Land Use Cover Change Research Articles

Assessing and predicting the dynamics of land use/land cover in northern Bangladesh using cellular Automata-Markov chain model

ABSTRACT Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

AbstractThis study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk’s hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk’s hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings.

Integrating Remote Sensing and GIS-Based Map Analysis in Determining Spread of Built-Ups and Land-Use Dynamics of Terrain of Onitsha Metropolis, Anambra State, Nigeria

Land scarcity in most cases hampers development and encourages the misuse of land. The suitability of land must be considered before appropriating or allocating land for any use. Land supports the livelihood of every being on the Earth and therefore determines survival, success, and sustainability (sustainable living). This study aimed at integrating remote sensing and GIS-based analysis to determine the rate at which built-up areas have spread across the terrain of Onitsha Metropolis, Anambra State, Nigeria, and the dynamics of other land uses. This research involved both primary and secondary data. The primary data included measurements, direct field observations, and key informant interviews to understand people’s perceptions of the land use in the area. The secondary data included satellite images of the area obtained from USGS and analyzed using ArcGIS 10.2 for variations in the terrain of the Onitsha Metropolis; to determine the land use and land cover change (LULCC) of the Onitsha Metropolis over 40 years, published and unpublished articles and books were also consulted. The geological analysis of the study showed that the area of the Ogwashi/Asaba formation is 318.57 km2; the areas of the Nanka sands and Bende-Ameke are 423.07 km2 and 259.42 km2, respectively. The Nanka sands and Bende-Ameke formations are best suited for engineering construction purposes, while the Ogwashi/Asaba formation is suitable for agriculture and should be designated as a buffer zone or park. However, due to the unavailability of land as a result of the growing population and the proximity of the area to the city center, the area is being encroached upon, and a large area (about 30.40%) has been converted to built-up areas as of 2022. Forecast analysis showed that if the trend continues, 158.28 km2 (49.68%) of the alluvium soils of the Ogwashi/Asaba formation will be covered with buildings by 2072. The geology and the terrain of the Onitsha Metropolis determine the soil characteristics and the land use suitability; mapping the geological formations and overlaying these with the land use and land cover change of the area revealed the extent of the encroachment on the Ogwashi/Asaba formation, which must be discouraged.

Research on the coupled evolution of LULCC and habitat quality in the Ganqing section of the Yellow River Basin based on multi-scenario simulations

IntroductionThis study focuses on the Ganqing section of the Yellow River Basin, exploring four land use scenarios: natural development, cropland protection, ecological protection, and rapid development. Given the ecological importance of this area, the research aims to evaluate how each scenario impacts habitat quality and land use sustainability by 2030.MethodsThe Future Land Use Simulation (FLUS) model and the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model were applied to simulate land use for each scenario. A habitat quality pattern and coupling coordination degree model was used to assess the interactions between land use and land cover change (LULCC) and habitat quality under different scenarios.ResultsFindings show that over 70% of the Ganqing section of the Yellow River Basin is primarily grassland. By 2030, the ecological protection scenario is predicted to have the highest habitat quality, followed by the natural development, rapid development, and cropland protection scenarios. Between 1990 and 2030, the area demonstrates predominantly high or moderate coordination between land use and habitat quality. Spatial analysis reveals lower coordination values in the southeast and higher values in the northwest, with imbalanced recession zones distributed around valley basins.DiscussionThis study highlights the value of strategic scenario planning in enhancing habitat quality and promoting sustainable land management in the Ganqing section of the Yellow River Basin. The ecological protection scenario shows the most promise for balancing development with habitat preservation, underscoring the importance of adopting land use policies that support ecological sustainability in vulnerable areas.

