Scientifically delineating ecological source areas surrounding urban environments and constructing ecological resistance surfaces are essential for understanding landscape connectivity and enhancing regional ecological security. They are essential for achieving high-quality, sustainable development in the construction of modern urban ecological civilization. This study selected key indicators that encompass both natural and socio-economic elements as factors contributing to ecological resistance. By employing the CatBoost algorithm with SHAP (Shapley Additive exPlanations), Circuit Theory and a systematic analysis of the ecological security pattern in Huzhou is conducted. The results demonstrate that (1) 20 ecological sources patches were identified, covering a total area of 1117 km2; (2)based on interpretable machine learning results, areas of high ecological resistance in the northeast and central-southern parts of the research area is 500.75 km2, accounting for 8.6 % of the total area of the area; (3) circuit theory identified 40 ecological corridors, 348 ecological pinch points, and a total of 11 ecological barrier surfaces. These findings support the development of a spatial framework consisting of 'two screens, three belts, four corridors, and five zones'. Furthermore, the 3D Realistic Geospatial Landscape Model (3dRGLm) was employed to enhance spatial understanding and provide visually optimized planning insights within ecologically critical areas. This research provides policy guidance insights to promote high-quality and sustainable regional development, offering significant practical implications for ecological planning and management.

Developing factories in rural areas suited to their local conditions is very important for rural revitalization and integrated urban-rural development. Ecological and environmentally friendly efficient land use of rural areas play a key role in a regional sustainable development, which needs the information of land use of rural factories in detail in order to make their land use ecological and efficient. It is difficult to obtain the land use information in detail of rural factories because their distribution is extremely scattered. In order to overcome the difficulty, a new methodology is developed here. Several indicators proposed here which are used to describe the land use of rural factories in detail include building density, plot ratio, vegetation coverage rate, cement pavement coverage rate and water coverage rate. The methodology includes: (1) obtaining images of a rural factory by using Dajiang unmanned aerial vehicle (Dajiang UAV); (2) processing the images with camera lens model, producing ortho-images and mosaicking images; (3) extracting the area of a rural factory, the number of floors of buildings, vegetation area, cement pavement area and waterbody area in the rural factory from the UAV images, and creating their database; (4) calculating the indicators by using the database. A rural factory in Huanglong village of Chengdu plain, located in Qingliu Town of Xindu District of Chengdu city, is used as an example to explore the methodology here. The research shows that the methodology is of low cost, high efficiency and accuracy, and easy to be grasped, which has a broad application value in quickly and accurately obtaining the land use information in detail of rural factories which are sparsely scattered in vast rural area. The land use information in detail is essential for rural revitalization and construction of beautiful China. The research also discovered that the rural factory in Huanglong village is of 0.378 of building density, 0.435 of plot ratio, 23.69 % of vegetation coverage rate, 28.59 % of cement pavement coverage rate and 2.83 % of waterbody coverage rate.

Against the backdrop of rapid urbanization in western China, which has triggered remarkable land-use changes and habitat degradation, Chengdu, as a developed city in China, plays a demonstrative and leading role in the economic and social development of China during the transition period. Therefore, integrated modeling approaches are required to balance development and conservation. This study responds to this need by conducting a scenario-based assessment of urbanization-induced land-use changes and regional habitat quality dynamics in Chengdu (1990–2030), using the FLUS-InVEST model. By integrating remote sensing-derived land-use data from 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we simulate future regional habitat quality under three policy scenarios: natural development, ecological priority, and cropland protection. Key findings include the following: (1) From 1990 to 2020, cropland decreased by 1917.78 km2, while forestland and built-up areas increased by 509.91 km2 and 1436.52 km2, respectively. Under the 2030 natural development scenario, built-up expansion and cropland reduction are projected. Ecological priority policies would enhance forestland (+4.2%) but slightly reduce cropland. (2) Regional habitat quality declined overall (1990–2020), with the sharpest drop (ΔHQ = −0.063) occurring between 2000 and 2010 due to accelerated urbanization. (3) Scenario analysis reveals that the ecological priority strategy yields the highest regional habitat quality (HQmean = 0.499), while natural development results in the lowest (HQmean = 0.444). This study demonstrates how the FLUS-InVEST model can quantify the trade-offs between urbanization and regional habitat quality, offering a scientific framework for balancing development and ecological conservation in rapidly urbanizing regions. The findings highlight the effectiveness of ecological priority policies in mitigating habitat degradation, with implications for similar cities seeking sustainable land-use strategies that integrate farmland protection and forest restoration.

