Patch Density Research Articles

Impacts of spatio-temporal change of landscape patterns on habitat quality across Zayanderud Dam watershed in central Iran

The biodiversity of an ecosystem is greatly influenced by the spatio-temporal pattern of the landscape. Understanding how landscape type affects habitat quality (HQ) is important for maintaining environmental and ecological sustainability, preserving biodiversity, and guaranteeing ecological health. This research examined the relationship between the HQ and landscape pattern. The study presented an interpretation of the biodiversity variation associated with the landscape pattern in the Zayanderud Dam watershed area by integrating the Land Change Modeler and the InVEST model. Landsat images and maximum likelihood classification were used to analyze the spatio-temporal characteristics of the landscape pattern in 1991 and 2021. The future landscape pattern in 2051 was simulated using a Land Change Modeler. Subsequently, the InVEST model and the landscape maps were used to identify the spatial distribution of HQ and its changes over three periods. The mean values of the HQ in the study area were 0.601, 0.489, and 0.391, respectively, demonstrating a decreasing trend. The effect of landscape pattern change on HQ was also assessed based on landscape metrics, including PD, NP, SHDI, and CONTAG. HQ had a significant positive correlation with the CONTAG parameter (R = 0.78). Additionally, it had a significant inverse correlation with NP (R = − 0.83), PD (R = − 0.61), and SHDI (R = − 0.42). The results showed that the habitats in the northern region had lower quality compared to those in the southern parts of the Zayanderud Dam watershed. The density, diversity, and connectivity of landscape patches significantly influence the HQ in the study area. This research has the potential to enhance understanding of the impacts of land change patterns on biodiversity and establish a scientific basis for the conservation of natural habitats. Additionally, it can facilitate efficient decision-making and planning related to biodiversity conservation and landscape management.

Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas

With the acceleration of urbanization, the ecosystem around cities is facing severe challenges. The drastic changes in the landscape pattern, especially in urban–suburban areas, are usually regarded as one of the main drivers. However, the spatiotemporal heterogeneous impacts of landscape patterns on the ecosystem services in this region remain unclear. To address this issue, we propose a novel framework integrating the InVEST-based ecosystem service assessment and spatiotemporal weighted regression (STWR)-based analysis of the spatiotemporal heterogeneity in urban–suburban areas, and apply it to the empirical study of Fuzhou City from 2000 to 2020. It first utilized the InVEST model to build a comprehensive ecosystem service index (CES) from five aspects (i.e., habitat quality, carbon storage, water yield, soil retention, and water purification capacity). Then, four landscape pattern indices (LPIs) (i.e., patch density (PD), area-weighted mean fractal dimension (FRAC_AM), splitting (SPLIT), and Shannon’s diversity (SHDI) index) were selected to build the STWR model. We compared and analyzed the differences in the spatial coefficient surfaces and significance tests generated by the STWR model in urban, urban–suburban, and rural areas. Results show that the following: (1) The CES in Fuzhou shows an upward trend from the urban area to the urban–suburban and rural areas, with significant gradient differences. (2) Compared with other areas, the LPIs in urban–suburban areas show more fragmentation, discreteness, and diversity, indicating more socioeconomic activities. (3) Although LPIs’ impacts on CES change over time (increasing from 2005 to 2010 and 2020 but decreasing in 2015), their effects are relatively low in urban–suburban areas, significantly lower than in urban areas. (4) Interestingly, the LPI coefficients near the urban–suburban boundary seem more significant. (5) This framework can effectively reveal the spatiotemporal heterogeneous relationships between various LPIs and CES, thus guiding concrete policies and measures that support decision-making for improving the ecosystem services surrounding cities through shaping landscape patterns.

