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Location, location, location. This mantra of real estate agents and their clients alike is now the target of a new report from the Brookings Institution linking housing prices and zoning practices to effectively depriving low-income students of high-quality schools. Using test scores from schools in the 100 largest metropolitan areas in the country, senior research analyst Jonathan Rothwell found that housing costs an average of 2.4 times more--close to $11,000 more per year--near a high-scoring public school than near a low-scoring one. High-priced homes are linked to zoning practices because they are typically located in areas that have intentionally been zoned to keep population density low, according to the study released last week. It shows that the average low-income student attends a school that scores at the 42nd percentile on state exams, while the average middle-/high-income student attends a school that scores at the 61st percentile on state exams. These test-score gaps between poor and more-affluent students were especially pronounced in the Northeastern part of the country, which claimed six of the top 10 largest gaps among the regions ranked by the report. While the idea that economic segregation is a function of zoning practices isn't new, Mr. Rothwell said his research is among the first to explicitly link the two and tie the results to access to high-quality schools. In this case, quality was determined by state test scores as listed on GreatSchools.org.

An Illinois school district leased a vacant school building to a local hospital, and was then ruled by the county tax office to have lost the building's tax-exempt status and to owe property taxes. Districts interested in leasing school property are advised to hire an attorney who specializes in real estate. (MLF)

Leasing closed schools provides a viable alternative to their disposal. Suggestions from a school business administrator are intended to sensitize other administrators to some common areas of difficulty in the real estate world. (MLF)

Analysis of problems faced by school systems with surplus schools due to declining enrollment has led to this list of suggestions on achieving flexibility in new building construction. (WD)

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses "pretzel noise", and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation. [ABSTRACT FROM AUTHOR]

Taking fast action, and effectively utilizing the available resources, are important when conducting time-critical surveillance missions. In addition, the potential complexity of the search, such as the ruggedness of the terrain or large size of the search region, should be considered. Such issues can be tackled by using a heterogeneous fleet of manned and unmanned vehicles equipped with different sensors by considering speed, coverage and flexibility. This article centres on designing a fast solution approach for planning coverage missions through zoning and routing. The challenge is to cover an area where targets can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of search in sub-areas, where the scores represent the importance of tasks. Heuristic approaches are proposed to automate search processes and plan missions, given a small time budget. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth. [ABSTRACT FROM AUTHOR]

To ensure that ecotourism development remains sustainable, the best place for such activities should be chosen based on the ecological potential. This study attempts to identify suitable ecotourism sites by developing a quantitative geographic model using multi-criteria evaluation (MCE), optimized by a non-dominated sorting genetic algorithm (NSGA-II). Three criteria (physical, biological, and socio-economic features), 13 sub-criteria, and 33 indices were first collected from primary and secondary data sources. Then, MCE method was applied to find ecotourism suitable areas, in which two methods of fuzzy overlay and weighted linear combination (WLC) were used to overlay criteria maps. Finally, NSGA-II was used to optimize ecotourism zoning through defining three objectives, including minimizing the distance from the sub-criteria of natural attractions, vegetation, and historical-cultural sites. Results show the WLC method is better than the fuzzy method at combining different layers to determine suitable zones for ecotourism, through which more than 50% of the study area, about 28,000 hectares, was classified as suitable for ecotourism. Matching 85% of suitable areas obtained by NSGA-II with high and very high suitable classes obtained by WLC shows that combining the MCE method with NSGA-II provided a more suitable hybrid method for ecotourism site suitability evaluation. This study creates a valuable tool for those responsible for planning and carrying out ecotourism initiatives, allowing them to further assess and conduct ecotourism projects. [ABSTRACT FROM AUTHOR]

Capital constraints are one of the major challenges for the supplier to invest in activities to enhance environmental and social responsibility (ESR) in production. Responsible buyer direct financing has emerged as an important solution to motivate supplier ESR by pegging the interest rate to the supplier's ESR level. Motivated by real practices, we develop game‐theoretical models to explore the value of conducting blockchain‐assisted due diligence to inspect the borrowing supplier's ESR qualification in a two‐tier supply chain structure where the downstream buyer directly finances the capital‐constrained supplier. Conventional wisdom tells us that driving greater supplier transparency (e.g., conducting blockchain‐assisted due diligence) will motivate the supplier to strengthen environmental and social compliance. However, we identify the necessary and sufficient "backfiring condition" under which the blockchain‐assisted due diligence may "backfire" by reducing the supplier's ESR efforts. The supplier devotes more ESR efforts if and only if the due diligence cost is sufficiently high. Our result implies that the heavy cost of adopting blockchain technology is not necessarily bad from the responsibility perspective as long as the backfiring condition does not hold. Moreover, we identify the Pareto zone in which conducting blockchain‐assisted due diligence could benefit both supply chain members. We extend our model by examining the impact of a shift from profit orientation to social welfare orientation. Our findings provide managerial insights into the implementation of blockchain‐assisted due diligence to enhance supplier ESR and improve supply chain members' profits. [ABSTRACT FROM AUTHOR]

