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.

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High Nature Value (HNV) farmland is typically characterised by low-intensity farming associated with high biodiversity and species of conservation concern. Mapping the occurrence and distribution of such farmland are useful for appropriate targeting of conservation measures and supporting associated rural communities. We mapped the likely distribution of HNV farmland in the Republic of Ireland using a linear regression model incorporating established European indicators, adapted for Ireland and weightings based on expert opinion. The indicators used were semi-natural habitat cover, stocking density, hedgerow density, river and stream density and soil diversity, with highest weightings placed on the first two indicators (40% and 30%, respectively). The map provides information on the likely occurrence and distribution of HNV farmland in each electoral division as a reference point for future monitoring of the distribution of HNV farmland in the Republic of Ireland in order to assist with planning and policy development for the rural environment.ARTICLE HISTORY

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The development and growth of geospatial techniques offer many advantages and challenges to the study of biodiversity, especially in the present era of climate change.We are now at the beginning of the international decade for biodiversity and by the time we travel through the decade, there would be sea-changes in the measurement and monitoring approaches, database management options, and inter-linked studies on biodiversity. With the onset of geoinformatics techniques comprising remote sensing, global positioning system (GPS), integrative tools, such as GIS, is realized as a complimentary system to ground-based biodiversity studies. Recently, a nationwide biodiversity study at landscape level using geoinformatics modeling techniques for India has been completed. The study has assessed plant diversity using a three-tier approach, wherein six biodiversity attributes (i.e., spatial, phytosociological, social, physical, economical, and ecological) were linked together based on their relative importance to stratify biological richness of forest vegetation (non-agricultural) of India. It has enumerated 7,964 plant species from 20,000 nested quadrate sampling plots of 0.04 ha each, delineated and mapped 120 vegetation classes; and organized the geo-spatial database on bisindia web portal. Here, we have (i) proposed a method to incorporate the fauna component in-line up with the existing methodology and (ii) utilized the GPS-gathered positional information on the distribution of two species (i.e., Medicago sativa and Poa annua to simulate their distribution for the year 2020 (SRES A1-B scenario, IPCC) using Maxent model. The study conducted in a test site of western Himalayas estimated (i) 24% increase in the overall biologically rich areas on supplement of fauna data and (ii) distribution of both the species would tend to increase in favor of shorter cold season. The study highlights the importance of geoinformatics techniquebased biodiversity study for its amenability to incorporate any further change or modification, and utility of the geo-spatial biodiversity database for simulating various species