Forest biomass is an important parameter for assessing the status of forest ecosystems. In the present study, forest biomass was assessed by integrating remotely-sensed satellite data and field inventory data using an artificial neural network (ANN) technique in Barkot forest, Uttarakhand, India. Spectral and texture variables were derived from Resourcesat-1 (RS1) LISS-III (Linear Imaging Self-Scanning Sensor) data of April 24, 2013. ANN was used for finding the relation of spectral and texture variables to field-measured biomass. The top 10 variables, namely shortwave infrared (SWIR) band reflectance, near infrared (NIR) band reflectance, normalized difference vegetation index (NDVI), difference vegetation index (DVI), green band contrast, green band variance, SWIR band contrast, NIR band dissimilarity, SWIR band second angular moment, and red band mean, were selected for generating a multiple linear regression model to predict the biomass. The predicted biomass showed a good relationship (R² = 0.75 and root mean square error (RMSE) = 85.32 Mg ha⁻¹) with field-measured biomass. The model was validated yielding R² = 0.74 and RMSE = 93.41 Mg ha⁻¹. The results showed that RS1 LISS-III satellite data have good capability to estimate forest biomass, and the ANN technique can be used to enhance the scope of biomass estimation with a minimum number of spectral and texture variables.

This paper tries to shift the focus of research on the impact of natural disasters on economic growth from global and national levels to sub-national levels. Inadequate sub-national level information is a significant lacuna for planning spatially targeted climate change adaptation investments. A fixed-effect panel regression analyses of 19 states from 2001 to 2015 assess the impacts of exposure to floods and droughts on the growth of gross state domestic product (GSDP) and human development index (HDI) in India. The flood and drought exposure are estimated using satellite data. The 19 states comprise 95% of the population and contribute 93% to the national GDP. The results show that floods indeed expose a large area, but droughts have the most significant impacts at the sub-national level. The most affected GSDPs are in the non-agriculture sectors, positively by the floods and negatively by droughts. No significant influence on human development may be due to substantial investment on mitigation of flood and drought impacts and their influence on better income, health, and education conditions. Because some Indian states still have a large geographical area, profiling disasters impacts at even smaller sub-national units such as districts can lead to effective targeted mitigation and adaptation activities, reduce shocks, and accelerate income growth and human development.

Forests sequester large quantity of carbon in their woody biomass and hence accurate estimation of forest biomass is extremely crucial. The present study aims at combining information from spaceborne LiDAR (ICESat/GLAS) and high resolution optical data to estimate forest biomass. Estimation of aboveground biomass (AGB) at ICESat/GLAS footprint level was done by integrating data from multiple sensors using two regression algorithms, viz. random forest (RF) and support vector machine (SVM). The study used forest height and canopy return ratio (rCanopy) for determination of effective size of ICESat/GLAS footprints for field data collection. The forest height was predicted with root mean square error (RMSE) of 1.35 m. The study showed that six most important parameters derived from LiDAR, and passive optical data were able to explain 78.7% (adjusted) variation in the observed AGB with an RMSE of 13.9 Mg ha–1. It was also observed that 15 most important parameters were able to explain 83% (adjusted) variation in the observed AGB. It was found that SVM regression algorithm explained 88.7% of variation in AGB with an RMSE of 13.6 Mg ha–1 on the combined datasets while RF regression algorithm explained 83.5% of variation in AGB with an RMSE of 20.57 Mg ha–1. The study demonstrated that RF regression algorithm performs equally well on datasets irrespective of the correlation of underlying variables with the predicted variable whereas SVM regression was found to perform well on those datasets which had a subset of underlying variables that are correlated with the predicted variable. The study highlighted that sensor integration approach is more accurate than single sensor approach in predicting the AGB.

Located on the south-facing slope of the Himalayas, Nepal receives intense, long-lasting precipitation during the Asian summer monsoon, making Nepal one of the most susceptible countries to flood and landslide hazards in the region. However, sparse gauging and irregular measurement constrain the vulnerability assessments of floods and landslides, which rely highly on the accuracy of precipitation. Therefore, this study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region by comparing different datasets and identifying the best alternative of gauge-based precipitation for hydro-meteorological applications. We compared eight SPPs using statistical metrics and then used the Multi-Criteria Decision-Making (MCDM) technique to rank them. Secondly, we assessed the hydrological utility of SPPs by simulating them through the GR4J hydrological model. We found a high POD (0.60–0.80) for all SPPs except CHIRPS and PERSIANN; however, a high CC (0.20–0.40) only for CHIRPS, IMERG_Final, and CMORPH. Based on MCDM, CMORPH and IMERG_Final rank first and second. While SPPs could not simulate daily discharge (NSE < 0.28), they performed better for monthly streamflow (NSE > 0.54). Overall, this study recommends CMORPH and IMERG_Final and improves the understanding of data quality to better manage hydrological disasters in the data-sparse Himalayas. This study framework can also be used in other Himalayan regions to systematically rank and identify the most suitable datasets for hydro-meteorological applications.