Land Use And Land Cover Dynamics Research Articles

Spatio-temporal analysis of land use and land cover changes in a wetland ecosystem of Bangladesh using a machine-learning approach

This study investigates quantifiable and explicable changes in Land Use and Land Cover (LULC) within the context of a freshwater wetland, Hakaluki Haor, in Bangladesh. The haor is a vital RAMSAR site and Ecologically Critical Area (ECA), which needs to be monitored to investigate LULC change patterns for future management interventions. Leveraging Landsat satellite data, the Google Earth Engine Database, CART algorithm, ArcGIS 10.8 and the R programming language, this study analyses LULC dynamics from 2000 to 2023. It focuses explicitly on seasonal transitions between the rainy and dry seasons, unveiling substantial transformations in cumulative LULC change patterns over the study period. Noteworthy changes include an overall reduction (~51%) in Water Bodies. Concurrently, there is a significant increase (~353%) in Settlement areas. Moreover, vegetation substantially declines (71%), while Crop Land demonstrates varying coverage. These identified changes underscore the dynamic nature of LULC alterations and their potential implications for the environmental, hydrological, and agricultural aspects within the Hakaluki Haor region. The outcomes of this study aim to provide valuable insights to policymakers for formulating appropriate land-use strategies in the area.

Monitoring LULC Dynamics and Landscape Pattern Analysis Using Landsat at Mongla and Surrounding Cities in Bangladesh

This study presents a thorough analysis of the temporal dynamics in land use and land cover (LULC) within the Mongla Port and its adjacent regions in Bangladesh, spanning from 2018 to 2022 and utilizing Landsat 8 satellite imagery. A seven-category classification system, including water bodies, trees, flooded vegetation, crops, built-up areas, bare ground, and rangeland, facilitated a detailed exploration of changing landscape patterns. Over the study period, a 1.87% reduction in tree cover was observed, contrasting with consistent expansion in built-up areas, which increased by 2.42%. Noteworthy fluctuations in agricultural lands revealed a net decrease of 165.95 km², while water bodies experienced fluctuations, ultimately recording a net increase of 142.29 km². The robust accuracy assessment, with overall accuracy ranging from 86.7% to 88.8%, underscores the reliability of the classification process. This research holds significant implications, offering valuable insights into sustainable development, conservation strategies, and disaster preparedness in a region undergoing transformative changes due to port establishment, especially considering the inclusion of the ecologically critical Sundarbans in the study area.

Spatiotemporal analysis of urban expansion in Srinagar city, Kashmir

Urbanization is a multifaceted process and a global phenomenon that encompasses various environmental and land cover changes. The expansion of Srinagar city, Kashmir, has changed its Land Use and Land Cover (LULC) due to an increase in population, horizontal expansion of the city, migration, and an increase in the built-up area. This has resulted in a change in Land Surface Temperature (LST). This study evaluates the dynamics of LULC in Srinagar city and its effect on LST using remote sensing, geographic information systems (GIS), statistical analysis techniques, and Landsat 4–5 TM and Landsat-8 OLI datasets. We analysed the LULC in Srinagar city from 2000 to 2020. The study calculated the Normalized Difference Vegetation Index (NDVI), LST, Fractional Vegetation Cover (FVC), and Emissivity from satellite remote sensing data of Srinagar city and analysed the relationship between them. The study shows that in terms of percentage change in LULC in Srinagar city from the year 2000 to 2020, built-up area showed the maximum change (75.58), followed by dense forest (− 62.74), vegetation (− 40.2), water bodies (− 25.41%), crop (23.16%), swamp (− 8.22), sparse forest (− 7.89), and open spaces (− 6.02). The findings of this study hold significance for researchers, urban planners, and policymakers to understand urbanization trends and their implications in the city.

