Search Results (256)

Landscape metrics are one of the main tools for studying changes in the landscape and the ecological structure of the territory. However, the calculation of some metrics yields significantly different values depending on the configuration of the “Cell neighborhood” (CN) used. This makes the comparison of different analysis results often impossible. In fact, although the metrics are defined in the same way for all software, the choice of a CN with four cells, which includes only the elements on the same row or column, or eight cells, which also includes the cells on the diagonal, changes their value. QGIS’ LecoS plugin uses the value eight while GRASS’ r.li module uses the value four and these values are not modifiable by users. A previous study has shown how the value of the CN used for the calculation of landscape metrics is rarely explicit in scientific publications and its value cannot always be deduced from the indication of the software used. The difference in value for the same metric depends on the CN configuration and on the compactness of the patches, which can be expressed through the Aggregation Index (AI), of the investigated landscape. The scope of this paper is to explore the possibility of deriving an analytical relationship between the Aggregation Index and the variation in the values of some landscape metrics as the CN varies. The numerical experiments carried out in this research demonstrate that it is possible to estimate the differences in landscape metrics evaluated with a four and eight CN configuration using polynomials only for few metrics and only for some intervals of AI values. This analysis combines different Free and Open Source Software (FOSS) systems: GRASS GIS for the creation of test maps and R landscapemetrics package for the calculation of landscape metrics and the successive statistical analysis. Full article

This study investigates the genetic diversity and population structure of Castanopsis tribuloides, a vital tree species in Asian forest ecosystems. Understanding the genetic patterns of keystone forest species provides critical insights into forest resilience and ecosystem function and informs conservation strategies. We analyzed population samples collected from three distinct locations within Doi Suthep Mountain in northern Thailand using Short Tandem Repeat (STR) markers to assess both intra- and inter-population genetic relationships. DNA was extracted from leaf samples and analyzed using a panel of polymorphic microsatellite loci specifically optimized for Castanopsis species. Statistical analyses included the assessment of forensic parameters (number of alleles, observed and expected heterozygosity, gene diversity, polymorphic information content), population differentiation metrics (G_ST), inbreeding coefficients (F_IS), and gene flow estimates (N_m). We further examined population history through bottleneck analysis using three models (IAM, SMM, and TPM) and visualized genetic relationships through principal coordinate analysis and cluster analysis. Our results revealed significant patterns of genetic structuring across the sampled populations, with genetic distance metrics showing statistically significant differentiation between certain population pairs. The PCA and cluster analyses confirmed distinct population groupings that correspond to geographic distribution patterns. These findings provide the first comprehensive assessment of C. tribuloides population genetics in this region, establishing baseline data for monitoring genetic diversity and informing conservation strategies. This research contributes to our understanding of how landscape features and ecological factors shape genetic diversity patterns in essential forest tree species, with implications for managing forest genetic resources in the face of environmental change. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

Fire is a key ecological driver in tropical savannas, yet its effects on plant biodiversity remain understudied in Amazonian savannas. This study investigates how fire recurrence influences taxonomic and functional diversity in savanna ecosystems in northeastern Amazonia. We conducted vegetation surveys across five phytophysiognomies in Amapá State, Brazil, and compiled trait data for 226 plant species. Generalized Additive Mixed Models (GAMMs) were used to evaluate the relationships between fire frequency and diversity metrics across five landscape scales. The results showed that taxonomic diversity—particularly Shannon diversity—exhibited a unimodal response to fire recurrence, with peak diversity occurring at intermediate fire frequencies. Abundance increased with fire frequency, indicating potential dominance by fire-tolerant species. Functional diversity responded more subtly: functional richness and dispersion showed weak, non-linear associations with fire, while functional evenness remained stable. These findings suggest that recurrent fire can reduce taxonomic diversity without strongly altering functional structure, possibly due to functional redundancy among species. The use of multiscale models revealed that biodiversity–fire relationships vary with spatial context. In conclusion, this study highlights the moderate resilience of Amazonian savannas to fire recurrence and emphasizes the need to incorporate these ecosystems into fire management plans in climate change scenarios. Full article

