This PDF file contains the front matter associated with SPIE Proceedings Volume 10005, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Long range land surveillance is a critical need in numerous military and civilian security applications, such as threat detection, terrain mapping and disaster prevention. A key technology for land surveillance, synthetic aperture radar (SAR) continues to provide high resolution radar images in all weather conditions from remote distances. State of the art SAR systems based on dual-use satellites are capable of providing ground resolutions of one meter; while their airborne counterparts obtain resolutions of 10 cm. Certain land surveillance applications such as subsidence monitoring, landslide hazard prediction and tactical target tracking could benefit from improved resolution. The ultimate limitation to the achievable resolution of any imaging system is its wavelength. State-of-the-art SAR systems are approaching this limit. The natural extension to improve resolution is to thus decrease the wavelength, i.e. design a synthetic aperture system in a different wavelength regime. One such system offering the potential for vastly improved resolution is Synthetic Aperture Ladar (SAL). This system operates at infrared wavelengths, ten thousand times smaller radar wavelengths. This paper presents a SAL platform based on the INO Master Oscillator with Programmable Amplitude Waveform (MOPAW) laser that has a wavelength sweep of Δλ=1.22 nm, a pulse repetition rate up to 1 kHz and up to 200 μJ per pulse. The results for SAL 2D imagery at a range of 10 m are presented, indicating a reflectance sensibility of 8 %, ground-range and azimuth resolution of 1.7 mm and 0.84 mm respectively.

Estimation of the essential climate variables (ECVs), such as photosynthetically active radiation (FAPAR) and the leaf area index (LAI), is largely based on satellite-based remote sensing and the subsequent inversion of radiative transfer (RT) models. In order to build models that accurately describe the radiative transfer within and below the canopy, detailed 3D structural (geometrical) and spectral (radiometrical) information of the canopy is needed. Close-range remote sensing, such as terrestrial remote sensing and UAV-based 3D spectral measurements, offers significant opportunity to improve the RT modelling and ECV estimation of forests. Finnish Geospatial Research Institute (FGI) has been developing active and passive high resolution 3D hyperspectral measurement technologies that provide reflectance, anisotropy and 3D structure information of forests (i.e. hyperspectral point clouds). Technologies include hyperspectral imaging from unmanned airborne vehicle (UAV), terrestrial hyperspectral lidar (HSL) and terrestrial hyperspectral stereoscopic imaging. A measurement campaign to demonstrate these technologies in ECV estimation with uncertainty propagation was carried out in the Wytham Woods, Oxford, UK, in June 2015. Our objective is to develop traceable processing procedures for generating hyperspectral point clouds with geometric and radiometric uncertainty propagation using hyperspectral aerial and terrestrial imaging and hyperspectral terrestrial laser scanning. The article and presentation will present the methodology, instrumentation and first results of our study.

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

Solar radiation takes in today's world, an increasing importance. Different devices are used to carry out spectral and integrated measurements of solar radiation. Thus the sensors can be divided into the fallow types: Calorimetric, Thermomechanical, Thermoelectric and Photoelectric. The first three categories are based on components converting the radiation to temperature (or heat) and then into electrical quantity. On the other hand, the photoelectric sensors are based on semiconductor or optoelectronic elements that when irradiated change their impedance or generate a measurable electric signal. The response function of the sensor element depends not only on the intensity of the radiation but also on its wavelengths. The radiation sensors most widely used fit in the first categories, but thanks to the reduction in manufacturing costs and to the increased integration of electronic systems, the use of the photoelectric-type sensors became more interesting. In this work we present a study of the behavior of different optoelectronic sensor elements. It is intended to verify the static response of the elements to the incident radiation. We study the optoelectronic elements using mathematical models that best fit their response as a function of wavelength. As an input to the model, the solar radiation values are generated with a radiative transfer model. We present a modeling of the spectral response sensors of other types in order to compare the behavior of optoelectronic elements with other sensors currently in use.

Coastal regions, especially river deltas are highly resourceful and hence densely populated; but these extremely low-lying lands are vulnerable to rising sea levels due to global warming threatening the life and property in these regions. Recent IPCC (2013) predictions of 26-82cm global sea level rise are now considered conservative as subsequent investigations such as by Met Office, UK indicated a vertical rise of about 190cm, which would displace 10% of the world’s population living within 10 meters above the sea level. Therefore, predictive models showing the hazard line are necessary for efficient coastal zone management. Remote sensing and GIS technologies form the mainstay of such predictive models on coastal retreat and inundation to future sea-level rise. This study is an attempt to estimate the varying trends along the Krishna–Godavari (K–G) delta region. Detailed maps showing various coastal landforms in the K-G delta region were prepared using the IRS-P6 LISS 3 images. The rate of shoreline shift during a 31-year period along different sectors of the 330km long K-G delta coast was estimated using Landsat-2 and IRS-P6 LISS 3 images between 1977 and 2008. With reference to a selected baseline from along an inland position, End Point Rate (EPR), Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) were calculated, using a GIS–based Digital Shoreline Analysis System (DSAS). The results showed that the shoreline migrated landward up to a maximum distance of 3.13km resulting in a net loss of about 42.10km² area during this 31-year period. Further, considering the nature of landforms and EPR, the future hazard line is predicted for the area, which also indicated a net erosion of about 57.68km² along the K-G delta coast by 2050 AD.

Land Surface Temperature (LST) is an important parameter related to land surface processes that changes continuously through time. Assessing its dynamics during a volcanic eruption has both environmental and socio-economical interest. Lava flows and other volcanic materials produced and deposited throughout an eruption transform the landscape, contributing to its heterogeneity and altering LST measurements. This paper aims to assess variations of satellite-derived LST and to detect patterns during the latest Fogo Island (Cape Verde) eruption, extending from November 2014 through February 2015. LST data was obtained through four processed Landsat 8 images, focused on the caldera where Pico do Fogo volcano sits. QGIS’ plugin Semi-Automatic Classification was used in order to apply atmospheric corrections and radiometric calibrations. The algorithm used to retrieve LST values is a single-channel method, in which emissivity values are known. The absence of in situ measurements is compensated by the use of MODIS sensor-derived LST data, used to compare with Landsat retrieved measurements. LST data analysis shows as expected that the highest LST values are located inside the caldera. High temperature values were also founded on the south-facing flank of the caldera. Although spatial patterns observed on the retrieved data remained roughly the same during the time period considered, temperature values changed throughout the area and over time, as it was also expected. LST values followed the eruption dynamic experiencing a growth followed by a decline. Moreover, it seems possible to recognize areas affected by lava flows of previous eruptions, due to well-defined LST spatial patterns.

Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km2 along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm; moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.

This paper presents observations based on in-situ and time series MODIS Terra/Aqua and NOAA-AVHRR satellite data on derived anomalies of multi geophysical parameters (land surface temperature -LST, outgoing long-wave radiation- OLR, and mean air temperature- AT for some seismic events recorded in Vrancea seismic region in Romania. Starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (°C) and the increased OLR values higher than the normal have been recorded around epicentral areas, function of the magnitude and focal depth, which disappeared after the main shock. A developed Lithosphere-Surfacesphere-Atmosphere-Ionosphere Coupling (LSAIC) model can explain most of these presignals as a synergy between different anomalies of geophysical/geochemical parameters. These anomalies prior to medium to strong earthquakes are attributed to the thermodynamic, degassing and ionization processes in the Earth- Atmosphere system and micro-fracturing in the rocks especially along area’s active faults. The main outcome of this paper is an unified concept for systematic validation of different types of earthquake precursors of which Land Surface Temperature (LST), outgoing Long wave Radiation (OLR), Air Temperature (AT), radon gas concentration, are the most reliable parameters within the chain of the processes described by a LSAIC model.

In this research, we applied the Global Navigation Satellite System (GNSS) with Global Positioning System (GPS) to create new geodetic network, which is referred to ITRF2000. GPS observation data in 2010 and 2012 were used for network adjustment by Least Square Method (Minimally Constrained Adjustment and Fully Constrained Adjustment), then adjusted coordinates were used to determine updated magnitude and direction.

The use of Remotely Piloted Aerial System (RPAS) for remote sensing applications is becoming more frequent as the technologies on on-board cameras and the platform itself are becoming a serious contender to satellite and airplane imagery. MicMac is a photogrammetric tool for image matching that can be used in different contexts. It is an open source software and it can be used as a command line or with a graphic interface (for each command). The main objective of this work was the integration of MicMac with QGIS, which is also an open source software, in order to create a new open source tool applied to photogrammetry/remote sensing. Python language was used to develop the application. This tool would be very useful in the manipulation and 3D modelling of a set of images. The main objective was to create a toolbar in QGIS with the basic functionalities with intuitive graphic interfaces. The toolbar is composed by three buttons: produce the points cloud, create the Digital Elevation Model (DEM) and produce the orthophoto of the study area. The application was tested considering 35 photos, a subset of images acquired by a RPAS in the Aguda beach area, Porto, Portugal. They were used in order to create a 3D terrain model and from this model obtain an orthophoto and the corresponding DEM. The code is open and can be modified according to the user requirements. This integration would be very useful in photogrammetry and remote sensing community combined with GIS capabilities.

An accurate estimation of solar radiation availability is vital for planning solar energy generation systems. Classically, this type of estimation is made by cumulating data for periods of one year and serves to determine locations with the highest solar radiation availability. However, the integration of high shares of technologies such as photovoltaics in the energy matrix and the evaluation of the economic viability of these systems under time-dependent promotion mechanisms, also requires estimations in a high temporal resolution. When looking at the yearly solar resource availability, the north-west of Argentina is one of the regions of the world with the highest solar radiation potential. Yet estimations are available mainly in low spatial resolutions and there are only few studies that try to characterize the temporal variability of the solar resource in this part of the world. This paper presents a methodology to integrate satellite imagery derived data and a GIS-based solar radiation algorithm in order to generate a high resolution solar irradiance spatiotemporal data set for the province of Salta, north-west Argentina. This data set describes in a better way the differences in solar resource availability between flat and mountainous regions in the province, serves to accurately identify locations with the highest global solar radiation and to characterize its variability on time. Furthermore, the presented methodology can be easily replicated for the rest of South America that is covered by Down-welling Surface Shortwave Flux (DSSF) product provided by the Land Surface Analysis Satellite Applications Facility.

Currently in archaeological studies digital elevation models are mainly used especially in terms of shaded reliefs for the prospection of archaeological sites. Hesse (2010) provides a supporting software tool for the determination of local relief models during the prospection using LiDAR scans. Furthermore the search for relicts from WW2 is also in the focus of his research. In James et al. (2006) the determined contour lines were used to reconstruct locations of archaeological artefacts such as buildings. This study is much more and presents an innovative workflow of determining historical high resolution terrain surfaces using recent high resolution terrain models and sedimentological expert knowledge. Based on archaeological field studies (Franconian Saale near Bad Neustadt in Germany) the sedimentological analyses shows that archaeological interesting horizon and geomorphological expert knowledge in combination with particle size analyses (Koehn, DIN ISO 11277) are useful components for reconstructing surfaces of the early Middle Ages. Furthermore the paper traces how it is possible to use additional information (extracted from a recent digital terrain model) to support the process of determination historical surfaces. Conceptual this research is based on methodology of geomorphometry and geo-statistics. The basic idea is that the working procedure is based on the different input data. One aims at tracking the quantitative data and the other aims at processing the qualitative data. Thus, the first quantitative data were available for further processing, which were later processed with the qualitative data to convert them to historical heights. In the final stage of the workflow all gathered information are stored in a large data matrix for spatial interpolation using the geostatistical method of Kriging. Besides the historical surface, the algorithm also provides a first estimation of accuracy of the modelling. The presented workflow is characterized by a high flexibility and the opportunity to include new available data in the process at any time.

Accurately describing forest surface fuel load is significant for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, as well as assessing potential fire hazards. In this study, the Light Detection and Ranging (LiDAR) data was used to estimate surface fuel load, due to its ability to provide three-dimensional information to quantify forest structural characteristics with high spatial accuracies. Firstly, the multilayered eucalypt forest vegetation was stratified by identifying the cut point of the mixture distribution of LiDAR point density through a non-parametric fitting strategy as well as derivative functions. Secondly, the LiDAR indices of heights, intensity, topography, and canopy density were extracted. Thirdly, these LiDAR indices, forest type and previous fire disturbances were then used to develop two predictive models to estimate surface fuel load through multiple regression analysis. Model 1 was developed based on LiDAR indices, which produced a R² value of 0.63. Model 2 (R² = 0.8) was derived from LiDAR indices, forest type and previous fire disturbances. The accurate and consistent spatial variation in surface fuel load derived from both models could be used to assist fire authorities in guiding fire hazard-reduction burns and fire suppressions in the Upper Yarra Reservoir area, Victoria, Australia.

