Ecotones, i.e., transition zones between habitats, are important landscape features, yet they are often ignored in landscape monitoring. This study addresses the challenge of delineating ecotones at multiple scales by integrating multisource remote sensing data, including ultra-high-resolution RGB images, LiDAR data from UAVs, and satellite data. We first developed a fine-resolution landcover map of three plots in Yunnan, China, with accurate delineation of ecotones using orthoimages and canopy height data derived from UAV-LiDAR. These maps were subsequently used as the training set for four machine learning models, from which the most effective model was selected as an upscaling model. The satellite data, encompassing Synthetic Aperture Radar (SAR; Sentinel-1), multispectral imagery (Sentinel-2), and topographic data, functioned as explanatory variables. The Random Forest model performed the best among the four models (kappa coefficient = 0.78), with the red band, shortwave infrared band, and vegetation red edge band as the most significant spectral variables. Using this RF model, we compared landscape patterns between 2017 and 2023 to test the model’s ability to quantify ecotone dynamics. We found an increase in ecotone over this period that can be attributed to an expansion of 0.287 km2 (1.1%). In sum, this study demonstrates the effectiveness of combining UAV and satellite data for precise, large-scale ecotone detection. This can enhance our understanding of the dynamic relationship between ecological processes and landscape pattern evolution.

Understanding the factors and mechanisms that influence the impact of urbanization on vegetation growth is crucial for climate change mitigation and urban greening initiatives. However, the long-term evolution mechanisms of these impacts, particularly in the context of changing climate and water scarcity, are not yet fully understood. In this study, we evaluated the long-term indirect impacts of urbanization on vegetation growth across 2,385 county-level cities in China. We found that the trend of vegetation enhancement induced indirectly by urbanization is less pronounced in arid zones compared to humid zones. In addition to the vegetation removal caused by urbanization, the expansion of urban spaces can compensate for vegetation loss by fostering growth, surpassing the effects of economic and population growth. However, in arid zones, the positive impacts and regulation amplitudes of urban spatial expansion on vegetation growth are constrained due to high urban water scarcity. These insights may contribute to a more accurate assessment of carbon gains or losses in urbanized vegetation.

Plastic greenhouse (PG), as a new type of modern agricultural measure, has been used widely due to its significant benefits for agricultural production. However, it also raises concerns about its potential environmental impact. Timely monitoring of PG is necessary for agricultural sustainability. However, extracting PGs in fragmented terrains is difficult due to the variety of types of PGs and high environmental heterogeneity. In this study, a modified plastic greenhouse index (MPGI) was proposed based on the differences in spectral signatures using the Landsat-8 Operational Land Imager (OLI), and it was used to detect PG's spatiotemporal changes. After precision validation (the F1 score is 92.8% and 88.60% in 2015 and 2020, respectively), the results indicate that MPGI is suitable for extracting PGs in fragmented terrains. Its application to PGs mapping in the Southwest Plateau region demonstrates its potential for heterogeneous surface digital mapping.

Light availability (LAv) dictates a variety of biological and ecological processes across a range of spatiotemporal scales. Quantifying the spatial pattern of LAv in three‐dimensional (3D) space can promote the understanding of microclimates that are critical to fine‐scale species distribution. However, there is still a lack of tools that are robust to evaluate spatiotemporal heterogeneity of LAv in forests. Here, we propose the Forest Light Analyzer python package (FLApy), an open‐source computational tool designed for the analysis of intra‐forest LAv variation across multiple spatial scales. FLApy is freely invoked by Python, facilitating the processing of LiDAR point cloud data into a 3D data container constructed by voxels, as well as traversal calculations related to the LAv regime by high performance synthetic hemispherical algorithm. Furthermore, FLApy incorporates 37 indicators, enabling users to expediently export and visualize LAv patterns and the evaluation of heterogeneity of LAv at two scales (voxel scale and 3D‐cluster scale) for a range of fine‐scale ecological study purposes. To validate the efficacy of the FLApy, we employed a simulated point cloud dataset that simulates forests (varying in canopy closure). Furthermore, to evaluate real world forest, we executed the standard workflow of FLApy utilizing drone‐derived data from three subtropical evergreen broad‐leaved forest dynamics plots within the Ailao Mountain Reserve. Our findings underscore that a series of indices derived from FLApy provide a robust characterization of light availability heterogeneity within diverse forest settings. Additionally, when juxtaposed with conventional monitoring techniques, the metrics offered by FLApy demonstrated better generality in our field assessments. FLApy offers ecologists a solution for rapid quantification of understory light 3D‐regimes across multiple scales, addressing the disparity between traditional manual approaches and the precision required for contemporary ecological studies. Moreover, FLApy provides robust support for the establishment and expansion of heterogeneity indices based on 3D micro‐environments, enhancing our understanding of the largely uncharted 3D structural patterns. Anticipated outcomes suggest that FLApy will enhance our knowledge concerning the intra‐forest climatic conditions into a 3D context, proving pivotal in the delineation of microhabitats and the development of detailed 3D‐scale species distribution models.

