Landscape Pattern Metrics Research Articles

Building landscape ecological network with multi-scenario connectivity based on network fault tolerance index and networking technology in graph theory

Existing methods for constructing Landscape Ecological Networks (LEN) either focus on functional attributes or structural attributes and also do not address the dynamic impact of urban development on ecological spaces. To address these limitations, this paper proposed evaluating the maximum connectivity (k) of potential LEN, composed of existing ecological spaces, using a network fault tolerance index. We then used graph-based networking technology to construct the optimal LEN structure based on this index. Specifically speaking, first, we identified the existing LEN with the highest ecosystem service capacity and lowest ecological cost in the study area. Using four landscape pattern metrics, we comprehensively evaluated the fault tolerance index as the maximum connectivity k of potential LEN and determined that k = 7. We then modeled the existing LEN as a weighted graph and transformed the network optimization problem into a k-edge addition problem. This calculation yielded seven LEN schemes, each corresponding to different priority orders of new ecological corridors based on k value. This index captures changes in ecological space patterns and offers diverse LEN optimization solutions. It provides practitioners with flexible spatial strategies to address the dynamic impacts of urbanization.

Assessing the effects of urban green spaces metrics and spatial structure on LST and carbon sinks in Harbin, a cold region city in China

In the face of accelerated urbanization, urban green spaces (UGSs) are crucial in alleviating urban heat island effects. This study investigates the link between UGS landscape patterns, plant community characteristics, Land surface temperature (LST), and Annual carbon sequestration of plant community (ACSPC) across different seasons and scales in Harbin by remote sensing and field measurements. The results show that (1) The LST in urban built-up areas shows an increasing trend, and its correlation with UGS exhibits seasonal fluctuations. (2) Landscape pattern metrics significantly relate to LST, particularly at 1500 and 1260 m window scales. (3) In summer and winter, Aggregation index (AI) , Mean patch area (AREA_MN), Edge density (ED), Percentage of landscape (PLAND), Mean patch shape index (SHAPE_MN), Mean diameter at breast height (MDBH), Mean canopy diameter (MCD), Average carbon sequestration capacity(ACSC), and Leaf area index (LAI) are positively correlated, whereas Sky view factor (SVF) is negatively correlated with ACSPC. (4) LAI, SVF and MCD (winter) significantly affect LST. MDBH, MCD, and SVF in summer significantly influence ACSPC. This research elucidates the effect of UGS landscape patterns and plant community characteristics on urban heat islands and carbon sinks, providing vital insights for urban ecological improvement.

From isolation to linkage: Holistic insights into ecological risk induced by land use change

While landscape ecological risk assessment (LERA) has the potential to positively impact environmental concerns by informing land use policies, a holistic perspective has yet to be adopted in previous investigations of LERA in relation to land use change. To address this gap, our study introduces a novel research framework that applies landscape ecology principles and methodologies, utilizing the concept of ‘landscape’ as a bridge connecting ecological risk and land use change. Building on relevant literature, we propose three hypotheses regarding the systemic nature of ecological risks. We then elucidate the principles, logic, and methods of LERA and employ high-precision land-use data and landscape pattern metrics to evaluate ecological risks resulting from land-use changes in Xiapu County between 2009 and 2013. Our ecological risk and land use overlay analysis validates three assumptions regarding the systematic character of ecological risks: (1) ecological risks in a local area are influenced by more than just land use changes within its spatial boundaries, (2) similar land use shifts can have varying impacts on ecological risk over time, and (3) ecological risk change cannot be solely attributed to the change in land use type. Finally, based on our enhanced understanding of ecological risks, we propose policy recommendations for land use planning and development at both global and local spatial scales. Our study provides a comprehensive understanding of ecological risks and their relationships with land use change, offering valuable insights for policymakers and researchers.

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.

The landscape altered the interaction between vegetation and climate in the desert oasis of Hotan River Basin, Xinjiang, China