Salt minerals are an important natural resource. The ability to quickly and remotely identify and quantify salt deposits and salt contaminated soils and sands is therefore a priority goal for the various industries and agencies that utilise salts. The advent of global hyperspectral imagery from instruments such as Hyperion on NASA’s Earth-Observing 1 satellite has opened up a new source of data that can potentially be used for just this task. This study aims to assess the ability of Visible and Near Infrared (VNIR) spectroscopy to identify and quantify salt minerals through the use of spectral mixture analysis. The surface and near-surface soils of the Atacama Desert in Chile contain a variety of well-studied salts, which together with low cloud coverage, and high aridity, makes this region an ideal testbed for this technique. Two forms of spectral data ranging 0.35 – 2.5 μm were collected: laboratory spectra acquired using an ASD FieldSpec Pro instrument on samples from four locations in the Atacama desert known to have surface concentrations of sulfates, nitrates, chlorides and perchlorates; and images from the EO-1 satellite’s Hyperion instrument taken over the same four locations. Mineral identifications and abundances were confirmed using quantitative XRD of the physical samples. Spectral endmembers were extracted from within the laboratory and Hyperion spectral datasets and together with additional spectral library endmembers fed into a linear mixture model. The resulting identification and abundances from both dataset types were verified against the sample XRD values. Issues of spectral scale, SNR and how different mineral spectra interact are considered, and the utility of VNIR spectroscopy and Hyperion in particular for mapping specific salt concentrations in desert environments is established. Overall, SMA was successful at estimating abundances of sulfate minerals, particularly calcium sulfate, from both hyperspectral image and laboratory sample spectra, while abundance estimation of other salt phase spectral end-members was achieved with a higher degree of error.

Hollow village is a special phenomenon in the process of urbanization in China, which causes the waste of land resources. Therefore, it's imminent to carry out the hollow village recognition and renovation. However, there are few researches on the remote sensing identification of hollow village. In this context, in order to recognize the abandoned homesteads by remote sensing technique, the experiment was carried out as follows. Firstly, Gram-Schmidt transform method was utilized to complete the image fusion between multi-spectral images and panchromatic image of WorldView-2. Then the fusion images were made edge enhanced by high pass filtering. The multi-resolution segmentation and spectral difference segmentation were carried out to obtain the image objects. Secondly, spectral characteristic parameters were calculated, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), the normalized difference Soil index (NDSI) etc. The shape feature parameters were extracted, such as Area, Length/Width Ratio and Rectangular Fit etc.. Thirdly, the SEaTH algorithm was used to determine the thresholds and optimize the feature space. Furthermore, the threshold classification method and the random forest classifier were combined, and the appropriate amount of samples were selected to train the classifier in order to determine the important feature parameters and the best classifier parameters involved in classification. Finally, the classification results was verified by computing the confusion matrix. The classification results were continuous and the phenomenon of salt and pepper using pixel classification was avoided effectively. In addition, the results showed that the extracted Abandoned Homesteads were in complete shapes, which could be distinguished from those confusing classes such as Homestead in Use and Roads.

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a “remote sensing” approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) is a collaborative research project funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) – Heritage Plus in 2015–2018 (www.prothego.eu). PROTHEGO aims to make an innovative contribution towards the analysis of geohazards in areas of cultural heritage, and uses novel space technology based on radar interferometry (InSAR) to retrieve information on ground stability and motion in the 400+ UNESCO's World Heritage List monuments and sites of Europe. InSAR can be used to measure micro-movements to identify geo-hazards. In order to verify the InSAR image data, field and close range measurements are necessary. This paper presents the methodology for local-scale monitoring of the Choirokoitia study site in Cyprus, inscribed in the UNESCO World Heritage List, and part of the demonstration sites of PROTHEGO. Various field and remote sensing methods will be exploited for the local-scale monitoring, static GNSS, total station, leveling, laser scanning and UAV and compared with the Persistent Scatterer Interferometry results. The in-situ measurements will be taken systematically in order to document any changes and geo-hazards that affect standing archaeological remains. In addition, ground truth from in-situ visits will provide feedback related to the classification results of urban expansion and land use change maps. Available archival and current optical satellite images will be used to calibrate and identify the level of risk at the Cyprus case study site. The ground based geotechnical monitoring will be compared and validated with InSAR data to evaluate cultural heritage sites deformation trend and to understand its behaviour over the last two decades.

The presence of modern structures and infrastructures is relevant if you want to plan an archaeological or cultural heritage project in a populated area (e.g., cities and countryside). Both natural and manmade objects “hidden” in the subsurface (like tree roots, electrical cables, pipelines, tunnels, etc.) can interfere in preservation of buried heritage. The main advantage of the remote sensing (RS) approach is the application of different non-destructive techniques (NDTs) to obtain the best result, in terms of both resolution and accuracy, without digging. One of these NDTs, i.e., the Ground Penetrating Radar (GPR) method, is used in this paper. The examples shown here demonstrate not only that the use of the GPR technique, as a remote sensor, represents an effective and non-destructive methodology for discovering, recovering, and understanding archeological data but also it can be applied to better understand the evolution of the ancient Past through the development of the Present.

