Land Use Cover Research Articles

The past and future dynamics of ecological resilience and its spatial response analysis to natural and anthropogenic factors in Southwest China with typical Karst

With the global land use/land cover (LULC) and climate change, the ecological resilience (ER) in typical Karst areas has become the focus of attention. Its future development trend and its spatial response to natural and anthropogenic factors are crucial for understanding the changes of ecologically fragile areas to human behavior. However, there is still a lack of relevant quantitative research. The study systematically analyzed the characteristics of LULC changes in Southwest China with typical Karst over the past 20 years. Drawing on the landscape ecology research paradigm, a potential-elasticity-stability ER assessment model was constructed. Revealing the characteristics and heterogeneity of the spatial distribution, annual evolution, and development trend of ER in the past and under different scenarios of shared socioeconomic pathways and representative concentration pathways (SSP-RCP) in the future. In addition, the spatial econometric model was utilized to reveal the spatial effect response mechanism of ER, and adaptive development strategies were proposed to promote the sustainable development of Southwest China. The study found that : (1) In the past 20 years, the LULC in Southwest China showed an accelerated change trend, the ER decreased declined in general, and there was significant spatial heterogeneity, showing the spatial distribution pattern of “west is larger than east, south is larger than north, and reduction in the west was slower than that in the east.” (2) Under the same SSP scenario, with the increase of RCP emission concentration, the area of the lowest-resilience increased significantly, and the area of the highest-resilience decreased. (3) The woodland was the largest contributor to ER per unit area in the Southwest China, and grassland was the main LULC type, which had a prominent impact on the ER of the study area. (4) The average precipitation and the normalized difference vegetation index (NDVI) were significant natural drivers of ER in the study area, and the economic growth, innovation, and optimization of industrial structure contributed to the ER of Southwest China. Overall, the integration of quantitative assessment and multi-scenario-based modeling not only provides new perspectives for understanding the pattern of change and response mechanisms, but also provides valuable references for other typical Karst regions around the world to achieve sustainable development.

Spatiotemporal Variations in Urban Wetlands in Kazakhstan: A Case of the Taldykol Lake System in Astana City

Acquiring a comprehensive understanding of the spatiotemporal dynamics of urban wetlands in Kazakhstan is crucial for their effective preservation and sustainable urban development. Our findings identify past and present Land Use Land Cover (LULC) in the capital city, providing policymakers with scientific evidence for improved management. Using remote sensing and Geographic Information System (GIS) techniques, this study examines the spatiotemporal changes in the Taldykol catchment area during the rapid development of Astana. In 1992, over 90% of the catchment area was grassland and vegetation. By 2022, 30% of the area became barren land. Urban areas increased by 127%, and water areas decreased by 24%. The most significant changes occurred in lakes Taldykol and Kishi Taldykol, whose areas shrank by 91% and 54%, respectively. The near-disappearance of the Taldykol wetlands is likely to contribute to rising land surface temperatures (LST), decreased natural flood control capacity, reduced biodiversity, and diminished recreational opportunities. The fate of Taldykol lakes underscores the urgent need to raise public awareness about the role of wetlands in Kazakhstan’s ecosystems and take action to preserve urban wetlands.

Seasonal dynamics of land surface temperature and urban thermal comfort with land use land cover pattern in semi-arid Indian cities: Insights for sustainable Urban Management

The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 °C in Udaipur to 26.18 °C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.