[目的]探究川东地区植被空间分布与时空变化特征,揭示影响植被变化的主要因素和具体范围,进而为该地区生态文明建设与高质量发展提供科技支撑.[方法]基于MODIS-NDVI数据与地面气象站点数据,利用趋势分析、偏相关系数和残差分析等方法探究了川东地区植被覆盖变化趋势与影响因素间的关系.[结果](1)2000-2020年川东地区植被NDVI呈"东北高西南低"的分布格局,植被覆盖整体呈上升趋势,并具有明显阶段特征.21a来川东地区大部分区域植被覆盖明显改善,局部地区植被退化严重.(2)气候变化与人类活动对植被NDVI均起促进作用,两者对植被影响区域有所差异,并且人类活动对植被影响更加显著.(3)2000-2020年川东地区植被NDVI与气温和降水呈正相关,且气温因子影响更为显著,但通过0.05显著性检验区域面积较小,表明植被变化主要受非气候因素影响.(4)人类活动对植被影响具有两面性,一方面城镇化导致植被NDVI下降,另一方面通过林业生态工程实施,林、草地植被恢复明显.[结论]川东地区植被整体上得到有效恢复,其变化主要受人类活动因素影响,对影响因素的研究有助于为生态环境保护与可持续发展提供理论支撑.

The measurement of carbon and carbon-related ecosystem services (CCESs) has garnered considerable global attention, primarily due to dual‑carbon goals, which are crucial for the rational allocating of ecosystem service (ES) resources and the enhancement of terrestrial carbon sinks. This study developed a novel research framework on CCESs to quantitatively measure carbon storage (CS), food production (FS), habitat quality (HQ), soil conservation (SC), and water yield (WY), and examined the spatiotemporal patterns of the supply-demand and trade-off/synergy processes related to CCESs in the Huaihe River Ecological Economic Belt (HREEB). The findings are as follows: (1) From 2000 to 2020, the supply-demand of the CCESs generally increased, except for carbon storage and food demand. Overall, the supply level of the CCESs exceeds the demand level, with a median ratio of supply and demand ratio (ESDR) of 1.13. (2) During the study period, the synergy relationship of the CCESs is mainly determined by the supply side of the CS-HQ and CS-SC, while on the demand side, it is determined by the CD- FD. And the ESDR of all C-related ecosystem services showed a significant synergy strengthening with CS in the HREEB. (3) Spatially, “high-low” spatial matching of the ESDR decreased, suggesting a gradual reduction in the spatial mismatch of CCESs. (4) We identified seven ecological functional zones and proposed corresponding strategies for promoting ecological management. Our research emphasized the spatiotemporal patterns of supply and demand imbalance in CCESs and the spatial optimization paths of trade-offs/synergies, providing valuable insights for achieving regional dual‑carbon goals.

探究成渝城市群土地利用冲突问题对促进区域可持续发展具有重要意义.对成渝城市群2010-2020年土地利用变化特征进行分析,并运用景观生态风险评价方法对土地利用冲突进行测算,通过空间自相关和地统计学方法研究其分异特征.结果表明:(1)成渝城市群土地利用以耕地、林地为主,土地利用类型面积呈"三增三减"的变化趋势,建设用地、林地面积增长明显.(2)成渝城市群土地利用冲突整体以可控级别为主,但冲突强度有所上涨,并在空间分布上具有明显的空间集聚性.不同土地类型冲突强度有所不同,冲突主要集中在耕地、林地、建设用地类型上.(3)人类活动对土地利用冲突影响逐渐增强,2010-2020年成渝城市群土地利用冲突块基比由0.197上升至0.487,空间相关性减弱,自然因素对土地利用冲突的影响逐渐减弱.2010-2020年成渝城市群土地利用冲突加剧,人类活动对冲突影响加强,后续发展中应加强土地监管和完善法律保护措施.

