Change Detection Analysis Research Articles

Spatiotemporal analysis of urban sprawling using change detection: a case study of Shaki district, Azerbaijan

Introduction. The contemporary globalized world characterizes the rapid population growth, its significant concentration in cities, and an increase in the urban population. Currently, many socio-cultural, economic, environmental, and other challenges are arising in modern cities. Cities are therefore emerging from the common understanding and displaying new characteristics: reduced density, dispersed development, poor accessibility and monofunction. However, the concentration of population in cities also brings its own set of issues. The purpose of article. The purpose of the study was to identify Shaki City's urban development process, and how its land use has evolved over time. It examines the population growth in the region and the increase in the specific weight of the urban population between 2016 and 2023, analyzing the population growth trend over a 20-year period. The direction and extent of urban land use has been studied by determining the relationship between the growth rate of the urban population and the extension of the urban area, and by analyzing the changes which have occurred during the period of land use. Research methods. Statistical data has also been used for this purpose, together with data from the Azersky satellite. Machine Learning (ML), which is widely used in remote sensing systems, was applied, Support Vector Machine Learning (SVM), and image classification and processing were performed. On the basis of the obtained data, a comparative analysis of the previous and current conditions was carried out and the area of changes in the area between the classified areas was calculated. Simultaneously, the changes between categories during the use of the area and the recent changes in the direction of land use were shown. Classification performance has been assessed, user and producer accuracies have been determined and kappas have been calculated. Main findings. The increase in the population of the Shaki district led to an increase in the specific weight of the urban population and the extension of the town to the south and south-east where the population previously lived sparsely. It is mainly due to construction of new housing estates in region, as well as construction of a central clinic, an ASAN service, and a regional education division. A 'flight to the centre' was observed, resulting in noticeable changes in the land use structure between 2016 and 2022, in line with the growth rate of urbanisation and economic development. The decoding of the distribution images of the region shows that there has been an increase in the area of settlements over the six-year period. By 2016, settlements cover 22.4 per cent of the city, and by 2022, the figure rises to 39 per cent. From 34% to 32.9%, the total area of forest reserves decreased. Scientific novelty and practical value The article describes for the first time the urban sprawling and territorial transformations in Shaki district in the context of population growth by using change detection analyses. The practical value of the study is the possibility of using its algorithm and method to conduct similar studies in other cities of Azerbaijan. The results of the study are significant in the context of justifying regional measures to adapt urban expansion to population growth.

Evaluating decadal space-temporal mangrove dynamics using geographic object-based image analysis (GEOBIA) on the Doce River delta-ES, southeastern Brazilian coast

Mangroves are ecosystems present in a large part of the Brazilian coastal zone that are home to a wide diversity of organisms, providing direct and indirect resources, as well as nursery and foraging habitats. Using images in 5-year intervals (2010, 2015, and 2020), the present study carried out a decadal mapping of the remaining mangroves of the Doce River delta along the tidal flats of the Mariricu River, in the coastal region of the city of São Mateus-ES. In this context, object-oriented classification methodology (GEOBIA) was used, which allowed the generation of high-resolution data. This methodology achieved excellent results, as evidenced by the global accuracy numbers and Kappa index, which gave an average of 96.78% and 93.5% respectively; and by low values of disagreement, with an average of 3.22%. The change detection analysis showed alterations in the landscape over time, with 9% of the mangrove areas becoming extinct, 12% of the areas expanding, and 79% of the areas remaining preserved. Therefore, our mapping data are consistent with the data published by the State Institute for the Environment and Water Resources of the State of Espírito Santo (IEMA), as well as with scientific works that recorded areas of reduction and expansion for mangroves, but mostly with large preserved areas. With that, our results demonstrate the highly effective application of geotechnologies for coastal environmental analyses with low cost and high speed.

Optical and Thermal Image Processing for Monitoring Rainfall Triggered Shallow Landslides: Insights from Analogue Laboratory Experiments

This study explores the innovative use of digital image processing (DIP) techniques, also named PhotoMonitoring, for analysing the triggering conditions of shallow landslides. The approach, based on the combination of optical and infrared thermographic imaging (IRT), was applied to a laboratory-scale slope, reproduced in a flume test apparatus. Three experiments were conducted to replicate rainfall-induced shallow landslides, applying change detection and digital image correlation analysis to both optical and thermal images. The method combines IRT’s ability to measure ground surface temperature changes with DIP’s capacity to track movement and displacement. Results showed the high reliability of the displacement time-series obtained through IRT-DIP with respect to the reference optical-DIP. The IRT-DIP technique also detects anomaly signals two minutes before landslide occurrence that can be regarded as a possible failure precursor. This study testifies to the potential of image analysis as a remote sensing technique, demonstrating the ability of DIP to capture the dynamics of shallow landslides, as well as the advantages of optical–IRT combinations to follow slope deformation processes during night-time. This approach, if properly adapted to real-scale scenarios, may contribute to a better understanding of landslide behaviour, improve landslide monitoring strategies, and promote more effective early warning systems (EWS).

