Arijit Roy

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.

Evaluation and integration of ERS-1-SAR and optical sensor data (TM and IRS) for geological investigations

The C-band imaging radar of ERS-1, due to its high sensitivity to terrain surface features, holds tremendous potential in topographic terrain mapping for various applications. This is being examined for geological applications, mainly structural and lithological mapping in a mineral belt of Bihar and Orissa, India. The high image contrast that facilitates structural interpretation and highlights topography on the SAR images, reflects the high sensitivity of the ERS-1-SAR to change in terrain slope in the study area.Extensive lineaments, fold structure and major lithological contacts are easily mappable from the SAR imagery. Many of the lineaments, lithological contacts and fold pattern are mapped equally from optical data (Landsat-TM and IRS-1B FCC). The close association of fold pattern and mineral deposits in the region has necessitated the study of those structures carefully from various remote sensing data products. Synergism between SAR and TM provided useful results regarding structure and lithology of the region.The advantage of SAR in highlighting topography and detecting lineaments are affected to a great extent by the speckle noise and low pixel resolution. The present study shows that future geologic interpretation demands high spatial resolution and efficient data processing technique which reduces the speckle noise more significantly.

Applicability of NDVI temporal database for western Himalaya forest mapping using Fuzzy-based PCM classifier

ABSTRACT Information about the spatial distribution of the different tree species is important for sustainable ecosystem management and planning in the western Himalaya. Remote sensing has proved to be useful to assess the spatial and qualitative distribution of vegetation cover over large areas. Present investigation has been carried out to discriminate three different gregarious forest types, i.e. sal (Shorea robusta), chir pine (Pinus roxburghii) and oak (Quercus spp.) in part of western Himalaya. The use of existing classical classifiers has limitation in classification of overlapping classes and in mixed vegetation formations. To overcome this, fuzzy-based possibilistic C-means (PCM) classifier was used to separate the classes. Temporal Landsat 8 imagery (representing the different phenological states) has been used to classify the three forest types. Phenological information and spectral variability were used to select the best suitable dates, i.e. temporal NDVI to use in PCM classifier. It was observed that the satellite data of March, April, May and November were the best suited for discrimination of sal, pine and oak. The overall accuracy of the classified image was found to be 86%. This method can be used for automated extraction of different species in mixed vegetation formations with appreciable accuracy.

Forest fire risk modeling in Uttarakhand Himalaya using TERRA satellite datasets

Abstract Forest fire is one of the major causes of degradation in western Himalaya, and is an annual phenomenon in more than 50% in the forests of Uttarakhand state. Fire danger models are useful for the fire managers to mitigate and suppress the fire activates. MODIS 8 day products viz. MODIS Terra Land surface reflectance (MOD09A1), MODIS Terra Land surface Temperature (MOD11A2) and ASTER digital Elevation Model (DEM) were used to develop fire danger model in this paper. Three parameters Modified Normalized Difference Fire Index (MNDFI), Perpendicular Moisture Index (PMI) and potential surface temperature were computed from the above mentioned satellite products. MNDFI has been used for determining the actual fire occurrence in thermal anomaly pixels and PMI has been used for the estimation of live fuel moisture content in the vegetation and litter. The Potential surface temperature was computed using the MODIS Land Surface Temperature and ASTER DEM. Spatial model was developed based on the above parameters and MODIS terra and Aqua thermal anomaly product (fire location) was used for the validation of the model in the study area. The fire danger models showed an accuracy of 87.31%, i.e. the model accurately predict the fire danger over the study area. Further analysis was done based on composite fire danger image and vegetation types; composite fire danger image and fragmentation map of the study area.

Nepal highway alignment survey and aspects of integrated approach for route alignment study

Satellite and aerial remote sensing techniques can provide useful, indicative information on most of the factors that are pertinent to a good route location. This information because it is obtained quickly, economically, and with fair to excellent dependability for large areas, can then be translated into better planning, design and constructions as well as lower maintenance cost.The Nepal highway project involved, besides study of geology, the mapping and delineation of land facets through which the route would pass, and included the study of (i) rock, (2) soil, (3) weathering, (4) slopes, (5) landslides, (6) hydrology and (7) construction material availability in an integrated manner.Remote sensing techniques helped in efficient selection of potential route than could have been possible by conventional ground techniques alone.