Understanding the processes that shape biodiversity patterns is an important challenge in ecology. Land-use change is often recognized as a pivotal factor influencing biodiversity at large scales, with habitat heterogeneity being one of the most critical drivers of community composition and diversity. In this study, we evaluate the influence of landscape structure on the functional diversity of bird assemblages in the Upper Magdalena River Valley, Colombia. We used Generalized Linear Models to assess the effects of landscape structure on functional diversity, incorporating landscape metrics such as the number of patches, patch area and shape, and Shannon’s diversity and evenness indices. Additionally, we analyzed the influence of landscape structure on functional beta diversity—including its components of functional turnover and nestedness—using a distance-based redundancy analysis. We also examined the relationship between species traits and landscape metrics through a RLQ and fourth-corner analysis. We found a negative effect of habitat loss and fragmentation on functional diversity. Our results show that bird assemblages exhibit higher diversity in non-fragmented landscapes (>75% forest area; <1% urban cover), retaining greater functional richness and functional evenness (FRic > 0.24; FEve > 0.60). Moreover, non-fragmented landscapes seem to support a higher number of nectarivores and forest specialist species. In contrast, bird functional richness decreased with landscape fragmentation (FRic < 0.07). These findings highlight the importance of forest conservation for maintaining species persistence, ecological processes, and ecosystem services provided by birds. Full article

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. Full article

Urban forests play a crucial role in enhancing vegetation cover and bolstering the ecological functions of cities by expanding green space, improving ecological connectivity, and reducing landscape fragmentation. This study examines these dynamics in Jinhua City, China, utilizing Landsat 8 satellite imagery for all four seasons of 2023, accessed through the Google Earth Engine (GEE) platform. Fractional vegetation cover (FVC) was calculated using the pixel binary model, followed by the classification of FVC levels. To understand the influence of landscape structure, nine representative landscape metrics were selected to construct a landscape index system. Pearson correlation analysis was employed to explore the relationships between these indices and seasonal FVC variations. Furthermore, the contribution of each index to seasonal FVC was quantified using a random forest (RF) regression model. The results indicate that (1) Jinhua exhibits the highest average FVC during the summer, reaching 0.67, while the lowest value is observed in winter, at 0.49. The proportion of areas with very high coverage peaks in summer, accounting for 50.6% of the total area; (2) all landscape metrics exhibited significant correlations with seasonal FVC. Among them, the class area (CA), percentage of landscape (PLAND), largest patch index (LPI), and patch cohesion index (COHESION) showed strong positive correlations with FVC, whereas the total edge length (TE), landscape shape index (LSI), patch density (PD), edge density (ED), and area-weighted mean shape index (AWMSI) were negatively correlated with FVC; (3) RF regression analysis revealed that CA and PLAND contributed most substantially to FVC, followed by COHESION and LPI, while PD, AWMSI, LSI, TE, and ED demonstrated relatively lower contributions. These findings provide valuable insights for optimizing urban forest landscape design and enhancing urban vegetation cover, underscoring that increasing large, interconnected forest patches represents an effective strategy for improving FVC in urban environments. Full article

As an ecological transition zone, the ecosystem of the Qin River Basin in the middle reaches of the Yellow River is of great significance to the regional ecological balance. With the rapid socio-economic development, land use changes are significant, and the spatial and temporal patterns of ecosystems are evolving. Exploring its dynamics and driving mechanisms is crucial to the ecological protection and sustainable development of watersheds. This research systematically examines the spatiotemporal dynamics and driving mechanisms of ecosystem patterns in the middle Yellow River’s Qin River Basin (1990–2020). Quantitative assessments integrating ecosystem transition metrics and redundancy analysis reveal three critical insights: (1) dominance of agricultural land and woodland (74.81% combined coverage), with grassland (18.58%) and other land types (6.61%) constituting secondary components; (2) dynamic interconversion between woodland and grassland accompanied by urban encroachment on agricultural land, manifesting as net reductions in woodland (−13.74%), farmland (−6.60%), and wetland (−38.64%) contrasting with grassland (+43.34%) and built-up area (+116.63%) expansion; (3) quantified anthropogenic drivers showing agricultural intensification (45.03%) and ecological protection measures (36.50%) as primary forces, while urbanization account for 18.47% of observed changes. The first two RDA ordination axes significantly (p < 0.01) explain 68.3% of the variance in ecosystem evolution, particularly linking land-use changes to socioeconomic indicators. Based on these findings, the study proposes integrated watershed management strategies emphasizing scientific land-use optimization, controlled urban expansion, and systematic ecological rehabilitation to enhance landscape stability in this ecologically sensitive region. The conclusions of this study have important reference value for other ecologically sensitive watersheds in land use planning, ecological protection policy making, and ecological restoration practice, which can provide a theoretical basis and practical guidance. Full article

