Land Use Cover Change Research Articles

Spatiotemporal Variation and Prediction of Carbon Storage in Terrestrial Ecosystems at Multiple Development Stages in Beijing City Based on the Plus and Integrated Valuation of Ecosystem Services and Tradeoffs Models

Terrestrial ecosystems play a critical role in the global carbon cycle, and their carbon sequestration capacity is vital for mitigating the impacts of climate change. Changes in land use and land cover (LULC) dynamics significantly alter this capacity. This study scrutinizes the LULC evolution within the Beijing metropolitan region from 1992 to 2022, evaluating its implications for ecosystem carbon storage. It also employs the Patch-Generating Land Use Simulation (PLUS) model to simulate LULC patterns under four scenarios for 2035: an Uncontrolled Scenario (UCS), a Natural Evolution Scenario (NES), a Strict Control Scenario (SCS), and a Reforestation and Wetland Expansion Scenario (RWES). The InVEST model is concurrently used to assess and forecast ecosystem carbon storage under each scenario. Key insights from the study are as follows: (1) from 1992 to 2022, Beijing’s LULC exhibited a phased developmental trajectory, marked by an expansion of urban and forested areas at the expense of agricultural land; (2) concurrently, the region’s ecosystem carbon storage displayed a fluctuating trend, peaking initially before declining, with higher storage in the northwest and lower in the central urban zones; (3) by 2035, ecosystem carbon storage is projected to decrease by 1.41 Megatons under the UCS, decrease by 0.097 Megatons under the NES, increase by 1.70 Megatons under the SCS, and increase by 11.97 Megatons under the RWES; and (4) the study underscores the efficacy of policies curtailing construction land expansion in Beijing, advocating for sustained urban growth constraints and intensified afforestation initiatives. This research reveals significant changes in urban land use types and the mechanisms propelling these shifts, offering a scientific basis for comprehending LULC transformations in Beijing and their ramifications for ecosystem carbon storage. It further provides policymakers with substantial insights for the development of strategic environmental and urban planning initiatives.

Landscape transition-induced ecological risk modeling using GIS and remote sensing techniques: a case of Saint Martin Island, Bangladesh.

Uncontrolled human activity and nature are causing the deterioration of Saint Martin Island, Bangladesh's only tropical island, necessitating sustainable land use strategies and ecological practices. Therefore, the present study measures the land use/cover transition from 1974 to 2021, predicts 2032 and 2042, and constructs the spatiotemporal features of the Landscape Ecological Risk Index based on land use changes. The study utilized Maximum Likelihood Classification (MLC) on Landsat images from 1974, 1988, 2001, 2013, and Sentinel 2B in 2021, achieving ≥ 80% accuracy. The MLP-MC approach was also used to predict 2032 and 2042 LULC change patterns. The eco-risk index was developed using landscape disturbance and vulnerability indices, Bayesian Kriging interpolation, and spatial autocorrelations to indicate spatial clustering. The research found that settlements increased from 2.06 to 28.62ha between 1974 and 2021 and would cover 41.22ha in 2042, causing considerable losses in agricultural areas, waterbodies, sand, coral reefs, and vegetation. The area under study showed a more uniform and homogenous environment as Shannon's diversity and evenness scores decreased. The ecological risk of Saint Martin Island increased from 4.31 to 31.05ha between 1974 and 2042 due to natural and human factors like erosion, tidal bores, population growth, coral mining, habitat destruction, and intensive agricultural practices and tourism, primarily in Nazrul Para, Galachipa, and Western Dakhin Para. The findings will benefit St. Martin Island stakeholders and policymakers by providing insights into current and potential landscape changes and land eco-management.

Land Use Land Cover Dynamics around a Western Himalayan Wetland using Geospatial Techniques

Despite having multitude of environmental advantages, wetlands are facing serious anthropogenic threats due to changes in the surrounding area. Therefore, the present investigation has been conducted to evaluate the landuse land cover (LULC) around a 6 km buffer strip of Nowgam wetland located in Kashmir valley. The detection of Land Use and Land Cover (LULC) changes was conducted using Landsat imagery within ArcGIS, spanning a 22-year period from 2000 to 2022. The Landsat imageries of three years (2000, 2010 and 2022) were classified using the supervised classification algorithm (maximum likelihood classification) in ArcGIS. Five LULC classes, viz., water, agriculture, settlement, vegetation and bareland were identified in the study region. The exploration of the classified images revealed the area under water decreased by about 56% while as area under agriculture and settlement increased by 44.7% and 30.9%, respectively. The area under vegetation decreased by 7.3% and area under bareland increased by 8.0%. The outcomes of the present discourse reveal that the changes in LULC around the Nowgam wetland are mostly anthropogenic which may pose serious threat to wetland ecosystem in near future. The present study provides a baseline data regarding ecosystem transformations and acts as an important stimulus for all the stakeholders of wetland in planning and implementation of the strategic conservational measures in Nowgam wetland (Bandipora).