In the arid region, the oasis is characterized by a desert substance and a mosaic of vegetation. The biophysical processes and interactions between vegetation and the local climate in this kind of region are determined by the macroscopic structure of the ecosystems, i.e. landscape patterns. To understand how these landscape patterns impact regional hydro-heat coupling across space and evolve over time, we utilized remote sensing observational data and methods to examine the relationships among these factors. In this case, we focused on the oasis along the upstream of the Hotan River Basin in the Taklamakan Desert in Xinjiang of western China and employed the satellite imagery datasets of Landsat from 1993 to 2019 to investigate the dynamics of vegetation–climate factors. Based on the land use and cover change datasets, the landscape pattern metrics, including patch density (PD), contagion index (CONTAG), fractal dimension (FRAC), were calculated to measure the landscape features on the different aspects, i.e. the fragmentation, the connectivity, and the complexity. With the algorithm of land surface energy balance, the land surface indicators, including the soil-adjusted vegetation index (SAVI), albedo, surface irradiance temperature (Ts), and evapotranspiration (ET), were calculated to represent the key process in the interaction of vegetation–climate. The temporal-spatial dynamics of the landscape patterns and the vegetation–climate metrics were mapped and demonstrated in a quantitative manner. The statistical results revealed that during the past decades, the agricultural land in the study area had significantly increased by 17 %. Grassland and shrubs had also expanded, while the desert area decreased from 82.57 % to 78.82 % of the total area, with an overall reduction rate of 1.4 %/10a. It was found the study area was getting warmer and dryer based on the general trends of Ts and ET observed during the period of 1993–2019. The agricultural land had the highest PD and CONTAG, and the lowest FRAC. The agricultural land had the lowest Ts and the highest ET. The results of structural equation model identified the decoupling effects of PD and CONTAG on the regional hydro-heat environment, while confirmed that FRAC had positive impact on the coupling between Ts and ET. Our study bridged the landscape pattern with the regional vegetation–climate interaction and provided the suggestions for the landscape planning and management for a more sustainable arid region.

Greenness and its composition and configuration in association with allergic rhinitis in preschool children

BackgroundFew studies focus on the associations of green space composition and configuration with children's allergic rhinitis (AR). MethodsA multi-center population-based cross-sectional study was performed in 7 cities in mainland of China between 2019 and 2020, recruiting 36,867 preschool children. Information on the current AR symptoms and demographics were collected by questionnaire. Exposure to residential greenness was estimated by Normalized Difference Vegetation Index (NDVI, 1000 m buffer) around the residences. Greenness composition was estimated in 3 main categories: forest, grassland, shrubland. Configuration of each category and total greenness (a spatial resolution of 10 m × 10 m) was estimated by 6 landscape pattern metrics to quantify their area, shape complexity, aggregation, connectivity, and patch density. Exposure to daily ambient particulate matter (PM1, PM2.5 and PM10, a spatial resolution of 1 km × 1 km) was estimated. Multilevel logistic regression models were applied to analyze the associations of greenness and its composition and configuration with AR, and mediation effects by PMs were examined by mediation analysis models. ResultsThe prevalence of self-reported current AR in preschool children was 33.1%. Two indicators of forest, Aggregation Index of forest patches (AIforest) (odds ratio (OR):0.92, 95% Confidential Interval (CI): 0.88–0.97), and Patch Cohesion of forest (COHESIONforest) (OR: 0.93, 95% CI:0.89–0.98) showed significantly negative associations with AR symptoms. Mediation analyses found the associations were partially mediated by PMs. Age, exclusive breastfeed duration and season were the potential effect modifiers. The associations varied across seven cities. ConclusionOur findings suggest the inverse associations of the aggregation and connectivity of forest patches surrounding residence addresses with AR symptoms. Since the cross-sectional study only provides associations rather than causation, further studies are needed to confirm our results as well as the underlying mechanisms.

Identifying the effects of landscape pattern on soil conservation services on the Qinghai-Tibet Plateau

Land use patterns affect soil erosion processes and soil conservation services. Identifying key landscape pattern affecting soil conservation services is critical for improving soil conservation services and optimizing land use patterns. In this study, the Qinghai-Tibet Plateau (QTP), where ecosystem services are important, was selected as research area. Based on land use, meteorological, soil and vegetation data, the RUSLE model was used to estimate soil conservation quantitatively from 2000 to 2020. The temporal changes in landscape metrics were analyzed using Fragstats, and the response of soil conservation services to landscape metrics was analyzed by correlation analysis. The landscape pattern showed an obvious temporal change trend. In general, the overall landscape of the QTP became more diversified and fragmented, and the landscape shape tended to become more complicated. Patch density (PD), edge density (ED), landscape shape index (LSI), splitting index (SPLIT), Shannon's diversity index (SHDI), and Shannon's evenness index (SHEI) showed increasing trends, while largest patch index (LPI), mean patch area (AREA_MN), aggregation index (AI), and contagion index (CONTAG) showed decreasing trends. At the class level, the variations of landscape pattern metrics of different land use types were big difference. In the 20-year period, soil conservation services were generally on the rise. The spatial heterogeneity of soil conservation services decreased from the south-east to the north-west. PD and ED were positively correlated with soil conservation function, while AREA_MN and AI had a negative correlation with soil conservation function. The effect of landscape pattern metric variation on soil conservation capacity significantly differed among different land use types. Results of the research offer reasonable evidence for landscape management, water and soil retention function improvement and regional ecological protection on the QTP.

