This PDF file contains the errata for “JRS Vol. 3 Issue 01 Paper JRS ERR-2009-3” for JRS Vol. 3 Issue 01

This PDF file contains the errata for “JRS Vol. 3 Issue 01 Paper JRS ERR-2009-2” for JRS Vol. 3 Issue 01

This PDF file contains the errata for “JRS Vol. 3 Issue 01 Paper JRS ERR-2009-1” for JRS Vol. 3 Issue 01

This paper uses multi-sensor remote sensing data to study the type and spatio-temporal variability of aerosols emitted from forest fires. The study is based on the Okefenokee Swamp fire that ravaged parts of Georgia and Florida between May and June of 2007. Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to study the aerosol type and its spatial distribution. Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data is used to study the vertical distribution of aerosols. The results show that there is a high concentration of fine mode aerosols during the fire episode. It is also observed that the 24 hour averaged PM2.5 concentration was above unhealthy levels on several occasions, in some instances reaching values over 50µg/m3. The PM10 concentration on the other hand was below unhealthy levels although there were numerous instances of episodic non attainment of the PM10 air quality standard. The study shows that the vertical reach of the aerosol plume over the land ranged from 2 to 3 kilometers.

In the southeastern United States, most wildland fires are of low intensity. A substantial number of these fires cannot be detected by the MODIS contextual algorithm. To improve the accuracy of fire detection for this region, the remote-sensed characteristics of these fires have to be systematically analyzed. Using an adjusted algorithm, this study collected a database including 6596 remote-sensed fire pixels in 72 MODIS granules, of which 3809 fire pixels are missed by the MODIS contextual algorithm. The statistical distributions of the sensor-observed fire reflectance and brightness temperature at relevant spectral channels are analyzed. The study explains the reasons that the detection of low intensity fires by the MODIS contextual algorithm is significantly influenced by view angles, especially when view angles are greater than 40 degrees. This paper discusses and suggests several aspects which could improve regional detection of low intensity fires. The results indicate that 1) the R₂ threshold R₂ < 0.3 is still valid for detecting low intensity fires omitted by the MODIS contextual algorithm; 2) the threshold T₄ > 310 K is too high, and a lower threshold of T₄ > 293 K should be adopted instead; 3) the threshold Δ T > 10 K is also too high, and both algorithms that use it risk omitting small fires because of this threshold.

The retrieval of Live Fuel Moisture Content (LFMC) over fire prone grasslands is important for fire risk and drought assessment. Radiative transfer (RT) model based inversion of measured reflectances for retrievals of LFMC offers a promising method for estimating LFMC. This paper evaluates the extent to which inverse RT model based LFMC retrievals over grasslands can be improved by the use of prior information on soil moisture and LAI. However due to the uncertainty in the procedures used in obtaining the pre-retrieval information about LAI and soil moisture, the prior information is more likely to be in terms of an expected range for LAI and soil moisture rather than exact values. This study uses simulations from coupled soil-leaf-canopy radiative transfer models to investigate the extent to which such categorical prior information may reduce the uncertainty in LFMC retrievals. Results show that under the experimental conditions used in this study, prior information on LAI and soil moisture improves LFMC estimation on the average by about 2.3 to 3.4% (absolute LFMC) depending on the quality and accuracy of the prior information. This can be equivalent to a relative improvement of about 18-27%. This can be significant, since at the dry conditions represented by this study, when fire spread is highly sensitive to LFMC, such improvements in LFMC could considerably improve fire spread predictions and aid fire management decision making. Uncertainty analysis in terms of prediction intervals and standard deviation of errors also show that improvements are significant.

Biomass burning is significant to emission estimates because: (1) it is a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite.

This PDF file contains the editorial “Guest Editorial: Recent Advances in Remote Sensing of Wildland Fires in the Eastern United States” for JRS Vol. 3 Issue 01

We have developed a visual three-dimensional simulation and assessment system of the Wenchuan earthquake zone using the Digital Earth concept and highly efficient spatial information visualization technology. Our goal is to provide a foundation for earthquake disaster relief and reconstruction. A number of major collapses, landslides, mud-rock flows and other geological disasters are analysed in the system, and the system can be used to monitor and analyse earthquake relief, rescue and insurance to provide first-hand source material for disaster situation assessment. Multi-sensor, multi-temporal, multi-resolution remote sensing data and multi-scale DEM data are integrated into this system, and produce a sequence of comprehensive resolution datasets. We present management methods for a dynamic multi-resolution terrain model and multi-level scene. These methods are based on the model division, delaminating algorithm, and make use of high-speed buffering mechanisms to achieve a multi-level large-scale real-time rendering of scenes in the disaster areas, which also effectively reduces the rendering delay, and achieves smooth, seamless roaming at different model levels.

The Wenchuan earthquake triggered large numbers of secondary mountain hazards which caused great casualties and losses. Based upon remote sensing interpretation, field investigation, and disaster surveys, we describe the general situation of the earthquake, study the spatial distribution of the secondary mountain hazards, establishe a hazard degree index model according to disaster-forming factors and seismic parameters, and make a hazard assessment on the secondary mountain hazards. Moreover, using geographical information system and remote sensing technologies and the principle of regional factors, the study area is classified into four hazard zones: no-hazard zone, low-hazard zone, moderate-hazard zone, and high-hazard zone. The results show that the high-hazard zone is distributed along the three main fault zones of the Longmen Mountain, mainly along the Minjiang River valley, the Yuzi Creek, the Jianjiang River valley, and other river valley areas. The other hazard zones are distributed outside the high-hazard zone in a ring shape, respectively, and their hazard degrees decrease gradually, especially in the low elevation areas and the Chengdu Plain where the hazard degree is the lowest. The developmental trend of secondary mountain hazards is analyzed in view of geodynamics.

Transportation networks are among the most important lifelines for post-seismic relief and reconstruction. It is imperative to investigate, monitor, and analyze transportation network damage caused by earthquake disasters in near real-time. Herein, we present a method for the analysis of seismic hazards and the subsequent assessment of the impact of the Wenchuan earthquake on transportation networks employing remote sensing and geographical information systems. In this method, the locations, shapes, lengths, and areas of the main damaged segments of state and provincial highways are interpreted and surveyed based on airborne ADS40 data and diverse remotely sensed satellite images of varying resolutions before and after the disaster. Next, the spatial distributions of geological disasters such as landslides, land-collapses, mud-rock flows, bank-collapses, earthquake rifts, and faults, as well as barrier lakes, were analyzed. These types of geological disasters commonly cause transportation network blockage and damage. Finally, geographical factors, including geological structures, topography, and landscapes, were collected and integrated with the disaster statistics to quantitatively analyze the primary transportation seismic disaster indices, and evaluate the geographical characteristics and genetic mechanisms of seismic disasters. Our results indicate that transportation network blockage and damage occurred in 808 segments, with a total length of 170.2 km, and occupied 29.66% of the total length of the state and provincial highways in the core disaster regions. The distribution of transportation network blockage and damage has obvious geographical characteristics. It is concentrated in regions near geological faults, folds, rock crushes, and breaks, especially near the Longmenshan-controlling fault, which played a decisive role in the Wenchuan earthquake. (partial abstract)

The Minshan and Qionglai mountains, the major distribution range of the Giant Panda (Ailuropoda melanoleuca), was struck by the Wenchuan earthquake. In this study, we evaluated the impact of the earthquake and subsequent geo-disasters on the Giant Panda and its habitat using Wenchuan county (where the epicenter was located) as a case study. Habitat characteristics before and after the earthquake were analyzed based on TM images taken pre and post-earthquake and previous research in this region. Results showed that about 252.0 km2 of panda habitat was lost in Wenchuan County after the earthquake, which accounted for 13.9% of the total habitat in this county. Regions with high earthquake intensity, low elevation, steep slope, and near river had a higher proportion of habitat loss than in other regions. In addition to habitat loss, the earthquake and its resulting geo-disasters also caused habitat fragmentation. After the earthquake, the number of habitat patches increased by a factor of 2 and mean patch size was only 28.3% of pre-earthquake conditions. Habitat restoration and corridor reparation along Road 303 are necessary for facilitating panda's migration between isolated patches. It is necessary to take some measures including relocation of residents and earthquake compensation to decrease the impact of reconstruction on the Giant Panda and its habitat.

Damage to vegetation caused by secondary disasters of the Wenchuan earthquake in severely damaged counties was estimated through the visual comparison of SPOT images acquired before the earthquake and ADS40 aerial images acquired after the earthquake, and a series of spatial analyses. In this paper, we (1) interpret 2-meter resolution aerial images that cover areas severely affected by the earthquake, and obtain statistical information on vegetation damage for the counties of Beichuan, Wenchuan, Maoxian, Lixian, Pingwu, Qingchuan, Anxian and Jiangyou; (2) spatially analyze the relationships between vegetation damage and slope gradient and distance from active faults using ArcGIS software to obtain information on vegetation damage under different geologic and geomorphologic settings; and (3) estimate the area of vegetation damage for the whole region using the above results for the areas covered by imagery. The results indicate that (1) farmland and grassland were less damaged than forestland was since they are mostly located on less steep slopes; (2) Wenchuan was the worst damaged county; and (3) the proportion of damage to vegetation first decreased and then increased with increasing distance from the three main faults of the Longmenshan fault zone owing to the combined effects of the three faults and the effects of regional geology and landforms.

The Wenchuan earthquake on May 12, 2008, triggered many secondary disasters, among which the barrier lakes formed by landslides were extremely serious. We monitored the number and spatial distribution of the barrier lakes in the earthquake-hit area from ADS40 airborne images, which covered areas of about 23,700 km². The results showed that there were 51 barrier lakes in the monitored area; among these, 10 were large-scale lakes and 14 were middle-sized lakes. The barrier lakes were distributed along the Longmen Mountain fault from the northeast to southwest direction. We used the dimensionless blockage index (DBI) to assess the potential risk of the barrier lakes. A small DBI value indicated a stable barrier lake, but the lake might have a higher risk with potential accumulative secondary disasters. Our study emphasized the monitoring and analysis of the high-risk Tangjiashan Barrier Lake from the multitemporal ADS40 airborne images acquired on May 16, 19, 23, and 27. We found that the water level at this barrier lake reached 66 m within 15 days after the barrier lake was formed, and the reservoir storage capacity reached 1.2 × 10⁸ m³ with an increase of 8 × 10⁶ m³ of water per day. Therefore, it faced a very real and urgent risk of dam break and overflow, considering the predicted storm rainfall and the continuous aftershocks. According to the analysis results, airborne remote sensing demonstrated the advantages of being intelligent, being able to maneuver, and providing high resolution. These advantages allowed us to quickly monitor and assess the distribution and dynamic changes of the barrier lakes in the earthquake-hit region using multitemporal airborne remote sensing images.