Ecological effects of land use and land cover changes on lakes in urban environments

AbstractUrban lakes confront significant threats due to changes in land use and land cover (LULC) resulting from urbanization and subsequent climate change. This review discusses the intricate effects of the urban heat island phenomenon on lakes, specifically attributed to LULC changes. Utilizing the Scopus and Web of Science databases, this study gathers the most pertinent earlier research in the LULC and water bodies. This study systematically categorizes variables into five distinct groups and scrutinizes the drivers, parameters, tools, and management strategies influencing the LULC dynamics on lakes. A research gap is identified in understanding the conjoined impacts of LULC and urban heat island effects within urban lake environments. The review further investigates diverse ways in which LULC impacts lakes, intersecting with multiple United Nations sustainable development goals (SDGs), notably SDGs 6, 11, 13, and 15. Consequently, this review serves as a valuable contribution to the understanding of LULC to provide substantial benefits to the urban climate research toward meeting SDGs.

Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta

Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused deep models and time series reconstruction techniques. The proposed method first reconstructed cloud-contaminated pixels through time series filtering, during which the cloud masks initialized by a deep model were refined and updated during the reconstruction process. Then, the reconstructed time series images were fed into a spectral-indices-fused deep model trained on samples collected worldwide for classification. Finally, post-classification processing, including spatio-temporal majority filtering and time series refinement considering land–water interactions, was conducted to enhance the LULC mapping accuracy and consistency. We applied the proposed method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA) and used time series Sentinel-2 images as the experimental data. The proposed method enabled seamless LULC mapping at a temporal frequency of 2–5 days, and the production of 10 m resolution annual LULC products in the GBA. The assessment yielded a mean overall accuracy of 87.01% for annual mapping in the four consecutive years of 2019–2022 and outperformed existing mainstream LULC products, including ESA WorldCover (83.98%), Esri Land Cover (85.26%), and Google Dynamic World (85.06%). Our assessment also reveals significant variations in LULC mapping accuracies with different cloud masks, thus underscoring their critical role in time series LULC mapping. The proposed method has the potential to generate seamless and near real-time maps for other regions in the world by using deep models trained on datasets collected globally. This method can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions. Notwithstanding the difficulties of obtaining high-quality LULC maps in cloud-prone areas, this paper provides a novel approach for the mapping of LULC dynamics and the provision of reliable annual LULC products.

ASSESSING ANTHROPOGENIC PRESSURE ON LAND USE AND LAND COVER IN THE MAFERE SUB-PREFECTURE IN SOUTH-EAST COTE DIVOIRE

Several hectares of forest disappear every year in Cote dIvoire, particularly in the Aboisso Department. Despite the fight to preserve biodiversity and forest cover, numerous pressures (anthropogenic pressures) continue to be exerted on the forests, leading to their destruction and even increased deforestation. This deforestation has many consequences, including reduced biodiversity and climate change. This study was initiated to gain a better understanding of this reduction in forest area. The aim of the study is to assess the level of forest degradation in the Mafere sub-prefecture. The activities involve mapping and analyzing the dynamics of land use and land cover (LULC). To achieve these objectives, the study is based on satellite images (Landsat 5, 7 and 8) from 1986, 2000 and 2020. The results of LULC dynamics show, on the one hand, a decline in closed forest/swamp forest (96.29%), degraded forest (76.75%), crop/fallow (67.03%) and annual crops (38.35%) and, on the other hand, an increase in cocoa (675.25%), oil palm (193.13%), rubber (14.39%) and housing/bare land (101.26%). The extension of agricultural areas in search of resources (natural and financial), demographic pressures, still traditional cultivation techniques and climate change are the main factors causing the degradation of forests in the Mafere sub-prefecture.

An Investigation of the Impact of Spatiotemporal Changes of Land Use and Land Cover and Land Surface Temperature Using RS Techniques for Selected Cities in Sri Lanka

