Future LULC Research Articles

Spatiotemporal Prediction of Future LULC changes, Northern and Northeastern Parts of Iraq with MOLUSCE

Spatiotemporal Prediction of Future LULC changes, Northern and Northeastern Parts of Iraq with MOLUSCE

An investigation of past and future LULC using multilayer perceptron-Markov chain techniques: a case study of a Himalayan smart city (2005–2040)

An investigation of past and future LULC using multilayer perceptron-Markov chain techniques: a case study of a Himalayan smart city (2005–2040)

Assessment and Multiscenario Simulation of Land Use and Ecosystem Services Interactions in Inner Mongolia

ABSTRACTBalancing land development and ecological protection poses significant challenges for sustainable development in arid and semi‐arid regions like Inner Mongolia. This study uses the integrated valuation of ecosystem services (ESs) trade‐offs model and patch‐generated land‐use simulation model to analyze the impacts of land‐use/cover (LULC) changes on ESs from 2005 to 2020 and simulate scenarios for 2035. The results show that (1) ecological governance efforts improved the comprehensive ecological index (CEI), habitat quality, and soil conservation capacity but led to a decrease in the annual water yield (AWY) in the eastern region. Additionally, the conversion of grassland and forest land to construction land and farmland caused slight declines in carbon sequestration and increased nitrogen export. (2) The external driving factors of different ESs vary, and the internal relationships within ESs have also changed. Notably, changes in LULC transformed the synergy between AWY and habitat quality into a trade‐off relationship after 2010 due to increased forest and grassland. (3) Among the five land‐use scenarios, the comprehensive development scenario combined with zonal management strategies can achieve the sustainable development goals of economic growth, ecological protection, and food security. This study clarifies the internal and external factors affecting ESs in Inner Mongolia and provides a scientific basis for future LULC management and ecological governance policies. It fills a gap in the spatiotemporal simulation of ESs under multiple scenarios in this region. These findings offer valuable references for promoting sustainable development in similar arid and semi‐arid regions, highlighting the importance of integrating multi‐objective strategies and adaptive management practices to balance ecological and economic objectives.

Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions

The African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990–2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda’s sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment’s ecological integrity, in alignment with national and global goals.Graphical

Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia

Watershed reactions to environmental changes is critical for water resource use and management planning. The change in land use/land cover (LUCC) alters surface water hydrology and water balance, subsequently it affects the availability of water resources. This study examined Akaki River catchment surface hydrological responses to future land use scenarios. The future LULC simulated using Cellular Automata-Markov Chain (CA-Markov) model in IDRISI software for 2031, 2041, and 2051 under Business as Usual (BAU) and Forest Patch Protection (FPP) scenarios. Water yield (WY), sediment export (SE), and soil loss (SL) were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The findings indicate that under BAU, forest and settlement areas increased 42.64% and 31.47%, whereas water body, grass land, and agriculture dropped 31.99%, 25.72%, and 10.4%, respectively, from 2021 to 2051. The FPP scenario shows a 66.42% increase in forest coverage and a 31.97% decrease in cultivated land between 2021 and 2051. Overall settlement expansion will expand by 31.44%, while water body and grassland will decline by 11.12% and 24.9%. As a result of LULC changes in BAU scenario, the proportions of WY (4.36%), SE (26%), and SL (22.45%) increased. The hydrological components exhibited more gradual variations of 0.55%, 18.8%, and 14.24% for the WY, SE, and SL, respectively, under the FPP scenario. Therefore, the alteration has resulted in a concerning state within the area, necessitating the implementation of comprehensive management techniques to restore the hydrology of the catchment.

