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The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4–5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City. [ABSTRACT FROM AUTHOR]

Simple Summary: Water colour is an important physicochemical property of water that affects aquatic communities. Land-use change has led to water colour darkening in lotic habitats, such as streams and rivers. Here, I evaluate whether urban land-use change affects water colour in urban ponds, a type of lentic habitats, and how diving beetles (Dytiscidae) respond to the water colour gradient in the Helsinki Metropolitan Area, Finland. The results show that urbanisation may not drive water darkening in urban ponds, possibly because urban ponds are often not connected to stormwater pipes. Diving beetles responded to the increasing pond water colour differently in the presence or absence of fish. Diving beetle species richness and abundance significantly increased along the water colour gradient in ponds with fish, but not in ponds without fish. Some species, such as the great diving beetle (Dytiscus marginalis), appear tolerant to brown water, whereas some species, such as the cherrystone beetle (Hyphydrus ovatus), prefer clear water. This study highlights that not all species benefit from increasing water colour. It is important to provide ponds with a gradient of water colour from clear to brown water in the landscape to meet the habitat requirements of different species for urban biodiversity conservation. Dramatic land-use changes in urban landscapes can drive water colour darkening by washing compounds, such as organic matter and iron, from terrestrial ecosystems into urban blue space, consequentially affecting aquatic communities. Here, I studied how pond water colour changes along an urban gradient and how diving beetles (Dytiscidae) respond to the water colour gradient in 11 ponds with fish and 15 ponds without fish in the Helsinki Metropolitan Area, Finland. I found that the pond water colour exhibited a non-significant decreasing pattern along the urban gradient, indicating that urbanisation may not necessarily drive brownification in urban ponds. Dytiscid species richness and abundance exhibited significant positive correlations with increasing water colour in ponds with fish but no significant correlation in ponds without fish. Some species, such as Agabus spp. and Dytiscus spp., appeared tolerant to highly coloured water, whereas some species, such as Hyphydrus ovatus and Hygrotus spp., tended to occur in clear water, indicating that brown water may provide dytiscids with prey refuges, but some species are intolerant to brown water. The study highlights the importance of urban pondscape heterogeneity to meet the needs of aquatic invertebrates that prefer different water colours and for the multifunctioning of urban ponds. [ABSTRACT FROM AUTHOR]

Urbanization and climate change exacerbate groundwater overexploitation and urban flooding. The infiltration basin plays a significant role in protecting groundwater resources because it is a prevalent technology of managed aquifer recharge. It could also be utilized as a retention pond to mitigate city waterlogging. The goal of this study was to explore the offsets of artificial recharge on the extra runoff induced by urbanization and extreme storms via infiltration basins. To achieve this objective, a lumped infiltration basin module was developed and integrated into a semi-distributed hydrologic model. Then, the enhanced model was applied to an agriculture watershed with urban areas. Finally, the functionalities of the infiltration basins were evaluated under the scenarios of the predicted urbanization and extreme storms. The results demonstrated the capability of the infiltration basins to influence both artificial recharge and flood mitigation. To mitigate floods, especially peak flows, larger areas are needed for infiltration basins than for artificial recharge purposes only. Based on different demands, the intermittent regulation of infiltration basins according to different hydrologic periods is recommended. The offsets of artificial recharge on the extra surface runoff provide insight into the comprehensive preservation and management of surface water resources and groundwater resources. [ABSTRACT FROM AUTHOR]

Soil degradation is one of the greatest challenges faced by arid and semiarid zones, as it causes a progressive loss of the capacity of soils to sustain life and human activities. In the present study, five spectral indices, individually associated with erosion, desertification, salinity, and soil sealing, were evaluated in a semi-arid region of the Ecuadorian Andes to study of soil degradation. The results were validated through field observations and samples processed in the laboratory. The spectral indices presented correlation coefficients between 0.5278 and 0.8809 with field observations. These indices provided useful information to understand the current state of the soils and their potential deterioration. The study area showed greater vulnerability to erosion caused by the combination of scarce vegetation, irregular topography, and significant seasonal rainfall. The advance of urbanization, mechanized agriculture, and overgrazing had a high impact by impermeabilizing considerable surfaces, while the advance of desertification and soil salinization showed little incidence in the study area. The systematic application of these indices would become a valuable means for monitoring soil degradation in Andean watersheds. [ABSTRACT FROM AUTHOR]

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding. [ABSTRACT FROM AUTHOR]

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.

Accurate urban impervious surface (UIS) extraction from open-source remote sensing data remains challenging, especially for cities with heterogeneous climatic backgrounds. Contemporary, state-of-the-art techniques achieve promising results at a global scale, but accuracy is compromised at the city level. Therefore, a ensemble machine learning approach using open-source Optical-SAR remote sensing datasets was implemented to enhance the accuracy of UIS mapping. Initially, we integrated optical and radar datasets with modified urban indices to generate input features. Then, we applied four ensemble machine learning algorithms, including AdaBoost, Gradient Boost (GB), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), and fine-tuned them via a soft voting ensemble approach. The optimized UISEM approach showed a model accuracy of 98%. The UISEM method achieved a classification accuracy of 92% and consistently performed across 32 cities globally with heterogeneous climatic zones. Regarding accuracy and predictive power, the XGB ensemble classifier outperformed other ML classifiers in mapping UIS. Furthermore, a comparative analysis against three well-known datasets (ESA World Cover, ESRI Land Cover, and Dynamic World) was also performed. The proposed UISEM model outperformed renowned global datasets with a 92% classification accuracy, followed by DW with 83%, ESA with 86%, and ESRI with 82%. In the future, developing a spatial–temporal version of UISEM can support diverse urban applications globally. The datasets and (GEE and Python) codes are available at https://github.com/mnasarahmad/UISEM.

Accurate and timely monitoring of functional urban impervious surfaces (FUISs), such as ports, roads, and buildings, is essential yet challenging for complex coastal cities due to their cloudy weather and diverse land surfaces. Synthetic aperture radar (SAR) provides unique all-weather observation capabilities for prompt and regular urban mapping. However, SAR scattering information is limited to distinguish impervious surfaces with similar scattering responses but different functions. This study develops a scattering–compactness fusion network (SCFN), which integrates SAR polarimetric scattering and object compactness characteristics for enhanced FUIS recognition. Central to our approach is the scattering object compactness index, which is specifically designed to capture the distinct spatial patterns and compactness of scattering objects and complement their intrinsic scattering signatures. The dual-branch SCFN concurrently extracts and fuses object-scale scattering and compactness features using tailored network architectures. Experiments on L-band and C-band fully polarimetric ALOS-2 and GF-3 data in Hong Kong, as well as L-band dual-polarized ALOS-2 data, are undertaken to verify SCFN's effectiveness, achieving up to 8% improvement in the overall FUIS classification accuracy over baselines. The transferability of SCFN is further validated using fully polarimetric ALOS-2 data in Shenzhen, where consistent performance improvements are observed. The successful application of SCFN in both coastal cities highlights the potential of joint scattering–compactness modeling for advanced SAR-based urban mapping and its robustness across different urban landscapes.

