Search Results (84)

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization. Full article

The Huang-Huai-Hai Plain, a vital agricultural area in China with a significant amount of arable land, plays a pivotal role in influencing grain production, ecological carbon cycles, and global climate change through its shifts in land use. Within this research, we have employed the ArcGIS tool and the In-VEST-Geodetector-PLUS methodology to scrutinize the shifts in carbon storage from the year 2000 to 2020, determine the pivotal influences behind these shifts, and anticipate the projected carbon storage for 2030. Although there has been a slight increase in forested areas as a result of environmental policies, the conversion of cropland to impervious surfaces due to urbanization has led to a persistent decrease in carbon storage, with a cumulative loss of 272.79 million metric tons over the two decades. The Normalized Difference Vegetation Index (NDVI), Night-Time Lights (NTL), Gross Domestic Product (GDP), and Population (POP) are critical factors impacting carbon storage, reflecting the intricate connection between socio-economic development and natural ecosystems. The multi-scenario simulations for 2030 suggest that the least reduction in carbon storage would occur under the scenario of protecting arable land, while the most significant decrease would be under the urban expansion scenario, highlighting the impact of urbanization. The study’s results emphasize the critical need to harmonize agricultural land conservation with economic progress for the enduring growth of the Huang-Huai-Hai region. Full article

Urbanization has significantly altered the ecological resources, functions, and services, thereby imposing specific constraints on particulate matter (PM) mitigation through green spaces. To investigate the effect of green spaces on mitigating PM_10,2.5 under multiple urban stressors, this study employed combined remote sensing imagery and small-scale quantitative measurements to identify the PM within green space and street tree, and their PM differences with the square underlying surface according to a continuous scale of 60~3000 m. The results indicated that urban stressors significantly influenced air PM₁₀ and PM_2.5 mitigation, with stressors LST (land surface temperature) and RD (traffic road density) as key stressors on air PM₁₀, while LST, ISA (impervious surface area), BH (building height), NDVI (normalized difference vegetation index), GA (green space area), and WA (water body area) were key stressors on air PM_2.5. Furthermore, stressors exhibited a significant scale effect on air PM_10,2.5 mitigation; for air PM_2.5, stressors ISA, RD, BH and BD (building density) had a notable impact on air PM_2.5 mitigation at 1500~3000 m scales, while NDVI, GA, and WA showed a significant impact at 450~600 m. For air PM₁₀, stressors ISA, BH, NDVI, and GA revealed a continuous scale effect, with the key scales occurring at 450 m and 3000 m. In summary, urbanization stressors can combine to affect air PM₁₀ and PM_2.5 mitigation by green spaces, especially at different spatial scales, to provide practical guidance for urban planning. Full article

Urban green spaces (UGSs) are integral to urban ecosystems, providing multiple benefits to human well-being. However, previous studies mainly focus on the quantity or quality of UGSs, with less emphasis on a comprehensive analysis. This study systematically examined the spatiotemporal UGS dynamics in the Pearl River Delta urban agglomeration (PRDUA) in China from the perspectives of the area, spatial configuration, and quality, using the high spatial resolution (30 m) Landsat-derived land-cover data and Normalized Difference Vegetation Index (NDVI) data during 1985–2021. Results showed the UGS area in both the old urban districts and expanded urban areas across all nine cities in the PRDUA has experienced a dramatic reduction from 1985 to 2021, primarily due to the conversion of cropland and forest into impervious surfaces. Spatially, the fragmentation trend of UGSs initially increased and then weakened around 2010 in nine cities, but with an inconsistent fragmentation process across different urban areas. In the old urban districts, the fragmentation was mainly due to the loss of large patches; in contrast, it was caused by the division of large patches in the expanded urban areas of most cities. The area-averaged NDVI showed a general upward trend in urban areas in nearly all cities, and the greening trend in the old urban districts was more prevalent than that in the expanded urban areas, suggesting the negative impacts of urbanization on NDVI have been balanced by the positive effects of climate change, urbanization, and greening initiatives in the PRDUA. These findings indicate that urban greening does not necessarily correspond to the improvement in UGS states. We therefore recommend incorporating the three-dimensional analytical framework into urban ecological monitoring and construction efforts to obtain a more comprehensive understanding of UGS states and support effective urban green infrastructure stewardship. Full article

