Search Results (135)

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

This study examines the impact of geomagnetic disturbances quantified by the Kp and Dst indices on the accuracy of single-frequency GPS positioning across mid-latitudes and the equatorial zone, with a focus on temporal and spatial positioning errors variability. GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. The selection of stations encompassed equatorial and mid-latitude regions (approximately ±45°), strategically aligned with the distribution of the Dst index during geomagnetic storms. Satellite navigation data were processed using RTKLIB software in standalone mode with standardized atmospheric and orbital corrections. The GPS was chosen over GLONASS following preliminary testing, which revealed a higher sensitivity of GPS positional accuracy to variations in geomagnetic indices such as Kp and Dst, despite generally lower total error magnitudes. The ECEF coordinate system calculates the total GPS error as the vector sum of deviations in the X, Y, and Z axes. Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. The results of the variance analysis confirm that the variation in the total GPS positioning error is non-random and can be attributed to the influence of geomagnetic storms. However, regression analysis reveals that the impact of geomagnetic storms (quantified by Kp and Dst) displays spatiotemporal variability, with no consistent correlation to GPS positioning error dynamics. The findings, as well as the developed methodology, have qualitative implications for GNSS-dependent operations in sensitive sectors such as navigation, timing services, and geospatial monitoring. Full article

The equitable allocation of healthcare resources reflects social equity. Previous studies of healthcare accessibility have overlooked the impact of inter-city patient mobility on local residents’ and local residents’ multi-mode travel choices, distorting accessibility calculation outcomes. Taking the area within Beijing’s Sixth Ring Road as an example, this study established a Multi-Mode Accessibility Model for Local Residents (MMALR) to tertiary hospitals, using the proportion of non-local patients to adjust hospital supply capacity and considering the various travel mode shares from residential communities to hospitals to calculate the number of potential patients. We compared the changes in geospatial accessibility under different travel modes and employed the Gini coefficient to evaluate the geospatial equity of accessibility for different regions when using different accessibility methods. The results indicate that the spatial distribution of healthcare accessibility via different methods is similar, and it gradually decreases along subway lines from the urban center to the periphery. We found that the equities in access to high-level healthcare for Dongcheng District, Xicheng District, the area between the Third and Fourth Ring Road, and the area between the Fourth and Fifth Ring Road, display different ranking results across different methods, revealing that an unreasonable analysis framework could mislead the placement decisions for new hospitals or the allocation of medical resources. These findings emphasize the impact of inter-city patient mobility and the diversity of travel mode choices on accessibility. Our model can assist stakeholders in more accurately evaluating the accessibility and equity of local residents in terms of tertiary hospitals, which is crucial for cities with abundant medical resources and superior conditions. Our analytical findings provide a scientific basis for the location decisions of tertiary hospitals. Full article

Amid declining fish diversity and human pressures in freshwater ecosystems, robust basin-scale assessments are vital for effective fisheries management. This study collated nearly four decades of fishery yields from the Pearl and Yangtze Rivers to identify conservation priorities in the Pearl River Basin. It introduced a novel cumulative effect indicator based on zeta diversity—a biodiversity pattern metric—integrated with cumulative effects analysis for management decision-making. The research employed a multi-site generalized dissimilarity model to examine the non-linear relationships between fish species composition (ζ_n) and human pressures, environmental factors, and geospatial variations across elevation gradients. The cumulative effect indicator, reflecting responses to anthropogenic stress when assessing ζ₂ (related to β diversity), helped evaluate basins for conservation or restoration needs based on their unique or homogenized biotic communities. The results suggest that ζ diversity in low-elevation sub-basins has a stronger filtering effect on ζ by human pressures than in mid- to high-elevation sub-basins, where community aggregation is more random. The impact varied with diversity aspects (nestedness vs. turnover) and zeta order. A negative correlation between cumulative effects and community uniqueness validated the novel cumulative effect indicator’s effectiveness for guiding restoration in the Pearl River Delta, potential fishing bans, and karst conservation. This approach offers a theoretical basis for prioritizing areas for freshwater fish diversity conservation and fishing restrictions in the Pearl River Basin. Full article

