Land use/Land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth Observation (EO) technology provides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sensors has increased remarkably over the past decades, as more and more platforms are placed in orbit and new applications emerge in different disciplines. The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems. One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen.

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.

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.

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.