Land Use Cover Change Research Articles

Examining the relationship between land-use cover change and sociodemographic characteristics: A case study of Madison County, Tennessee

Examining the relationship between land-use cover change and sociodemographic characteristics: A case study of Madison County, Tennessee

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Machine learning for urban land use/ cover mapping: Comparison of artificial neural network, random forest and support vector machine, a case study of Dilla town

The ability to accurately classify land use/cover (LULC) is critical for environmental monitoring and land use planning. This study compares three machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) for LULC classification using Google Earth images from the years 2006, 2014, and 2022. The objective of this study is to evaluate and identify the best classifier for LULC classification and change detection. Four LULC categories (Built-up, Open area, Farmland, and Agroforestry) were identified. The evaluation criteria included overall accuracy, kappa coefficient, producer's accuracy, user's accuracy, computing time, algorithm stability, and visual quality. The results showed that the RF algorithm outperformed both SVM and ANN algorithms with an average overall accuracy of 0.97, kappa coefficient of 0.98, producer's accuracy of 0.99, and user's accuracy of 0.97, surpassing the accuracies achieved by SVM (0.96, 0.97, 0.98, and 0.97) and ANN (0.89, 0.81, 0.94, and 0.88), with corresponding computing times of 6.33, 15, and 30 s. All classifiers performed stably with different training sizes. Visual quality assessment revealed that RF had the highest precision. Consequently, the built-up change detection result shows, the net change in built-up area between 2006 and 2022 was increased by 0.74 Km2for ANN, 1.74 Km2 for SVM, and 1.66 Km2for RF. The comparison reveals that the RF algorithm showcasing high precision in detecting change, consistent with the data (increased by 1.65 Km2) obtained from Dilla town land administration office. To validate the results, the study considered field surveys, reference images, local experts, and previous studies. Based on the findings, the study concludes that using RF classifier with an object-based approach is an effective way to map LULC and detect changes in the study area over time. Future researchers are recommended to utilize this effective algorithm for addressing LULC related problems in the study area.

Evaluating water balance components in a tropical ecological zone under land use changes

Water availability for domestic, institutional, and agricultural production is threatened by the continuous change in land use land cover (LULC). These changes in LULC features affect the dynamics of the hydrological processes such as surface runoff, infiltration, and evapotranspiration, resulting in variations in water availability especially, for human consumption and crop yield. Therefore, the Soil and Water Assessment Tool (SWAT) hydrological model was deployed to simulate the Pra River Basin (PRB)’s water balance response to changing LULC from 1986 to 2020. Open and closed forests reduced from an area of 11,071 km2 to 5902 km2 and 5125 km2 to 4990 km2 while settlement and cropland increased from 1100 km2 to 2392 km2 and 5303 km2 to 9320 km2 representing an average annual expansion rate of +24.7 km2/yr and +118 km2/yr respectively over the three decades. The growth in settlement and cropland resulted in an upsurge of more than 33 % and 20 % in surface runoff and water yield respectively, but decreases of over 4 % and 17 % in evapotranspiration and baseflow respectively. These changes have resulted in a seasonal increase in surface runoff, baseflow, and evapotranspiration (ET) during the wet season but a decrease during the dry season. The PRB has experienced a dramatic decline in forest areas, chiefly in areas where settlement and farmlands have been expanded. The areas where surface runoff and water yield showed significant upsurge and reduction in ET are the northwestern and southern enclaves of the basin. These findings can aid in effectively planning and managing land use to control the gradually depleting water resources in the basin.

Spatial Optimization of Land Use and Carbon Storage Prediction in Urban Agglomerations under Climate Change: Different Scenarios and Multiscale Perspectives of CMIP6

Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals1. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.

