Landscape Configuration Metrics Research Articles

The cooling capacity of urban vegetation and its driving force under extreme hot weather: A comparative study between dry-hot and humid-hot cities

Extreme heat events in cities necessitate closer examination of urban vegetation’ capacity to mitigate extreme hot weather. However, the cooling capacity of vegetation (CCV) and its driving forces of 2D and 3D landscape metrics under extreme hot weather conditions in dry-hot and humid-hot cities, particularly within different functional zones is still not well understood. The results indicate that the CCV is more pronounced in humid-hot cities than in dry-hot cities, particularly during periods of extreme hot weather. This CCV varied significantly across different functional zones, being the lowest in commercial zones and the highest in green space zones. Moreover, residential zones in humid-hot cities have higher CCV when the weather is more extreme. In dry-hot and humid-hot cities, urban vegetation landscape configuration metrics (such as patch and edge densities) contribute significantly to the CCV, and landscape composition (vegetation coverage) also contributes, with a significant increase during periods of extreme hot weather, particularly in humid-hot cities. Additionally, the 3D building morphology plays a crucial role in moderating the CCV; however, its impact diminishes during extreme heat. We also found that the CCV does not increase further after vegetation coverage is greater than 30–40 % in dry-hot cities, compared to approximately 60–80 % in humid-hot cities. The CCV increased nonlinearly with increasing urban vegetation largest patch index and decreasing urban vegetation patch density in dry-hot and humid-hot cities, with different thresholds of vegetation coverage and largest patch index under normal and extreme hot weather. A higher building height increased the CCV in humid-hot cities and reduced the CCV in dry-hot cities, particularly under extreme hot weather, and sparse high-rise buildings were better suited to humid-hot cities for increasing urban CCV. These findings provide valuable insights for urban planners to enhance the urban CCV, contributing to long-term urban resilience and public health protection during extreme heat events.

Landscape context affects patch habitat contributions to biodiversity in agroecosystems

AbstractEffective conservation schemes are needed to advance the dual objectives of biodiversity conservation and agronomic production in agricultural landscapes. Understanding how plant and arthropod taxa respond to both local habitat patch characteristics and landscape complexity is crucial for planning effective agri‐environment schemes. This study investigated the relative effects of local variables (plant and insect diversity ≤100 m from patch habitat center) and landscape variables (landscape composition and configuration metrics ≤5 km from patch habitat center) on the diversity of plants and arthropods within noncrop habitat patches (1) at different spatial extents ranging from 0.1 to 5 km, while (2) quantifying differential effects of local and landscape variables on particular components of diversity (i.e., species richness and abundance), and accounting for (3) particular components of landscape extent (0.1‐, 0.5‐, 1‐, 2‐, and 5‐km radii) and complexity (i.e., landscape composition and configuration). Landscape variables were significantly correlated with local plant and arthropod species richness and abundance at all spatial extents. Biodiversity responses to landscape variables were largely scale‐dependent, as pairwise comparisons were significantly different between all spatial extents except between 1‐ and 2‐km extents, and correlations were lowest at the 5‐km extent. Partial R2 values for predicting local biodiversity were highest when both local and landscape variables were included as predictors of species richness and abundance, increasing from 0.163 to 0.469 when landscape variables were included, underscoring the importance of considering both local and landscape effects on local diversity. Landscape configuration variables accounted for more variation in plant and arthropod species richness than composition variables. However, models performed best when composition and configuration were considered together rather than alone, suggesting that both components of landscape complexity should be considered for identifying and managing conservation areas in crop fields. Existing conservation schemes that incentivize farmers to create or conserve seminatural patch habitat within crop fields may be more effective when combined with landscape‐scale designs that enhance landscape complexity across the Northern Great Plains. Local conservation efforts should be coordinated with landscape‐level efforts to ultimately enhance biodiversity and desired ecosystem service outcomes across agricultural landscapes.