Changes in land use and land cover (LULC) have an impact on land surface temperature (LST) locally, regionally, and globally. This research focused on the spatiotemporal dynamics of LULC and LST between 2000 and 2021 at six selected meteorological stations, belonging to the different climatic zones of Sri Lanka. The LULC were classified from Landsat ETM+ and OLI/TIRS data using the maximum likelihood classification approach, while LST was retrieved using the thermal band of both Landsat images. Resultsreveal that the built-up areas have increased in all selected locations and bare land, vegetation cover, and agricultural land have all declined in the last 20 years. In Galle and Batticaloa areas, built-up areas have grown significantly by 11.9% and 11.14% respectively, by 2021. The thermal environment spatiotemporal alterations were consistent with the urbanization trend. Since many other land use types have been converted into urban areas, the average LST in Vavuniya, Anuradhapura, Batticaloa, and Galle has been rising steadily. This showed that increasing built-up area density has played a significant role in raising LST in the study areas. The vegetation cover, agricultural land, and water bodies showed the least LST change. By examining the correlation analysis between the Normalized Difference Vegetation Index (NDVI) and LST, a somewhat positive relationship was revealed only in the areas of Galle, Batticaloa, and Hambantota. Nevertheless, the findings of this study will serve as a helpful benchmark for future landscape and urban design initiatives aimed at reducing the detrimental effects of LST on urban sustainability.

Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning.

Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km2 in 1990 to 338.74 km2 in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71°C in 1990 to 50.46°C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76°C in 2000 to 29.69°C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.

An analysis of the impacts of land use change on the components of the water balance in the Central Rift Valley sub-basins in Ethiopia

Water resources are influenced by changes in land use and land cover (LULC), such as industrialization, urbanization, forestry, and agriculture. This study has aimed to analyze past and predicted LULC dynamics and their impacts on the components of the water balance in the Central Rift Valley (CRV) sub-basins in Ethiopia. The Soil and Water Assessment Tool (SWAT) and the Land Change Modeler (LCM) were employed to evaluate the impacts of past and future LULC dynamics in the Ketar, Meki and Shalla sub-basins. The SWAT models were calibrated with flow data from 1990 to 2001 and were validated with flows from 2004 to 2010, using SWAT-CUP in the SUFI-2 algorithm. LCM with Multi-Layer Perceptron (MLP) neural network method for land transition scenario analysis and a Markov Chain method for predictions, as well as SWAT models with fixing-changing methods for simulations, were used to evaluate the condition of hydrological processes under the influence of changes in LULC. The analyses resulted in an annual runoff variation from − 20.2 to 32.3%, water yield from − 10.9 to 13.3%, and evapotranspiration from − 4.4 to 14.4% in the sub-basins, due to changes in LULC. Integrated land use planning is recommended for the management of water resources.

Groundwater Recharge and Surface Runoff Modeling Response to Land Use and Land Cover Dynamics in a Mae Wong Watershed of Thailand

This study aims to develop efficient management strategies and assess the spatiotemporal dynamics of land use and land cover (LULC) on surface runoff modeling response dynamics for the long-term sustainability of watersheds. The soil and water assessment tool (SWAT) model was used to evaluate the LULC dynamics on GRSR in the Mae Wong Watershed (MWW) of Thailand. Using Landsat images, three different LULC maps (2011, 2021, and 2031) were created using the cellular automata markov chain (CA-Markov) model, and TerrSet 2020 geospatial monitoring and modeling software. In the overall MWW, the forestland has undergone deforestation and decreased by 2.10% of the total area and 2.72% of the total area has been transformed into agricultural lands due to human activity and population growth. The soil, LULC, weather, and the digital elevation model (DEM) were all used in the SWAT simulation procedure. To understand the groundwater recharge and surface runoff (GRSR) responses of each hydrologic response units (HRUs), the SWAT model was calibrated and verified using streamflow and the sequential uncertainty fitting (SUFI-2) technique from the SWAT calibration and uncertainty program (SWAT-CUP). The results indicate that there is a good agreement for both the calibration and validation phases of all LULC simulations. The study indicated that groundwater recharge has decreased over the last two decades while surface runoff has increased due to the forest area being converted to agricultural land. Thus, the study can support maximizing water management and strategies for systematically attaining sustainability.