Limitations in the use of Sentinel-1 data for morphological change detection in rivers

ABSTRACT The identification of morphological changes occurring along river channels is essential to support river process understanding, assess sediment budgets and evaluate the effectiveness of river management. Among available remote sensing techniques, space-borne synthetic aperture radar (SAR) could potentially provide a powerful complement to optical imagery for this task. However, very few studies have been carried out on the use of SAR datasets to study erosion and deposition processes in river channels. In this work, we investigate the potential of change detection analysis based on Sentinel-1 data, by comparing variations of radar backscattering to river morphology changes identified through high-resolution drone acquisitions. We considered a time series of two years of Sentinel-1 data relative to a period where, despite a moderate fluvial event occurred, morphological changes have been significantly detected in multitemporal drone point clouds. Satellite optical imagery (planet.com) and hydro-meteorological data were used to support the analysis and interpret results. The results show that the spatial and temporal resolution of Sentinel-1 is currently not suitable for accurate discrimination of morphological changes related to river dynamics at local scale. Other spaceborne sensors with sub-metric ground sampling distance and/or daily revisit time would be probably suitable; however, so far, this option would need the use of commercial solutions with a consistent increase of the costs of the investigation.

Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change detection analysis has been limited. This study monitors and analyzes LULC changes in the study area from 1991 to 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). The results showed that the RF algorithm produced the most accurate maps of the three-decade study period, accompanied by a high kappa coefficient (0.93–0.97) compared with the SVM (0.91–0.95), ANN (0.91–0.96), KNN (0.92–0.96), and XGBoost (0.92–0.95) algorithms. Consequently, the RF classifier was implemented to categorize all obtainable satellite images. Socioeconomic changes throughout these transition periods were revealed by the change detection results. Rangeland and barren land areas decreased by 11.33 % (−402.03 km2) and 6.68 % (−236.8 km2), respectively. The transmission increases of 13.54 % (480.18 km2), 3.43 % (151.74 km2), and 0.71 % (25.22 km2) occurred in agricultural land, forest, and built-up areas, respectively. The outcomes of this study contribute significantly to LULC monitoring in developing regions, guiding stakeholders to identify vulnerable areas for better land use planning and sustainable environmental protection.

Uncertainty assessment of Sentinel-2-retrieved vegetation spectral indices over Europe

ABSTRACT Vegetation spectral indices (VIs) from multispectral remotely sensed imagery provide useful information in several sectors, especially if longing for change detection analyses or land monitoring. In this context, estimating uncertainty of VI values is crucial to recognize significant differences in both space and time domains. Unexpectedly, most applications reported in literature and involving VI do not take care about this issue, thus making unreliable a significant part of deductions. In this work, authors present an approach aimed at mapping in time and space the theoretical uncertainty of some widely used VIs basing their approach on the so-called variance propagation law (VPL). VPL can be consequently used to get an estimate of the theoretical VI uncertainty, starting from one of the bands involved in VI computation. VI uncertainty all along the year 2020 was then mapped at pixel level by Google Earth Engine over the whole Europe to test seasonal trends. Uncertainty of VI differences, as possibly resulting from a change detection approach, was tested by comparing monthly composites of VI and computing the expected uncertainty of differences along the year. An example was reported involving two NDVI maps (June–September) proving that about 30% of ΔVI were not significant.

Spatio-Temporal Analysis of Lahore’s Resilience Against Heat Wave by Using Remote Sensing and GIS