Effects of reservoir system historical evolution on water system landscape patterns: the case of Changshou, China.

Reservoirs are a crucial form of water ecological infrastructure that offers a wide range of ecosystem services. The impact of reservoir construction on land use and landscape patterns is a matter of considerable importance. However, the specific effects of local-scale reservoir development on water system landscape patterns have not been comprehensively investigated. The Changshou district of Chongqing, located in southwest China and having an artificial lake system primarily composed of reservoirs, is an excellent example of examining the effects of human activities on the landscape patterns of water systems. This research reconstructed the historical evolution of reservoirs and rivers in Changshou District by historical maps and remote sensing data over seven decades from 1950 to 2020. On this basis, the landscape pattern metrics and graph-theoretical metrics of water systems in four crucial years, 1950, 1960, 1990, and 2020, were analyzed. The results indicate that from 1950 to 2020, the reservoir area in Changshou District increased by 64.195 km2, while the river area decreased by 7.297 km2; the two periods with the highest reservoir construction intensity were the 1950s and the 1970s to 1980s, when the reservoir area increased by 58.456 km2 and 3.089 km2, respectively; the construction of reservoirs diminished the connectivity of water systems and increased their fragmentation, dispersion, and edge complexity, but had little effect on their concentration and shape complexity. The findings can be applied to the optimization of water system patterns at the local level, the management of reservoir groups, the decision-making of water ecological infrastructure, and ecological effect studies of artificial lakes.

Mapping landscape ecological patterns using numeric and categorical maps.

The reciprocal relationships between ecological process and landscape pattern are fundamental to landscape ecology. Landscape ecologists traditionally use raster maps portraying classified features such as land use or land cover categories, and metrics suggested by the patch-corridor-matrix conceptual model of pattern. Less attention has been given to the landscape gradient conceptual model and raster maps portraying numeric features such as greenness or percent vegetation cover. We introduce the open-source tool GraySpatCon to calculate and map a variety of landscape pattern metrics from both conceptual models using either categorical or numeric maps. The 51 metrics, drawn mostly from the landscape ecology and image processing literatures, are calculated from the frequencies of input pixel values and/or the pixel value adjacencies in an analysis region. GraySpatCon conducts either a moving window analysis which produces a continuous map of a pattern metric, or a global analysis which produces a single metric value. We describe an implementation in the GuidosToolbox desktop application which allows novice users to interactively explore GraySpatCon functionality. In the R desktop environment, we demonstrate several metrics using an example map of percent tree cover and illustrate a multi-scale moving window analysis to identify scale domains. Comparisons of computational efficiency indicate a substantial GraySpatCon advantage over related software in the R environment.

Seasonal and multi-scale difference of the relationship between built-up land landscape pattern and PM2.5 concentration distribution in Nanjing

The urban landscape pattern, which can affect the air pollutants distribution in urban areas, was significantly changed due to the expansion of built-up land. Deeply understanding of the mechanisms by which urban landscape pattern influence the PM2.5 concentration distribution is fundamental for urban pollution control. However, the relationship between the built-up land landscape pattern and PM2.5 concentration distribution is still unclear at different grid scales in different seasons. In this study, four landscape metrics were calculated to quantify the distribution characteristics of built-up land in Nanjing, and the impact of built-up land landscape pattern on PM2.5 concentration distribution at different grid scales in different seasons were further analyzed. Results showed that the dominant landscape pattern metrics affecting PM2.5 concentration in different areas of Nanjing were closely related to the topography: in the main urban areas with flat terrain, the aggregation, shape, and proportion of built-up land significantly affected PM2.5 concentration, while the influence of the shape and proportion of built-up land was more noticeable in the hilly southwest suburbs. The effect of built-up land landscape pattern on PM2.5 concentration in winter was more significant than that in summer in general, and the relatively greater seasonal differences in this effect can be seen in the aggregation and proportion of built-up land. With the grid increasing, the correlation between the built-up land landscape pattern and the PM2.5 concentration distribution increased in the main urban areas but mostly decreased in the southwest suburbs, indicating that the PM2.5 concentration distribution in the main urban areas was mostly contributed by spatial transmission of pollutants, while the PM2.5 concentration distribution in the southwest suburbs was mostly dominated by local emissions. We suggest that the planning of flexible urban strategies with respect of PM2.5 pollution control in different areas should be implemented considering the influence of topography.