This study presents an automated methodology to generate training data to map up-to-date artificial impervious surface (AIS) extent maps using two dates (winter and nonwinter) of a Sentinel-2 granule across six international sites (Egypt, India, Qatar, U.K., Eastern USA, and Western USA). It uses a series of spectral, textural, and distance decision functions combined with an outdated AIS layer to create nontarget and target binary masks from which to generate a balanced set of training data applied to a random forest classifier. Two outdated global AIS layers (GMIS-2010 and GISA-2016) were evaluated within the framework to create AIS maps from more recent years (e.g., 2020). For the decision functions, stepwise threshold adjustments applied to normalized difference vegetation index (NDVI) and Euclidean distance layers were evaluated on the binary masks (low-density AIS, high-density AIS, and nontarget land covers) with 729 permutations and 115 permutations for global impervious surface area (GISA) and global manmade impervious surface (GMIS), respectively. The optimal thresholds were determined globally (all six scenes), individually (scene) and grouped by climate for adaptive thresholds. The accuracy assessment found both GMIS-output and GISA-output with global thresholds can accurately map current AIS with 86.9% (±1.7%) (GISA) and 82.7% (±2.3%) (GMIS) accuracy. Adaptive climate thresholds yielded slightly higher accuracies for temperate, tropics, and arid scenes. A novel beach bare ground sampling mask and annual NDVI standard deviation were also evaluated for performance and improved the accuracy in 5/6 sites. Lastly, the global GISA output was compared with a manually labeled deep learning model (Esri) with slightly lower overall accuracy (86.9% vs 88.6%).

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications. [ABSTRACT FROM AUTHOR]

The China–Europe Railway Express (CRE) plays a crucial role in promoting local economic development and bolstering regional connectivity. Quantitative assessments of urban changes along the CRE are essential for understanding its influence on the local eco-environment and the economy, which is a cornerstone for fostering sustainable growth. Urban boundaries are vital indicators of city development; however, accurately extracting these boundaries over multiple years at a large scale and fine granularity remains challenging. On the basis of a global impervious surface area (GISA2) dataset from 2010 to 2019, this study extracted fine-scaled city boundaries annually for European countries and analyzed the spatiotemporal patterns of urbanization in areas along and not along the express during the past decade. The generated enhanced urban boundaries (EUBs) show strong agreement with the Landsat time-series data and align well with existing global urban time-series products (GUB, GAUD, and NTL). From 2010 to 2019, urban areas and clusters expanded across the entire region, with notable disparities observed between areas along the express (noted as ON-CRE) and those not along (noted as OFF-CRE). ON-CRE cities experienced more substantial urban growth near the express, resulting in new mega urban agglomerations and expansion in small and moderate cities. In contrast, most OFF-CRE cities exhibited stable development with limited urban sprawl. Dynamic proportions of the impervious surface area (ISA) within the EUB indicated more compact impervious areas within ON-CRE boundaries compared to OFF-CRE. This study contributes to understanding urban expansions driven by the long-term CRE operations. The EUB dataset developed herein provides a valuable resource for future investigations into the impacts of urbanization on biotic stress and urban health in European countries, thus supporting the achievement of sustainable development goals. [ABSTRACT FROM AUTHOR]

Evaporation from impervious surfaces plays a vital role in the catchment water cycle. Exploring the spatiotemporal variation patterns and influencing mechanisms of impervious surface evaporation at the catchment scale can improve the understanding and evaluation of the evaporation process. This study downloaded 0.5 m resolution images of the Baiyangdian catchment (BYD) from Google Earth and used deep learning to identify impervious surfaces. This was used to revise impervious surfaces of the China land cover dataset in 1985 and 1990–2020. Potential evaporation (PET) from three types of impervious surfaces (roofs, ground affected or not affected by the building height) was calculated by modifying the parameters of the Penman–Monteith equation, and daily precipitation and water‐storage capacity of impervious surfaces were taken into account to estimate impervious surface evaporation (E). The results showed that E values of the three types of impervious surfaces were between 72.1 and 178.2 mm/year and all exhibited the spatial distribution of high in the northwest and low in the southeast of the BYD in 1980–2020. Compared with that in 1980, in 2020, the cumulative evaporation (EAP) increased by 134.4%. The ratio of EAP to cumulative precipitation ranged from 3.0% to 6.9%, increasing significantly in a fluctuating manner. The increments in precipitation days and impervious surface area played a major role in the increase of EAP, and the decrease in precipitation was the fundamental reason for the increase in the proportion of impervious surface evaporation and water resource pressure in the BYD. Excluding the continuous evaporation from the remaining water can likely lead to underestimating the impervious surface evaporation. This study provides an efficient and reasonable novel approach for calculating impervious surface evaporation in long series and large‐scale catchments. [ABSTRACT FROM AUTHOR]

Complex ecosystems as wetlands are very sensitive to changes and disturbances. Human activities causing habitat fragmentation that alter landscapes affect the wetland ecosystems and negatively influence biodiversity. The health of the ecosystem is measured as a homeostasis between the system components and stability or resilience of the system against disturbance; therefore, any anthropogenic disturbances can cause dramatic changes in the balance of wetland ecosystem. Burullus, the second largest of the Egyptian northern coastal wetlands along the Mediterranean coast, has been recognized recently as critically endangered ecosystem according to an assessment using IUCN Red List of ecosystems. The study sought to assess Burullus wetland's health based on multispectral indicators related to the structure, function, and resilience of the ecosystem. Three indices were calculated to quantify the health of the wetland: the structure index, vigor index, and resilience index. The overall status of the wetland indicates a dynamic state and rapid transformation, which occurred over the study period. The outcomes indicated that Burullus's health has progressively deteriorated in the recent investigated years of 2016 and 2019 compared to the reference years of 1984 and 1986. The assessed indicators revealed a trend towards more deterioration of its ecological system due to the LULC transformations, anthropogenic interventions, and overexploitation of its natural resources. In addition, the growth in the impervious surfaces by 9% in the recent years due to expansion in urbanization was detected. All of these factors may affect the ecological balance and the health of the wetland to some extent. The fragility of this wetland and the continuous fragmentation of it occurs due to human activities that threaten its important and unique habitats. The integrated framework for the assessment of Burullus wetland's health based on remotely sensed data and using geospatial techniques can be utilized for the assessment of other similar coastal wetlands at national and regional scales. [ABSTRACT FROM AUTHOR]