With the acceleration of urbanization in China, most rural areas formed a widespread phenomenon, i.e., destitute village, labor population loss, land abandonment and rural hollowing. And it formed a unique hollow village problem in China finally. The governance of hollow village was the objective need of the development of economic and social development in rural area for Chinese government, and the research on the evaluation method of rural hollowing was the premise and basis of the hollow village governance. In this paper, several evaluation methods were used to evaluate the rural hollowing based on the survey data, land use data, social and economic development data. And these evaluation indexes were the transition of homesteads, the development intensity of rural residential areas, the per capita housing construction area, the residential population proportion in rural area, and the average annual electricity consumption, which can reflect the rural hollowing degree from the land, population, and economy point of view, respectively. After that, spatial analysis method of GIS was used to analyze the evaluation result for each index. Based on spatial raster data generated by Kriging interpolation, we carried out re-classification of all the results. Using the fuzzy clustering method, the rural hollowing degree in Ningxia area was reclassified based on the two spatial scales of county and village. The results showed that the rural hollowing pattern in the Ningxia Hui Autonomous Region had a spatial distribution characteristics that the rural hollowing degree was obvious high in the middle of the study area but was low around the study area. On a county scale, the specific performances of the serious rural hollowing were the higher degree of extensive land use, and the lower level of rural economic development and population transfer concentration. On a village scale, the main performances of the rural hollowing were the rural population loss and idle land. The evaluation method of rural hollowing constructed in this paper can effectively carry out a comprehensive degree zoning of rural hollowing, which can make orderly decision support plans of hollow village governance for the government.

In 1989 about 1.5 million soldiers were stationed in Germany. With the political changes in the early 1990s a substantial decline of the staff occurred on currently 200,000 employees in the armed forces and less than 60,000 soldiers of foreign forces. These processes entailed conversions of large areas not longer used for military purposes, especially in the new federal states in the eastern part of Germany. One of these conversion areas is the former military training area Konigsbruck in Saxony. For the analysis of vegetation and its development over time, the Normalized Difference Vegetation Index (NDVI) has established as one of the most important indicators. In this context, the questions arise whether MODIS NDVI products are suitable to determine conversion processes on former military territories like military training areas and what development processes occurred in the ”Konigsbrucker Heide” in the past 15 years. First, a decomposition of each series in its trend component, seasonality and the remaining residuals is performed. For the trend component different regression models are tested. Statistical analysis of these trends can reveal different developments, for example in nature development zones (without human impact) and zones of controlled succession. The presented workflow is intended to show the opportunity to support a high temporal resolution monitoring of conversion areas such as former military training areas.

The Central Asian (CA) rangelands is a part of the arid and semi-arid ecological zones and spatial extent of drylands in CA (Tajikistan, Kazakhstan, Uzbekistan, Kyrgyzstan, and Turkmenistan) is vast. Projections averaged across a suite of climate models, as measured between 1950-2012 by Standardised Precipitation-Evapotranspiration Index (SPEI) estimated a progressively increasing drought risks across rangelands (Turkmenistan, Tajikistan and Uzbekistan) especially during late summer and autumn periods, another index: Potential Evapotranspiration (PET) indicated drought anomalies for Turkmenistan and partly in Uzbekistan (between 1950-2000). On this study, we have combined a several datasets of drought indices ( SPIE, PET, temperature_T°C and precipitation_P) for better estimation of resilience/non-resilience of the ecosystems after warming the temperature in the following five countries, meanwhile, warming of climate causing of increasing rating of degradations and extension of desertification in the lowland and foothill zones of the landscape and consequently surrounding experienced of a raising balance of evapotranspiration (ET₀). The study concluded, increasing drought anomalies which is closely related with raising (ET₀) in the lowland and foothill zones of CA indicated on decreasing of NDVI indices with occurred sandy and loamy soils it will resulting a loss of vegetation diversity (endangered species) and raising of wind speeds in lowlands of CA, but on regional level especially towards agricultural intensification (without rotation) it indicated no changes of greenness index. It was investigated to better interpret how vegetation feedback modifies the sensitivity of drought indices associated with raising tendency of air temperature and changes of cold and hot year seasons length in the territory of CA.

Monitoring of soil aggregate breakdown remains, even at the micro-plot scale, a challenge. Remote sensing has shown its potential to assess many different soil properties and is a fast and non-destructive method to investigate soil susceptibility to water erosion. We designed an outdoor experiment to monitor soil aggregates breakdown under natural rainfall at a micro-plot scale using a regular camera. Five soils susceptible to detachment (silty loam with various organic matter content, loam and sandy loam) were photographed once per day. We collected images and rainfall data from November 2014 until February 2015. Considering that the soil surface roughness causes shadow cast, the blue/red band ratio is used to observe the soil aggregates changes. In addition, a Gray Level Co-occurrence Matrix (GLCM) is used to extract the image texture entropy which reflects the process of soil aggregates breakdown. In our research the entropy calculated at 135 degrees along the direction of shadows gives best results. Our results show that both entropy and shadow index follow the wetting and drying cycles with a decrease due to a rain event. This decrease is small due to low rainfall intensity (< 2.5 mmh^-1) for the entire period that the experiment ran. However, the biggest rain event of 20 mmday^-1 resulted in a decrease in entropy, meaning that sufficient rainfall energy was present to trigger the soil aggregates break down. This research concludes that both entropy and shadow index obtained with a regular camera enable the monitoring of soil aggregate breakdown at a high spatial resolution.

Most studies indicate that L-band synthetic aperture radar (SAR) has a great capacity to estimate biomass due to its ability to penetrate deeply through canopy layers. Many applications using L-band space-borne data have showcased their own significant contribution in biomass estimation but some limitations still exist. New data have been released recently that are designed to overcome limitations and drawbacks of previous sensor generations. The Japan Aerospace Exploration Agency (JAXA) launched the new sensor ALOS-2 to improve wide and high-resolution observation technologies in order to further meet social and environmental objectives. In the list of priority tasks addressed by JAXA there are experiments utilizing these new data for vegetation biomass distribution measurement. This study, therefore, focused on investigating the capabilities of these new microwave data in above ground biomass (AGB) estimation. The data mode used in this study was a full polarimetric ALOS-2/PALSAR-2 (L-band) scene. The experiment was conducted on a portion of a tropical forest in a Central Highland province in Vietnam.

The aim of this research work is to understand natural and environmental vulnerability situation and its cause such as intensity, distribution and socio-economic effect in the Indigirka River basin, Eastern Siberia, Russia. This paper identifies, assess and classify natural and environmental vulnerability using landscape pattern from multidisciplinary approach, based on remote sensing and Geographical Information System (GIS) techniques. A model was developed by following thematic layers: land use/cover, vegetation, wetland, geology, geomorphology and soil in ArcGIS 10.2 software. According to numerical results vulnerability classified into five levels: low, sensible, moderate, high and extreme vulnerability by mean of cluster principal. Results are shows that in natural vulnerability maximum area covered by moderate (29.84%) and sensible (38.61%) vulnerability and environmental vulnerability concentrated by moderate (49.30%) vulnerability. So study area has at medial level vulnerability. The results found that the methodology applied was effective enough in the understanding of the current conservation circumstances of the river basin in relation to their environment with the help of remote sensing and GIS. This study is helpful for decision making for eco-environmental recovering and rebuilding as well as predicting the future development.

