Water use efficiency (WUE) links terrestrial carbon uptake and water loss, yet its nonlinear responses across aridity gradients remain poorly understood in China. Previous studies have largely focused on single hydroclimatic regimes, emphasizing linear relationships and static assessments of WUE drivers. Using the aridity index, China was divided into Arid, Semi-arid, Sub-humid, and Humid zones. WUE during 2001–2020 was derived from MODIS products. The mean WUE across China was 1.168 ± 0.518 gC·kg−1 H2O, exhibiting increasing patterns from west to east and from Arid to Humid zones. Trend analysis showed that 63.51% of pixels experienced increasing WUE trends, although only 16.92% were statistically significant. The Semi-arid zone exhibited the highest increasing rate (0.0025 gC·kg−1 H2O·a−1), whereas the Arid and Sub-humid zones showed the highest proportions of increasing and decreasing persistence patterns (47.50% and 59.49%, respectively), based on Hurst analysis. Zonal-specific XGBoost models combined with SHAP and GAM smoothing captured nonlinear relationships and interactions between WUE and environmental factors, with mean test R2 values ranging from 0.84 to 0.93 across the four aridity zones. Leaf area index (LAI) was the most important and consistent contributor across zones; vapor pressure deficit (VPD) dominated in Arid to Sub-humid zones and water-availability variables became more influential in Humid zones. Response curves and transition points varied along the aridity gradient, with interaction patterns shifting from heterogeneous climate–vegetation interactions in arid zones to stronger multi-factor coupling under humid conditions. These findings provide new insights into ecosystem water–carbon coupling across contrasting hydroclimatic backgrounds.

Urbanization and global warming have led to more frequent extreme heat events, highlighting the importance of Park Cooling Islands. This study analyzes the cooling effect (PCE) of 50 urban parks in Fuzhou to explore the relationship between park area and cooling effect. The results indicate that there is no simple positive correlation between park area and cooling effect. Specifically, while larger parks may have greater cooling potential, a larger area does not necessarily lead to better cooling effects. The optimal park area for cooling effect ranges from 0.594 to 56 hm2; beyond this range, an increase in park area does not significantly enhance the cooling effect. A low proportion of impervious surfaces, a high proportion of water bodies and vegetation, as well as complex patch patterns can enhance PCE, while excessive edge density and landscape fragmentation can weaken PCE. Based on importance analysis, the external morphological characteristics and internal patch characteristics of parks significantly influence cooling effects. Furthermore, the cooling effect of parks is jointly determined by internal and external conditions, with internal conditions having a more significant impact. Therefore, merely pursuing a “large” park area does not guarantee a “good” cooling effect; instead, greater emphasis should be placed on optimizing park design and layout, simplifying boundary shapes, reducing impervious surface ratios, and increasing vegetation diversity to maximize cooling effects.

As a geological disaster widely distributed in the southern regions of China, rainfall-induced shallow landslides pose a significant threat to affected areas. Timely detection of landslides is crucial in the effective response to such disasters. However, landslide detection faces adverse impacts from various factors, such as insufficient sample data, complex model structures, and limitations in detection accuracy during the actual detection process. In this study, high-quality image samples were collected from multiple landslide disaster areas in southern China, and a rainfall-induced shallow landslide sample database was constructed in the region. Based on this, a lightweight attention-guided YOLO model (LA-YOLO) was proposed to improve the detection performance of YOLO model for rainfall-induced shallow landslides. First, CG block is introduced to enhance the C2f module, enriching the feature representation capability through multiscale feature fusion and reducing the model's parameters and computational complexity. Second, the SimAM attention module is used to focus on the target regions, improving feature extraction effectiveness. Experimental results show that the model parameters of LA-YOLO were reduced by approximately 30%, with precision, recall, and mean average precision (mAP) on the landslide sample dataset increasing by 2.6%, 0.7%, and 2.2%, respectively. While ensuring model detection performance, the model structure was significantly optimized, achieving both lightweight and accuracy goals, confirming the model's superiority in monitoring rainfall-induced shallow landslide disasters.

The nighttime economy is instrumental in driving economic growth, particularly in the post-pandemic era. Nighttime Light (NTL) data is a key source in nighttime economy remote sensing study, with its angular effect directly affecting result accuracy. This study compares the accuracy of identifying nighttime economic agglomerations (NEAs) in Shanghai using Black Marble NTL and POI data at three observation angles: near-nadir, off-nadir, and all-angle. The results indicate that under all three angles, landmark NEAs can be identified fairly well. However, near-nadir demonstrates superior sample library identification accuracy and Theil index performance compared to all-angle and off-nadir. The study reveals that near-nadir observations offer higher accuracy and better suppression of "pseudo-accuracy units", making them more suitable for studying the nighttime economy. Furthermore, the study analyzes the spatial distribution characteristics of NEAs in Shanghai and finds a distinct "center-periphery" development pattern, suggesting imbalances in overall development. The presence of buildings with scattered high-low distribution and complex urban structures contributes to the variations in NEA identification under different satellite-observed angles. This study provides valuable insights into selecting the appropriate satellite-observed angle for studying NEAs using NTL data. It also explores the potential application of Black Marble NTL data products in socioeconomic remote sensing.