Western Himalayan Forests in Climate Change Scenario

The Himalayan mountain range is amongst the largest, newest and highest mountain chains on earth that form over 2400-km-long arch from east to west direction in the north of South Asia. Himalaya is home to over hundred million people with a small population inhabiting very high altitudes. The range prompts orographic precipitation and impacts weather of the region including the South Asian monsoon, acts as a storage of water in the form of snow and ice and goes about as a wellspring of vast rivers/waterways, for example, Ganges, Indus and Brahmaputra, thus making it the ‘water tower’ for millions of people of the Indo-Gangetic plains. In addition to this, they act as major stores of valued biodiversity resources due to their unique location and physiographic features and are a centre of age-old human cultural diversity. However, the Himalayan mountains are particularly vulnerable to climate change and variability due to their young and fragile nature coupled with sharp gradients; with the increase in population pressure, natural and socioeconomic systems in these mountain regions are at threat, especially with reference to rapid globalization. The quick change in the biological community, driven by both natural and anthropogenic determinants, represents a remarkable danger not exclusively to the source of revenue of the native people, biota and art but also to the people living in the downstream that are dependent on these natural resources and ultimately to the global environment.

Himalayan Spatial Biodiversity Information System

Spatial distribution of environmental resource and its management issues are determined by complex processes and relationships. It involves several interrelating elements with many attributes and a dynamic behavior that required advanced spatial analytical capabilities in the GIS software. The technological solutions required to analyze the system include spatially distributed simulation and optimization models, interactive information system, decision support tools, and expert systems based on geospatial technologies. The primary paradigm of a GIS is the map, an inherently static concept of limited attributes. While modern GIS extends the scope of what can be done within this paradigm toward digital cartography considerably, elaborate applications can be built within existing GIS systems and powerful and flexible tool that involves spatial elements can be developed for different environmental applications. The Eastern Himalayan region is known as one of the global biodiversity hotspots. It includes several Global 200 eco-regions, two Endemic Bird Areas, and several centers for plant diversity. The high biological diversity of the Himalaya is mainly due to the multiple biogeographic origins. The climate variability as a result of being associated with the huge, complex, and steep terrain also gives the Himalayan region a plethora of habitats for the occurrence of the biodiversity hotspot in the region. Apart from being a storehouse of natural resources, the Himalaya is also prone to innumerable natural and anthropogenically induced disasters. This is evident by the recurrent calamities like Kedarnath tragedy, which results in huge loss of life and property.

Spatial landscape model to characterize biological diversity using R statistical computing environment

Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output.

Assessment of forest fire danger using automatic weather stations and MODIS TERRA satellite datasets for the state Madhya Pradesh, India

Forest fires are the most frequently occurred phenomenon during summer seasons in the state Madhya Pradesh. Monitoring and assessment of forest fires are the crucial steps in effective forest fire management. Forest fire danger estimation helps the disaster management authorities to take necessary mitigation measures for minimizing the losses and to evacuate the local people. Fire danger rating systems predict the fire danger based on the meteorological station parameters and ground datasets. McArthur Forest Fire Danger Index (FFDI) is the most popularly used fire danger rating systems using in the country Australia. This index requires large amount of ground datasets for the computation of drought parameter. In India, it is very difficult to compute the drought parameter due to the unavailability of instruments and man power. In the present research, McArthur Fire Danger Index was modified by inducing Normalized multiband drought index (NMDI) that was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA surface reflectance product MOD09GA as a substitute for fuel availability parameter. To test the robustness of modified FFDI, the research was carried out on Madhya Pradesh state for the assessment of forest fire danger. The results obtained from modified McArthur fire danger index were validated by using MODIS active fire hot spot location data (MOD14) and achieved an overall accuracy of 82%. The research concludes that modified FFDI can be used for assessing the forest fire danger in case of unavailability of fuel availability data for a particular forest.

Developing the static fire danger index using geospatial technology

Forest fires in Uttarakhand state have considerable economic, social and environmental impacts on humans and biodiversity. Forest fire danger indices are important tools for mitigating and suppressing forest fires, but, operational fire danger index system has not been developed for the India. In general, Fire danger indices have been developed based on the parameters which are associated for the cause of ignition, spreading of forest fires. These properties include forest fuel type, topographic conditions and moisture conditions. Vegetation and topographic conditions are static, i.e. they do not change frequently, whereas moisture conditions are dynamic. Dynamic properties such as air temperature, relative humidity, wind speed changes regularly in a day. In this study, Static Fire danger Index has been developed using MODIS Land cover type yearly L3 global 500 m SIN grid (MCD12Q1) and ASTER GDEM datasets. International Geosphere-Biosphere Programme (IGBP) land cover type has been generated from MCD12Q1, which has been used to compute the forest fuel type index based on historical fire data. Slope danger index, Aspect danger index, Elevation danger index and Terrain rugged Index has been computed from the ASTER GDEM datasets. Finally, Static Fire Danger Index has been developed by integrating the above mentioned indices. Estimated accuracy of static fire danger index was around 95%, i.e. developed static fire danger index was accurately model the fire danger over the study area.