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development. Full article

Urban green spaces (UGSs) provide important support for the health of urban residents and the realization of sustainable urban development. However, the spatiotemporal pattern of urban resident exposure to UGSs in cities is unclear, especially at the national scale in China. Based on the annual 30 m resolution Normalized Difference Vegetation Index (NDVI) data of the Landsat satellite, we quantitatively analyzed the change in UGS coverage from 2000 to 2020 for 320 cities in China and combined it with population data to understand the changing patterns of urban population exposure to different UGS coverage. The results indicated that the average UGS coverage decreased from 63% to 44% from 2000 to 2020 in China, which could be divided into two stages: a rapid decline phase (2000–2014) and a progressive decline phase (2015–2020). Geographically, UGS coverage declined faster in southwestern and eastern cities than in other regions, particularly in medium-sized cities. We also found that urban pixel-based areas in cities with the highest UGS coverage (80–100%) decreased rapidly, and the proportion of the urban population exposed to the highest UGS coverage also declined significantly from 2000 to 2020. Urban pixel-based areas with low UGS coverage (20–40%) continued to expand, and there was a rapid increase in the proportion of the urban population exposed to low UGS coverage, with an increase of 146 million people from 2000 to 2020. The expansion of impervious surfaces had the most significant effect on the change in UGS coverage during different periods (2000–2020, 2000–2014, and 2015–2020). Natural factors such as precipitation, surface maximum temperature, and soil moisture also affected UGS coverage change. These findings provide insights into the impact of urbanization on the natural environment of cities, availability of UGS for residents, and sustainable urban development under rapid urbanization. Full article

Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R² value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery. Full article

In the context of global climate change, there is a projected increase in land surface temperature (LST) worldwide, amplifying its impacts. This poses a particular concern for countries with hot climates, including the Kingdom of Bahrain as an example for the Gulf Cooperation Council countries (GCC), which are countries with a hot climate. With a surge in population growth, there is a heightened demand for land to accommodate additional residential developments, creating an opportunity to investigate the influence of land use changes on LST variations. To achieve this goal, a residential development project spanning from 2013 to 2023 was undertaken. Landsat 8 OLI/TIRS remote sensing datasets were selected for four climate seasons, each set comprising images before and after development. The analysis involved extracting the LST, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-Up Index (NDBI) on various dates, followed by correlation and regression analyses to explore their interrelationships. The results revealed a significant increase in the mean LST during spring and autumn post-development. A consistent positive association between the LST and NDBI was observed across all seasons, strengthening after development completion. Conversely, there was a pre-development negative correlation between the LST and NDVI, shifting to a positive relationship post-development. These findings empirically support the idea that small-scale residential developments contribute to notable LST increases, primarily due to expanded impervious surfaces. These insights have the potential to inform localized adaptation strategies for small-scale residential development projects, crucial for managing the impacts of rising land surface temperatures. Full article