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of "population heat--land use scale" to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Water scarcity occurs in the agriculturally dominated Upper catchment area of Narmada River, Central India because of the overexploitation of underground water for residential, industrial, and other uses. Delineating the Ground Water Potential Zone (GWPZ) is critical to meeting the area's water demand. Finding the Upper catchment Narmada River groundwater potential zone is the primary goal of this study. The study uses geographical methodologies based on the Analytical Hierarchical Process (AHP). To create a GWPZ map, ArcGIS 10.4 software compiles eight thematic layers, including elevation, slope, drainage density, geology, rainfall, soil texture, modified normalized difference water index, topographic wetness index, and land use/cover. There are five classifications for land use land cover map: Very low, low, moderate, high, and Very high. Each theme map in this study was given a weight based on its unique attributes and contribution to the GWP capacity. The AHP method, which takes into account each layer's relative relevance regarding the others, was used to establish the weights. Four groups were created from the resulting groundwater potential map: excellent, good, moderate, and poor. According to the study, 26.05% of the basin was categorized as excellent, 34.59% as good, 23.97% as moderate, and 15.4% as poor groundwater potential.The results of this study further indicate that a sizable section of the Narmada River Basin has well to moderate groundwater potential, pointing to encouraging prospects for the area's sustainable groundwater use. The study offers crucial insights for planners and policymakers to conscientiously harness groundwater resources, fostering sustainable development across diverse land uses in the fragile zone of the upper catchment of the Narmada, and it serves as a model for simulation in other sensitive river basins. The implications of the study are geared towards enhancing groundwater prospects, revitalizing fragile riverine ecosystems, and achieving the target outlined in Goal 6 of the Sustainable Development Goals (SDGs) of 2030. [ABSTRACT FROM AUTHOR]

Activity-based travel demand models emerged mainly to fix the conceptual, statistical, and operational deficiencies of conventional trip-based models. This is done by microstimulating the activity scheduling behaviour of individuals/households instead of modelling the number of trips between the zones of an urban area. In the “Next Generation” of activity-based models (ABMs), researchers are making an effort to improve their capacity to replicate the travel-activity patterns of urban populations more realistically. Expanding the modelling time frame from a single day to an entire week is one of the essential aspects of the “Next Generation” of ABMs. Although there is still a long way to go before a comprehensive and operational week-long ABM can be developed, the literature on its different aspects, the theoretical and conceptual frameworks, and the efforts to collect multi-day travel-activity diaries are now at a stage that is worth a comprehensive and systematic review. Therefore, the current study is devoted to exploring the existing literature on multi-day activity-based modelling, categorising its elements in a systematic manner, searching for the research gaps in the existing models and proposing a comprehensive framework to fill those gaps. [ABSTRACT FROM AUTHOR]

Urban periphery belts play a crucial role in preserving the morphological characteristics of cities. These belts often encompass various zones, including designated Military Zones, each of which contributes to structural integrity of urban spaces. Due to the vast expanse of military areas and associated security concerns, the relocation of these zones to the city's outskirts is a recurring topic of discussion in Türkiye. Presuming the occurrence of such a possibility, how these zones will transform into functional urban spaces in terms of urban morphology is going to be vital. This study focuses on the potential of converting military zones in the urban periphery of Ankara into green spaces, highlighting the importance of such transformations for urban ecology and the preservation of green areas. To identify the existing green spaces within the military zones of the study area, vegetation indices such as Sentinel-2's NDVI, NDMI, GCI, RVI, NDBI, and NISI were utilized, and comprehensive green space analyses were conducted. The findings indicate that the current green space allocation per capita in the region is 5 m2. However, when military zones are incorporated as regional parks alongside existing urban green spaces, this figure increases significantly to 14.4 m² per person. Additionally, to determine the accessibility of military zones converted into regional parks in terms of walking distance and their serviceable radius, several analyses have also been conducted. The conversion of military zones into regional parks and the development of related projects in Ankara would provide substantial benefits from a public policy perspective. [ABSTRACT FROM AUTHOR]