An application of the remote sensing derived indices for drought monitoring in a dry zone district, in tropical island

Recent research has shown that droughts have intensified in South Asia over the past two decades. As a natural disaster, this has severely impacted people’s livelihoods, especially in the dry zones of Sri Lanka. Thus, to ensure human well-being, security of water resources, and ecosystem health, it is essential to minimize the impacts of droughts using reliable information. Remote sensing (RS) data and techniques help bridge the gap by enabling the analysis of drought phenomena through a diverse array of indices developed in the fields. However, until now, there has been a lack of systematic monitoring and reliable data for accurately characterizing droughts in the study area. As the first comprehensive analysis, we tried to evaluate the spatial–temporal dynamics of drought conditions in two Divisional Secretariat Divisions of the Anuradhapura district, Sri Lanka using eight standardized remote sensing-derived indices over a decade (2013–2023) including the Standardized Precipitation Index (SPI). SPI values indicated that the region has experienced notably below-average precipitation. According to SPI results, a significant portion of the Medawchchiya area experienced arid conditions in 2023. All other indices proved that 2018 was the driest year and 2013 was the wettest year among the three time points, as reflected by their low and high index values. However, according to NVSMI and LST, the wettest year is 2023, with only 1.78 % of areas experiencing severe drought and a maximum LST of 31.4 °C. LULC change detection revealed that 14.3 % of agricultural lands and 5.1 % of forest areas were converted into barren lands over a decade. Overall, this conversion may be another leading factor contributing to increased LST and dryness in the area during the concerned period while increasing the mean LST of barren land by around 4.7 °C per decade. Land surface-related and vegetation-related indices, such as NDWI, NDMI, LST, and NDVI, exhibited a more pronounced impact on short-term drought occurrences. The findings revealed that average precipitation coincides with short-term drought episodes in the area, with 2018 standing out as having the least rainfall and the driest year. The study’s findings may provide additional insights for planning authorities, supporting environmental protection and enhancing agricultural production by mitigating droughts’ impacts through short- and long-term strategies. Although, the study focused on a small area, a similar approach could be extended to other areas by incorporating advanced machine learning techniques and additional drought indices in the future.

Assessing Future hydrological response of an urban watershed using machine learning based LULC forecasting models

Urbanization in terms of land-use/cover (LULC) change has a long-term significant impact on the hydrological cycle as the LULC is one of the most important influencing parameters to produce curve number (CN). The drastic change in LULC changes the CN. This change directly affects surface water including peak flows. This study aims to assess the change in surface runoff due to changes in LULC. Hydrological modeling is done for the consistent long-term behavioral study of surface runoff. The area focused on the study is the Kharun river, a tributary of the Mahanadi River. For the assessment of the impact of LULC change on the catchment discharge, a daily-step conceptual model soil and water assessment tool (SWAT) was applied. Landuse maps were prepared with the Landsat Thematic Mapper satellite images. The future land use was forecasted with the technique of spatial statistical modeling. The machine learning (ML) tool is used for quantifying the influences on the LULC change dynamics and producing the LULC map for 2024 and 2030. Remote sensing (RS) and geographic information system (GIS) analysis were coupled with hydrological SWAT modeling to investigate the connection between the LULC change and hydrologic regime. The SWAT Model’s calibration efficiency is verified by comparing the simulated and observed discharge time series at the Patharidih gauge and discharge station. The monthly and daily calibrations were quite satisfactory, with Nash-Sutcliffe an efficiency coefficient of 0.86 and 0.67. This modeling provides reliable information for sustainable management of available water resources of the catchment.

Temporal dynamics of land use land cover (LULC) changes and assessment of environmental and climatic parameters using remote sensing and GIS: A case study in Limboti Reservoir, Loha Taluka, Maharashtra, India