YouthMappers chapters, utilizing OpenStreetMap (OSM), play a pivotal role in tackling climate challenges through education and activism. This study investigates the influence of a booster grant project on enhancing Climate Activism and Education efforts through YouthMappers chapters in Sri Lanka. Through a geometric approach, the research integrates measurable survey data from OSM platform data from 223 YouthMappers chapter respondents at four (04) universities in Sri Lanka to evaluate five critical factors/dimensions: Capacity Building and Funding Support (CBFS), Climate Activism and Education (CAE), Community Engagement and Collaboration (CEC), Technical Skills and Resources (TSR), and Sustainability and Policy Integration (SPI). The Friedman test confirmed statistically significant differences across all factors’ variables (p < 0.001), highlighting strengths in technical competence and educational integration, with gaps identified in community engagement and sustainability. A Radial Basis Function (RBF) model revealed moderate predictive accuracy, excelling in variables like CAE and TSR but indicating higher error rates in SPI and CEC. Practical outcomes include flood risk maps, curriculum-integrated teaching schemes, and localized mapping workshops. These results underscore the booster grant’s role in enabling impactful, youth-led geospatial initiatives. However, challenges such as internet access, training gaps, and language barriers remain. This study recommends expanding student and community participation, refining training strategies, and integrating OSM into university curricula. These scalable interventions offer valuable insights for replication in other vulnerable regions, enhancing climate resilience through community-driven, data-informed youth engagement. Full article

As an important part of the emerging energy portfolio, the coordinated development of the photovoltaic (PV) industry and ecological environment is a core factor in realizing the high-quality development of the energy industry. Xinjiang, located in northwestern China, possesses vast open land, abundant solar radiation, and low land-use conflict, making it a strategic hub for large-scale PV power station deployment. However, the region’s fragile ecological background is highly sensitive to land-use changes induced by PV infrastructure expansion. Therefore, scientifically evaluating the ecological impacts of PV construction is essential to support environmentally informed operation and maintenance (O&M) strategies.This study investigates the spatial distribution of PV installations and their macro-scale ecological effects across Xinjiang from 2000 to 2020. Utilizing multi-temporal satellite remote sensing data and geospatial analysis techniques on the Google Earth Engine (GEE) platform, we constructed a Remote Sensing Ecological Index (RSEI) model to quantify the long-term ecological response to PV development. It was found that PV installations were concentrated in unutilized land (37.10%) and grassland (34.45%), with the smallest proportion being found in forested land (1.68%). Nearly 70% of the PV areas showed an improving trend in the ecological environment index, and there were significantly more ecological quality-improving areas than degraded areas (69% vs. 31%). There were significant regional differences, and the highest ecological environment index was found in 2020 for the Northern Xinjiang Altay PV area (0.30), while the lowest (0.10) was observed in Hetian in southern Xinjiang. The results of this study provide a spatial optimization basis for the integration of PV development and ecological protection in Xinjiang and provide practical guidance to help the government to formulate a comprehensive management strategy of “PV + ecology”, which will help to realize the synergistic development of clean energy development and ecological safety. Full article

Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R² = 0.612), outperforming the global GWRF (R² = 0.59) and local GWRF (R² = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article