Collapsing houses are one of the indicators to assess earthquake damage intensity. We monitored and analyzed the house collapse ratio and its spatial distribution in the Wenchuan Earthquake of May 12, 2008 by interpreting the aerial images. The results show that the houses were widely damaged, especially in Wenchuan County, Mianzhu City, Shifang City, and Pengzhou City in which Wenchuan County experienced the severest damage. We analyzed the spatial variation of the house collapse ratio and its relationship with earthquake intensity, geological structure, and stratum lithology. The results demonstrate that the house collapse ratio and the earthquake intensity have a positive relationship, which is controlled by the geological structure, stratum lithology, and building structure. Analysis of the collapsed houses over an extensive earthquake-damaged region using aerial images provides not only an effective assessment for the damages and losses, but also the foundation data for the analysis of earthquake intensity.

The secondary disasters induced by the Wenchuan earthquake of May 12, 2008, such as landslides, collapsing rocks, debris flows, floods, etc., have changed the local natural landscape tremendously and caused heavy soil erosion in the earthquake-hit areas. Using thematic mapper images taken before the earthquake and airborne images taken after the earthquake, we extracted information about the destroyed landscape by utilizing remote sensing and geographical information system techniques. Then, taking into account multi-year precipitation, vegetation cover, soil type, land use, and elevation data, we evaluated the soil erosion area and intensity using the revised universal soil loss equation. Results indicate that the soil erosion in earthquake-hit areas was exacerbated, with the severe erosion area increasing by 279.2 km², or 1.9% of the total statistical area. Large amounts of soil and debris blocked streams and formed many barrier lakes over an area of more than 3.9 km². It was evident from the spatial distribution of soil erosion areas that the intensity of soil erosion accelerated in the stream valley areas, especially in the valleys of the Min River and the Jian River.

A strong earthquake, with a magnitude of 8.0, hit in the Wenchuan area of China on May 12, 2008, resulting in significant changes to the land surface. Remote sensing (RS), especially synthetic aperture radar (SAR) RS technology can play a key role in detecting changes of the land surface. In the present paper, multitemporal Advanced Land Observation Satellite- Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) data acquired on Feb. 17 and May 19, 2008 were used to analyze the land surface changes caused by the Wenchuan earthquake. The characteristics of the land surface in several sites hit by the earthquake are presented, by comparison of pre- and post-seismic images. Subsequently, the land surface changes caused by the earthquake were extracted using change detection and classification methods. To this end, land surface change detection can provide a detailed basis for assessing an earthquakes impact.

More than 69,000 people died following the magnitude 8.0 Wenchuan earthquake of May 12, 2008. Bad weather hampered relief efforts, and in some cases rescuers had to trek into the disaster area on foot and search for trapped survivors by hand as roads were blocked by debris. Due to travel difficulties, spatial information needs to be extracted in the disaster area by remote sensing techniques. The main problem focused on in this paper is how to use the all-weather and all-day/night capability of Synthetic Aperture Radar (SAR) to extract primary seismic disaster information after the earthquake. Using air- and space-borne SAR images with different bands, polarizations and incidence angles, including multi-polarization X-band air-borne data, C-band polarimetric Radarsat-2, X-band TerraSAR-X with high resolution, multi-polarization X-band COSMO-SkyMed and L-band multi-polarization ALOS-PalSAR space-borne data, we perform image characteristics analysis of landslides. Obvious differences can be recognized between old and new landsides in SAR images with different bands. Multi-polarization SAR can play an important role in landslide discrimination. Two SAR images at different incidence angles do not provide much more information if the difference between the two angles is small. Landslide recognition accuracy strongly depends on the direction of view, especially for large incidence angles, in which case the characteristic difference for landslide recognition is great. There are different polarization responses between a landslide and its surroundings that can be used to recognize the landslide. Interferometric SAR images, on the other hand, do not provide good recognition capability due to temporal decorrelation and resolution. Meanwhile, information extraction of barrier lakes using different resolution and incidence angle SAR images is analyzed in this paper; small incidence angles and high resolutions improve the object recognition and information extraction of barrier lakes.

The devastating Wenchuan Earthquake occurred in Sichuan Province, Southwestern China, with a magnitude of 8.0 on May 12, 2008. Most buildings along the seismic zone were ruined, resulting in infrastructure damage to factories, traffic facilities and power supplies. The earthquake also triggered geological disasters, such as landslides, debris flow, landslide lakes, etc. During the rescue campaign the remote sensing aircrafts of the Chinese Academy of Sciences (CAS), equipped with synthetic aperture radar (SAR) and optical sensors, flew over the disaster area and acquired many high resolution airborne SAR images. We first describe the basic characteristics of SAR imagery. The SAR images of buildings are simulated, and the backscattering mechanism of the buildings is analyzed. Finally, the various disaster phenomena are described and analyzed in the high resolution airborne SAR images. It is shown that certain phenomena of ruins could be identified clearly in high resolution SAR images in proper imaging conditions, while the functional destruction is quite difficult to detect. With calibrated data, the polarmetric SAR interferometry could be used to analyze the scattering mechanism and 3D distribution of the scattering center, which are redound to earthquake damage assessment.

The large 8.0 scale earthquake that occurred in Wenchuan, Sichuan province, China on May 12, 2008 caused huge damage to people's lives and property. Airborne and spaceborne remote sensing can be used accurately and effectively in almost real-time to monitor and assess earthquake disasters, providing an important scientific basis and decision-making support for government emergency command and post-disaster reconstruction. The high resolution, multi-band, multi-polarization, and full-polarization synthetic aperture radar (SAR) system and theories developed in recent years provide important data resources and the basic methodology for post-earthquake monitoring and evaluation. In this paper, the cities of Beichuan and Dujiangyan in the Wenchuan earthquake region are chosen as study sites. Using advanced high-resolution, multi-band, multi-polarization, and full-polarization SAR data, and applying urban building backscattering models and target backscattering and polarimetric target decomposition theory, the backscattering characteristics, polarimetric characteristics and texture features between collapsed and intact buildings post-earthquake are extensively compared and analyzed. Subsequently, a new SAR detection method for collapsed urban buildings is proposed from these characterizations. Preliminary results from comparisons between this method and high-resolution optical data show that the proposed method is effective and powerful in detecting collapsed urban buildings devastated by an earthquake.

This PDF file contains the editorial “Guest Editorial: Remote Sensing of the Wenchuan Earthquake” for JRS Vol. 3 Issue 01

Since launch, Terra and Aqua MODIS have successfully operated on-orbit for more than 9 and 6.5 years, respectively. MODIS, a key instrument for the NASA's EOS missions, was designed to make continuous observations for studies of Earth's land, ocean, and atmospheric properties and to extend existing data records from heritage earth-observing sensors. In addition to frequent global coverage, MODIS observations are made in 36 spectral bands, covering both solar reflective and thermal emissive spectral regions. Nearly 40 data products are routinely generated from MODIS observations and publicly distributed for a broad range of applications. Both instruments have produced an unprecedented amount of data in support of the science community. As a general reference for understanding sensor operation and calibration, and thus science data quality, this paper provides an overview of the MODIS instruments and their pre-launch calibration and characterization, and describes their on-orbit calibration algorithms and performance. On-orbit results from both Terra and Aqua MODIS radiometric, spectral, and spatial calibration are discussed. Currently, both instruments, including their on-board calibration devices, are healthy and are expected to continue operation for several years to come.

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multi-spectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG's ability to generate scenes "on demand." To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, we have developed a comprehensive workflow aimed at reducing the man-in-the-loop requirements for many aspects of synthetic hyperspectral scene construction. Through a fusion of 3D lidar data with passive imagery, we have been able to partially-automate many of the required tasks in the creation of high-resolution urban DIRSIG scenes. This paper presents a description of these techniques.

The marginality index for agricultural land use, which was originally defined on a global scale, was used to evaluate agricultural land resources of Benin. For the assessment of the index, several biophysical factors limiting agricultural production under low capital input are analyzed using a fuzzy logic based algorithm. For Benin, we determined the marginality index (MI) successfully in a spatial resolution of 1km x 1km using influencing factors with a higher spatial resolution and an adapted fuzzy logic based algorithm. The results of the approach proved that the chosen indicators on a global scale are also useful indicators on a national scale. The necessary modifications were slight and mostly with the aim to increase the tangibility for national decision makers. On a national scale, data derived from remote sensing like MODIS or SRTM are interesting and embolden sources to derive input data. To support national decision makers, input data and algorithms were implemented within a computer-based Spatial Decision Support System (SDSS). With the developed SDSS 'AGROLAND' the user is able to visualize and analyze agricultural land resources based on the MI. Additionally, advanced model based raster analyses as well as the possibility of user interactions during runtime are implemented.

The detection and tracking of naval targets, including low Radar Cross Section (RCS) objects like inflatable boats or sea skimming missiles requires a thorough knowledge of the propagation properties of the maritime boundary layer. Models are in existence, which allow a prediction of the propagation factor using the parabolic equation algorithm. As a necessary input, the refractive index has to be known. This index, however, is strongly influenced by the actual atmospheric conditions, characterized mainly by temperature, humidity and air pressure. An approach is initiated to retrieve the vertical profile of the refractive index from the propagation factor measured on an onboard target. The method is based on the LS-SVM (Least-Squares Support Vector Machines) theory. The inversion method is here used to determine refractive index from data measured during the VAMPIRA campaign (Validation Measurement for Propagation in the Infrared and RAdar) conducted as a multinational approach over a transmission path across the Baltic Sea. As a propagation factor has been measured on two reference reflectors mounted onboard a naval vessel at different heights, the inversion method can be tested on both heights. The paper describes the experimental campaign and validates the LS-SVM inversion method for refractivity from propagation factor on simple measured data.

Hyperspectral imaging (HSI) data in the 0.4 - 2.5 micrometer spectral range allow direct identification of materials using their spectral signatures, however, spatial coverage is limited. Multispectral Imaging (MSI) data are spectrally undersampled and may not allow unique identification, but they do provide synoptic spatial coverage. We have developed an approach that uses coincident HSI/MSI data to extend mineral mapping to larger areas. Hyperspectral data are used to model and extend signatures to multispectral Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) data. Analysis consists of 1. Atmospheric correction of both the hyperspectral and multispectral data, 2. Analysis of the hyperspectral data to determine spectral endmembers and their spatial distributions, 3. Spectral modeling to convert the hyperspectral signatures to the multispectral response, and 4. Analysis of the MSI data to extend mapping to the larger spatial coverage of the multispectral data. Comparing overlapping area with extensive field verification shows that ASTER mineral mapping using these methods approaches 70% accuracy compared to HSI for selected minerals. Spot checking of extended ASTER mapping results also shows good correspondence. While examples shown are specific to ASTER Short Wave Infrared (SWIR) data, the approach could also be used for other multispectral sensors and spectral ranges.