Expansion of urban impervious surface (UIA) and increased urban pluvial flooding (UPF) have an impact on urban dynamics, socioeconomic activities, and our environment. Therefore, monitoring the increase in UIS and its effect on UPF is essential. The notion of this research is based on the mapping of impervious surface area increase in three major cities of Pakistan. There were two key objectives: (i) Mapping impervious surface area growth using the global impervious surface area index (GISAI) on Google Earth Engine from 1992 to 2022 and (ii) mapping the pluvial flood extent in selected urban areas using Sentinel-1 Ground Range Detected (GRD) data. Thus, we have utilized the GISAI for mapping urban impervious surface area (UISA) using Landsat time-series data on GEE. Our research findings revealed that about 16.8%, 23.5%, and 16.4% of the impervious surface have been increased in Islamabad, Lahore, and Karachi, respectively. Also, Lahore city has the highest overall accuracy, aiming at the GISAI of 93%, followed by Karachi and Islamabad with an overall accuracy of 86% and 85%, respectively. The results indicated that urban flooding has occurred in those areas where the ISA has grown during the last three decades. It shows significant changes in the impervious surface area that cause enhanced urban pluvial flooding in major cities of Pakistan. Also, Sentinel-1 data and the SNAP tool significantly mapped flooded areas in the selected zones. So, providing cities and local governments with increased quick flood detection capabilities is essential. It can also provide feasible policy recommendations for Pakistan decision-makers in city management. Therefore, we suggest a modeling-based solution to identify high-risk locations in major cities for upcoming UPF events. [ABSTRACT FROM AUTHOR]

The process of urbanization alters the distribution of land use and gives rise to certain climatic modifications that have a direct influence on vegetation phenology. Mountainous areas have a fragile biological environment, and vegetation phenology is relatively sensitive to urbanization. However, there is a paucity of research on the impact of urbanization in mountainous areas on vegetation phenology. The Urban Agglomeration of Central Yunnan (UACY) is located on the Yunnan–Guizhou Plateau in China. This study explored the vegetation phenological changes in different terrains from 2001 to 2020 in the UACY based on remote sensing data. Using the dynamic gradient method, we examined the response of vegetation phenology to urbanization from three aspects: urban–rural gradient, urbanization intensity (UI), and population density. The results showed that landform has a significant impact on SOS (start of growing season), with a topographic relief difference of 200 m acting as the dividing line. The findings derived from the dynamic gradient analysis indicate that UI has the most significant effect. SOS advances by 5.77 days (R2 = 0.96), EOS (end of growing season) advances by 2.30 days (R2 = 0.83), and LOS (length of growing season) lengthens by 2.59 days (R2 = 0.87) for every 10% increase in UI. This study has the potential to serve as a valuable resource for future urban planning and administration in the UACY. Additionally, it could provide decision-making support for the development of mountainous urban agglomerations in ecological environments. [ABSTRACT FROM AUTHOR]

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Wild bee communities persist in cities despite major disruption of nesting and food resources by urban development. Bee diversity and abundance is key for urban agriculture and maintenance of plant diversity, and assessing what aspects of cities enhance bee populations will promote our capacity to retain and provision bee habitat. Here, we assessed how variation in land cover and neighborhood development history affected bee communities in the midwestern US urban landscape of Madison, Wisconsin. We sampled bee communities across 38 stratified sites with relatively high (>55%) or low (<30%) levels of impervious surface, and assessed effects of land use and neighborhood development history on bee abundance and species richness. We show abundance and richness of soil nesting bees was lower in newer neighborhoods. Soil nesting bees and bee community richness decreased as cover of impervious surface increased, but above ground nesting bees were minimally impacted. Bee community similarity varied spatially and based on dissimilar local land cover, only for soil nesting bees, and the overall bee community. Impervious surface limited bee abundance and diversity, but new neighborhoods were associated with greater negative effects. We suggest that enhancing the structural diversity of new neighborhoods in urban ecosystems may imitate the structural benefits of older neighborhoods for bee populations. [ABSTRACT FROM AUTHOR]

Large-scale, high-resolution and multi-temporal impervious surface maps from remote sensing are essential for socioeconomic and environmental studies. However, in complex regions like subtropical China, climate and terrain severely contaminate optical and synthetic aperture radar (SAR) imagery, resulting in relatively poor accuracy here. Therefore, this paper presents a novel network for accurately extracting impervious surfaces, in which a terrain-guided gated fusion module is proposed to adaptively fuse Sentinel-2 optical and Sentinel-1 SAR imagery. To eliminate the tedious manual annotation over vast and mixed regions, an automatic samples generation and weighting strategy is further introduced. Then, the framework monitors impervious surfaces in subtropical China from 2016 to 2021 with the support of Google Earth Engine. The final impervious surface map scores an overall accuracy and Kappa of 93.19–94.04% and 0.843–0.859, respectively. Moreover, its consistent performance in various scenarios and superior visual details than most products evidence the feasibility and reliability of the proposed framework. This map provides precise dynamics of impervious surfaces that will help to inform urban studies.

This study focuses on the problem of mapping impervious surfaces in urban areas and aims to use remote sensing data and orthophotos to accurately classify and map these surfaces. Impervious surface indices and green space assessments are widely used in land use and urban planning to evaluate the urban environment. Local governments also rely on impervious surface mapping to calculate stormwater fees and effectively manage stormwater runoff. However, accurately determining the size of impervious surfaces is a significant challenge. This study proposes the use of the Support Vector Machines (SVM) method, a pattern recognition approach that is increasingly used in solving engineering problems, to classify impervious surfaces. The research results demonstrate the effectiveness of the SVM method in accurately estimating impervious surfaces, as evidenced by a high overall accuracy of over 90% (indicated by the Cohen’s Kappa coefficient). A case study of the “Parkowo-Lesne” housing estate in Warsaw, which covers an area of 200,000 m², shows the successful application of the method. In practice, the remote sensing imagery and SVM method allowed accurate calculation of the area of the surface classes studied. The permeable surface represented about 67.4% of the total complex and the impervious surface corresponded to the remaining 32.6%. These results have implications for stormwater management, pollutant control, flood control, emergency management, and the establishment of stormwater fees for individual properties. The use of remote sensing data and the SVM method provides a valuable approach for mapping impervious surfaces and improving urban land use management.

Soil degradation is one of the greatest challenges faced by arid and semi-arid zones, as it causes a progressive loss of the capacity of soils to sustain life and human activities. In the present study, five spectral indices, individually associated with erosion, desertification, salinity, and soil sealing, were evaluated in a semi-arid region of the Ecuadorian Andes to study of soil degradation. The results were validated through field observations and samples processed in the laboratory. The spectral indices presented correlation coefficients between 0.5278 and 0.8809 with field observations. These indices provided useful information to understand the current state of the soils and their potential deterioration. The study area showed greater vulnerability to erosion caused by the combination of scarce vegetation, irregular topography, and significant seasonal rainfall. The advance of urbanization, mechanized agriculture, and overgrazing had a high impact by impermeabilizing considerable surfaces, while the advance of desertification and soil salinization showed little incidence in the study area. The systematic application of these indices would become a valuable means for monitoring soil degradation in Andean watersheds.