Landscapes of risk: A comparative analysis of landscape metrics for the ecotoxicological assessment of pesticide risk to bees

Abstract Pesticide use in agricultural landscapes creates environmental contamination that is heterogenous in space and time. Mobile organisms, such as bees, are exposed to multiple contamination sources when visiting patches that vary in the amount, timing and toxicity of pesticides used. Yet, environmental risk assessments (ERA) typically fail to consider this heterogeneity, in part because of the complexities of estimating exposure to different pesticides, and subsequent risk at organism‐relevant scales. We use pesticide assays of 269 bee‐collected pollen samples to understand the spatiotemporal variability of risk across a network of 41 field sites in southern Sweden. Observed bee pesticide risk is calculated based on compound‐specific residue quantifications in pollen and standardized toxicity data. We then compare the ability of three classes of landscape‐scale variables to predict this risk: (1) landscape composition and configuration metrics, (2) landscape load based on national pesticide use data and (3) predictions from a newly developed bee pesticide exposure model. Based on use data, 10 crops account for 81% of the total risk. We detected 49 pesticide compounds in bee‐collected pollen. Although herbicides and fungicides constitute the bulk of detected pesticides, both in frequency and amount quantified, unsurprisingly, insecticides contribute the most to risk. Landscape composition and configuration metrics did not predict observed pesticide risk, and interactions with bee species indicate taxa‐dependency in predictions. Landscape load predicted observed risk consistently between taxa. Risk estimates from our exposure model were strongly predictive but only when considering realized risk (i.e., risk estimates based on prior pesticide use information). Synthesis and applications. Predicting pesticide risk based on landscape patterns could enable landscape‐scale ERA. However, simple metrics of landscape pattern, such as proportion of agricultural land, are not sufficient. We found that risk observed in bee‐collected pollen was best predicted when integrating spatialized pesticide use in the pesticide exposure model, underscoring the importance of such data for research, monitoring and mitigation. Further, we propose a guidance framework for future landscape ecotoxicological risk analyses that clarifies data needs relative to risk prediction goals.

Impacts of land use patterns on river water quality: the case of Dongjiang Lake Basin, China

Landscape pattern can regulate water quality, but determining the thresholds at which landscape metrics impact water quality remains a key issue. Thus, in this study, we investigated landscape metrics that affect the water quality of the rivers flowing into Dongjiang Lake, China, and their spatial and seasonal differences, were investigated using redundancy and partial redundancy analyses. Further, a raw water quality parameter-landscape metric data set was constructed. Threshold values of landscape metrics that induced sudden changes in water quality were analyzed using the bootstrap method applied to 1000 random samples extracted from data set. Finally, mechanisms of landscape patterns that affected river water quality were quantitively explored. The following results were obtained: (1) The key area of landscape that impacted water quality in the Dongjiang Lake Basin was the 400 m buffer zone; (2) Seasonal differences were observed in water quality changes, which better explained by landscape metrics in the dry season than in the wet season. Moreover, water quality was most influenced by landscape configuration metrics. The influence of landscape topographic metrics was greater than that of composition metrics in the dry season, whereas the opposite was true in the wet season; and (3) In this watershed, the largest patch index of grassland and landscape shape index of built-up land were the key metrics at the sub-basin and 400 m buffer zone, respectively. If the largest patch index of grassland was below 8.5% or the landscape shape index of built-up land exceeded 1.5, the water quality of the rivers flowing into the lake was prone to a risk of significant decline, which has an important guiding significance to the optimization of land use pattern.

A regionally varying habitat model to inform management for greater sage-grouse persistence across their range