We estimate the age of humid lowland tropical forests in Rondônia, Brazil, from a somewhat densely spaced time series of Landsat images (1975-2003) with an automated procedure, the Threshold Age Mapping Algorithm (TAMA), first described here. We then estimate a landscape-level rate of aboveground woody biomass accumulation of secondary forest by combining forest age mapping with biomass estimates from the Geoscience Laser Altimeter System (GLAS). Though highly variable, the estimated average biomass accumulation rate of 8.4 Mg ha^-1 yr^-1 agrees well with ground-based studies for young secondary forests in the region. In isolating the lowland forests, we map land cover and general types of old-growth forests with decision tree classification of Landsat imagery and elevation data. We then estimate aboveground live biomass for seven classes of old-growth forest. TAMA is simple, fast, and self-calibrating. By not using between-date band or index differences or trends, it requires neither image normalization nor atmospheric correction. In addition, it uses an approach to map forest cover for the self-calibrations that is novel to forest mapping with satellite imagery; it maps humid secondary forest that is difficult to distinguish from old-growth forest in single-date imagery; it does not assume that forest age equals time since disturbance; and it incorporates Landsat Multispectral Scanner imagery. Variations on the work that we present here can be applied to other forested landscapes. Applications that use image time series will be helped by the free distribution of coregistered Landsat imagery, which began in December 2008, and of the Ice Cloud and land Elevation Satellite Vegetation Product, which simplifies the use of GLAS data. Finally, we demonstrate here for the first time how the optical imagery of fine spatial resolution that is viewable on Google Earth provides a new source of reference data for remote sensing applications related to land cover.

A necessary condition for accumulating fundamental climate data records is the use of observation instruments whose stability and accuracy are sufficiently high for climate monitoring purposes; the number of instruments and their distribution in space should be sufficient for measurements with no spatial or temporal gaps. The continuous acquirement of data over time intervals of several decades can only be possible under the condition of simultaneous application of instruments produced by different manufacturers and installed on different platforms belonging to one or several countries. The design of standard sources for pre-flight calibrations and in-flight monitoring of instruments has to meet the most stringent requirements for the accuracy of absolute radiometric measurements and stability of all instruments. This means that the radiometric scales should be stable, accurate, and uniform. Current technologies cannot ensure the high requirements for stability and compatibility of radiometric scales: 0.1% per decade within the 0.3 - 3 μm band and 0.01 K per decade within the 3 - 25 μm band. It is suggested that these tasks can be aided through the use of the pure metals or eutectic alloy phase transition phenomenon that always occur under the same temperature. Such devices can be used for pre-flight calibrations and for on-board monitoring of the stability of radiometric instruments. Results of previous studies of blackbody models based upon the phase transition phenomenon are quite promising. A study of the phase transition of some materials in small cells was conducted for future application in onboard monitoring devices and its results are positive and allow us to begin preparations for similar experiments in space.

Many algorithms exist to invert airborne imagery from units of either radiance or sensor specific digital counts to units of reflectance. These compensation algorithms remove unwanted atmospheric variability allowing objects on the ground to be analyzed. Low error levels in homogenous atmospheric conditions have been demonstrated. In many cases however, clouds are present in the atmosphere which introduce error into the inversion at unacceptable levels. For example, the relationship that is defined between sensor reaching radiance and ground reflectance in a cloud free scene will not be the same as in an adjacent region with clouds in the surround. A novel method has been developed which utilizes ground based measurements to modify the empirical line method (ELM) approach on a per-pixel basis. A physics based model of the atmosphere is used to generate a spatial correction for the ELM. Creation of this model is accomplished by analyzing whole-sky imagery to produce a cloud mask which drives input parameters to the radiative transfer (RT) code MODTRAN. The RT code is run for several different azimuth and zenith orientations to create a three-dimensional representation of the hemisphere. The model is then used to achieve a per-pixel correction by adjusting the ELM slope spatially. This method is applied to real data acquired over the atmospheric radiation measurement (ARM) site in Lamount, OK. Performance of the method is evaluated with the Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument. The sensitivity to spectral sampling is also assessed by down-sampling the HYDICE data to the spectral response of the multi-spectral system Wildfire Airborne Sensor Program LITE (WASP Lite). Finally a method to utilize this approach when additional sensors (like a sky camera) are not available is suggested.

A physically-sound, non-local excess phase delay observation operator is developed for simulating excess phase delay measurements from GPS radio occultation (RO) missions. By approximating an observed ray by a straight line, the refractivity gradient information along an observed ray path is included in the simulated excess phase delay. This observation operator is used to simulate observations from the German CHAllenging Minisatellite Payload (CHAMP) RO mission based on large-scale analysis. The need to use such an observation operator for GPS RO data assimilation in spherically asymmetric regions is shown by results from a set of forward simulation and data assimilation experiments. A modification that renders the non-local excess phase delay observation operator more suitable for parallel implementation of GPS RO data assimilation is proposed.

A primary objective of the effort described here is to optimize the performance of a modeling environment for radiometric instruments capable of predicting their complete end-to-end behavior, integrating the optical, electrothermal, and electronic systems. The numerical environment consists of a Monte Carlo ray-trace (MCRT) model of the optical system coupled to a transient three-dimensional finite-difference electrothermal model of the detector assembly with an analytical model of the signal-conditioning circuitry. The resulting model provides a complete simulation of the dynamic optical and electrothermal behavior of the instrument. The modeling environment has been used to create an end-to-end model of the CERES scanning radiometer, and its performance compared to the calibration performance of an operational CERES total channel as a benchmark. To optimize the accuracy of the electrothermal model, the nominal properties of certain key parameters in that model are modified using an evolutionary search algorithm such that the model's simulated output exactly matches the actual instrument ground calibration data. Results indicate that varying the layer thickness, effective thermal conductivity, and effective thermal capacitance of the thermistor, kapton, and epoxy layers in the thermistor bolometer within reasonable uncertainty bounds provides an excellent match with the recorded instrument data.

Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study.

The Aglite Lidar is a portable scanning lidar that can be quickly deployed at agricultural and other air quality study sites. The purpose of Aglite is to map the concentration of PM₁₀ and PM_2.5 in aerosol plumes from agricultural and other sources. Aglite uses a high-repetition rate low-pulse energy 3-wavelength YAG laser with photon-counting detection together with a steerable pointing mirror to measure aerosol concentration with high spatial and temporal resolution. Aglite has been used in field campaigns in Iowa, Utah and California. The instrument is described, and performance and lidar sensitivity data are presented. The value of the lidar in aerosol plume mapping is demonstrated, as is the ability to extract wind-speed information from the lidar data.

We present a change detection method for terrain covers from multi-temporal SAR images based on a spatial chaotic model which is known to adequately characterize the coherent process of SAR imaging. The major problem of SAR change detection rises from both the presence of speckle noise and the pixel mis-registration that are commonly seen in the remote sensing image. By means of chaotic model, we first transform the images to fractal domain and then perform the CFAR detection. Simulated tests are conducted to quantitatively evaluate the impacts of these two major error sources on detection rate. Results from satellite SAR for landcover change detection clearly show that the proposed algorithm not only the speckle noise can be effectively suppressed without scarifying the spatial resolution; the excruciating mis-registration error was taken into account and removed.

Hyperspectral imaging sensors commonly employ multiple apertures or focal planes for broad spectral coverage. These designs often result in spatial misregistration artifacts between the spectral regions. Unknown misregistration errors of fractions of a pixel cannot be corrected and can have a negative impact on target detection performance, especially for targets that are subpixel. The work here analyzes the impact of band-to-band misregistration on hyperspectral target detection performance. Synthetic imagery was used to simulate various amounts of sub-pixel misregistration between the visible (VIS) and near infrared (NIR) regions of the optical spectrum. Scenes were created with vehicles placed as targets. Target detection algorithms were applied using both the full spectrum misregistered imagery, and the VIS and NIR bands separately. Receiver operating characteristic curves were used to assess the performance of each algorithm for each target. Results indicate that statistical target detection algorithms are less sensitive to band-to-band misregistration than geometric algorithms. Results also indicate that in some cases it is more beneficial to use full spectrum misregistered imagery rather than applying target detection algorithms to the VIS and NIR bands separately, even for large amounts of sub-pixel misregistration.

Geostationary Operational Environmental Satellite (GOES)-10 was the National Oceanic and Atmospheric Administration's (NOAA) operational GOES-West satellite for approximately eight years until it was retired as an operational satellite due to an ever increasing inclination in its orbit. Since its retirement, GOES-10 has been used for a number of applications, such as, special 1-minute imagery over parts of North America during its move to 60° West longitude, routine imagery of the Southern Hemisphere, the first operational Sounder coverage over South America, initialization of regional numerical weather prediction models, and even temporary recalled as the operational GOES-East satellite during a major GOES-12 anomaly. Products from the GOES-10 Sounder and/or Imager include: imagery, cloud-top parameters, atmospheric stability indices, total precipitable water vapor, motion vector winds, volcanic ash detection, fire detection and characterization, and precipitation. As the mission of GOES-10 has continued beyond its retirement as an official operational US satellite, already lasting more than double its five-year life expectancy, many countries have been afforded the opportunity to benefit from on-going GOES-10 measurements. The purpose of this paper is to summarize the history of GOES-10, especially the unique situation of GOES-10 operating in support of central and South America after its operational use.

Precise registration and orthorectification of remote sensing images are the basic processes for quantitative remote sensing applications, especially for multi-temporal image analysis. In this paper, we present an automated precise registration and orthorectification package (AROP) for Landsat and Landsat-like data processing. The Landsat and Landsat-like satellite images acquired from different sensors at different spatial resolutions and projections can be re-projected, co-registered, and orthorectified to the same projection, geographic extent, and spatial resolution using a common base image through a combined resampling strategy; this allows us to perform multi-temporal image analysis directly. This paper presents and tests the AROP package on Landsat and Landsat-like data. The package is now freely available from our research web site.

On the basis of Landsat 7 ETM+ imagery, a prototype land-cover map was prepared for the Huascarán Biosphere Reserve (Peru). This document should contribute to the sustainable management of the Huascarán Biosphere Reserve, while making it possible to establish a regional planning policy and to prepare a natural risks map, which is still lacking in the region. The influence of the topography on radiometry was attenuated by using the normalized difference snow index (NDSI) and normalized difference vegetation index (NDVI), which were segmented using their histogram. A digital elevation model (DEM) was introduced to define the "highlands" and "lowlands". In the latter, the slope derived from the DEM was combined with the NDVI to map the agricultural surfaces. Twenty-one spectral classes were defined and their correspondence with land-cover themes was checked by field observations. The land-cover map provides original information on the extent of the glacial cover, debris-covered glaciers, 881 lakes, vegetation density, agricultural surfaces, urban zones and mines.