Identifying the driving forces of cultivated land fragmentation in China.

Cultivated land fragmentation (CLF) has severely affected China's agricultural production efficiency, large-scale operations, agricultural modernization, and food security. Exploring the spatiotemporal evolution patterns and driving forces of CLF is crucial for agricultural modernization. However, the driving forces of CLF in different agricultural regions in China still need to be clarified. In this study, CLF was measured in 2000, 2010, and 2020 based on remote sensing data with landscape pattern metrics, and the driving forces of spatial differentiation were detected based on a geographical detector model. The overall cultivated land area has slightly declined during the study period while the CLF has intensified. CLF showed significant spatial autocorrelation, with CLF increased-cultivated land decreased (2000 to 2010) and CLF decreased-cultivated land decreased (2010 to 2020). The contribution rate of land use intensity on CLF was the highest among all agricultural regions, excluding the Qinghai Tibet Plateau. In contrast, the contribution rates of other factors significantly varied across agricultural regions. These findings provide scientific support in formulating policies to conserve cultivated land for sustainable use of agricultural resources and CLF management in China.

Integrating Landscape Pattern Metrics to Map Spatial Distribution of Farmland Soil Organic Carbon on Lower Liaohe Plain of Northeast China

Accurate digital mapping of farmland soil organic carbon (SOC) contributes to sustainable agricultural development and climate change mitigation. Farmland landscape pattern has changed greatly under anthropogenic influence, which should be considered an environmental variable to characterize the impact of human activities on SOC. In this study, we verified the feasibility of integrating landscape patterns in SOC prediction on Lower Liaohe Plain. Specifically, ten variables (climate, topographic, and landscape pattern variables) were selected for prediction with Random Forest (RF) and Support Vector Machines (SVMs). The effectiveness of landscape metrics was verified by establishing different variable combinations: (1) natural variables, and (2) natural and landscape pattern variables. The results confirmed that landscape variables improved mapping accuracy compared with natural variables. R2 of RF and SVM increased by 20.63% and 20.75%, respectively. RF performed better than SVM with smaller prediction error. Ranking of importance of variables showed that temperature and precipitation were the most important variables. The Aggregation Index (AI) contributed more than elevation, becoming the most important landscape variable. The Mean Contiguity Index (CONTIG-MN) and Landscape Contagion Index (CONTAG) also contributed more than other topographic variables. We conclude that landscape patterns can improve mapping accuracy and support SOC sequestration by optimizing farmland landscape management policies.

Assessing the Contributions of Urban Green Space Indices and Spatial Structure in Mitigating Urban Thermal Environment

Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, and the contribution weights of various typical cooling factors in mitigating the thermal environment have rarely been analyzed. For this research, three periods of Landsat 8 data captured between 2014 and 2018 of Xuzhou during the summer and autumn seasons were selected along with corresponding meteorological and flux measurements. The mono-window method was employed to retrieve LST. Based on the characteristics of the vegetation and spatial features of the green space, eight factors related to green space were selected and computed, consisting of three indices that measure vegetation and five metrics that evaluate landscape patterns: vegetation density (VD), evapotranspiration (ET), green space shading degree (GSSD), patch area ratio (PLAND), largest patch index (LPI), patch natural connectivity (COHESION), patch aggregation (AI), and patch mean shape index distribution (SHPAE_MN). Linear regression and bivariate spatial autocorrelation analyses between each green space factor and LST showed that there were significant negative linear and spatial correlations between all factors and LST, which proved that the eight factors were all cooling factors. In addition, LST was strongly correlated with all factors (|r| > 0.5) except for SHPAE_MN, which was moderately correlated (0.3 < |r| < 0.5). Based on this, two principal components were extracted by applying principal component analysis with all standardized green space factors as the original variables. To determine the contribution weight of each green space factor in mitigating the urban heat island (UHI) effect, we multiplied the influence coefficient matrix of the initial variables with the standardized multiple linear regression coefficients between the two principal component variables and LST. The final results indicated that the vegetation indices of green space contribute more to the alleviation of the UHI than its landscape pattern metrics, and the contribution weights are ranked as VD ≥ ET > GSSD > PLAND ≈ LPI > COHESION > AI > SHAPE_MN. Our study suggests that increasing vegetation density is preferred in urban planning to mitigate urban thermal environment, and increasing broadleaf forests with high evapotranspiration and shade levels in urban greening is also an effective way to reduce ambient temperature. For urban green space planning, a priority is to multiply the regional green space proportion or the area of largest patches. Second, improving the connectivity or aggregation among patches of green space can enhance their ability to cool the surrounding environment. Altering the green space spatial shape is likely the least significant factor to consider.