LAND USE LAND COVER (LULC) CHANGE ANALYSIS IN BAGUIO CITY USING GIS-BASED TRANSITION POTENTIAL MODELING

Abstract. Baguio City experienced rapid LULC changes in the past decades due to rising population resulting in increasing demand in residential and commercial areas. The LULC change from 2003 to 2011 was quantified through a transition potential model using Artificial Neural Network and Cellular Automata. Potential driving factors considered in this study were barangay population and population density, land category, elevation, slope, soil type, and distances from the Central Business District, roads, and sinkholes. Multilayer Perceptron with Backpropagation technique was employed in the modeling simulations where various value combinations for the five hyperparameters were tested. Among the combinations of hyperparameter values tested, the combination that achieved the highest simulation accuracy was 1, 0.001, 1000, 10, and 0.01 for Neighborhood, Learning Rate, Iterations, Hidden Layers, and Momentum respectively. The prediction results for the year 2035 show that built-up areas in Baguio City will increase by 760.79 hectares while vegetation and bare soil will decrease by 347.64 hectares and 413.26 hectares, respectively. Built-up areas are expected to form mostly in Alienable and Disposable lands, barangays with high population density, and areas near roads and pre-existing development. On the other hand, minimal built-up expansion is expected in vacant forests, forest reserves, and areas near sinkholes. These findings are in line with the city government's expectations. With an accuracy of 72.80% and a Kappa statistic of 0.61, it can be concluded that the model is capable of predicting future LULC change and may serve as a viable guide for future land use development plans.

Modeling hydrological consequences of 21st-Century climate and land use/land cover changes in a mid-high latitude watershed

The Naoli River Basin (NRB), a pivotal agricultural production area in China, is poised to undergo substantial impacts on water resources due to projected climate and land use/cover (LULC) changes. Despite its significance in the context of China's expanding farmland construction in the NRB, there exists limited research on the potential repercussions of future shifts in runoff, soil water content (SWC), and evapotranspiration (ET) on crop productivity and water availability (both in terms of quantity and timing). This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models (GCMs) across three scenarios to drive the well-calibrated distributed hydrological model, ESSI-3. The objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term (2026–2050), middle-term (2051–2075), and far-term (2076–2100) future in comparison to the baseline period of 1990–2014. Results consistently indicate an increase trend in annual average ET, runoff, and SWC in the NRB across all three future periods under the three SSP scenarios. LULC changes emerge as the primary driver influencing regional hydrological processes in the near future. Notably, under high-emission scenarios, monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the baseline. This shift is attributed to the anticipated warming of winter and spring, leading to a transition in peak snowmelt from April to March. Concurrently, the expansion of cropland intensifies crop evapotranspiration demand, potentially exacerbating water stress during the early stages of crop growth in April. The findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling processes. Early planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing water-related challenges in the NRB and neighboring mid-high latitude regions.

A combined impact assessment of climate and land use/land cover change in an Eastern Himalayan watershed in northeast India.

The current study investigates the joint impact of projected land use/land cover change (LULCC) and climate change on the discharge of river Puthimari using Soil and Water Assessment Tool (SWAT). Puthimari, flowing through part of Bhutan and the northeastern region of India, earns its significance by contributing a fairly huge amount of discharge to the mainstream Brahmaputra causing frequent floods downstream, specifically in the monsoon season. The analysis was carried out from 2025 to 2099, by dividing this entire period into three sub-periods, 2025‒2049, 2050‒2074, and 2075‒2099, each of 25 years duration. To evaluate the impact of climate change, this study considered future climate data of five different CMIP5 (Coupled Model Intercomparison Project) climate models and their ensemble for RCP4.5 and RCP8.5 (Representative Concentration Pathways). The changes in LULC were incorporated by projecting the future LULC for 2035, 2065, and 2085 for each of the periods using the CA (Cellular Automata)-Markov model. SWAT performed well for both calibration and validation. The respective Nash-Sutcliffe efficiency (NSE) values for calibration and validation were found to be 0.74 and 0.77. Also, 0.75 and 0.79 coefficient of determination (R2) values were obtained for calibration and validation, respectively. The analyses reveal a 19.76% increase in rural settlement, and a 6.30%, 16.45%, and 8.76% decrease in forest, cropland, and waterbodies in the watershed by the end of this century. The average monsoon rainfall would increase by 14.16% and 38.92%, with a corresponding increase in discharge by 34.27% and 64.67%, under RCP 4.5 and RCP 8.5, respectively. This comprehensive study represents a pioneering effort to thoroughly analyze the future hydrological dynamics of the Puthimari River. This research serves as a vital resource for policymakers and government agencies, offering valuable insights to guide both structural and non-structural measures aimed at safeguarding the river from potential flood devastation. Additionally, it provides essential information to support the implementation of effective watershed management practices.