Abstract. Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. Crop models require that vegetation characteristics extracted from an image be the correct crop type. This study compares four techniques to create land-use classifications using MODIS data. These classifications were compared to the ground data that had been set aside, to a mask where each MODIS sized pixel were at least 80% of a single land-used based on a Landsat TM classification for the same year, and to the Landsat TM classification. Using a decision tree method and comparing the classification to an 80% mask resulted in an accuracy of 73% which was the highest accuracy obtained in this study. The study showed that accuracies could range from 37% to 73% depending on the classification process and if segment data, an 80% mask, or a Landsat TM classification were used for accuracy assessment.

Two optimization techniques from the class of direct search solvers, the genetic and generalized pattern search algorithms, are applied to hyperspectral source prediction. The number of filter bands to be sampled and their placements are the variables subject to optimization. The algorithms provide the optimal placements by sampling the stable source radiances at multispectral resolution, estimating the original source spectrum upon interpolation to hyperspectral resolution, and retaining the most accurate prediction. The integrated absolute error between the source prediction and the hyperspectral source truth is minimized. The interpolation estimate of the source approaches zero error asymptotically, as the number of allowed band samples is increased. The source spectrum of 2.15 μm bandwidth is reconstructed with 13 bands and relative absolute error of less than 0.5%. The method can be applied to any radiance spectrum or subspectrum partition, and is useful in transfer radiometer design or minimizing the number of spectrometer measurements required for hyperspectral source prediction.

This work describes the calibration and characterization of a commercial digital camera that is being evaluated as a potential inexpensive imaging system to measure the bidirectional reflectance factor of the test site surfaces that are used by the Remote Sensing Group for the ground-based vicarious calibration of airborne and spaceborne systems. The main advantage to using a lightweight digital camera in combination with a fisheye lens is that a test site can be simultaneously spatially and spectrally sampled with greater ease. The motivation for using an inexpensive, lightweight digital camera to measure surface bidirectional distribution factor is the ability to spatially sample many points on the ground. An additional benefit to such cameras is their ability to operate without the need for cooling, which was required on a previous camera system developed at the Remote Sensing Group. Laboratory measurements include the gain and offset, spectral responsivity, linearity, cosine response, and point spread function. The results show that the uniformity of the Nikon detector array ranges from 0.7%-1.2%. The spectral bandwidth is approximately 90 nm for the blue channel, and 110 nm for the green, and 75 nm for the red channel. Linearity measurements show that the camera responsivity is nonlinear due to the internal image-processing algorithms, and a logarithmic fitting function is suggested. Measurements of the camera response versus the angle of incidence reveal that the camera does not exhibit a cos⁴ falloff on detector irradiance as one would expect, which is another consequence of the internal algorithms.

Currently, there are two nearly identical moderate resolution imaging spectroradiometer (MODIS) instruments operated in space: one on the Terra spacecraft launched in December 1999 and another on the Aqua spacecraft launched in May 2002. MODIS has 36 spectral bands with wavelengths covering from visible (VIS) to long-wave infrared (LWIR). Since launch, MODIS observations and data products have significantly enabled studies of changes in the Earth system of land, oceans, and atmosphere. In order to maintain its on-orbit calibration and data product quality, MODIS was built with a comprehensive set of on-board calibrators. MODIS reflective solar bands (RSB) are calibrated on-orbit by a system that consists of a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) on a regular basis. Its thermal emissive bands (TEB) calibration is executed on a scan-by-scan basis using an on-board blackbody (BB). The MODIS Characterization Support Team (MCST) at NASA/GSFC has been responsible for supporting sensor calibration and characterization tasks from pre-launch to post launch. In this paper, we describe current MCST efforts and progress made to examine sensor stability and inter-calibration consistency using observations over Dome Concordia, Antarctica. Results show that this site can provide useful calibration reference for Earth-observing sensors.

Spatial, spectral and temporal variations in operating conditions are major contributors to the expected variability/uncertainty in system performance. The ratio of signal-to-noise ratio (SNR) based on climatological data to a standard atmosphere is the primary performance metric used, with results presented in the form of histograms and maps of worldwide LADAR performance variation. This metric is assessed at 2 wavelengths, 1.0642 μm and 1.557 μm, for a number of widely dispersed land and maritime locations worldwide over oblique and vertical air to surface paths in which anticipated clear air aerosols and location specific heavy rain and 150 m thick fog occur. Seasonal, boundary layer, and time of day variations for a range of relative humidity percentiles are also considered. In addition to realistic vertical profiles of molecular and aerosol extinction, air-to-ground cloud free line of sight (CFLOS) probabilities as a function of location for this geometry are computed. Observations from the current study strongly indicate that use of the standard atmosphere to predict performance will produce overly optimistic, in many cases extremely so, estimates of expected performance. Locally heavy rain, when present, severely limits LADAR system performance at these wavelengths. Some operational capability exists for vertical looks through fog.

Lidar (LIght Detection And Ranging) provides the means to quantitatively evaluate the spatial and temporal variability of particulate emissions from agricultural activities. AGLITE is a three-wavelength portable scanning lidar system built at the Space Dynamic Laboratory (SDL) to measure the spatial and temporal distribution of particulate concentrations around an agricultural facility. The retrieval algorithm takes advantage of measurements taken simultaneously at three laser wavelengths (355, 532, and 1064 nm) to extract particulate optical parameters, convert these parameters to volume concentration, and estimate the particulate mass concentration of a particulate plume. The quantitative evaluation of particulate optical and physical properties from the lidar signal is complicated by the complexity of particle composition, particle size distribution, and environmental conditions such as heterogeneity of the ambient air conditions and atmospheric aerosol loading. Additional independent measurements of particulate physical and chemical properties are needed to unambiguously calibrate and validate the particulate physical properties retrieved from the lidar measurements. The calibration procedure utilizes point measurements of the particle size distribution and mass concentration to characterize the aerosol and calculate the aerosol parameters. Once calibrated, the Aglite system is able to map the spatial distribution and temporal variation of the particulate mass concentrations of aerosol fractions such as TSP, PM₁₀, PM_2.5, and PM₁. This ability is of particular importance in the characterization of agricultural operations being evaluated to minimize emissions and improve efficiency, especially for mobile source activities.

In rapidly changing peri-urban environments where biophysical and socio-economic processes lead to spatial fragmentation of agricultural land, remote sensing offers an efficient tool to collect land cover/land use (LCLU) data for decision-making. Compared to traditional pixel-based approaches, remote sensing with object-based classification methods is reported to achieve improved classification results in complex heterogeneous landscapes. This study assessed the usefulness of object-oriented analysis of Quickbird high spatial resolution satellite data to classify urban and peri-urban agriculture in a limited peri-urban area of Hanoi, Vietnam. The results revealed that segmentation was essential in developing the object-oriented classification approach. Accurate segmentation of shape and size of an object enhanced classification with spectral, textural, morphological, and topological features. A qualitative, visual comparison of the classification results showed successful localisation and identification of most LCLU classes. Quantitative evaluation was conducted with a classification error matrix reaching an overall accuracy of 67% and a kappa coefficient of 0.61. In general, object-oriented classification of high spatial resolution satellite data proved the promising approach for LCLU analysis at village level. Capturing small-scale urban and peri-urban agricultural diversity offers a considerable potential for environmental monitoring. Challenges remain with the delineation of field boundaries and LCLU diversity on more spatially extensive datasets.

Frequency Domain (FD) fluorimetry, capitalizes on the frequency response function of a fluorophore and offers independence from light scatter and excitation/emission intensity variations in order to extract the sample's fluorescent lifetime. Mercury vapor lamps, a common source of industrial facility lighting, emit radiation that overlaps the UV/blue absorption spectrum of many fluorophores and may be used as an efficient and portable excitation source. The AC power modulation of mercury vapor lamps modulates the lamp's intensity at 120 Hz (in the United States) and higher harmonics. The fluorescent lifetimes for 3 different materials (willemite, uranium doped glass and U₃O₈) are measured with conventional techniques and compared with the FD technique using the power harmonics from a mercury vapor lamp. The mercury lamp measurements agree to within 25% of the conventional methods.

Airborne laser scanning provides reliable terrain data in terms of 3D coordinates. High resolutions Digital Terrain Models (DTM) are in use for many applications, including change detection of surfaces. Also the estimation of the snow depth by making use of Airborne Laser Scanning (ALS) data acquired in summer and winter is a subject of current investigations. However estimating snow depth seems problematic in vegetation covered areas. This work focuses on the investigation of the snow depth estimation using snow covered vegetation effect. In this aspect a method based on segmentation filter is proposed for snow depth calculation in vegetation covered area by using ALS data. The method demonstrates the performance of the segmentation filter with respect to the adaptability on the vegetated snow covered area. Additionally the influence of the snow covered biomass on the snow depth model is also investigated.

The Halogen Occultation Experiment (HALOE) operated in orbit for over 14 years, providing high quality measurements from the upper troposphere into the lower thermosphere. Since the quality of this data set depended on the long-term stability of the instrument, a series of analysis tests were designed to routinely monitor instrument performance. These tests evaluated possible changes in the gas cells, electronic gains, optical performance, and signal temperature dependencies. The gas cell stability was determined from an analysis of the Doppler shift signature in retrieved mixing ratios. Electronic gain stability was determined by instrument scans of the solar disk, each with different balance settings. Optical and tracking performance was also determined from solar scan data. The only statistically significant changes detected were: 1. a small methane gas cell change, causing less than 0.5% per decade change in retrieved methane, and 2. a small optical alignment change in the HF channel that only affects HF results below 25 kilometers. These detailed analyses indicate that the HALOE instrument remained stable throughout the mission, adding confidence to the long-term atmospheric trends deduced from HALOE products.

The inherent dimensionality of a spectral image can be estimated in a number of ways, primarily based on statistical measures of the data cloud in the hyperspace. Methods using the eigenvalues from Principal Components Analysis, a Minimum Noise Fraction transformation, or the Virtual Dimensionality algorithm are widely applied to entire hyperspectral images typically with the goal of reducing the overall dimensionality of an image. Additionally, in complex scenes containing non-natural materials, the lack of multivariate normality of the data set implies that a statistically-based estimation is less than optimal. However, it is desirable to understand the dimensionality of individual components and small multi-material clusters within a hyperspectral scene, as there is no a priori reason to expect all distinct material classes in the scene to have the same inherent dimensionality. For this reason, the inherent dimensionality may be useful as an indicator of the presense of manmade or natural materials within small subsets of the image. Here, a geometric approach is developed based on the spatially local estimation of dimensionality in the native data hyperspace. It will be shown that the dimensionality of a collection of data points in the full n dimensions (where n is the number of spectral channels measured) can be estimated by calculating the change in point density as a function of distance in the full n dimensional hyperspace. Simple simulated examples to demonstrate the concept will be shown, as well as applications to real hyperspectral imagery collected with the HyMAP sensor.

