Land Use Cover Change Detection Research Articles

Evaluating the Climate Change Trends and Spatiotemporal Variations of Evapotranspiration: A Case Study in El-Beheira Governorate, Egypt

Aims: Evapotranspiration (ET) is crucial for determining crop water requirements, while climate change and global warming are major concerns. Therefore, in this study, we aimed at evaluating the climate change trends and the spatiotemporal variations in ET in a selected area located in El-Beheira Governorate, Egypt, while accounting for potential land use/land cover (LULC) changes. Methodology: We analyzed three Landsat images acquired in 1984, 2001, and 2020 to assess LULC changes in the study area. Climate change was studied from 1984 to 2021 using data from NASA's Prediction of Worldwide Energy Resource (POWER) project. We also used the Terra MODIS MOD16A3GF Version 6.1 ET product from 2000 to 2021 to evaluate the spatiotemporal variations of ET. Results: LULC change detection showed that the unchanged agricultural area from 1984 to 2020 accounted for 29.3% of the study area and was used to evaluate the changes of ET. Evaluating climate change revealed that there was an increased trend in temperature, relative humidity (RH), and precipitation, while no change was observed in wind speed. Similarly, the anomalies changes of temperature, RH, and precipitation had an increasing trend while wind speed was constant. On the other hand, the yearly ET values calculated from 2000 to 2021 had an increasing trend. There was a moderate correlation between RH and precipitation, as well as between ET and precipitation. Conclusion: The increased trends in climatic parameters such as temperature will eventually require changes in crop patterns to adjust to these changes and maintain a profitable yield. Furthermore, the increase in ET will result in an increase in crop water requirements, which is problematic, especially considering the limited water resources in Egypt.

Modelling and forecasting of urban flood under changing climate and land use land cover

Abstract Chennai is a rapidly urbanizing Indian mega-city and experiences flooding frequently. Literature state that climate change and land use change have a significant impact on the runoff generated every year, making it essential to study the historical trend and forecast changes in land use land cover (LULC) and climate to model runoff. This study considered Adyar watershed for LULC change detection, climate change analysis, and flood forecasting for 2030 and 2050 based on LULC and runoff from 2005 and 2015. A coupled hydrologic–hydraulic model (HEC-HMS and HEC-RAS) was developed to assess flooding for future LULC and climate scenarios. LULC analysis shows an increase in built-up cover by 6%. Climate analysis shows 74% probability of increase in precipitation intensity between 2015 and 2050 compared to 2015. It was observed that depth of flooding increased by 19.4% in 2030 and 60.4% in 2050 compared to 2015. This study makes a structural proposition for flood mitigation through flood carrier canals on the downstream reach of the river which flows through Chennai. The canals were found to significantly reduce overbanking, providing protection against flooding. This is the best measure for providing the highest flood reduction for the study area.

An integrative climate and land cover change detection unveils extensive range contraction in mountain newts

The global decline of amphibian populations, driven by anthropogenic activities, is a pressing conservation issue, with salamanders being of particular concern, as these species serve as ecological indicators vulnerable to environmental change. Mountain newts of the Neurergus genus, which are endemic to the Zagros Mountain chain from southeastern Turkey to northern Iraq and southwestern Iran, face a multitude of threats. Among these threats, climate change and land use alterations have been identified as major contributors to the decline of these species. Given the varying spatiotemporal scales at which these factors operate, in this study we aimed to assess the impacts of climate and land use/land cover (LULC) changes (LULCC) on the distribution of the Neurergus genus. We employed MaxEnt model to predict their habitat suitability under current climatic conditions. We projected the predicted model to the future, i.e., 2050, under two climate change scenarios. We then proceeded to map the LULC patterns of the identified suitable habitats for each species using Landsat satellite images, and conducted a hindcast of LULCC within these habitats for three time-slices 1988, 2005, and 2020. Finally, we evaluated the efficiency of current network of protected areas (PAs) and key biodiversity areas (KBAs) in Iran, Iraq, and Turkey to cover suitable habitats of the species. Our results revealed that climate changes would negatively influence all Neurergus species, with southern species in Iran and Iraq, i.e., N. derjugini, N. kaiseri, and N. crocatus exhibiting the greatest range loss. Conversely, LULC change detection indicated that northern species in Turkey, i.e., N. strauchii and N. barani, are more exposed to cropland developments and have experienced greatest habitat changes over the past 30 years. Ultimately, our findings underscore the insufficiency of extant conservation areas in protecting Neurergus habitats and urge the need for comprehensive conservation measures. We recommend promoting less strictly conserved areas, e.g., KBAs, implementing trans-boundary conservation plans, and designating new reserves to ensure long-term preservation of amphibians in the regions.