Disproportionate contributions of land cover and changes to ecosystem functions in Kazakhstan and Mongolia

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics—function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)—we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions.

Multi-Scenario Simulation of Land-Use/Land-Cover Changes and Carbon Storage Prediction Coupled with the SD-PLUS-InVEST Model: A Case Study of the Tuojiang River Basin, China

Land-use and land-cover changes (LUCCs) significantly impact carbon sequestration by modifying the structure and function of terrestrial ecosystems. This study utilized GIS and remote sensing techniques to forecast future LUCC patterns and their influence on regional carbon budgets, which is essential for sustainable development. We devised a coupled system dynamics (SD) model integrated with a patch-generating land-use simulation (PLUS) model to simulate LUCCs under diverse future scenarios using multisource environmental data. Additionally, the InVEST model was employed to quantify carbon storage in terrestrial ecosystems. By establishing three scenarios—ecological priority (EP), highly urbanized (HU), and coordinated development (CD)—this study’s aim was to predict the LUCC patterns and carbon storage distribution of the Tuojiang River Basin (TRB), China, up to 2035. The results showed that (1) from 2000 to 2020, significant LUCCs occurred in the TRB, primarily involving the conversion of cultivated land into construction areas and forestland; (2) LUCCs had a substantial impact on carbon storage in the TRB, with the EP scenario demonstrating the highest carbon storage by 2035 due to extensive forest expansion, while the HU scenario indicated a decline in carbon storage associated with rapid urbanization; and (3) the mountainous regions of the TRB, dominated by forestland, consistently exhibited higher carbon storage, whereas the Chengdu Plain region in the upper basin displayed the lowest. In conclusion, we recommend prioritizing the CD scenario in future development strategies to balance economic growth with ecological protection while simultaneously enhancing carbon storage. Our findings offer valuable insights to shape future LUCC policies in the Tuojiang River Basin, underscoring the adaptability of the coupled model approach to a wide range of geographic scales and contexts.

Potential impacts of land use and land cover change (LUCC) and climate change on evapotranspiration and gross primary productivity in the Haihe River Basin, China

Both land use and land cover change (LUCC) and global warming play crucial roles in regulating water and carbon exchanges between the surface and atmosphere. However, the individual contributions of LUCC and global warming, particularly in the mid-latitudes, remain poorly understood. This study disentangled the impacts of LUCC and global warming on evapotranspiration (ET) and gross primary productivity (GPP) during 1980–2015 in the Haihe River Basin (HRB), China, using the Community Land Model version 5. The model experiments suggest that decreases in natural vegetation and expansions of croplands in the HRB reduced annual GPP and ET. Conversely, increasing natural vegetation coverage enhanced annual GPP and ET. Neither LUCC nor global warming exhibited a statistically significant (p < 0.05) impact on the spatially averaged annual ET during 1980–2015. However, both LUCC and global warming significantly (p < 0.05) increased spatially averaged annual GPP. LUCC enhanced the spatially averaged GPP, possibly due to reforestation and afforestation since the late 1990s. Global warming increased GPP mainly due to the CO2 fertilization effect. When averaging across the whole HRB, LUCC increased ET with a magnitude of approximately 3.5 mm yr−1 between 1980 and 2015, while global warming reduced ET by about 3.0 mm yr−1. On average, LUCC enhanced the spatially averaged annual GPP with a comparable magnitude to global warming. The findings demonstrated that natural vegetation restoration emerges as an important strategy for climate warming mitigation in mid-latitudes experiencing intense anthropogenic disturbances.