Monitoring and management of land use plays an important role in the economic development of agriculture regions around the world. In this regard, field boundary extraction for agriculture land parcels is crucial for further analysis and mapping. An effective image analysis needs to perform this task by automatically extracting the features with minimum operator efforts. Many researchers have attempted land use/land cover classification using IRS P6 LISS IV data. However little emphasis has been given to agriculture field boundary extraction. This paper explores the potential of IRS P-6 LISS IV dataset for agriculture field boundary extraction. The segmentation of field areas based on the tonal and textural gradients of the imagery is carried out. The segmented regions were classified to derive preliminary field boundaries. Finally, the derived field boundaries are geometrically refined using snakes. The use of snakes resulted in marked improvement in preliminary results. It was observed that traditional snakes can be effectively applied on single closed boundary at a time, which makes it suitable for single target detection. An attempt has been made in this study to develop looping snakes which can deal with multiple boundaries as will be the case with objects like agriculture fields.

This paper describes a novel physically-based approach for estimating leaf area index (LAI) and leaf chlorophyll content (C_ab) at regional scales that relies on radiance data acquirable from a suite of aircraft and operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool integrates leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths. REGFLEC adopts a multi-step LUT-based inversion approach and incorporates image-based techniques to reduce the confounding effects of land cover specific vegetation parameters and soil reflectance. REGFLEC was applied to agricultural and natural vegetation areas using 10 m and 20 m resolution SPOT imagery, and variable environmental and plant development conditions allowed for model validation over a wide range in LAI (0 - 6) and C_ab (20 - 75 μg cm^-2). Validation against in-situ measurements yielded relative root-mean-square deviations on the order of 13% (0.4) for LAI and between 11 - 19% (4.9 - 9.1 μg cm^-2) for C_ab. REGFLEC demonstrated good utility in detecting spatial and temporal variations in LAI and Cab without requiring site-specific data for calibration. The physical approach presented here can quite easily be applied to other regions and has the potential of being more universally applicable than traditional empirical approaches for retrieving LAI and C_ab.

Giant reed (Arundo donax L.) is a perennial invasive weed that presents a severe threat to agroecosystems and riparian areas in the Texas and Mexican portions of the Rio Grande Basin. The objective of this study was to use QuickBird satellite imagery to map giant reed infestations and estimate infested areas in the Mexican portion of the Rio Grande Basin (MPRGB). Intensive ground surveys were conducted and global positioning system (GPS) points were collected to document the locations along major tributaries and other infested areas in the MPRGB. To map the scattered infestations, QuickBird image scenes covering a total area of 7190 square km are needed. In this study, 40 archived and newly-tasked QuickBird image scenes acquired from 2002 to 2007 and covering a total area of 3252 square km were used. These images were classified using a maximum likelihood classifier and the areas for giant reed and water in each scene were determined. The total giant reed-infested area determined from the 40 images was 2160 ha. Since the 40 images constituted about 45.2% of the total area to be imaged, the total infested area in the MPRGB was estimated to be 4775 ha. Based on the images acquired along the tributaries, the ratio of giant reed-infested area to water area was 1.6 ha/ha and the ratio of giant reed-infested area to river length was 4.6 ha/km. Although actual infested areas are larger than the estimated areas because some of the infested areas may have not been counted during the ground surveys, this study provides the first and accurate estimates of giant reed infestations in the MPRGB. These results will be useful for both land owners and government agencies for the management and control of this invasive weed.

The ability to assimilate microwave radiance observations of the Earth's atmosphere affected by precipitation is investigated with an emphasis on channels that are sensitive to frozen hydrometeors. Mesoscale numerical weather prediction and radiative transfer models, as well as their corresponding adjoint models are utilized in sensitivity and data assimilation experiments. Special Sensor Microwave / Imager observations of Hurricane Bonnie (1998) are compared with model results that are transferred to radiance space with the radiative transfer model. Sensitivity results indicate that the model error in radiance space in areas of precipitation at the initial time is most dependent on the initial hydrometeor fields. At later forecast times, the model error is more sensitive to initial conventional model variables such as water vapor and temperature. When radiance data is assimilated, the model fields have better agreement with the observations compared to a control experiment for all observed channels at the initial time. However, at the next observation time 12 h later, the quantitative error measurements for the control and post assimilation forecasts are approximately the same value. Although this study demonstrates the ability to assimilate observations sensitive to atmospheric ice (as well as liquid) concentrations in a variational framework, important aspects such a background error correlation and bias have been ignored for simplification. More observations in a more complex data assimilation system will be needed in order to fully maximize the forecast impact of these observations.

The NASA Icing Remote Sensing System was operated for the Alliance Icing Research Study II field program during the winter of 2003 around Montreal, Canada and around Cleveland, Ohio during the winter of 2005. Icing research aircraft flights from these field programs provided verification data on liquid water content, air temperature and also cloud particle imagery and distributions. The purpose of this work is to show that the NASA Icing Remote Sensing System X-band radar reflectivity profiles could be used beyond merely defining vertical cloud boundaries, by operationally deriving a qualitative small drop icing hazard warning flag. Several case studies are presented which depict a variety of synoptic weather scenarios. These cases demonstrate that X-band reflectivities below -10 dBZ and above the minimum detectable are uniquely indicative of a particle population dominated by small, liquid droplets. A discussion is included for each case on how an in-flight icing hazard flag from the radar reflectivity profile would improve the operational hazard detection system. Comparison of the NASA Icing Remote Sensing System's X-band radar data to a nearby similar X-band from McGill University is done to ensure data quality and consistency.

A study of wavelet denoising on hyperspectral reflectance data, specifically the red edge position (REP) and its first derivative is presented in this paper. A synthetic data set was created using a sigmoid to simulate the red edge feature for this study. The sigmoid is injected with Gaussian white noise to simulate noisy reflectance data from handheld spectroradiometers. The use of synthetic data enables better quantification and statistical study of the effects of wavelet denoising on the features of hyperspectral data, specifically the REP. The simulation study will help to identify the most suitable wavelet parameters for denoising and represents the applicability of the wavelet-based denoising procedure in hyperspectral sensing for vegetation. The suitability of the thresholding rules and mother wavelets used in wavelet denoising is evaluated by comparing the denoised sigmoid function with the clean sigmoid, in terms of the shift in the inflection point meant to represent the REP, and also the overall change in the denoised signal compared with the clean one. The VisuShrink soft threshold was used with rescaling based on the noise estimate, in conjunction with wavelets of the Daubechies, Symlet and Coiflet families. It was found that for the VisuShrink threshold with single level noise estimate rescaling, the Daubechies 9 and Symlet 8 wavelets produced the least distortion in the location of sigmoid inflection point and the overall curve. The selected mother wavelets were used to denoise oil palm reflectance data to enable determination of the red edge position by locating the peak of the first derivative.

The research on land use / land cover change (LUCC) can provide an important means to understand the relationship between ecological environmental change and human being's activity. The study area, in this paper, Jintai District and Weibin District, is the suburban area of Baoji City, which is located at the frontier of Western Development in China. To explore the typical pattern of land cover change in western China, the LUCC of the study area from 1988 to 2004 is analyzed, using remote sensing technology. Based on these, Markov model is applied to predict the tendency of the LUCC of this area in the next 16 years, and the results indicate that human being's activity, especially in the western cities, will have an increasingly great influence on the regional ecological environment in the current pattern of land use. Faced with the contradiction between land and people and severe ecological environment, establishing land use regulation indices and spatial optimal designs favorable to ecological environment by setting up general land use planning scientifically is important to satisfy reasonable demand of land with economic development and accelerated urbanization and improve ecological environment in western cities in China.

In the past few years, imaging spectroscopy has been used widely. However, it only acquires intensity information in a narrow electromagnetic band, ignoring the polarimetric information of the electromagnetic wave, resulting in inaccurate material classification. Imaging spectropolarimetric technology as a new sensing method can acquire the polarimetric information at a narrow electromagnetic band sequence, but there are few results showing how to combine the redundant and complementary features provided by spectropolarimetric imagery. In this paper, an unsupervised spectropolarimetric imagery classification method is proposed to jointly utilize the spatial, spectral and polarimetric information to make material classification more accurate. First, a spectropolarimetric projection scheme is proposed to divide the spectropolarimetric data set into two parts: a polarimetric spectrum data set and a polarimetric data cube. Then, a kernel fuzzy c-means clustering method is used to cluster the polarimetric spectrum data set and polarimetric data cubes. At last, kernel fuzzy c-means clustering results are combined by evidence reasoning to get better clustering performance. Through experimentation and simulation, the effects of classifying different materials with similar surface colour can be enhanced greatly.

Map production and revision in mountain areas by means of high resolution satellite images showed to be a difficult task. This paper investigates the influence of the elevation accuracy in the updating of large scale geo-databases (geo-DBs) in a mountain urban area of the Italian Western Alps using high resolution panchromatic IKONOS images. A simulation of 1:10,000 scale geo-DB update was performed for the city of Sauze d'Oulx (Susa Valley, Italy) and tested with respect to the Regional technical specifications recently adopted in northern Italy. Finally, results were compared to the official geo-DB available for the test site. Results show that, depending on the digital elevation model's accuracy used, it is possible to satisfy the requirements for the residual's tolerance but not those for residual's mean and standard deviation, thus high resolution DEMs may be needed for updating large scale geo-BDs in mountain areas.

The overarching goal of this research was to develop spatial models and demonstrate their use in selecting the most suitable areas for the inland valley (IV) wetland rice cultivation. The process involved comprehensive sets of methods and protocols involving: (1) Identification and development of necessary spatial data layers; (2) Providing weightages to these spatial data layers based on expert knowledge, (3) Development of spatial models, and (4) Running spatial models for determining most suitable areas for rice cultivation. The study was conducted in Ghana. The model results, based on weightages to 16-22 spatial data layers, showed only 3-4 % of the total IV wetland areas were "highly suitable" but 39-47 % of the total IV wetland areas were "suitable" for rice cultivation. The outputs were verified using field-plot data which showed accuracy between 84.4 to 87.5% with errors of omissions and commissions less than 23%. Given that only a small fraction (<15% overall) of the total IV wetland areas (about 20-28% of total geographic area in Ghana) are currently utilized for agriculture and constitute very rich land-units in terms of soil depth, soil fertility, and water availability, these agroecosystems offer an excellent opportunity for a green and a blue revolution in Africa.

There have been major improvements in our understanding of the role of lakes and impoundments in global carbon cycle. Estimating the true role of lakes as sentinels, regulators and integrators of climate change requires analyzing carbon content of vast number of lakes. This is not realistic without using remote sensing methods. There are no satellite sensors at the moment that provide full global coverage and at the same time have spatial and radiometric resolutions suitable for lake color dissolved organic matter (CDOM) mapping. Therefore, the global study has to be performed using more sensitive sensors to create regional lake CDOM statistics for as many sites as possible and extrapolating the results on global lake map that can be created from less sensitive sensor data with full global coverage (Landsat). As a first step towards the global lake carbon mapping we show that the Advanced Land Imager (ALI) allows to study regional variations in lake CDOM content and consequently estimate closely correlated DOC (dissolved organic carbon) and CO₂ saturation values. The results show also that there may be regional differences in lake CDOM content even if the study sites are geographically relatively close to each other and occupying zones with similar land cover and annual runoff. In one occasion the difference can be explained with human impact that has caused acidification of lakes but the other occasion needs further studies.