Evaluating the influence of COVID-19 pandemic on socioeconomic development in Wumeng Mountain area based on multi-source remote sensing data

ABSTRACT Assessing the socioeconomic development of rural areas is important for the targeted implementation of rural revitalization in China, especially after three years of COVID-19 pandemic restrictions. However, the influence of COVID-19 on socioeconomic development in rural areas remains unclear. Therefore, this paper constructed a comprehensive socioeconomic development index (SEDI) by considering county-level statistics on natural environment, social resources, and economy as proxies for assessing local socioeconomic development, and analyzed the influence of COVID-19 from the changing pattern of SEDI before and after COVID-19, with Wumeng Mountain (WM) area as the study case. In response to the absence and untimeliness of statistics, we employed both nighttime light (NTL) and land use/land cover (LULC) features to fill in these missing SEDIs from 2013 to 2022, using random forest regression. With the assistance of LULC, the overall R2 increased from 0.7763 to 0.9056, and the estimation accuracy improved in 36 out of 38 counties. All counties in WM experienced an increase in SEDI, and the growth rate did not slow down, proving the effective implementation of rural revitalization strategy. Also, the spatial clustering pattern remained relatively stable. These findings provide scientific foundations for the local government to assess comprehensive development and formulate policies.

Impact of changing urban form and production-living-ecological space on changing ecosystem services of a smart city of Eastern India, Durgapur.

Worldwide land use land cover (LULC) transformation become a serious issue in the last few decades due to its immense importance in environmental and human well-being perspectives. Expansion of urban areas at the expense of natural land covers and changing urban form is mainly responsible for changing environmental conditions. This study focused on identifying the impacts of LULC change on environmental conditions through the assessment of changing ecosystem services (ESs) of the Durgapur Municipal Corporation (DMC) from 1990 to 2020. Changing ESs are assessed based on changing urban forms and production-living-ecological space (PLES) components. Results found that the compactness of urban areas is increasing along with the outward expansion. The core urban area of DMC has risen from 8.11% to 30.11% during 1990-2020. Similarly, living space increased from 15.57% to 42.60%, production space decreased from 53.06% to 25.59%, and ecological space fluctuated from 1990 to 2020. This transformation of PLES components negatively affects DMC's environmental condition, affecting the achievement of Sustainable Development Goals (SDGs). These significant results may be utilized to understand changing environmental conditions and priority issues for DMC's future sustainable urban development.

Spatiotemporal Monitoring of Land Use-Land Cover and Its Relationship with Land Surface Temperature Changes Based on Remote Sensing, GIS, and Deep Learning

Spatiotemporal Monitoring of Land Use-Land Cover and Its Relationship with Land Surface Temperature Changes Based on Remote Sensing, GIS, and Deep Learning

Assessing the impact of Land Use Land Cover changes on land surface temperature over Kigali, Rwanda in the past three decades

Land use changes and urban activities cause environmental degradation and climate change. This study aims to assess the impacts of Land Use Land Cover (LULC) changes on land surface temperature (LST) in Kigali, Rwanda over the past three decades. The combined techniques of remote sensing and GIS were applied to generate good quality Landsat images, categorize land use classes and retrieve urban indices. The modified Mann-Kendall test and Sen’s slope estimator were used to analyze trends in LST. The results indicate that between 1990 and 2020, the extent of builtup and forest areas have increased at an average rate of 3.39 km2/year and 2.42 km2/year respectively, while open land has decreased at an average rate of 5.81 km2/year. In the same period, water bodies and wetlands showed minimal changes with an increase of 1.56 km2 and a decrease of 1.66 km2 for water bodies and wetlands respectively. The slope magnitudes of LST are predominantly positive (p ≤ 0.05) with a high increase observed in dry seasons (0.51 °C per decade for Tmin in JJA and 0.49 °C per decade for Tmax in JF). The positive correlations between LST and urban indices were observed in all studied subregions (all values are above 0.61, p ≤ 0.05). The findings of this study are useful for the development of future urban land use schemes and the adoption of mitigation and adaptation strategies in response to climate change.