Identifying habitat needs for species with large distributions is challenging because species-habitat associations may vary across scales and regions (spatial nonstationarity). Furthermore, management efforts often cross jurisdictional boundaries, complicating the development of cohesive conservation strategies among management entities. The greater sage-grouse (Centrocercus urophasianus) is a rapidly declining species that spans 11 U.S. states and responds to habitat conditions across a wide range of spatial scales and regions. Allowing for regional variance in species-habitat associations and suitability predictions could systematically identify important habitats at levels relevant to management. We collaboratively developed a model with Bureau of Land Management (BLM) biologists that: (1) evaluated the scale of effect for different environmental covariates; (2) accounted for regional differences in population-level responses; and (3) predicted probabilities of persistence across the U.S. occupied range. We modeled range-wide lek persistence data (6615 communal breeding sites classified as active or inactive) as a function of environmental covariates. Environmental covariates included sagebrush cover, pinyon-juniper cover, topography, precipitation, point and line disturbance densities, and landscape configuration metrics. Our model treated habitat assessment areas – regionally delineated by BLM biologists – as random intercepts and slopes that allowed for geographic variation in species-habitat associations and predicted probabilities of lek persistence. Our final model indicated support for 12 environmental covariates predicting lek persistence at scales extending between 1- to 15-km radii from lek centers, and a covariate measuring distance to the occupied range boundary. Five of these covariates showed significant regionally varying responses: sagebrush clumpiness (a measure of habitat aggregation), pinyon-juniper cover, point disturbance of anthropogenic features such as energy infrastructure and communication towers, elevation, and a topographic index associated with mesic habitats. This spatial nonstationarity indicates unitary range-wide recommendations, or rules-of-thumb with respect to their effects on lek persistence, may be problematic for these environmental conditions. For covariates that did not include random slopes, and which were potentially amenable to management actions, we found that leks were predicted to become extirpated when sagebrush cover fell below 9.6 % (summarized at the 3.2-km radius extent), and the proportion of classified sagebrush habitat fell below 0.7 (1-km). We produced a continuous predictive probability surface of lek persistence which we binned based on model sensitivity thresholds to produce habitat quality categories. The highest quality habitat (capturing 50 % of active leks) covered 25.5 % of the occupied range, while the combined lowest through highest quality habitats (capturing 95 % of active leks) covered 65.0 %. Accommodating regional environmental differences in models that are relevant to habitat management planning will help ensure their applicability to targeted goals. Continuous collaboration between modelers and land managers early in the modeling process increases the likelihood of this outcome.

Heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation in a semiarid region of China.

Wetlands in semiarid regions have important ecological functions and provide services that are partly dependent on the landscape pattern and structure; therefore, it is important to assess the heterogeneity of wetland landscape conditions for wetland conservation and management. Widespread wetland degradation resulting from main factors in semiarid regions highlights the need for exploring the heterogeneity of wetland landscapes and their relationships with anthropogenic disturbances and precipitation. Wetlands in our study referred to natural wetlands, including marshes, rivers, and lakes. We used overall landscape metrics to assess wetland landscape conditions and used grid landscape metrics at a scale of 30km using a moving window method to explore the heterogeneity of wetland landscapes during the period (1986-2019) for western Jilin Province. We performed Spearman correlation analyses of the landscape metrics with the Hemeroby index (HI) and mean annual precipitation (MAP). We suggested that wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED and AI) had positive correlations with HI by 2019 (p < 0.01); in addition, wetland landscape composition metrics (e.g., NP and PD) and configuration metrics (e.g., ED) were negatively correlated with MAP during 1986-2019. In conclusion, wetland landscapes have become increasingly fragmented over the past few decades (1986-2019) in western Jilin Province and the reasons for this result mainly include the household contract responsibility system, along with rural productivity, severe overgrazing and reclamation, and climate change. Wetland landscape composition was positively correlated with human interference and negatively correlated with mean annual precipitation.

Exploring spatial nonstationarity for four mammal species reveals regional variation in environmental relationships

AbstractBroad‐scale ecological research on species distributions commonly presumes that the correlative relationships discovered are stationary over space. This is an assumption of most species distribution models (SDMs) that combine observations of species occurrence with environmental characteristics to understand current ecological correlates and to predict distributions based on those relationships. However, ecological relationships may vary spatially because of changes in the environment (i.e., resource availability) or the organism itself (i.e., local adaptation). Discovering this within‐species variation typically requires dense datasets over large geographic areas, which are now being provided by the recent proliferation of open‐access biodiversity occurrence records. Using nearly 4000 sampling locations from an open‐access, state‐wide camera‐trapping project, we explore the space‐varying effects of covariates on the distribution of four mammal species at two scales: region‐specific and fine resolution, with the latter estimated using spatially varying coefficients (SVC) models, to understand the scale of spatial variation in ecological relationships. Among the four species tested, the ecological relationships for two were best explained with the regional models, equivocal results for one species, while the SVC model had superior fit and predictive performance for the final species (white‐tailed deer, Odocoileus virginianus). Spatial nonstationarity was more common in relationships with landscape composition characteristics, such as housing density, than in landscape configuration metrics, such as patch richness density. One of the most appealing results of an SVC approach is not only the improved predictions across large landscapes but also understanding how animals are responding to the environment differently at the management unit level. For example, we found that deer's spatially varying relationship with forest cover was best explained by an interactive effect of deer management units (i.e., differences in deer populations) and predator pressure. These findings lead to a new hypothesis about how deer may be differentially using forested environments across space and could be a promising area of future research. Given sufficient data, accounting for nonstationarity in SDMs can show large‐scale ecological patterns while also detecting local level changes in animal ecology in areas small enough that management or protection can be readily implemented.

