Land Cover Types Research Articles

The vegetation–topography heterogeneity coupling in the Loess Plateau, China

As a result of adaptation to the environment, the great environmental spatial heterogeneity leads to the high spatial heterogeneity of vegetation status. This coupling may be more apparent in water-limited drylands, where topography is the main determinant of small-scale variation in water availability and energy. Metrics describing this coupling may contribute to the detection of the extension of vegetation reshaped by human intervention and other driven forces. In this study, the heterogeneity index of coupling (HIC) was developed to indicate the coupling between spatial heterogeneity of vegetation status (Hv) and the spatial heterogeneity of topography (HT) in the Loess Plateau in northern China. The 16-day composed MODIS normalized vegetation index (NDVI) with a resolution of 250 m and SRTM DEM were employed to quantify the heterogeneity of vegetation status and the topographical heterogeneity. The results show that HIC varies among geomorphic zones, land cover types, and land cover change categories. Among all land cover types, HIC of sandy areas was the largest, followed by the HIC of the forest, shrub, farmland, and grassland. Among geomorphic zones, the highest HIC value appeared in plains with dense residential areas, followed by sandy land that is frequently reshaped by wind, rocky mountainous areas, hilly and gully loess plateaus, and loess tableland. It was revealed that the alternation of vegetation by human activities and natural disturbances shaped greater HIC. Results of this study approved the effectiveness of the HIC in reflecting the coupling of the vegetation status with topography at regional scale.

Time-series land cover change detection using deep learning-based temporal semantic segmentation

The process of sustainable urban development is accompanied by frequent and complex land cover changes, and thus, clarify accurate information on land cover changes can provide scientific data for urban management. To characterize urban development at an accurate spatiotemporal scale, a change detection model is not only required to provide accurate location (Where) and time (When) of the changes, but also semantic information on the change types (What). Accordingly, this study proposed a deep learning method for temporal semantic segmentation change detection (TSSCD) that obtains information on the where, when, and what of changes simultaneously. TSSCD model bridges the semantic gap between remote sensing time series abrupt changes and land cover changes by learning the month-to-month mapping from spectral information to land cover types. We implemented a temporal semantic segmentation model based on the most classic fully convolutional network, where all two-dimensional convolutions and pooling operations were replaced with one-dimensional. We conducted tests on the TSSCD in several urban study areas, and it consistently exhibited good accuracy. In most cases, it outperformed the BFAST and CCDC algorithms, except when only a single spectral band was used. Simultaneously, we analyzed the minimum data requirements for training a TSSCD. The TSSCD currently faces challenges in achieving strong generalization beyond the training data distribution. Additionally, we observed that change detection for specific land cover types can be achieved through the flexible configuration of TSSCD. Finally, we explored a method for constructing datasets using existing products to minimize data annotation efforts, yielding promising results. However, there is still some gap compared to complete manual annotation. Overall, the TSSCD model provided a novel solution to accurately characterize sustainable urban development at the spatiotemporal scale.

Microclimate Impacts of Land Cover Types in Halu Oleo University Botanical Garden and Its Surroundings

Halu Oleo University (UHO) Botanical Garden is one of the green open spaces that contribute to maintaining climate stability and environmental comfort in Kendari City, along with the high land use change due to increased population and city progress. The study aimed to determine the effect of land cover types on microclimate in UHO Botanical Garden and its surroundings. The research was conducted from June 2021 to January 2022. The research was conducted on three types of vegetation land cover. The variables measured were microclimate, including light intensity, temperature, relative humidity, and temperature humidity index. Measurements were conducted in the morning, afternoon, and evening. The method used was descriptive quantitative and statistical analysis using the analysis of variance and the least significant difference (LSD) test. The results showed that the microclimatic conditions of UHO Botanical Garden in tree-dominant land cover are more comfortable and significantly different from grass-dominant and settlement-dominant, but they are not significantly different between grass-dominant and settlement-dominant. This fact indicates the importance of the conservation of tree vegetation and the need for supervision so that there is no diversion of functions into settlements to maintain the preservation and sustainability of UHO Botanical Garden as one of the green open spaces that control climate stability and environmental comfort in Kendari City. Keywords: botanical garden, microclimate, settlement, temperature humidity index, vegetation

Losing lemurs: Declining populations and land cover changes over space and time.