Developing a size effect model that can encompass the surface effect and the fracture process zone (FPZ) is still challenging. Here, a combined size effect model (CSE model) is formulated by integrating the surface effect size model and the size effect model of fracture mechanics (SEFM model) associated with the FPZ for rocks under compression. Parametric studies indicate that an increased volume fraction of the interior zone as the sample size increases is responsible for the anomalous size effect (ASE). The normal size effect (NSE) is a result of the decrease in the surface layer's volume fraction and the interior zone's nominal strength as the sample size increases. Moreover, the mixed type ASE–NSE is caused by the competition among the reduced surface volume fraction, the weakened interior zone strength, and the increased interior zone volume fraction as the sample size increases. A validation study demonstrates that the CSE model accurately predicts the ASE, the NSE, and the mixed type ASE–NSE. It was observed that the determination coefficient R2 of the CSE model is greater than that of the SEFM model for the NSE, equivalent to that of the size effect model of surface effects for the ASE and close to that of the improved USEL (IUSEL) for the mixed type ASE–NSE. [ABSTRACT FROM AUTHOR]

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site. [ABSTRACT FROM AUTHOR]

In the context of hyperspectral image (HSI) analysis, a widely used feature extraction method, Principal Components Analysis (PCA) suffers from limitations such as wavelength bias and a lack of consideration for local spectral information. While various segmentation based PCA methods attempt to address these issues by incorporating local relationships, they still overlook band similarity beyond immediate neighbours. To address these challenges, this paper introduces a novel approach called dependency based segmented PCA (dPCA). This method employs hierarchical clustering-driven mutual information-based segmentation, facilitating more comprehensive feature extraction from HSI data. By utilizing this dependency-based segmentation, both global and local structures are effectively captured, providing enhanced details for classification tasks. The proposed dPCA is evaluated on four prominent HSI datasets in remote sensing for land use classification, and the experimental results underscore its superiority over conventional PCA, and other segmentation based PCA methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

Large-scale, high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to improvements in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labelled data is still a challenge, hindering the large-scale application of land cover classification methods. In this study, a Transformer-based weakly supervised method for cross-resolution land cover classification using outdated data is proposed. First, to capture long-range dependencies without overlooking the fine-grained details of objects, a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention is designed. Second, an anti-noise loss calculation module based on optimal transport (OT) is proposed. The anti-noise loss calculation identifies confident areas and vague areas based on the OT matrix, which relieves the effect of noises on outdated land cover products. By introducing a weakly supervised loss with weights and using an unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolutions and the corresponding ground truths of six states in the United States were used to validate the performance of the proposed method. The experiments used outdated land cover products with 30 m resolutions from 2013 as training labels and produced land cover maps with 1 m resolutions from 2017. The results showed the superiority of the proposed method over state-of-the-art methods. The code is available at https://github.com/yu-ni1989/ANLC-Former. [ABSTRACT FROM AUTHOR]

AbstractAlthough land use planning in the United States has long privileged single-family homes, the dominance of single-family zoning is weakening. At least eight states have passed laws opening single-family neighborhoods to accessory dwelling units and, in some cases, to middle housing, such as townhouses, duplexes, triplexes, and quadplexes. Opponents of laws liberalizing single-family zoning often seek to prohibit for-profit developers from densifying single-family neighborhoods, by restricting development to owner-occupiers. Often these opponents invoke concerns about gentrification and social equity. But for recent zoning reforms to put a meaningful dent in the housing affordability problems confronting many regions, the planning profession must accept that the physical transformation of neighborhoods at scale will require significant, though by no means exclusive, involvement of for-profit builders. In this Viewpoint we explain why the relevant empirical evidence from cities such as Houston (TX), Portland (OR), and Auckland (New Zealand) indicates that single-family zoning reform can foster more affordable types of housing through for-profit development, while allaying fears concerning gentrification and displacement. Cities and states should promote equity and neighborhood stability through tools such as tenant protections, but they should eliminate owner-occupancy restrictions and other regulations that bar for-profit developers from building middle housing. [ABSTRACT FROM AUTHOR]

In conversation with Quinn Slobodian's Crack-up capitalism, the commentary explores the book's innovative but for the most part implicit methodological and expositional strategy, reflecting on some of the implications for the geographical analysis of ideas and ideation. Ideation certainly matters, but never mechanically or predictably, so the challenging questions concern how to specify, and to assign explanatory weight to, particular ideas in particular situations. [ABSTRACT FROM AUTHOR]