Hydrochemical properties of groundwater and land use and land cover changes impact on agricultural productivity: An empirical observation and integrated framework approaches

Land use and land cover (LULC) dynamics of the Bankura district effect groundwater resources and agricultural productivity, particularly the expansion of urban land, discharge of fertiliser mixed water from agriculture and reduction of scrubland. This study investigates the Spatio-temporal relation between groundwater qualities (GWQ), LULC and agricultural productivity of the Bankura district in 2009 and 2020. The twelve standard water qualities parameters; “pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), iron (Fe3+), bicarbonate (HCO3¯), chloride (Cl−), sulfate (SO42−), nitrate (NO3−), and fluoride (F−)” were considered for the study. Landsat-5, 7 and 8 images were classified for 2009 and 2020 to detect LULC dynamics related to GWQ. The results showed that GWQ was depleted in 2020 compared to 2009, with agricultural land suffering from lower GWQ than other land uses. Some of the water quality indices (Mean value of pH, EC, Ca2+, Mg2+, and NO3−) drastically declined in 2020 compared to 2009. The degree of contamination (CD) analysis showed the above value of zero (>CD) is highly vulnerable for dug well users. The mean nitrate (7.281 to 51.387 mg/l), calcium (38.366 to 58.082 mg/l), chloride (121.861 to 125.981 mg/l), and potassium (15.261 to 18.363 mg/l) concentration levels drastically increase over the agricultural land. To know the relationship between LULC and agriculture productivity, block-wise production and yield analysis of both years have been presented. It was seen that there are low crop and high productivity observed from the 2009 to 2015 block of Bankura. The overall result indicates that human activities are the primary cause of spatial-temporal changes in GWQ and agricultural productivity. The geospatial-based water quality assessment benefited in understanding changes in GWQ and developing appropriate site-specific agricultural and health policies.

Assessing the status and spatial-temporal dynamics of the Bamenda Mountains (BM), North West region of Cameroon.

Change in land use and land cover (LULC) contributes in worsening ecological issues. Studying the trends of change in land use is highly significant to deal with global climate change and sustainable development. The aim of this paper is to evaluate the spatial-temporal dynamics of LULC of the Bamenda Mountains (BM) in the North West region of Cameroon, over a period of 34 years (1988-2022) and predict 34 years (2022-2056) future land use scenario of this site using time series satellite imagery (MSS, TM, ETM+, and OLI-TIRS) and ancillary data and to comprehend the driving forces of land use/land cover change (LULCC). The trends of LULCC were quantified; LULC maps were derived by classifying time series satellite images. Six LULC categories were identified during the study period (1988-2022). The research revealed a significant LULCC of the BM which can be justified by increase in the human population observed in the study area and the desire to extend agricultural lands to sustain the growing population. Overall, cultivated area 5684 ha (10.47%), 10680 ha (19.57 %), and 15163 ha (27.78%) and built-up area 449 ha (0.83%), 996 ha (1.83%), and 3242 ha (5.94%) for the study years 1988, 2003, and 2022, respectively, were all on the increase throughout the study period at the expense of other land cover types. The predicted figures of 2056 showed a continuous reduction of montane forest and savanna: 2401.92 ha (4.40%) and 25,862.67 ha (47.39%), respectively. Bare area is expected to drop in 2056 (2905.92 ha (5.32%)). The above decrease, when compared to 2022 figures, represents a loss of 3.97%, 4.53%, and 0.57%, respectively. The losses observed are gained by built-up and cultivated land (5.72% and 3.39%, respectively), covering surfaces areas of 6364.89 ha (11.66%) and 17,008.56 ha (31.17%), respectively. The above findings suggest that population growth is likely the major menace to the natural environment. It is thus safe to say that substantial LULCC was observed throughout the study period and will undoubtedly continue if nothing is done. This necessitates urgent measures such as reforestation and afforestation, encouraging off-farm activities and even improving technologies to combat the rate of forest degradation of the BM. Additionally, rebuilding trust between the French and English Cameroons through dialogue is premodial, to end the curent conflictual civil war and lessen the landscape configuration in Bamenda.

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.