The ecological environment of giant panda（Ailuropoda melanoleuca） habitat directly determines the reproduction and continuation of giant panda population. The quantitative evaluation of giant panda ecological environment and its influencing factors is of great scientific and application significance for the planning and management of Giant Panda National Park（GPNP） in the Qionglai-Daxiangling Mountain region of Sichuan Province. Based on the remote sensing ecological index（RSEI）, we analyzed ecological environment status and its spatiotemporal variation of the study area before（2015） and after（2021） establishment of GPNP. The contribution of natural and social driving factors to the ecological environment status of the park was quantitatively analyzed by using the geographical detector. The results showed that:（1） The proportion of “excellent” and “good” grades of ecological environment status in the park was high（～70% in total）, and most of them were distributed in the core zone of the GPNP. The overall ecological environment status of the region was good.（2） Compared with 2015, the RSEI levels of “excellent” and “poor” in 2021 were reduced and transferred to the other levels. The ecological quality of the region changed slightly as a whole, with obvious regional differences. The ecological environment status fluctuated greatly in the northwest region with higher elevation and the general control zone with more human activities in the southwest of the study area.（3） Natural factors were the main driving forces leading to the change of ecological environment status, followed by human factors. The order of contribution of the top six factors was: the normalized vegetation index（NDVI, 0.87） ＞ dryness index（0.58） ＞ wetness（0.54） ＞ altitude（0.38） ＞ land-cover type（0.37） ＞ annual average temperature（0.36）, with strong interactions between NDVI and the other factors. More attention should be paid to the most suitable areas, such as those of NDVI value of 0.875-1, altitude of 1720-2150 m, annual average temperature of 14.1-16.3 ℃, and those covered by coniferous forests. This study can provide an important reference for the evaluation and protection of giant panda habitat quality in the GPNP.

为了了解川西高原植被EVI的时空变化特征,以MODIS-EVI数据、DEM数据和气象格点数据为基础,基于相关性分析、趋势分析和最大值合成等方法,探讨了川西高原2001-2020年植被EVI时空变化特征及不同海拔高程下植被EVI分布和变化规律.在此基础上,对研究区植被EVI时空变化的气候因子驱动力进行了分析研究.结果表明:(1)川西高原20年间植被EVI均值介于0～0.88,空间分布具有明显的地域分异.(2)近20 a来,川西高原植被EVI整体增长趋势,速率为1.0％/10 a,植被EVI的相对年际变化率介于-4.26％～13.58％.有13.09％的地区植被EVI变化通过显著性检验,其中约10.18％的区域植被EVI呈增加趋势.(3)川西高原近20 a不同海拔高程下植被EVI都呈波动增加趋势,变化速率以及增加趋势的显著性都有明显的差异.在＜2 500 m,2 500～3 000 m,4 500～5 000 m 3个海拔高程区间内,植被EVI增加趋势显著.(4)川西高原植被EVI与气温和降水呈正向相关的区域面积占比分别为56.42％,64.09％.在0.05显著性水平下,川西高原植被EVI变化受气候因子驱动的地区约占研究区总面积的21.87％.整体而言,近20 a来川西高原植被EVI呈增加趋势,且具有明显空间差异,EVI与气温和降水整体呈正向相关.川西高原大部分地区的植被EVI变化受非气候因子驱动.