Impact of Land Use and Land Cover Changes on Soil Physico-Chemical Properties in Southern Tigray, Northern Ethiopia

Soil quality declines due to the conversion of natural vegetation into farmland and grazing areas. The response of soil properties to land use and land cover changes (LULC) exhibits both spatial and temporal variations. This study aimed to assess the effects of LULC changes on the physico-chemical properties of soil in the Wojic watershed. Soil samples were collected from natural forests, bushland, shrubland, and cultivated lands across three landforms of the watershed (upper, middle, and lower) to evaluate the physical and chemical properties of the soil associated with different LULC types. The LULC categories were compared using mean values and critical thresholds for selected physicochemical soil properties. Soil analysis was conducted using R software, employing one-way analysis of variance (ANOVA). A normality test was performed before the post hoc analysis, and Tukey’s honest significance difference (HSD) test was utilized for mean separation among the LULC types. Additionally, a normalized difference vegetation index (NDVI) was calculated for the years 1997 and 2017. A simple linear regression model was developed to estimate soil physico-chemical properties over the past 20 years, using laboratory soil parameters and the NDVI for 2017. The pH values showed a slight decrease in cultivated land (from 5.7 to 5.4), bushland (from 7 to 6.6), and shrubland (from 6.5 to 6.2) over the study period. The analysis indicated a declining trend in physico-chemical properties, attributed to changes in vegetation cover and management practices. Consequently, the government needs to enforce policies and regulations that promote effective land resource management and utilization, with particular emphasis on the proper management and conservation of forests, bushlands, and shrublands, as well as measures to prevent increased land resettlement.

Analysis of Land Use and Land Cover Dynamics in the Highlands of Northern Ethiopia

In the highlands of northern Ethiopia, many land use and land cover changes (LULCC) have occurred during different activities of the populations. Despite its coverage and unwise use of natural resources, these land uses and land covers are facing increasing or decreasing their area coverage. Information derived from land use and land cover change detection is important to land conservation, sustainable development, and management of Natural resources. This purpose of this study is therefore concerned with identifying the change in land use and land cover detection of the Wejic watershed. To identify land cover changes detection; remote sensing data, satellite imagery and image processing techniques had done within two dates of 1998 and 2017 using Land sat TM 30 m resolution images using Arc GIS 10.1. Hence, a field survey was carried out in 2017 to identify the major land cover types in the watershed. Detection Analysis of land use and land cover dynamics were calculated from the selected watershed in Southern Tigray, Northern Ethiopia. These land use types were cultivated, forest, bush, shrub, and homestead lands. Furthermore, a normalized difference vegetation index (NDVI) was developed for both years. The land use and land cover change results indicated that forest, cultivated land, and bush land decreased by 4.4%, 2.2%, and 2.4%, respectively. However, shrub land and homestead increased by 5.5% and 3.6%, respectively. Therefore The change detection analysis using GIS and remote sensing could deliver useful information to understand the seasonal patterns of land use dynamics for planners and decision makers; consequently, sustainable land management planning is possible.

Geo-spatial analyses of meandering rivers, assessing past and future impacts on bank landforms and LULC changes

ABSTRACT Morphological investigation of rivers is difficult but is one of the vital topics for the management of highly meandering river systems. The study employs GIS to analyse 32 years of Barak River data, addressing challenges in quantifying the morphology of meandering rivers. Earth observatory data from 1990 to 2022 track channel bank shifts and Land Use/Land Cover (LULC) alterations. Although the digital shoreline analysis system (DSAS) model identified long-term bank line migration analysis of shorelines, it's use is very new for rivers, especially in terms of the meandering of rivers. Conversely, research on long-term meandering river morphology in the Barak River channel has not been conducted despite evident land-use changes. Using DSAS for bank positions and CA–Markov models for LULC , future projections until 2043 reveal ongoing Barak River bank shifts and significant LULC pattern alterations. The findings of the research were validated through RMSE, Student's t-test, chi-square, and the kappa coefficient. Forecasts indicate that rapid urbanization impacts river morphology. The methodology is crucial for managing highly meandering rivers, particularly in contexts involving vulnerable river systems.