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin. [ABSTRACT FROM AUTHOR]

The uneven development in the Lancang-Mekong River Basin(LMRB) makes the changes in population and impervious surface more complex, thus affecting the economic and social targets of river water resource allocation in the trans-boundary basin. This study employed remote sensed images and LandScan database to obtain impervious surface and population data, then constructed decoupling model to explore the decoupling relationship of population and impervious surface in LMRB from 1995 to 2015. Results showed: ①Impervious surface expansion intensity is bigger than population growth intensity in basin and national level. There is a spatial dislocation between the growth intensities of population and impervious surface in provincial level. ②At the basin level, the whole basin is weakly decoupled. At the national level, Thailand changes from weakly decoupled to strongly decoupled, while the other five countries remained weakly decoupled but the gap between population and impervious surface growth decreases. At the provincial level, LMB experiences the process from "weak decoupling" to "weak decoupling and strong decoupling" with the reasonable provinces increasing. ③Population growth and im‐ pervious surface expansion accelerates industrial-domestic water consumption of the basin. In China, Lao's and Myanmar parts of the basin, population growth is the leading factor to industri‐ al-domestic water consumption increasing, while impervious surface expansion dominates in Vietnam. In Thailand and Cambodia parts of basin, the effect of the two factors on industrial-do‐ mestic water consumption diminish. Basin countries should strengthen cooperation to coordinate the relationship between population and impervious surface, and adjust their water use plans and industrial structures for a better development. [ABSTRACT FROM AUTHOR]

Various physical characteristics of urban impervious surfaces (ISAs) and urban green spaces (UGSs) collectively regulate environmental temperatures through heating and cooling processes. However, current research often analyzes each regulating factor as an independent variable when examining its relationship with land surface temperature (LST), with limited studies considering the combined contribution weights of all regulating factors. Based on multi-source remote sensing data and ground observations from the near summers of 2014, 2016, 2017, and 2018 in the built-up area of Xuzhou City, numerical values and spatial distributions of 15 regulating factors, including ISA density (fi), land surface albedo (Albedo), population density (Population), anthropogenic heat flux (AHF), maximum ISA patch index (LPIISA), natural connectivity of ISA patches (COHESIONISA), aggregation index of ISA patches (AIISA), average shape index of ISA patches (SHAPE_MNISA), UGS density (fv), evapotranspiration (ET), UGS shading index (UGSSI), maximum UGS patch index (LPIUGS), natural connectivity of UGS patches (COHESIONUGS), aggregation index of UGS patches (AIUGS), and average shape index of UGS patches (SHAPE_MNUGS), were separately extracted within the study area. Using geographically weighted regression models and bivariate spatial autocorrelation models, we separately obtained the quantitative and spatial correlations between the 15 regulating factors and LST. The results revealed that all selected regulating factors exhibited high goodness-of-fit and significant spatial correlations with LST, which led to their categorization into eight warming factors and seven cooling factors. The factor detection of the Geographic Detector further reveals the combined contribution of all regulating factors to LST. The results indicate that cooling factors collectively have higher explanatory power for LST compared to warming factors, with UGSSI contributing the most to LST, while Population contributed the least. Furthermore, the interaction detection results of the Geographic Detector have highlighted variations in the explanatory power of different factor combinations on LST. Ultimately, it has identified factor combinations that have proven to be most effective in mitigating the urban heat environment across three scenarios: warming factors alone, cooling factors alone, and a combination of both warming and cooling factors. The suggested factor combinations are as follows: fi ∩ Albedo, fi ∩ LPIISA, UGSSI ∩ fv, UGSSI ∩ LPIUGS, fi ∩ UGSSI, and Albedo ∩ UGSSI. Therefore, our findings hold the potential to provide a valuable reference for urban planning and climate governance. Tailoring factor combinations to the local context and selecting the most effective ones can enable cost-effective mitigation of the urban heat environment. [ABSTRACT FROM AUTHOR]

The objective of this study was to discern the spatial and temporal patterns of areas in Nanning that are susceptible to waterlogging, particularly during various phases of urban expansion. Furthermore, this study presents a proposal outlining strategies aimed at preventing and controlling waterlogging. These strategies are based on the integration of the concepts of sponge city and resilient city construction. This study employed remote sensing (RS) and geographic information system (GIS) techniques to provide technical support. The supervised classification method and normalized difference index method were utilized to compare and extract impervious surfaces in Nanning from 2013 to 2020. The present investigation utilized the acquired impervious surfaces to compute the fractal dimension as a weighting factor, incorporating a digital elevation model (DEM) for the purpose of conducting a hydrological analysis in ArcGIS. Based on the findings of the study, several conclusions can be derived. The following conclusions can be drawn from the study: (1) The fractal dimension of Nanning varied over the study period, with values of 1.32, 1.41, and 1.58 in 2013, 2017, and 2020, respectively. The distribution of impervious surfaces showed a decreasing trend from the city center to the periphery. Urban planning and construction activities have significantly influenced the distribution of impervious surfaces, resulting in a progressively more complex and unstable structure. (2) From 2013 to 2020, the urban expansion fractal dimension increased from 1.32 to 1.58, indicating a decrease in the stability of impervious surfaces. The areas with higher concentrations of impervious surfaces coincided with frequent waterlogging-prone areas. Furthermore, the distribution of waterlogging-prone points transformed from a concentrated pattern to a scattered one. (3) In terms of waterlogging prevention and control strategies, the old urban areas are recommended to be transformed into sponge city projects, and the new development areas are planned, designed and implemented with the concept of "resilience". [ABSTRACT FROM AUTHOR]

