A. K. Saha

Land Cover Classification Using IRS LISS III Image and DEM in a Rugged Terrain: A Case Study in Himalayas

Abstract Digital image classification is generally performed to produce land cover maps from remote sensing data, particularly for large areas. The performance of image classifiers that utilize only the remote sensing data may deteriorate, especially in mountainous regions, due to the presence of shadows of high peaks. In this study, a multisource classification approach to map land cover in Himalayan region with high mountain peaks having elevations up to 4785 m above mean sea level has been adopted. Remote sensing data from IRS LISS III image along with NDVI and DEM data layers have been used to perform multi‐source classification using maximum likelihood classifier. The results show a substantial improvement in accuracy of classification on incorporation of NDVI and DEM as ancillary data over the classification performed solely on the basis of remote sensing data.

Heat waves, temperature extremes and their impacts on monsoon rainfall and meteorological drought in Gujarat, India

In the Indian Subcontinent, droughts occur generally due to delayed arrival or early retreat of the south-west monsoon associated with poor precipitation. However, initiation and intensification of droughts may also result from heat waves. With an arid to semi-arid climate, the Gujarat State of India experiences seasonal temperature variations and frequent heat waves during summer and inconsistent rainfall during the rainy (monsoon) season, resulting in recurrent droughts. In the present study, ground data of rainfall and temperature were used to assess meteorological drought in Gujarat during 1981–2010. Geographical information systems (GIS)-based analysis revealed an association between rainfall deficit and heat waves, and an indirect influence of heat waves and temperature extremes on drought development and intensification. The study indicates a relationship between climate extremes and climate change. It also points to the fact that, in certain years, local factors are more influential than global factors such as El Niño and the Southern Oscillation (ENSO) for drought occurrence and non-occurrence in Gujarat.

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat, India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.

A comparative analysis of four drought indices using geospatial data in Gujarat, India

Rainfall deficiency results in drying up of soil, leading to a drought situation, invariably with grave consequences. The magnitude and pattern of drought can be measured with various drought indices using remote sensing and meteorological data. The present study attempts to analyze spatio-temporal pattern of drought severity, and compare meteorological and vegetative drought indices in Gujarat, an agrarian and drought-prone state in western India. Open source remote sensing and rainfall datasets were analyzed for the monsoon and non-monsoon seasons from 1982 to 2001, a period underscored by 12 major droughts. Four indices, namely Standardized Precipitation Index (SPI), Rainfall Anomaly (RFA), Vegetation Condition Index (VCI), and NDVI Anomaly Index (NAI) were used for analysis. Drought patterns, thus delineated, were found to have very good correlation with rainfall. It was observed that both RFA and NAI could be used as an indicator for assessment of the percentage of area affected by meteorological and vegetative drought in the study area. A comparison between the four indices was of much help in understanding the impact of meteorological drought on vegetative drought. Furthermore, multiple regression analysis revealed that RFA and NAI were better-suited drought indices, among the four under consideration.

Analysing Meteorological and Vegetative Drought in Gujarat

The impact of drought on vegetation can have significant consequences on livelihood and socio-economic development. Delay in monsoon, high temperature and lack of water resources lead to recurrent droughts in Gujarat. The present work attempts to study the spatio-temporal coverage of drought and its characteristics. Normalized Difference Vegetation Index (NDVI) Anomaly and Rainfall Anomaly Index (RAI) derived through CRU Global Climate dataset and NOAA-AVHRR data respectively for the period 1982–2001 were used for monitoring and comparison of meteorological and vegetative drought situations. Drought patterns, thus delineated, were found to have very good correlation with rainfall. It was observed that both Rainfall Anomaly Index and NDVI Anomaly Index can be used as an indicator for assessment of area affected by meteorological and vegetative drought. The latter showed a high correlation with Rainfall Anomaly Index. The impact of rainfall on vegetation health is, thus, clearly visible. The study was able to delineate the zones more prone to drought with the help of these two indices. This technique proved useful for analysing the spatial and temporal trend of drought, its prevalence, severity level and persistence with the help of freely available meteorological and satellite data. The findings will be of great value for planners and resource managers in quick decision making and forecasting.