[Objective] The vegetation change and important climate factors influencing fractional vegetation coverage (FVC) in Sichuan Province were analyzed in order to provide theoretical support for the sustainable development of natural resources in this area. [Methods] Based on the MODIS-NDVI dataset for Sichuan Province from 2000 to 2020, FVC for the study area from 2000 to 2020 was statistically analyzed, and its spatiotemporal variation characteristics and relationships with climate factors were analyzed. [Results] ① The FVC of Sichuan Province tended to be stable, and the average FVC value was about 0.50. ② FVC exhibited obvious spatial heterogeneity. FVC in the east was higher than in the west, and presented an increasing spatial distribution pattern from northwest to southeast. ③ The areas with high FVC in Sichuan Province accounted for about 70% of the total area, and the overall vegetation status was good. There were signs of slow growth in FVC over time. ④ FVC changes in the study area were positively and negatively correlated with air temperature and precipitation, and the area proportions were close to each other. ⑤ The driving forces of FVC change in Sichuan Province were mainly non-climate factors, and the area driven by climate factors accounted for 21.17% of the total area. [Conclusion] Sichuan Province has complex topography. The good climatic conditions, vigorous vegetation growth, and stable FVC experienced over a period of many years has maintained the ecological environment of the middle and upper reaches of the Yangtze River. In the future, Planners and officials should give more attention to the impacts of human activities FVC, and actively build an ecological barrier in the middle and upper reaches of the Yangtze River.

Extracting spatial objects and their key points from remote sensing images has attracted great attention of worldwide researchers in intelligent machine perception of the Earth’s surface. However, the key points of spatial objects (KPSOs) extracted by the conventional mask region-convolution neural network model are difficult to be sorted reasonably, which is a key obstacle to enhance the ability of machine intelligent perception of spatial objects. The widely distributed artificial structures with stable morphological and spectral characteristics, such as sports fields, cross-river bridges, and urban intersections, are selected to study how to extract their key points with a multihot cross-entropy loss function. First, the location point in KPSOs is selected as one category individually to distinguish morphological feature points. Then, the two categories of key points are arranged in order while maintaining internal disorder, and the mapping relationship between KPSOs and the prediction heat map is improved to one category rather than a single key point. Therefore, the predicted heat map of each category can predict all the corresponding key points at one time. The experimental results demonstrate that the prediction accuracy of KPSOs extracted by the new method is 80.6%, taking part area of Huai’an City for example. It is reasonable to believe that this method will greatly promote the development of intelligent machine perception of the Earth’s surface.

利用地理信息系统技术与层次分析法对东川区生态环境进行敏感性分析,本文共选取5个评价指标,首先对其进行单因子评价;然后基于GIS空间分析功能,将东川区综合生态敏感性分为5个等级;最后构建东川区综合生态环境敏感性分布图.研究结果显示:①5个生态评价因子中,坡度因子对东川区生态环境敏感性影响最大,权重值为0.36.根据对生态敏感性影响程度从大到小排序为:坡度、高程、土地利用、NDVI和水域缓冲区.②东川区生态环境敏感性总体偏高,极高敏感区与高敏感区共占区域总面积的44.17％;中敏感区占比最高,为26.1％;低敏感区和极低敏感区两者占比之和为29.73％.③东川区生态环境极高敏感区和高敏感区主要分布在西北部;极低敏感区和低敏感区主要分布在东北部及中部河谷地区.

基于1986、1995、2011和2019年4期Landsat TM/OLI遥感数据,以四川省剑阁县为研究区域,利用归一化植被指数和像元二分模型,结合DEM高程数据,分析该地区在经历长江防护林建设、天然林资源保护工程和退耕还林等生态工程过后植被覆盖的变化情况及其地形分异特征.研究结果显示:(1)经过诸多生态工程的改善,剑阁县的植被变化情况较好,以高植被覆盖度和中高植被覆盖度为主,高植被覆盖度所占比例持续增加;(2)通过差值法分析,研究区域内植被变化以稳定和改善为主,在1986-2019年的年际变化中,除低植被覆盖度外,各等级植被覆盖度大多向高植被覆盖度转变;(3)与地形因子结合分析,植被改善区域主要发生在海拔500～700 m、陡坡地和半阳坡等地带,退化主要发生在海拔500～600 m、斜坡地和阳坡等地带,改善面积均大于退化面积.