Monitoring the spatiotemporal dynamics of building footprints (BF) is necessary for understanding urbanization growth. It is a difficult task to extract residential sites, mainly BF, because of the complexity of their makeup and spectral variety. Additionally, conventional methods for building mapping typically rely on abundant training data and expertise from human operates. This study presents a new unsupervised Feature-Based Building Footprint Extraction (F2BFE) strategy using Sentinel-1&2 satellite images and the SRTM Digital Elevation Model (DEM). The newly developed radar index (NRI) from Sentinel-1 images was utilized to extract the Primary Building Footprints (PBF) through histogram analysis and thresholding techniques, based on the mean of annual Sentinel-1 VV and VH Backscatter channels in the Ascending orbit. In this research, the integration of the Otsu and Unimodal thresholding technique was developed as an optimal thresholding method for feature extraction. Furthermore, Sentinel-2 images were applied to extract spectral indices related to vegetation (NDVI, GNDVI, RDVI indices), water (NDWI index), and residential/built-up (NDBI, BuEI). The qualitative and quantitative validation results indicate that the NRI-based BF map achieved higher Overall Accuracy (OA) values of 98.14%, 90%, and 91% in Region of Interest-1 (ROI-1), ROI-2, and ROI-3, respectively. Additionally, the Kappa Coefficients (KC) for these regions were 0.96, 0.97, and 0.85, respectively. The NRI index provides an excellent OA result when vegetation, water, and slope features are carefully eliminated. Finally, it can be inferred that the simultaneous use of the sentinel-1&2 and slope data in feature space leads to increased BF accuracy. [ABSTRACT FROM AUTHOR]

This Special Issue focuses on the data, methods, techniques, and empirical outcomes of urban heat island studies from a time and space perspective. We showcase research papers, empirical studies, conceptual or analytic reviews, and policy-related tasks to help achieve urban sustainability. We are interested in target methodologies and datasets capturing urban heat island phenomena, including novel techniques for urban heat island monitoring and forecasting with the integration of remote sensing and GIS, the spatial relationship between urban heat island intensity and land use/cover distribution in metropolitan areas, the geographical patterns and processes of urban heat island phenomena in large cities, spatial differences in urban heat island intensity between developing and developed countries, urban heat island disaster mitigation and adaptation for future urban sustainability, and prediction and scenario analysis of urban heat island formation for policy and planning purposes. [ABSTRACT FROM AUTHOR]

It is an important link for optimizing land and resource management and coordinating the relationship between people and land to analyze the spatio-temporal expansion characteristics and driving forces of impervious surface. But the research on the difference of impervious surface expansion and its influence mechanism in Yunnan needs to be improved. Therefore, data from 2006 to 2015 Statistical Yearbook of Yunnan and Tsinghua University􀆳s GAIA (Global Artificial Impervious Area) data are used to analyze the spatio-temporal expansion characteristics of impervious surfaces in the whole province, then the driving factors affecting expansion of impervious surfaces in Yunnan by using geographic detector is analyzed. The results are as follows: (1) From the perspective of space, from 2006 to 2015, the impervious surfaces of 16 prefectures and cities in Yunnan experienced unprecedented expansion, but there were regional differences, uneven expansion and other problems, among which Kunming, Qujing, Honghe Prefecture and Yuxi had a strong expansion. (2) In the time series, the impervious surface of Yunnan shows a state of continuous accelerating expansion. (3) The main factors influencing the expansion of impervious surface in Yunnan from 2006 to 2015 are the secondary industry, the tertiary industry and highway mileage, and the interaction among different factors has enhanced the expansion of impervious surface. The results can provide reference for understanding the mechanism of impervious surface expansion and promote sustainable development and coordinated development in Yunnan. [ABSTRACT FROM AUTHOR]

Dramatic land-use changes in urban landscapes can drive water colour darkening by washing compounds, such as organic matter and iron, from terrestrial ecosystems into urban blue space, consequentially affecting aquatic communities. Here, I studied how pond water colour changes along an urban gradient and how diving beetles (Dytiscidae) respond to the water colour gradient in 11 ponds with fish and 15 ponds without fish in the Helsinki Metropolitan Area, Finland. I found that the pond water colour exhibited a non-significant decreasing pattern along the urban gradient, indicating that urbanisation may not necessarily drive brownification in urban ponds. Dytiscid species richness and abundance exhibited significant positive correlations with increasing water colour in ponds with fish but no significant correlation in ponds without fish. Some species, such as Agabus spp. and Dytiscus spp., appeared tolerant to highly coloured water, whereas some species, such as Hyphydrus ovatus and Hygrotus spp., tended to occur in clear water, indicating that brown water may provide dytiscids with prey refuges, but some species are intolerant to brown water. The study highlights the importance of urban pondscape heterogeneity to meet the needs of aquatic invertebrates that prefer different water colours and for the multifunctioning of urban ponds.

Urbanization and climate change exacerbate groundwater overexploitation and urban flooding. The infiltration basin plays a significant role in protecting groundwater resources because it is a prevalent technology of managed aquifer recharge. It could also be utilized as a retention pond to mitigate city waterlogging. The goal of this study was to explore the offsets of artificial recharge on the extra runoff induced by urbanization and extreme storms via infiltration basins. To achieve this objective, a lumped infiltration basin module was developed and integrated into a semi-distributed hydrologic model. Then, the enhanced model was applied to an agriculture watershed with urban areas. Finally, the functionalities of the infiltration basins were evaluated under the scenarios of the predicted urbanization and extreme storms. The results demonstrated the capability of the infiltration basins to influence both artificial recharge and flood mitigation. To mitigate floods, especially peak flows, larger areas are needed for infiltration basins than for artificial recharge purposes only. Based on different demands, the intermittent regulation of infiltration basins according to different hydrologic periods is recommended. The offsets of artificial recharge on the extra surface runoff provide insight into the comprehensive preservation and management of surface water resources and groundwater resources.

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

Since impervious surface is one of the core urban ecological indices, its high-precision time series monitoring at regional or global scales is highly desirable. Spectral features, spatial features, and temporal features have been successfully applied to regional impervious surface monitoring. However, few studies focus on the extraction of optimal features for subpixel impervious surface monitoring at the regional or global scales. In order to solve the above problem, this study proposes a new method for subpixel time series impervious surface estimation based on optimal spectral-temporal features. The proposed method first extracts various spectral indices from time series Landsat imagery to construct a spectral-temporal feature library. A feature scoring model is then established to optimize spectral-temporal features from the feature library. Based on the optimal spectral-temporal features, a modified semi-supervised Fuzzy C-means (FCM) method is proposed to identify stable pixels and extract endmembers from stable pixels. Finally, the endmembers are applied to the Fully Constrained Least Squares (FCLS) for time series impervious surface abundance extraction. The optimal spectral-temporal features can effectively distinguish impervious surfaces from pervious surfaces through feature optimization selection, and improve the feature utilization performances, thereby obtaining high-precision impervious surface mapping. The proposed method is applied in Wuhan, China, from 2013 to 2021, using seasonal time series Landsat 8 OLI images. Experimental results show that the impervious surface area of Wuhan expands from 567.27 km2 in winter 2013 to 1056.95 km2 in winter 2021 at the pixel scale. The impervious surface area expands from 545.80 km2 in winter 2013 to 921.01 km2 in winter 2021 at the subpixel scale. The subpixel impervious surface monitoring effectively solve the overestimation and underestimation problem of the pixel-scale impervious surface monitoring. This study characterizes the long-term dynamics of impervious surfaces at the subpixel scale and can provide reliable urban growth information for evaluating the impact of urbanization on the ecological environment.

