In the present study, trend of satellite based annual evapotranspiration (ET) and natural forcing factors responsible for this were analyzed. Thirty years (1981-2010) of ET data at 0.08° grid resolution, generated over Indian region from opticalthermal observations from NOAA PAL and MODIS AQUA satellites, were used. Long-term data on gridded (0.5° x 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) ERS scatterometer at 25 km resolution and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mann-Kendall tests were performed with time series data for trend analysis. Mean annual ET loss from Indian ago-ecosystem was found to be almost double (1100 Cubic Km) than Indian forest ecosystem (550 Cubic Km). Rainfed vegetation systems such as forest, rainfed cropland, grassland showed declining ET trend @ - 4.8, -0.6 &-0.4 Cubic Kmyr-1, respectively during 30 years. Irrigated cropland initially showed ET decline upto 1995 @ -0.8 cubic Kmyr-1 which could possibly be due to solar dimming followed by increasing ET @ 0.9 cubic Kmyr-1 after 1995. A cross-over point was detected between forest ET decline and ET increase in irrigated cropland during 2008. During 2001-2010, the four agriculturally important Indian states eastern, central, western and southern showed significantly increasing ET trend with S-score of 15-25 and Z-score of 1.09-2.9. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states no significant trend in rainfall was observed though significant increase in ET was noticed. The study recommended to investigate the influence of anthropogenic factors such as increase in area under irrigation, increased use of water for irrigation through ground water pumping, change in cropping pattern and cultivars on increasing ET.
Climate change has become a cause of concern as well as the challenge of this century. Himalayan mountain ranges with high snow fields and numerous valley glaciers may bear the brunt of such changes already being reported including Intergovernmental Panel on Climate Change (IPCC). Gangotri is one of the most prominent snow-fed catchments of Indian Himalayan Region (IHR) due to origin of river Ganga situated within it. Spatio-temporal changes in snow covered area of this basin were examined for melting seasons of the years 2006 to 2010 and a latest reference year of 2012 as a special test case. Standard snow data products (MOD10A2) of Moderate Resolution Imaging Spectroradiometer (MODIS)-Terra sensor with spatial resolution of 500 m were used. For all the years of reference, snow covered area percentage was derived for respective months representing usual ablation or melting periods. Snow depletion curves (SDCs) were generated for such periods of the respective years. CARTOSAT digital elevation model (DEM) was used for topographic information of terrain. Relationship of SDCs with the land surface temperatures (LST) of the basin was worked upon using MODIS-Terra LST (MOD11A2) product (version 5) with 1 km resolution at 8-day interval for the day time temperature for respective months of above reference years. Thereafter, interpolation and simulation of snow covered areas was carried out on the basis of LST data. The study thus produced snow cover maps for the years of reference as well as their relationship with LST for climate change inferences.
Landslide hazard assessments using computational models, such as artificial neural network (ANN) and frequency ratio (FR), were carried out covering one of the important mountain highways in the Central Himalaya of Indian Himalayan Region (IHR). Landslide influencing factors were either calculated or extracted from spatial databases including recent remote sensing data of LANDSAT TM, CARTOSAT digital elevation model (DEM) and Tropical Rainfall Measuring Mission (TRMM) satellite for rainfall data. ANN was implemented using the multi-layered feed forward architecture with different input, output and hidden layers. This model based on back propagation algorithm derived weights for all possible parameters of landslides and causative factors considered. The training sites for landslide prone and non-prone areas were identified and verified through details gathered from remote sensing and other sources. Frequency Ratio (FR) models are based on observed relationships between the distribution of landslides and each landslide related factor. FR model implementation proved useful for assessing the spatial relationships between landslide locations and factors contributing to its occurrence. Above computational models generated respective susceptibility maps of landslide hazard for the study area. This further allowed the simulation of landslide hazard maps on a medium scale using GIS platform and remote sensing data. Upon validation and accuracy checks, it was observed that both models produced good results with FR having some edge over ANN based mapping. Such statistical and functional models led to better understanding of relationships between the landslides and preparatory factors as well as ensuring lesser levels of subjectivity compared to qualitative approaches.
This study describes the use of SAR interferometric technique, coherence technique and backscattering coefficient data
for the estimation of canopy height in forest area. The study area is Tundi Reserved Forest falling in Dhanbad district of
Jharkhand state in India. Most predominant species is Shorea robusta (Sal) constituting the top storey of the forest with
maximum height up to 20 m. For the purpose of validation, Ground Truth locations were taken on the basis of variability
in the area. At ground truth location, sample plots were selected for the measurement of stand heights for the forest
stands of different age and height categories. Interferometric SAR technique was used to generate Digital Elevation
Model (DEM) in terms of Top of the Canopy Digital Surface Model (TCDSM). For this, Radarsat SAR interferometric
data pair of 12 February and 7 March, 2004 were selected. The baseline for this pair was 643 m. The TCDSM at ground
truth location was subtracted from the DEM obtained at clear-cut areas. The value of tree height was 8.1 m as against the
measured value of 9m. The two date SLC data set (17 April and 22 May, 2004) of Envisat- ASAR were also used for the
study of coherence pattern and its relation to forest height. Averages of coherence values for each of the GT sites with
stand heights as well as ground height were derived using 5×5 pixel windows. The coherence values with the ground
measured stand heights showed a high correlation. Coherence has been shown to be efficient for establishment of forest
stand height in a forest environment. Thus, this paper demonstrates that SAR derived TCDSM and coherence can be
used as a useful tool along with intensity data for the measurement of tree parameters.
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