Urban Matrices Threaten Patch Occurrence of Howler Monkeys in Anthropogenic Landscapes

Habitat modification due to human activities threatens species survival. While some species can inhabit habitat patches in anthropogenic landscapes, their occurrence often depends on landscape structure. We assessed the effects of landscape structure on brown howler monkey (Alouatta guariba clamitans) occurrence in an urban scenario. We conducted censuses in 59 forest patches from 2014 to 2016 in Rio Grande do Sul State, Brazil. We evaluated patch occurrence (presence/absence) in response to landscape composition (forest cover, arboreal crops, urban areas, open areas, and water) and configuration (patch density), considering the scale of effect. Water, urban, and open areas were the most important predictors of howler presence. Their presence was notably higher in landscapes with more water, likely because these landscapes consist of rural areas with low urbanization, less farming, and relatively high forest cover. Presence of howlers was positively associated with forest cover and negatively related to urban areas, open areas, and arboreal crops. Resource scarcity and increased mortality risks from human pressures, such as domestic dog attacks, electrocution, and roadkill on these land covers may explain these relationships. We highlight the importance of conserving and increasing forest cover in anthropogenic landscapes to protect species reliant on forested habitats, like howler monkeys.

Impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations in the Yangtze River delta urban agglomeration: Exploration and planning strategies

PM2.5-O3 composite pollution has become a pressing atmospheric challenge in China. Assessing the impact of territorial spatial landscape pattern on PM2.5 and O3 concentrations within the context of territorial spatial planning will provide ideas for atmospheric governance and spatial planning. Based on the agricultural-urban-ecological space system, this study first reclassifies territorial space within the Yangtze River Delta urban agglomeration (YRDUA) and calculates multilevel and multidimensional landscape metrics. Then, variables are screened by the random forest model and collinearity test. Furthermore, the spatiotemporally heterogeneous impact of selected landscape metrics on PM2.5 and O3 concentrations is explored using the geographically and temporally weighted regression (GTWR) model, with a tradeoff-synergy analysis being conducted to propose planning strategies. The conclusions show: (1) Two pollutant concentrations have both numerical and spatial correlations. The temporal stability of concentrations' responses to metrics is increasing, while the spatial heterogeneity continues to be significant. (2) Aggregation index of agricultural production space (AP_AI) and percentage of forest area (FO_PLAND) are trade-off and synergy factors of coordinated control, respectively. Largest patch index of grassland (GR_LPI) and patch density of other ecological spaces (OT_PD) are individual factors of PM2.5 reduction. (3) Spatial zoning supported by intra-zonal and inter-zonal cooperation policies, and coordinated control guidelines centered around the utilization of agricultural production space, have been advocated. This study aims to provide theoretical supplement and practical support for urban agglomeration's atmospheric governance and territorial spatial landscape pattern optimization.

Enhancing urban blue-green landscape quality assessment through hybrid genetic algorithm-back propagation (GA-BP) neural network approach: a case study in Fucheng, China

This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network’s approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.

Monitoring vegetation patterns and their drivers to infer resilience: Automated detection of vegetation and megaherbivores from drone imagery using deep learning

Ecological pattern theory has highlighted spatial vegetation patterns that can be used as indicators of ecosystem resilience. Combining this spatial pattern theory with aerial imagery from drones and automated image processing with deep learning methods, we show how monitoring of natural ecosystems can be enhanced through quantifying vegetation spatial patterns. We demonstrate this approach in a tropical seagrass ecosystem with a high abundance of turtles that generate vegetation patches when grazing. Past field observations suggest that patch size and density reflect the seagrass meadow resilience, but understanding the natural variation in vegetation patchiness is crucial. Employing the deep learning methods of semantic segmentation and object detection, we quantify vegetation patchiness metrics and turtle distribution across 12 ha of seagrass meadow in the years 2012 and 2022. The resulting output facilitates spatial and temporal comparisons, revealing areas of low resilience. In 2012, turtle grazing across the entire site yielded vegetation patch sizes averaging 2 ± 0.2 m2 (95% confidence interval). Reduced patch sizes of 0.24 ± 0.05 m2 and 0.67 ± 0.6 m2 at the reef edge and beach slope respectively, in conjunction with a reduced patch density, indicated lower resilience at the seagrass meadow edges. Analysis of the 2022 dataset indicates a general decrease in patch size over time, suggesting declining resilience. A retraining experiment of the semantic segmentation model was conducted where the initial model was retrained on the 2022 dataset and demonstrated the adaptability of the deep learning methods. Despite using different equipment, the model achieved high accuracy with only 5–10 additional training images. By providing the tools to conduct these analyses, we aim to stimulate the uptake of deep learning for enhancing the data obtained from aerial imagery to improve the monitoring and conservation of natural ecosystems.