Accurately mapping impervious surface has great significance and application for urbanization monitoring, urban sustainable planning, ecosystem protection, etc. In this study, an automatic and high-performance multi-level classification approach based on time-series similarity, spectral feature, and logical reasoning was proposed to accurately map China's annual impervious surface dataset (CAID) from 1992 to 2021 using Landsat time-series images. Firstly, the synthesized image of 30-year night-time light maximum value was used to mask the areas where humans are inactive, which will preliminarily screen out the impervious surface and mitigate the computational volume. Then, similarity measurement based on dynamic time warping was performed, respectively, for periodic index series, which were weighted by an amplitude scale factor. Furthermore, similarity measurement, spectral features, and logical reasoning of long-time-series land cover type were combined to construct a multi-level approach to mapping impervious surface. Finally, for the computational efficiency of pixel-level time-series classification, we built a distributed parallel high-performance computing framework to fully utilize local computing resources for efficiently developing CAID. Using 58,839 validation samples based on stratified random sampling, the overall accuracy and F1-score of CAID were 90.05% and 88.49%. A comparison with five existing 30 m impervious surface products further indicated that our CAID achieved better impervious surface extraction accuracy and was capable of finely delineating impervious surface landscapes. Therefore, it was concluded that our CAID is an accurate and credible product that can provide an important data base for urbanization and urban-related research in China. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of mapping impervious surfaces in urban areas and aims to use remote sensing data and orthophotos to accurately classify and map these surfaces. Impervious surface indices and green space assessments are widely used in land use and urban planning to evaluate the urban environment. Local governments also rely on impervious surface mapping to calculate stormwater fees and effectively manage stormwater runoff. However, accurately determining the size of impervious surfaces is a significant challenge. This study proposes the use of the Support Vector Machines (SVM) method, a pattern recognition approach that is increasingly used in solving engineering problems, to classify impervious surfaces. The research results demonstrate the effectiveness of the SVM method in accurately estimating impervious surfaces, as evidenced by a high overall accuracy of over 90% (indicated by the Cohen's Kappa coefficient). A case study of the "Parkowo-Lesne" housing estate in Warsaw, which covers an area of 200,000 m², shows the successful application of the method. In practice, the remote sensing imagery and SVM method allowed accurate calculation of the area of the surface classes studied. The permeable surface represented about 67.4% of the total complex and the impervious surface corresponded to the remaining 32.6%. These results have implications for stormwater management, pollutant control, flood control, emergency management, and the establishment of stormwater fees for individual properties. The use of remote sensing data and the SVM method provides a valuable approach for mapping impervious surfaces and improving urban land use management. [ABSTRACT FROM AUTHOR]

The biophysical composition index (BCI)-based linear spectral mixture model (LSMM) is used in this study to extract the impervious surface (IS), vegetation, and soil coverage of the main urban region (MUR) of Hefei City over the 2001–2021 period. In addition, the Soil Conservation Service-Curve Number (SCS-CN) model is first applied to simulate the surface runoff (SR) in the MUR of Hefei City over the past 21 years, then assessed for simulation accuracy using typical waterlogging points in the study area. On this basis, the spatiotemporal evolution of IS and SR and their relationships in the MUR of Hefei City are investigated and discussed in this study. The obtained results showed that (1) the root-mean-square error (RMSE), mean absolute error (MAE), and systematic error (SE) values of the BCI index-based LSMM are smaller than those of the LSMM, demonstrating a higher extraction accuracy of urban IS extraction of the BCI index-based LSMM. (2) The IS area of the MUR of Hefei City exhibits an increasing trend from 107.555 km2 in 2001 to 387.660 km2 in 2021. In addition, the change rate and change intensity values indicate an increasing–decreasing–increasing trend. The highest change rate and change intensity values are 24.839 km2/year and 23.094%, respectively, and were observed in the 2001–2005 period. (3) The simulated SR (165–195 mm) in the MUR of Hefei City demonstrates an increasing trend in the 2001–2021 period at a rainfall intensity value of 200 mm/d. In addition, the simulated SR amount in the central area exhibits slight changes, while that in the surrounding areas shows substantial variations. (4) The distribution of IS and SR in the MUR of Hefei City reveals strong directional variations, which are all affected by geographical conditions. The IS coverage and SR show high positive correlation coefficients in different years. (5) The present study provides primary data for effective urban planning, water resources management and regulation, and disaster prevention and mitigation in Hefei City, as well as a scientific reference for future studies on urban IS, SR, and their quantitative relationships in other regions. [ABSTRACT FROM AUTHOR]

Uncontrolled urban expansion with rapid population growth causes great pressure on the urban environment. The index of impervious surface area (ISA) has recently been used for the dynamic monitoring of urban expansion. This paper takes Dar es Salaam, the largest city in Tanzania, as an example, combined with multi-source data to obtain urban impervious surface component data on an annual scale, based on which the spatial–temporal characteristics of the city's expansion are concluded: first, urban expansion showed an evidently phased pattern, with a steady annual growth rate of 2.9% during 2000–2005, and a low rate of 1.4% during 2006–2010, then a relatively high rate of 3.3% in the period of 2011–2019. Secondly, urban expansion hotspots emerged in areas along the main roads extending from the center to the south and north, but coastal expansion and infilling development became evident after 2015, with the impervious surface significantly increasing in areas with poor traffic. Third, urban land expansion, which lags significantly behind population growth, has eased, as the ratio of population growth rate to land expansion is declining, from 2.9 in the period 2002–2012 to 2.0 in the period 2012–2019. Finally, the influence of population, economy, traffic and nature on the evolution of impervious surfaces are analyzed. This paper puts forward corresponding strategic suggestions for land-use policies and analyzes the trend of urban growth spatiotemporal patterns in the future. [ABSTRACT FROM AUTHOR]