Classifying emission reduction zones on different scales has important implications for the ecological protection and high‐quality development of the Yellow River Basin. Based on remote sensing data and a light‐carbon conversion model, carbon footprints at provincial, municipal, and county scales in the Yellow River Basin are measured. The spatiotemporal evolution critical paths of carbon footprints at the three spatial scales are compared and classified into different zones using spatiotemporal evolution analysis methods. The conclusions are as follows: (1) The carbon footprint increased over the years. The spatial distributions of carbon footprints at the three scales are not only consistent but also different. The study of carbon footprints at the county scale is more conducive to the summary of the spatiotemporal evolution and the formulation of detailed emission reduction schemes. (2) Four provinces, 48 cities, and 373 counties are designated as a "core protected zone"; three provinces, 29 cities, and 177 counties are designated as a "strictly governed zone"; one province, 12 cities, and 47 counties are designated as a "key restricted zone"; four cities and 39 counties are designated as an "alert diffusion zone." (3) The agglomeration expansion trend and the spillover effect of high‐carbon footprint units at the county scale are more obvious. Further enhancement of the path‐locking characteristics of the carbon footprints of counties will make governance more difficult. Effective governance of carbon footprint at the county scale is of urgent concern. These results provide scientific evidence for multiscale carbon emission control and zoning policy formulation in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Simple Summary: This study analyzed the effect of forest plantations on the taxonomic and functional diversity of dung beetles in comparison to natural forests in southern Ecuador. The results indicated a greater abundance of generalist species in the plantations, likely due to their high level of adaptation to disturbances and their presence in nearby forests. This could be attributed to the proximity of these areas to urban zones, which may influence the dung beetle community, as their primary food source depends on mammals. However, this study highlights the lack of information on this topic in the Neotropics, particularly in Ecuador, suggesting that further research is needed to compare and validate these findings. This study sheds light on the complex relationship between land use, biodiversity, and the functional traits of dung beetles in Ecuador. The results indicate that the richness and abundance of dung beetles vary across different land uses and regions, with forests generally having a positive impact, while eucalyptus and pine plantations have a negative effect in certain areas. Specific indicator species, such as Homocopris buckleyi for forest areas and Onthophagus curvicornis for eucalyptus plantations, were identified. This study also found that functional diversity analysis, based on morphological traits, revealed that certain traits, such as biomass, pronotum width, head width, and elytra length, were significant contributors to differences in dung beetle communities across various land uses and regions. This study highlights the potential conservation value of certain modified habitats and emphasizes the importance of considering both taxonomic and functional diversity when assessing the impact of land use on the ecosystem services provided by dung beetles. It underscores the potential value of plantations as refuges for dung beetle communities and the need for long-term assessments to better understand biodiversity changes over time. [ABSTRACT FROM AUTHOR]

Rapid urbanization has led to many urban thermal environment problems. Most studies focus on analysing the urban thermal environment from the perspective of land-use type, and often at a large scale. Thus, research of urban spatial thermal environment at a block scale with different urban functional zone (UFZ) becomes the focus of this paper. UFZ refers to urban planning units with similar socio-economic functions, which often have similar energy consumption and outdoor thermal environment. In this paper, the urban weather generator (UWG) model is used to quantitatively analyse the spatial thermal environment of Beijing. To accurately simulate the urban thermal environment, the underlying surface used in the simulation is replaced by the finely classified underlying surface of the urban functional area. In this paper, we first use POI + OSM data to divide the functional areas of Beijing. The recognition accuracy of functional area is 85.6%. Then, the UWG model is used to simulate the temperature and humidity differences in different functional areas, and finally the corresponding urban and rural weather stations were used to validate its accuracy. The results show that the model calibration method can effectively improve the simulation accuracy of UWG thermal environment, the R2 is increased by about 0.12, and the RMSE is reduced by about 0.84. The canopy temperature of different urban functional areas from high to low is: industrial area>commercial area>residential area>public area>road and traffic area>green space and square area. This model is more suitable for areas with relatively homogenous urban morphology and sparse urban vegetation. The thermal environment simulation results of the same urban functional area are basically the same, with slight differences in the temperature peaks, and the average temperature in the city centre is higher than that in the suburbs. [ABSTRACT FROM AUTHOR]

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management. [ABSTRACT FROM AUTHOR]