With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.

The detection of changes using remote sensing imagery has become a broad field of research with many approaches for many different applications. Besides the simple detection of changes between at least two images acquired at different times, analyses which aim on the change type or category are at least equally important. In this study, an approach for a semi-automatic classification of change segments is presented. A sparse dataset is considered to ensure the fast and simple applicability for practical issues. The dataset is given by 15 high resolution (HR) TerraSAR-X (TSX) amplitude images acquired over a time period of one year (11/2013 to 11/2014). The scenery contains the airport of Stuttgart (GER) and its surroundings, including urban, rural, and suburban areas. Time series imagery offers the advantage of analyzing the change frequency of selected areas. In this study, the focus is set on the analysis of small-sized high frequently changing regions like parking areas, construction sites and collecting points consisting of high activity (HA) change objects. For each HA change object, suitable features are extracted and a k-means clustering is applied as the categorization step. Resulting clusters are finally compared to a previously introduced knowledge-based class catalogue, which is modified until an optimal class description results. In other words, the subjective understanding of the scenery semantics is optimized by the data given reality. Doing so, an even sparsely dataset containing only amplitude imagery can be evaluated without requiring comprehensive training datasets. Falsely defined classes might be rejected. Furthermore, classes which were defined too coarsely might be divided into sub-classes. Consequently, classes which were initially defined too narrowly might be merged. An optimal classification results when the combination of previously defined key indicators (e.g., number of clusters per class) reaches an optimum.

The current generation of SAR satellites such as TerraSAR-X, TanDEM-X and COSMO-SkyMed provide resolutions below one meter, permitting the detailed analysis of urban areas while covering large zones. Furthermore, as they are deployable independently of daylight and weather, such remote sensing SAR data are particularly popular for purposes such as rapid damage assessment at building level after a natural disaster. The purpose of our study is the investigation of techniques for the detection of changes based on one pre-event and one post-event SAR amplitude image. We provide a comparison of several methods for detecting changes in urban areas. Especially, changes at building locations are looked for. We analyzed two areas affected differently in detail. First, a suburban area of Paris, France, was considered due to changes caused by an urbanization project. Here, we have two TanDEM-X acquisitions available, before (November 4, 2012) and after (May 10, 2013) the changes. Second, we investigated changes that happened in Kathmandu, Nepal, after the April 25, 2015 earthquake. For this analysis, we have two TerraSAR-X acquisitions, one before (October 13, 2013) and one immediately after (April 27, 2015) the earthquake. Both areas differ by the building types, the image resolution and the available reference, which makes it an interesting challenge. In this paper, we compare six different methods for change detection. The investigated methods contain both standard criteria such as Log Ratio, Kullback-Leibler and the Difference of Entropies detector, and methods developed by the authors such as a Log Ratio combined with an Alternating Sequential Filter. All change detection results are presented and discussed by considering the available ground truth.

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal components).

In many parts of the world, ecosystems change compromises the supply of ecosystem services (ES). Better ecosystem management requires detailed and structured information. Ecosystem accounting has been developed as an information system for ecosystems, using concepts and valuation approaches that are aligned with the System of National Accounts (SNA). The SNA is used to store and analyse economic data, and the alignment of ecosystem accounts with the SNA facilitates the integrated analysis of economic and ecological aspects of ecosystem use. Ecosystem accounting requires detailed spatial information at aggregated scales. The objective of this paper is to explore how remote sensing images can be used to analyse ecosystems using an accounting approach in the Orinoco river basin. We assessed ecosystem assets in terms of extent, condition and capacity to supply ES. We focus on four specific ES: grasslands grazed by cattle, timber and oil palm harvest, and carbon sequestration. We link ES with six ecosystem assets; savannahs, woody grasslands, mixed agro-ecosystems, very dense forests, dense forest and oil palm plantations. We used remote sensing vegetation, surface temperature and productivity indexes to measure ecosystem assets. We found that remote sensing is a powerful tool to estimate ecosystem extent. The enhanced vegetation index can be used to assess ecosystems condition, and net primary productivity can be used for the assessment of ecosystem assets capacity to supply ES. Integrating remote sensing and ecological information facilitates efficient monitoring of ecosystem assets, in particular in data poor contexts.

Ecometrica, together with partners in the UK, Mexico and Brazil, have collaborated on a UK Space Agency international partnership space programme (IPSP) project to advance EO applications in forests. A key objective was to improve EO derived information management for forest protection. Ecometrica’s cloud-based mapping platform was used to establish regional EO Labs within the partner organizations: ECOSUR (Mexico), INPE and FUNCATE (Brazil) and the University of Edinburgh (UK). The regional networks of EO Labs have provided a unified view of forestry-related data that is easy to access. In Mexico and Brazil the EO Labs enabled collaboration between research organisations and NGOs to develop applications for monitoring forest change in specified study areas and has enabled the compilation of previously unavailable regional EO and other spatial datasets into products that can be used by researchers, NGOs and state governments. Data on forest loss was linked to dynamic earth system models developed by the University of Edinburgh and INPE, utilising the EO Labs to provide an intuitive and powerful environment in which non-expert end- users can investigate the results from the huge datasets produced by multi-run model simulations. This paper demonstrates and discusses examples of mapping applications created on Ecometrica EO Labs by ECOSUR, INPE and the University of Edinburgh as part of this project, illustrating how cloud technology can enhance the field of forest protection.