The Implications of Plantation Forest-Driven Land Use/Land Cover Changes for Ecosystem Service Values in the Northwestern Highlands of Ethiopia

In the northwestern Highlands of Ethiopia, a region characterized by diverse ecosystems, significant land use and land cover (LULC) changes have occurred due to a combination of environmental fragility and human pressures. The implications of these changes for ecosystem service values remain underexplored. This study quantifies the impact of LULC changes, with an emphasis on the expansion of plantation forests, on ecosystem service values in monetary terms to promote sustainable land management practices. Using Landsat images and the Random Forest algorithm in R, LULC patterns from 1985 to 2020 were analyzed, with the ecosystem service values estimated using locally adapted coefficients. The Random Forest classification demonstrated a high accuracy, with values of 0.97, 0.98, 0.96, and 0.97 for the LULC maps of 1985, 2000, 2015, and 2020, respectively. Croplands consistently dominated the landscape, accounting for 53.66% of the area in 1985, peaking at 67.35% in 2000, and then declining to 52.86% by 2020. Grasslands, initially the second-largest category, significantly decreased, while wetlands diminished from 14.38% in 1985 to 1.87% by 2020. Conversely, plantation forests, particularly Acacia decurrens, expanded from 0.4% of the area in 2000 to 28.13% by 2020, becoming the second-largest land cover type. The total ecosystem service value in the district declined from USD 219.52 million in 1985 to USD 39.23 million in 2020, primarily due to wetland degradation. However, plantation forests contributed USD 17.37 million in 2020, highlighting their significant role in restoring ecosystem services, particularly in erosion control, soil formation, nutrient recycling, climate regulation, and habitat provision. This study underscores the need for sustainable land management practices, including wetland restoration and sustainable plantation forestry, to enhance ecosystem services and ensure long-term ecological and economic sustainability.

Lacustrine Wetlands Landscape Simulation and Multi-Scenario Prediction Based on the Patch-Generating Land-Use Simulation Model: A Case Study on Shengjin Lake Reserve, China

Landscape simulation and prediction are crucial for understanding the dynamic evolution and future trends of wetlands. However, only a few existing studies have focused on the applicability and limitations of commonly used land-use/cover change (LUCC) simulation models in lake wetland landscapes. Taking Shengjin Lake Reserve in China as the study area, we firstly analyzed landscape variations during 2010–2020 using multisource remote sensing images. Then, the patch-generating land-use simulation (PLUS) model was employed to simulate wetland landscapes in 2020, the accuracy and limitation of which in simulating lacustrine wetlands were also explored. Lastly, the changing trends of wetland landscapes in 2030 under different development scenarios were predicted. The results show that the landscape of Shengjin Lake Reserve has changed significantly during 2010–2020, with increases in mudflats, reservoirs/ponds, woodlands, and built-up land, and there has been decreases in lakes, grass beaches, and croplands. The PLUS model demonstrated an ideal simulation accuracy for Shengjin Lake Reserve, with the overall accuracy exceeding 80%, kappa coefficient greater than 0.75, and figure of merit (FOM) coefficient of 0.35, indicating that the model can capture the dynamic changes in wetland landscapes accurately. The simulation accuracy can be effectively improved with the adjacent initial year, shorter time interval, and the primary driver factors. Under the natural development scenario, the number of patches in the Shengjin Lake Reserve increased sharply, and landscape fragmentation intensified. Under the urban development scenario, the expansion of built-up land increased, and the average patch area increased. In the ecological protection scenario, the Shannon diversity index and Shannon evenness index of the landscape improved significantly, and the natural wetlands such as grass beaches and lakes can be protected effectively. Our study confirms the applicability of the PLUS model in simulating and predicting lacustrine wetlands landscapes, and the conclusions provide a scientific basis for formulating reasonable development strategies to realize wetland resource conservation and management.

Spatiotemporal dynamics of LULC change and implications for sustainable urban growth in Saudi Arabia