Integrated assessment of future climate and land use changes on urban floods: A Markov chain and PCSWMM-based approach for Hyderabad case study.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1-10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

Spatiotemporal change analysis of LULC using remote sensing and CA-ANN approach in the Hodna basin, NE of Algeria

Spatiotemporal studies of land use/land cover change (LULCC) have become an important issue due to the rapid conversion of landscape and its impact on the environment and ecosystems. This study aims to identify the LULC change's impact on the Hodna basin (26,000 km2) from 2000 to 2020 using Landsat images data and forecast the possible change scenarios to the horizon of 2050. The Maximum Likelihood Supervised Classification (MLSC) was applied to Landsat data between 2000 and 2020 with a five-year interval. The confusion matrix of image classification indicates an overall accuracy of 85% and a kappa coefficients range between 0.82 and 0.96. To forecast the possible change scenarios of each class of LULC a transitional simulation using an artificial neural network (ANN) and machine learning algorithms. While, the cellular automata (CA) algorithm was used to simulate and predict the future LULC for the years 2030, 2040 and 2050 after validation of 2020 LULC map. The overall kappa calculated is 68.5% indicating a good accuracy of the validation process. The results show that for 2000–2020, agricultural lands decreased by 2.16% and sandy sails increased by 9.45%, indicating the expansion in the desertification phenomenon. Over the period 2000 to 2050, bare land decreased by 10% in 2015, recovered by 15% in 2020, and is projected to increase by 8% by 2050, reaching 73%; mixed and dense forest exhibited similar trends, growing from 0.7% in 2000 to 1.66% in 2050 with greater stability; built-up areas in the study zone surged by 150%, from 0.86% in 2000 to 2.5% in 2020, while clear forest remained relatively consistent, covering 4–6% of the surface; agricultural land diverged from bare land due to agricultural investment policies favoring new farms over bare land expansion. Urban sprawl is observed in the major cities like M'sila and Bordj Bou Arreridj and accelerated during the study period. Urban tissue evolved at the expense of agricultural land and focused mainly on slight slope areas and valleys.

A modeling approach for evaluating and predicting the impacts of land use land cover changes on groundwater recharge in Walga Watershed, Upper Omo Basin, Central Ethiopia

Study regionWalga watershed, Upper Omo basin, Ethiopia (2349 km2). Study focusThis research aimed to understand and predict the impacts of LULC changes on groundwater recharge in Walga watershed. Thus, the historical LULC changes in Walga watershed was assessed from Landsat: 4–5 TM, 7 ETM + and 8 OLI-TIRS (1990, 2006, and 2021), through Hybrid classification of unsupervised and supervised scheme using Erdas Image 2015, while future LULC was predicted using CA–Markov chain analysis approach in QGIS-MOLUSCE plugin. The LULC change impact scenarios on groundwater recharge was assessed using SWAT model. New hydrological insightsThe evaluation of LULC changes pointed out that Walga watershed has undergone high intensity of LULC changes (1990–2021) due to anthropogenic activities and predicted to instantly change in the coming (2021–2035). The hydrological assessments indicate that groundwater recharge was declined over the last three decades by 20.84 mm and predicted to decline in future (2021–2035) by 6.17 mm. This implies that the declined recharge was linked to LULC changes over time with deforestation and agricultural land expansion. This means due to changes in agriculture (+26.3 %, +15.4 %), mixed forest (−3.7, −2.3 %), rangelands (−23.8 %, −8.8 %), grazing land (−2.6 %, −2.0 %), and urban (+1.14 %, +0.46 %) during (1990–2021) and (2021–2035). These outcomes provide baseline information for decision makers concerning LULC and groundwater for better planning and management strategies in the watershed.

Examining the spatio-temporal relationship between LST, NDVI, NDBI and LULC change of Pachhua dun, Dehradun, Uttarakhand (India)

Recent climate change has had a negative impact on a wide range of human and natural systems, and it is clear that humans influence the climate. Because, as anthropogenic influence increases, the heat output from the land surface increases, speeding up the rate of climate change. In this regard, the use of RS and GIS techniques has provided various opportunities for research to examine these changes. The current analysis is based on the Landsat 1989, and 2020. Over the study period of 31 years, the built-up regions increased in size from 44.23 km2 to 154.56 km2. Whereas, the area covered by scrubland, water bodies, and vegetation cover has significantly decreased. The LST study further supports the outcome, showing that the mean and standard deviation increased from 14.81°C±1.32(1989) to 18.82°C±1.57(2020). The study also made an effort to examine how LULC affected LST; while vegetation cover has consistently helped to lower mean LST, built-up areas and scrubland are the main drivers of mean LST rise. The LST and NDBI revealed a positive correlation, while the NDVI/SLOPE and LST showed a negative correlation. Subsequently, the multiple linear regression (MLR) models concluded that the BUAs has evolved into a serious threat to the increase in LST, but increase in vegetation cover and SLOPE would result in slight decrease in LST. the study recommended that the government create policies that restrict future land encroachment and conversion, notably of forested area and water bodies, and make an immediate effort to increase the quantity and quality of urban green cover in the study area. So that we may, respectively, minimize the potential hazard posed by future LST rise and LULC change.