Spatio-Temporal Analysis of Mangrove Loss in Vulnerable Islands of Sundarban World Heritage Site, India

Mangroves are unique ecosystem found mainly in tropical coastal region in saline environment and under tidal influence. It has enormous ecological and economic value to the environment and local people. However, the problems are arising in tropical coastal region like Sundarban, where both natural and ever increasing anthropogenic activities have complicated the growth and development of mangroves. Therefore, spatio-temporal monitoring of mangroves has huge importance for their conservation in Sundarban World Heritage Site, the largest mangrove population in the world. Remote sensing has been proven as an important tool to monitor such ecosystem, but the traditional pixel based approach has several drawbacks. Recently, Object-based Image Analysis (OBIA) approach in remote sensing has helped to overcome such drawbacks. The present study attempts to analyse the status of mangroves over the time period of 40 years (1975–2015) in the study area using Landsat time series images through OBIA. The result reveals that the mangroves are gradually reducing over the last 40 years and about 4% mangrove area has been converted into water. It is a major indication of increase in sea water level, making many islands vulnerable. The time series analysis in some islands, like Bhangaduni, Bulchery, Dalhousie and Halliday shows the land area as well as mangroves have been destroyed more than one-third. If the process continues at the same rate, these islands may soon completely disappear.

Facility Management System: A Case Study of University Campus

Remote Sensing and GIS play very important role in creating future smart cities. Facilities management being an important component of smart cities assimilates infrastructural functions and processes. Moreover it defines scheduled approaches toward the optimization of resources, in turn, promoting efficiency and simplifying complex decisions. This study aims at developing a better facility management system at Delhi University North campus by utilizing an integrated approach of information technology and GIS. The case study pertains to meet the objectives like collection of information on various facilities (viz., Banks and ATM, Photocopy and Printout shops, Food Joints, Health Care and Medical shops, Hostels, etc.) in the Delhi University North Campus and collating that information to develop the facility management system in a GIS framework. For this study, high resolution satellite imageries of QuickBird (60 cm resolution), EICHER Delhi City Map and Google Earth have been used. The heads-up digitization has been performed for feature extraction (e.g., road network, canteen, Railway Reservation centre, parks, shopping centres, etc.) from the very high resolution satellite imagery and ArcPAD mobile GIS has been used to perform the survey related to facilities in the campus. The mapped facilities have been brought into a GIS-based network analysis to find out basic closest facilities, optimized route identification, service area identification, origin-distance matrix etc. The mapped facilities have been then published using open-source ArcGIS Explorer toolbox for common users.

Land Cover Change and Rhino Habitat Mapping of Kaziranga National Park, Assam

Kaziranga National Park in Assam is a habitat for the highest population of one-horned rhino in the world. Conservation management of the park has become a serious concern to maintain the wildlife in the park. The present study investigates land cover change within Kaziranga National Park during the last two decades (1990–2009) using remote sensing-GIS techniques and analyses habitat suitability for rhino to understand possible effect of land cover change on the rhino habitat. The change detection analysis has shown considerable reduction of grasslands areas and small water bodies. Furthermore, Habitat Suitability Model for rhino has been developed based on semi-quantitative Analytical Hierarchy Process. The result shows decline in the suitable habitats for rhino during this period. Assessment of rhino habitat change indicates that any change in the land cover trigger substantial change in the suitable habitats for rhino. Moreover, increase in rhino population as reported by census and reducing suitable habitat further limit the carrying capacity of the national park. The results from the present study may be used as baseline for future rhino habitat monitoring.

LaSIRF: Landslide Safe Intelligent Route Finder for Mountainous Terrain in GIS Environment

Route planning in hilly areas is a compound job as it involves consideration of a number of factors. The conventional route planning practice is time consuming and does not consider factors related to geo-hazards such as landslide hazard zones, geological faults etc., thereby resulting in increased cost of road design, maintenance etc. The aim of this chapter is to develop a Geographic Information System (GIS) based software for planning a road route that passes through landslide safe areas. A number of thematic cost factors have been integrated in GIS. Dijkstra’s least-cost finding algorithm together with improved neighbourhood analysis to compute the neighbourhood movement cost has been used to find landslide safe route. Working examples have been presented to demonstrate the utility of the software for route planning in highly landslide prone area in the Himalayas.