Evaluation of Different Classification Algorithms for Land Use Land Cover Mapping

For efficient sustainable management and monitoring landscape changes over times, reliable land use land cover (LULC) mapping using the most accurate classification algorithms is required. Increasing innovative classification algorithms and satellite data demands finding the most suitable classifier to create accurate maps of different features efficiently. The challenge addressed in this study is to identify the most accurate algorithm for classifying and generating reliable LULC. The objective of this research was to identify the best classification among several algorithms both overall and in each individual class by using ArcGIS Pro and Google Earth Engine with Landsat 8 and Sentinel-2 datasets for Ranya city as the study area. Support vector machine (SVM), maximum likelihood, random tree, classification and regression tree, K-Nearest Neighbor and iterative self organizing cluster algorithms were used to classify the satellite image of the study area. The kappa coefficient matrix was used to assess the performance of each classifier and method. The study showed that the random tree algorithm achieved highest overall accuracy using Sentinel-2 with 83%. Meanwhile, when the specific class accuracy is priority, the result suggests the use of SVM algorithm using Sentinel-2 for building footprint extraction with 92% accuracy. The result also showed that the outcomes of most algorithms were better using Sentinel-2 rather than Landsat 8, making Sentinel-2 more suitable for accurate LULC mapping. The outcomes of the research assessed different classification algorisms to find the best algorithms and methods that can be used to generate accurate and efficient LULC maps.

Effects of localized development on land use and coastline dynamics: A focus on recent changes along the Lekki Peninsula

Human-induced activities significantly impact the natural coastlines of island cities, primarily due to the processes of urbanization and population concentration in developing nations. Over the past decades, affluent communities have rapidly urbanized the Lekki Peninsula on the Nigerian Coast and provided adequate coastal protection. However, little attention is paid to its effects on its adjacent coastlines. This study highlights these coastline alterations and corresponding patterns of land use/land cover (LULC) along the ∼88 km stretches of the study area. The study employs six multispectral Landsat 7 ETM+ and Landsat 8 OLI images within the geographic information system (GIS) framework to analyze these changes. Results reveal that urbanized areas increased from 5.36 % in 2002 to 42.37 % in 2022, accompanied by a significant retreat of approximately 56 % (49 km) along the entire coast over the entire study period. These changes are resultant effects of few protected and isolated major infrastructures exhibiting developmental levels significantly higher than the rest of the coastline. Therefore, addressing these pressing issues is essential for sustainable environmental management and the well-being of ecosystems and human societies.

Spatial prioritization of groundwater resources in mountain region of Nepal: a GIS and AHP integrated framework

ABSTRACT Climate change and anthropogenic activities pose major challenges to water resource management in water-scarce mountain regions. Managing and monitoring groundwater effectively is crucial in mitigating these challenges. Understanding groundwater resources and their distribution is necessary for effective management. The present study covers Doramba rural municipality of Nepal. It employed the analytic hierarchy process (AHP) as a multi-criteria decision-making (MCDM) tool integrated with remote sensing (RS) data and geospatial techniques to identify groundwater potential zones (GPZs). The parameters for analysis include drainage density, slope, rainfall, lineament density, land use/land cover (LULC), soil, geology, curvature, roughness, topographic wetness index, and topographic position index. These 11 thematic layers of the study area were prioritized using Satty's AHP method for the delineation of GPZs. The results classified the study area into very high, high, moderate, low, and very low groundwater potential classes, covering 26.71, 27.98, 46.61, 34.71, and 4.89 km2, respectively. The delineated map accuracy was confirmed with a 70.7% prediction rate based on the spatial distribution of 56 springs in the study area. The study's results can be applied to the sustainable management of groundwater resources in the study region and other areas with similar hydrogeological conditions.

Use of Socio-economic, Climatic, and Land use Land Cover Patterns in Solid Waste Forecasting with Integrated Gradient LSTNet Based Model in Lomé, Togo