As a core element of comprehensive land consolidation, cultivated land serves as both a fundamental resource and strategic platform for driving rural revitalization and advancing ecological civilization development. Based on the five periods of remote sensing monitoring data of land use from the 1980 to 2020 in Beichuan Qiang Autonomous County, this study systematically examines cultivated land transfer dynamics and quantitatively assesses fragmentation levels through landscape metrics analysis, with the ultimate objective of informing strategic land consolidation planning at the county scale. The results indicate that (1) the cultivated land transformation in Beichuan Qiang Autonomous County exhibited distinct temporal patterns demarcated by 2010. During the initial phase, limited land transfers predominantly involved woodland transfers, characterized by cross-regional occupation–compensation dynamics and a northwest-oriented spatial shift. The subsequent phase witnessed substantial transfer intensification, incorporating grassland and construction land transfers alongside woodland. This period demonstrated balanced intra-township occupation–compensation mechanisms and a marked southeastward migration of transfer concentration; (2) cultivated land transfer dynamics demonstrated greater intensity in topographically moderate townships, whereas northwestern mountainous townships characterized by elevated altitudes and pronounced gradients maintained comparative spatial stability in transfer patterns; (3) cultivated land fragmentation exhibited topographic modulation, with reduced spatial disaggregation in low-lying plains contrasting elevated indices across northwestern highland terrains; and (4) the cultivated land area showed a predominant reduction in low-elevation and gentle-slope regions, accompanied by a decrease in landscape fragmentation. Conversely, in areas with higher elevations and steeper slopes, expansions in both cultivated land area and fragmentation were observed. Full article

Understanding the interplay between climate change, landscape patterns, and carbon sequestration is critical for sustainable ecosystem management. This study investigates the spatiotemporal evolution of vegetation Net Primary Productivity (NPP) and landscape patterns in Inner Mongolia, China, from 2000 to 2020, and evaluates their implications for carbon sink capacity under climate change. Using remote sensing data, meteorological records, and landscape metrics (CONTAG, SPLIT, IJI), we quantified the relationships between vegetation productivity, landscape connectivity, and fragmentation. Results reveal a northeast-to-southwest gradient in NPP, with high values concentrated in forested regions of the Greater Khingan Range and low values in arid western deserts. Over two decades, NPP increased by 73% in high-productivity zones, driven by rising temperatures and ecological restoration policies. Landscape aggregation (CONTAG) and patch connectivity showed strong positive correlations with NPP, while higher fragmentation values (SPLIT, IJI) negatively impacted carbon sequestration. Climate factors, particularly precipitation variability, emerged as critical drivers of NPP fluctuations, with human activities amplifying regional disparities. We propose targeted strategies—enhancing landscape connectivity, regional differentiation management, and optimizing patch structure—to bolster climate-resilient carbon sinks. These findings underscore the necessity of integrating climate-adaptive landscape planning into regional carbon neutrality frameworks, offering feasible alternatives for mitigating climate impacts in ecologically vulnerable regions. Full article

Urban environments pose challenges for pollinators due to habitat loss and limited floral resources. However, green infrastructure, particularly street and ornamental trees, can play a critical role in supporting urban pollinator communities. In this study, we evaluated nectar volume, sugar content, and amino acid composition across eight urban tree species commonly planted in South Korea. Using standardized productivity metrics at the flower, tree, and hectare scales, we compared their nutritional contributions. Our results revealed substantial interspecific differences in nectar quantity and composition. Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda exhibited high nectar yields or amino acid productivity, whereas species such as Cornus kousa, though lower in nutritional yield, may offer complementary value due to their distinct flowering periods or other phenological traits. These findings underscore the importance of selecting tree species not only for aesthetic value but also for ecological function, providing an evidence-based approach to pollinator-friendly urban biodiversity planning and landscape management. Full article