Climate and land use changes have a significant impact on the runoff generation process in urban environments, and these effects could get worse in the future. The combined contributions of these changes have increased the risk of flooding. Therefore, there is a need for integrated modeling to better understand the runoff variability, especially in small urban catchments. To quantify and separate the effects of land-use changes and climate change on the hydrological response of urban catchments with a distributed hydrological model (Storm Water Management Model, SWMM), this study introduces a new integrated approach based on the Machine Learning based land use change modeler and climate change scenarios under CMIP6. Based on supervised classification and land use change model analysis, accumulated impervious area increase from 22% (in 2023) to 33% (in 2060) was observed in the study area. Furthermore, integrating this projected increase in imperviousness with future climate change into SWMM by considering three different scenarios i.e., S1 (Climate Change), S2 (Combined Land Use and Climate Change), and S3 (Land use Change) resulted that climate change could cause an increase in runoff from 13.2% to 18.3% in peak runoff and the contribution of land use could range from 9.1% to 18.6%. Similarly, in response to the coupled effects of climate and land-use change, the runoff would likely change from 24.53% to 39.66%. Conclusively, the study showed that despite climate change, intensive urban development by the substitution of impervious surfaces could also have a severe impact on the microclimate and hydrology of small catchments. Lastly, this study could provide a way forward for the future planning and management of water resources in small catchments which could be extended to larger catchments. [ABSTRACT FROM AUTHOR]

Formal rules define urban governance, yet in democratic systems like the United States, institutions governing public input shape decision-making. Governance literature needs more breadth on how civic engagement varies across types of nature and its implications for urban social-ecological systems. We analyzed five cities' ordinances within the Minneapolis-St. Paul metropolitan region, identifying nature-based rules and civic engagement mechanisms. We found ten engagement mechanisms across six nature types, primarily discussed within zoning ordinances—farm animals being the exception. Public hearings were the most frequently mentioned engagement mechanism, yet they have documented biases toward elite perspectives. Despite codification of engagement opportunities, it remains unclear whether utilized mechanisms support substantive input and influence into urban nature governance. Our study not only lays groundwork for research on how to address the inequitable distribution of nature-related benefits and burdens felt by some urban communities, but also inspires further exploration into this crucial area of study. [ABSTRACT FROM AUTHOR]

• Lake Edku, Egypt, sequester carbon which worth of $1.7 million. • NDVI and SAVI indicated that carbon stocks were best predicted during year of 2022. • Land use classification showed that many coastal areas were lost since 1994. As a case study to assess changes of blue carbon stocks among different land use classes; lake Edku, Egypt, was selected at the north Mediterranean coast of Egypt. Landsat 5 and 8 satellites imageries (1994, 2008, and 2022) were downloaded and upon classification, there were five land use classes identified: artificial surfaces (including barren and urban areas), crops, flooded vegetation, sabkha, and water. Using InVEST model, the quantity and economic value of the coastal blue carbon sequestration (CBCS) service offered by lake Edku were modelled. Based on land use classification of lake Edku, those area that were classified as surface water and croplands were decreased from 1994 to 2022. On the other hand, aerial coverage of the artificial surface increased. Analyses for Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegeation Index (SAVI) indicated that the amount of carbon stocks estimated by ground inventory was best predicted during year of 2022 (R2= 0.449, P value < 0.01). Our economic analyses of CBCS provided by lake Edku between 1994 and 2022 was 0.6507 (106 Mg C ha−1), which has a value of about $49 millions. Per year, lake Edku sequester carbon that worth about $1.7 million. Our study concluded that CBCS service can hasten the adoption of wetland rehabilitation measures, which have a favourable effect on climate regulation. [ABSTRACT FROM AUTHOR]

Zoning of protected areas increases the sustainability of these areas by reducing the conflict between protection and public uses. In the implementation of the biosphere reserve model, as a key lever for promoting sustainable development, zoning has been neglected. The purpose of this research is to zoning Dana district based on the spatial analysis of changes in the structural integrity of the landscape and the ethnography of place values from the perspective of communities. For this purpose, adaptation to the habitat, conservation value, distance from the area of popular use, and changes in the structural integrity of the land surface have been mapped, classified, and superimposed. The zones have been allocated by comparing the obtained units with the logical letter model. Based on the results, during about thirty years, the spatial changes in the structural coherence of the landscape of the land have been significant. At higher altitudes, the landscape structure has remained relatively unchanged, but at lower and middle altitudes, it has undergone a destructive process. The biggest reduction in structural integrity has occurred around settlements, rivers, and highways. Areas with conservation value are assigned to the core area. In the shield area, it is desired to maintain the connection between a large genuine spot inside the core and the surrounding spots. Public uses are considered outside the protection core and as far as possible outside the shield zone. Large areas of Dena district have been identified as public use areas, which are highly compatible with the transition. In this area, it is necessary to train and empower local communities by relying on alternative livelihoods and entrusting them with part of the protection supervision. Based on comparing the old and proposed zoning, the new approach, in addition to allocating wider areas for protection, with the allocation of public use zones, is the basis for creating a native people's protected area. [ABSTRACT FROM AUTHOR]

