Land Use Cover Dynamics Research Articles

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

A GIRS-based analysis of urban green space losses with land-use changes and its relationship with surface urban heat island in the city of Tabriz

This research investigates the relationship between urbanization, land-use land-cover (LULC) changes, and surface urban heat islands (SUHIs) in Tabriz, Iran. Using Landsat satellite data from 2000 to 2021, the study analyzed changes in urban green space (UGS) and their impact on SUHIs. The maximum likelihood classification algorithm was employed to assess LULC changes, while the perceptron neural network model and cellular automaton Markov chain model were used to simulate and predict future LULC dynamics. Various indices, including the UGS Change Intensity Index (CII), Land Use Dynamic Degree Index (LUDD), and UGS Land Index, were used to quantify urban expansion, green space changes, and per capita green space (PCG) availability. The findings revealed a significant increase in built-up area and a subsequent decline in green space. While green space initially increased, it later decreased, leading to a reduction in both UGS area and PCG. Surprisingly, the analysis of heat islands associated with green space and built-up areas showed that built-up areas had a more pronounced mitigating effect on SUHIs compared to green space. Consequently, the green space heat island effect intensified, while the built-up area heat island effect decreased over time.

A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo

A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo

Predicting Prayagraj's Urbanization Trajectory using CA-ANN Modelling: Population Pressures and Land Use Dynamics

Land Use/Land Cover (LULC) dynamics provide a crucial role in the monitoring, planning, and management of resources. They also offer valuable information for developing strategies to balance conservation efforts, resolve conflicts between different land uses, and address pressures from growth. The present study focuses on the assessment of LULC dynamics, their forecasting, and their changes for Prayagraj city (including its surroundings) of India. Using long-term spatiotemporal Landsat datasets (1988–2018), we have explored the interlinkages between the change dynamics and human population pressure to explore the impact of agriculture and urbanization on the city landscape. Future growth prediction is carried out by incorporating Cellular Automaton (CA) and Artificial Neural Network (ANN) models. Six exploratory layers (viz., roads, educational institutes, railway transition, slope, river, and restricted area) are used in the learning process to determine LULC change (1997–2008) simulation. The validation of real and predicted LULC is carried out for 2018, where the correctness percentage and kappa value are found to be 90.29% and 0.87, respectively. Then, the ANN- Multilayer perceptron (MLP) and CA model are applied to predict LULC–2028 using the same trained transition probabilities. Results show that Built-land has grown highly by 10.03%, whereas Agriculture land and Forest land have significantly decreased by 13.43% and 3.03%, respectively, from 1988 to 2018. The predicted LULC of 2028 reveals that Built-land will keep growing by 2.83% during 2018–2028 at the cost of Agriculture land and Forest land, especially in northern, south–western and southern region, including city's inner sphere. United Nations' human population projection reveals that the city is expected to reach a population of 1.625 million by 2028. This indicates that tremendous pressure will be placed on land resources, particularly on agricultural, barren, and forested areas. To address this alarming scenario, it is imperative to delineate future development areas, ensuring better urban planning for the environmental sustainability and economic prosperity of Prayagraj city.

Impact of land use and land cover change on land surface temperature: Comparative studies in four cities in southwestern Ethiopia

The urban climate has undergone significant changes due to rapid population growth, leading to a decline in vegetation cover and an increase in land surface temperature (LST). This study aims to assess the influence of land use land cover (LULC) changes on LST in four major urban areas in southwestern Ethiopia, namely Jimma, Bonga, Mattu and Nekemte, during the period from 2002 to 2024. To investigate the impact of LULC dynamics on LST, 30 m spatial resolution images from Landsat were utilized, including Thematic Mapper (TM) for the year 2002 and Landsat Operational Land Imager (OLI) and Thermal Infrared (TIRS) for the years 2014 and 2024. Over the past 22 years, the mean LST has increased by 2.81 °C, 2.94 °C, 3.37 °C, and 3.96 °C for Bonga, Nekemte, Mattu, and Jimma, respectively. The increase in LST can be attributed to various factors, but one of the primary reasons is linked to the rapid urbanization and decrease in forest cover. Changes in LULC triggered by rapid urbanization significantly influences LST in major cities. The results highlight the increment of impervious surface and the decline in vegetation cover as key factors contributing to the upward trend in LST. The results indicate that urban centers with less vegetation cover experienced higher LST compared to their surroundings. The results of this study indicate the necessity of effective urban planning to increase vegetation cover through urban greenery and parks to mitigate the increasing trends of LST, which can improve urban thermal comfort levels.

