India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for Remote Sens. 2015, 7 2403 comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20 m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90 m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially >0.55 m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of <2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities.

A comprehensive analysis of climate data (1958-2018) is carried out at the national scale in India to assess spatiotemporal variation in aridity. The aridity is analyzed using UNEP (United Nations Environment Programme) Aridity Index (AI), which is the ratio between Precipitation (P) and Potential Evapotranspiration (PET). Freely available Terra-Climate database, P and PET variables, offered an unprecedented opportunity for monitoring variations in AI and aridity index anomalies (AIA) at interseasonal and inter-decadal basis. The study also assesses longer term patterns of P and AI anomalies with vegetation anomalies. The results indicate that significant clustered areas with maximum dryness are located at west-central part of India, the state of Maharashtra. Overall, there is a gradual increase in the extent of arid zone during 60-year period and spatially maximum extent of percentage change in aridity area is observed. The change patterns of AI in India are largely driven by the changing patterns of precipitation. The maximum impact of decline in precipitation on AIA was observed during Kharif season frequently, for every 4-5 years during 1972-1992. The pattern repeated in the last few recent years (2013- 2018), the decline in precipitation resulted increased aridity. The study also reveals that the availability and usage of irrigation sources have increased from 2014 to 2018. Thus, despite of less precipitation positive vegetation has been resulted in this period. The findings are important to understand the impacts of climate change on land use pattern, and land and water resource management.