ABSTRACT Few studies have explored recurrent neural network models in municipal solid waste (MSW) generation forecasting regarding the inefficiency of conventional and probabilistic tools. This study aimed to develop a new approach of Integrated Gradient Long- and Short-term Time series Network (LSTNet+IG)-based models, assess the baseline neural network models, and provide features influences explanation on MSW generation by using socio-economic, climatic, and Land Use Land Cover (LULC) patterns. Hence, the Random Forest Regressor and Gradient Boosting Regressor methods were used for feature selection, and the IG component was incorporated to LSTNet model for feature influence explanation alongside baseline models implementation. Moreover, the metrics such as root relative square error (RSE) and relative absolute error (RAE) were used to evaluate models’ performance. As a result, the LSTNet+IG outperforms others with the lowest RSE (0.9727) and RAE (0.9108). Moreover, five key influencing features were found, namely the number of households and institutions, mean relative humidity, electricity consumption, built area, and mean temperature. Hence, the socio-economic, climatic, and LULC features in MSW generation forecasting have shown to be important in decision-making for effective MSW management. Therefore, the LSTNet+IG model adoption by stakeholders could help to anticipate and mitigate MSW mismanagement through better planning.

Time series monitoring and analysis of Pakistan’s mangrove using Sentinel-2 data

Sustainable Development Goal-14 (SDG-14) directly demands the protection, conservation, restoration, and sustainable management of the global mangrove ecosystem. Assessment of the development Pakistan has made toward the sustainable management of mangrove ecosystems necessitates the remote sensing-based evaluation of national-level mangrove cover. Using Google Earth Engine (GEE) for geoprocessing 12,000+ 10-m high-spatial resolution Sentinel-2 time-series images (2016–23) and applying random forest (RF) classifier, the current research provides the latest spatial distribution of mangroves along Pakistan’s coastline and changes observed for a duration of 8 years. Additionally, this research provides the first spatiotemporal health assessment of Pakistan’s national mangrove cover as well. Rational analysis of the results indicated splitting the entire timeline based on two seasons (Jan–Jun and Jul–Dec). Results revealed an overall increase of 1,210 km2 (2023) in the mangrove cover on the national-level; a 3.42 km2 average annual increase from 2016 (1,186 km2). Mangrove gain/loss assessment based on the land use land cover (LULC) transition matrix illustrated 223 km2 gain and 199 km2 loss; a 24 km2 net gain. Declines of 20.28% and 7.91% were found in maximum- and mean-NDVI (2016-23), depicting the deteriorating mangrove health conditions. Likewise, significant Sen’s slope analysis (p < 0.05) indicated that 88.8% of all the mangrove-NDVI pixels exhibited an overall decrease, whereas 11.2% pixels showed an overall increase (2016-23). It was concluded that despite showing a growth in the extent, mangrove cover in Pakistan has shown a decline in health, primarily due to deforestation for urban operations and sea-level rise, still making them vulnerable and potentially leading to a disrupted ecosystem, including carbon release in the atmosphere. This study will assist in the formulation of mangrove conservation and management strategies, whereas future research can explore the potentials of land surface temperature (LST) and evapotranspiration in combination to the NDVI for an in-depth analysis of the health status of mangroves.

The effect of land use and land cover on soil carbon storage in the Yellow River Delta, China: Implications for wetland restoration and adaptive management

Gaining a comprehensive understanding of the effect of land use/land cover (LULC) and soil depth on soil carbon storage, through the manipulation of external carbon input and turnover processes, is crucial for accurate predictions of regional soil carbon storage. Numerous research investigations have been conducted to examine the impact of LULC on the storage and cycling of carbon in the surface soils of coastal wetlands. Nevertheless, there remains a dearth of understanding concerning the implications of this phenomenon on subterranean soils, a crucial factor in discerning the capacity for carbon sequestration in coastal wetlands and implementing measures for their preservation. The study focused on the Yellow River Delta (YRD) in China, which serves as a representative model system. It aimed to assess the impact of LULC as well as soil depth on carbon storage. This was achieved by a combination of remote sensing interpretation and field samplings. The findings of the study indicate that there was an increase in soil organic carbon storage with both the area covered and the depth of the soil across the four different land use types, namely forest, grass, tidal flat, and cultivated land. Cultivated land was identified as the predominant LULC type, encompassing 41.73% of the entire YRD. Furthermore, it accounted for a substantial carbon storage of 76.08%. In comparison to soil layers at depths of 0–20 cm and 20–40 cm, 40–60 cm was discovered to have the maximum carbon storage, accounting for 42.29% of total carbon storage. Furthermore, one of the main factors influencing carbon storage is salinity, which shows a negative association with carbon storage. Moreover, the aforementioned findings underscore the significance of the conjoined physical and chemical properties induced by LULC in influencing the dynamics of soil carbon. This suggests that the inclusion of deep soil carbon in the estimation and restoration of soil carbon storage is necessary. This inclusion will support the realization of the United Nations' “Toward Zero Carbon” effort and facilitate the implementation of China's national carbon neutrality objectives.