A critical role of the capital green belt in constraining urban sprawl and its fragmentation measurement

Urban green belts (UGBs), an important green infrastructure of cities, have the functions of constraining urban sprawl, which are of great significance to urban green, resilient and sustainable development. However, it remains unclear how to quantify the constraining effect of UGBs on urban sprawl and the degree of fragmentation after UGBs constrain urban sprawl. Here, we proposed a UGBs extraction method to explore the spatial evolution of UGBs in the rapidly expanding megacity Beijing from 2000 to 2020. We also quantitatively investigated the role of UGBs in constraining urban sprawl and measured the degree of fragmentation of the UGBs after constraining urban sprawl with the help of the constraint assessment index, the Urban Annual Ring model, and the landscape pattern index. Results showed that: (1) Beijing’s UGBs area showed a “V”-shaped trend of first decreasing and then increasing from 2000 to 2020, decreasing from 332.61 km2 in 2000 to 303.41 km2 in 2015, and then increasing to 369.42 km2 in 2020, which is equivalent to about 54 Beijing Olympic Forest Parks. (2) The UGBs’ role in constraining urban sprawl was characterized by gradual enhancement in three periods from 2005 to 2020. The boundary containment rate (BCR) of urban sprawl was 0.48, 0.22, and 0.14 respectively. After the construction of the newly planned UGBs in 2005, the speed of urban sprawl from 2010 to 2015 decreased by 10.90%. (3) The fragmentation of UGBs showed an upward trend and connectivity showed a downward trend. The patch density (PD) characterizing fragmentation increased from 17.22 in 2000 to 31.60 in 2020, and the patch aggregation index (AI) characterizing connectivity decreased from 86.92 to 79.73. The research can provide a reference for other similar sized cities in UGB planning and government decision-making.

Abstract 5135: Tertiary lymphoid structures and T-cell aggregates signal a robust anti-tumor immune response in a subset of leiomyosarcoma patients

Abstract Cancer immunotherapy has ushered in a new era for treatment of many difficult to treat cancers such as Leiomyosarcoma (LMS), one of the most common subtypes of sarcomas. There are few biomarkers which can be used to identify patients who might benefit from immunotherapy. There is an urgent need to discover novel biomarkers which might be prognostic of a strong anti-tumor response. We thus sought to comprehensively profile the microenvironment of LMS tumors to identify features which might be suggestive of an active immune response and molecular features prognostic of response to immunotherapy. We began our analysis by classifying the LMS tumors from The Cancer Genome Atlas (TCGA) into 4 subtypes based on previously identified molecular features and comparing the enrichment of various immune gene signatures. Two of the subtypes had a high expression of various immune gene signatures including B Cells, M2 macrophages and Cytotoxic T cells. Visual inspection of the Hematoxylin and Eosin-stained tissue slides available with TCGA pointed to a presence of lymphoid aggregates or Tertiary lymphoid structures (TLS) in 10-15% of the tumors - predominantly belonging to one of the two subtypes. This subtype displayed features of smooth muscle differentiation and was also associated with improved survival. To validate our findings, we performed immunohistochemistry (IHC) and bulk RNA sequencing of LMS specimens (n=66) obtained from surgical resections from the Johns Hopkins Hospital. We identified mature CD21+ TLS in 15% of these cases and non-germinal center T cell aggregates present in more than 30% of the cases and these features were associated with improved survival. We digitally merged the different IHC sections to study if the markers were spatially correlated. Interestingly, spatial analysis showed that these CD8 T cells aggregates co-localized with neighborhoods of high density of PD-L1+ cells, hinting at the possibility of immune mediated upregulation of PD-L1 as means of immune suppression. The deconvolution of the transcriptome of these samples showed a marked presence of cytotoxic CD8 T cells and plasma B cells in this subset of patients, an enrichment of pathways related to T cell trafficking, activation and proliferation. Single cell RNA sequencing of tumor infiltrating immune cells showed an oligoclonal expansion of B and T cells. Taken together, this study shows that a subset of LMS patients have a strong anti-tumor immune response which is characterized by the presence of Tertiary Lymphoid Structures and patches of high density of PD-L1 expressing cells. These features might serve as biomarkers in screening LMS patients which might benefit from immunotherapy. Citation Format: Aditya Suru, Alexandre Maalouf, Lingling Chen, Ada Tam, William Villasi, Kaushal Sharma, Lindy Zhang, Christian Meyer, Jonathan Greer, Fabian Johnston, Carol Morris, Sophia Strike, Adam Levin, Daniel Rhee, Brian Ladle, Kornel Schuebel, Yan Zhang, Rulin Wang, Srinivasan Yegnasubramanian, John Gross, Robert Anders, Andrew Pardoll, Nicolas Llosa. Tertiary lymphoid structures and T-cell aggregates signal a robust anti-tumor immune response in a subset of leiomyosarcoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5135.

