The relative impacts of vegetation, topography and weather on landscape patterns of burn severity in subtropical forests of southern China.

Understanding the landscape patterns of burn severity is vital for managing fire-prone ecosystems. Relatively limited research has been done about fire and burn severity patterns in subtropical forests. Here, we derived the pre-fire forest type data from a global land-cover product at 30 m resolution based on time-series Landsat imageries. Using Landsat 8 OLI remote sensing imagery and field-based composite burn index (CBI), this study spatially mapped the burn severity of 27 forest fires in the subtropical forest ecosystems in southern China from 2017 to 2021. The landscape pattern of patches with different burn severity was quantified using landscape indices. In addition, factors influencing the patterns of burn severity across the landscape were determined using the Geodetector model. Burn severity of patches varied significantly over space. High burn severity was common in forest patches with low fragmentation, low patch density, and regular shape. In contrast, moderate and low burn severity was prevalent in patches with smaller patch size, high patch density, and complex shapes. Extensively burned forest patches were located at higher elevations, while more fragmented patches were located in gently sloping areas. Topographic factors were the most significant factors influencing variances in burn severity across the forest patches, followed by weather conditions. Compared to low elevation areas, vegetation types at the high elevation areas (dominated by Masson pine) are more singular, with higher fuel loads, thus resulting in a more regularly-shaped distribution of highly severe burning patches. A detailed understanding of burn severity patterns and driving factors in a landscape can help develop sustainable forest management and restoration strategies. Practically, fire managers should conduct mechanical fuel treatments or thinning of forests at high-elevation areas to reduce the potential of severe fire behavior and the continuity of fire spread.

Assessing the influence of simulated environmental gradients on the spatial heterogeneity of landscape patterns in the Tibetan Plateau.

Landscape patterns are pivotal in the realms of land use planning and ecological development, yet there remains a dearth of comprehensive research pertaining to the prediction of changes in landscape pattern characteristics. Within this study, we adopt the PLUS-CA-Markov and Fragstats models to forecast landscape patterns on the Tibetan Plateau spanning the period from 2030 to 2050. Through qualitative and quantitative analyses, we explore the spatiotemporal characteristics of landscape pattern changes between 2000 and 2050, concurrently identifying correlations among landscape pattern indices. Moreover, acknowledging the distinctive environmental gradients encompassing the plateau, notably elevation, slope, temperature, and precipitation, we investigate their implications on landscape pattern changes. Our findings indicate that: (1) Grassland degradation exhibited the utmost severity between 2000 and 2020, primarily attributed to overgrazing and climate-induced glacial melt. In contrast, cropland, forest, and water showcased divergent trends from 2020 to 2050 when compared to the preceding two decades, indicative of the efficacy of climate change control measures. (2) The distribution of landscape patterns on the Tibetan Plateau exhibited a considerable level of instability, marked by a decline in aggregation, reduced diversity and complexity, and amplified ecological connectivity between 2000 and 2020, signifying a partial amelioration in ecological quality. Between 2020 and 2050, landscape aggregation decreased alongside landscape fragmentation and the number of connectivity paths, signifying a discernible degradation of the plateau's ecosystem. (3) The most significant trade-off relationship was observed between landscape division index and largest patch index, while the synergistic relationship between landscape shape index and mean shape index was more pronounced. (4) Landscape aggregation, division, and largest patch index demonstrated non-linear quadratic trends in relation to elevation and temperature. Landscape shape index and patch density exhibited irregular non-linear effects. Largest patch index was predominantly influenced by slope, whereas division index was most affected by precipitation.

Driving mechanism of land use and landscape pattern to phytoplankton and zooplankton community and their trophic interactions in river ecosystems.

The trophic interactions between phytoplankton and zooplankton communities are essential for maintaining river ecosystem integrity and health. However, the driving mechanisms of land use and landscape patterns (LULP) affecting their trophic interactions are not fully understood. Therefore, the research objective of this study was to reveal the driving mechanisms of LULP on the interaction of phytoplankton with zooplankton through remote sensing interpretation of LULP in different buffer scales (500 m, 1000 m, 1500 m, and catchment), combined with water environment factors and plankton community structures analyzed. Results showed that LULP had the most significant effect on the phytoplankton and the zooplankton community structure at 500 and 1500 m buffer scales, respectively. Construction land (CON) and edge density (ED) most influenced phytoplankton and zooplankton community structure and their influence mechanisms were identified, i.e., CON increased the species (S) of phytoplankton by increasing the concentration of NO3-N in river water at the 500 m buffer scale. ED reduced the biological density (BD) of zooplankton by decreasing the concentration of heavy metal (HM) in river water at the 1500 m buffer scale. The water area (WAT) and ED showed the most significant influence on plankton interaction. Three pathways were found to explain their influence mechanisms, i.e., ED decreased the BD or Shannon-Weiner index (H') of zooplankton by increasing the dissolved oxygen (DO) to enhance BD of phytoplankton in river water at the 1500 m buffer scale; the WAT increased the BD of phytoplankton by increasing water temperature to reduce the H' of zooplankton at the 500 m buffer. These findings have implications for effective ecological planning of future human activities in the stream domain and maintaining river ecosystem health.