The Impact of land use change on peatland degradation: A case Nathong and Saming Village, Champhone district, Champasack Province, Lao PDR

The major impact on land use change and peatland degradation was directly from human activities and inappropriate management. Changing of land use and land cover (LULC) influence directly on peat land drained and degraded. Nathong and Saming Village, Champhone district, Champasack Province, Lao PDR is one of proposed peatland area and rapid LULC changes from 2016 due to a social-economic growth. Therefore, the assessment of land use change is required for sustainable peatland management. The main objectives of this study were to assess LULC and its change between 2011 to 2016 and 2016 to 2021. In this study, remote sensing data has been used for LULC classification using random forest classifier in Google Earth Engine (GEE). Then, the post-comparison change detection algorithm was applied for detected LULC changes. The result revealed that mixed deciduous area was continually decrees 523.44 ha and 607.05 ha from 2011 to 2016 and 2016 to 2021. Additionally, cassava and dry dipterocarp were increase from 138.24 ha in 2011 to 2016 to 386.1 ha in 2016 to 2021 and 93.15 ha in 2011 to 2016 to 156.42 ha in 2016 to 2021 respectively. Additionally, the derived overall accuracy and Kappa hat coefficients of the land use and land cover map in 2011,2016 and 2021 were higher than 85%. The average producer’s accuracy and user’s accuracy for all land use and land cover types were also more than 83%. The derived information of land use and land cover data of this study can be used as information to support decision-makers and land use planners for to awareness and sustainable peatland management.

Land Use and Land Cover Change Detection Using Remote Sensing in the Kal River Basin, Raigad District, Maharashtra, India

Land use and land cover (LULC) are distinct yet interrelated concepts that describe the characteristics and utilization of land. Land cover refers to the physical surface, such as vegetation, water bodies, or man-made features, while land use denotes the purpose of land utilization. Changes in LULC significantly impact water resources, making it a critical component in water resource studies. This study aims to detect changes in LULC over a five-year period (2017–2021) in the Kal River basin, a sub-basin of the Savitri River in Maharashtra, India. Sentinel-2 satellite imagery with a 10 m resolution was used, and the supervised classification method, specifically Maximum Likelihood Classification (MLC), was applied to classify LULC into six categories: cropland, bare ground, built-up areas, trees, rangeland, and water bodies. The results show that in 2017, the areas under cropland, tree cover, bare ground, built-up areas, and water bodies were 35.20 km², 187.61 km², 0.133 km², 7.77 km², and 1.89 km² respectively. By 2021, cropland, tree cover and bare ground decreased by 3.82%, 4.85%, and 0.01% respectively while water bodies, rangeland, and built-up areas increased by 0.02%, 8.34%, and 0.32% respectively. The overall accuracy of LULC classification was 77% for 2017 and 91% for 2021 with validation using the Kappa coefficient indicating good to excellent accuracy. This study highlights the importance of monitoring LULC changes for understanding the impacts of human activities and climate on watershed development and water resource management. Such analysis provides valuable insights for decision-makers to plan for sustainable development, land conservation and environmental management, ensuring balanced growth while protecting natural resources in the Kal River basin.

Research Progress in Spatiotemporal Dynamic Simulation of LUCC

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels.

Impact of Land Use/Cover Change on Soil Erosion and Future Simulations in Hainan Island, China

Soil erosion (SE) is a critical threat to the sustainable development of ecosystem stability, agricultural productivity, and human society in the context of global environmental and climate change. Particularly in tropical island regions, due to the expansion of human activities and land use/cover changes (LUCCs), the risk of SE has been exacerbated. Combining the RUSLE with machine learning methods, SE spatial patterns, their driving forces and the mechanisms of how LUCCs affect SE, were illustrated. Additionally, the potential impacts of future LUCCs on SE were simulated by using the PLUS model. The main results are as follows: (1) Due to LUCCs, the average soil erosion modulus (SEM) decreased significantly from 108.09 t/(km2·a) in 2000 to 106.75 t/(km2·a) in 2020, a reduction of 1.34 t/(km2·a), mainly due to the transformation of cropland to forest and urban land. (2) The dominant factor affecting the spatial pattern of SE is the LS factor (with relative contributions of 43.9% and 45.17%), followed by land use/cover (LUC) (the relative contribution is 28.46% and 34.89%) in 2000 and 2020, respectively. (3) Three kinds of future scenarios simulation results indicate that the average SEM will decrease by 2.40 t/(km2·a) under the natural development scenario and by 1.86 t/(km2·a) under the ecological protection scenario by 2060. However, under the cropland protection scenario, there is a slight increase in SEM, with an increase of 0.08 t/(km2·a). Sloping cropland erosion control remains a primary issue for Hainan Island in the future.