Effects of Landscape Patterns on Atmospheric Particulate Matter Concentrations in Fujian Province, China

Based on the observation data from 37 national ambient air automatic monitoring stations and the land use/cover data in Fujian Province, the effects of land use/cover on the annual and seasonal variations in the air quality index (AQI), PM2.5 concentration and PM10 concentration are analyzed. In addition, buffer zones with different radii are established to calculate landscape pattern metrics and the influences of landscape patterns on the AQI, PM2.5 concentration and PM10 concentration at different scales are discussed. The results show that land use/cover types have remarkable impacts on the variations in the atmospheric particulate matter concentrations. The AQI and PM10 concentration are the highest for construction land, and the PM2.5 concentration is the highest for cultivated land. The seasonal variations in all air pollutant concentrations show similar characteristics, i.e., high in spring and winter and low in summer and autumn. Different landscape metrics have diverse effects on atmospheric particulate matter concentrations at different scales. In the buffer zone with a 5000 m radius, the patch number and patch density of forest land are positively correlated with the PM2.5 concentration, while the edge density of construction land has a negative correlation with it, indicating that landscape fragmentation affects the PM2.5 concentration. More fragmented forest land has a weaker effect on the reduction in the PM2.5 concentration, and more fragmented construction land has a weaker effect on the increase in the PM2.5 concentration. Moreover, the seasonal variations in the atmospheric particulate matter concentrations are different under different land use/cover types. Except for autumn, the AQI and PM2.5 concentration are most noticeably affected by forest land in all seasons, showing negative correlations. In autumn, the impacts of cultivated land on the AQI and PM2.5 concentration are more pronounced. The PM10 concentration is substantially affected by forest land in spring and summer and is markedly influenced by construction land in autumn and winter. The analysis of the landscape metrics of forest land and construction land at different scales indicates that the optimal scale is 5000 m for studying the annual average of the AQI and PM10 concentration and is 3000 m for investigating the annual average of the PM2.5 concentration. The optimal scales to research the seasonal variations in the AQI, PM2.5 and PM10 concentrations are 4000–5000 m for forest land and construction land, while the optimal scale is 1000 m for cultivated land to research the AQI and PM2.5 in autumn. This study can provide a scientific basis for the optimization of land use/cover and landscape patterns in Fujian Province, the planning and management of green space and the selection of research scales in the future.

Evolution of the landscape pattern in the Xin'an River Basin and its response to tourism activities

Tourism activities may interfere with the landscape pattern as a type of human activity.Scientific decision-making in the river basin's tourism development requires a precise understanding of the tourism activities' complicated effects on landscape patterns. The research object is to ascertain the direction and magnitude of the influence of tourism activities on the landscape pattern in the Xin'an River Basin. The response index (RI) was calculated by comparing the dynamic characteristics of landscape pattern metrics and Morphological Spatial Pattern Analysis (MSPA) inside and outside the 24 scenic areas. The results show the RI (=−2.56) of the scenic areas is eight times higher than that of the other areas, which indicates the intensity of the tourism activities' negative impact on the landscape pattern. Cultural and low-level scenic areas have more serious landscape fragmentation in general, through the comparative analysis of different types and scales of scenic areas. Combined with the analysis of tourism socio-economic data, scenic area construction is the main factor leading to landscape fragmentation. This study is an effective review of the impact of China's tourism industry on landscape patterns in the past 40 years, and the proposed RI will better help to quantify the effect of tourism activities on landscape patterns.

Spatiotemporal Landscape Pattern Analyses Enhanced by an Integrated Index: A Study of the Changbai Mountain National Nature Reserve

The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics.

How Do Landscape Heterogeneity, Community Structure, and Topographical Factors Contribute to the Plant Diversity of Urban Remnant Vegetation at Different Scales?