Assessment and Spatial Analysis of Land Use and Land Cover (LULC) Dynamics in the Black Volta Basin

The Black Volta Basin, a critical hydrological region, is experiencing rapid changes in Land Use and Land Cover (LULC) patterns due to diverse anthropogenic and environmental factors. This study employs a comprehensive Multi-Sensor Remote Sensing and Geographic Information System (GIS) methodology to assess and analyze the spatiotemporal dynamics of LULC in the basin. Utilizing data from multiple remote sensing platforms, including optical and radar sensors, as well as historical GIS datasets, a detailed LULC classification is performed. The integration of multi-temporal satellite imagery enables the detection and quantification of changes across distinct land cover categories. Through rigorous spatial analysis and statistical techniques, the study reveals the extent and magnitude of LULC transformations over a defined period. Land cover transitions are identified, characterized, and linked to potential drivers such as urbanization, agricultural expansion, and natural processes. The outcomes of this research contribute to an enhanced understanding of the evolving LULC dynamics in the Black Volta Basin, shedding light on both natural and human-induced changes. The results underscore the significance of a multi-sensor approach in accurately capturing complex LULC changes and their implications for regional sustainability and resource management. This study's insights serve as valuable inputs for policymakers, researchers, and stakeholders engaged in land-use planning, environmental conservation, and sustainable development within the Black Volta Basin and similar ecologically sensitive areas.

Impact of land use and land cover dynamics on urban heat island in the Sunyani Municipality using satellite remote sensing

The rapid urbanization coupled with tremendous growth in population and built-up areas in the cities has led to issues associated with global climate change impacts such as urban heat islands. This study is concerned with the spatial impact of the dynamics of urbanisation on urban heat island (UHI) in the Sunyani Municipality using Satellite Remote Sensing techniques. Sunyani municipal is a growing area in Ghana with increasing population and urbanisation. Many people in Ghana are moving to settle in Sunyani for different purposes. In this process, vegetated areas have been converted to urban areas and this has led to a reduction in the vegetation cover and increased the urban temperature in the municipality. Therefore, there is a need for a study on how the dynamics in the land use and land cover (LULC) in the municipal are contributing to urban heat. The main aim of this research is to identify the impact of LULC dynamics on UHI using multi-temporal Landsat images from 2001 to 2021 with a 5-year epoch in the Sunyani Municipal. Landsat images acquired over Sunyani and its environs for the period from 2001 to 2021 with a 5-year epoch, were download freely from the USGS Earth Explorer website. These images were pre-processed in QGIS 3.16 using the Semi-automatic classification plugin (SCP). The pre-processed images were classified using the Random Forest (RF) algorithm. The supervised classification was done using the semi-automatic classification plugin in QGIS 3.16. This study considered two (2) classes (urban and vegetation). The thermal infrared bands of the Landsat images were used to compute the land surface temperature (LST) for each year, starting from 2001 up to 2021, at a 5-year interval. The standard deviation and mean LST were extracted and used to extract all the UHIs in the municipality. Regression analyses were used to determine the impact of LULC dynamics on LST and UHIs in the Sunyani Municipality. The analysis yielded a strong impact with an R-squared value of 75% and 77% for UHI and LST respectively. The vegetated covers yielded a strong negative correlation with UHIs while Urbanisation yielded a strong positive correlation with the intensity of UHIs. On the other hand, Vegetation reduced the intensity of UHIs in the municipality. This gives a clear understanding of how dynamics in urbanisation and vegetation are affecting UHIs in the Sunyani Municipality. A Linear model was developed for LULC dynamics, LST and UHIs in the Sunyani municipality. It was concluded that LULC dynamics have a strong impact on UHI. It is recommended that the environmental Agencies in the Municipality should ensure the protection of the vegetated areas as well as improve the green areas in the Municipality to minimize urban heat.

Land use-land cover dynamics, and local perceptions of change drivers among Nyangatom agro-pastoralists, Southwest Ethiopia

