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One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area. [ABSTRACT FROM AUTHOR]

Many Indian capitals are rapidly becoming megacities due to industrialization and rural-urban emigration. Land use within city boundaries has changed dynamically, accommodating development while replacing traditional land-use patterns. Using Landsat-8 and IRS-P6 data, this study investigated land-use changes in urban and peri-urban Hyderabad and their influence on land-use and land-cover. Advanced methods, such as spectral matching techniques with ground information were deployed in the analysis. From 2005 to 2016, the wastewater-irrigated area adjacent to the Musi river increased from 15,553 to 20,573 hectares, with concurrent expansion of the city boundaries from 38,863 to 80,111 hectares. Opportunistic shifts in land-use, especially related to wastewater-irrigated agriculture, emerged in response to growing demand for fresh vegetables and urban livestock feed, and to easy access to markets due to the city's expansion. Validation performed on the land-use maps developed revealed 80-85% accuracy. [ABSTRACT FROM AUTHOR]

This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T1) and a more recent (T2, with T2 > T1) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T1 and T2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (±2 ha) in respect to HR (±32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This work explores the spatial distribution of monsoonal flooded areas using ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR) in the semi-arid region of N. Gujarat, India. The amplitude component of SAR Single Look Complex (SLC) images has been used to estimate the extent of surface and near-surface water dynamics using the mean amplitude (MA) of monsoonal (July to September) and post-monsoonal (October to January) seasons. The integration of SAR-derived maps (seasonal flooding maps and seasonal MA change) with archaeological data has provided new insights to understand present-day landscape dynamics affecting archaeological preservation and visibility. Furthermore, preliminary results suggest a good correlation between Mid-Holocene settlement patterns and the distribution and extension of seasonal floodable areas within river basin areas, opening interesting inroads to study settlement distribution and resource availability in past socio-ecological systems in semi-arid areas. [ABSTRACT FROM AUTHOR]

The Cauvery basin of Karnataka State encompasses a range of land cover types, from dense forest areas and plantations in the Western Ghats hills, to fertile agricultural lands in the river valley. Recent demographic changes, rapid economic development and urbanization have led to the conversion of vast stretches of forested land into plantations and permanent agriculture. We examine the human drivers of forest cover change between 2001 and 2006, using MODIS 250 m data at multiple spatial scales of nested administrative units i.e., districts and taluks. Population density does not emerge as a major driver of forest distribution or deforestation. Protected areas and landscape accessibility play a major role in driving the distribution of stable forest cover at different spatial scales. The availability of forested land for further clearing emerges as a major factor impacting the distribution of deforestation, with new deforestation taking place in regions with challenging topography. This research highlights the importance of using a regional approach to study land cover change, and indicates that the drivers of forest change may be very different in long settled landscapes, for which little is known in comparison to frontier forests. [ABSTRACT FROM AUTHOR]

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon's entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years. [ABSTRACT FROM AUTHOR]

This study integrated multi-temporal Landsat images, the Markov-Cellular Automation (CA) model, and socioeconomic factors to analyze the historical and future farmland loss in the Delhi metropolitan area, one of the most rapidly urbanized areas in the world. Accordingly, the major objectives of this study were: (1) to classify the land use and land cover (LULC) map using multi-temporal Landsat images from 1994 to 2014; (2) to develop and calibrate the Markov-CA model based on the Markov transition probabilities of LULC classes, the CA diffusion factor, and other ancillary factors; and (3) to analyze and compare the past loss of farmland and predict the future loss of farmland in relation to rapid urban expansion from the year 1995 to 2030. The predicted results indicated the high accuracy of the Markov-CA model, with an overall accuracy of 0.75 and Kappa value of 0.59. The predicted results showed that urban expansion is likely to continue to the year of 2030, though the rate of increase will slow down from the year 2020. The area of farmland has decreased and will continue to decrease at a relatively stable rate. The Markov-CA model provided a better understanding of the past, current, and future trends of LULC change, with farmland loss being a typical change in this region. The predicted result will help planners to develop suitable government policies to guide sustainable urban development in Delhi, India. [ABSTRACT FROM AUTHOR]