In this study, multispectral satellite data from Landsat 4-5 TM and Landsat 8 OLI/TIRS was used to analyse Land Use Land Cover (LULC) change detection and to retrieve environmental parameters. The LULC change detection was done between 2007 and 2021. This study used the supervised classification-maximum likelihood approach in ArcGIS 10.3 software to detect LULC in Loha Taluka, Maharashtra. The taluka was classified into four major LULC classes, viz. water, vegetation, settlement and barren-land. Changes in LULC directly or indirectly will also induce a change in the environmental parameters. Environmental parameters (water surface temperature, chlorophyll-a concentration and total suspended solids) were analysed for Limboti Reservoir. Data were predicted for next two years (2022 and 2023) using multiplicative seasonal decomposition and Holt-Winter’s multiplicative method and trends of each parameter were analysed using Mann-Kendall trend method. For both years, vegetative land was the most extensive LC in Loha Taluka, accounting for more than 60% of the total land area. However, throughout these years, urbanisation was rampant and vegetative land was converted to settlement. In these years, open water resources such as reservoirs, lakes and rivers covered a very small percentage of the total area, which is a serious threat to the ecosystem. As a result, proper water body management is essential; otherwise, these resources will be destroyed and will no longer be able to contribute to the area’s socioeconomic growth. Keywords: Change detection, Chlorophyll content, Land use land cover, Water quality, Water surface temperature

Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region

Human activities related to land use and land cover (LULC) conversion have been the primary factor driving changes to mangrove distribution over recent decades. In order to quantify the anthropogenic influences associated with LULC changes on mangroves in the Vietnamese Southern Coast (VSC), we investigated the variations and trends in mangrove distribution between 1988 and 2023. We used a time-series of Landsat spectral indices from Google Earth Engine and applied hot spot analysis and machine learning algorithms to analyse mangrove variations and LULC classification, respectively. Our findings revealed that over the past 36 years, approximately half of the mangrove area has been lost due to LULC conversions. The most significant losses in mangrove cover occurred during the 1998–2011 period, with a decline of 46.79% in total area (an average of 3.6% per annum). The rate of mangrove deforestation more than halved to 17.49% (1.5% per annum) in the period between from 2011 to 2023. We attribute the reduction in mangrove loss to conservation efforts and natural regeneration processes. The emerging hot spot analysis indicated that the most significant restoration of new mangrove areas occurred between 1988 and 1998, totalling 1795 ha (1.4%), while the highest rate of mangrove deforestation was observed between 1998 and 2011, amounting to 2249 ha (2.0%). The primary causes of these variations in mangrove distribution were the conversion of mangrove areas to shrimp farming (38.91%), followed by other agricultural land use (5.82%) and the expansion of impervious surfaces (3.34%). In contrast, a result of enhanced conservation efforts and natural regeneration was associated with a 17.91% of mangrove area gain in the 2011–2023 period. Despite the regeneration potential of mangroves, our study highlighted the ongoing need to manage and protect mangrove forests to facilitate their expansion in the VSC. The analytical approach adopted in this study is applicable to other coastal areas when assessing changes in mangroves and land use practices.

Reviews on the Land Use and Land Cover Changes in Bangkok

Remote sensing has been a highly effective and efficient method for spatial and socio-political analyses for several decades. It utilizes satellite images to map land use and land cover (LULC) and detect changes in this regard. One of Southeast Asia’s megacities, Bangkok has undergone significant land use dynamics and related socioeconomic activity changes. Recently, the new Land and Buildings Tax Act B.E. 2562 (A.D. 2019) has been implemented, which could impact land-use changes, particularly in vacant lands. This study aims to review the literature on LULC changes in Bangkok since the implementation of the land tax and to provide a summary of the main findings. The reviews indicate that previous literature on LULC in Bangkok has primarily focused on environmental or spatial factors, rather than socio-economic ones. This highlights a gap in our understanding of the socio-economic and LULC impacts of land taxation.

Spatio-temporal analysis of urbanization effects: unravelling land use and land cover dynamics and their influence on land surface temperature in Aligarh City