An investigation of temporal dynamics of El Niño–Southern Oscillation (ENSO) and spatial patterns of dryness/wetness period over arid and semi-arid zones of Central Asia and their relationship with Normalized Difference Vegetation Index (NDVI) values (1982-2011) have explored in this article. For identifying periodical oscillations and their relationship with NDVI values have selected El Nino 3.4 index and thirty years of new generation bi-weekly NDVI 3g acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites time-series data. Based on identification ONI (Oceanic Nino Index) is a very strong El Nino (warm) anomalies observed during 1982-1983, 1997-1998 and very strong La Nino (cool) period events have observed 1988-1989 years. For correlation these two factors and seeking positive and negative trends it has extracted from NDVI time series data as “low productivity period” following years: 1982-1983, 1997 -1998; and as “high productivity period” following years: 1988 -1989. Linear regression observed warm events as moderate phase period selected between moderate El Nino (ME) and NDVI with following periods:1986-1987; 1987-1988; 1991-1992; 2002-2003; 2009-2010; and moderate La Niña (ML) periods and NDVI (1998-1999; 1999-2000; 2007-2008) which has investigated a spatial patterns of wetness conditions. The results indicated that an inverse relationship between very strong El Nino and NDVI, decreased vegetation response with larger positive ONI value; and direct relationship between very strong La Niña and NDVI, increased vegetation response with smaller negative ONI value. Results assumed that significant impact of these anomalies influenced on vegetation productivity. These results will be a beneficial for efficient rangeland/grassland management and to propose drought periods for assessment and reducing quantity of flocks’ due to a lack of fodder biomass for surviving livestock flocks on upcoming years in rangelands. Also results demonstrate that a non-anthropogenic drivers of variability effected to land surface vegetation signals, understanding of which will be beneficial for efficient rangeland and agriculture management and establish ecosystem services in precipitation-driven drylands of Central Asia.

On October 15, 2015, a severe and devastating flood hit the region of Sannio, Southern Italy, and the city of Benevento. Benevento and the hilly area of Sannio, have already experienced similar disasters, but the natural disasters occurred in the past did not help to better cope with current ones. The flood in this almost unknown area of Campania reached its climax with the flooding of the Tammaro and Calore rivers. The extent of the damage to the region, businesses and people was very heavy. Benevento is the most affected area. Utilizing a combination of remote-sensing techniques, Geographic Information System (GIS) data, this project employed Sentinel-1/2 and Landsat 8 imagery taken before and during the floods to calculate total inundated area and delineate flood extent. This data was then used to assess pre-existing flood hazard maps of the area. The resulting maps and methodologies from this project were delivered to the local governments and organizations as they work to better understand this historic event and plan for recovery throughout the region. The main goal of this study is to map flood inundation using principally open, free and full data acquired by Sentinel and Landsat satellite platforms operated by European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA) respectively.

Mapping of forest coverage and forest changes became an increasing issue due to deforestation and forest degradation. Moreover, the estimation of related indicators such as carbon reduction, biomass and wood capacity is of large interest for industry and politics. As forest height is an important contributing parameter for these indicators, the region-wide estimation of forest heights is an essential step. This article investigates the accuracy potential of forest height estimation that can be reached by the current configuration of the two SAR satellites TerraSAR-X and TanDEM-X. Depending on the chosen acquisition mode and flight geometry, products of different quality can be achieved. Eight InSAR data sets showing different characteristics in geometric resolution, length of baseline, and mapping time, are processed and analyzed. To enable a thorough evaluation of the estimated heights, first-pulse LIDAR point clouds and aerial ortho-images are used as reference data.

In last decades, there have been a decline in natural resources, becoming important to develop reliable methodologies for their management. The appearance of very high resolution sensors has offered a practical and cost-effective means for a good environmental management. In this context, improvements are needed for obtaining higher quality of the information available in order to get reliable classified images. Thus, pansharpening enhances the spatial resolution of the multispectral band by incorporating information from the panchromatic image. The main goal in the study is to implement pixel and object-based classification techniques applied to the fused imagery using different pansharpening algorithms and the evaluation of thematic maps generated that serve to obtain accurate information for the conservation of natural resources. A vulnerable heterogenic ecosystem from Canary Islands (Spain) was chosen, Teide National Park, and Worldview-2 high resolution imagery was employed. The classes considered of interest were set by the National Park conservation managers. 7 pansharpening techniques (GS, FIHS, HCS, MTF based, Wavelet ‘à trous’ and Weighted Wavelet ‘à trous’ through Fractal Dimension Maps) were chosen in order to improve the data quality with the goal to analyze the vegetation classes. Next, different classification algorithms were applied at pixel-based and object-based approach, moreover, an accuracy assessment of the different thematic maps obtained were performed. The highest classification accuracy was obtained applying Support Vector Machine classifier at object-based approach in the Weighted Wavelet ‘à trous’ through Fractal Dimension Maps fused image. Finally, highlight the difficulty of the classification in Teide ecosystem due to the heterogeneity and the small size of the species. Thus, it is important to obtain accurate thematic maps for further studies in the management and conservation of natural resources.

This paper examines how radar and optical imagery combined can be employed for the study of land degradation. A case study was conducted in the Randi Forest, Cyprus, a known overgrazed area for the past 70 years. Satellite optical imagery was used for the calculation of the Normalised Difference Vegetation Index (NDVI) for the time period between December 2015 to July 2016 and C-Band Synthetic Aperture Radar imagery was used to derive correlative changes in backscatter intensity (σ⁰). The results are indicative of the overgrazing in the area with the temporal and spatial variations of grazing defined. Both the NDVI and the σ⁰ values demonstrate sudden shifts in vegetation cover following the start of the grazing period with the greatest shifts being evident in close proximity to the location of farms. NDVI and backscatter coefficient correlation was measured at 0.7 and 0.8 for the months of February and April respectively. Shifts in NDVI value by 0.1 correspond to a shift in σ⁰ by 4 db. VH cross-polarization showed greater sensitivity to changes in vegetation than VV. The paper also examines the capability of C-Band Synthetic Aperture Radar to measure changes in plant structure and vegetation fraction as the result of grazing. Depending on grazing intensity, backscatter coefficient varies according to vegetation density.

This paper presents a novel algorithm for calculation of plant area density based on surface and volume convex hull which is applied to each horizontal cut of a point cloud data. This method can be used as an alternative to conventional voxelization approaches to improve accuracy and computation efficiency. The terrestrial data was collected from a boreal forest at Peace River, Alberta, Canada during summer and fall in 2014. This technique can be applied to an arbitrary point cloud data to calculate all other metrics of forests including plant area index, leaf area density, and also leaf area index.