Abstract 905: ML quantification of tumor-Infiltrating lymphocytes distinguishes immune-phenotypes and reveals phenotypic heterogeneity

Abstract Immune phenotypes (IP), defined by the tumor-infiltrating lymphocyte (TIL) distribution within the tumor microenvironment (TME), is prognostic and predictive of treatment response. Here, machine learning (ML) models that characterize the TME were deployed in non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC) to exhaustively label TILs directly from hematoxylin and eosin (H&E)-stained whole slide images (WSI), compute IP and quantify TIL distribution, tasks which are manually untenable. ML-powered TME models (PathAI, Boston, MA; commercially available as PathExplore™) for NSCLC and HNSCC that quantify tissue regions (e.g. tumor, epithelium, and stroma) and cells (e.g. lymphocytes) in H&E-stained WSI were deployed on 126 NSCLC and 103 HNSCC commercial samples. Ground truth CD3 and CD8 scores were obtained from paired immunohistochemically-stained WSI (HALO, Indica Labs, Albuquerque, NM). Inflamed, desert, or excluded IPs (based on data-driven cutoffs) were inferred from the mean TIL density in epithelium and stroma (slide-level IP, sIP), and from the fraction of “hot” patches in all 100um x 100um patches tiling the tumor and stroma areas (patch-level IP, pIP). TIL spatial distribution was measured by the Morisita-Horn index that computes the patch-wise overlap between TILs and cancer cells (MHI, ranging from 0 to 1), and epithelial-stromal interface distance index (EDI) indicating the hot stromal patch bias toward the epithelium (-EDI) or stroma (+EDI). ML-predicted TIL densities highly correlated with ground truth CD3+ and CD8+ cell densities (NSCLC/HNSCC CD3, CD8: tumor Pearson r= 0.88/0.85, 0.74/0.74; epithelium r= 0.76/0.71, 0.88/0.62; stroma r= 0.74/0.87, 0.61/0.76). High s- and pIP agreement was seen (NSCLC 93% and HNSCC 83%); 6/26 discordant cases were driven by TIL hotspots with high density (116%-310% of the cutoff) but few (median = 26%) hot patches in the epithelium. MHI was higher for the inflamed vs excluded IP (p < 0.0001 for NSCLC and HNSCC) and intra-group variability was high (NSCLC/HNSCC inflamed: 0.66±0.11/0.31±0.14, excluded: 0.51±0.12/0.26±0.17, desert: 0.48±0.09/0.28±0.17; mean±std). EDI was lowest and negative in inflamed IP but near zero in desert and excluded IP (NSCLC/HNSCC inflamed: -43±4um/-184±19um, excluded: 1.6±4um/-41±13um, desert: 18±4um/-14±13; mean±sem). Excluded and desert IPs had roughly equal + and - EDI cases (NSCLC +/-: 57/46; HNSCC +/-: 39/29). ML-powered IP prediction using TIL distribution enables accurate and rapid profiling of the TME using routine histopathology. pIPs were concordant with sIPs and highlight TIL heterogeneity. Spatial markers (MHI and EDI) reveal differences between IP classes and intra-group heterogeneity relevant for drug discovery and patient stratification. Investigating prognostic associations of these markers is a promising direction for future studies. Citation Format: Nhat Le, Bahar Rahsepar, Jennifer Hipp, Jake Conway, Ylaine Gerardin, Emma Krause, Ciyue Shen, Raymond Biju, Michael Nercessian, Nicholas Indorf, Sandrine Degryse, Miles Markey, Victoria Mountain, Pranjal Vaidya, William Wijaya, Aditee Shrotre, Patrick Caplazi, David Inzunza, Joann Palma, Erik Huntzicker, Catherine Tribouley, Diana Chen, Raluca Prediou, Francine Chen, Kevin Kolahi. ML quantification of tumor-Infiltrating lymphocytes distinguishes immune-phenotypes and reveals phenotypic heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 905.