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) change in Saudi cities between 2013 and 2023. Using Landsat satellite imagery and a Maximum Likelihood Classifier, LULC changes are analyzed in nine cities to understand the impact of Saudi Arabia’s rapid urbanization following the oil crisis. The objectives are: (1) to evaluate the LULC changes in nine cities between 2013 and 2023; (2) to explore the spatiotemporal patterns of LULC changes in this period; and (3) to interpret the urban expansion that occurs in Saudi Arabia in the light of the kingdom sustainable development plan. The results identifiy four primary LULC change models: Urbanization, Vegetation Recovery, Land Degradation, and Ecological Transition Models. The Urbanization and Land Degradation Models determine Saudi Arabia’s urban growth trend, posing significant challenges to sustainable development. Most cities experienced substantial increases in impermeable surfaces and developed lands, while concurrently losing high terrain and barren areas. However, the small cities showed lower urbanization and land degradation rates than major cities, showing an opportunity to sustainably grow by adopting sustainable planning approaches. The study’s findings show that more targeted and comprehensive policies are needed to address the challenges of urbanization and land degradation, mitigate environmental impacts, and ensure long-term resilience. It underscores the importance of considering Ecological Urbanism in urban development plans.

Modeling future projections of land use and land cover in the Kakataibo territory

Abstract. The Amazon region is undergoing a dynamic and extensive transformation from forested areas to human-altered landscapes, significantly impacting forests and indigenous territories. This research aims to create future land use and land cover (LULC) scenarios by examining past trends (2014–2022) and predicting future patterns for 2030, focusing on the Kakataibo territory in central Peru. Using the Dinamica EGO modelling platform, we projected LULC changes based on historical data and various predictive factors. Our findings reveal notable forest loss between 2014 and 2022, with this trend likely continuing through 2030. Within indigenous territories, deforestation was less severe, with a reduction of 30,247.4 hectares compared to the more significant loss in non-indigenous areas. This underscores the vital role these territories play in mitigating forest loss. However, the rapid expansion of agriculture and infrastructure within these regions signals an urgent need for stronger legal protections to halt further deforestation. This study highlights the critical importance of indigenous territories in forest conservation and offers valuable insights for future land management and conservation strategies in the Amazon.

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

Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275×108t, 81.210×108t, and 84.13×108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.

Social-ecological system trajectories of peri-urban watersheds based on a spatial analysis of vulnerability components: A case study in Mexico City, 1999-2039.

Urban periphery watersheds play a crucial role in providing diverse ecosystem services, especially hydrological services (HES), for society at different temporal and spatial scales; moreover, local populations directly influence ecosystem functionality through their decisions and actions. The interactions between social and ecological factors create social-ecological systems (SESs), whose trajectories continuously change in response to internal factors such as land use cover change (LUCC) and external factors such as climate change (CC). This situation influences the vulnerability of SESs in terms of exposure, sensitivity and adaptation capacity. In this study, the social-ecological vulnerability (SEV) of the periphery of Mexico City was investigated based on the Collaborative Protocol for Ecosystem Services Assessment and Social-ecological Vulnerability Mapping (ECOSER) and a quantitative method approach. For this purpose, spatial analysis was performed using the ecological and social spatial data for LUCC tendencies calculated for 1999-2019 and projected for short-term CC scenarios and using LUCC calculated for 2039 in trend-based (TREND) and restrictive (REST) scenarios. The results reveal that increases in the SEV in 2039 will be related to important decreases in the HES; furthermore, the REST scenario suggests decreases in the SEV due to decreases in the HES, assuming that environmental public policy instruments will be preserved in this region. The present work aims to contribute to decision-making for HES preservation at local and regional scales and to help develop adaptation strategies under LUCC and CC scenarios.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Understanding the relationship between land use/land cover changes and air quality: A GIS-based fuzzy inference system approach.

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m3) and 2023 (PM10: 383.70 ± 61.49 µg/m3), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.

Impacts of Participatory Forest Management on Land Use/Land Cover of Adaba-Dodola Forest in South Eastern Ethiopia and its Implication to REDD+ Implementation.