Wind energy is one of the best options for renewable energy such that, many researchers work on wind resource assessment, specifically using numerical weather prediction (NWP) model to forecast atmospheric behavior on a given domain. In addition, every combination of parameterization configuration influences wind assessment. At the same time, choosing the optimum vertical and horizontal resolution may affect its output and processing time. Regardless of available researches, most of them focuses on mid-latitude area but not in tropical areas like the Philippines. In the study, sensitivity analysis of Weather Research and Forecasting (WRF) model version 3.6.1 with 4 configurations was performed. The duration of the simulation was from January 1, 2014 00:00 to December 31, 2014 23:00. The parameters involved were horizontal resolution and vertical levels. Also, meteorological input data from NCEP Final Analysis with 1 degree resolution every 6 hours was used. For validation, wind speed measurements at 10 m height from NOAA Integrated Surface Database (ISD) were utilized, of which, the 3 weather stations are located in Manila, Science Garden and Ninoy Aquino International Airport (NAIA). The results show that increasing horizontal resolution from 4 km to 1 km have no significant increase to wind speed accuracy. In majority, higher vertical levels tend to increase its accuracy. Moreover, the model has higher accuracy during the rainy season and months of April and May. Overall, the model overestimated the observed wind speed but the diurnal cycle of wind speed follows all the simulation.

Mangroves are trees or shrubs that grows at the surface between the land and the sea in tropical and sub-tropical latitudes. Mangroves are essential in supporting various marine life, thus, it is important to preserve and manage these areas. There are many approaches in creating Mangroves maps, one of which is through the use of Light Detection and Ranging (LiDAR). It is a remote sensing technique which uses light pulses to measure distances and to generate three-dimensional point clouds of the Earth's surface. In this study, the topographic LiDAR Data will be used to analyze the geophysical features of the terrain and create a Mangrove map. The dataset that we have were first pre-processed using the LAStools software. It is a software that is used to process LiDAR data sets and create different layers such as DSM, DTM, nDSM, Slope, LiDAR Intensity, LiDAR number of first returns, and CHM. All the aforementioned layers together was used to derive the Mangrove class. Then, an Object-based Image Analysis (OBIA) was performed using eCognition. OBIA analyzes a group of pixels with similar properties called objects, as compared to the traditional pixel-based which only examines a single pixel. Multi-threshold and multiresolution segmentation were used to delineate the different classes and split the image into objects. There are four levels of classification, first is the separation of the Land from the Water. Then the Land class was further dived into Ground and Non-ground objects. Furthermore classification of Nonvegetation, Mangroves, and Other Vegetation was done from the Non-ground objects. Lastly Separation of the mangrove class was done through the Use of field verified training points which was then run into a Support Vector Machine (SVM) classification. Different classes were separated using the different layer feature properties, such as mean, mode, standard deviation, geometrical properties, neighbor-related properties, and textural properties. Accuracy assessment was done using a different set of field validation points. This workflow was applied in the classification of Mangroves to a LiDAR dataset of Naawan and Manticao, Misamis Oriental, Philippines. The process presented in this study shows that LiDAR data and its derivatives can be used in extracting and creating Mangrove maps, which can be helpful in managing coastal environment.

On-shore, hyperspectral imagery is currently used to detect and measure remotely oil spill extension for environmental purpose and hydrocarbon seepage for petroleum exploration. In this study, variations of hyperspectral signatures of vegetal species have been analyzed at the laboratory scale to detect indirectly the potential impacts on the plants of crude oil seepage and spills in the soil. Experimental study has been performed under greenhouse to simulate the exposure of two species of plants to a co-contamination of hydrocarbons and heavy metals contained in sludge from mud pit. Maize and bramble have been selected for this study since they are cultivated and spontaneous species respectively located in the region of interest. Five levels of exposure were performed over a period of 100 days. Reflectance evolution of each plant was measured with a spectroradiometer from 350 nm to 2500 nm with a dedicated leaf clip. Net morphological impacts were observed for maize with a global reduction of plants and leaves sizes correlated to the level of cocontamination. Hyperspectral measurement on maize revealed a higher reflectance in the absorption wavelength of water at 1450 and 1900 nm due to contamination and water stress. Reflectance in the visible increased at 600 nm (red interval) for bramble plants exposed to co-contamination. Then, the level of reflectance in the NIR decreased between 700 and 800 nm (red-edge) and absorption of water also decreased at 1450 and 1900 nm as described previously for the maize.

The new European Observatory radar data of polar orbiting satellite system Sentinel-1 provide a continuous and systematic data acquisition, enabling flood events monitoring and mapping. The study area is the basin of Sperchios River in Fthiotida Prefecture, Central Greece, having an increased ecological, environmental and socio-economic interest. The catchment area and especially the river delta, faces several problems and threats caused by anthropogenic activities and natural processes. The geomorphology of Sperchios catchment area and the drainage network formation provoke the creation of floods. A large flash flood event took place in late January early February 2015 following an intense and heavy rainfall that occurred in the area. Two space born radar images, obtained from Sentinel-1 covering the same area, one before and another one during the flood event, were processed. Two different methods were utilized so as to produce flood hazard maps, which demonstrate the inundated areas. The results of the two methods were similar and the flooded area was detected and delineated ideally.

Land Use Land Cover Changes (LULCC) data provide objective information to support environmental policy, urban planning purposes and sustainable land development. Understanding of past land use/cover practices and current landscape patterns is critical to assess the effects of LULCC on the Earth system. Within the framework of soil sealing in Italy, the present study aims to assess the LULCC of the Nola area (Naples metropolitan area, Italy), relating to a thirty year period from 1984 to 2015. The urban sprawl affects this area causing the impervious surface increase, the loss in rural areas and landscape fragmentation. Located near Vesuvio volcano and crossed by artificial filled rivers, the study area is subject to landslide, hydraulic and volcanic risks. Landsat time series has been processed by means of the supervised per-pixel classification in order to produce multitemporal Land Use Land Cover maps. Then, post-classification comparison approach has been applied to quantify the changes occurring between 1984 and 2015, also analyzing the intermediate variations in 1999, namely every fifteen years. The results confirm the urban sprawl. The increase of the built-up areas mainly causes the habitat fragmentation and the agricultural land conversion of the Nola area that is already damaged by unauthorized disposal of urban waste. Moreover, considering the local risk maps, it was verified that some of the new urban areas were built over known hazardous sites. In order to limit the soil sealing, urgent measures and sustainable urban planning are required.