Ratio-based bottom depth-retrieval algorithms are conceptually simple relative to other algorithms and can be effective. The objective of this study was to determine the utility of imposing a spatial-smoothing assumption on three ratio-based, feed-forward remote-sensing bathymetry algorithms: Polcyn et al., Stumpf et al., and Dierssen et al. We consider three smoothing operators: median, Savitzky-Golay, and linear diffusion with data fidelity, applied in three domains: spatial, spectral, and spectral-spatial. Thus, we consider nine smoothing methods. In addition, we consider two points at which smoothing is applied: one before the inversion process (pre-smoothing) and the other after the inversion process (post-smoothing). Our new formulations were tested with synthetic data, in situ remote-sensing reflectance, and simultaneous acoustic bathymetry, acquired in optically shallow waters. Analysis and results from the synthetic-data experiment indicate that pre-smoothing method is more effective than post-smoothing method. The field-data experiments indicate that spatial-domain smoothing is effective regardless of the type of smoothing operator, whereas spectral smoothing is not. Spectral-spatial-domain smoothing is as effective as spatial-domain smoothing, but is prone to over-segmentation. Effectiveness of spatial pre-smoothing was observed with every ratio-based inversion method, which suggests potential universal applicability of smoothing operators to ratio-based algorithms.

The detection of long term trends in woody vegetation in Queensland, Australia, from the Landsat-5 TM and Landsat-7 ETM+ sensors requires the automated prediction of overstorey foliage projective cover (FPC) from a large volume of Landsat imagery. This paper presents a comparison of parametric (Multiple Linear Regression, Generalized Linear Models) and machine learning (Random Forests, Support Vector Machines) regression models for predicting overstorey FPC from Landsat-5 TM and Landsat-7 ETM+ imagery. Estimates of overstorey FPC were derived from field measured stand basal area (RMSE 7.26%) for calibration of the regression models. Independent estimates of overstorey FPC were derived from field and airborne LiDAR (RMSE 5.34%) surveys for validation of model predictions. The airborne LiDAR-derived estimates of overstorey FPC enabled the bias and variance of model predictions to be quantified in regional areas. The results showed all the parametric and machine learning models had similar prediction errors (RMSE < 10%), but the machine learning models had less bias than the parametric models at greater than ~60% overstorey FPC. All models showed greater than 10% bias in plant communities with high herbaceous or understorey FPC. The results of this work indicate that use of overstorey FPC products derived from Landsat-5 TM or Landsat-7 ETM+ data in Queensland using any of the regression models requires the assumption of senescent or absent herbaceous foliage at the time of image acquisition.

This paper addresses the issue on airborne image positioning model and its application in FY-3A experiment. First, the FY-3A Medium Resolution Spectral Imager (MERSI)'s viewing vector is derived from MERSI's imaging pattern. Then, the image positioning model is analyzed mathematically in detail which is based on Earth-aircraft geometry. The model parameters are mainly determined by both the sensor - aircraft alignment and the onboard discrete measurements of the positioning and orientation. Flight trials are flown at an altitude of 8300 m over the Qinghai Lake China. It is shown that the image positioning accuracy (about 1~4 pixels) is better than previous methods (more than 7 pixels, [G. J. Jedlovec et al. NASA Technical Memorandum TM - 100352 (1989) and D. P. Roy et al. Int. J. Rem. Sens. 18(9), 1865 - 1887 (1997)]). It is also shown that the model has the potential to hold the image positioning errors within one pixel. The model can operate automatically, and does not need ground control points data. Since our algorithm get the image positioning results through an observation geometric perspective which is in computing the point at which the sensor viewing vector intersects the earth surface, our algorithm assumes the airborne data are from the plain area.

Rangeland comprises as much as 70% of the Earth's land surface area. Much of this vast space is in very remote areas that are expensive and often impossible to access on the ground. Unmanned Aerial Vehicles (UAVs) have great potential for rangeland management. UAVs have several advantages over satellites and piloted aircraft: they can be deployed quickly and repeatedly; they are less costly and safer than piloted aircraft; they are flexible in terms of flying height and timing of missions; and they can obtain imagery at sub-decimeter resolution. This hyperspatial imagery allows for quantification of plant cover, composition, and structure at multiple spatial scales. Our experiments have shown that this capability, from an off-the-shelf mini-UAV, is directly applicable to operational agency needs for measuring and monitoring. For use by operational agencies to carry out their mandated responsibilities, various requirements must be met: an affordable and reliable platform; a capability for autonomous, low altitude flights; takeoff and landing in small areas surrounded by rugged terrain; and an easily applied data analysis methodology. A number of image processing and orthorectification challenges have been or are currently being addressed, but the potential to depict the land surface commensurate with field data perspectives across broader spatial extents is unrivaled.

Monitoring deforestation and forest degradation is central to assessing changes in carbon storage, biodiversity, and many other ecological processes in tropical regions. Satellite remote sensing is the most accurate and cost-effective way to monitor changes in forest cover and degradation over large geographic areas, but the tools and methods have been highly manual and time consuming, often requiring expert knowledge. We present a new user-friendly, fully automated system called CLASlite, which provides desktop mapping of forest cover, deforestation and forest disturbance using advanced atmospheric correction and spectral signal processing approaches with Landsat, SPOT, and many other satellite sensors. CLASlite runs on a standard Windows-based computer, and can map more than 10,000 km², at 30 m spatial resolution, of forest area per hour of processing time. Outputs from CLASlite include maps of the percentage of live and dead vegetation cover, bare soils and other substrates, along with quantitative measures of uncertainty in each image pixel. These maps are then interpreted in terms of forest cover, deforestation and forest disturbance using automated decision trees. CLASlite output images can be directly input to other remote sensing programs, geographic information systems (GIS), Google Earth^TM serif}, or other visualization systems. Here we provide a detailed description of the CLASlite approach with example results for deforestation and forest degradation scenarios in Brazil, Peru, and other tropical forest sites worldwide.

Full-waveform lidar data are emerging into the commercial sector and provide a unique ability to characterize the landscape. The returned laser waveforms indicate specific reflectors within the footprint (vertical structure), while the shape of the return convolves surface reflectance and physical topography. These data are especially effective in vegetative regions with respect to canopy structure characterization. The objective of this research is to evaluate the performance of waveform-derived parameters as input into a supervised classifier. Extracted waveform metrics include Gaussian amplitude, Gaussian standard deviation, canopy energy, ground energy, total waveform energy, ratio between canopy and ground energy, rise time to the first peak, fall time of the last peak, and height of median energy (HOME). The classifier utilizes a feature selection methodology which provides information on the value of waveform parameters for discriminating between class pairs. For this study area, energy ratio and Gaussian amplitude were selected most frequently, but rise time and fall time were also important for discriminating different tree types and densities. The lidar classification accuracy for this study area was 85.8% versus 71.2% for Quickbird imagery. Since the lidar-based input data are structural parameters derived from the waveforms, the classification is improved for classes that are spectrally similar but structurally different.

Remote Sensing using high resolution imagery (HRI) is fast becoming an important tool in detailed land-cover mapping and analysis of plant species invasion. In this study, we sought to test the separability of Pteronia incana invader species by pixel content aggregation and pixel content de-convolution using multi-temporal infrared HRI. An invaded area in Eastern Cape, South Africa was flown in 2001, 2004 and 2006 and HRI of 1x1m resolution captured using a DCS 420 colour infrared camera. The images were separated into bands, geo-rectified and radiometrically corrected using Idrisi Kilimanjaro GIS. Value files were extracted from the bands in order to compare spectral values for P. incana, green vegetation and bare surfaces using the pixel based Perpendicular Vegetation Index (PVI), while Constrained Linear Spectral Unmixing (CLSU) surface endmembers were used to generate sub-pixel land surface image fractions. Spectroscopy was used to validate spectral trends identified from HRI. The PVI successfully separated the multi-temporal imagery surfaces and was consistent with the unmixed surface image fractions from CLSU. Separability between the respective surfaces was also achieved using reflectance measurements.

The use of hyperspectral imaging is a fast growing field with many applications in the civilian, commercial and military sectors. Hyperspectral images are typically composed of many spectral bands in the visible and infrared regions of the electromagnetic spectrum and have the potential to deliver a great deal of information about a remotely sensed scene. One area of interest regarding hyperspectral images is anomaly detection, or the ability to find spectral outliers within a complex background in a scene with no a priori information about the scene or its specific contents. Anomaly detectors typically operate by creating a statistical background model of a hyperspectral image and measuring anomalies as image pixels that do not conform properly to that given model. In this study we compare the performance over diurnal and seasonal changes for several different anomaly detection methods found in the literature and a new anomaly detector that we refer to as the fuzzy cluster-based anomaly detector. Here we also compare the performance of several anomaly-based change detection algorithms. Our results indicate that all anomaly detectors tested in this experimentation exhibit strong performance under optimum illumination and environmental conditions. However, our results point toward a significant performance advantage for cluster-based anomaly detectors in the presence of adverse environmental conditions.

The evidence of phytoplankton bloom in response to the cyclone Sidr in the central Bay of Bengal (BoB) basin has been reported. Satellite observations of surface chlorophyll-a concentration (Chl-a) from MODIS-Aqua, sea surface temperature (SST) from MODIS-Aqua and TMI were studied for before, during and after cyclone periods. The mixed-layer depth (MLD) derived from the nearest Argo float that was closest to the cyclone track along with the float temperature and salinity (T/S) profiles near the cyclone track were studied to further complement the satellite observations. Nearly 900 km long phytoplankton bloom was observed along the cyclone path. Along the path of cyclone Sidr, the average increase in Chl-a due to Sidr during the cyclone and after cyclone period was about 285 per cent and 150 per cent respectively, from the before-cyclone period. The mean decrease in SST from before cyclone period was about 1.75°C and 1.27°C between during and after cyclone period respectively, with maximum cooling of 3.2°C observed during cyclone period. The Argo float observations indicate deepening of MLD by 15 m, during the cyclone and shoaling of MLD after the cyclone by about 35 m. Upward Ekman pumping due to cyclonic winds caused upwelling of nutrient rich subsurface waters, which led to the bloom in the BoB.