Machine intelligent perception (MIP) provides a novel way for human beings to recognize geographical locations automatically. MIP of geographical locations enables computers to describe locations automatically and quantitatively by extracting Earth's surface features and building relationships. The earth surface fingerprint is established here by mining the relationship between spatial objects with stable characteristics extracted from urban high-resolution remote sensing images, which realizes intelligent perception of geographical location innovatively. Mask Region-based Convolutional Neural Network is used to automatically extract the spatial objects such as playgrounds, crossroads, and bridges from the images. Then, the extracted spatial objects are encoded according to the landuse type, distance, and angle of 24 nearest objects to construct urban surface fingerprint database. The urban surface fingerprint database is used to match the geographical location of spatial objects in local images so that the matching algorithm can be used for machine recognition of the geographical location of specific objects in the target image. Taking the main cities in China as the experimental area, the success rate of location perception is 92%. We have made a useful exploration in the field of MIP of geographical location, hoping to promote the development of human cognition of geographical location.

植被物候是植被生长发育、枯萎凋落的周期性变化现象,揭示植被物候变化及其对水热因子的响应机制具有重要的生态意义.采用两种动态阈值分别提取川西高原近20 a森林植被、草地的生长季开始期(Start of Growing Season,SOS)和生长季结束期(End of Growing Season,EOS),利用Theil-Sen Median(Sen)趋势分析、偏相关系数分析植被物候的变化特征及其对季节性气温、降水的响应.结果表明,(1)川西高原植被平均SOS以70—130 d为主,平均EOS以260—290 d为主;植被SOS整体呈提前趋势、EOS整体呈推迟趋势,SOS的平均变化速率大于EOS;植被SOS平均提前速率为2.4 d·(10 a)?1(α=0.05),EOS平均推迟速率为1.4 d·(10 a)?1(α=0.05);草地和森林植被SOS平均提前速率大致相同,分别为2.4 d·(10 a)?1、2.3 d·(10 a)?1;草地EOS平均推迟速率1.4 d·(10 a)?1,小于森林植被2.2 d·(10 a)?1.(2)温度是影响植被SOS、EOS变化的主要因素.春季温度上升导致大部分植被SOS提前,这种现象在草地和森林植被间都很常见;冬季温度上升并不能使植被SOS出现普遍提前,且由于冬季温度升高可能会影响植被的春化作用,部分植被SOS甚至与冬季温度呈正相关;秋季温度上升是植被EOS推迟的主要因素,对草地的影响尤其显著;部分植被EOS与夏季温度呈负相关,这可能与夏季温度上升产生的水分胁迫以及植被生长周期的提前结束有关.(3)冬春季降水对植被SOS的影响不明显;夏季降水减少虽然对EOS有提前效应,但秋季降水增加以及升温对植被EOS的推迟作用强于夏季降水减少所带来的影响.

为探究乡村聚落空间重构策略并助力乡村振兴,本文以德阳市罗江区为例,基于2018年高分一号(GF-1)遥感影像数据,利用面向对象方法提取聚落斑块信息,运用核密度估计法分析乡村聚落空间分布特征,并结合自然和社会经济因素构建聚落用地适宜性评价体系,对现有乡村聚落进行评价,最终提出差异化重构策略.结果表明:①罗江区乡村聚落分布呈现"一心多极圈层扩散"的特征.②聚落布局适宜性总体偏高,不同乡镇适宜性差异显著.③现有乡村聚落布局倾向于较高级别适宜区,适宜性较好的乡镇布局合理性越高.④根据不同的集聚和适宜性特征,将罗江区乡村聚落划分为集聚发展型、提升建设型、限制协调型、生态转化型4种类型,并探讨其差异化重构建议.

Automatic registration of high-resolution remote sensing images (HRRSIs) has always been a severe challenge due to the local deformation caused by different shooting angles and illumination conditions. A new method of characteristic spatial objects (CSOs) extraction and matching is proposed to deal with this difficulty. Firstly, the Mask R-CNN model is utilized to extract the CSOs and their positioning points on the images automatically. Then, an encoding method is provided to encode each object with its nearest adjacent 28 objects according to the object category, relative distance, and relative direction. Furthermore, a code matching algorithm is applied to search the most similar object pairs. Finally, the object pairs need to be filtered by position matching to construct the final control points for automatic image registration. The experimental results demonstrate that the registration success rate of the proposed method reaches 88.6% within a maximum average error of 15 pixels, which is 28.6% higher than that of conventional optimization method based on local feature points. It is reasonable to believe that it has made a beneficial contribution to the automatic registration of HRRSIs more accurately and efficiently.