Hydropower is the main source of renewable energy and the most feasible for implementation in remote areas without access to conventional energy grids. Therefore, knowledge of actual, potential, and future perspectives of sustainable hydropower projects is decisive for their viability. This study aims to estimate the present and future potential capacity of Peru’s hydropower system and from the potential small hydroelectric plants, specifically Run-of-River class. First, we employed geospatial databases and hydroclimatological products to describe the current hydropower system and potential sites for Run-of-River projects. The findings identified 11,965 potential sites for Run-of-River plants. Second, we executed and validated a hydrological model to estimate historical daily streamflows (1981–2020) and hydropower parameters for actual and potential sites. It was determined there is an installed capacity of 5.2 GW in the current hydropower system and a total potential capacity of 29.1 GW for Run-of-River plants, mainly distributed in the northern and central Andes. Finally, we evaluated future changes driven by ten global climate models under three emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5), compared with the baseline period of 1981–2010 with two future time slices. The main results about capacity indicated that operational hydroelectric plants (Run-of-River plants) are projected to decrease by 0.5 to −5.4% (−7.2 to −2.2%) during 2036–2065 and by −9.2 to 3.8% (1.8 to −11.9%) during 2071–2100. These outcomes provide relevant information to support policymakers in addressing sustainable development gaps in the coming decades and stakeholders involved in the implementation and mitigation of climate change impacts on hydropower projects in Peru. Full article

In the Taihang Mountain Tourism Development Plan (2020–2035), the Taihang Mountain Expressway is included in the construction of the National Tourism Scenic Road around Taihang Mountain to promote the integrated development of regional transportation and tourism. The Lai-Qu Expressway is part of the Baoding section of the Taihang Mountain Expressway. Based on the data of traffic flow on the Lai-Qu Expressway, data of regional tourism resources, and data of regional economic and social development, this paper studies the interaction between the traffic and tourism space of the Lai-Qu Expressway by using spatial interaction, geographically weighted regression (GWR), and other geospatial analysis theories and methods. The results show that the traffic flow of the Baishishan Tollgate is directly correlated with the passenger flow of the Baishishan scenic spot. The spatial pattern of two tourism resource cluster centers and one sub-center, and one residential cluster center and one sub-center is expected to be formed along the Lai-Qu Expressway. The newly built traffic routes extend the influence of the traffic space and overlaps with the regional tourism space, not only providing new opportunities and possibilities for the development of regional tourism, but also promoting the change in the regional tourism spatial pattern and the cluster form of tourism resources. The research on the interaction between the traffic–tourism space in this paper can help to enrich the theoretical connotation of the research on the integration of transport and tourism, and can also be used to evaluate the tourism impact of newly built transport routes and serve the regional tourism development. Full article

The coordinated improvement of urban land green use efficiency (ULGUE) requires an in-depth exploration of its spatial correlation network characteristics and impacts. However, the existing studies on the matter have not yet formed a unified evaluation index system, ignoring energy inputs and positive ecological outputs, and the spatial correlation analysis is limited to geographic proximity rather than revealing the characteristics of the network structure. Therefore, taking 283 Chinese cities as the research object, this investigation constructs an evaluation index system for ULGUE, based on its core attributes. It then examines the spatial correlation network structure and impact by conducting social network analysis and applying Pearson’s correlation coefficients. The results reveal the following: (1) Between 2003 and 2020, China’s overall ULGUE trended upward in stages, with reduced disparities among cities and a significant geospatial clustering phenomenon. (2) The spatial correlation network of China’s ULGUE increased gradually, transforming from a unipolar-dominant to a multi-core-led network structure centered around Beijing, Shanghai, Wuhan, and Chongqing. Moreover, the overall balanced development of ULGUE in eastern, central, and western China was strengthened. (3) The spatial equilibrium of the ULGUE was impeded by a more efficient hierarchical structure of the network, while improved degree centrality, betweenness centrality, and closeness centrality had significant positive impacts on the ULGUE. These findings can serve as a theoretical reference for strengthening the coordinated improvement of ULGUE in China, offering significant insights for other developing nations that are seeking to make similar improvements. Full article

This review synthesizes historical and contemporary research on wildlife–vehicle collisions and roadkill, outlining its evolution from early documentation to modern road ecology. It discusses how early efforts in North America and Europe that quantified animal casualties and developed standardized methodologies formed current studies that use advanced geospatial tools, citizen science, and artificial intelligence to analyze spatiotemporal patterns. We examine key ecological, methodological, and economic impacts of roadkill on wildlife populations and human safety, highlighting the role of road density, vehicle speed, and seasonal factors. The framework presented also underscores a commitment to sustainability by integrating environmental conservation with infrastructural development and socio-economic resilience. The review details various mitigation strategies, from fencing and wildlife crossings to dynamic signage, and evaluates their effectiveness in reducing mortality rates, thereby supporting sustainable development in transportation infrastructure and wildlife management. It also identifies research gaps and outlines future directions, advocating for integrated, multidisciplinary approaches to improve wildlife conservation, infrastructure planning, and public awareness in the context of rapidly expanding road networks. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