A neural network is presented for estimating cloud water and ice paths, effective scattering heights of cloud water and ice, and column water vapor. The cloud water and ice are then used to classify scenes as either clear or cloudy using a simple threshold test of 2 gm^-2 for water and 10 gm^-2 for ice. Training of the neural networks was performed using high resolution spectra in micro-windows of O₂ and CO₂ near infrared absorption bands generated from an ensemble of analyzed meteorological fields from ECMWF and surface properties from MODIS. An independent test data set was generated using the same radiative transfermodel, but coupled with atmospheric profiles derived from CloudSat and Calipso data. Analysis indicates that the algorithmprovides approximately 75-90% accuracy with a 95-99% confidence level for classifying scenes as either cloudy or clear over land surfaces in nadir viewing geometry. These estimates are shown to be robust, in the sense that they are insensitive to realistic instrumental errors, errors in the meteorological analyses and surface properties, and errors in the simulations used for training.

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

In this paper, we propose a novel learning algorithm, the nearest reduced convex hull (NRCH), to tackle the issue of limited training information in practical remote sensing. Before classification, the "underlying" prototypes in the training subspace of each class are approximated by constrained convex combinations of the "existing" ones of this class. By this means, the original training set of each class is eventually expanded to a reduced convex hull (RCH) manifold through which the representational capacity of the training set is greatly enlarged. During this process, good separations of different classes are well maintained by the reduced factor. Based on these RCHs, the nearest neighbor decision rule is then utilized to classify a query sample. Experimental results, obtained on different kinds of data (synthetic data and real multispectral images), show the potential of NRCH for remote sensing classification in comparison with some famous traditional classifiers including Maximum Likelihood Classifier (MLC), Back-propagation Neural Network Classifier (BP), and Support Vector Machine (SVM).

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Automated orthorectification of raw image products is now possible based on the comprehensive metadata collected by Global Positioning Systems and Inertial Measurement Unit technology aboard aircraft and satellite digital imaging systems, and based on emerging pattern-matching and automated image-to-image and control point selection capabilities in many advanced image processing systems. Automated orthorectification of standard aerial photography is also possible if a camera calibration report and sufficient metadata is available. Orthorectification of historical imagery, for which only limited metadata was available, was also attempted and found to require some user input, creating a semi-automated process that still has significant potential to reduce processing time and expense for the conversion of archival historical imagery into geospatially enabled, digital formats, facilitating preservation and utilization of a vast archive of historical imagery. Over 90 percent of the frames of historical aerial photos used in this experiment were successfully orthorectified to the accuracy of the USGS 100K base map series utilized for the geospatial reference of the archive. The accuracy standard for the 100K series maps is approximately 167 feet (51 meters). The main problems associated with orthorectification failure were cloud cover, shadow and historical landscape change which confused automated image-to-image matching processes. Further research is recommended to optimize automated orthorectification methods and enable broad operational use, especially as related to historical imagery archives.

The advances in polarimetric synthetic aperture radar (SAR) interferometry techniques provide a promising way to extract sub-canopy surface parameters using processed SAR images. In this paper, we evaluate the fully maximum likelihood decomposition model of polarimetric SAR interferometry for sub-canopy soil moisture estimation. We further propose a methodology for sub-canopy soil estimation using repeat pass space-borne SIR-C (Shuttle Imaging Radar C) L-band polarimetric SAR interferometric data. The comparison of the inversion results with the field measurements and the climate data of Hotan region from 1951 to 2006 suggests good inversion potential of the proposed method.

In order to overcome the difficulty of automatic registration between the images of different rotations and resolutions, this paper presents a new image registration algorithm. In the algorithm, Harris-Laplace detector is applied to extract image features; a method named scale-space projection based on scale-invariance of Harris-Laplace is proposed; and a feature space on the projection information is constructed in the direction of the main feature orientation, which makes feature space rotation-invariant and resolution-invariant. In addition, the two steps are taken to accomplish feature matching: Euclidean distance is used to match two features coarsely, and then Random Sample Consensus (RANSAC) is applied to identify the correct matches, which implements feature matching accurately and efficiently. Experiments show that the algorithm in this study can accomplish image registration automatically and accurately between images even with large differences in resolutions and rotations.

Using a seven-year history of the satellite measurements and meteorology data over Georgia, USA, this study investigated the capability of a series of satellite-derived vegetation water indices for drought monitoring, as well as the connection between fire occurrence and drought conditions. The vegetation water indices are normalized calculations between MODIS near infrared band 2 and shortwave infrared bands 5, 6, 7, and the difference between bands 6 and 7, denoted by NDWI_2,5, NDWI_2,6, NDWI_2,7 and NMDI, respectively. Results show that the drought conditions indicated by NMDI, NDWI_2,6 and NDWI_2,7 agree well with what have been identified by meteorology data. NMDI, however, has demonstrated more dependable results regarding seasonal moisture variations, as well as stronger responses to drought conditions than NDWI_2,6, and NDWI_2,7. The annual fire number exhibits high correspondence to drought conditions. Drought intensity also influenced fire extent and the most widespread fires occurred during the driest years. Analysis reveals that NMDI can be used as an effective indicator for fire risk monitoring. It is expected that this research will greatly benefit many applications, such as monitoring drought and detecting fire danger potential.

High resolution field spectroradiometers are important for spectral analysis and mobile inspection of vegetation disease. The biggest challenges in using this technology for automated vegetation disease detection are in spectral signatures pre-processing, band selection and generating reflectance indices to improve the ability of hyperspectral data for early detection of disease. In this paper, new indices for oil palm Ganoderma disease detection were generated using band ratio and different band combination techniques. Unsupervised clustering method was used to cluster the values of each class resultant from each index. The wellness of band combinations was assessed by using Optimum Index Factor (OIF) while cluster validation was executed using Average Silhouette Width (ASW). 11 modified reflectance indices were generated in this study and the indices were ranked according to the values of their ASW. These modified indices were also compared to several existing and new indices. The results showed that the combination of spectral values at 610.5nm and 738nm was the best for clustering the three classes of infection levels in the determination of the best spectral index for early detection of Ganoderma disease.

The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m³, 34% had moderate WP of 0.3-0.4 kg/m³, and only 11% area had high WP > 0.4 kg/m³. The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations.

This paper presents a neural-fuzzy inference approach to identify the land use and land cover (LULC) patterns in large urban areas with the 8-meter resolution of multi-spectral images collected by Formosat-2 satellite. Texture and feature analyses support the retrieval of fuzzy rules in the context of data mining to discern the embedded LULC patterns via a neural-fuzzy inference approach. The case study for Taichung City in central Taiwan shows the application potential based on five LULC classes. With the aid of integrated fuzzy rules and a neural network model, the optimal weights associated with these achievable rules can be determined with phenomenological and theoretical implications. Through appropriate model training and validation stages with respect to a groundtruth data set, research findings clearly indicate that the proposed remote sensing technique can structure an improved screening and sequencing procedure when selecting rules for LULC classification. There is no limitation of using broad spectral bands for category separation by this method, such as the ability to reliably separate only a few (4-5) classes. This normalized difference vegetation index (NDVI)-based data mining technique has shown potential for LULC pattern recognition in different regions, and is not restricted to this sensor, location or date.

This study presents our methods to produce multi-day Terra, Aqua, and Terra-Aqua moderate resolution imaging spectroradiometer (MODIS) snow cover composite images respectively from daily Terra MODIS, Aqua MODIS, and both Terra and Aqua MODIS snow cover products, with flexible starting and ending dates and a user-defined cloud cover threshold. The methods are applied to Colorado Plateau, USA and northern Xinjiang, China. The statistical comparison gives the following results. For the 2003-2004 hydrologic year, the daily Terra-Aqua composite images exhibited ~10-15% less on annual mean cloud cover and ~1-4% more on annual mean snow cover, compared with their daily Terra or Aqua counterparts. Using 10% cloud cover as a user-defined threshold, we produced 152 (northern Xinjiang) and 162 (Colorado Plateau) multi-day Terra-Aqua composite images for the 2003-2004 hydrological year, respectively. On average, it is 2.4 and 2.2 days per composite image for northern Xinjiang and Colorado Plateau, respectively. The multi-day Terra, Aqua, and Terra-Aqua composite products result in similar annual mean snow covers (~15% for the Colorado Plateau and ~30% for the northern Xinjiang), as those from the corresponding standard NASA 8-day products, ~3 times as those from the standard NASA daily products. The lower snow cover percentage retrieved from the daily standard products is mainly due to larger cloud cover in the daily products. The Aqua products always have lower annual mean snow cover and higher annual mean cloud cover than those of the Terra products. The daily Terra-Aqua composite products have higher agreement with ground measurements than either of the standard NASA daily Terra or Aqua products. The multi-day Terra-Aqua composite products have much higher agreement with ground measurements than that of the standard daily products and have similar agreement as that of the standard 8-day products. (partial Abstract)

Polarization knowledge is important to detect object characteristics. An approximate relationship between vertically and horizontally polarized reflectivities of specular surfaces is developed and validated using the refractive index datasets of water in the various wavelength ranges at various incidence angles. This study proposes a unique technique to estimate the refractive indexes of a specular surface at a given view angle by the direct inversion of the Fresnel equation and the decomposition of the unpolarized emissivity. The unpolarized emissivity is calculated using the Fresnel equation and the refractive index of water at wavelengths ranging from ultraviolet (200 nm) to microwave (18.75 cm). Consequently, the differences of reflectivity between the Fresnel equations and the Hong approximation are approximately less than 0.001 within the Brewster angles of a material. In addition, the results for refractive index show a reasonable range of retrievals within the Brewster's angle. The imaginary parts of the refractive indexes have larger errors than the real parts, due to the uncertainty of the direct inversion of the Fresnel equation.

A new and efficient method for incorporating the spatiality into difference-based change detection (CD) algorithms is introduced in this paper. It uses the spatial derivatives of image pixels to extract spatial relations among them. Based on this methodology, the performances of two famous difference-based CD methods, conventional polynomial regression (CPR) and multivariate alteration detection (MAD), are improved and called modified polynomial regression (MPR) and spatial multivariate alteration detection (SMAD), respectively. Various quantitative and qualitative evaluations have shown the superiority of MPR over CPR and SMAD over MAD. Also, the superiority of SMAD over all mentioned CD algorithms is shown. Moreover, it has been proved that both proposed methods enjoy the affine invariance property.

High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modeling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.

Changes in rice cultivation systems have been observed in the Mekong River Delta, Vietnam. Among the changes in cultural practices, the change from transplanting to direct sowing, the use of water-saving technology, and the use of high production method could have impacts on radar remote sensing methods previously developed for rice monitoring. Using Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) data over the province of An Giang, this study showed that the radar backscattering behaviour is much different from that of the reported traditional rice. At the early stage of the season, direct sowing on fields with rough and wet soil surface provides very high backscatter values for HH (Horizontal transmit - Horizontal receive polarisation) and VV (Vertical transmit - Vertical receive polarisation) data, as a contrast compared to the very low backscatter of fields covered with water before emergence. The temporal increase of the backscatter is therefore not observed clearly over direct sowing fields. Hence, the use of the intensity temporal change as a rice classifier proposed previously may not apply. Due to the drainage that occurs during the season, HH, VV and HH/VV are not strongly related to biomass, in contrast with past results. However, HH/VV ratio could be used to derive the rice/non-rice classification algorithm for all conditions of rice fields in the test province. The mapping results using the HH/VV polarization ratio at a single date in the middle period of the rice season were assessed using statistical data at different districts in the province, where very high accuracy was found. The method can be applied to other regions, provided that the synthetic aperture radar data are acquired during the peak period of the rice season, and that few training fields provide adjusted threshold values used in the method.