Forest loss and degradation due to land cover changes imperil biodiversity worldwide. Subtropical and tropical ecosystems experience high deforestation rates, negatively affecting species like primates. Madagascar's endemic lemurs face exceptionally high risks of population declines and extirpation. We examined how short-term land cover changes within a fragmented landscape in southeastern Madagascar impacted the density of lemur species. Using line transects, we assessed density changes in nine lemur species across five forest fragments. Diurnal surveys were conducted monthly from 2015 to 2019 on 35 transects (total effort = 1268 km). Additionally, 21 transects were surveyed nocturnally in 2015 and 2016 (total effort = 107.5 km). To quantify forest cover changes, we generated land use/land cover (LULC) maps from Sentinel-2 imagery using supervised classification for each year. For the LULC maps, we overlayed species-specific buffers around all transects and calculated the proportion of land cover classes within them. We observed declines in the annual densities of four diurnal and cathemeral lemur species between 2015 and 2019, with species-specific declines of up to 80% (Varecia variegata). While the density of two nocturnal species decreased, one increased fivefold (Cheirogaleus major) between 2015 and 2016. By 2019, Grassland was the dominant land type (50%), while Paddy Fields had the smallest coverage (1.03%). Mature Agricultural Land increased the most (63.37%), while New Agricultural Land decreased the most (-66.36%). Unexpectedly, we did not find evidence that higher forest cover supported a higher lemur population density within sampled areas, but we found support for the negative impact of degraded land cover types on three lemur species. Our study underscores the urgent need to address land-use changes and their repercussions for primate populations in tropical ecosystems. The diverse responses of lemur species to modified habitats highlight the complexity of these impacts and emphasize the importance of targeted conservation efforts.

Abandoned cropland mapping and its influencing factors analysis: A case study in the Beijing-Tianjin-Hebei region

Abandoned cropland (AC) is one of the important types of land cover and land use change that poses a threat to food security, nature conservation, and ecosystem protection. Despite its significance, detailed spatial distribution patterns of AC across expansive geographic regions remain under-explored. Therefore, this study 1) proposes a novel framework designed to precisely determine the timing and extent of AC, and evaluates its effectiveness in the Beijing-Tianjin-Hebei region from 2000 to 2020; 2) analyses the spatial and temporal evolution of AC; 3) investigates the relationship between AC and both physical geographic and socio-economic factors. The proposed AC recognition framework integrates the method of change detection. Initially, regions changing are pinpointed, followed by categorizing pre- and post-change images to ascertain the magnitude and timing of these shifts, enabling the pinpointing of AC locations and their temporal onset. The results showed that the accuracy of land cover classification mapping ranged from 80.83% to 99.08%. Specifically, the user's accuracy of the proposed method varied between 79.29% and 89.38%. We discerned that AC was mainly distributed in the northwest water conservation belt, adopting an inverted “L” pattern. Additionally, physical geographic factors predominantly influenced AC. The use of remote sensing plays a crucial role in assessing AC, which is of great significance for understanding the evolution of abandoned landscapes and analyzing the relationship between abandonment and geology, environment, and socio-economy.

A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai–Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

LCZ framework and landscape metrics: Exploration of urban and peri-urban thermal environment emphasizing 2/3D characteristics

Rapid urbanization has altered the environment and climate, necessitating a thorough grasp of urban thermal dynamics for sustainable development. The lack of detailed urban information poses a considerable challenge. This study employs the Local Climate Zone (LCZ) framework to delve into the intricate relationship between land surface temperature (LST) zones and both 2/3D urban characteristics. The LCZ map for Bojnourd city, Iran, was prepared, consisting of eleven classes categorized into five built types and six land cover types. The mean LST in the summer of 2021 was derived from Landsat8 using the split-window algorithm (SWA) and divided into seven thermal zones. The research reveals distinctive spatial patterns of LCZs across various Land LST zones. Notably, LCZ2 (Compact mid-rise) and LCZ 3 (Compact low-rise), despite differing heights, share the same thermal zone (Zone3), suggesting influences beyond height on thermal zoning. In contrast, LCZ5 (Open midrise), situated in a cooler thermal zone (Zone2), features more vegetation and taller trees, exhibiting a lower LST range compared to LCZ2 and LCZ3. Class-level metrics (AREA_MN and LPI) offer a similar description of LCZ spatial patterns, while landscape-level metrics like SHDI and IJI provide a more insightful depiction of spatial patterns. The study found that the most appropriate spatial pattern for improving urban thermal conditions involves considering a set of factors such as vegetation presence, land cover proximity, height, and density. This ensures sustainable and green urban development that is in tune with climate change and promotes balanced and harmonious thermal patterns in the urban environment.