Land use and land cover (LULC) in coastal areas is critical in shaping the ecological systems, regional economy, and livelihood of indigenous communities. This study analyzes LULC changes (LULCC) in Soc Trang Province, Vietnam Mekong Delta, from 2010 to 2020 and simulates future LULC for 2030 under four scenarios: natural growth (business as usual, BAU), climate change challenges, profit optimization, and adaptation strategies. Satellite-based LULC maps and geospatial datasets were integrated into a LULC simulation model based on a Markov Chain and Cellular Automata to predict LULC in 2030 under disparate scenarios. Simultaneously, this study also estimates economic values and ecosystem service values as proxies to evaluate benefits and trade-offs between the scenarios. The research findings reveal that the critical LULCC observed during 2010–2020 are transitions from triple rice crops to double rice crops, rice–shrimp to brackish aquaculture, and expansion of perennial plantations. These transitional trends will persist at a modest rate under the BAU scenario in 2030. The climate change challenge scenario will intervene up to 24.2% of the total area, with double rice crops reaching the most extensive area compared to other scenarios, about 106,047 ha. The profit optimization scenario will affect 16.03% of the total area, focusing on aquaculture expansion to the maximum shared proportion of 34% (approximately 57,000 ha). Adaptive solutions will emphasize reducing triple rice crops while expanding double rice crops and reviving rice–shrimp to different extents depending on development pathways. Economic evaluations show a growth trend across scenarios, with maximum returns under profit optimization. Yet, ecosystem service values notably highlight ecological trade-offs, raising concerns about balancing economic benefits and ecological trade-offs in land use planning. The research findings recommend a comprehensive and multitarget approach to land use planning that integrates ecosystem services into initial assessments to balance benefits and trade-offs in coastal areas commonly affected by LULCC. By adopting well-informed and strategic land use plans that minimize ecological and social impacts, local sustainability and resilience to climate change can be significantly enhanced. Full article

The ethnic villages in karst regions, where the ecosystem and social systems are intricately linked, face the dual challenges of poverty and ecological sustainability. Tourism, as an emerging strategy adopted for poverty alleviation, has inevitably posed complex impacts on social–ecological systems (SES). However, due to the particularity of the SES in this region, the mechanisms through which tourism influences social–ecological systems remain unclear, hindering the achievement of eco-friendly economic growth. In this study, we first applied the vulnerability spectrum diagram (VSD) model assessment framework to various remotely sensed and socially sensed data to evaluate ecological and social vulnerability, taking Leishan County, a typical karst region in Guizhou, southwest China as a case study. Then, advanced geospatial analysis methods were adopted to investigate the spatial characteristics of the vulnerability index. Finally, we utilized the geographical detector to identify influencing factors and investigated their synergistic effects. Our results reveal that, within the studied area, social vulnerability is generally lower in the north than the south, while ecological vulnerability shows the other way around. Social vulnerability is significantly influenced by several tourism-related factors, such as transportation convenience and the preservation of traditional dwellings. These factors collectively exert a pronounced effect on social vulnerability mitigation. Moreover, ecological vulnerability, with the exception of rocky desertification, shows significant co-directional changes with social vulnerability, reflecting the fact that tourism factors indirectly shape the ecosystem. The development of ethnic village tourism in villages with better socio-economic conditions tends to effectively improve the quality of the ecological environment, whereas those with poorer conditions tend to exacerbate ecological damage. The findings drawn from this study convey important practical implications that assist in identifying key vulnerable areas in karst ethnic villages and support their sustainable development goals. Full article