ABSTRACT Rapid urbanization causes significant changes in land use and land cover (LULC), contributing to Urban Heat Islands (UHIs) and environmental challenges. This study analyzes two decades of Landsat satellite imagery to examine LULC changes and their impact on Land Surface Temperature (LST) in Aligarh City, India. Supervised classification using maximum likelihood techniques produced LULC maps with 85% accuracy. The results show a 21.10% increase in built-up areas, from 2338.83 hectares in 2001 to 2832.66 hectares in 2021, alongside a 24.55% decline in vegetation, from 620.28 hectares to 467.91 hectares. LST increased by 11.35°C, with a total maximum rise of 10.70°C between 2001 and 2021. Regression analysis revealed strong correlations between LST and spectral indices, with NDVI showing a negative correlation (R² = 0.89, 0.83, 0.78) and NDBI a positive one (R² = 0.87, 0.89, 0.79). The Aligarh main city area consistently recorded higher LST values than the AMU campus, with 88.60% of the city area in the 40–45°C range by 2021, compared to 52.87% for the campus. This study underscores the need for sustainable urban strategies to mitigate UHI effects and promote climate resilience.

Attribution of climate change and human activities to spatiotemporal changes of ecological service value in Yunnan Province of China

Quantifying the influence of climate change and human activities on spatiotemporal changes of regional ecological service value (ESV) can provide key information for regional ecosystem protection. However, few studies have distinguished the contributions of climate change and human activities to the total ESV. In this study, Yunnan Province in Southwest China with high biodiversity and ESV was selected as a representative region for the attribution analysis of ESV changes. The Carnegie-Ames Stanford Approach (CASA) was used to calculate the vegetation net primary productivity (NPP), and improved equivalent factor method was used to calculate ESVs of different ecosystem service functions directly from NPP. Then, the multi-factor attribution analysis method was used to quantify the contribution rates of climate change and human activities to changes in ESV. Results show that the ESV in Yunnan Province increased by 12.9 % in 2000–2019 compared to 1980–1999 without considering economic development. Land use and cover change (LUCC) is the main cause for the increase in ESV with the contribution rate of 92.3 %, while that of climate change is only 7.7 %. Major components of ESV change are ESV changes in forests, grasslands, and farmland, which are all mainly caused by LUCC with the contribute rates of over 80 %. ESVs in different cities differ greatly, where the ESV and ESV per unit area in Pu'er City are 5.3 and 2.6 times as those in Zhaotong City, respectively. LUCC is also the main cause of ESV changes in all cities, with contributions rates generally exceeding 80 %. When considering economic development, the ESV in Yunnan has increased by 336.2 % in these two periods. Contribution rate of economic development to ESV increase in Yunnan is 80.3 %, while that of climate change and LUCC is only 19.7 %. The speed of ecological construction in Yunnan Province is far slower than that of economic development, and appropriate measures should be taken to improve the ecological services in the future.

Sustainable development goals, governance and assessment of the impact of urban growth on vegetation cover in Benin City, Nigeria, using land-use–land-cover change trajectories

Land-use/cover (LULC) and governance are key elements in the attainment of the Sustainable Development Goals. This study focused primarily on LULC changes in Benin City, Nigeria, from 1992 to 2022; to examine how urban growth affected vegetation cover across four time points: 1992, 2002, 2015 and 2022. Variable LULC classifications considered included: built-up areas, open spaces/bare lands, vegetations, waterbodies and wetlands. In three decades (1992–2022), increases in the built-up, open space/bare land and wetland LULC were observed and attributed to population increases and higher infrastructural demands whereas evident reductions in the sizes of vegetation and waterbody categories were ascribed to anthropogenic activities and climate events. The average annual rates of change in the built-up and vegetation LULC increased (7.28%) and decreased (−3.02%), respectively, between 1992 and 2022; with the built-up area experiencing a 218% increase from 6493 to 20,672 (km2) whereas vegetation decreased by 90% from 23,445 to 2239 (km2). The corollary is that urban growth, exemplified by the built-up LULC, was progressive while vegetation cover was depleted, in the 30-year period. Changes in five other periods were also interrogated. Government and stakeholders-promoted afforestation and agroforestry programs are recommended as plausible solutions to check vegetation-cover depletion. Other solutions proffered include the support of relevant farmers’ trainings in climate-smart and hydroponic agriculture, and the application of sustainable farming techniques. Policymakers and estate developers/managers should encourage sustainable land governance and cities with resilience and coping capacities; to tackle the problem of climate change, with high emphasis on green area developments.