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India

Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India

Land Use/Cover Dynamics and its Implication on the Sustainability of Urban Agriculture in Selected Urban Centers of Ethiopia

Abstract Analysis of urban Land Use/Land Cover (LULC) dynamics in light of urban agriculture (UA) helps to understand its implication for UA practice and in turn making the necessary interventions. Therefore, the main objective of this study was to examine the LULC dynamics in selected urban centers of Ethiopia (Addis Ababa, Bahir Dar, Adama, and Hawassa cities) for the last seventeen years (2006–2022). SPOT 5 satellite imagery for the year 2006 and 2016 and Sentinel image for the year 2022 were analyzed. In addition, data from key informant interviews, focus group discussions, and field observations were used to triangulate LULC analyses information and to identify the main causes of LULC dynamics in the studied urban centers. The findings reveled that there were rapid expansion of urban built-up areas at the expense of other urban LULC types mainly of peri urban horticultural lands for the last 17 years (2006‒2022) in Addis Ababa, Hawassa, Bahir Dar, and Adama cities of Ethiopia. This has adverse impact on the sustainability of UA. Rapid urban population growth mainly because of high rural urban migration, expansion of squatter settlement, and increment of investment were the main driving forces of LULC dynamics. Based on the findings of this study (changes in LULC and driving factors) the studied cities administrations or authorities need to develop sustainable development plans by considering UA.

Synergistic impact of climate and land use land cover change dynamics on the hydrological regime of Loktak Lake catchment under CMIP6 scenarios

Study region: Loktak Lake catchment, north-eastern Himalayan ranges, IndiaStudy focus: Assessing the potential synergistic impact of land use land cover (LULC) and climate change on water resources is crucial for watershed management especially in the data-scarce vulnerable wetlands. Using the SWAT hydrological model, the current study seeks to quantify the combined impacts of LULC and climate change on the water balance parameters of the Loktak Lake catchment. This study simulates the hydrological regime of the Loktak Lake catchment based on future climate and changes to LULC dynamics using four GCM models in combination with SWAT models under two alternative scenarios.New hydrological insights for the region: The findings showed that under the SSP 126 and SSP 585 scenarios, the annual mean temperature is anticipated to increase by a maximum of 1.7°C and 3.74°C, respectively, by the 2050 s and 2090 s, while the annual precipitation similarly shown a rising trend for both the scenarios for the mid and far future. The mean annual streamflow is projected to increase by 48.84 cumecs (31.65 %) and 57 cumecs (36.89 %) by the 2050 s and 2090 s decades, respectively, while the water yield will rise by 200.65 mm (30.25 %) and 216.1 mm (32.57 %) in comparable decades. The outcomes of this study might provide a scientific foundation for ecological protection as well as water resource management and development in response to the potential future risk due to climate change in the Loktak Lake catchment.

Land use-land cover dynamics in Boyo wetland using remote sensing and GIS for the period 1992–2022

ABSTRACT Land use changes are one of the main factors contributing to the degradation of wetland ecosystems. The objective of this study was to describe land use and land cover (LULC) change in the Boyo Wetland from 1992–2022. We employed remote sensing and GIS to obtain data for this study. As the main data source, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) images were used. We downloaded Landsat images from the USGS website that were cloud-free. A supervised maximum likelihood classifier algorithm was used for the classification process. Five LULCs are identified, which include farmland, built-up areas, bare land, grazing land, and wetland. For the duration of the research period, the rate of increase in the percent of farmland, built-up, and barren land was observed to be 18.24%, 9.48%, and 4.52%, respectively, while a decreasing pattern was observed in grazing land and wetland by − 19.44% and − 12.79%, respectively. The expansion of agriculture and built-up areas owing to population growth are the primary factors influencing change in the study area. Therefore, a rapid protective measure should be taken to rehabilitate the study wetland.

Assessing the Impacts of Landuse-Landcover (LULC) Dynamics on Groundwater Depletion in Kabul, Afghanistan’s Capital (2000–2022): A Geospatial Technology-Driven Investigation

Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts.