National forest inventories provide measurements of forest variables (e.g. growing stock) that can be used for the estimation of above ground biomass (AGB). Mapping growing stock brings knowledge about spatial distribution and temporal dynamics of ABG, which is necessary for carbon cycle analysis. Several studies have been conducted on the integration of ground and optical remote sensing data to map forest biomass over Europe. Nevertheless, more direct information on forest biomass could be obtained by LiDAR techniques, which directly assess vertical forest structure by measuring the distance between the sensor and the scattering elements located inside the canopy volume. Thus, global 1-km maps of forest canopy height have been recently obtained from the Geoscience Laser Altimeter System (GLAS). The current study aims to produce a forest growing stock map in Spain. Five different forest type areas were identified in three provinces along a North – South gradient accounting for different ecosystems and climatic conditions. Growing stock ground data from the Third Spanish National Forest Inventory were assigned to each forest type and aggregated to 1-km spatial resolution. GLAS-derived canopy height was extracted for the locations of selected ground data. A relationship between inventory growing stock and satellite canopy height was found for each class. The obtained relationships were then extended all over Spain. The accuracy of the resulting growing stock map was assessed at province level against the Third Spanish National Forest Inventory growing stock estimations (R = 0.85, RMSE = 21 m³ ha^-1).

Many factors such as geomorphic features, economic development, expansion of cities, the implementation of new policy, etc. are changing land cover. Therefore, it is necessary to monitor Land-Use and Land-Cover Change (LUCC) by using new technologies and methods at high tempo-spatial resolution. Based on one supervised classification approach combining object-oriented method and binary decision tree, this study mapped land cover of Zhejiang Province, China, at the scale of 1:250000 in 2000, 2005, and 2010. After image segmentation, object features such as normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), area, the ratio of length to width, density, etc. were applied to assign each object to specific class. Being verified through confusion matrix, the mapping results were satisfactory. Taking land cover map in 2010 as an example, the lowest user accuracy was 84.14%, with the average of 92.15%; The lowest production accuracy was 62.00%, with the average was 86.69%; The overall accuracy was 0.8928; The Kappa coefficient was 0.8752. Under the influence of geomorphic features and economic development, changes of land cover in Zhejiang were mainly distributed in the areas with lower elevation and higher GDP from 2000 to 2010. Under the influence of natural factors and human activities, many croplands and wetlands were lost from 2000 to 2010. For croplands, there were 2106.608 km² croplands changed into other types from 2000 to 2005, and 1897.809 km² from 2005 to 2010. Most of croplands were changed into artificial lands, with 1520.601km² from 2000 to 2005 and 1446.826 km² from 2005 to 2010. For wetland, there were 209.085 km² wetlands changed into other types from 2000 to 2005, and 292.975 km² from 2005 to 2010. Most of wetlands were changed into croplands, with 134.652 km² from 2000 to 2005 and 122.979 km² from 2005 to 2010.

Urban vegetation land cover change is a direct measure of quantitative increase or decrease in sources of urban pollution and the dimension of extreme climate events and changes that determine environment quality. This study addresses climate changes effects and anthropogenic impacts on urban green biophysical variables based on time series satellite data in synergy with in-situ data and new analytical methods. This paper explored the use of time-series MODIS Terra/Aqua Normalized Difference Vegetation Index (NDVI/EVI), Land Surface Temperature (LST) and Leaf Area Index (LAI), land surface albedo data to provide vegetation change detection information for Bucharest test area during 2000- 2015 period. Training and validation are based on a reference dataset collected from Landsat ETM remote sensing data. The mean detection accuracy for investigated period was 89%, with a reasonable balance between change commission errors (19.74%), change omission errors (24.72%), and Kappa coefficient of 0.74. Annual change detection rates across the urban/peri-urban green areas over the study period were estimated at 0.77% per annum in the range of 0.45% (2000) to 0.78% (2015).Vegetation dynamics in urban areas at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system.

The purpose of this study was to create leopard cat (Prionailurus bengalensis) habitat potential maps of South Korea using spatial information. To create maps, we gathered various environmental factors potentially affecting the species’ distribution from a spatial database: elevation, slope, land cover and so on. We analyzed the spatial relationships between the distribution of the leopard cats and the environmental factors using a frequency ratio model. Among the total number of known leopard cat locations, we used 50% for mapping and the remaining 50% for model validation. Our models were relatively successful and showed a high level of accuracy during model validation with existing locations (frequency ratio model 82.15%). These maps can be used to manage and monitor the habitat of mammal species and top predators.

Remote sensing image analysis systems and geographic information systems (GIS) show great promise for the integration of a wide variety of spatial information supporting tasks such as urban and regional planning, natural resource management, agricultural studies and topographic or thematic mapping. Current and future remote sensing programs are based on a variety of sensors that will provide timely and repetitive multisensor earth observation on a global scale. GIS offer efficient tools for handling, manipulating, analyzing and presenting spatial data that are required for sensible decision making in various areas. The Environmental Monitoring project may serve as a convincing example of the operational use of integrated GIS/remote sensing technologies. The overall goal of the project is to assess the capabilities of satellite remote sensing for the analysis of land use changes, especially in moor areas. These areas are recognized as areas crucial to the mission of the Department of Environment and, therefore, to be placed under an extended level of protection. It is of critical importance, however, to have accurate and current information about the ecological and economic state of these sensitive areas. In selected pasture and moor areas, methods for multisensor data fusion have being developed and tested. The results of this testing show which techniques are useful for pasture and moor monitoring at an operational level. A hierarchical method is used for extracting bog land classes with respect to the environmental protection goals. A highly accurate classification of the following classes was accomplished: deciduous- and mixed forest, coniferous forest, water, very wet areas, meadowland/farmland with vegetation, meadowland/farmland with partly vegetation, meadowland/ farmland without vegetation, peat quarrying with maximum of 50% vegetation, de- and regeneration stages. In addition, a change detection analysis is performed in comparison with the existing classification of 1994-96 [7] and methods are developed to improve the classification strategy.