Evaluating territorial management of integrated development region (RIDE-DF) and its effect on land use land cover transformation

ABSTRACT In Brazil, the decentralized federative structure of the State, consisting of four autonomous political levels – Union, states, Federal District, and municipalities – alongside its vast territory, necessitates innovative strategies for territorial management. The Integrated Development Region (RIDE) was regulated in 1985, establishing a standardized region composed of municipalities spanning multiple states, prioritizing coordinating federal actions with regional policies. This study aims to assess the impact of Federal District RIDE on land use and land cover changes (LULC) within its territory. As a data source, we utilized MapBiomas collection data for the region. This collection employs multi-temporal (1985–2022) Landsat satellite imagery classifications, ensuring accurate and current information. Additionally, socioeconomic indicators were incorporated to comprehend changes during and post-RIDE-DF implementation. Insights from this study may support decision-making processes concerning land management, urban planning, and conservation endeavors within the region.

Climate change driven by LUCC reduced NPP in the Yellow River Basin, China

Anthropogenic activities and the resulting climate change affect the type, structure, and function of ecosystems. Understanding vegetation dynamics related to anthropogenic activities and climate change is critical to address the terrestrial carbon cycle in the context of global warming. The objective of this study is to quantify the effects of human-induced land use and land cover change (LUCC) and LUCC-induced climate change on terrestrial net primary productivity (NPP) in the Yellow River Basin (YRB) during 2000–2020 using Weather Research and Forecasting (WRF) model and Integrated Biosphere Simulator (IBIS) model through different experimental scenarios. Results indicated that LUCC can cause an increase in NPP of 1.2 ± 0.67 gC m−2 yr−1 in YRB. The increased precipitation and decreased temperature due to LUCC showed weak negative effect on annual mean NPP in YRB (−0.2 ± 0.74 gC m−2 yr−1). The coupling of LUCC and LUCC-induced climate change increased annual mean NPP approximately 0.6 ± 0.86 gC m−2 yr−1. The impacts of LUCC and LUCC-induced climate change and their coupling effects on NPP were greatest in spring, increasing NPP by 5.1 ± 0.51, 3.4 ± 0.41, and 6.1 ± 0.79 gC m−2 yr−1, respectively. These findings provide important guidance for the sustainable and adaptive management of terrestrial ecosystems in river basin in the context of global change.

Evolution Process and Land Use/Land Cover Response of Urban–Rural Space in Wuhan under Polycentric Structure

Polycentric development facilitates urban–rural spatial reshaping and land use/land cover (LULC) protection. Previous studies have predominantly focused on urban areas, with spatial delineation methods biased towards the macro-level, lacking a holistic perspective that situates them within the urban–rural spatial framework. This study proposes a spatial delineation framework that is applicable to the polycentric structure, taking into account the social, economic, and natural characteristics of urbanization. It employs semivariance analysis and spatial continuous wavelet transform (SCWT) to analyze the effects of polycentric development on the urban–rural space of Wuhan from 2012 to 2021 and applies a land use transition matrix, landscape indices, and bivariate spatial autocorrelation to quantify the responses and differences of LULC within urban–rural space. The results indicate that 600m×600m is the best scale for exhibiting the multidimensional characterization of urbanization. The polycentric structure alleviates the compact development of the central city, and it drives rapid expansion at the urban–rural fringe, exacerbating the spatial heterogeneity in LULC change pattern, spatial configuration, and urbanization response within urban–rural spaces. The overall effects of urbanization on LULC are relatively weak along the urban–rural gradient, experiencing a transition from positive to negative and back to positive. This study employs a novel spatial delineation framework to depict the polycentric transformation of metropolitan areas and provides valuable insights for regional planning and ecological conservation in the urban–rural fringe.

Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India

The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables.

Assessing sediment dynamics and retention services in the vulnerable mountain ecosystem of the Indian Himalayas.

Sediment loss and export pose significant global environmental issues, profoundly affecting water quality, soil fertility, and ecosystem stability, particularly in vulnerable mountain ecosystems like the Indian Himalayas. The present study used remote sensing data and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) sediment delivery ratio (SDR) model to analyze spatial-temporal variations in soil loss (SL), sediment export (SE), and sediment retention (SR) capabilities in the South Shimla watershed, Himachal Pradesh, India, from 1993 to 2023. The findings showed significant changes in land use and land cover (LULC): evergreen forest and scrub land decreased sharply by 11.53% and 36.43%, respectively, while agricultural areas and built-up areas increased notably by 71.16% and 215.76%, respectively. Despite a decline of 19.18% in SL and 24.43% in SE, sediment loss and export varied across the study area, highlighting the heterogeneous nature of sediment dynamics. The overall retention capacity increased by 2.59%, with scrub forests playing a critical role in SR, while built-up areas showed the lowest retention. Northern and central sub-watersheds (SWs) experienced a significant decrease in retention capacity (from - 1.92 to - 11.6%), whereas those in the southern and eastern regions saw an increase in SR (from 3.69 to 28.24%). These results underscore the complex interactions between LULC changes, sediment dynamics, and retention services, highlighting the importance of preserving natural ecosystems and informing policy for landscape-based conservation and development planning in the vulnerable Himalayan region.

Panzara River Watershed Prioritization Based on Geomorphometric and LULC Change Analysis using Geo-Spatial Techniques

Human activities can significantly influence the quality of water flowing from a watershed, either positively or negatively. As water moves through the system, these impacts accumulate, with all land-based activities having the potential to affect the water quality and quantity experienced by downstream stakeholders. Similarly, the actions of upstream landowners impact the water that flows across others' properties. Geospatial techniques like remote sensing and geographic information systems (GIS) are invaluable tools for analysing drainage patterns within a watershed and the associated changes in land use and cover. This study focuses on the Panzara river basin, a principal tributary of the larger Tapi river basin, situated in central India between the westward-flowing Godavari and Narmada river systems, which both ultimately discharge into the Arabian Sea. The study area spans latitudes from 20°42'0" N to 21°18'0" N and longitudes from 74°06'0" E to 75°00'0" E, covering a geographical area of 2,986.05 square kilometers with a perimeter of 570.51 kilometers. The watershed delineation was carried out using Shuttle Radar Terrain Mapper (SRTM) data with a 30-meter resolution. For land use and land cover (LULC) analysis, Landsat 5 TM C2L1 and Landsat 8 OLI/TIRS C2L1 datasets, both with 30-meter resolution, were utilized. The present study conducts a morphometric analysis and assesses LULC changes within the Panzara river basin between 2000 and 2021. Morphometric parameters such as linear parameters [Drainage density (Dd), Stream frequency (Fs), Mean bifurcation ratio (Rbm), Drainage texture ratio (Dt), Length of overland flow (Lo)] and areal parameters [Elongation ratio (Re), Circulatory ratio (Cr), Form factor (Rf), Compactness coefficient (Cc)] were used to prioritize sub-watersheds. Furthermore, the study classifies the observed LULC changes between satellite imagery datasets from 2000 and 2021, quantifying the percentage changes in the respective LULC classes across the sub-watersheds over the two decades. The overall accuracy of the LULC classification was 81.82% for 2000 and 88.88% for 2021, with Kappa coefficients of 0.772 and 0.85, respectively. In terms of prioritizing sub-watersheds, common sub- watersheds such as SW-1, SW-10, and SW-15 were classified under moderate priority, while SW-5, SW-8, and SW-14 were classified under the lowest priority. The results of this study, particularly the prioritization of sub-watersheds, can be instrumental for hydraulic engineers in planning and managing water resources in the Panzara river basin.

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.

Shoreline and land use–land cover changes along the 2004-tsunami-affected South Andaman coast: understanding changing hazard susceptibility

Abstract. The 2004 tsunami affected the South Andaman coast, causing it to experience dynamic changes in the coastal geomorphology and making the region vulnerable. We focus on pre-and post-tsunami shoreline and land use–land cover changes from 2004, 2005, and 2022 to analyze the dynamic change in hazard. We used General Bathymetric Chart of the Ocean (GEBCO) data to calculate run-up [m], arrival times [min], and inundation [m] at a few locations using three tsunamigenic earthquake source parameters, namely the 2004 Sumatra, 1941 North Andaman, and 1881 Car Nicobar earthquakes. The Digital Shoreline Analysis System is used for the shoreline change estimates. Landsat data are used to calculate shoreline and land use–land cover (LULC) change in five classes, namely built-up areas, forests, inundation areas, croplands, and water bodies during the above period. We examine the correlation between the LULC changes and the dynamic change in shoreline due to population flux, infrastructural growth, and gross state domestic product growth. The Indian industry estimates the Andaman and Nicobar Islands losses exceeded INR 10 billion during 2004, which would today see a 5-fold increase in economic loss due to a doubling of built-up area, a 3-fold increase in tourist inflow, and population density growth. The unsustainable decline in the forest cover, mangroves, and cropland would affect sustainability during a disaster despite coastal safety measures.

Evolution and Analysis of Water Yield under the Change of Land Use and Climate Change Based on the PLUS-InVEST Model: A Case Study of the Yellow River Basin in Henan Province

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 108 m3; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 108 m3 higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.