M. D. Behera

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

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 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.

High plant endemism in an Indian hotspot – eastern Himalaya

A preliminary investigation in the Subansiri area of the eastern Himalaya recorded high plant endemism. As a regular exercise of field sampling of various forest types to map the biorich areas, the number of species observed was evaluated to analyze their endemic status in one of the important hotspot regions of the world. The total number of individuals, species, genera and families observed were recorded in various natural and semi-natural forest types. A total of 122 plots sampled randomly in various forest types recorded 764 plant species, of which 59 were found to be endemic. The overall species endemism is observed to be 13 per ha. These 59 species belong to 27 families and 46 genera. Fifty percent of species were from just five families, Rubiaceae, Lauraceae, Acanthaceae, Magnoliaceae and Rosaceae. Stem size class distribution of the most abundantly found endemic tree species in three primary forest types indicated a quantitative status. This study envisages the status of plant endemism carried out following a proportional stratified random sampling method for various classified vegetation cover types using satellite remote sensing data. Many primitive genera and families were recorded which are indicative of long evolutionary age and affinities of the area with respect to species endemism.

Future of Endemic Flora of Biodiversity Hotspots in India

India is one of the 12 mega biodiversity countries of the world, which represents 11% of worlds flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models.

Forest Vegetation Characterization and Mapping Using IRS-1C Satellite Images in Eastern Himalayan Region

Abstract IRS 1C LISS-III sensor data was used to generate a medium scale vegetation cover map. Four scenes with minimum cloud cover were acquired, pre-processed and geo-referenced to Survey of India (SOI) topomaps. The satellite images were then subjected to knowledge-based hybrid classification. A standard forest vegetation / land cover classification legend was used for this purpose. All the vegetation classes were visited on ground to collect information on their structure and composition, which was utilized in the classification exercise. Total land cover of over 20000 km2 of Subansiri Himalaya was classified into seventeen categories. The vegetation classes derived from digital classification were compared with the existing ground-based forest classification given by Champion and Seth. Area estimates were made for various land cover categories. Distribution of various forest vegetation types when compared with altitudinal zones of the area has shown good relationship. Correspondence using field-gathered GPS points for vegetation classes showed 89.25% overall accuracy. The methodology used here for classification exercise has contributed to improved classification accuracy. All the vegetation classes have been described with respect to their dominant species composition, spectral response on satellite images, occurrence zone with respect to altitude & climate and their correspondence with existing ground-based forest type classification given by Champion and Seth. This study envisages the use of satellite remote sensing and its kindred technologies like GIS and GPS supplemented by ground-based limited field survey for characterizing forest vegetation cover.

Wetland Monitoring, Serving as an Index of Land Use Change-A Study in Samaspur Wetlands, Uttar Pradesh, India

Wetlands are among the most productive ecosystems in the world and any alterations might lead to changes in their bio-physical, socio-economic and climatic conditions. Wetland dynamics as an index of land use change were studied. Satellite remote sensing was utilized to understand the periodic and seasonal dynamics of Samaspur wetlands using Landsat and RESOURCESAT-1 temporal data. Index-based (i.e., Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI)) classification resulted in meaningful discrimination of wetland classes. Results indicate (i) effective water spread areas have increased to optimum capacity at 1990 due to the influence of Sharda canal, (ii) expansion of the agricultural area has led to reduction of the wetland buffer area, and (iii) increase in vegetation biomass due to pesticide-fertilizer runoff and sedimentation load. We also reiterate (i) free availability of the Landsat satellite data in public domain facilitating such monitoring studies and (ii) availability and utility of SWIR band information in wetland classification exercise. The study concludes that policy-driven measures have both long and short term impacts on land use and its natural wetland ecosystems; and the characterizing the later serves as indictor of the former and perhaps vice versa.

Characterizing Shorea robusta communities in the part of Indian Terai landscape

Shorea robusta Gaertn. f. (Sal) is one of the important timber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its associations. The present study deals with delineation, mapping and characterization of various communities of Sal (Shorea robusta) forests in Terai landscape of Uttar Pradesh, India ranging across over 16 districts. Field survey and visual interpretation based forest vegetation type classification and mapping was carried out as part of the project entitled ‘Biodiversity characterization at landscape level using remote sensing and GIS’. Indian Remote Sensing-P6 (Resourcesat-1) Linear Imaging Self Scanner-III satellite data was used during the study. The total area covered by different Sal forests was found to be approximately 2256.77 km2. Sal communities were identified and characterized based on their spectral properties, physiognomy and phytosociological characteristics. Following nine Sal communities were identified, delineated and mapped with reasonable accuracy viz., Chandar, Damar, dry plains, moist plains, western alluvium, western alluvium plains, mixed moist deciduous, mixed dry deciduous and Siwalik. It is evident from the area estimates that mixed moist deciduous Sal is the most dominant community in the region covering around (1613.90 km2), other major communities were found as western alluvium plains Sal (362.44 km2), mixed dry deciduous Sal (362.44 km2) and dry plains Sal (107.71 km2). The Terai landscape of Uttar Pradesh faces tremendous anthropogenic pressure leading to deterioration of the forests. Community level information could be used monitoring the status as well as for micro level conservation and planning of the Sal forests in Terai Landscape of Uttar Pradesh.

Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985

Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanization, industrialization etc. We used on-screen digital interpretation technique to derive LULC maps from Landsat images at three decadal intervals i.e., 1985, 1995 and 2005 of two major river basins of India. Rain-fed, Mahanadi river basin (MRB) attributed to 55% agricultural area wherein glacier-fed, Brahmaputra river basin (BRB) had only 16% area under agricultural land. Though conversion of forest land for agricultural activities was the major LULC changes in both the basins, the rate was higher for BRB than MRB. While water body increased in MRB could be primarily attributed to creation of reservoirs and aquaculture farms; snow and ice melting attributed to creation of more water bodies in BRB. Scrub land acted as an intermediate class for forest conversion to barren land in BRB, while direct conversion of scrub land to waste land and crop land was seen in MRB. While habitation contributed primarily to LULC changes in BRB, the proximity zones around habitat and other socio-economic drivers contributed to LULC change in MRB. Comparing the predicted result with actual LULC of 2005, we obtained >97% modelling accuracy; therefore it is expected that the Dyna-CLUE model has very well predicted the LULC for the year 2025. The predicted LULC of 2025 and corresponding LULC changes in these two basins acting as early warning, and with the past 2-decadal change analysis this study is believed to help the land use planners for improved regional planning to create balanced ecosystem, especially in a changing climate.

Analysing land and vegetation cover dynamics during last three decades in Katerniaghat wildlife sanctuary, India

The change in the tropical forests could be clearly linked to the expansion of the human population and economies. An understanding of the anthropogenic forcing plays an important role in analyzing the impacts of climate change and the fate of tropical forests in the present and future scenario. In the present study, we analyze the impact of natural and anthropogenic factors in forest dynamics in Katerniaghat wildlife sanctuary situated along the Indo-Nepal border in Uttar Pradesh state, India. The study site is under tremendous pressure due to anthropogenic factors from surrounding areas since last three decades. The vegetation cover of the sanctuary primarily comprised of Shorea robusta forests, Tectona grandis plantation, and mixed deciduous forest; while the land cover comprised of agriculture, barren land, and water bodies. The classification accuracy was 83.5%, 91.5%, and 95.2% with MSS, IKONOS, and Quickbird datasets, respectively. Shorea robusta forests showed an increase of 16 km2; while Tectona grandis increased by 63.01 km2 during 1975–2010. The spatial heterogeneity in these tropical vegetation classes surrounded by the human dominated agricultural lands could not be addressed using Landsat MSS data due to coarse spatial resolution; whereas the IKONOS and Quickbird satellite datasets proved to advantageous, thus being able to precisely address the variations within the vegetation classes as well as in the land cover classes and along the edge areas. Massive deforestation during 1970s along the adjoining international boundary with Nepal has led to destruction of the wildlife corridor and has exposed the wildlife sanctuary to human interference like grazing and poaching. Higher rates of forest dynamics during the 25-year period indicate the vulnerability of the ecosystem to the natural and anthropogenic disturbances in the proximity of the sanctuary.

The charms and challenges of climate change biodiversity in a warming world

This Special Issue of Biodiversity and Conservation presents a series of 11 papers that document studies on the Indian subcontinent through experiments, measurements, and modelling, with or without geoinformatics technology, to enhance our understanding of the effects of climate change that may have on biodiversity of the region. The papers included here have been selected from those presented at the International Workshop on biodiversity and climate change held in the Indian Institute of Technology (IIT), Kharagpur, India, on 19–22 December 2010.

Evaluating Ecological Niche Models: A Comparison Between Maxent and GARP for Predicting Distribution of Hevea brasiliensis in India

Selection of appropriate ecological niche model for predicting species niche distribution has been a challenge considering the type of species and input variables. Therefore, two ecological niche modelling approaches (Maxent and GARP) are employed to predict the present distribution of a planted species (Hevea brasiliensis Muell. Arg.) in two bio-geographical regions of India: Western Ghats (WG) and North east (NE) regions. The difference between two approaches is in the algorithm and kinds of species data (presence-only or presence and absence) used for model training. GARP over-estimates were observed more in NE as compared to that of WG. Maxent predicts Hevea distribution more accurately in both regions as it considers presence-only data, which appears to be more accurate for this species. The over-prediction of Hevea niche distribution by GARP especially in NE may be attributed to inaccurate and insufficient ‘absence data’ as compared to ‘presence data’. The model accuracy estimator, AUC failed to attribute the difference in model predictability between Maxent and GARP, whereas partial-AUC is found to be better estimator of model spatial accuracy. Therefore, Maxent is found to be more appropriate model for predicting the niche distribution of a plantation species like Hevea.