Past and future land use/land cover, and climate change impacts on environmental sustainability in Vea catchment, Ghana

LULC and climate change are the two most common manifestations of global environmental change. The objective of this study was to quantify past and future LULC and climate change impacts on environmental sustainability in the Vea catchment of Ghana. The past LULC change (1998–2022) was analyzed using ERDAS Imagine 15 upon the application of the Support Vector Machine (SVM) classifier algorithm. The CA-Markov was used to predict land-use changes in 2038 and 2054. Climate change was projected under SSP 4.5 and SSP 8.5 emission scenarios using the R software. The projections from an ensemble mean of SSPs in the near future (2023–2052) and far future (2071–2100) were compared to the reference period (1993–2022). The results show that, between 1998 and 2022, cropland increased from 10.9% to 51.98%, while grassland and forest areas decreased from 54.8–18.14% and 31.7–22.73%, respectively. The results of the predicted LULC change showed an increase in cropland from 181 km2 in 2038 to 183 km2 in 2054 at the expense of grassland and forest, which are expected to decrease from 51–50 km2 and 73–71 km2, respectively. The findings of climate change show the projected increases in average temperature range from 2.10 to 3.5 °C under SSP4.5 and from 2.7 to 4.15 °C under SSP8.5. Under the SSP4.5, annual average precipitation is anticipated to decrease by 12.34–13.1%, while the SSP8.5 projects a decrease of 12.6–13.6%. Results will help in the setting up of long-term environmental management strategies in the catchment as well as the development of appropriate adaptation methods to mitigate the ongoing impacts of LULC and climate change on environmental sustainability.

Coupling RESI with Multi-Scenario LULC Simulation and Spatiotemporal Variability Analysis: An Ecological Spatial Constraint Approach

Southwest China’s arid river valleys represent ecologically vulnerable areas with intense human activity. Understanding the historical changes in LULC and land cover and projecting the impacts of various development scenarios on future LULC have become crucial for regional spatial information management and territorial spatial planning. This research analyzes the land-use changes in the Yuanmou dry-hot valley over a 30-year span from 1990 to 2020. Building upon the PLUS model, we established a coupled habitat quality spatial and multi-scenario land-use simulation model. Four development scenarios were proposed: natural progression, economic development, ecological conservation, and balanced development. We conducted simulations and evaluations of land-use in the Yuanmou dry-hot valley for 2030 using the PLUS mode, assessing the sustainability of future development scenarios under varying ecological constraints. During the simulation, three distinct RESI regions were employed as restricted development zones, integrating the three ecological constraints with the four simulation scenarios. We introduced a novel approach based on ecological environmental quality as the ecological constraint, providing a scientific reference for sustainable development in ecologically vulnerable areas. The results indicate that under ecological conservation scenarios with high-to-low RESI constraints, the ecological environment is superior to the other three scenarios. The results show the following: (1) From 1990 to 2020, aside from a continuous decrease in grassland area, there was an increasing trend in the areas of water bodies, forests, croplands, construction lands, and unused lands in the Yuanmou dry-hot valley. (2) By 2030, under all four development scenarios, the cropland area is expected to expand rapidly, while forested areas will decrease; grassland areas will decline under natural and economic development scenarios but show opposite trends under the other scenarios; and construction land and unused land areas will decrease under the ecological conservation and balanced development scenarios. (3) Land-use intensity analysis for the four scenarios indicated that, by 2030, unused lands in the Yuanmou dry-hot valley are more likely to be converted into water bodies, forests are more likely to be converted into croplands and grasslands, grasslands are more likely to be converted into croplands, croplands are more likely to be converted into grasslands, and construction lands are more likely to become unused lands. (4) Sustainable LULC management evaluations based on landscape indices reveal that ecological conservation and balanced development scenarios exhibit superior landscape connectivity and clustering. Thus, the balanced development scenario is the most appropriate LULC strategy for the Yuanmou dry-hot valley in the future. These findings provide scientific references for balancing ecological conservation and economic development in the arid river valleys of Southwest China.