Analysis of land use/ land cover changes and landscape fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia

This study investigated the relationship between land use/land cover (LULC) changes and forested landscape fragmentation in the southwestern region of Ethiopia. Satellite images from 1986, 2002 and 2019 were collected and analyzed using standard procedures in ERDAS 2015 software. Fragstat 4.2.1 software was utilized to assess landscape fragmentation by examining a raster datasets derived from the classified LULC map over the research period. The study identified seven LULC classes in the study area. Findings revealed a substantial reduction in shrubland by 46.3%, dense forest by 23.75%, open forest by 17.3%, and wetland by 32.63%, while cropland increased by 38.06%, agroforestry by 20.29%, and settlements by 163.8% during the study period. These changes varied across different agroecological zones and slope gradients. Landscape metrics results indicated an increase in the number of patches and patch density for all LULC classes, demonstrating significant fragmentation of the landscape. The largest patch index, mean patch areas, and the percentage of landscape occupied by open forest, dense forest, shrubland, and wetland declined as a result of conversion to cropland, agroforestry, and settlement. Conversely, the largest patch index, the mean patch area and the percentage of the landscape occupied by agroforestry, cropland and settlement increased, indicating their increasing dominance in the landscape over the study periods. The findings highlighted the potential deleterious impacts of ongoing land use change and fragmentation on the environment, ecosystem function and local livelihoods. Therefore, it is crucial to implement appropriate conservation efforts and sustainable land management practices to mitigate the rapid change and fragmentation of land use and its negative impacts on sub-watershed ecosystems.

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.

Overcoming confusion and stigma in habitat fragmentation research.

Anthropogenic habitat loss is widely recognized as a primary environmental concern. By contrast, debates on the effects of habitat fragmentation persist. To facilitate overcoming these debates, here we: (i) review the state of the literature on habitat fragmentation, finding widespread confusion and stigma; (ii) identify consequences of this for biodiversity conservation and ecosystem management; and (iii) suggest ways in which research can move forward to resolve these problems. Confusion is evident from the 25 most-cited fragmentation articles published between 2017 and 2021. These articles use five distinct concepts of habitat fragmentation, only one of which clearly distinguishes habitat fragmentation from habitat area and other factors ('fragmentation per se'). Stigmatization is evident from our new findings that fragmentation papers are more charged with negative sentiments when compared to papers from other subfields in the environmental sciences, and that fragmentation papers with more negative sentiments are cited more. While most empirical studies of habitat fragmentation per se find neutral or positive effects on species and biodiversity outcomes, which implies that small habitat patches have a high cumulative value, confusion and stigma in reporting and discussing such results have led to suboptimal habitat protection policy. For example, government agencies, conservation organizations, and land trusts impose minimum habitat patch sizes on habitat protection. Given the high cumulative value of small patches, such policies mean that many opportunities for conservation are being missed. Our review highlights the importance of reducing confusion and stigma in habitat fragmentation research. To this end, we propose implementing study designs in which multiple sample landscapes are selected across independent gradients of habitat amount and fragmentation, measured as patch density. We show that such designs are possible for forest habitat across Earth's biomes. As such study designs are adopted, and as language becomes more precise, we expect that confusion and stigma in habitat fragmentation research will dissipate. We also expect important breakthroughs in understanding the situations where effects of habitat fragmentation per se are neutral, positive, or negative, and the reasons for these differences. Ultimately this will improve efficacy of area-based conservation policies, to the benefit of biodiversity and people.