Propagation of atmospheric condition parameter uncertainty in measurements of landscape heterogeneity

ABSTRACT Remote sensing products are widely used in ecology and environmental science to understand how surfaces are composed and configured in a landscape and how they change through time. Land cover maps that describe the nature of habitats available to organisms have become critical tools in the study of anthropogenic impacts, such as fragmentation, on biodiversity and related ecological processes. Despite the increasing importance and widespread use of remote sensing derived landscape pattern indices measured using landscape composition and configuration metrics via land cover maps, these indices may conceal substantial and potentially problematic concerns about the uncertainty of reflectance data. This uncertainty derives from the presence of varying atmospheric conditions between the earth and the sensor and is often unreported. We asked how the magnitudes of measured landscape composition and configuration change and the dispersion in the changes caused by reflectance uncertainty compare. Using a Monte Carlo framework, we estimated multiple realizations of reflectance images of a simulated hyperspectral dataset depicting a peri-urban area. These estimates were influenced by uncertainty originating from atmospheric condition parameters (column water vapour, aerosol optical depth, and aerosol type). We generated multiple classified maps using Support Vector Machines and quantified standard landscape composition and configuration metrics. High dispersion in composition and configuration metrics caused by reflectance uncertainty strongly impacted our ability to detect and compare changes of the kind that natural scientists typically derive from such maps. We conclude that the propagation of uncertainty limits the extent to which current landscape patterns and Land Use and Land Cover change (LULC) analyses can be used for fine-scale decision-making for landscape management.

Multi-taxa ecological responses to habitat loss and fragmentation in western Amazonia as revealed by RAPELD biodiversity surveys

ABSTRACT Habitat loss and fragmentation caused by deforestation are important anthropogenic drivers of changes in biodiversity in the Amazon rainforest, and has reached its highest rate in recent decades. However, the magnitude and direction of the effects on species composition and distribution have yet to be fully understood. We evaluated the responses of four taxonomic groups − birds, amphibians, orchid bees, and dung beetles - to habitat loss and fragmentation at both species and assemblage level in the northern Ecuadorian Amazon. We sampled fifteen 250-m long plots in terra-firme forest remnants. We calculated one landscape fragmentation index (fragindex), which considers the proportion of continuous forest cover, edge density and isolation in the landscape, and nine landscape configuration metrics. Logistic regression models and multivariate regression trees were used to analyze species and assemblage responses. Our results revealed that over 80% of birds, amphibians or orchid-bee species, and 60% of dung beetles were negatively affected by habitat loss and fragmentation. Species composition of all taxonomic groups was significantly affected by differences in forest cover and connectivity. Less than 5% of all species were restricted to landscapes with fragindex values higher than 40%. Landscape metrics related to the shape and area of forest patches determined the magnitude and direction of the effect on species responses. Therefore, changes in the landscape configuration of Ecuadorian Amazonia should be minimized to diminish the effects of habitat loss and fragmentation on species occurrence and assemblage composition.

Understanding the spatiotemporal pollution dynamics of highly fragile montane watersheds of Kashmir Himalaya, India