Application of artificial neural network (ANN) for investigation of the impact of past and future land use–land cover change on streamflow in the Upper Gilgel Abay watershed, Abay Basin, Ethiopia

Changes in land area features, such as vegetation type and soil conditions, have an impact on carbon sources and sinks and support food production; this is critical in addressing global sustainability challenges such as climate change, biodiversity loss, and food security. The study's major goal was to determine how LULC changes in the past and future might affect streamflow in the Upper Gilgel Abay watershed. The modeling was conducted using the MOLUSCE Quantum GIS plugins cellular automata simulation method and streamflow modeled using SWAT. Landsat 5 TM (1995), Landsat 7 ETM + (2007), and Landsat 8 imaging (2018) satellites were used to collect the images, which were then categorized using ERDAS 2014 software, and the kappa coefficient becomes 84.04%, 82.37%, and 85.54% for 1995, 2007, and 2018 LULC, respectively. SWAT model better performed the simulation which is R2 of 0.77 for calibration and 0.68 for validation and ENS becomes 0.71 and 0.62 for calibration and validation, respectively. The output change in streamflow due to past and future LULC maps shows an increase in LULC in cultivated areas and resulted in 39%, 46.81%, and 52.45% in each of the years 1995, 2007, and 2018, respectively. The three LULC modifications in the land cover maps from 1995, 2007, and 2018 had simulated mean monthly peak discharges of 62.20 m3/s, 66.51 m3/s, and 72.10 m3/s, respectively. The projected LULC 2027 also shows a similar increase in the study area, and dominantly cultivated land illustrates the highest change at around 53.77% but the highest change occurs on grassland during (2018–2027) land use at around 12.29%. And the highest streamflow was found around a monthly average of 1400 m3/s. The study primarily provides insight into how LULC fluctuation affects streamflow, and it is crucial for water planners and natural resource professionals whose focus is on the Upper Gilgel Abay basin.

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Over the past few decades, the growth of population and the development of the economy have had a significant impact on the way land is used and covered (LULC) in the Nile Nyabarongo River basin. However, there is limited knowledge about the patterns of land use and the mechanisms that drive changes in these patterns because of human activities. Therefore, it is crucial to examine how land use and cover are shifting in this area, identify the factors responsible for these changes, and forecast future patterns. This study sought (1) to evaluate the changes in LULC from 1990 to 2020 and (2) to predict future fluctuations until 2060. By analyzing the LULC data for the years 1990, 2000, 2010, and 2020, this study simulated the future LULC patterns of the area for the next 30 years using the LULC transition matrix and the Markov chain model. The study classified the LULC into five categories: forestland, grassland, cropland, settlement, and water. The results revealed that there will be significant changes in the LULC of the study area from 2030 to 2060. The forest area is projected to decrease by 801.7 km, 771.6 km, and 508.2 km, while the cropland area will expand by 6307.2 km, 6249.2 km, and 6420.6 km during this period. The grassland area will experience a small increase of 761.1 km, 802.4 km, and 859.1 km, and the settlement area will also grow by 355.2 km, 407.4 km, and 453.2 km. In contrast, the water area will decrease by 55.9 km, 50.5 km, and 40 km. The ongoing pattern of LULCC is expected to persist over the next three decades, with an increase in cropland area and grassland. This study’s findings can provide valuable insights for land use planners and water resource managers in developing fair land use and water resource management policies for the entire region, enabling them to make well-informed decisions.

Quantifying the landscape changes within and outside the Dachigam National Park, Kashmir Himalaya, India using observations and models.

Protected areas are the cornerstone of biodiversity and serve as a haven for biodiversity conservation. However, due to immense anthropic pressures and ongoing changes in climate, the protected reserves are under immense threat. Human interference through land system changes is a major precusor of fragmentation of landscapes resulting in the decline of Himalayan biodiversity. In this context, this research assessed land use land cover changes (LULCCs) and fragmentation within and outside the Dachigam National Park (DNP) using remote sensing data, GIS-based models and ground truth over the past 55 years (1965-2020). Landscape Fragmentation Tool (LFT) helped to compute edge effect, patchiness, perforation and core areas. The Land Change Modeller (LCM) of IDRISI TerrSet was used for simulating the future LULC for the years 2030, 2050, 2700 and 2100. The analysis of LULCCs showed that built-up and aquatic vegetation expanded by 326% and 174%, respectively in the vicinity of the DNP. The area under agriculture, scrub and pasture decreased primarily due to intensified land use activities. Within the DNP, the area under forest cover declined by 7%. A substantial decrease was observed in the core zone both within (39%) and outside (30%) the DNP indicative of fragmentation of natural habitats. LCM analysis projected 10% increase in the built-up extents besides forests, shrublands and pastures. This knowledge generated in this study shall form an important baseline for understanding and characterising the human-wildlife relationship, initiating long-term ecological research (LTER) on naturally vegetated and aquatic ecosystems (primarily Dal Lake) of the region.