Quantifying of surface urban heat island intensity in Isfahan metropolis using MODIS\Terra\LST data

Heat island characteristics depend on the background climate of the site where the city is located. Therefore, an index was defined for the Isfahan metropolitan area to quantify the surface urban heat island intensity. This new index is based on the representative pixels of urban and non-urban areas. For this purpose, MODIS land cover type product (MCD12Q1) data were used to distinguish between urban and non-urban areas. Also, data from the MODIS/Terra land surface temperature product (MOD11A1) from 2000 to 2018 were utilized for daytime and nighttime to study the surface heat island intensity. Then, the representative pixels of urban and non-urban areas were identified using the spatial correlation method, and the heat island index was calculated for the metropolitan area of Isfahan. The study showed that the frequency distribution of the nighttime heat island index follows a normal distribution and is often 3.5 to 4º K above the temperature of the surrounding areas of the city. The 365-day floating mean of the surface urban heat island reveals that this index has increased in recent years. The research of temporal behavior showed that the intensity of the surface urban heat island reaches its maximum in January and becomes weaker in summer, while the survey of spatial behavior showed that the core of the surface urban heat island extends towards downtown areas, where the oldest part of the city is located.

Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India

Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). The present study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Gujarat State, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Gujarat, India, before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. The maps that result depict the geographical distribution of normalized burn ratio and difference normalized burn ratio and land surface temperature forecasts, providing valuable insights into spatial patterns and trends. The findings of this work show that an automated temporal analysis utilizing Google Earth Engine may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats. The impact of forest fires can be severe, leading to the loss of biodiversity, damage to ecosystems, and threats to human settlements.

Long-term evolution and driving mechanisms of the Baiyangdian wetland based on land cover frequency characteristics

For sustainable conservation and management of wetlands, it is essential to quantify the evolution progress and driving mechanisms of wetlands. This study examined Baiyangdian Wetland as a research area, and remote images and statistical yearbooks were used as data sources. Dominant zones of wetland were defined based on spatial characteristics of land cover frequency. A transfer matrix and random forest were used to analyze the evolution process and driving mechanism of typical wetlands in the North China Plain. Based on the results, following conclusions were arrived: (1) There was significant spatial differentiation of land cover frequency characteristics in the Baiyangdian wetland, with the cultivated land-dominated zone, the aquatic vegetation-dominated zone, and the water body-dominated zone being the three core zones of wetland ecosystem evolution. (2) There was a frequent and significant change of the Baiyangdian wetland due to extreme climatic conditions. Between 1987 and 2017, the built-up land area and the water body area increased by 4.62 and 16.17 km2, respectively, while the aquatic vegetation area decreased by 20.70 km2. A combination of natural and human factors caused changes in the wetland’s structure. (3) The driving mechanisms of ecosystem evolution in the Baiyangdian wetland were influenced significantly by space, time, and land cover types. Specifically, on a spatial scale, social factors were the predominant factors contributing to changes in water body and cultivated land in the cultivated land-dominant zone and the aquatic vegetation-dominant zone. In contrast, natural factors were the dominant factors leading to changes in aquatic vegetation. There was a greater contribution from social factors to the driving mechanism of changes in the water body, aquatic vegetation, and cultivated land in the water body-dominant zone than from natural factors. On a temporal scale, it was primarily influenced by natural factors in the early stages and socio-economic factors in the late stages, with the year 2002 serving as a turning point. The scale of land cover types can be seen primarily in the differences among the agricultural lands, aquatic vegetation, and water body. In particular, the Secondary Industry Out Value (SIOV) was highly sensitive to changes in the cultivated area. In addition, cumulative evaporation (EC) has been identified as the main factor responsible for the evolution of aquatic vegetation, which is closely related to the changes in water body.

A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland

The distribution of grassland communities significantly influences biodiversity conservation and resource management. Remote sensing datasets, such as Sentinel land cover images with high spatial and temporal resolution, have been commonly employed to simulate the distribution and community structure of plant species in grasslands. Nevertheless, the precision of grassland and community classification requires further enhancement for more effective grassland management. In response, our study presents a dynamic workflow for simulating the distribution of grassland plant species under different hydroclimatic conditions. Leveraging a random forest classifier with multi-source remote sensing features, we mapped the distribution of nine plant communities and six types of land cover in the upstream grasslands of the Xilingol River Basin, Inner Mongolia from 2017 to 2022. Utilizing the Google Earth Engine platform, our findings reveal a remarkable consistency in the overall spatiotemporal accuracy and classification precision of grassland communities across different years, even under varying hydroclimatic conditions. When critical features were carefully selected, the integration of Sentinel-1 (C-band synthetic aperture radar), Sentinel-2 (surface reflectance), SRTM DEM 30 m (Digital Elevation Model data), and MOD16A2.006 (evapotranspiration/latent heat flux product) data yielded a high overall accuracy ranging from 87.0 % to 89.5 % over multiple years. Our study not only determined the spatial patterns of various grassland plant communities but also explored the mechanism of their responses to different hydroclimatic conditions. The proposed approach holds practical implications for fine-scale grassland classification, providing valuable support for landscape classification and monitoring efforts. Furthermore, the insights derived from our findings offer valuable guidance for land managers and policymakers, aiding them in making informed decisions related to grassland conservation and sustainable land use planning.

Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

AbstractVegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems.

ML‐Based Hybrid SAR and Optical Image LULC Mapping and Change Analysis With Variations in the Air Quality of the Imphal Valley, North‐East India

AbstractImphal Valley, situated in Manipur, India, stands as an intermontane valley of great ecological significance, notably hosting Loktak Lake. This research delves into the Land Use Land Cover Change (LULCC) within the Imphal Valley from 2016 to 2021 and assesses their impact on air quality across distinct land cover types. We used Sentinel‐1 & 2 data, ALOS PALSAR Digital Elevation Model, and applied Random Forest (RF), a machine learning algorithm for effective land use and land cover (LULC) mapping. Additionally, Sentinel‐5P data was utilized to monitor air quality parameters (CO, HCHO, NO2, SO2, Aerosol Index) spanning 2019 to 2021. The overall accuracies for the LULC maps employing a k (k = 3) fold approach for accuracy assessment varied between 88% and 92%, with corresponding Kappa coefficient ranging from 0.85 to 0.90. Noteworthy trends emerged from our analysis, revealing an increase in settlements and horticulture farms and a decline in forested areas and phumdis (floating biomass). Our findings highlight a mean concentration of CO ranging between 0.045 mol/m2 and 0.055 mol/m2 in different land cover types during February and March (2019–2021). Furthermore, we observed maximum mean HCHO, NO2, SO2, and aerosol index concentrations in March. Pollution levels surged during forest fires and shifting agriculture seasons, while aerosol levels declined during the lockdown period. This integrated approach emphasized on the comprehensive analysis of the dynamic interplay between LULCC and air quality in the Imphal Valley. This intricate relationship between LULC changes and air quality dynamics in the Imphal Valley, contribute to our understanding of the environmental dynamics in this ecologically vital region.

Validating spatio-temporal environmental machine learning models: Simpson’s paradox and data splits

Machine learning has revolutionized environmental sciences by estimating scarce environmental data, such as air quality, land cover type, wildlife population counts, and disease risk. However, current methods for validating these models often ignore the spatial or temporal structure commonly found in environmental data, leading to inaccurate evaluations of model quality. This paper outlines the problems that can arise from such validation methods and describes how to avoid erroneous assumptions about training data structure. In an example on air quality estimation, we show that a poor model with an r 2 of 0.09 can falsely appear to achieve an r 2 value of 0.73 by failing to account for Simpson’s paradox. This same model’s r 2 can further inflate to 0.82 when improperly splitting data. To ensure high-quality synthetic data for research in environmental science, justice, and health, researchers must use validation procedures that reflect the structure of their training data.

Lowland forest fragment characteristics and anthropogenic disturbances determine alien plant species richness and composition

Loss of habitat and biological invasion are the main threats to biodiversity. In intensive agricultural or urban landscapes, forest fragments, even if they are small and isolated represent biodiversity refugia. Environmental variables such as landscape structure, abiotic conditions and anthropogenic disturbance affect the biodiversity of the fragments. In this study, we explored plant species richness in 48 forest fragments embedded in predominantly agricultural landscapes on the alluvial plains of the Mura and Drava rivers in NE Slovenia. We determined several forest fragment characteristics such as perimeter, area, shape complexity, length and proportion of edge shared with adjacent land-cover types and anthropogenic disturbance indicated by the presence of footpaths and waste disposed in fragments. The abiotic condition of these fragments was assessed by Ellenberg indicator values. We built generalized linear models and ordination analyses to assess the importance of environmental variables for the richness and composition of alien plant species and other ecologically meaningful plant groups. Shape complexity had a consistent positive effect on the richness of native and alien plants species. Major drivers of alien plant composition in forest fragments included adjacent land-cover and urbanization level. An increasing proportion of arable land along the forest fragment perimeter negatively affected the richness of alien plants, while the effect of urban areas was positive. Our results confirmed that forest fragments in the rural–urban matrix represent biodiversity refugia and support native plant species; however, they are not resistant to invasions; instead invasion depends on the land-cover type in the surroundings of a given forest fragment.