The study of changes in land use and land cover (LULC) is helpful in the understanding of change and management of environmental sustainability. As a result, the central Telangana districts are the focus of this study since they are under stress from both natural and human-caused problems. The examination of LULC variations and predictions for the region between 2007–2015 and 2021–2030 employed Landsat OLI datasets, TerrSet, and geographic information system (GIS) tools. The LULC image is produced using a Landsat dataset and classified using a support vector machine (SVM). Then, consecutively to project future LULC change, LULC maps were constructed using the CA Markov model. The four stages included were change analysis, transition possibility, change forecasting, and model validation. It is found that the vegetation and the arid landscape are stressed and accumulating. The total accuracy was above 87 percent, and the kappa statistic measurement was above 85 percent with a three-year target. The study has found using the Markov chain land change modeler that Medchal–Malkajgiri district urban settlements will grow by 46, 37, and 26% from 2021–2030, 2030–2050, and 2050–2100, respectively. On other hand, the Warangal (Hanamkonda) observed 39, 45, and 30% between 2021–2030, 2030–2050, and 2050–2100, respectively, and Rangareddy districts observed 60, 24, and 12% between 2021–2030, 2030–2050, and 2050–2100, respectively. Given that urban areas are especially susceptible to flash flooding, this research will offer policymakers advice and a framework on behalf of planning city growth and managing the available resources judiciously with utmost planning. [ABSTRACT FROM AUTHOR]

Combining various viewpoints to produce coherent and cohesive results requires decision fusion. These methodologies are essential for synthesizing data from multiple sensors in remote sensing classification in order to make conclusive decisions. Using fully polarimetric Synthetic Aperture Radar (PolSAR) imagery, our study combines the benefits of both approaches for detection by extracting Pauli's and Krogager's decomposition components. The Local Pattern Differences (LPD) method was employed on every decomposition component for pixel-level texture feature extraction. These extracted features were utilized to train three independent classifiers. Ultimately, these findings were handled as independent decisions for each land cover type and were fused together using a decision fusion rule to produce complete and enhanced classification results. As part of our approach, after a thorough examination, the most appropriate classifiers and decision rules were exploited, as well as the mathematical foundations required for effective decision fusion. Incorporating qualitative and quantitative information into the decision fusion process ensures robust and reliable classification results. The innovation of our approach lies in the dual use of decomposition methods and the application of a simple but effective decision fusion strategy. [ABSTRACT FROM AUTHOR]

Alluvial rivers are dynamic landscapes on the earth's surface that evolve with time. While many studies have examined the immediate effects of floods on river channels, there is a lack of research that investigates the longer-term evolution of river morphology following such events. The present study was carried out on the Upper Krishna River which flows between the southern part of Maharashtra and the northern part of Karnataka states in India for 375 Km. The morphological parameters were analyzed for three decades (1991–2021) and the year 2019 with the highest flood level was also considered for change analysis. The assessment was done for change in active channel area, mean width, bank line migration, sinuosity index, and erosion-accretion. The land use classification was also analyzed for the study period to understand the exposure to future floods. The spatial data was retrieved from different satellite missions and analyzed with the help of Remote Sensing (RS) and Geographical Information System (GIS). The river was divided into seven segments (R1, R2, R3, R4, R5, R6, and R7) and bank lines were digitised manually to minimise possible errors. The results show that during the study period, the river channel has been modified in terms of active channel area expansion in the R1, R5, R6, and R7, and erosion was found the dominating process while the left bank was more erosive than the right bank of the river. The built-up area was seen going through a major expansion than any other land use class. The discharge and sediment data confirm the flood years (1994, 2005, 2006, and 2019) which accelerated the morphological activity in the river segment. The results of the study provide new insights related to short-term morphological changes in the Upper Krishna River and can be used by policymakers and managers to carry out future development plans and river training work at affected sites. [ABSTRACT FROM AUTHOR]

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making. [ABSTRACT FROM AUTHOR]