Urban greening is becoming an important strategy in improving urban ecosystem services and sustainability. Identifying the response of vegetation to urbanization and urban landscape patterns can help in planning for urban greening. Urbanization may lead to both direct and indirect effects on vegetation, and the indirect effects of urbanization on vegetation growth (UIE-VG) have been paid much attention recently in large scale. In this study, we investigated the spatiotemporal evolution of UIE-VG and the effects of landscape patterns on UIE-VG using the boosted regression tree model and remotely sensed data. An increase in average UIE-VG from 4 to 56% was found during urbanization of Kunming, the case study area in southwest China. However, UIE-VG exhibited high variations due to landscape pattern changes at the local scale. Overall, area-related and aggregation-related landscape metrics had greater effects on UIE-VG than the other metrics. The increase and aggregation of built-up land enhanced UIE-VG by 3.1–81.3% while the increase and aggregation of unused land and waterbodies reduced UIE-VG by 0.7–20.6%. Moreover, we found that the large and aggregated vegetation areas may mitigate the negative UIE-VG in low urbanization areas. Our findings have important implications for integrating urban landscape planning into sustainable urban greening strategies.

作为探索可持续发展实现路径的关键工具,社会-生态系统理论研究框架的重要性日益凸显,但截至目前,对如何运用社会-生态系统理论研究框架解读各项可持续发展目标(SDGs)还缺乏比较清晰的认识.概述了社会-生态系统的主要研究框架,基于文献计量软件和可视化手段系统分析了面向 SDGs的社会-生态系统研究的现状和特点.结果表明:"SDG1-无贫困"、"SDG2-零饥饿"、"SDG8-体面工作和经济增长"、"SDG13-气候行动"和"SDG14-水下生物"是目前研究中关注的热点,涉及了多尺度的农林、淡水、海洋、城乡等典型系统,呈现出跨学科、数据多元化和方法集成化的显著特征;而有关"SDG4-优质教育"、"SDG5-性别平等"、"SDG7-经济适用的清洁能源"和"SDG10-减少不平等"等目标的研究相对较少;SDGs研究热点与国家发展阶段密切相关,基于社会-生态系统视角的多项目标关联关系的研究较少,该领域研究主要为可持续发展目标提供了"分析框架、达标评估、趋势预测和管理决策"的支撑服务作用.未来亟需加强以下四个方面的研究:(1)基于社会-生态系统视角的SDGs关联关系研究;(2)构建因地制宜的社会-生态系统研究框架;(3)SDGs导向的社会-生态系统动态反馈机制研究;(4)学科融合和数据平台建设.为探索适宜中国SGDs的实现路径提供科学参考.

建筑屋顶作为闲置的土地资源已成为光伏发电重要的潜在空间,屋顶光伏发电是脱碳电力供应的主要方式,将在实现城市碳中和进程中发挥重要作用.对建筑屋顶光伏发电潜力进行精确评估将有助于分布式光伏的科学规划和合理布局,提升土地利用效率.旨在对建筑屋顶光伏发电潜力影响因素和评估方法,以及光伏发电潜力主要评估模型进行系统性阐述,比较分析不同评估方法的优缺点,总结未来研究的重点方向.现有研究表明,建筑屋顶光伏发电潜力评估已从经验取值发展为定量空间分析,评估尺度、评估精度和评估成本已经成为不同评估方法综合权衡的重点.现有三种评估方法中,采样法计算成本和数据成本较低,但评估结果不确定性较大、精度较低;全面评估法评估精度较高,但数据获取成本和计算成本较高;机器学习法能够高效挖掘大数据潜力,且算法性能显著提升,因而相较于其他方法更适宜大尺度建筑屋顶光伏发电潜力评估.当前建筑屋顶光伏发电潜力评估仍然存在大尺度精细评估缺乏、评估结果不确定性大以及评估模型计算量大等问题.未来研究重点应关注三个方面:1)建立适宜不同区域的高精度简化模型并完善技术潜力评估模型;2)阐明建筑屋顶光伏发电潜力的影响因素,为代表性建筑分类体系的完善、关键特征值的选取提供理论依据;3)将光伏安装情景、农村屋顶质量、城市公共建筑屋顶产权和用能需求等因素对建筑屋顶光伏发电潜力的影响纳入评估框架.