Assessing Anthropogenic Impacts on Stubb’s Creek Forest Reserve, Nigeria: A Remote Sensing and GIS Approach

Land use-land cover (LULC) changes, particularly in tropical regions like the Niger Delta of Nigeria, are critical drivers of global environmental shifts. This study focuses on the spatial assessment of forest depletion in the Stubb’s Creek Forest Reserve (SCFR) in Akwa Ibom State, amidst rapid infrastructural and industrial developments. The data used includes Landsat 5 of 30m TM, Landsat 7 of 30m TM, and Landsat 8 of 30m ETM for 1986, 2003, and 2018 respectively. Using remote sensing and GIS techniques, the research analyzes LULC dynamics between 1986 and 2018 and projects future trends to 2028. Results reveal a significant depletion of forest covers, particularly the Dense Forest, due to anthropogenic activities like oil and gas exploration and infrastructure development. Over the study period, the Dense Forest cover decreased from 12,296 hectares (41.9% of the total area) in 1986 to 9,149 hectares (31.2%) in 2018. Conversely, built-up areas increased from 1,385 hectares (4.7%) to 3,358 hectares (11.4%) during the same period. The study forecasts a continued decline in forest cover, with a projected Dense Forest cover of 7,474 hectares (25.45%) in 2028. These findings underscore the urgent need for sustainable land management policies to mitigate biodiversity loss and environmental degradation. Remote sensing and GIS tools prove instrumental in monitoring and understanding LULC changes, offering valuable insights for informed decision-making in environmental conservation and land use planning.

Analysis of land use/land cover changes and driving forces during the period 1992–2022: a case study of Jinan City, China

AbstractThis research aims to quantify the spatial pattern of urban land use/land cover (LULC) change while considering environmental effects. This paper integrates historical Landsat imagery, The Environment for Visualizing Images (ENVI), geographical information system (GIS), and socioeconomic data to determine the spatial–temporal urban LULC dynamics and the conversion of LULC in response to the rapid urbanization from 1992 to 2022. Principle component analysis and multiple linear regression are used to determine and model the relationship between the socioeconomic factors and the changes for identifying the driving forces. The results indicate that impervious surfaces have exponentially increased, expanding more than two times from 2,348 to 4,795 km2, in contrast to bare lands, which drastically declined by 95%, from 1,888 to 87 km2. Water bodies have always been relatively fewer, at approximately 100 km2. In addition, the majority of farmland in Jinan City is concentrated in the northern region with a steady area in the range of 2,100–2,900 km2, while the majority of woodland located in the southern region declined from 3,774.52 km2 (37%) to 3,088.28 km2 (30%). Economic development, population growth, and climate change are the primary factors that have an obvious impact on LULC changes.

Altitudinal Appraisal Of Land Use Land Cover And Surface Temperature Change In The Satluj Basin, India

The land use change has affected nearly 32% of the global landscape from 1960 to 2019. Several studies have examined the impacts of land use land cover (LULC) on the surface temperature. Still, the spatiotemporal variation of LULC and LST with altitude is a less researched area. In the current study, we assess the LULC dynamics and its relation to altitudinal LST in the Himalayan Satluj River basin in Himachal Pradesh across the altitudinal range of 332 to 6558 meters. LULC, LST, NDVI, and NDMI were derived from Landsat data for 1980-2020. The spatial pattern was analyzed using Support Vector Machine (SVM) and a mono-window algorithm. The results of LULC denote that snow covered area (SCA) have decreased by nearly 56.19% since 1980 and vegetation cover has increased. However, a decline in vegetation density is pronounced at the same time. The mean surface temperature of the Satluj basin has amplified by 6°C (0.25°C/year) from 1996 to 2020. Mostly Zone 3 and 4 are under high hilly and temperate dry regions in Lahaul Spiti and Kinnaur district of Himachal Pradesh. The most important sign is that the mean surface temperature for Zone 3 (3000m-4500m) and Zone 4 (above 4500m) was the highest increase to 6°C (0.26°C/year) and 8°C (0.31°C/year) from 1996 to 2020. The increase in LST values is attributed to land cover dynamics precisely the decline of snow cover area and the emergence of vegetation zone at higher above the 4500 altitudes. Our study facilitates regional analysis.

Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia

Intense level of land use land cover (LULC) changes has been observed in Sub-Saharan Africa, particularly in the central highlands of Ethiopia, due to rapid population growth and urbanization process. However, quantifying and identifying the rural-urban landscape changes are challenging. In this study, LULC changes during the years 1984, 1990, 2000, 2010, and 2021 have been analyzed using satellite imageries and Support Vector Machine (SVM) algorithms in a heterogenous rural and urban landscape of the Akaki river catchment, central highlands of Ethiopia. The LULC change drivers were evaluated by applying LULC thematic change analysis combined with key informants’ interviews. Seven LULCs that include: Built-up area (BTA), Cropland (CL), Grassland (GL), Waterbody (WB), Plantation Forest (PF), Woodland (WL), and Bareland (BL) were detected. The result shows that 51.3 % of the catchment area has been transformed into other land uses. BTA increased by 24.7 % while GL and WL reduced by 18.1 % and 5.9 % respectively. Large areas of CL (61 %) and GL (22 %) were changed into an urban landscape. The spatial and non-spatial analysis revealed that the major spatiotemporal LULC change drivers between 1984 and 2005 were land use policy and legislation change and the Eucalyptus tree plantation campaign. Whereas, low-cost housing programs, informal settlers, market opportunity, and real estate development were the main drivers for the LULC changes between 2006 and 2021. The study also found the key informant observation and SVM image classification results are aligned and therefore, we found the SVM-based classifications are suited for such complex rural-urban landscape change and pattern analysis. The outcome of this research can contribute to improving land use policy, its management, and public understanding of the LULC dynamics and its implications.

Land Use Land Cover (LULC) Dynamics by CA-ANN and CA-Markov Model Approaches: A Case Study of Ranipet Town, India

The present study analyzed the spatio-temporal variations in the Land Use Land Cover types within Ranipet Municipal town in Ranipet District, Tamil Nadu State, India, using two different platforms (QGIS and IDRISI Selva v.17.0). The possible parameters driven the net changes in the Land Use Land Cover (LULC) types were also incorporated for the analysis. Results revealed the positive net changes in the built-up area are about 26.8%, and combined other classes like vegetation, barren land, and water bodies have net negative changes during 1997-2019. Particularly barren land was found to have a reduction of 17.4% due to the massive industrialization in the study area. Further, the LULC maps were used for future prediction (2029) using the dynamic models of CA-ANN (Cellular Automata and Artificial Neural Network) and CA-Markov. Predicted maps yielded a kappa index of 81.6% and 82.6% for CA-ANN and CA-Markov, representing their respective accuracy levels. The CA-Markov model is extended for determining the probable long-term changes for 2080 in LULC with a kappa index of 76.2%. Compared to the CA-ANN model using the QGIS platform, CA-Markov provided better analysis, particularly from one cell to the other. According to the survey and the ground truth in the locality, industrialization and occupational shift were the most influential drivers of LULC dynamics. Moreover, the results of this study assist the stakeholders in the decision-making process for future sustainable land use management.

Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia

Land Use/Land Cover (LULC) change has inhibited sustainable development for the last millennia by affecting climate, biological cycles, and ecosystem services and functions. In this regard, understanding the historical and future patterns of LULC change plays a crucial role in implementing effective natural resource management. This study aimed to model and characterize the spatiotemporal trajectories of landscape change between the 1984 and 2060 periods. The satellite image spectral information was segmented into seven LULC classes using a hybrid approach of image spectral recognition. The supervised classification technique of Support Vector Machine (SVM) was used to classify the satellite images, whilst the Land Change Modeler (LCM) Module in TerrSet software was used to assess the historical trend and future simulation of LULC dynamics. To predict future landscape changes, transition potential maps were generated using a Multi-layer Perceptron (MLP) neural network algorithm. The findings of the study demonstrated that the Goang Watershed has experienced significant LULC change since 1984. During the 1984–2001, 2001–2022, and 1984–2022 periods, farmland showed a dramatic increasing trend with 7.5 km2/yr−1, 110.3 km2/yr−1, and 64.3 km2/yr−1, respectively. A similar trend was also observed in built-up areas with 0.5 km2/yr−1, 3.2 km2/yr−1, and 2 km2/yr−1. The expansion of farmland and built-up area was at the expense of forest, shrubland, and grasslands. With a business-as-usual scenario, the extent of farmland will continue to increase between 2022 and 2060 while rapid reduction is expected by forest, shrubland, and grasslands. The alarming rate of farmland and built-up area expansion will put significant pressure on biodiversity and ecosystem services in the area. As a result, eco-friendly conservation approaches should be implemented as soon as possible to maintain ecosystem health and encourage sustainable development.