Pollution of riverine ecosystems through the multidimensional impact of human footprints around the world poses a serious challenge. Research studies that communicate potential repercussions of landscape structure metrics on snowmelt riverine water quality particularly, in climatically fragile Himalayan watersheds are very scarce. Though, worldwide, grasping the influence of land-use practices on water quality (WQ) has received renewed attention yet, the relevance of spatial scale linked to landscape pattern is still elusive due to its heterogenic nature across diverse geomorphic regions. In this work, therefore, we tried to capture the insights on landscape-aquascape interface by juxtapositioning the impacts of landscape structure pattern on snowmelt stream WQ of the whole Jhelum River Basin (JRB) under three varying spatial scales viz., watershed scale, riparian corridor (1000 m wide) and reach buffer (500 m wide). The percentage of landscape pattern composition and configuration metrics in the JRB were computed in GIS utilizing Landsat-8 OLI/TIRS satellite image having 30 m resolution. To better explicate the influence of land-use metrics on riverine WQ with space and time, we used Redundancy analysis (RDA) and multilinear regression (MLR) modeling. MLR selected land-use structure metrics revealed the varied response of WQ parameters to multi-scale factors except for total faecal coliform bacteria (TC) which showed perpetual presence. The reach-scale explained slightly better (76%) variations in WQ than riparian (75%) and watershed (70%) scales. Likewise, across seasonal scale, autumn (75%), winter (83%), and summer (77%) captured the most WQ variation at catchment, riparian, and reach scales respectively. We observed impairing WQ linkages with agriculture, built-up and barren rocky areas across watersheds, besides, pastures in riparian buffer areas, and fragmentation of landscape patches at the reach scale. Due to little appearance of spatial scale differences, a multi scale perspective landscape planning is emphasized to ensure future sustainability of Kashmir Himalayan water resources.

Reverse Thinking: A New Method from the Graph Perspective for Evaluating and Mitigating Regional Surface Heat Islands

Accurately locating key nodes and corridors of an urban heat island (UHI) is the basis for effectively mitigating a regional surface UHI. However, we still lack appropriate methods to describe it, especially considering the interaction between UHIs and the role of connectivity (network). Specifically, previous studies paid much attention to the raster and vector perspective—based on standard landscape configuration metrics that only provide an overall statistic over the entire study area without further indicating locations where different types of pattern and fragmentation occur. Therefore, by reverse thinking, here we attempt to propose a new method from the graph perspective which integrates morphological spatial pattern analysis (MSPA)—which is used to characterize binary patterns with emphasis on connections between their parts as measured at varying analysis scales, and habitat availability indices to evaluate and mitigate regional surface UHI. We selected the Pearl River Delta Metropolitan Region (PRDR), one of the most rapidly urbanized regions in the world as the case study (1995–2015). The results of the case study showed: (1) the core (UHI) type accounts for the vast majority of the MSPA model, with the relative land surface temperature (LST) rises, the proportion of the core type will increase, and it could influence the edge (UHI) type significantly; (2) the branch, bridge, and islet (UHI) types have similar results to the lower temperature (4 &lt; Relative LST ≤ 6) area and account for the majority, indicating that these types are more susceptible to their surrounding environment; (3) the importance and extreme importance area (node) from 1995 to 2015 have increased significantly and mainly distributed in the urbanized areas, which means cooling measures need to be implemented in these areas in order of priority. Shifting the research logic of UHI evaluation and mitigation from “patch” to “network”, we hold the point that the method (reverse thinking) has significant theoretical and practical implications for mitigating regional UHI and urban climate-resilience.

Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK

ContextLandscape structure is thought to affect the provision of ecosystem service bundles. However, studies of the influence of landscape configuration on ecosystem service trade-offs and synergies in urban areas are limited. This study used Bayesian Belief Networks to predict ecosystem service trade-offs and synergies in the urban area comprising the towns of Milton Keynes, Bedford and Luton, UK.ObjectivesThe objectives of this study were to test (1) a Bayesian Belief Network approach for predicting ecosystem service trade-offs and synergies in urban areas and (2) assess whether landscape configuration characteristics affect ecosystem service trade-offs and synergies.MethodsBayesian Belief Network models were used to test the influence of landscape configuration on ecosystem service interactions. The outputs of a Principal Component Analysis (PCA) on six ecosystem services and landscape configuration metrics were used as response and explanatory variables, respectively. We employed Spearman’s rank correlation and principal component analysis to identify redundancies between landscape metrics.ResultsWe found that landscape configuration affects ecosystem service trade-offs and synergies. A sensitivity analysis conducted on the principal components showed that landscape configuration metrics core area (CORE) and effective mesh size (MESH) are strong influential determinants of ecosystem service trade-offs and synergies.ConclusionsThis study demonstrates that landscape configuration characteristics affect ecosystem service trade-offs and synergies and that a core set of metrics could be used to assess ecosystem service (ES) trade-offs and synergies. The findings may be relevant to planning and urban design and improved ecosystem management.