This research work aims at finding urban resilience capacity of Lahore (study area) against the heat wave. Natural as well as anthropogenic disasters are threatening the urban systems and the urbanites. In the present scenario of fastest urbanization, the concepts of urban resilience and sustainability are sprouting and flourishing for making the cities more resilient and sustainable against disasters. Lahore, being the 2nd largest city of Pakistan with huge population, faces the disasters of various natures including urban flooding, smog and heat wave. The major function of a city is to provide all what is needed for its people and that is only possible when a city will be resilient enough to cope with the disasters and have capability to meet the social, economic and environmental needs of its people. This research focuses on identification of the major disasters being faced by Lahore and evaluation of its sustainability and resilience capacity against these disasters. Landsat images of the years 2002,2012 and 2022 were acquired from the USGS website. These images were processed and analyzed by using ArcGis to find out the relationship among various indices causing heat wave. In this regard, NDVI, NDBI, LULC, LST and UHI were obtained. The extracted information was later rearranged, over-layered and weighted to trace the zonal resilience of Lahore. The deduced findings reveal tremendous impact of decreasing vegetation cover and expansion in built-up area in creation of heat island and hindering the resilient capacity of Lahore. The eastern parts of the city are the most resilient followed by southern areas while central and western parts are least resilient against heat wave. Change detection analysis shows drastic change, the highest resilient areas of 2002 and 2012 are the least resilient in 2022. This research will further lead to new horizons of research pertinent to urban resilience and will be of great significance for developers and policy makers in making the cities resilient against disasters.

ASSESSMENT OF IMAGE CLASSIFICATION ALGORITHMS FOR LAND COVER CLASSIFICATIONS IN TULLY, NY

Abstract. The identification, delineation, and mapping of landcover is integral for resource management and planning as it establishes a baseline for thematic mapping and change detection analysis. The availability of high-resolution satellite imagery and the development of machine learning algorithms have significantly improved the prediction and accuracy of landcover classification. In this study, landcover classification is performed on seven-band Landsat 9 imagery and eight-band PlanetScope imagery for the village of Tully, NY, with an area of 900 square kilometers. The resolution of Landsat imagery is 30 meters, whereas the resolution of PlanetScope imagery is 3 meters. Classification schema is developed in ArcGIS Pro with five classification levels: conifer forest, hardwood forest, agriculture, developed, and water. Pixel-based supervised classification is performed using Support Vector Machine (SVM), Random Tress (RT), K-Nearest Neighbor (K-NN), and Maximum Likelihood Classifier (MLC). The reference dataset is acquired by an image interpreter using high-resolution imagery for map accuracy assessment. All the classification methods for Landsat imagery have more than 78% accuracy, but SVM performed best with 82% accuracy. For PlanetScope imagery, SVM performed best with 85% accuracy, whereas MLC had the lowest accuracy of 77%.

Monitoring temporal changes in coastal mangroves to understand the impacts of climate change: Red Sea, Egypt

This paper focuses on the current state of mangroves in Egypt, analyses how climate change is affecting mangrove forests and suggests a conservation strategy to safeguard this valuable maritime resource. Mangroves in Egypt are primarily found in regions around the Red Sea, making them popular for ecotourism across the globe. A crucial breeding and feeding ground for many commercial fishery species, mangroves are a maritime habitat that is both economically and environmentally significant and a variety of living things call them home. Mangroves are one of the most threatened tropical and subtropical ecoregions in the world. These threats come from both natural and human sources, including oil spills, human waste runoff, herbicide use, and coastal development, all of which have the potential to harm the environment. Mangrove habitats are immediately impacted by salinity changes and rising sea levels brought on by climate change. This is unfortunate as mangroves are one of the most important global carbon sinks, and their loss due to climate change may accelerate sea level rise and ocean acidification, which will cause the extinction of numerous commercial fish species. A conservation strategy should be implemented to safeguard the mangroves around the Egyptian Red Sea by using mitigation techniques, creating marine protected zones near the mangroves, and consulting the owners of the mangrove stalks. Analysis of multi-temporal satellite imagery is one of the most significant methods to assist decision-makers and environmental planners in obtaining high-precision information about environmental and climatic changes. Analysis of satellite imagery is a valuable way to detect, monitor, assess and map the environmental, human, and natural activities that are threatening the growth of the mangrove forest ecosystem. Change detection analyses using satellite imagery were conducted to evaluate the effects of rapid coastal sustainable development, including human urban and tourism activities; the decline and destruction of large areas of the mangrove forest for animal feeding along camel trade roads; and the threat of natural impacts such as flash flooding, coastal and soil erosion or human impacts such as established desalination plants and other human activities along the coastal zone of the Egyptian Red Sea.

A Review of Practical AI for Remote Sensing in Earth Sciences

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing.