Lessons Learnt from the Influencing Factors of Forested Areas’ Vulnerability under Climatic Change and Human Pressure in Arid Areas: A Case Study of the Thiès Region, Senegal

Understanding the factors influencing the vulnerability of forested areas is crucial for human well-being and effective governance of ecosystem supply and demand. Based on remote sensing data, this study also considered ten natural and human variables as indexes to explore the main influencing factors that may impact the vulnerability of the Thies region’s forested areas. The 2005, 2010, 2015, and 2020 satellite image data were processed using ArcGIS 10.6 and ENVI 5.1 software. The methodology includes using the transfer matrix approach and calculating the geographic landscape index to describe the dominant morphology of forested areas. Furthermore, a mixed linear regression model was built to establish the connection between forested areas and the potential contributing components. Our study revealed that the forested areas led to relative fragmentation, with an average of 88 patches for Aggregation Index (AI), 3.25 for Largest Patch Index (LPI), 2.50 for Patch Density (PD), and 112 for Landscape Shape Index (LSI) between 2005 and 2020. In addition, the transfer matrix indicated that the loss of forestry areas was about −78.8 km2 for agricultural land, −127.8 km2 for bare land, and −65.3 km2 for artificial surfaces. The most critical factors that influenced forested areas were agricultural and manufactural added value, rainfall (p < 0.05), slope, distance to the road, and agricultural sown area (p < 0.001). Overall, this investigation has revealed that the effective management of forested areas in the Thies region requires an understandable assessment. It was observed that both human anthropogenic and natural factors significantly contribute to the decline in forested areas.

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.

Artificial afforestation increased the distribution area and landscape complexity of planted forests in Tibet over the past 30 years

Afforestation plays an important role in improving the ecological environment and promoting local economic development in countries around the world. Although the artificial afforestation project in Tibet has been conducted for more than 30 years since in 1990 s, few studies have examined the dynamic changes in landscape patterns of planted forests in this region since the implementation of the project. This study used Google Earth Engine (GEE) and Landsat data from 1990, 2000, 2010 and 2020 to construct a Normalized Difference Vegetation Index (NDVI) time series curves and an optimal classification model based on the Jeffries-Matusita (J-M) distance. Then, the changes in landscape indices of planted forests in the Tibet Autonomous Region over the past 30 years were examined. The results showed that the area of planted forests increased from 9889.20 ha to 165,879.36 ha by artificial planting, with an average annualized growth rate of about 9.86%. In terms of the landscape pattern, the connectivity and aggregation degree of planted forests increased; the Cohesion (COH) and Aggregation Index (AI) increased by 44.60% and 26.03%, respectively. However, the fragmentation indices also increased to a certain extent due to the increased area and discontinuous planting; the Number of Patches (NP), Patch Density (PD) and Landscape Shape Index (LSI) increased by 32,262, 0.03 and 138.88, respectively. Among the seven regions of Tibet, these changes were most obvious in Lhasa, Qamdo and Shannan regions. Our results indicate that the planted forest area has significantly increased over the past 30 years although the landscape complexity has also increased because of discontinuous planting. These findings highlight the remarkable achievements of the Tibetan afforestation project over the past 30 years.