Spatial identification and determinants of trade-offs among multiple land use functions in Jiangsu Province, China

Understanding the relationships among multiple land use functions (LUFs) is crucial for land-based spatial planning that can guide targeted land use policy-making in complex socio-ecological systems. However, few studies concerned the interactions among various LUFs integrating the issues of economy, environment, and society at a fine scale. In this study, we quantified 12 LUFs using a geospatial model and statistical analysis at the grid scale in Jiangsu Province. Then, we identified the relationships among three primary LUFs—agricultural production function (APF), urban-rural living function (ULF), and ecological maintenance function (EMF)—and further explored the determinants of LUF trade-offs aimed to provide a reference for policy-makers to make decisions in future land use planning and management. The results revealed that the high trade-off areas for APF and ULF are mainly distributed in central and northern Jiangsu, and the trade-offs for both APF-EMF and ULF-EMF were higher in the area covered with water and forest. The determinants of LUF trade-offs mainly refers to land use/land cover, potential evapotranspiration, and vegetation coverage ratio. Moreover, landscape configuration metrics and distance to the nearest county and nearest road also have remarkable impacts on the trade-offs of APF-EMF and ULF-EMF. Finally, we proposed that the concepts of LUF trade-offs should be incorporated into the processes of delineating boundaries for urban growth, farmland, and natural areas. We also propose that land consolidation projects should be implemented in an orderly manner to alleviate LUF trade-offs.

Spatial scale effects of landscape metrics on stream water quality and their seasonal changes

Physiography and land use patterns influence streams water quality by affecting non-point source (NPS) pollution process. However, each landscape factor may affect the NPS pollution process differently with the variations of the spatial scale and season. Thus, quantitative analysis of each landscape metrics scale effect and determination of the abrupt change-point in the relationship between stream water quality and the metrics is very helpful for landscape planning of water quality protection. Based on water quality monitoring data for four years in 12 sub-watersheds of a typical headwater watershed in Eastern China, we adopted regular and partial redundancy methods to quantify the spatial scale effects and seasonal dependence of various landscape metrics impact on stream water quality, and then to identify the abrupt change-point of the water quality along the gradient of landscape metrics. Results revealed that the pure effects of different categories of landscape metrics on stream water quality were in the following order: landscape configuration metrics (20.5–31.6%) > physiographic metrics (4.0–15.9%) >landscape composition metrics (3.2–7.5%). The spatial scale effect of physiography impact on stream water quality was the most significant, while the impact of landscape configuration on water quality had the highest seasonal sensitivity. The overall water quality variation was better explained by buffer zone scale than by catchment scale landscape characteristics, and this phenomenon was more obvious during the wet season than during the dry season. In the studied watershed, we identified the largest patch index of farmland (LPIfar) and the landscape shape index of forest (LSIfor) as the key landscape metrics at sub-watershed scale and buffer zone scale, respectively. The LPIfar > 7.0% at the sub-watershed scale and LSIfor < 5.5 at the buffer zone scale were suggested as the preferred landscape planning parameters to protect the stream water quality efficiently. Results indicated that, to protect water quality, landscape regulation should follow the scale-adaptability measures and consider the landscape thresholds, which cause abrupt changes in water quality.

Spatiotemporal Variation of Surface Urban Heat Islands in Relation to Land Cover Composition and Configuration: A Multi-Scale Case Study of Xi’an, China

Urban heat islands (UHI) can lead to multiple adverse impacts, including increased air pollution, morbidity, and energy consumption. The association between UHI effects and land cover characteristics has been extensively studied but is insufficiently understood in inland cities due to their unique urban environments. This study sought to investigate the spatiotemporal variations of the thermal environment and their relationships with land cover composition and configuration in Xi’an, the largest city in northwestern China. Land cover maps were classified and land surface temperature (LST) was estimated using Landsat imagery in six time periods from 1995 to 2020. The variations of surface heat island were captured using multi-temporal LST data and a surface urban heat island intensity (SUHII) indicator. The relationship between land cover features and land surface temperature was analyzed through multi-resolution grids and correlation analysis. The results showed that mean SUHII in the study area increased from 0.683 °C in 1995 to 2.759 °C in 2020. The densities of impervious surfaces had a stronger impact on LST than green space, with Pearson’s correlation coefficient r ranging from 0.59 to 0.97. The correlation between normalized difference impervious surface index and LST was enhanced with the enlargement of the grid cell size. The correlations between normalized difference vegetation index and LST reached maxima and stabilized at grid cell sizes of 210 and 240 m. Increasing the total area and aggregation level of urban green space alleviated the negative impacts of UHI in the study area. Our results also highlight the necessity of multi-scale analysis for examining the relationships between landscape configuration metrics and LST. These findings improved our understanding of the spatiotemporal variation of the surface urban heat island effect and its relationship with land cover features in a major inland city of China.