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. Full article

Utilization of remote sensing-derived meteorological data is a valuable alternative for tropical insular territories such as Puerto Rico (PR). The study of ecosystem resilience in insular territories is an underdeveloped area of investigation. Little research has focused on studying how an ecosystem in PR responds to and recovers from unique meteorological events (e.g., hurricanes). This work aims to investigate how an ecosystem in Western Puerto Rico responds to extreme climate events and fluctuations, with a specific focus on evaluating its innate resilience. The Antillean islands in the Caribbean and Atlantic are vulnerable to intense weather phenomena, such as hurricanes. Due to the distinct tropical conditions inherent to this region, and the ongoing urban development of coastal areas, their ecosystems are constantly affected. Key indicators, including gross primary production (GPP), normalized difference vegetation index (NDVI), actual evapotranspiration (ET), and land surface temperature (LST), are examined to comprehend the interplay between these factors within the context of the Culebrinas River Watershed (CRW) ecosystem over the past decade during the peak of hurricane season. Data processing and analyses were performed on datasets provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 OLI TRIS, supplemented by information sourced from Puerto Rico Water and Energy Balance (PRWEB)—a dataset derived from Geostationary Operational Environmental Satellite (GOES) data. The findings revealed a complex interrelationship among atmospheric events and anthropogenic activities within the CRW, a region prone to recurrent atmospheric disruptions. NDVI and ET values from 2015 to 2019 showed the ecosystem’s capacity to recover after a prolonged drought period (2015) and Hurricanes Irma and Maria (2017). In 2015, the NDVI average was 0.79; after Hurricanes Irma and Maria in 2017, the NDVI dropped to 0.6, while in 2019, it had already increased to 0.8. Similarly, average ET values went from 3.2339 kg/m²/day in 2017 to 2.6513 kg/m²/day in 2018. Meanwhile, by 2019, the average ET was estimated to be 3.8105 kg/m²/day. Data geoprocessing of LST, NDVI, GPP, and ET, coupled with correlation analyses, revealed positive correlations among ET, NDVI, and GPP. Our results showed that areas with little anthropogenic impact displayed a more rapid and resilient restoration of the ecosystem. The spatial distribution of vegetation and impervious surfaces further highlights that areas closer to mountains have shown higher resilience while urban coastal areas have faced greater challenges in recovering from atmospheric events, thus showing the importance of preserving native vegetation, particularly mangroves, for long-term ecosystem stability. This study contributes to a deeper understanding of the dynamic interactions within urban coastal ecosystems in insular territories, emphasizing their resilience in the context of both natural atmospheric events and human activity. The insights gained from this research offer valuable guidance for managing and safeguarding ecosystems in similar regions characterized by their susceptibility to extreme weather phenomena. Full article

Problems such as the expansion of impervious areas and changes in underlying surfaces have occurred in cities due to rapid urbanization, along with an increasing probability of extreme rainfall. Based on the normalized building index (NDBI) of underlying surfaces, calculated from remote sensing images in the Pearl River Delta from 1990 to 2020, this study determines the underlying surface changes in the Pearl River Delta. Based on the hourly rainfall data of meteorological stations in the Pearl River Delta region from 1990 to 2020, the extreme rainfall indexes are calculated to analyze the changes in extreme rainfall in the Pearl River Delta. Based on the NDBI of underlying surfaces and extreme rainfall calculated in the Pearl River Delta, the evolution of underlying surfaces and extreme rainfall is analyzed, as is the correlation between them, and the main conclusions are as follows: (1) From 1990 to 2020, the NDBI in highly urbanized areas in the Pearl River Delta was higher than that in non-highly urbanized areas. The NDBI in highly urbanized areas showed an increasing trend, and the growth rate tended to slow down; (2) From 1990 to 2020, extreme rainfall in highly urbanized areas of the Pearl River Delta was higher than in non-highly urbanized areas. Extreme rainfall in both highly urbanized areas and non-highly urbanized areas showed an increasing trend, with that in highly urbanized areas increasing faster; (3) The positive correlation between the NDBI and extreme rainfall indicators in highly urbanized areas is more significant than that in non-highly urbanized areas. Full article

This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CN_cα) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CN_cα map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges. Full article

Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts. Full article

Urban heat vulnerability varies within and across cities, necessitating detailed studies to understand diverse populations’ specific vulnerabilities. This research assessed urban heat vulnerability at block group level in three Alabama cities: Birmingham, Montgomery, and Auburn-Opelika. The vulnerability index combines exposure, sensitivity, and adaptive capacity subindices, incorporating Landsat 8 satellite-derived Land Surface Temperature (LST), demographic, and socioeconomic data using factor analysis and geospatial techniques. Results showed strong positive correlations between LST and impervious surfaces in Auburn-Opelika and Montgomery, with a moderate correlation in Birmingham. An inverse correlation between LST and Normalized Difference Vegetation Index was observed in all cities. High LST correlated with high population density, varying across cities. Birmingham and Montgomery’s central areas exhibited the highest heat exposure, influenced by imperviousness, population density, and socioeconomic factors. Auburn-Opelika had limited high heat exposure block groups, and high sensitivity did not always align with exposure. Correlations and cluster analysis were used to dissect the heat vulnerability index, revealing variations in contributing factors within and across cities. This study underscores the complex interplay of physical, social, and economic factors in urban heat vulnerability and emphasizes the need for location-specific research. Local governance, community engagement, and tailored interventions are crucial for addressing unique vulnerabilities in each urban context. Full article

Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC’s impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle. Full article