Understanding the spatio-temporal dynamics of land use and land cover (LULC) and their drivers is instrumental in synthesizing knowledge for informed natural resource management, planning, and associated decisions. The objective of this study was to analyze LULC dynamics and their drivers in the Nyangatom district of southwest Ethiopia. This study adopted a mixed-method approach, comprising remote sensing and geographic information system-based analysis, focus-group discussions, key informant interviews, and structured interviews covering 384 agro-pastoral households. Land use and land cover maps were compiled with Landsat image data from 1992, 1999, 2010, and 2020 using supervised pixel-based classification with a maximum likelihood algorithm. The study revealed that grassland (33%) and shrub land (27%) are the dominant LULC classes in the study periods. The results show a significant increase in settlement, cropland, and bare land, between 1992 and 2020 following demand for fertile lands, overgrazing, and climate changes. Reductions in the water body, open woodland, riverine forest, and dense woodland of − 23%, − 18%, − 18%, and − 7%, respectively, were observed for the same period with an overall accuracy of 80%. Agro-pastoralists identified agricultural expansion, overgrazing, and drought as the main drivers of LULC changes, followed by conflicts, invasion of alien plant species, and change in policy institutions. The impacts of unplanned LULC change can be reduced through appropriate land-use planning with genuine stakeholder engagement. Updated information on present and future LULC dynamics can support these social dynamics and align a shared understanding among stakeholders that replaces polarization and erosion of mutual trust.

Remote sensing based investigation of coastal LULC dynamics in the coastal region of Bangladesh

Rapid land-use transition on the landscape is accelerating the overall land cover change by altering naturally productive land into more complex land-uses. This study explored the spatial and temporal dynamics of land use and land cover (LULC) as landscape exploration though satellite imagery in the southwest of Bangladesh from 2000 to 2020. The goal of this study was to quantify the overall trend for different LULC changes using satellite imagery from Landsat TM and Landsat OLI/TIRS. Sequential K-means-based unsupervised and more accurate maximum likelihood classification (MLC) algorithm-based supervised methods were used to analyze land-use change for each year. The trend of land-use changes and transition results between 2000 and 2020 showed a massive increase in built-up (2010.654 ha/year) land-use; 525546.18 ha of forest increase (1.7%) in two decades, whereas 13351.59 ha (1.10%) of forest or plantation area was transformed into the built-up area. Remote sensing (RS) based land dynamics is always land cover analysis, especially for the vast area of interest.

Forecasting land use and land cover dynamics using combined remote sensing, machine learning algorithm and local perception in the Agoènyivé Plateau, Togo

Knowledge of land use and land cover (LULC) dynamics helps policymakers set sustainable land management plans. Population growth, migration, and rural exodus have led to spatial expansion in many cities worldwide, especially in coastal urban settings. This research aims at assessing and predicting LULC over the Agoènyivé plateau (AP) in Togo, using remote sensing and Markov Chain along with local perceptions on LULC change, thus considering four scenarios (i.e., Business-as Usual, afforestation, wetland policy, and building policy). LULC was classified based on the maximum likelihood algorithm using Landsat images. The future scenarios maps were produced using the Multilayer Perceptron (MLP) neural networks-Markov chain modelling approach by considering five driver variables considered to affect future development. The historical change analysis was performed for the periods of 1986–2001, 2001–2011, and 2011–2020, while 1986–2020 was used for model validation and change prediction. Image accuracy assessment was performed using the error or confusion matrices, kappa coefficients (Kscores), Relative Operating Characteristics (ROC), and accuracy scores, with an overall accuracy (Kappa coefficient) of 87% (0.81), 88% (0.81), 90% (0.82), and 92% (0.84) for the years 1986, 2001, 2011, and 2020, respectively. The results showed an increase in built-up areas (38.71% land gain), while a decrease was observed in mixed vegetation/savannah (22.69% land loss), croplands/bare surfaces (22.89%), and wetlands (3.36% land loss) over the period of 1986–2020. An increase in built-up areas (7.79% and 19.79% land gain by 2030 and 2050, respectively) and a decrease in mixed vegetation (1.13% and 2.54% land loss by 2030 and 2050, respectively), croplands and bare surfaces (6.58% and 16.98% loss by 2030 and 2050, respectively), and wetlands (0.13% and 0.31% loss by 2030 and 2050, respectively), are expected under the BAU scenario. Contrary to the wetland policy, which induces change only in the wetlands (0.02% and 0.047% land loss by 2030 and 2050, respectively), the afforestation scenario results in a gain in forest/savannah (0.11% and 0.73% land gain by 2030 and 2050 compared to the BAU scenario), but also a decrease in croplands/bare surfaces (0.64% by 2050 compared to the BAU scenario), whereas the building policy scenario results in a long-term increase in croplands/bare surfaces (2.88% by 2050, compared to the BAU), and wetlands (0.027 and 2.88% by 2050) in addition to the mixed vegetation/savannah. LULC dynamics (including future urbanization trends) for the AP under various scenarios is a step ahead of time, highlighting the importance of the implementation of existing laws and policies, but also providing information to deal with the observed inharmonious spatial development of urban centres, and serving as a dataset source for LULC change impact assessments on related resources.