Performance Comparison of Deep Learning (DL)-Based Tabular Models for Building Mapping Using High-Resolution Red, Green, and Blue Imagery and the Geographic Object-Based Image Analysis Framework

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.

Crop and landscape heterogeneity increase biodiversity in agricultural landscapes: A global review and meta-analysis.

Agricultural intensification not only increases food production but also drives widespread biodiversity decline. Increasing landscape heterogeneity has been suggested to increase biodiversity across habitats, while increasing crop heterogeneity may support biodiversity within agroecosystems. These spatial heterogeneity effects can be partitioned into compositional (land-cover type diversity) and configurational heterogeneity (land-cover type arrangement), measured either for the crop mosaic or across the landscape for both crops and semi-natural habitats. However, studies have reported mixed responses of biodiversity to increases in these heterogeneity components across taxa and contexts. Our meta-analysis covering 6397 fields across 122 studies conducted in Asia, Europe, North and South America reveals consistently positive effects of crop and landscape heterogeneity, as well as compositional and configurational heterogeneity for plant, invertebrate, vertebrate, pollinator and predator biodiversity. Vertebrates and plants benefit more from landscape heterogeneity, while invertebrates derive similar benefits from both crop and landscape heterogeneity. Pollinators benefit more from configurational heterogeneity, but predators favour compositional heterogeneity. These positive effects are consistent for invertebrates and vertebrates in both tropical/subtropical and temperate agroecosystems, and in annual and perennial cropping systems, and at small to large spatial scales. Our results suggest that promoting increased landscape heterogeneity by diversifying crops and semi-natural habitats, as suggested in the current UN Decade on Ecosystem Restoration, is key for restoring biodiversity in agricultural landscapes.

Waste Management: Landfill Selection Using Gis and Analytical Hierarchy Process

The widespread use of landfilling as a convenient waste management method has become unsustainable due to the rapid development causing a surge in waste generation, threatening the lifespan of existing landfills. The closure of landfills presents waste management challenges, necessitating more extensive garbage disposal facilities to accommodate the growing urban population. However, poor site selection methods can have a negative impact on both the environment and human health, which goes against the objectives of sustainable development goal (SDG). Methods like Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) facilitate decision-making processes. This integration reduces analysis time and costs enhances accuracy and minimizes errors. The Analytical Hierarchy Process (AHP) provides a framework to validate judgments and derive measurable values in decision-making involving theories and concepts. In this study, several criteria were used to create a final suitability map which is the distance from the residential area, distance from the plantation area, distance from the main road, distance from waterways, type of soil, slope, and land cover. The results indicate that distance from residential has the highest weightage of importance, which is 27%. It also found that the highly suitable new landfill area is located at Batu in Kuala Langat, Pulau Carey, and Bukit Rotan in Kuala Selangor.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China

Land surface temperature (LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones (the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami (Tuha) Basin, and Pakakuna Mountain Group) using geographical detector (Geodetector) and geographically weighted regression (GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24°C/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09°C/10a in autumn, and showed a warming trend in other seasons. Digital elevation model (DEM), latitude, wind speed, precipitation, normalized difference vegetation index (NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover (LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Local floral abundance influences bumble bee occupancy more than urban‐agricultural landscape context

Abstract Loss of natural habitat due to increases in agricultural extent raises the question of whether human‐dominated landscape types can support biodiversity, particularly for declining insect pollinators. Compared to more rural agricultural landscapes, urban areas may confer benefits for bumble bee populations by providing stable and diverse floral resources. However, disentangling the effects of local‐ and landscape‐scale characteristics on bumble bee populations in human‐modified landscapes is challenging. Here, we assessed bumble bee occupancy using a repeated transect sampling design conducted during the summers of 2019 and 2020 within the metropolitan area of Madison, WI, and the surrounding agricultural landscape. We fit hierarchical occupancy models to estimate the detection (p) and occupancy () probabilities relative to local habitat quality (floral abundance and floral richness) and landscape (agricultural‐urban gradient) features for eight bumble bee species. We hypothesised that bumble bees were more likely to occupy urban areas, serving as refugia, relative to agricultural sites. We found that the detection probability of all bumble bee species was seasonal and influenced by maximum floral abundance at survey sites, independent of the surrounding land cover type. After accounting for species‐specific detection probabilities, the effect of urbanisation on bee occupancy was weak, and no species were less likely to occupy urban than rural agricultural areas. Our findings suggest that bumble bee occupancy is associated with a ‘honeypot effect’ where local resource availability, in the form of higher floral abundance, is most important in limiting the occupancy of bumble bees across urban and agricultural landscapes.