Spatio-temporal analysis and prediction of land use landcover (LULC) change in Wular Lake, Jammu and Kashmir, India.

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.

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

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

Land use mapping from sentinel satellite imagery using remote sensing and GIS techniques

Abstract The need to classify sentinel-2 satellite images to extract land use /land cover (LULC) are essential to analysis the processes of environment problems and to improve living conditions. Hence, this research aims to create LULC maps from sentinel-2 satellite images through Maximum likelihood (ML) algorithm using remote sensing and GIS. The selected study area for this research is Baghdad city because of it has a unique political stability and due to rapid urbanization, that lead to rise additional request for natural resources and affected on LULC in Baghdad city. After preprocessing and processing of satellite images, thematic maps were created and classified into five main classes based on visual interpretation and visit the field of the study area that containing: urban, vegetation, soil, asphalt roads, and water bodies. The results showed that classification accuracy assessment of ML algorithm is acceptable because of overall accuracy and Kappa index equal (86%, 0.82) respectively.

Uncovering the impacts of LUCC on ecological connectivity in suburban open-pit mining concentration areas: A pattern collection of changing relationships between ecological resistance and ecological network elements

The suburban open-pit mining concentration area couples two human activities, urban sprawl and open-pit mining, while destruction of surface vegetation and ecological restoration coexist. This has also resulted in complex land use shifts that significantly affect regional ecological connectivity. However, the impact of such complex Land use/land cover changes (LUCC) on ecological connectivity in this region is not well understood Therefore, this study takes the suburban open-pit mining concentration area of Jinan City as a case study and uses Land use/land cover (LULC) data from the years 2000, 2010, and 2020 to simulate the spatio-temporal evolution of ecological networks (ENs). Subsequently, a pattern collection of changing relationships was proposed that relate changes in ecological resistance to changes in EN elements, addressing the dynamic transmission process from "spatial field" to "spatial structure entity". Based on this approach, GeoDetector is employed to reveal the response extent of EN elements to LUCC. The results indicate: (1) In the past 20 years, the area of construction land has increased by 100 %, while mining land has increased by 69 %. This significant increase in LUCC has led to a notable enhancement in ecological resistance in the study area, driving the generation of 20 % more ecological pinch points and barriers. Additionally, it has resulted in a twofold increase in both the number and length of level 1 ecological corridors. (2) The dominant status and structure of grassland, agriculture land, and woodland remain relatively stable; however, the interflow among them has caused approximately 25 % of the ecological corridors to undergo changes such as disappearance and path alteration. (3) The implementation of ecological protection and restoration has led to a continuous increase in the area of ecological sources, particularly with the mining rehabilitation increasing by 32.7 times compared to 2000, thereby driving gradual improvement in 25 % of the ecological network elements. (4) Although the quantity and length of ecological corridors in the study area remain relatively stable, evaluation results indicate that its spatial structure is gradually deteriorating, with ecological connectivity decreasing by 7.7 %. This study highlights the importance of restoration of ecological processes in sub-urban open-pit mining concentration areas. The study results can provide a scientific basis for ecological restoration and sustainable development in resource-based cities.