Ecosystem service value and ecological compensation in Qilian Mountain National Park: Implications for ecological conservation strategies

Qilian Mountain National Park (QMNP) is a critical ecological barrier and water conservation area in northwestern China. Its diverse ecosystems provide significant ecosystem services, yet the valuation of these services and their dynamic responses to land use/cover (LULC) changes and ecological protection measures require detailed investigation. This study employs an improved equivalent factor method, tailored to the specific ecosystem types and LULC patterns of QMNP, to evaluate the ecosystem service value (ESV) across different LULC types. Using an elasticity analysis model, the sensitivity of ESV to LULC changes from 1990 to 2022 was quantitatively assessed. The results indicate that the ESV of QMNP increased from 46.13 billion RMB to 50.97 billion RMB between 1990 and 2022, mostly due to the growth of grassland and forest areas. The primary route for increasing the region’s ESV is the conversion of desert to grassland, and this should be a major area of emphasis for next ecological conservation initiatives. Regulating services form the core of the ESV in QMNP, with climate and hydrological regulation contributing significantly to the overall ESV. Maintaining the stability of forest, wetland, and water body areas is crucial for sustaining the total ESV of QMNP. Through the application of carbon emission coefficients and ESV methods, the average range for long-term ecological compensation standards in QMNP is expected to be between 3.78 billion RMB and 28.28 billion RMB annually. Additionally, we explore the impacts of ongoing ecological protection mechanisms on enhancing ESV. There is a pressing need to formulate integrated policy frameworks that balance ecological conservation with sustainable economic development. The study also highlights the necessity of strengthening the protection and foundational research of grasslands and water bodies to enhance the ESV of QMNP. These insights serve as a foundation for potential planning and management strategies in QMNP, aiming to maintain and enhance its ecosystem functions and services, thereby contributing to broader regional and global ecological stability.

Investigating the changing land use patterns and their impacts on ecosystem in the Teesta River Basin

This study investigated the changing land use patterns and their impacts on ecosystem in the Teesta River Basin of northwestern Bangladesh. Although anthropocentric land use patterns, including agricultural land use, settlements, built areas, and waterbody loss, have been increasing in the Nilphamari district, by negatively affecting local ecosystems, they have not been identified by prior research. Limitations of contemporary literature motivated me to work on this crucial ground in the Teesta River Basin in Northwestern Bangladesh. This study applied a mixed research approach to identify the study objectives. Firstly, the land use and land cover (LULC) changes which occurred between 2000 and 2020 were detected using satellite imagery and supervised classification method. In addition to the detection of LULC changes, the study explored the people’s perceptions and experiences about the ecosystem changes resulted from the LULC changes over the last 20 years, conducting stakeholders’ consultations and household surveys utilizing a semi-structured questionnaire. The findings indicated that waterbodies in Nilphamari district have significantly decreased from 378 km2 in 2000 to 181 km2 in 2020. In the same way, the vegetation coverage has reduced 187 km2 between the years 2000 and 2020. On the contrary, agricultural lands (croplands) have increased from 595 km2 to 905 km2 and settlements have increased from 81 km2 to 206 km2 between the years 2000 and 2020. From the chi-square test, it was found a significant association between ecosystem change and biodiversity loss. It was further identified that waterbody decreases have significant impacts on aquatic ecosystems. The results of this study also indicated that due to the introduction of foreign tree species, local and native species have been significantly decreasing over the time. This study emphasizes the non-anthropocentric and inclusive land use policy implications for protecting life on land and preserving the aquatic ecosystem in Bangladesh.

Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51°C in 1990 to 46.73°C in 2020 and the highest temperature category (50-60°C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

Geospatial Approach to Identify the Indicators of Wetland Change: A Study for Kabartal (Ramsar Wetland), India

This study assesses the trend of LULC change in Wetlands from 1989 to 2023 using LANDSAT data for the core wetland zone of 26.02 km2 and the extended buffer zone of 623.08 km2. A physicochemical analysis of soil and water quality was conducted, and geospatial tools such as NDVI, MNDWI, and LULC were used in the study. ICP-OES study shows high levels of Ca, Mg, K, Fe and Cu in soil. Water quality analysis reveals that all parameters are within the permissible limit. Pearson correlation analysis shows a significant positive correlation between precipitation and vegetation cover in the buffer zone and with the water in wetland in the core zone. Nitrate and phosphate have a strong positive correlation with each other in both seasons, implying the same source, which could be due to agricultural runoff. Higher electrical conductivity indicates the presence of mineral ions and is strongly correlated with pH, temperature alkalinity and TDS. NDVI, MNDWI and LULC studies show decreased vegetation and water areas in core and buffer zones. The driving forces of wetland degradation are agriculture and built-up in core and buffer zones. The results of the kappa coefficient show an accuracy of 88%. Conservation strategies should be developed based on the indicators of the driving forces of degradation.

Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China

The alteration of landscape spatial patterns (LSPs) and ecosystem services (ESs) in watersheds can have detrimental effects on the local environment and community. However, a comprehensive understanding of the current state of LSPs and ESs in watersheds around Winter Olympic venues in China is limited. Here, we assessed current LSPs and ESs and developed optimization strategies for the Xigou watershed around Winter Olympic venues in the northern agro-pastoral ecotone of China. The results indicated that the main land use type was grassland in the Xigou watershed, and landscape types were relatively homogenous. All three ESs (water yield, sediment retention, and carbon storage) generally improved from 2004 to 2020. For ESs, there was the lowest total volume of water yield in 2004 (637.44 × 104 m3). But sediment retention (10.54 × 106 t, 18.13 × 106 t, 13.28 × 106 t, and 16.85 × 106 t) had an upward, then downward, then upward trend before and after ERP. Carbon storage grew steadily. Correlation analysis suggested that the three ESs were closely related to the landscape spatial indices of average patch area (AREA_MN), contagion index (CONTAG), and Shannon’s evenness index (SHEI). AREA_MN, CONTAG, and SHEI in the eastern part of the study area promoted sediment retention and carbon storage, while in the southwestern part of the study area, they inhibited water yield and sediment retention. The results suggest that improving sediment retention by optimizing land use and cover change (LUCC) and LSPs is the main approach to further enhance ESs in the study area. Our study suggests that the inclusion of multiple landscape pattern indices can provide a more comprehensive representation of regional ecosystem service.

Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.

Navigating Urban Sustainability: Urban Planning and the Predictive Analysis of Busan’s Green Area Dynamics Using the CA-ANN Model

While numerous studies have employed deep learning and high-resolution remote sensing to predict future land use and land cover (LULC) changes, no study has integrated these predictive tools with the current urban planning context to find a potential issues for sustainability. This study addresses this gap by examining the planning context of Busan Metropolitan City (BMC) and analyzing the paradoxical objectives within the city’s 2040 Master Plan and the subordinate 2030 Busan Master Plan for Parks and Greenbelts. Although the plans advocate for increased green areas to enhance urban sustainability and social wellbeing, they simultaneously support policies that may lead to a reduction in these areas due to urban development. Using the CA-ANN model in the MOLUSCE plugin, a deep learning-based LULC change analysis, we forecast further urban expansion and continued shrinkage of natural green areas. During 1980–2010, Busan Metropolitan City (BMC) underwent high-speed urban expansion, wherein the urbanized areas almost doubled and agricultural lands and green areas, including forests and grassland, reduced considerably. Forecasts for the years 2010–2040 show continued further expansion of urban areas at the expense of areas for agriculture and green areas, including forest and grasslands. Given the master plans, these highlight a critical tension between urban growth and sustainability. Despite the push for more green spaces, the replacement of natural landscapes with artificial parks and green areas may threaten long-term sustainability. In view of these apparently conflicting goals, the urban planning framework for BMC would have to take up increasingly stronger conservation policies and adaptive planning practices that consider environmental preservation on a par with economic development in the light of the planning context and trajectory of urbanization.