Applying Remote Sensing Methods to Estimate Alterations in Land Cover Change and Degradation in the Desert Regions of the Southeast Iberian Peninsula

Numerous drylands worldwide have experienced degradation of both soil and vegetation in proximity to watering areas. Degradation can be observed in satellite imagery as fading radial brightness belts extending away from the water sources. The main objective of this study was to examine the spatio-temporal patterns of land degradation and rehabilitation in the drylands of the southeast Iberian Peninsula. The brightness index of tasseled cap was discovered to be the best form of spectral transformation for enhancing the contrast between the bright-degraded areas near the points and the darker surrounding areas far from and in between these areas. To comprehend the spatial structure present in spaceborne imagery of two desert sites and three key time periods, semi-variograms were created (mid-late 2000s, around 2015 and 2020). To assess spatio-temporal land-cover patterns, a kriging was used to smooth the brightness index values extracted from 30 m spatial resolution images. To assess the direction and intensity of changes between study periods, a change detection analysis based on kriging prediction maps was performed. These findings were linked to the socioeconomic situation prior to and following the EU economic crisis. The study discovered that degradation occurred in some areas as a result of the region’s agricultural activities being exploited.

Geospatial Analysis of Informal Settlement Development in Cape Town

Informal settlements are a major influence in the urban growth of developing countries such as South Africa. There are also associated with negative socio-economic factors such as unemployment and are lacking in terms of secure land tenure arrangements. This research focuses on developing a geospatial understanding of the internal dynamics of informal settlement development within the City of Cape Town. To investigate how informal settlements are established and developed in a local context, the informal settlements of Imizamo Yethu, Langa, and Siqalo were monitored for the period 2011-2019 using image classification to determine the development, complexity, and compactness of the dwellings. The overall accuracy of the classified maps thus developed ranged between 88 and 96%. Change detection analysis was subsequently used to identify the geospatial trends for each informal settlement across all three. The combination of linear regression and ordinary least squares analysis determined that the major spatial trend driving growth was densification, which was correlated with the availability of open space, unemployment, poverty, and GDP. Furthermore, densification was identified along the major formal external transport routes and informal internal transport networks. It was found that individual settlements present unique internal geospatial development dynamics in the macroeconomic context of Cape Town, but that these tend to differ in the microeconomic context of the city. Among the explanatory variables for this situation were sloped lands, employment opportunities, and neighbouring areas where the incomes of the residents were higher. Across all the informal settlements, open space proved to be the most significant factor, while GDP played the most influential role in explaining shack compactness over time. This study could be used to contribute to policy and decision-making in the formalisation process in these informal settlements.

Oil Spill Detection Using Otical Sensors: A Multi-Temporal Approach

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Spatiotemporal Analysis of Land Use Land Cover Mapping and Change Detection in Dambatta Local Government Area

This research studied the spatiotemporal changes in land use (LU)/land cover (LC) in Dambatta local government area, with a view to identifying the effect arising from the observable changes in land use patterns. The imageries used in the study were obtained from the National Space Research and Development Agency (NARSDA), Abuja. Spatial analytical techniques and descriptive statistical techniques were employed to analyze the data. The results showed 66.8% reduction in agricultural lands, 45.5% reduction in vegetation cover, 223.2% increase in built-up areas, 269.1% increase in bare lands and 70% increase in water bodies within the 20 years. Spatio-temporal analysis of the three imageries revealed that agricultural lands were largely been taken over by urbanization while vegetation had rapidly given way to bare lands within the 20 years. It was observed that these changes resulted from anthropogenic activities, environmental factors and climate change. These result in the loss of farmlands, inadequate food supply, unemployment, inadequate industrial raw materials, reduction in revenue generated, forest depletion, desertification, wildlife extinction and temperature increase. While it is recommended that reforestation, land reclamation and irrigation agriculture should be promoted in the area, it is also suggested that further research should focus on the impact of climate change on land cover change in the area.

Land use change detection using the intensity analysis at the Bosomtwe District, Ashanti Region, Ghana

Changing environmental conditions owing to increasing resource degradation and urbanization in the Bosomtwe district of Ghana make the land cover and change detection analysis an extremely important consideration for Sustainable Environmental Management. The district, where lies the only natural occurring Lake in Ghana, Lake Bosomtwe, is a highly populated urban district that is threatened by rapid vegetation losses, subsequently destroying important biodiversity within the watershed of the UNESCO biosphere reserve. This research investigates the significance of transitions among the major land use/land cover categories in the Bosomtwe district using GIS and remote sensing technologies. Support Vector classification algorithm was adopted for classifying satellite imageries of the years 2002, 2008, 2016, and 2022 while the analysis of the dynamics of change was conducted using the land use change intensity analysis. The study results provide evidence of the prominence of LULC changes within the 20 years interval of assessment. Significant changes in the structure and arrangement of the LULC within the Bosomtwe area were observed for 20 year period. The period between 2016 and 2022 experienced rapid changes and the land use transformation accelerated over the whole period as compared to the 2002-2008- and 2008-2016- year intervals. The study concludes that the major changes are Built-up and Agricultural activities which have claimed forest lands around the Lake's catchment and has intensified in recent years (2016 – 2022). Based on the findings, the study recommends that spatial planning of the spaces in the district be strictly implemented to protect the remnant forest from anthropogenic activities. Afforestation projects should be carried out within the district to reclaim the lost biodiversity. Implementation of such projects are critical in conserving the Lake's biosphere and ecosystem to ensure its sustainability.