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN.

Continuous and unplanned urbanization, combined with negative alterations in land use land cover (LULC), leads to a deterioration of the urban thermal environment and results in various adverse ecological effects. The changes in LULC and thermal characteristics have significant implications for the economy, climate patterns, and environmental sustainability. This study focuses on the Province of Naples in Italy, examining LULC changes and the Urban Thermal Field Variance Index (UTFVI) from 1990 to 2022, predicting their distributions for 2030. The main objectives of this research are the investigation of the future seasonal thermal characteristics of the study area by characterizing land surface temperature (LST) through the UTFVI and analyzing LULC dynamics along with their correlation. To achieve this, Landsat 4-5 Thematic Mapper (TM) and Landsat 9 Operational Land Imager (OLI) imagery were utilized. LULC classification was performed using a supervised satellite image classification system, and the predictions were carried out using the cellular automata-artificial neural network (CA-ANN) algorithm. LST was calculated using the radiative transfer equation (RTE), and the same CA-ANN algorithm was employed to predict UTFVI for 2030. To investigate the multi-temporal correlation between LULC and UTFVI, a cross-tabulation technique was employed. The study's findings indicate that between 2022 and 2030, there will be a 9.4% increase in built-up and bare-land areas at the expense of the vegetation class. The strongest UTFVI zone during summer is predicted to remain stable from 2022 to 2030, while winter UTFVI shows substantial fluctuations with a 4.62% decrease in the none UTFVI zone and a corresponding increase in the strongest UTFVI zone for the same period. The results of this study reveal a concerning trend of outward expansion in the built-up area of the Province of Naples, with central northern regions experiencing the highest growth rate, predominantly at the expense of vegetation cover. These predictions emphasize the urgent need for proactive measures to preserve and protect the diminishing vegetation cover, maintaining ecological balance, combating the urban heat island effect, and safeguarding biodiversity in the province.

Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model.

Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role played by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in general. In Gedaref landscape of Eastern Sudan, there is limited or no knowledge of LULC structure and size, degree of change, transition, intensity and future outlook. Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988-2018) using Landsat imageries and the random forest classifier, (2) determine the underlying dynamics that caused the changes in the landscape structure using intensity analysis, and (3) predict future LULC outlook for the years 2028 and 2048 using cellular automata-artificial neural network (CA-ANN). The results exhibited drastic LULC dynamics driven mainly by cropland and settlement expansions, which increased by 13.92% and 319.61%, respectively, between 1988 and 2018. In contrast, forest and grassland declined by 56.47% and 56.23%, respectively. Moreover, the study shows that the gains in cropland coverage in Gedaref state over the studied period were at the expense of grassland and forest acreage, whereas the gains in settlements partially targeted cropland. Future LULC predictions showed a slight increase in cropland area from 89.59% to 90.43% and a considerable decrease in forest area (0.47% to 0.41%) between 2018 and 2048. Our findings provide reliable information on LULC patterns in Gedaref region that could be used for designing land use and environmental conservation frameworks for monitoring crop produce and grassland condition. In addition, the result could help in managing other natural resources and mitigating landscape fragmentation and degradation.

Four-decade (1977-2017) landscape tale of tourist reservoir hotspot El Piñol-Guatapé, Colombia.

Efficient management of land use/land cover (LULC) features is vital for a balanced sustainable ecosystem. Thus, this work aimed to document the LULC changes in the less studied El Peñol-Guatapé reservoir, Antioquia, Colombia, especially in the reservoir area due to the construction of a hydro-electric power plant. For this study, Landsat images of 1977, 1986, 1997, and 2017 were used and the results indicated an increase in the settlement area and road networks by 0.10 and 0.60%, respectively, while during 1986 to 2017, cropland, plantation, dense forest, and open forest areas presented an increase of 0.52, 1.06, 2.87 and 2.61%, respectively. However, the marshy vegetation, scrub forest and fallow land decreased to - 0.51, - 3.79 and - 4.29%, respectively, in the same period. The water body before and after the completion of reservoir project denoted an increase from 13.1 km2 in 1977 to 45.7 km2 in 1986. This study provides a first-hand report on LULC dynamics in this tourism dominated municipalities that will serve as a reference for ecosystem management to reconcile the conflicts between different LULC classes in ecologically enriched regions.