[ObjectiveH Landscape ecological risk is an effective indicator to identify regional ecological risks and measure regional ecological security. Based on the construction of impervious surface, the environmental response of human activities to the formation and exacerbation of risks can be intuitively determined. Regional landscape ecological risk assessment and the influencing factors of the aggravated risk areas are analyzed, which has scientific significance and practical value for curbing the emerging and spreading of risk exacerbation zones. Methods] Nanchang City was taken as an example, the information of impervious surface was obtained to construct a landscape ecological risk index. The spatiotemporal evolution characteristics of landscape ecological risk were analyzed. The influencing factors of landscape ecological risk in the study area was measured by using geographic detectors. --Results] (1) From 201 0 to 2020, the area of impervious surface in Nanchang City increased dramatically, from 648.63 km' to 1 ()75.12, and the resources of agricultural land and ecological land continued to decline. The impervious surface was mainly converted from landscapes such as cultivated land, forest land, and water areas, and the degree of interference from human activities increased. (2) From 201 0 to 2020, the spatial distribution of Nanchang's landscape ecological risk index was presented as low in the middle and high on both sides. The average value of landscape pattern index decreased as a whole. Tn 2()1(): 2015, and 2020, the areas of high-risk areas of landscape ecology in Nan ch ang City were 519.73, 324.82 km2, and 365.04 km2, respectively. The area of landscape ecological high-risk areas first decreased and then increased. The areas were dominated by landscape ecological medium-high-risk areas and medium-risk areas. (3) The influencing factors of landscape ecological risk in Nan ch ang City in 2010, 2015 and 2()2() were dominated by socio-economic factors and distance factors. The interaction effects of the influencing factors were all larger than the explanation degree of a single factor. The distance factor had a positive effect on natural climate factors and socioeconomic factors. (4) The landscape ecological risk exacerbation zone in Nan ch ang City was dominated by landscape types such as forest land, water area and cultivated land. Exacerbation zones were caused by multiple factors such as natural environment, socioeconomics, and accessibility, but the impact of human activities was more prominent. Conclusion] The landscape ecological status of Nanchang City has been improving. The impacts of natural environment, social economy, distance and other factors on the landscape ecological risk index and risk exacerbation areas are different and interactive. These research results are of great significance for maintaining regional ecological security and sustainable development. [ABSTRACT FROM AUTHOR]

The effective extraction of impervious surfaces is critical to monitor their expansion and ensure the sustainable development of cities. Open geographic data can provide a large number of training samples for machine learning methods based on remote-sensed images to extract impervious surfaces due to their advantages of low acquisition cost and large coverage. However, training samples generated from open geographic data suffer from severe sample imbalance. Although one-class methods can effectively extract an impervious surface based on imbalanced samples, most of the current one-class methods ignore the fact that an impervious surface comprises varied geographic objects, such as roads and buildings. Therefore, this paper proposes an object-oriented deep multi-sphere support vector data description (OODMSVDD) method, which takes into account the diversity of impervious surfaces and incorporates a variety of open geographic data involving OpenStreetMap (OSM), Points of Interest (POIs), and trajectory GPS points to automatically generate massive samples for model learning, thereby improving the extraction of impervious surfaces with varied types. The feasibility of the proposed method is experimentally verified with an overall accuracy of 87.43%, and its superior impervious surface classification performance is shown via comparative experiments. This provides a new, accurate, and more suitable extraction method for complex impervious surfaces. [ABSTRACT FROM AUTHOR]

Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from remote sensing images and applying three different machine learning algorithms. Through this process, we obtained the optimal combination of features and a machine learning algorithm. Utilizing this model, our objective is to map the evolution of IS in the Dianchi Lake Basin, from 2000 to 2022, and analyze its dynamic changes. Our results showed the following: (1) The optimal model for IS extraction in the Dianchi Lake Basin was IMG-SPESVM based on the support vector machine, remote sensing images, and spectral features. (2) From 2000 to 2022, the spatial distribution and shape of the IS in the Dianchi Lake Basin changed significantly, but they all developed in the area around Dianchi Lake. (3) From 2000 to 2015, the rate of expansion of IS gradually accelerated, while from 2015 to 2022, it contracted. (4) From 2000 to 2022, the center of mass of IS moved to the northeast, and the standard deviation ellipse shifted greatly in the south–north direction. (5) Natural factors negatively affected the expansion of IS, while social factors positively affected the distribution of the IS. [ABSTRACT FROM AUTHOR]

With an increase in human population over the past 30 years, regional land use in south Texas has shifted from grassland and shrubland to a peri-urban matrix. Despite this shift from natural areas to more anthropogenically modified habitats, native red harvester ants (Pogonomyrmex barbatus) have maintained nest sites within parts of these matrices. To determine which habitat characteristics in a peri-urban landscape may play a role in red harvester ant nest site selection, we mapped the location of nests in 2020 and 2021. We then evaluated nest presence and absence relative to elevation, percentage of surrounding impervious surfaces, distance to roadways, and tree canopy cover (using NDVI). For a sub-sample of the study site, we also measured soil moisture and estimated the potential foraging area per colony with Voronoi tessellation. We found that nests were clustered together near high human-use areas such as athletic fields, lawns, sidewalks, and railroad tracks. Nests were more likely to be found in areas with higher elevation and lower tree canopy cover, with no impact from surrounding impervious surfaces or soil moisture. In fact, many nests were observed immediately adjacent to roadways and in paved parking lots. Red harvester ants are highly adept at nesting in disturbed, urbanized matrices, but still appear to be constrained by certain environmental factors like shading, potential flood risk (elevation), and access to food resources (foraging area). [ABSTRACT FROM AUTHOR]

Fast and accurate impervious surface extraction is important to monitor urban dynamics and ensure people's well-being. In order to improve the extraction accuracy of impervious surface, a composite impervious surface extraction index CBI based on NDBI, NDVI and MNDWI is proposed in the paper. The impervious surface extraction images in different land cover areas were compared with the NDBI and NDISI, respectively, and were assessed by using visual and statistical analysis. The results show that CBI has good robustness, less affected by the type of ground objects and sensors. The Kappa coefficients of CBI are the highest in different sensors and OLI images with different land cover areas, and the overall accuracy is above 91%. In the area with high coverage of vegetation and water, the extraction accuracy of CBI was significantly improved. CBI is more suitable for areas with high coverage of vegetation and water and has an improved Kappa coefficient of 0.1116 than that of the areas containing only a small amount of water and vegetation. As the NDISI algorithm uses the thermal infrared band, when the area of the study area is small, its extraction accuracy is low, indicating that the accuracy of the algorithm is affected by the size of the study area.

Urbanization as a human activity alters the surface structure, modifying the urban ecological environment. In this study, the greenness, wetness, building index, and heatness of Dalian, a coastal city in northeast China, were extracted from the summer MODIS remote sensing data of 2010 and 2022, and the remote sensing ecological index (RSEI) was created based on spatial principal component analysis. The artificial impervious surface fraction (AISF) in Dalian was extracted to analyze the impact of urban expansion on urban ecological environment with the background of urbanization, the spatial and temporal change characteristics of the urban ecological environment in Dalian from 2010 to 2022 were thoroughly evaluated. The results show that: (1) Dalian's RSEI in 2010 and 2022 primarily ranged from 0.2 to 0.8. During 2010–2022, the RSEI in urban built-up areas decreased from 0.2–0.4 to 0–0.2. However, the areas of RSEI approaching 1 have increased significantly in the past ten years, mainly distributed in original forest land or ecological land in the northern areas of Dalian. (2) From 2010 to 2022, Dalian's natural environment showed a positive development in which, the area with enhanced ecological quality accounted for 59.09%; however, the proportion of ecological land with RSEI greater than 0.8 declined from 76.64% to 51.05% and the proportion of urban building land rose from 1.11% to 11.85%, demonstrating that urbanization has had a substantial impact on the urban natural environment and has altered the urban land use structure. (3) The AISF of Dalian's urban center area was found to exceed 70% and is centered in the south's densely built-up area. The urban expansion boundary extending from the city center to the surrounding territory has an AISF ranging from 20% to 70%. This study objectively analyzed the geographical and temporal features of the ecological environment, as well as the impact of urbanization, and offers constructive recommendations for urban building design and population distribution optimization.