Analysis of forest tree dieback using UltraCam and UAV imagery

ABSTRACT In recent years, increasing tree diebacks and mortality in some forests, particularly in forest parks, created a need amongst forest managers to find effective methods to gather information about the rate of dieback and mortality and their reasons. High-quality air and space-born remote sensing data has established as an alternative to field surveys for certain inventory tasks. This study used high-quality UltraCam-Xp and UAV drone images from 2016 and 2021 to map tree dieback and mortality in Daland Forest Park, Golestan Province, Iran. High-quality ortho mosaics and Digital Surface Models (DSMs) were generated from UltraCam (2016) and UAV (2021) images. The images were then classified through object-based classification by Nearest Neighbor (NN), Support Vector Machine (SVM), and Bayes algorithms using various input data sets including spectral bands, Canopy Height Model (CHM), vegetation indices, and texture analysis features. Our results indicate that the Bayes algorithm is more precise in mapping tree dieback for the two time steps compared to other algorithms. The best tree dieback map on UltraCam images was obtained using the spectral bands with CHM, texture analysis features, and vegetation indices. This combination resulted in an overall accuracy of 91.20% and a Kappa coefficient of 0.88. It was also found that combining the UAV main bands with CHM and texture features did produce a high-accuracy map with an overall accuracy of 88.46% and a Kappa coefficient of 0.84. Change detection analysis of tree dieback showed that between 2016 and 2021, the number of healthy trees decreased, and the number of gaps and open areas increased in the study area. We conclude that UltraCam and UAV photographs can serve to identify and map tree dieback and dead trees with good accuracies and can hence support forest health monitoring.

Anthropogenic Climate Change Negatively Impacts Vegetation and Forage Conditions in the Greater Four Corners Region

AbstractThe Southwestern U.S. has been in a megadrought for the past two decades, stressing vegetation across the region and leading to poor forage conditions on rangelands. How has human‐induced climate change affected air temperatures and atmospheric evaporative demand in the greater Four Corners region of the Southwest, and what have the corresponding impacts been to vegetative productivity? We address this question via a multi‐step climate change detection and attribution analysis. We first relate evaporative demand (quantified by Vapor Pressure Deficit, or VPD) and precipitation to the Normalized Difference Vegetation Index (NDVI) in six unique plant‐precipitation zones. VPD is strongly negatively related to NDVI, and the strength of this relation is similar in magnitude to precipitation. We then use a large ensemble of climate change simulations to estimate the magnitude of VPD increases attributable to human‐induced climate change. We find that modest increases in air temperatures have led to large increases in VPD, with the influence of climate change about twice the inter‐annual standard deviation by 2020. Finally, we estimate the reductions in NDVI due to increased VPD and present the reductions in terms of total annual Aboveground Net Primary Productivity. We conclude that human‐induced warming has roughly doubled the magnitude of recent vegetation deficits, especially in mid‐precipitation areas (receiving 250–500 mm of precipitation annually), concentrated in New Mexico. These results indicate that increased temperatures from human‐caused climate change are having persistent and damaging impacts on vegetation productivity, with significant implications for ranchers and other land users in the region.

LAND USE/LAND COVER CHANGE DETECTION IN GIRNAR WILDLIFE SANCTUARY- JUNAGADH

As the time plays an important role in leading the life processes and its surroundings, this research work presented about the Land use/Land cover changes in Girnar Wildlife Sanctuary of Junagadh, situated in the Gujarat State of India between the year 1996, 2008 and 2017. Remote sensing and GIS have been used to describe different features of the study area. A supervised signature extraction with the maximum likelihood algorithm was employed to classify the digital data of IRS 1D georeference and merged LISS III for land use/land cover mapping for the year 1996, 2008, and 2017. This work carried out to get understanding of land-cover changes in last twenty years in the Girnar Wildlife Sanctuary areas. As change detection analysis is a major part of this study, it shows that some areas are increasing more than other classes mainly the forest area had been increased followed by barren land while other areas like scrub land decreases subsequently. This habitat change lead to fluctuation in the dispersion of lions in the area with the course of changing resource availability.

Change detection analysis using Landsat images on Balurghat Municipality, West Bengal, India

Change detection analysis using Landsat images on Balurghat Municipality, West Bengal, India