In highly fragmented urban areas, plant diversity of remnant vegetation may depend not only on community structure and topographical factors, but also on landscape heterogeneity. Different buffer radius settings can affect the relative importance of these factors to plant diversity. The aim of this study was to examine the relative importance of landscape heterogeneity, community structure, and topographical factors on plant diversity under different buffer radii in biodiversity hotspots. We established 48 plots of remnant vegetation in Guangzhou city, one of the biodiversity hotspots. A buffer radius of 500 m, 1000 m, and 2000 m was established around the center of each sample plot, and 17 landscape heterogeneity indices in each buffer were calculated by FRAGSTATS 4.2 software. Combined with the community structure and topographical factors, the impact factors of plant diversity under different buffer radii were analyzed by multiple regression analysis. We found the following: (1) The combined explanatory power of the three factors accounted for 43% of the species diversity indices and 62% of the richness index at its peak. The three impact factors rarely act independently and usually create comprehensive cumulative effects. (2) Scale does matter in urban landscape studies. At a 500 m buffer radius, community structure combined with road disturbance indices was strongly related to diversity indices in herb and shrub layers. The stand age was negatively correlated with the tree-layer richness index. As the scale increased, the diversity indices and richness index in the three layers decreased or increased under the influence of comprehensive factors. (3) The richness index in the herb layer was more responsive to impact factors than other biodiversity indices. Except for the herb layer, the interpretation of landscape heterogeneity for each plant diversity index was more stable than that for the other two factors. Road disturbance indices, combined with the other six landscape pattern metrics, can well indicate species diversity and richness. We suggest that the vegetation area of remnant patches within a radius of 500-2000 m should be appropriately increased to protect plant diversity, and the negative effects of road disturbance should also be considered.

Long‐Term Changes and Influencing Factors of Water Quality in Aquaculture Dominated Lakes Unveiled by Sediment Records and Time Series Remote Sensing Images

AbstractHydraulic connectivity has great effects on water quality. Enclosure aquaculture can largely alter lake flow regime and thus deteriorate water quality. Understanding the dynamics and influencing factors of water quality in enclosure aquaculture lakes is of great significance to ecosystem restoration of degraded lakes. However, it remains challenging due to the lack of long time series data. In this study, the dynamics of water quality in aquaculture dominated lakes were captured through 210Pb and 137Cs dating based on sediment records. Meanwhile, the effects of landscape pattern within aquaculture lakes on water quality were revealed by long time series of remote sensing images. Results showed the 210Pb and 137Cs sediment dating could provide an effective way to obtain long‐term series of lake water quality data in the unmonitored area. The contents of lake sediment nutrients showed an upward trend from 1960 to 2018. The transformation of lakes from agriculture dominated to aquaculture dominated had increased sediment nutrients level. The long‐term changes of lake sediment nutrients could be well explained by the landscape pattern metrics within aquaculture lakes, with an average explain power of 87.6%. The configuration metrics at class level had the most contribution (73.6%) to the changes of lake sediment nutrients, followed by the composition metrics (48.8%) and the configuration metrics at landscape level (33.4%). This study can provide a promising method to understand water quality changes in lakes with no historical monitoring data available and is of great benefit to water quality management in aquaculture dominated lakes.

Combining catastrophe technique and regression analysis to deduce leading landscape patterns for regional flood vulnerability: A case study of Nanjing, China

Urbanization leads to changes in land use, and the expansion of impervious surfaces leads to an increase in flood vulnerability. Predicting and analyzing these landscape pattern changes are important in the early stages of urban planning. In practice, the threshold for obtaining comprehensive and detailed hydrological and meteorological data is high, which makes it difficult for landscape and urban planners to quickly evaluate urban floods. To compensate for these trends, we took Nanjing, China, as the study site and discussed the leading flood vulnerability landscape patterns based on quantitative assessments. We introduced catastrophe theory to integrate three indicators and seven subfactors for flood vulnerability assessment: exposure, including precipitation; sensitivity, including elevation, slope, soil and drainage density; and adaptability, including land use and forest coverage. Then, we calculated the landscape pattern metrics (shape index, fractal dimension index, related circumscribing circle, contiguity index and landscape division index) at the class level. Finally, we divided the city into four subregions, established regression models for the subregions and the whole city, and deduced the leading flood vulnerability landscape patterns in each region and the whole city. We found that the leading landscape patterns varied among different regions. According to the research results, the landscape pattern indexes identified in this paper can be interpreted intuitively, which can provide a reference for modifying the planning layout of regional green infrastructure, optimizing the vulnerability of urban floods, and providing a basis for further improving Nanjing urban planning and alleviating the urban flood vulnerability. The methods proposed herein also will benefit land use and green infrastructure management in other regions lacking meteorological and hydrological data.

Resilient landscape pattern for reducing coastal flood susceptibility

Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience.