Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India

Abstract

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.

Dynamics and determinants of land change in India: integrating satellite data with village socioeconomics

“…We have a dedicated article describing the technical details and validation of the LULCC database, with basic land cover area statistics (Roy et al 2015). In contrast, this study presents detailed land conversion analysis of the LULCC database.…”

“…Our data have an overall mapping accuracy of 95% (across eleven land classes defined in Table S7), thus providing accurate and reliable information on LULCC. See Roy et al (2015) for further details on validation.…”

“…Our study differs from existing satellite-based national assessments of LULCC in India on two aspects. First, our land cover conversion estimates rely on Landsat analysis that covers longer time period, uses uniform classification scheme, maps patch to patch land dynamics, and is validated using ground data (Roy et al 2015). Earlier highresolution land cover mapping activities at national scale were one-time effort (see review by Roy et al 2015) hence unavailable for monitoring at regular time intervals; their project-specific classification scheme and varying data quality make compilation of consistent time series images difficult.…”

“…First, our land cover conversion estimates rely on Landsat analysis that covers longer time period, uses uniform classification scheme, maps patch to patch land dynamics, and is validated using ground data (Roy et al 2015). Earlier highresolution land cover mapping activities at national scale were one-time effort (see review by Roy et al 2015) hence unavailable for monitoring at regular time intervals; their project-specific classification scheme and varying data quality make compilation of consistent time series images difficult. Tracking patch-level dynamics is crucial because the environmental impacts vary depending on the preceding and replaced land cover class (Don et al 2011;Mahmood et al 2014).…”

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

“…For example, Harada et al [44] prepared the MODIS based vegetation map of year 2001 in Japan by manually labeling the clusters obtained from the Iterative Self-Organizing Data Analysis Technique. Roy et al [45] used on-screen visual screen technique for the preparation of land use and land cover database in India using medium-resolution Indian remote sensing satellite data. More recently, Sharma et al [35] employed machine learning and automated classification approach for the production of nationwide vegetation physiognomic map in Japan using MODIS data.…”