Evaluation and interpretation of landscapes from satellite imagery

Satellite imagery allows us to view landscapes from a bird’s eye view, providing a new dimension in appreciating the environments we inhabit. This alternative perspective has the potential to shape individual perceptions of landscapes and play a pivotal role in land management decision-making and communication. However, the interpretation and appreciation of landscapes seen in satellite imagery may vary among observers. This study investigates the relationship between individuals’ ability to interpret images from eye-level and satellite perspectives, their familiarity with the landscape, and their appreciation of land cover from this viewpoint. To achieve this, a survey was conducted presenting respondents with images of land cover classes captured at eye level and from satellite imagery of the Yungay municipality in Chile. Participants were asked to interpret the primary land use land cover (LULC) depicted in the imagery and indicate their appreciation of that landscape. Variation in the interpretation of LULC was observed between the image source and land cover type. For instance, forest classes seen in eye-level imagery were more accurately interpreted compared to satellite imagery, while the reverse was true for agriculture. These differences in interpretation also impacted the appreciation scores assigned to the landscapes in the images. Specifically, if respondents perceived an image to be dominated by a traditionally appreciated land cover (e.g., Native Vegetation), they provided a higher score, even if the image depicted another class (e.g., Plantation Forestry). These findings highlight that considering the influence of satellite imagery in shaping perception is crucial in supporting land management activities.

Urban Sprawl Analysis Using Landscape Metrics in Chiba Prefecture, Japan from 1990-2021

Abstract Urban sprawl, characterized by unregulated development on the outskirts of urban areas, leads to agricultural land loss, reduced biodiversity, and environmental issues. The study employs landscape metrics to analyze the spatial patterns and effects of urban sprawl by measuring the fragmentation and structure of urban areas in Chiba Prefecture, Japan. The research employs data collected from Landsat imagery 5 (1990) and 8 (2021) and utilizes the Google Earth Engine platform for spatial and temporal analysis. The Land Use Land Cover (LULC) assessment reveals the transformation of Chiba’s landscape from 1990 to 2021, with urban areas expanding significantly and vegetation decreasing. The analysis using 8 selected landscape metrics highlights the varying degrees of urban sprawl across Chiba Prefecture. Cities near Tokyo show higher levels of sprawl, while some areas outside the edge experience growing development. Notably, there is a trend of increasing urban sprawl toward the edge. As Chiba Prefecture faces challenges from an aging population, the findings emphasize the need for controlled urban development to mitigate the negative impacts of sprawl and the importance of sustainable urban planning especially towards the edge area to ensure a balance between development and environmental preservation.

Multi-criteria analysis of low impact development practices: a case study in the Tan Hoa - Lo Gom basin, Ho Chi Minh city, Vietnam

Abstract Urban flooding poses a significant challenge for cities globally. In recent years, Low Impact Development (LID) has been recognized as an effective and sustainable solution for managing urban floods. This study employs the EPA-Storm Water Management Model (EPA-SWMM), social surveys, and multi-criteria analysis (MCA) to assess the effectiveness of LID practices from environmental, social, and economic sustainability perspectives. Results demonstrated that implementing LID techniques such as rain gardens, infiltration trenches, permeable pavements, and their combinations in the Tan Hoa - Lo Gom basin can reduce the average total runoff volume by 8.77% to 30.80% under the current land use-land cover and the design hyetograph with a 3-hour duration and a 5-year return period. Moreover, there is a reduction of approximately 7.42% to 30.59% in the average total runoff volume for the design hyetograph with a 3-hour duration and a 5-year return period, aiming to assess the climate change impact. Additionally, sociological and economic aspects are integrated to comprehensively assess each LID practice through MCA via the Analytical Hierarchy Process (AHP) method. Overall, the analysis results indicated that the scenarios involving the application of rain gardens, infiltration trenches, and their combinations are more effective than other scenarios. These findings will provide valuable insights for decision-making and planning of other LID projects in Ho Chi Minh City (HCMC) to mitigate urban flooding, particularly in light of the escalating challenges posed by urbanization and climate change.