Patchiness-driven loss of soil organic carbon and total nitrogen could be offset by vegetation recovery

Patchiness acts as an indicator of terrestrial ecosystem degradation and can lead to considerable loss of soil organic carbon and total nitrogen. However, quantitative assessments of the effects of patchiness on soil organic carbon and total nitrogen stocks and their associated mechanisms remain limited. This study aimed to explore the influence mechanisms of patchiness on soil organic carbon and total nitrogen stocks and to project the quantitative contribution of the further expansion of patchiness and vegetation recovery. Soil properties, soil organic carbon and total nitrogen stocks were investigated using a combination of field sampling and aerial photography in four grassland types, alpine meadow, alpine steppe, temperate grassland, and desert grassland, at 47 sites in northwestern China. Soil organic carbon and total nitrogen densities in the bare patches were 34–54 % and 23–41 % lower, respectively, compared to the original vegetation. At the plot-scale, current soil organic carbon and total nitrogen stocks ranged from 30.85 to 77.80 T/ha and 3.26 to 10.19 T/ha, respectively, across grassland types; with a 10–27 % and 7–24 % potential loss of soil organic carbon and total nitrogen stocks, respectively, from the further expansion of patchiness but a 10–50 % and 9–37 % potential increase in soil organic carbon and total nitrogen stocks, respectively, from vegetation recovery. Soil organic carbon and total nitrogen stocks were positively correlated with vegetation biomass, soil clay content, and precipitation (p < 0.001), whereas they were negatively correlated with patchiness (p < 0.001). In summary, patchiness reduced soil organic carbon and total nitrogen stocks by decreasing vegetation inputs and increasing erosion outputs, while vegetation recovery showed potential for increasing carbon and nitrogen stocks. Our results highlight that maintaining intact vegetation cover is critical for preserving terrestrial ecosystem carbon and nitrogen storage.

Landscape pattern optimization of small watershed based on water purification (Danjiangkou reservoir area, China)

In recent years, landscape pattern optimization has been regarded as a cutting-edge science in various fields, and basin hydrology is no exception. Its effect on basin water purification has also been focused on. Based on the analysis of land use types and landscape patterns in the Danjiangkou Reservoir Basin, and combined with the determination of water quality indicators in the Danjiangkou Reservoir Basin, the relationship between the spatial pattern of Landscape Morphology and water quality was studied; It is found that among the basic characteristic indicators of many basins, the main influencing factors of water quality with high Variable importance for the projection (VIP>1) are IJI (Interspersion and Juxtaposition Index), COHE (Patch Cohesion Index), AI (Aggregation Index), PD (Patch Density), ED (Edge Density); Using PLSR (Partial Least Squares Regression) analysis, the main influencing factors of water quality were discussed. It was found that there was a significant negative regression relationship (Rc < 0) with CODMn (permanganate index), TP (Total Phosphorus), TN (Total Nitrogen) and NH4+-N (ammonia nitrogen) in the basin, and the influencing factors were topographic humidity index, topographic relief, slope, and forest area; The landscape patch connectivity index was used to determine the ecological source areas in the Danjiangkou reservoir basin that have improved connectivity and need to be protected; The priority of ecological corridor construction and protection was determined; With the help of the minimum cumulative resistance model, the ecological corridor between various ecological sources is constructed; The ecological grips on the ecological corridor are identified. In order to provide technical support for the optimization of landscape configuration for the purpose of water quality purification in Danjiangkou reservoir area.

Combined effects of urban forests on land surface temperature and PM2.5 pollution in the winter and summer

The interactions and mechanisms among green space patterns, air quality and thermal environments are not well understood. This study aimed to identify co-benefits of urban forest pattern indicators (UFIs) in reducing PM2.5 on highly polluted winter days and providing cooling on hot summer days. We selected urban core areas of seven cities in Jiangsu Province, China as a study case, and obtained data from MODIS, Landsat 8, Sentinel-1 and Sentinel-2 remote sensing images and products. The results showed that on a heavily polluted winter day, all four UFIs (area, patch density, mean shape index and aggregation index) had significantly negative total effects on PM2.5. The effect of area on PM2.5 was suppressed by LST, and other UFIs’ effects on PM2.5 were partially or fully mediated by LST. On a hot summer day, all four UFIs except patch density had significantly negative total effects on LST. The effect of patch density on LST was suppressed by PM2.5, and the effects of other UFIs on LST were partially mediated by PM2.5. This study indicated that increasing area, shape complexity and aggregation level of urban forest patches could reduce PM2.5 on highly polluted winter days and provide cooling on hot summer days.