Agricultural Landscape Composition Linked with Acoustic Measures of Avian Diversity

Measuring, monitoring, and managing biodiversity across agricultural regions depends on methods that can combine high-resolution mapping of landscape patterns with local biodiversity observations. This study explores the potential to monitor biodiversity in agricultural landscapes by linking high-resolution remote sensing with passive acoustic monitoring. Land cover maps produced using a small unmanned aerial system (UAS) and PlanetScope (PS) satellite imagery were used to investigate relationships between landscape patterns and an acoustically derived biodiversity index (vocalizing bird species richness) across 12 agricultural sample locations equipped with acoustic recorders in Iowa, USA during the 2018 growing season. Statistical assessment revealed a significant direct association between vocalizing bird richness and percent noncrop vegetation cover. High spatial resolution (1 m) UAS mapping produced stronger statistical associations than PS-based maps (3 m) for landscape composition metrics. Landscape configuration metrics (Shannon’s diversity index, contagion, perimeter-area-ratio, and circumscribing circle index) were either cross-correlated with composition metrics or unusable owing to complete landscape homogeneity in some agricultural landscape samples. This study shows that high resolution mapping of noncrop vegetation cover can be linked with acoustic monitoring of unique bird vocalizations to provide a useful indicator of biodiversity in agricultural landscapes.

How the landscape features of urban green space impact seasonal land surface temperatures at a city-block-scale: An urban heat island study in Beijing, China

Urban greening has been widely regarded as one of the most effective strategies for alleviating the risk of urban heat island (UHI). In this study, the metropolitan region of Beijing in China, was chosen as the study area. We used urban blocks as the special units for spatial analysis, and 17 landscape factors, including both landscape composition and configuration metrics, were chosen as the explanation variables for seasonal land surface temperature (LST). Based on these, we examined the potential differences in the relationship between urban LST and the landscape characteristics of urban green space (UGS) in different seasons and UGS gradients. Our results confirmed the significant cooling effect by UGS at a city-block-scale. However, the roles of UGS landscape composition and configuration on impacting LST varied in different seasons and UGS gradients. Generally, the cooling effect of UGS is more significant in locations with warmer climate or lower UGS coverage, and the landscape composition characteristics of UGS had a dominant effect on the variation of LST. On the contrary, in cold seasons or higher UGS ratios, the landscape configuration characteristics of UGS, such as the edge density of urban forest patches, would predominantly function to alleviate UHI than landscape composition in urban blocks. Moreover, the geometry features of urban blocks were also found to have a certain influence on LST. These findings demonstrated that the influence of UGS landscape features on urban surface thermal environment is very complex at city-block-scale. Therefore, for UHI mitigation, a prerequisite for multifactor analysis must be taken into consideration during the implementation of urban greening.

Showcasing Relationships between Neighborhood Design and Wellbeing Toronto Indicators

Cities are the keystone landscape features for achieving sustainability locally, regionally, and globally. With the increasing impacts of urban expansion eminent, policymakers have encouraged researchers to advance or invent methods for managing coupled human–environmental systems associated with local and regional sustainable development planning. Although progress has been made, there remains no universal instrument for attaining sustainability on neither regional nor local planning scales. Previous sustainable urbanization studies have revealed that landscape configuration metrics can supplement other measures of urban well-being, yet few have been included in public data dashboards or contrasted against local well-being indicators. To advance this sector of sustainable development planning, this study had three main intentions: (1) to produce a foundational suite of landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; (2) to visualize and interpret spatial patterns of neighborhood streetscape patch cohesion index (COHESION), Shannon’s diversity index (SHDI), and four Wellbeing Toronto indicators across the 140 Toronto neighborhoods; (3) to quantitatively assess the global collinearity and local explanatory power of the well-being and landscape measures showcased in this study. One-hundred-and-thirty landscape ecology metrics were computed: 18 class configuration metrics across seven land cover categories and four landscape diversity metrics. Anselin Moran’s I-test was used to illustrate significant spatial patterns of well-being and landscape indicators; Pearson’s correlation and conditional autoregressive (CAR) statistics were used to evaluate relationships between them. Spatial “hot-spots” and/or “cold-spots” were found in all streetscape variables. Among other interesting results, Walk Score® was negatively related to both tree canopy and grass/shrub connectedness, signifying its lack of consideration for the quality of ecosystem services and environmental public health—and subsequently happiness—during its proximity assessment of socioeconomic amenities. In sum, landscape ecology metrics can provide cost-effective ecological integrity addendum to existing and future urban resilience, sustainable development, and well-being monitoring programs.