科学构建城市快速扩张背景下的区域生态安全格局是缓解生态保护与土地开发矛盾的有效途径之一.云南省临沧市作为西南边境城市,其独特的地形地貌与丰富的生物多样性使得该区域在云南省"三屏两带"的生态安全基本格局中占据十分重要的地位.以临沧市为例,运用InVEST模型评估区域生态系统服务,通过地形、植被覆盖度等非生物因素对其脆弱性进行描述,耦合生态系统服务和脆弱性识别具有较高生态保护重要性的区域作为生态源地,最后运用最小累积阻力模型识别生态廊道,以此构建该区域的生态安全格局并提出生态安全保护策略.结果表明:临沧市生态源地总面积8804.25 km2,占该市土地总面积的37.27％,主要分布于凤庆县澜沧江流域、镇康县西部、耿马县、沧源县及永德县大雪山片区;生态廊道总长度为2151.96 km,根据该结果提出"一屏一带一轴多核心"的生态安全格局优化模式,为临沧市新一轮国土空间规划的"山水林田湖草"要素配置及生态环境建设提供科学指导.

Local climate zones (LCZs) provide a comprehensive framework to examine surface urban heat islands (SUHIs), but information is lacking on their thermal contributions and spatial effects in different macroclimate cities. A standard framework for distinguishing between the cooling effect and heating effect and spatial effect analysis based on the LCZ scheme was conducted in five distinct macroclimate cities, i.e., Yuanjiang (arid climate), Jinghong (tropical climate), Kunming (subtropical climate), Zhaotong (temperate climate), and Shangri-La (alpine climate). The results indicated that (1) built-up zones presented heating effects in Jinghong and Shangri-La, but opposite results were observed in Yuanjiang and Zhaotong. (2) The thermal contributions of natural zones with dense trees (LCZAs) and waterbodies (LCZGs) showed cooling effects in the five cities regardless of season. (3) The spatial effect of heating LCZs on land surface temperature (LST) was more significant than that of cooling LCZs in Jinghong and Shangri-La, but the opposite results occurred in Yuanjiang and Kunming. Moreover, the spatial effect was lower in Zhaotong than in other cities. (4) Lower LST differences between natural zones and built-up zones in winter than in summer decreased the spatial effects. In summary, the thermal contributions of LCZs and their spatial heating/cooling effects were different among five distinct climate backgrounds, which implies that targeted measures must be used in different macroclimates.

Karst rocky desertification is a major ecologic and geologic problem in Southwest China that has restricted the sustainable development of society and the economy. Many methods have been used to evaluate rocky desertification based on satellite remote sensing, but the results of these methods are affected by the heterogeneous surroundings of the karst region and the low resolution of sensors. In this study, a new method that combines satellite images and unmanned aerial vehicle (UAV) images was used to quantitatively extract information and evaluate rocky desertification. First, we extracted the bare rock ratio from the local high‐resolution UAV images, and then the regression models were established between the bare rock ratio of UAV images and the band reflectivity and eight rock indices of satellite images to invert the bare rock ratio at the county scale. The results showed that the overall accuracy and F1 of the classification of UAV images was 97% and 90%, respectively. The linear regression model between the reflectivity of the pixels in band 2 of the LANDSAT image and the bare rock ratio extracted from the UAV images was the best (R2 = 0.86). In addition, our method in which rocky desertification was assessed by the inversion model based on UAV images and LANDSAT images was superior to the traditional approach based on vegetation coverage. These results suggested that we can extract information on rocky desertification based on high‐resolution UAV images and assess rocky desertification by the inversion model from the local to regional scale.

建立自然保护区是保护和维持生物多样性最有效、最基本的措施.在有限的资金和人力条件下,如何参考不同人类干扰在保护优先区建设中的影响并选择合适的保护规划方案,在更大程度上的保护本区域的生物多样性,一直以来都是保护生物学家争论的焦点.以横断山南段区为例,重点关注保护区建设过程中不同的人类干扰程度,以人类干扰的高低为切入点,基于多准则决策分析的原理和方法,以横断山南植被生态系统和人类干扰强度因子为基础,对比分析横断山南的生态系统保护价值分布、人类干扰格局和保护成效,结果显示:一是区域内森林生态系统(针阔混交林、常绿阔叶林、落叶阔叶林、落叶混交林、常绿针叶林)、湿地生态系统(湖泊、河流、草本湿地)、高山生态系统(冰川和永久积雪、高寒草甸、高寒草原)价值高且分值在21分以上;二是从人类干扰高低分析入手,基于维持和提升区域生物多样性的角度出发,提出了不同的保护优先区域和保护策略.三是识别出横断山南段区的保护优先区主要包括峨边县、马边县、石棉县、越西县、保山市、腾冲市、维西县、德钦县、察隅县等区域.