Coupled relationships between landscape pattern and ecosystem health in response to urbanization.

Rapid urbanization has highlighted ecological problems in the metropolitan area, with increasing landscape fragmentation and severe threats to ecosystem health (EH). Studying the spatio-temporal coupled relationship between landscape pattern and EH and its response to urbanization in the Fuzhou metropolitan area (FMA) can provide scientific reference for its long-term development planning. We examined the coupled relationship between landscape pattern and EH and its driving mechanism in the FMA at grid and township scales to address the gap. The results show that landscape heterogeneity, diversity, and dispersion are gradually increasing, and EH is rising progressively in the FMA from 2000 to 2020. The spatial distribution of landscape pattern indices and EH indicators showed a "high in the south and low in the north" trend. During the study period, the coupled relationship between landscape patterns and EH was increasingly powerful but with remarkable spatial heterogeneity. The study also found an inverted U-shaped relationship between urbanization and coupled relationships. Ecological landscapes' heterogeneity, diversity, and connectivity in low-urbanization areas are conducive to EH. The opposite is true for high-urbanization areas. This study provides a valuable reference for optimizing landscape planning and ecological management in metropolitan areas.

Construction of ecological security pattern in combination with landslide sensitivity: A case study of Yan'an City, China.

The ecological security pattern can harmonize the relationship between natural environmental protection and socio-economic development. This study proposes a regional ecological security pattern optimization framework by integrating theory and practice with landslide sensitivity and landscape structure. Using Yan'an City as an example, this study optimizes the landscape layout of preliminary ecological sources. The landslide sensitivity index is generated using the information value model and then used to adjust the ecological resistance surface. The Minimum Cumulative Resistance (MCR) approach is used to extract ecological corridors, locate ecological nodes utilizing circuit theory, and outline crucial ecological control areas. The results demonstrate: (1) the ecological sources are primarily composed of forestlands, with a total area of 2,352.2400 km2, concentrated in the southwest, central, and southeast regions. The optimal landscape granularity for the source patches is 600 m. (2) Yan'an is divided into four landslide sensitivity level zones: extremely high, high, medium, and low, with the overall landslide sensitivity of the region being high. (3) The highest ecological resistance is observed in built-up land and the lowest in forestland. The total number of ecological corridors is 26, avoiding most of the highly sensitive areas of landslides. (4) The number of ecological pinch points is 61, while the ecological barrier points amounted to 54. The critical ecological control areas consist mainly of cropland, forestland, and grassland, and differentiated restoration strategies are proposed to address their unique characteristics. The findings of the research can offer scientific guidance for the practice of ecological security protection in geohazard-prone areas.

Analysis of changes and driving forces of landscape pattern vulnerability at Qianping Reservoir in Central China.

Reservoir projects often have significant impacts on ecosystems. The resulting environmental problems hinder the ecologically sustainable development of project areas. Research focusing on landscape pattern vulnerability could shed light on ecological restoration in disturbed sites. However, few studies have specifically examined reservoir areas in this context. This study investigates the spatial distribution characteristics, change rules, spatial autocorrelation, and driving forces of landscape pattern vulnerability in the Qianping Reservoir area (3859.16 hm2) from 2000 to 2020 using land use data. The findings reveal several key points: (1) Over the study period, cultivated land, grassland, and forest land are the key landscape types, covering more than 90% of the area. Cultivated land decreased by 481.57 hm2 as other land use types expanded. (2) Vulnerability remained stable in the first decade but sharply increased from 2010 to 2020, showing a trend of spatial aggregation. Reservoir construction and simultaneous ecological restoration efforts led to shifts in vulnerability zones across the landscape. (3) Spatial distribution of landscape pattern vulnerability shows a positive correlation, which strengthened by 2020 compared to earlier years. (4) Man-made factors, particularly land use changes, significantly influence landscape pattern vulnerability, with their impact growing over time. These findings not only provide a scientific basis for ecological restoration and landscape reconstruction in the Qianping Reservoir area but also offer insights applicable to similar environments. Overall, this study enhances theoretical understanding of reservoir landscape pattern vulnerability and contributes valuable perspectives on ecological restoration strategies for reservoir areas.