An AI framework for Change Analysis and Forecast Modelling of Temporal Series of Satellite Images

The study focuses on change analysis and predicting future LULC map of capital city of Karnataka state, India. The chosen study area is more prone to urbanisation and greatly affected by population in recent years. Spatial-temporal data from 1989-2019 are considered. LULC classes comprise of Water bodies, Urban, Forest, Vegetation and Openland. An optimal LULC maps from 1989 to 2019 obtained by deep neural network technique are used to perform change analysis which would mainly give the change LULC map with number and percentage of change pixels. According to the analysis performed major change as environmental affecting factor was noticed between 2009 and 2019 where in urban with the area of 189.3861 sq. km remain unchanged and noticeable transitions from other LULC classes to urban. Later, time series classification was performed using Cellular Automata, Cellular Automata-Neural Networks, techniques to predict the LULC map of 2024. Among these CA-NN outperformed with an average kappa coefficient of 0.83. Also, this was validated with projected LULC map of 2024 provided by USGS.

Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made available for LULC change modeling and prediction via the QGIS MOLUSCE plugin. Transition potential modeling was computed, and future LULC changes were predicted using the CA-ANN model. An overall accuracy of 86.53% and an overall kappa value of 0.82 were obtained by comparing the actual data of 2022 with the simulated LULC data from the same year. The study findings revealed that between 2022 and 2037, agricultural land (63.09%) and shrubland (5.74%) showed significant increases, and forest (−48.10%) and grassland (−0.31%) decreased. From 2037 to 2052, the built-up area (2.99%) showed a significant increase, and forest and agricultural land (−2.55%) showed a significant decrease. From 2052 to 2067, the projected LULC simulation result showed that agricultural land (3.15%) and built-up area (0.32%) increased, and forest (−1.59%) and shrubland (−0.56%) showed significant decreases. According to the study’s findings, the main drivers of LULC changes are the expansion of built-up areas and agricultural land, which calls for a thorough investigation using additional data and models to give planners and policymakers clear information on LULC changes and their environmental effects.

Impact of Climate and Land Use/Cover Changes on Streamflow in Yadot Watershed, Genale Dawa Basin, Ethiopia

Climate and land use/cover (LULC) changes are essential factors that influence hydrological regimes by altering the groundwater recharge and river flow. This study investigated the separate and combined impact of climate and LULC changes on streamflow. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow under near-term (2021–2050) and mid-term (2051–2080) period against 1985 to 2015 baseline period. The Cellular Automata (CA)-Markov chain model was used to predict the future LULC change. The three-ensemble average of regional climate models (CCLM4.8, RACMO22T, EC-EARTH) under RCP 4.5 and RCP 8.5 emission scenarios were applied for future climate projection. The LULC predictions between 2035 and 2055 showed an increase in agricultural land, grassland, settlement areas and woodlands by 44.02%, 30.35%, 69.2%, and 55.05%, respectively, while forest and scrub/bush lands showed a decrease by decrease by 21.53% and 11.08%, respectively. The annual, wet, and dry seasons rainfall projections increased by 0.13%, 0.02%, and 0.85% respectively, during the near term period under RCP 4.5 scenarios. Overall, the annual, wet, and dry season rainfall projections showed slightly increasing tendency. The temperature projection consistently indicated a warmer future with the highest mean annual projected temperature being 2.0°C under high emission scenario during the midterm period. The projected streamflow under the combined impact of climate and LULC changes will increase by up to 8.72% in wet seasons and by up to 6.62% in dry seasons during the near-term period under RCP4.5 scenarios. Similarly, the projected mean annual streamflow will increase by up to 8.13%. The annual, wet and dry season’s streamflow projections showed a consistent increase during both near and midterm periods under both climate scenarios. Understanding the future response of streamflow under climate and LULC changes is crucial to plan adaptation options for water resources management under future warming condition.