Land consumption is increasing rapidly with the exponential growth of population. The built-up environment configuration influences the management processes for development and other municipality works. Population growth also affects the availability of land for different purposes in its spatial distribution. The present study was conducted using satellite remote sensing data Landsat MSS (Multi-spectral Scanner), ETM+ (Enhanced Thematic Mapper), IRS P-6 (Indian Remote Sensing Satellite), LISS IV (Linear Imaging Self-Scanner), and IRS P-5 Cartosat-1 for the assessment of urban area change dynamics between years 1976 and 2008 in Bhagalpur city in the state of Bihar in India. The ground truth and coordinate points were collected using a Global Positioning System (GPS) for the location of the built-up themes prepared in the Geographic Information System (GIS). Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC) were introduced to aid in the quantitative assessment changes. The results show a rapid growth in land consumption rate till 1976, reduction in 1999 and again increase in 2008.

Land Use Land Cover (LULC) change detection helps the policy makers to understand the environmental change dynamics to ensure sustainable development. Hence, LULC feature identification has emerged as an important research aspect and thus, a proper and accurate methodology for LULC classification is the need of time. In this study, Landsat-7 satellite data captured by Enhanced Thematic Mapper (ETM+) were used for LULC classification employing the maximum likelihood supervised classification (MLC) algorithm. The study targets the improvement of classification accuracy with the combined use of thermal and spectral information from satellite imagery. Land surface temperature (LST) is sensitive to land surface features and hence can be used to extract information on LULC features. The classification accuracy was found to improve on integrating the thermal information from the thermal band of Landsat ETM+ with spectral information. Two thermal vegetation indices, namely Thermal Integrated Vegetation Index (TLIVI) and Advanced Thermal Integrated Vegetation Index (ATLIVI), proposed in this study showed fairly good correlations (R2=0.65 and 0.7, respectively) with the derived surface temperature. These indices based on empirical parameterization of the relationship between surface temperature (Ts) and vegetation indices showed an increase of nearly 6% in the overall accuracy for land-use/land-cover (LULC) classification in comparison to MLC algorithm using Standard False Colour Composite (FCC) satellite image of Landsat ETM+ as reference

Purpose – The purpose of study is linked to management and policy-making strategies, such as forest management, land use planning and sustainable management of natural resources. It aims to help prevent forest fire by taking precautions. It also aims to be helpful for authorities coping during the event of occurrence of fire. Design/methodology/approach – The methodology paradigm applied here is based on knowledge-based and analytic hierarchy process (AHP) techniques. Knowledge-based criteria involve topographic and different themes for risk assessment. The assignment of value given to equation is significant due to its importance. Findings – Results are in strong agreement with actual fire occurrences in the past years. The risk zones are identified according to past occurrence of fire. The gradients of low- to high-risk zones are according to fuel, topographic features and weather conditions. Direction and aspect value were taken accordingly. Originality/value – The paper presents forest fire risk zones designed on knowledge-based information. Crisp and fuzzy AHP approaches were applied to improve the efficacy of the model. The mapping results were in accordance with actual fire occurrences in the past years.