Quantifying Spatiotemporal Characteristics and Identifying Influential Factors of Ecosystem Fragmentation in Karst Landscapes: A Comprehensive Analytical Framework

Guanling-Zhenfeng County, a microcosm of the ecologically fragile karst area in southwest China, experiences rapid population growth and urban expansion which intensifies land use transformation and ecological landscape fragmentation. Exploring the spatiotemporal characteristics of landscape fragmentation and its causes in Guanling-Zhenfeng County is of great significance in maintaining the stability of the ecosystem and ecological protection in karst areas. In this study, a comprehensive landscape fragmentation index (FI), geographic probe, multi-scale geographically weighted regression (MGWR), and PLUS model were used to quantitatively explore the spatiotemporal characteristic heterogeneity, causes, and future scenario projections of landscape fragmentation in Guanling-Zhenfeng County from 2000 to 2020. The results showed that: (1) the distribution of each landscape index was characterized by obvious spatial differentiation. Among them, the spatial distribution trends of patch density (PD) and largest patch index (LPI) were opposite and the distribution trends of Shannon diversity index (SHDI) and Shannon evenness index (SHEI) were similar. There were fewer heterogeneous patches in the study area from 2000 to 2020, and the landscape shape was more regular and less fragmented. (2) The overall landscape fragmentation in Guanling-Zhenfeng County from 2000 to 2020 was dominated by moderate fragmentation, with the smallest percentage of extreme fragmentation, and heavy fragmentation was mainly distributed in the north-central part of the study area. (3) Natural and social factors jointly affect the landscape fragmentation in Guanling-Zhenfeng County, and there is a significant interactive enhancement effect among the factors, with population density being the most important influence factor. In addition, the effects of the factors on landscape fragmentation showed significant spatial non-stationarity. (4) The characteristics of landscape fragmentation changes in Guanling-Zhenfeng County under different scenarios varied significantly, with the largest percentage of increase in heavy landscape fragmentation under the business-as-usual scenario (BAU), the next under the land use planning scenario (LUP), and the smallest under the ecological protection scenario (ESP).

Spatial pattern of urban forest diversity and its potential drivers in a snow climate city, Northeast China

Urban forest diversity is the cornerstone of providing ecosystem services and improving urban environment. However, the spatial pattern of urban forest (UF) diversity and its driving mechanisms are still not well understood. In our study, using 330 UF sample plots and the corresponding plot-based standard deviation values of remote sensing parameters with different spatial resolutions, we developed prediction models for the spatial pattern of UF diversity, and the driving factors of UF diversity were further explored in our study. Our findings indicated that the 3-m spatial resolution of remote sensing was optimal for predicting UF diversity. Both excessively high and excessively low resolutions can increase misclassification errors. In addition, among the three prediction models, the boosted regression trees (BRT) model exhibited greater robustness than the support vector machine (SVM) and random forest (RF) models for predicting UF diversity. UF species were abundant, with 112 tree species in Changchun City. Spatially, the UF diversity showed a significant gradient decreasing trend from suburban areas (outer rings) to downtown areas. We also observed higher diversity in the older communities with high housing prices, supporting the “legacy effect and luxury effect”. Additionally, our research revealed that socioeconomic factors, landscape cover, and patterns collectively explained 41.25% of the UF diversity variation, wherein socioeconomic factors contributed the most variation (17.33%). We further found that UF diversity does not increase more with increasing house prices when the house prices are higher than ¥ 12,000/m2. The higher patch density reduced UF diversity, while a higher UF landscape shape index (LSI) promoted diversity. The LSI threshold, which significantly impacted UF diversity, was about 12. Our research showed that high-resolution remote sensing offers a rapid, cost-effective approach to acquire UF diversity patterns, which could enhance urban forest diversity and contribute to sustainable urban development.