Spatio-temporal assessment of landscape ecological risk using spatial statistical analysis in a basin of Turkiye.

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas.

Assessing Regional Ecosystem Conditions Using Geospatial Techniques-A Review.

Ecosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks. Vigor, organization, and resilience (VOR) and pressure-stress-response (PSR) are two commonly adopted conceptual models for indicator selection and organization. The analytical hierarchy process (AHP) is primarily used to determine model weights and indicator combinations. Although there have been many successful efforts in assessing regional ecosystems, they remain affected by a lack of spatially explicit data, weak integration of natural and human dimensions, and uncertain data quality and analyses. In the future, regional ecosystem condition assessments may be advanced by incorporating recent improvements in spatial big data and machine learning to create more operative indicators based on Earth observations and social metrics. The collaboration between ecologists, remote sensing scientists, data analysts, and scientists in other relevant disciplines is critical for the success of future assessments.

Integrating source apportionment and landscape patterns to capture nutrient variability across a typical urbanized watershed.

Effective integrated watershed management requires models that can characterize the sources and transport processes of pollutants at the watershed with multiple landscape patterns. However, few studies have investigated the influence of landscape spatial configuration on pollutant transport processes. In this study, the SPARROW_TN and SPARROW_TP models were constructed by combining direct pollution source data and landscape pattern data to investigate the source composition and nutrient transport processes and to reveal the influence of landscape patterns on nutrient transport in the urbanized Beiyun River Watershed. The introduction of landscape metrics significantly improved the simulation results of both models, with R2 increasing from 0.89 to 0.85 to 0.93 and 0.91, respectively. Spatial variations existed in TN and TP loads and yields, as well as the source compositions. Pollution hotspots were effectively identified. Source apportionment showed that for the entire watershed, TN came from atmospheric nitrogen deposition (35.25%), untreated sewage (28.23%), agricultural sources (22.60%), and treated sewage (13.92%). In comparison, TP came from untreated sewage (44.94%), agricultural sources (40.22%), and treated sewage (11.51%). In addition, the largest patch index of grassland correlated positively with both TN and TP, whereas the largest shape index of buildup land and interspersion and juxtaposition index of forest were negatively correlated with TN and TP, respectively. The results of this study will provide insight into effective nutrient control measures that consider spatially varying nutrient sources and associated nutrient transport processes.

Identifying the impacts of land use landscape pattern and climate changes on streamflow from past to future.

Identifying the individual and combined hydrological response of land use landscape pattern and climate changes is key to effectively managing the ecohydrological balance of regions. However, their nonlinearity, effect size, and multiple causalities limit causal investigations. Therefore, this study aimed to establish a comprehensive methodological framework to quantify changes in the landscape pattern and climate, evaluate trends in streamflow response, and analyze the attribution of streamflow events in five basins in Beijing from the past to the future. Future climate projections were based on three general circulation models (GCMs) under two shared socioeconomic pathways (SSPs). Additionally, the landscape pattern in 2035 under a natural development scenario was simulated by the patch-generating land use simulation (PLUS). The Soil and Water Assessment Tool (SWAT) was applied to evaluate the streamflow spatial and temporal dynamics over the period 2005-2035 with multiple scenarios. A bootstrapping nonlinear regression analysis and boosted regression tree (BRT) model were used to analyze the individual and combined attribution of landscape pattern and climate changes on streamflow, respectively. The results indicated that in the future, the overall streamflow in the Beijing basin would decrease, with a slightly reduced peak streamflow in most basins in the summer and a significant increase in the autumn and winter. The nonlinear quadratic regression more effectively explained the impact of landscape pattern and climate changes on streamflow. The trends in the streamflow change depended on where the relationship curve was in relation to the threshold. In addition, the impacts of landscape pattern and climate changes on streamflow were not isolated but were joint. They presented a nonlinear, non-uniform, and coupled relationship. Except for the YongDing River Basin, the annual streamflow change was influenced more by the landscape pattern. The dominant factors and the critical pair interactions varied from basin to basin. Our findings have implications for city planners and managers for optimizing ecohydrological functions and promoting sustainable development.