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.

Spatio-Temporal Change Detection Analysis of Land Use Land Cover of Bathinda District, Punjab, India

ABSTRACT: Due to rapid industrialization and urban sprawl in the last few decades, the land use pattern and its consumption takes place at a large scale that could lead to problems such as over-exploitation of land resources, food insecurity and pollution. It becomes imperative to carry out monitoring and subsequent modelling of land use land cover (LULC) changes. An attempt was made to study the changes in the LULC pattern of district of Bathinda, Punjab, India. Remote sensing (RS) and geographical information system (GIS) were used to perform the analysis of satellite data using image processing and classification procedures. For preparing LULC maps, supervised classification was carried out using maximum likelihood classification (MLC) algorithm, aided with Earth Resources Data Analysis System (ERDAS) Imagine 2014 and ArcGIS 10.3 software. Further, change detection study was done using multi-temporal Linear Imaging Self Scanning Sensor-III (LISS-III) data sets of the year 2006 and 2018 to analyze the temporal changes. It was observed that the region is occupied by various ground features such as water, built-up area, agricultural land, vegetation/trees and fallow land. The results revealed that the area under water bodies have increased by 0.413km2 in 2018. The built-up areas including human settlements, commercial infrastructures, roads and other pavements, have increased from 584.448km2 to 852.140km2 between 2006 and 2018, whereas the agricultural land has reduced from 2686.121km2 to 2398.384km2 during the period. The area under vegetation (trees) indicated that there was an increasing trend from 28.490km2 to 54.678km2 during 12years of time span whereas, the fallow land/barren land showed a decreasing trend from 26.361km2 to 18.367km2. It is suggested that the LULC change detection studies are very significant to conserve the land resources and to avoid further degradation.

Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data.

The dynamic change in land use/land cover (LULC) caused by rapid urbanizationhas become a major concern in Lahore, causing a variety of socioeconomic and environmental issues relating to air quality. As a result, it is important to monitor existing LULC change detection, future projection, and the increase in atmospheric pollutants in Lahore. This research work makes use of Landsat, GIOVANNI, SRTM DEM, and vector data. The four key steps of the research approach are as follows: (i) LULC classification from 2000 to 2020 using Lansat data and semi-automatic classification plugin (SCP); (ii) simulation of future prediction using Modules of Land Use Change Evaluation (MOLUSCE) prediction model; (iii) assessment of effects of land use change on air quality using GIOVANNI-NASA product; and (iv) monitoring, change detection, and result interpretation. According to the research findings, there was an increase in metropolitan areas and a decrease in vegetation, barren land, and water bodies for both historical and future projections. The findings also indicated that from 2000 to 2020, about 27.41% of the urban area expanded, with a decline of 42.13% in vegetation, 2.3% in the barren land, and 6.51% in water bodies, respectively. Between 2020 and 2040, the urban area is predicted to increase by 23.15%, while vegetation, barren land, and water bodies are expected to decrease by 28.05%, 1.8%, and 12.31%, respectively. Also, the atmospheric pollutants have been increased including NO2 (1.60%), SO2 (1.02%), CO2 (0.71%), CO (1.56%), O3 (0.15%), and CH4 (0.20%), respectively. And it is projected that by 2040, the average annual atmospheric concentration of NO2, SO2, CO2, CO, O3, and CH4 will be increased by 28.80%, 18.36%, 12.78%, 28.08%, 2.70%, and 3.60%, respectively. In addition, it was also observed that the majority of the city's urban area expansion was found in the city's eastern and southern regions. Therefore, government should focus on natural resource conservation especially vegetation cover to reduce air pollutants concentration and the LULC effect.

Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices.

Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000-2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km2 in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km2 of forest lands and the increase of agricultural lands by 14.90 km2 and wetlands by 9.31 km2. These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity.

Ecosystem Service Valuation along Landscape Transformation in Central Ethiopia

Land degradation and discontinuation of ecosystem services (ES) are a common phenomenon that causes socio-economic and environmental problems in Ethiopia. However, a dearth of information is known about how ES are changing from the past to the future with regard to land use land cover (LULC) changes. This study aimed at estimating the values of ES based on the past and future LULC changes in central Ethiopia. Maximum likelihood classifier and cellular automata-artificial neuron network (CA-ANN) models that integrate the module for land use change evaluation (MOLUSE) were used to classify and predict LULC. The CA-ANN model learning and validation was employed to predict LULC of 2031 and 2051. Following LULC change detection and prediction, the total ES values were estimated using the benefit transfer method. Results revealed that forests, wetlands, grazing lands, shrub-bush-woodlands, and water bodies were reduced by 9755 ha (37%), 4092 ha (38.4%), 21,263 ha (81%), 63,161 ha (25.7%), and 905 ha (1%), respectively, between 1986 and 2021. Similarly, forests, wetlands, grazing lands, shrub-bush lands, and water bodies will experience a decline of 1.5%, 0.5%, 2.6%, 19.6%, and 0.1%, respectively. Meanwhile, cultivated lands, bare-lands, and built-up areas will experience an increase between 1986 and 2051. The estimated total ES values were reduced by US$58.3 and 85.4 million in the period 1986–2021 and 1986–2051. Food production and biological control value increased while 15 other ES decreased throughout the study periods. Proper land use policy with strategic actions, including enforcement laws for natural ecosystems protection, afforestation, ecosystems restoration, and conservation practices, are recommended to be undertaken to enhance multiple ES provision.

Change of land use / land cover in kurdistan region of Iraq: A semi-automated object-based approach

During the conflict and in the years afterward, the Kurdistan Region of Iraq (KRI) saw substantial changes in land use. The mapping and monitoring of land use/land cover (LULC) is critical for its long-term development and natural resource management. Therefore in this study, we developed a semi-automated object-based land use/land cover classification to identify and quantify LULC changes and change detection analysis in the Kurdistan Region of Iraq for the period 1990 to 2020 using Landsat satellite data (TM, ETM+, and OLI). To determine the optimum segmentation scales for each phase, we first applied and evaluated different scales of a multi-resolution segmentation technique. After that, spatial (digital elevation information) and spectral information were combined in an object-based image analysis (OBIA) technique. For each LULC class, object features were found. We then used the standard nearest neighbor (SNN) approach to derive their individual properties. Field data and validation units collected from high resolution Google earth pro and historical maps in SAS planet open source were used to conduct accuracy evaluations based on the error matrix and kappa coefficient for each reference year. With overall accuracies ranging from 86.072% to 88.9% and Kappa coefficients of 0.845–0.878, ten LULCs were effectively recorded. After that, a post classification comparison was used to perform a change analysis. The findings demonstrated that LULC change trends were notably different, as all categories were altered at different times throughout the study. Strikingly, 52.1% of the land use/land cover in the Kurdistan Region has changed between 1990 and 2020. All changes corresponded to the KRI's own challenges throughout the last three decades. The OBIA-based approaches and features, we conclude, have a lot of potential for LULC mapping and monitoring. The results shall support state institutions and experts to manage the land in a more controlled way.

Forest Cover Change Detection using Geospatial Technologies in Chandaka National Park, Odisha, India

Forest plays a vital role in carbon sequestration and climate regulation. A crucial tool for managing forest, particularly in protected regions, is keeping track of how the land cover changes in natural places. Using geospatial approaches, such as remote sensing and geographic information system (GIS), the present study has revealed spatio-temporal changes in land use categories and forest cover in the Chandaka National Park of Odisha, India, throughout the period of 1980-2020. The Landsat, LISS III and Sentinel satellite images of the year 1980, 2000 and 2020 were utilized respectively to map five land use land cover categories i.e. deciduous broadleaf forest, crop land, mixed forest, scrub land and water bodies in this preserved area. The satellite images were classified using a Supervised Classification method using Maximum Likelihood algorithm and ground control points (GCPs) were used for the spatial statistical analyses. The overall accuracies of the classification method in land cover categories in year 1980, 2000 and 2020 were 90.45%, 92.76% and 94.68%, respectively. Elsewhere, in order to study land use land cover (LULC) change and loss in forest of the Chandaka National Park, LULC classification, per-pixel scales post classification and self-knowledge on the LULC change process were used. The LULC change detection results showed that deciduous broadleaf forest decreased from 179.01 sq. km (76.66%) in 1980 to 132.75 sq. km (56.85%) in 2020, while mixed forest cover increased by 8.17 sq. km (3.50%) in 1980 to 38.84 sq. km (16.63%) in 2020. Crop land, Scrub land and Water bodies were also increased by 3.34%, 3.27% and 0.07%, respectively. Two significant change processes in the area are the logging activities in several places for timber and the conversion of natural forests with plantation. Agriculture expansion in the forest’s periphery is linked to the dramatic decline in forest cover change. The decline in forest cover is also a result of the production of charcoal and lumber exploitation. Overall, our findings indicated that more public awareness and participatory forest management are necessary to preserve Chandaka National Park. This study highlights the use of geospatial technologies in understanding the changes in LULC in the Chandaka National Park.

Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine

With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) bring data-driven techniques to the desktops of researchers while changing workflows and making excessive data downloads redundant. We present a study that utilizes machine learning algorithms on the GEE cloud computing platform for land use/land cover (LULC) mapping and change detection analysis using a Landsat satellite image time series. We applied different machine learning techniques to data from an environmentally sensitive area in Northern Iran. We tested their efficiency for LULC mapping and change detection analysis using the support vector machine (SVM), random forest (RF) and classification and regression tree (CART). We obtained LULC maps for the years 2000, 2005, 2010, 2015 and 2020. Training data was collected from field operations and historical datasets, and the respective LULC maps were validated using ground control points. In addition, we validated the reliability of the results through a spatial uncertainty analysis using Dempster-Shafer Theory (DST). The resulting accuracies of the classification outcomes varied significantly. SVM performed best with accuracies of 90.25%, 91.84%, 89.02%, 93.35% and 95.65% for 2000, 2005, 2010, 2015 and 2020, respectively. The spatial uncertainty analysis also validated the efficiency of SVM compared to RF and CART. The results confirm the potential of machine learning techniques for time series LULC mapping on the GEE platform while lowering the barriers to analyzing large amounts of satellite data. The results are also critical for decision-makers and authorities for analyzing the LULC changes and developing the respective environmental protection and polices in Northern Iran.

Impact of land use/land cover changes on groundwater resources in Al Ain region of the United Arab Emirates using remote sensing and GIS techniques

Urbanisation causes land degradation problems, including an increased pressure on natural resources and management of water resources. This study aims to investigate the impact of the spatio-temporal dynamics of land use land cover (LULC) changes on groundwater table in the region of Al Ain, United Arab Emirates (UAE), from 2006 to 2016. The Landsat images, Landsat ETM for 2006 and Landsat 8 for 2016, were acquired from the earth explorer site. A semi-supervised hybrid classification method was used for image classification and post-classification techniques for LULC change detection. The study area was categorised into six major LULC classes. These are agriculture/farms/oasis, gardens/playgrounds, urban areas, sandy areas, lake and mixed urban/sandy areas. Accuracy assessment of LULC were evaluated using confusion matrix and ground truthing. The obtained land use and land cover maps were also correlated with spatial groundwater table maps prepared with groundwater data. It was found that agriculture/farms/oasis and urban areas expanded from 42,560 ha to 45,950 ha (7.38%) and from 8150 ha to 9105 ha (10.49%) from 2006 to 2016, respectively. The corresponding water demand was increased by 9.56% and 22.22%, respectively. Natural sandy area was found to decrease by 8.10%. As groundwater is major source of water for agriculture in this region, the spatial maps also revealead average declining rate of groundwater depth 40.44% with expansion of urban and agricultural areas over the last 10 years. The outcomes of the study would help concerning authorities for a sustainable management of its land and groundwater resources.

Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

Evaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

Dynamic change of land use/land cover patterns and driving factors of Nansihu Lake Basin in Shandong Province, China

Anthropogenic activities and natural factors have a significant effect on land use/land cover (LULC) patterns of Nansihu Lake basin. Understanding the LULC change is essential for regional sustainable development. Based on LULC change detection methods, we characterized the spatial–temporal change of LULC patterns. Logistic regression model and the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model were used in driving factors analysis and scenario simulation. The results showed that cultivated land and construction land were the dominant LULC types. As construction land gradually expanded to the surrounding areas, a large amount of cultivated land was occupied, especially in the peripheral areas of the central cities. During 1987–2017, 4109.22 km2 of cultivated land was converted into construction land, accounting for 86.38% of cultivated land change, and the contribution rate to construction land was 95.24%. Because of the severe drought in 2002, waters first decreased and then increased. The dynamic degree of waters reached 2.11%, and the increment of waters was mainly converted from cultivated land and unused land. The degree of LULC fragmentation was strengthened, and the distribution showed a diversified trend. The LULC changes were deeply influenced by natural environment and socio-economic factors. Under different scenarios, the expansion of construction land and occupation of cultivated land will continue to be the main features of LULC changes in the future. The LULC patterns will become more regular and more aggregated. This study can help address the interwoven challenges of urbanization development and ecological protection.

Land Use/Land Cover Change Detection Study Using Remote Sensing and GIS Technique in Puthimari River Basin-A Transboundary Basin Between Bhutan and India

Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.

Detection and prediction of land use change impact on the streamflow regime in Sahelian river basin, northwestern Nigeria

Abstract Detecting and predicting the impact of land use/land cover (LULC) changes on streamflow are crucial sources of information for the effective management and protection of land and water resources in Sahelian ecosystems such as the Hadejia river basin. In this study, LULC change detection was performed using ENVI, while the LULC modeling was conducted using the cellular automata (CA)–Markov in the IDRISI environment. However, the streamflow trend and variation were assessed using the Mann–Kendall (MK) trend test and the inverse distance weightage (IDW). Before the LULC modeling and projection (2030), the LULC was classified for 1990, 2000, and 2010 using supervised classification. Model output revealed a strong relationship between LULC and streamflow trend, thus, the decade 1990–2000 was the decade with high forest clearance and streamflow output, and consequently severe floods. However, the decade 2000–2010 witnessed land use expansion mainly via construction (3.4%). Meanwhile, the scenario will slightly change in the future as agriculture is projected to expand by about 9.3% from 2010 to 2030 due to the increased human population. Thus, food insecurity aggravated by climate change should be anticipated, and measures to avert/reduce their effects must be initiated.

Multi-temporal image analysis for LULC classification and change detection

ABSTRACT Land use/Land cover (LULC) plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem. This study mainly focuses on the usage of the geographic information system (GIS) and land usage to identify the changes in Ananthapuramu, which is located in the Ananthapur district of Andhra Pradesh (AP) state, South India. This paper mainly focuses on the classification and identification of the changes in LULC in the period of 1999–2019 based on a survey of India’s topographic map and temporal images gathered from the satellite. The experimental results indicate that over the specified period of study, the rate of increment and decrement in the built-up area, rate of underwater bodies, forest bodies that may demonstrate a significant impact on the environmental ecosystem.