Investigating urban heat island through spatial analysis of New York City streetscapes

Cities experience the urban heat island (UHI), which continue to pose challenges for humanity's increasingly urban population. Past research has revealed that land cover composition and configuration, along with other geographical phenomena (i.e., albedo), can explain much of the spatial pattern of UHI, yet advances await. In response, this research was made to: (i) assess the spatial pattern of mean ambient night temperature across 34 streetscapes in New York City (NYC); (ii) create and differentiate global and local regression models between-natural and built streetscape characteristics- and mean ambient night temperature; and (iii) use geographically weighted regression (GWR) to assess local patterns of correlated associations. Urban canopy layer (UCL) temperatures were recorded across 34 weather stations, and landscape metrics calculated from 0.914 m land cover data with 96% accuracy. Local Getis-Ord Gi* statistic exhibited significant spatial cold and hot spots of UHI in NYC. Global inferential tests revealed that sky-view factor, photosynthesis activity, elevation, and road configuration were the strongest predictors of mean ambient night temperature. Six multiple regression models were ultimately made with GWR fitting the UHI aptly (R2 = 65–74%). Important explanatory covariates were illustrated using local pseudo-t statistics and linked to mean ambient night temperature, supporting the importance of GWR for understanding local UHI interactions. Results also confirm that landscape configuration metrics are stronger predictors of UHI than composition measures. Streetscape design, particularly road patterns and process, requires more consideration when attempting to mitigate UHI during future sustainability planning, urban renewal projects, and research.

A global assessment of primate responses to landscape structure.

Land-use change modifies the spatial structure of terrestrial landscapes, potentially shaping the distribution, abundance and diversity of remaining species assemblages. Non-human primates can be particularly vulnerable to landscape disturbances, but our understanding of this topic is far from complete. Here we reviewed all available studies on primates' responses to landscape structure. We found 34 studies of 71 primate species (24 genera and 10 families) that used a landscape approach. Most studies (82%) were from Neotropical forests, with howler monkeys being the most frequently studied taxon (56% of studies). All studies but one used a site-landscape or a patch-landscape study design, and frequently (34% of studies) measured landscape variables within a given radius from the edge of focal patches. Altogether, the 34 studies reported 188 responses to 17 landscape-scale metrics. However, the majority of the studies (62%) quantified landscape predictors within a single spatial scale, potentially missing significant primate-landscape responses. To assess such responses accurately, landscape metrics need to be measured at the optimal scale, i.e. the spatial extent at which the primate-landscape relationship is strongest (so-called 'scale of effect'). Only 21% of studies calculated the scale of effect through multiscale approaches. Interestingly, the vast majority of studies that do not assess the scale of effect mainly reported null effects of landscape structure on primates, while most of the studies based on optimal scales found significant responses. These significant responses were primarily to landscape composition variables rather than landscape configuration variables. In particular, primates generally show positive responses to increasing forest cover, landscape quality indices and matrix permeability. By contrast, primates show weak responses to landscape configuration. In addition, half of the studies showing significant responses to landscape configuration metrics did not control for the effect of forest cover. As configuration metrics are often correlated with forest cover, this means that documented configuration effects may simply be driven by landscape-scale forest loss. Our findings suggest that forest loss (not fragmentation) is a major threat to primates, and thus, preventing deforestation (e.g. through creation of reserves) and increasing forest cover through restoration is critically needed to mitigate the impact of land-use change on our closest relatives. Increasing matrix functionality can also be critical, for instance by promoting anthropogenic land covers that are similar to primates' habitat.