Urbanization, habitat fragmentation, and intensifying urban heat island (UHI) effects accelerate biodiversity loss and diminish ecological resilience in cities, particularly in climate-vulnerable regions. To address these challenges, we developed TreeGrid, a functionality-based spatial tree planning tool designed specifically for urban settings in the Northern Plains of India. The tool integrates species trait datasets, ecological scoring metrics, and spatial simulations to optimize tree placement for enhanced ecosystem service delivery, biodiversity support, and urban cooling. Developed within an R Shiny framework, TreeGrid dynamically computes biodiversity indices, faunal diversity potential, canopy shading, carbon sequestration, and habitat connectivity while simulating localized reductions in land surface temperature (LST). Additionally, we trained a deep neural network (DNN) model using tool-generated data to predict bird habitat suitability across diverse urban contexts. The tool’s spatial optimization capabilities are also applicable to post-fire restoration planning in wildland–urban interfaces by guiding the selection of appropriate endemic species for revegetation. This integrated framework supports the development of scalable applications in other climate-impacted regions, highlighting the utility of participatory planning, predictive modeling, and ecosystem service assessments in designing biodiversity-inclusive and thermally resilient urban landscapes. Full article

Cork oak (Quercus suber) woodlands hold significant ecological, cultural, and economic value in the Mediterranean basin, particularly due to cork production, one of the most valued non-wood forest products worldwide. However, cork oak ecosystems are increasingly threatened by climate change, land-use intensification, and rural abandonment, leading to widespread signs of decline. To address these challenges, data-driven and scalable methods are more essential than ever. Satellite-based remote sensing (RS) offers a promising approach for large-scale, cost-effective, and timely monitoring of cork oak forests dynamics and health, but an exhaustive review about this topic is missing. This study reviews 35 peer-reviewed articles published between 2010 and 2025, assessing how satellite RS has been applied to monitor cork oak landscapes. The results show that key research topics include forest disturbances, land cover classification, and forest and environmental variables monitoring. Landsat is the most frequently used satellite mission, and NDVI is the most applied vegetation index. Although machine learning techniques and accuracy metrics are heterogeneous, with results that are difficult to compare, relevant performances have been achieved. For instance, the highest classification accuracy (98%) was reached in mapping cork oak mortality. However, the field remains fragmented, with limited attention to key ecological indicators such as biodiversity, resilience, and ecosystem services. RS for cork oak monitoring is still a relatively young discipline with high potential for development, requiring greater methodological consistency and stronger integration with conservation strategies to support adaptive management in the face of future environmental pressures. Full article

As essential components of arid region ecosystems, terminal lakes play a critical role in enhancing the functions of ecosystem services (ESs) and improving ecological structure. Despite the increasing degradation of ESs and landscape stability due to climate and human pressures, comprehensive assessments of water provision, carbon storage, soil conservation, and habitat integrity in arid terminal lake regions are still lacking. Focusing on the Ebinur Lake Basin (ELB), this study employed the InVEST model to quantify ES changes from 2000 to 2020, combined with univariate regression, Pearson, and Spearman correlation analyses to explore their dynamic evolution. Landscape pattern indices calculated via Fragstats 4.2 further revealed trends in fragmentation, boundary complexity, and diversity. Results show that most ESs exhibited synergistic relationships, particularly between carbon sequestration and habitat quality (r = 0.45), observed clear trade-offs, such as between water yield and carbon sequestration (r = −0.47), underscoring the complexity of ecosystem interactions. Enhanced ES functions were associated with increased patch number, density, and shape complexity, while landscape diversity fluctuated. NDVI growth improved ES performance and reduced fragmentation, though changes in landscape metrics were largely driven by climate variability and socio-economic pressures, exacerbating fragmentation and weakening ecological stability. Overall, understanding the trade-offs and synergies among ESs in the ELB is crucial for informing sustainable development strategies. Full article

PM_2.5 pollution presents a significant risk to urban habitability. The urban landscape pattern (ULP) serves as a crucial regulator that profoundly influences the spatiotemporal distribution features of PM_2.5. Analysis of the driving mechanisms of the ULP is therefore essential for optimizing urban ecological spatial planning. However, the driving mechanism is dynamic and exhibits seasonal variations. This study selected four landscape metrics and four control variables, developed a geographically and temporally weighted regression (GTWR) model, and examined the spatiotemporal and seasonal effects of ULP on PM_2.5 concentrations in the central urban area of Urumqi (CUA) from 2003 to 2023. The results show the following: (1) Over the past two decades, the four ULP metrics have shown an increasing trend in the CUA. (2) The spatial distribution of PM_2.5 concentrations follows a latitudinal gradient, with higher concentrations observed in the northern regions and lower concentrations in the southern regions, initially increasing and then declining over time. (3) The driving mechanisms of ULP on PM_2.5 exhibited significant variations across different locations and time scales. (4) Seasonal variations arise from pronounced meteorological contrasts and intensified pollution from central heating, which is particularly evident in central CUA. Full article