Despite various interventions to protect forests, many developing countries, including Ethiopia, continue to face substantial forest conservation challenges, particularly where local communities heavily rely on forests for their livelihoods. Recognizing the urgency of this issue, the government of Ethiopia introduced Participatory Forest Management (PFM) and devolved forest management responsibilities to enhance forest conservation. Therefore, this assessment examines the impacts of PFM on forest cover based on an analysis of the Land Use/Land Cover Change (LULCC) over the last 23 years in Adaba-Dodola, and its implications for REDD+ implementation. The study involved determining the LULCC of the Adaba-Dodola forest after the introduction of PFM from 2000 to 2023. Landsat images of 2000, 2012, and 2023 were analyzed to detect LULCC. The study result showed that the Adaba-Dodola forest cover increased by 1.83% since the PFM was introduced. The decreased agricultural land by 0.87% was the main factor attributed to the increase in shrub cover, while shrubland attributed to the rise in forest cover. Net areas of about 148 ha/year of shrublands were converted into forest land owing to significant forest regeneration, while shrublands had a net gain of 110.5 ha/year from agriculture and grasslands between 2000 and 2023. The increase in forest cover is attributed to the effectiveness of PFM in halting deforestation and promoting forest conservation. Thus, the PFM approach is a tool for preserving forest ecosystems and mitigating the adverse effects of deforestation and forest degradation, therefore would be used as an umbrella for implementing REDD+.

Evolving road networks and urban landscape transformation in the Himalayan foothills, India.

The amalgamation of Himalayan and Indo-Burmese biodiversity has made the state of Manipur, India, a unique ecosystem. In addition, the region is a strategic place for the country to establish an economic corridor to Southeast Asia. In recent times, the region has witnessed tremendous infrastructural/road development. Subsequently, forest fragmentation related to urbanization and road expansion has emerged in the Himalayan foothills. The development of roads brought rapid changes in land use and land cover (LULC) and thus subsequent environmental degradation. The current study attempts to understand how the development of road networks has impacted the natural cover in Manipur, India. A spatio-temporal analysis was performed to assess the relationship between the development of road networks and LULC changes using the Landsat satellite images over a decade (2012-2022). The results showed significant changes in the area coverage of LULC categories such as agricultural land, built-up areas, forest, and water bodies with the increase in road density. To have a holistic view, the study area was segregated into three functional zones based on their urban land use pattern, i.e., urban center, peri-urban, and urban peripheral fringes. Urban sprawl in the urban center has led to the rapid conversion of forested lands into built-up areas and agricultural lands in the peri-urban and urban peripheral fringes, respectively. The decline of forest areas to urbanization in peri-urban and urban peripheral fringes calls for conservation and restoration initiatives. The study also emphasizes how different stakeholders can be involved and empowered to strengthen public-private partnerships for conservation and restoration in such sensitive ecosystems. Urban planners and developers should be critical in making informed decisions through understanding ecological concerns in tandem with infrastructural development.

Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico

Abstract. This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development.

Significant negative impact of human activities on carbon storage in the Yellow River Delta over the past three decades

With the increasing intensification of human activities, significant changes in land use and land cover (LULC) have posed a severe threat to the carbon storage capacity of wetland ecosystems. A deep understanding of this impact is crucial for protecting regional ecosystems and promoting sustainable development. This study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the human activity intensity (HAI) index to conduct detailed grid analysis and global analysis of carbon storage through creating fishnet system and explored the complex relationship between carbon storage and HAI in the Yellow River Delta (YRD), China. The results indicated that over the past 30 years, natural wetlands such as meadow wetlands and salt marshes in the study area had undergone significant degradation due to escalating human activities, while artificial wetlands and non-wetland areas expanded. Concurrently, the total regional carbon storage had declined by 2.08 Tg, representing a significant drop of 8.22 % in the YRD from 1990 to 2020. Among them, dry land, as the primary land type, served as the most crucial carbon pool. Additionally, the human activity intensity of land surface (HAILS) increased significantly, with a growth rate of 37.27 %. HAI mapping revealed a continuous expansion of areas with high HAI. In contrast, the Yellow River Delta National Nature Reserve (YRDNNR) maintained relatively low HAI. Correlation analysis further showed the significant negative correlation (p < 0.01) between carbon storage and HAI, with r values of grid analysis ranging from −0.1395 to −0.0344, while that for global analysis was −0.9643, respectively. This reflected the spatial heterogeneity and agglomeration effects of data analysis across different scales. This study provides valuable insights for achieving the “dual carbon” goals and supporting the conservation and management of wetland ecosystems.