The use of multisource remote sensing data to obtain urban impervious surface has become a popular research topic. Multisource remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association between similar object data. To fill this gap, in this article, we propose a dual-view learning fusion classification method (DvLF) based on multiview learning. First, DvLF uses cotraining algorithm to combine multiple data sources for accurate classification, extracting easy-to-classify area while separating difficult-to-classify regions for further analysis. Second, a canonical correlation analysis method is adopted to mine the degree of association of similar object data for constructing a subspace projection field of each object sample. The data in the difficult-to-classify regions are classified in the projection field of each object, and then the results of each classification are fused by voting. Finally, the classification results of the two regions are combined into the classification results of the whole image to achieve impervious surface mapping. The proposed method is applied to the dual-sensor (high-resolution image and LiDAR) Buffalo dataset and the dual-sensor (RGB and multispectral LiDAR) Houston dataset. The experimental results show that our method achieved a significant improvement in classification accuracy compared to other methods. The overall classification accuracy of this new DvLF fusion method on the Buffalo and Houston datasets is 83.35% and 88.84%, respectively, leading to accurate high-resolution impervious surface mapping.

Remote sensing techniques have proved its efficacy for the impervious surface mapping, which is a significant indicator of urbanization process and environmental status. However, systematic and random errors in the existing methods still impact the reliability of subpixel impervious surface estimation, generating compounded errors when conducting multitemporal monitoring. The compounded errors of the conventional methods often significantly impact the temporal consistency of the results. In this study, a novel method based on a straightforward pixel change detection approach was put forward to improve the estimation of multitemporal impervious surface area. Two experimental areas located in Rome in Italy and Shenzhen in China were chosen to testify the generality of the proposed method to estimate different types of impervious surfaces worldwide. By reducing the compounded errors, the proposed method demonstrated its efficiency in achieving higher accuracy in both study areas without involving extensive data sources and intensive manual tasks. Compared with the conventional classification and regression tree algorithm, the overall mean average error and root mean square error of this study declined by more than 15.55% and 8.63%, respectively, and R2 increased from approximately 0.93 to 0.96. The proposed method also drastically reduced the standard deviation of the multitemporal percent ISA of the stable pixels. The accurate change estimation of percent ISA has been a fundamental but challenging issue associated with monitoring and understanding the urban environment. Therefore, our proposed method, with its improved ability to estimate impervious surface change both spatially and temporally, can provide accurate information required for urban environment research.

Accurate extraction of urban impervious surface (UIS) is essential for urban planning and environmental monitoring. However, multispectral remote sensing data for UIS extraction suffers from the inter-class spectral confusions, e.g. UIS and bare soil, and intra-class variations of sub-class UIS. Hyperspectral and full/dual-polarization synthetic aperture radar (full/dual PolSAR) data provide opportunities for reducing such confusions and have potential for fine UIS mapping, i.e., roads, buildings, and grounds. In this study, we first investigated the hyperspectral data (Gaofen-5) capability to reduce the intra/inter-class misclassification in comparison with multispectral data (Landsat-8). Then, we explored contributions of synergistically using full and dual PolSAR (ALOS-2 and Sentinel-1) with hyperspectral and multispectral data using optical-SAR sparse representation classification (OSSRC). Results showed that both the hyperspectral and the SAR polarization features helped better delineation between UIS and bare soil, and sub-class UIS (roads and buildings). The relative contribution of PolSAR was higher in multispectral data than in hyperspectral data, with full PolSAR contributed significantly. The combined hyperspectral and full PolSAR data using OSSRC delivered the best result, with an overall accuracy higher than 90%. The results indicate the promising capability of synergizing hyperspectral and full/dual PolSAR data for improving UIS extraction from advanced satellite data.

It is critically important to understand the variation of urban thermal environment since urban heat island (UHI) effect is one of the most worrisome urban environment and impedes urban sustainable development. Taking Huai’an central urban area in China as a case, Landsat images from 2000 to 2020 were used, we first estimated land surface temperature (LST) and land covers based on google earth engineer (GEE) platform. Then explored annual variation of SUHI and urban expansion types using statistics method and urban landscape Analysis tools. Finally, discussed the heat effect of urban expansion types and directions. The results showed that (1) SUHI patches gradually concentrated in Phase I, while in Phase II, concentrated SUHIs spread continuously from urban centre to rural area, changing from two centres to a polycentric pattern. (2) Urban expansion was characterized as a diffusion and coalescence process, extension type is the most dominant urban growth mode, second is leapfrog type, and the smallest is infilling type. (3) Annual trend of NLST shows an increasing trend for all three urban expansion types during the last two decades, which of extension mode was biggest, while, that of infilling growth was smallest. This research can attribute a better understanding for the impact of urban expansion on surface heat island effects.

ABSTRACTImpervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.

Nepal’s political institutions and administrative units were thoroughly restructured in 2015 with the promulgation of the new Constitution. Several rural areas were combined to meet the definition of urban threshold criteria to classify rural areas into urban categories. Accordingly, over 3,900 local political and administrative units were amalgamated into 753 units, of which, 293 units are classified as urban. Within these newly defined urban areas, many natural environments have been converted into impervious surfaces such as paved roads, sidewalks, and building roofs. These impervious surfaces have drastically increased the amount of surface run-offs—often termed as “urban floods” --under increasing precipitation caused by global climate change. These incidences have negatively impacted to the groundwater recharge processes in the urban areas. Data on groundwater recharge rates are needed in the context of global climate change to understand the status of groundwater recharge processes in the urban areas of Nepal. However, due to various limitations, this study only focuses around the Kathmandu Valley of Nepal to understand: a) how the expansion of urban, peri-urban, and associated areas have resulted in decreasing groundwater recharges; b) how groundwater is affected by the year-to-year variability of precipitation amount (low and high intensity) with the conversion of the natural landscape into built-up areas; and c) how the changing trends in precipitation and evapotranspiration may impact future groundwater availability. This study is based on a review of the literature and the analysis of secondary data available from the government and various social media and authors' professional experiences. The study ends with some recommendations based on experiences from other parts of the world on groundwater recharge processes.