The Response of Sensitive LULC Changes to Runoff and Sediment Yield in a Semihumid Urban Watershed of the Upper Awash Subbasin Using the SWAT+ Model, Oromia, Ethiopia

Land use and land cover (LULC) changes in many parts of river basins have caused water shortages, flood risks, land degradation, soil loss, biodiversity loss, and ecosystem deterioration. LULC change and topography are the main factors that cause land degradation and soil erosion in the Ethiopian highlands. The aim was to evaluate the rate of the LULC change and its effects on runoff and sediment yield in the semihumid subtropical Awash watershed using the SWAT + model. The land use maps of 2000, 2010, and 2020, along with constant climate data from 1992 to 2020, were used to investigate the effects of LULC dynamics on runoff and sediment yields. Agriculture and urbanization both increased at 7.1% and 7.95%, respectively. In contrast, the forest area decreased by 8.8% and shrubland by 3.25% from 2000 to 2020. Bare soil and urban areas covered the majority of the landscape units that were labeled as potential runoff generators. The majority of the soil erosion-prone areas that were classified as severe in the second and third scenarios covered a sizable area of urban, agricultural, and shrubland. These soil erosion hotspots covered an area of 3,777.3 ha (3.18%) and 13,413.1 ha (11.3%), with a total annual sediment yield of 361.93 m/ton and 1239.24 m/ton, respectively. In general, the change in LULC results in the annual sediment yield, with mean annual amounts of 241.8 tons/ha, 408.7 tons/ha, and 732.4 tons/ha for each scenario in the sequence. The model performance was tested using R2 = 0.88, NSE = 0.9, and PBIAS = −2.36, which indicate good agreement between simulated and observed flow, and R2 = 0.82, NSE = 0.86, and PBIAS = 4.38 for the simulated against recorded sediment yield. The increases in sediment yields have serious implications for reservoir siltation downstream of the watershed and warn land use managers to take action.

Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia

Understanding the spatiotemporal changes in land use and land cover (LULC) in the watershed is crucial for maintaining the sustainability of land resources. This study intents to understand the historical (1972–2015) and future (2030–2060) spatiotemporal distribution of LULC changes in the Upper Awash Basin (UAB). The supervised Maximum Likelihood Classifier technique (MLC) was implemented for historical LULC classification. The Cellular Automata-Markov (CA–Markov) model was employed to project two scenarios of LULC, ‘business-as-usual’ (BAU) and ‘governance’ (Gov). Results from the historical LULC of the study area show that urban and cropland areas increased from 52.53 km2 (0.45%) to 354.14 km2 (3.01%) and 6040.75 km2 (51.25%) to 8472.45 km2 (71.97%), respectively. Whereas grassland, shrubland, and water bodies shrunk from 2052.08 km2 (17.41%) to 447.63 km2 (3.80%), 2462.99 km2 (20.89%) to 1399.49 km2 (11.89%) and 204.87 km2 (1.74%) to 152.44 km2 (1.29%), respectively, from 1972 to 2015. The historical LULC results indicated that the forest area was highly vulnerable and occupied by urban and cropland areas. The projected LULC under the BAU scenario shows substantial cropland and urban area expansion, increasing from 8472.45 km2 (71.97%) in 2015 to 9159.21 km2 (77.71%) in 2060 and 354.14 km2 (3.1%) in 2015, 1196.78 km2 (10.15%) in 2060, respectively, at the expense of vegetation cover. These results provide insight intothe LULC changes in the area, thus requiring urgent attention by watershed managers, policymakers, and stakeholders to provide sustainable practices for the UAB. Meanwhile, the Gov scenario indicates an increase in vegetable covers and a decrease in cropland, encouraging sustainable development compared to the BAU scenario.