Land use regulations have played a critical role in the siting and operation of renewable energy technologies. While there is a growing literature on the siting of wind and solar technologies, less is known about the relationship between local codes and planning decisions and the development of wood-based bioenergy technologies, particularly in rural places. This research examines the relationship between local land use policies and the siting and operation of different types of wood-based bioenergy technologies in northern Michigan, USA. Land use codes including zoning laws and ordinances related to wood-burning devices from 506 cities, townships, and villages within 36 counties in northern Michigan were combined with US Census data in a GIS database. ArcGIS was used to examine geographical differences between communities and socioeconomic factors related to different regulatory approaches. We found that areas with greater population densities and higher income and education levels tended to have more nuanced land use codes related to all scales of wood-burning, including residential wood heating, commercial-scale heating, and power generation. This paper emphasizes the importance of local decision-making and land use policies in shaping the development of wood-based energy technologies, and suggests the need for greater attention to rural community dynamics in planning the shift to a lower-carbon economy. [ABSTRACT FROM AUTHOR]

The main principles of optimal flow regulation in small rivers by reservoirs are presented. New methodological approaches are considered, taking into account the need of differential analysis of the physiographic conditions of the areas where the basins of the examined rivers are located. This is of particular importance in the case of formation of regional response to global climate warming with a combination of unfavorable economic and environmental conditions. The methodological basis of the study includes the geosystem approach and a basin principle, which allow comprehensive incorporation of the effect of the totality of natural and anthropogenic factors on the formation of variations of quantitative and qualitative characteristics of water in watercourse and the ecological conditions in them. The methods of studies include the physiographic zoning of the Ural River basin within the boundaries of the Russian Federation, statistical and graphical analysis of materials of long-term hydrometeorological observations, cartography, etc. The results of the study include methodological provision and substantiation of methodological principles for optimal flow regulation of small rivers in the Ural River basin by reservoirs within the boundaries of the Russian Federation, taking into account environmental and economic criteria. They form a part of a vast list of proposals aimed at hydrological-environmental rehabilitation of small rivers. [ABSTRACT FROM AUTHOR]

Most children grow up in urbanised settings with a low possibility to experience biodiversity and nature. However, experiencing nature and other species increases children's wellbeing, health, learning abilities and their understanding of nature values. Play biotopes is one solution for supporting a co-existence between children and different species in nature-based play settings. Play biotopes are based on ecological theories, where structures in the morphology of landscapes at different scales and the content of flora and fauna can support children's interplay with a part of the landscape. However, traditional landscape management is not adapted to support the dynamic nature of play biotopes, especially when considering multiple scales. This makes it interesting to explore more dynamic management concepts arching over multiple scales. Accordingly, we here explore creative management as a scale-based framework for design by management to further develop the concept of play biotopes. Using examples from a landscape laboratory in southern Sweden, we propose that a creative management framework combining the scales of landscape, biotope, place, and object together with play connectivity can support the creation and management of multifunctional play biotopes. [ABSTRACT FROM AUTHOR]

The Inner Mongolia mining area in western China are characterized by the development of numerous penetrating fissures, resulting in severe land damage. It is significant to reveal the underlying evolution mechanism and identify treatment timing for restoring the ecological environment. The Guanbanwusu mining subsidence area in Inner Mongolia, China was selected as the research case for this study. The evolution mechanism of different penetrating fissures was revealed by field measurement, physical simulation and theoretical analysis. The treatment timing prediction model for the mining subsidence area was established based on the enhanced Weibull time function. The results show that the ground fissures are mainly step-type and collapse-type fissures. The breaking form of overlying strata determines their vertical opening and horizontal dislocation. The high mining intensity in the western mining area results in a shortened period of dynamic fissure expansion and reduced closure degree. The damage extent of the overlying strata exhibits zoning characteristics both vertically and horizontally. The relative standard deviation of the prediction model is only 3.7%. Concurrently, the prediction model is employed to determine the optimal timing for treatment in the study area, estimated to be 259 days. Subsequently, once this threshold is reached, the study area undergoes treatment and restoration of its e cological environment. This study addresses the knowledge gap in this field by highlighting the interconnectedness between rock strata structure and evolution mechanism of penetrating fissures, thereby providing a method for determining the treatment timing in mining subsidence areas. [ABSTRACT FROM AUTHOR]