Airborne laser detection and ranging (LADAR) systems are used for applications such as three-dimensional imaging, topographic mapping, and target recognition. Typical systems transmit laser beams through apertures that are a few inches wide and use pulses that are roughly a nanosecond in duration. If the pulse reflects off a target that is tilted with respect to the LADAR's line-of-sight, then the reflection process will elongate the received signal as compared to the transmitted pulse. If the range is more than a few kilometers and the target is tilted more than about forty-five degrees, the increase in the width of the received pulse produces a signifcant drop in range precision as compared to when the target is perpendicular to the line-of-sight.

Dechirping is a technique widely used to reduce sampling rate. It is well suited for the illumination of small scenes. In this paper, we extend this idea to mono/bistatic spotlight synthetic aperture radar (SAR) imaging. An azimuth preprocessor based on the spectral analysis (SPECAN) convolution is presented. The convolution overcomes the Doppler aliasing of echoed signals, while the wavenumber analytic formula keeps unchanged. Since the spatial characteristic of the signal is preserved, the preprocessing is well compatible with conventional focusing approaches, such as chirp scaling algorithm and frequency scaling algorithm The proposed method is validated by simulations in both monostatic and bistatic cases.

Soft classification methods can be used for mixed-pixel classification on remote sensing imagery by estimating different land cover class fractions of every pixel. However, the spatial distribution and location of these class components within the pixel remain unknown. To map land cover at subpixel scale and increase the spatial resolution of land cover classification maps, in this paper, a prediction model combining wavelet transform and Radial Basis Functions (RBF) neural network, abbreviated as Wavelet-RBFNN, is constructed by predicting high-frequency wavelet coefficients from low-frequency coefficients at the same resolution with RBF network and taking wavelet coefficients at coarser resolution as training samples. According to different land cover class fraction images obtained from mixed-pixel classification, based on the assumption of neighborhood dependence of wavelet coefficients, subpixel mapping on remote sensing imagery can be accomplished through two steps, i.e., prediction of land cover class compositions within subpixels and hard classification. The experimental results obtained with artificial images, QuickBird image and Landsat 7 ETM+ image indicate that the subpixel mapping method proposed in this paper can successfully produce super-resolution land cover classification maps from remote sensing imagery, outperforming cubic B-spline and Kriging interpolation method in visual effect and prediction accuracy. The Wavelet-RBFNN model can also be applied to simulate higher spatial resolution image, and automatically identify and locate land cover targets at the subpixel scales, when the cost and availability of high resolution imagery prohibit its use in many areas of work.

A multilayer soil model is presented for improved estimation of soil moisture content using the first-order small perturbation method (SPM) applied to measurements of radar backscattering coefficient. The total reflection coefficient of the natural bare soil including volume scattering contribution is obtained using the multilayer model. The surface reflection terms in SPM model are replaced by the total reflection coefficient from the multilayer soil surface in estimating soil moisture. The difference between the modified SPM model and the original SPM surface model is that the modified SPM model includes both the surface scattering and the volumetric scattering of the natural bare soil. Both the modified SPM model and the original SPM model are tested in soil moisture retrievals using experimental microwave backscattering coefficient data in the literature. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are 5.2%, while errors from the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved when the surface reflection terms are replaced by the total reflection coefficients of multilayer soil model over bare or very sparsely vegetation covered fields.

Remote sensing provides critical information for broad scale assessments of wildlife habitat distribution and conservation. However, such efforts have been typically unable to incorporate information about vegetation structure, a variable important for explaining the distribution of many wildlife species. We evaluated the consequences of incorporating remotely sensed information about horizontal vegetation structure into current assessments of wildlife habitat distribution and conservation. For this, we integrated the new NLCD tree canopy cover product into the US GAP Analysis database, using avian species and the finished Idaho GAP Analysis as a case study. We found: (1) a 15-68% decrease in the extent of the predicted habitat for avian species associated with specific tree canopy conditions, (2) a marked decrease in the species richness values predicted at the Landsat pixel scale, but not at coarser scales, (3) a modified distribution of biodiversity hotspots, and (4) surprising results in conservation assessment: despite the strong changes in the species predicted habitats, their distribution in relation to the reserves network remained the same. This study highlights the value of area wide vegetation structure data for refined biodiversity and conservation analyses. We discuss further opportunities and limitations for the use of the NLCD data in wildlife habitat studies.

The temperate forest is a complex biome due to the diversity of forest types, forest cover change dynamics and forest use management practices. While temperate forests play an important role in the global carbon cycle, their net carbon exchange is uncertain. Quantifying forest cover change is an important step in documenting disturbance regimes and carbon exchange estimates. Biome-wide gross forest cover loss was estimated using a probability-based sampling approach that integrated moderate and high spatial resolution satellite data sets. Area of gross forest cover loss from 2000 to 2005 within the temperate forest biome is estimated to be 1.03% of the total biome area, or 18.41 Mha. Estimated forest cover loss represented a 3.5% reduction in year 2000 forest area. About 68% of the total forest cover loss occurred in Eastern North America and in Europe. The mid-latitude forests of the United States exhibited the highest forest cover loss rates within the biome. Biome-wide rate of gross forest cover loss gradually increased from 2001 to 2005. The lowest change was detected in 2004, followed by the year of the highest change over the 5-year period. The regional forest cover change dynamics were confirmed by official forest fire and timber production statistics. The validation of the MODIS-based product demonstrated its efficiency in forest cover mapping and monitoring. Forest cover change monitoring using the approach presented should bring greater understanding on forest cover dynamics in temperate forests and enable improved carbon accounting.

Widespread disturbance has brought a large amount of narrow-linear and small-area disturbance features (e.g., trails, seismic lines, forest roads, well sites, and cut blocks) to forest areas throughout the past decade. This issue has prompted research into finding the appropriate data and methods for mapping these narrow-linear and small-area disturbance features in order to examine their impacts on wildlife habitat. In this paper, we first described the characteristics of small forest disturbances and presented the nature of problem. We then presented a framework for detecting and extracting narrow-linear and small-area forest disturbance features. Using a SPOT 5 high spatial detail image and existing GIS databases, we applied the framework to map narrow-linear and small-area forest disturbance features in a Bear Management area (BMA) in the eastern slopes of the Rocky Mountains in Alberta, Canada. The results indicated that the proposed framework produced accurate disturbance maps for cut blocks, and forest roads & trails. The high errors of omission in the cut lines map were attributed to inconsistent geometric and radiometric patterns in the 'rarely-used' or 'old' cut lines. The study confirmed the feasibility of rapidly updating incomplete GIS data with linear and small-area disturbance features extracted from high spatial detail SPOT imagery. Future work will be directed towards improvement of the framework and the extraction strategy to remove a large amount of spurious features and to increase accuracy for cut lines mapping.

This paper proposes an automatic approach for building extraction in suburban and rural areas by integrating the light detection and ranging (LIDAR) data and WorldView imagery. This approach consists of two major steps: building detection and 2D building reconstruction. In the first step, a normalized digital surface model (nDSM) is produced from LIDAR data. Then a building mask is obtained from nDSM based on the elevation and roughness analyses. In the second step, the building mask, along with orthoimage are used to determine preliminary position of the building boundaries. Then a least square line fitting process is applied to produce line segments. Finally the boundaries of buildings are reconstructed by grouping line segments, and intersection of the favorable groups. The proposed approach has shown a 92.5% completeness and 94.75% correctness.

This study couples Laplacian-of-Gaussian filter (LoG) and wavelet multi-scale edge detection approaches to extract the waterlines of Chongming Island from Landsat images. The digital elevation model (DEM) of the Chongming tidal flat is constructed by means of the "waterline method". From multi-temporal images obtained under different tidal conditions, it is possible to build up a set of heighted waterlines within the tidal zone, and gridded DEM is then generated by the Australian National University Digital Elevation Model (ANUDEM) method. The results indicate that the methods proposed by this paper can effectively reproduce the coastal near shore topography of Chongming Island, and the precision of the DEM of Dongtan tidal flat is acceptable.

Most ecosystems and crops experience water stress in arid and semiarid areas of the Inner Mongolia grassland, Northern China. Yet the lack of long-term in situ monitoring data hinders the managerial capacity of changing water vapor environment, which is tied with sustaining the grassland in the Inner Mongolia. Environmental remote sensing monitoring and modeling may provide synergistic means of observing changes in thermodynamic balance during drought onset at the grassland surface, providing reliable projections accounting for variations and correlations of water vapor and heat fluxes. It is the aim of this paper to present a series of estimates of latent heat, sensible heat, and net radiation using an innovative first-principle, physics-based model (GEOMOD: GEO-model estimated the land surface heat with MODis data) with the aid of integrated satellite remote sensing and in situ eddy covariance data. Based on the energy balance principle and aerodynamics diffusion theory, the GEOMOD model is featured with MODIS (Moderate Resolution Imaging Spectroradiometer) data with 250 m spatial resolution to collectively reflect the spatial heterogeneity of surface properties, supplement missing data with the neighborhood values across both spatial and temporal domains, estimate the surface roughness height and zero-plane displacement with dynamic look-up table, and implement a fast iterative algorithm to calculate sensible heat. Its analytical framework is designed against overreliance on local micro-meteorological parameters. Practical implementation was assessed in the study area, the Xilin Gol River Basin, a typical grassland environment, Northern China. With 179 days of MODIS data in support of modeling, coincident ground-based observations between 2000 and 2006 were selected for model calibration. The findings indicate that GEOMOD performs reasonably well in modeling the land surface heat exchange process, as demonstrated by a case study of Inner Mongolia.

In the last fifty years, the landscape of Southern Italy has been intensely modified by human activities. A series of analyses have been carried out by using three orthophotos whose acquisition time spans range from 1953 to 2002. The change detection analysis indicates two main phases of changes. The first main change from 1953 to 1973 concerns mainly woodland and wild vegetation while the second one is driven by agricultural practices. The analysis also allows for the detection of some paleao structures known as "lama" that partly coincide with the hydrographic features and present a considerable transformation from that of their original wild vegetation by being transformed into agricultural fields. The loss of wild vegetation in the lama channel beds and the occurrence of severe floods in this area during the last decade have led to the speculation that intensive agricultural practices have increased the frequency and magnitude of flooding. Based on the availability of more than 50 years of aerial photography, the site is suitable for an investigation about the interaction between historical land use change and watershed storm flow response.