为探究南京市绿色植被覆盖时空动态变化规律,基于Landsat影像,借助ENVI、PCI、ArcGIS等软件操作平台构建绿色植被信息提取模型,利用模型提取了南京市2000年、2009年、2017年3期植被覆盖信息,并在此基础上进行了时空动态变化及其驱动因素分析.结果表明:(1)时间上,南京市2000年、2009年、2017年植被覆盖面积分别为4607 km2、4909 km2、4513 km2,总体呈现出2000—2009年上升,2009—2017年下降的趋势;(2)空间上,2000—2009年:南京市最北部、东部和南部植被呈现不同程度增加的趋势,最南部明显减少;2009—2017年:八卦洲以北区域植被增加,居民点及城市中心附近植被减少.这种植被的空间变化是由于城市化进程加快、农业类型变化、政策调控等综合因素的作用.研究结果对南京市植被的规划、建设和管理,以及对南京市的生态环境建设具有参考价值.

基于MOD10A2数据对2001-2020年川西高原积雪的时空变化特征及其影响因素进行分析.结果表明:1)川西高原积雪覆盖率(SCP)年内分布呈双峰型周期变化趋势.2)2001-2020年来川西高原SCP整体呈缓慢减少的趋势.3)川西高原积雪覆盖频率(SCF)的空间分布差异明显,SCF随海拔的增高而增长,且迎风坡和阴坡的SCF高于背风坡和阳坡.4)整体上,川西高原SCF与气温总体相关性为负、与降水总体相关性为正.气温对积雪覆盖频率变化的影响更显著.

以2005、2010、2015年三期四川省土地利用栅格数据为基础,应用ENVI和GIS技术对数据进行预处理,采用土地利用转移矩阵与土地利用动态度模型,并结合四川省土地利用实际情况分析四川省2005—2015年土地利用变化特征,并利用SPSS软件对2005—2015年间统计年鉴上相关的社会经济数据进行因子分析,研究影响四川省土地利用变化的主要驱动力因素并分析其作用机理,为后来的四川省土地利用规划提供参考依据.结果表明:(1)四川省近10年土地利用类型面积变化较为频繁,表现为城乡居民工矿用地大幅度增加,但各类土地占比总量土地面积变化不大.(2)耕地、林地、草地始终为四川省的主要土地利用类型,占比较为均衡且保持性好,耕地处于25％左右,林地和草地始终处于35％左右.而城乡居民工矿用地、水域以及未利用地面积虽有变化但占比始终较小.(3)四川省土地利用存在明显的区域差异,东部土地利用程度高,西部利用程度低.林地和草地大量且长期集中在西部,城乡居民工矿用地则在东部集中,东西分布极不均衡,不利于四川省区域经济均衡发展.(4)影响四川省土地利用变化的主要驱动力因子为常驻人口数和GDP,常驻人口始终保持较大数额且稳步增长,保持劳动力人口数量的同时带来社会经济快速发展的结果,从而促使土地利用结构不断发生变化.

冰川时空演化不仅对河川径流、生物生存环境、地表形态产生巨大的影响,而且冰川本身对气候变化有着强烈的响应. 在全球气候变暖背景下,对冰川长时间演化过程进行监测具有重要意义. 文章利用TM影像、OLI影像,通过非监督分类法、监督分类法、比值阈值法、雪盖指数法(NDSI)、基于多尺度分割的面向对象法、基于神经网络的冰川识别方法对梅里雪山地区的冰川信息进行提取. 结果表明,基于神经网络的冰川识别方法对于裸冰区及冰碛覆盖区的冰川信息提取效果相对较好,提取精度最高. 在此基础上,基于ENVI深度学习模块,利用神经网络分类法解译1989、1998、2009年和2019年梅里雪山地区的冰川信息,并结合Google Earth和DEM数据,对其进行目视修正,最终得到了1989—2019年梅里雪山地区冰川边界变化图,结果显示1989—2019年梅里雪山地区的冰川退缩了23.77 km2,年均退缩0.79 km2,面积相对退缩率为17.03%,年均相对退缩率为0.57%.