Accurate monitoring of irrigation areas is of great significance to ensure national food security and rational utilization of water resources. The low resolution of the Moderate Resolution Imaging Spectroradiometer and Landsat data makes the monitoring accuracy insufficient for actual demand. Thus, this paper proposes a method of extracting the irrigated area in arid regions based on Sentinel-2 long time-series imagery to realize the accurate monitoring of irrigation areas. In this paper, a typical irrigation area in the arid region of Northwest China–Xinjiang Santun River is selected as the study area. The long time series Sentinel-2 remote sensing data are used to classify the land use of the irrigation area. The random forest, CART decision tree, and support vector machine algorithms are used to combine the field collection of the typical irrigation point and non-irrigated sample points. The irrigation area is extracted by calculating the Normalized Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) time series data as the classification parameters. The results show that (1) the irrigated area of the dryland irrigation region can be effectively extracted using the SAVI time-series data through an object-oriented approach combined with the random forest algorithm. (2) The extracted irrigated areas were 44,417, 42,915, 43,411, 48,908, and 47,900 hm2 from 2019 to 2023, and the overall accuracies of the confusion matrix validation were 94.34%, 90.22%, 92.03%, 93.23%, and 94.63%, with kappa coefficients of 0.9011, 0.8887, 0.8967, 0.9009, and 0.9265, respectively. The errors of the irrigated area compared with the statistical data were all within 5%, which demonstrated the effectiveness of the method in extracting the irrigated area. This method provides a reference for extracting irrigated areas in arid zones. [ABSTRACT FROM AUTHOR]

Alongside the advent of industrial capitalism in the nineteenth century, Europe witnessed a profound transformation in its social, economic and political structures. This resulted in a shift in architectural styles that reflected the changing needs and aspirations of the age. This transformation was not merely a material one, manifesting in formal architectural expressions, but also an inherent one, characterized by a transformation in living styles. The Siedlung (settlement) was a typology of architecture that emerged in Germany, Austria, Switzerland, the Netherlands and Poland in response to the socio-spatial reorganization of urban areas. It was part of a larger urban zoning plan and constituted a housing district within an industrial city. However, in addition to residential buildings, the Siedlung also included facilities such as educational, commercial and religious institutions, integrated into the settlement area with the aim of fostering a sense of community. The organization of living space in each Siedlung was not entirely identical; rather, their design was formed within a conceptual understanding that sought to address the needs of its users or the context in which it was built. This study aims to interpret the diverse architectural formulation of the Siedlung and its interaction with society. Furthermore, the objective is to propose a comparative scheme for these distinctive settlements. To this end, three cases are selected from diverse time periods, contexts and places: the Siedlung Praunheim in Germany; the Siedlung Frankendaal in Netherlands; and the Siedlung Halen in Switzerland. These cases are presented and discussed in order to illustrate the multiplicity of design concepts. [ABSTRACT FROM AUTHOR]

Dubai in the UAE is a poster child of the region. The ‘Chinatown’ within Dubai Mall is an artificially-made theme-park-style place and a commodified space designed for consumption, which stands in contrast to traditional and naturally-occurring Chinatowns in North America, Europe and Southeast Asia. A new addition to the luxurious Dubai Mall, the ‘Chinatown’ features shops and restaurants in a China-inspired environment. Representing a different ethnolinguistic ecology, the ‘Chinatown’ is a unique existence in a superdiverse and mobile country made up of local Arabs and foreign workers/expats from the Indian subcontinent, Southeast Asia and beyond. This study explores how various linguistic and multimodal elements participate in the making of the linguistic/semiotic landscape of Dubai Mall ‘Chinatown’. This study highlights that both linguistic (Chinese characters) and non-linguistic elements (e.g. red colour and neon lights) are strategically combined as meaning-making resources to give the area authenticity and a unique image and identity. Conceptually, this ‘Chinatown’ is theorised as a case of ‘translation’ and cultural (re)contextualisation, where cultural elements from a far-away land are localised/recontextualised to adopt new social meanings. This study contributes to scholarship in sociolinguistics and applied linguistics, especially in commodified man-made spaces in transnational urban zones in our globalised world. [ABSTRACT FROM AUTHOR]

Analysis of people’s perceptions toward wildlife is fundamental in conservation planning. Also fundamental is dissemination of information about wildlife ecology. In Mexico, conservation of carnivores, who frequently come into conflict with human communities, is of utmost importance. We designed a workshop about the ecological aspects of pumas and jaguars; 232 children in elementary schools in rural, semiurban, and urban zones of Central Mexico participated. We analyzed the children’s knowledge of, perceptions of, and behavior in relation to pumas and jaguars before and after the workshop. We compared the three aspects by separating the data by grade and zone and found positive changes after the workshop. Knowledge was greater in the rural zone, but pro-conservation behavior was higher in the urban zone. Sixth-grade children displayed pro-conservation behavior and had more knowledge and more positive perceptions. We discuss the importance of involving children in conservation projects and disseminating information about large carnivores. [ABSTRACT FROM AUTHOR]