Land use Land Cover Monitoring and Change Detection of Tinsukia, India

This study is driven towards land use land cover (LULC) mapping and LULC change detection in Tinsukia district, India. LULC mapping and change detection provides land planner and environmental scientists a better understanding of the land surface processes occurring in a given landscape so that they can come up with a strategy for sustainable development keeping degradation of natural environment from anthropogenic activities at bay. This study utilized remote sensing data products and software’s for LULC mapping and LULC change. Landsat data has been utilized in ENVI for the classification of LULC and LULC change detection during the period 1991-2020. The LULC classification was achieved through Maximum Likelihood Classification (MLC) which is a widely preferred classificatory method. Image change detection was achieved through ENVI thematic change workflow. On top of that ArcGIS version 10.2 was used for preparing all map layouts. Results reveal that the study area has undergone significant changes in its LULC pattern. Substantial increases were recorded in agricultural area (862.4 sq. km to 1186 sq. km), built up area (473.4 sq. km to 699.5 sq. km) and waterbodies (81 sq. km to 146.7 sq. km). A declining trend was evident in degraded vegetation (772.2 sq. km to 274.3 sq. km) and barren land (798.8 sq. km to 641 sq. km). In the short study period, the study area already seems to be changing in its LULC pattern due to anthropogenic activities. The steady increases to the agricultural land and built up area (BUA) is a potential threat to the LULC balance and it may have manifold impacts to LULC dynamics in the future if proper land utilization policy is not adopted.

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

BackgroundForest plays an important role in climate regulation and carbon sequestration. Komto Forest is one of the remnant natural forests found in Guto Gida district of East Wollega zone, Ethiopia, has been supporting the local community for construction, energy and household furniture. Currently, influenced by land use land cover change (LULCC), this forest has been declining at an alarming rate. Detecting LULCC and understanding the driving forces has important for supporting decision making processes. We examine variation in forest cover dynamics over the period 1991–2012 using Landsat TM image of 1991, ETM + of 2002 and OLI-TIRS of 2019.ResultsThe LULCC detection results show that a dramatic increase of agricultural land from (24.78%) in 1991 to (33.5%) in 2019 with annual expansion rate (23.68%) per annum, where forest cover declined by 20.1% in 1991 and 37.38% in 2019 with annual decreasing rate of 4.18% per annum. Our finding indicates the increment of agricultural land, grassland, and settlement, while the dense and open forest cover shows a declining trend. The declining of forest coverage is likely to cause unpleasant environment and affects human wellbeing.ConclusionsThe massive declined in forest cover change are often associated with agricultural expansion in the periphery of the forest. Timber exploitation and charcoal production are other problems that contribute for the declining of forest coverage. Overall, our results suggest the need of participatory forest management and public awareness creation to sustain the Komto remnant forest.

Land-use–land-cover change detection and application of Markov model: A case study of Eastern part of Kolkata

Land-use change of a region acts as an indicator of human impact on the landscape. Increasing urban growth has induced adverse landscape alterations which need to be predicted and controlled, especially in the urban areas and ‘rurban’ fringes, to prevent the trends of urbanization from engulfing the ecology. The present discussion makes an attempt to address this issue by assessing the present and predicting the future spatio-temporal dynamisms in land use and land cover along the urban and rurban fringe area of eastern Kolkata, stretching from the Eastern Metropolitan Bypass to Bhangar areas in West Bengal, India. For the fulfilment of the work, Landsat imageries of 1991 and 2016 have been chosen to depict the present urban growth, following which the results have been predicted and validated to show how the various land-use categories might change, using the Markov model. The study has depicted that urban growth continues to shift eastwards, resulting in greater number of urban patches in eastwards. The validation of positive and negative growth of respective land-use patterns with Markov model is within 10%. So, if the current reclamation activities continue, the original land cover shall decrease by 70% of the study area and these shall be replaced by urban areas.