The Russia-Ukraine conflict has persisted for over a year, posing challenges in assessing and verifying the extent of damage through on-site investigations. Nighttime light (NTL) remote sensing, an emerging approach for studying regional conflicts, can complement traditional methods. This study employs NASA's Black Marble products to reveal the response characteristics of NTL intensity at national and state scales during the first anniversary of the conflict (January 2022 to February 2023) in Ukraine. The study used the nighttime light ratio index (NLRI) to assess the relative intensity of NTL and month-on-month change rate (MoM), nighttime light change rate index (NLCRI), and the rate (R value) of linear regression analysis to depict spatiotemporal dynamics. In addition, Theil-Sen median trend analysis and Mann-Kendall tests were employed to analyze intensity trends, with a “dual-threshold method” to reduce extensive noise interference. The results showed: At the national scale, the conflict resulted in an 84.0% decrease in NTL across Ukraine. At the state scale, the most severe NTL decline occurred near the southwestern border and eastern conflict zone under Ukrainian government control, witnessing over 80% decline rates. The correlation of decreases in NLCRI and R values with population displacement, infrastructure damage, or curfew measures demonstrated that the concentration of refugees and electricity facility restoration led to increased NLCRI and R values. Overall, NTL reflects critical moments at the national scale and provides insights into military intentions and humanitarian measures at the state scale. Therefore, NTL can effectively serve as a tool for observation and assessment in military conflicts.

Bamboo groves predominantly thrive in tropical or subtropical regions. Assessing the efficacy of remote sensing data of various types in extracting bamboo forest information from bright and shadow areas is a critical issue for achieving precise identification of bamboo forests in complex terrain. In this study, 34 features were obtained from Sentinel-1 SAR and Sentinel-2 optical images using the Google Earth Engine platform. The normalized shaded vegetation index (NSVI) was then employed to segment the bright and shadow woodlands. Different features from diverse data sources were evaluated to extract bamboo forest information in the bright and shadow areas, then use the random forest (RF) classification algorithm to extract bamboo forest. The results showed that (1) the red-edge and short-wave infrared bands of Sentinel-2 optical images and their corresponding vegetation indices are significant in bamboo forest information extraction. (2) The dissimilarity and homogeneity of Sentinel-2 texture features in the bright area and dissimilarity in the shadow area, the Sentinel-1 backscatter features in the bright area and the VV and VH in the bright area and VV-VH in the shadow area have some variability between bamboo and nonbamboo forests, which can be used as effective features for bamboo forest extraction. (3) The combination of spectral, texture and backscatter features yields the highest overall classification accuracy and Kappa coefficient, at 87.96% and 0.7435, respectively. This study has the potential for remote sensing refinement of bamboo forest identification in complex terrain areas by utilizing subregion classification methods combined with optical and radar image features.

Chlorophyll is an important physiological parameter reflecting the health status of green vegetation. The change mechanism of chlorophyll and leaf spectrum under pest stress is complex. It is of great significance to analyze the relationship between chlorophyll and leaf spectrum in depth for pest detection. Taking Shunchang County, Nanping City, Fujian Province as the experimental area, the leaf SPAD and leaf spectrum of Phyllostachys pubescens under different damage scenarios were measured. Pearson correlation method was used to screen the leaf spectrum characteristic indexes, and multiple linear regression, ridge regression, random forest and XGBoost estimation models of leaf SPAD were established. By comparing the screening results of spectral characteristics and the estimation effect of the model, the relationship between chlorophyll and leaf spectral characteristics of Phyllostachys pubescens under the stress of Pantana phyllostachysae was analyzed. The results showed that: (1) SPAD of Phyllostachys pubescens leaves showed a downward trend with the increase of insect pests; (2) Compared with the undamaged state, the spectral characteristics of Phyllostachys pubescens leaves changed obviously under the stress of Pantana phyllostachysae, and the "green peak" and "red valley" tended to disappear, the slope of "red edge" decreased, and the reflectance of near infrared wavelength decreased. (3) The best spectral characteristics of leaf SPAD based on full sample fitting are VOG(2), R-515/R-570, CIred, PRI and NDVI705, and the best estimation model is multiple linear regression model (R-2=0.7537, RMSE=3.0150). (4) SPAD of Phyllostachys pubescens leaves was fitted based on samples with different damage degrees. The optimal spectral characteristic indexes were health: CIred, VOG(2), ARVI, R-515/R-570, DVI; mild hazard: RENDVI, RERVI and REDVI; moderate hazard: RENDVI, RERVI and REDVI; severe hazard: VOG(2), CIred, NDVI705; off year: PRI, NDVI705, VOG(1), CIred. The best estimation model is the multiple linear regression model, and the model accuracy is healthy (R-2 = 0.8823; RMSE=1.6388); mild hazard(R-2=0.1802; RMSE=3.3354); moderate hazard(R-2 = 0.3604; RMSE=3.8867); severe hazard (R-2=0.4677; RMSE=2.6018); off year (R-2=0.7324; RMSE=2.3754). It was found that with the increase of the damage grade, the spectral characteristic index of Phyllostachys pubescens leaves changed, and the estimation accuracy of the relational model showed a trend of sharp decline at first and then slowly rising. The model had better estimation effect on SPAD of healthy and young leaves, but poor estimation effect on SPAD of light-medium-severe damaged leaves. When the relationship between SPAD and spectral characteristics of Phyllostachys pubescens leaves tends to be disordered, it indicates that the harm of Pantana phyllostachysae may occur.

To control the negative effects resulting from the disorderly development of aquaculture ponds and promote the development of the aquaculture industry, rapid and accurate identification and extraction techniques are essential. An aquaculture pond is a special net-like water body divided by complex roads and dikes. Simple spectral features or spatial texture features are not sufficient to accurately extract it, and the mixed feature rule set is more demanding on computer performance. Supported by the GEE platform, and using the Landsat satellite data set and corresponding DEM combined with field survey data, we constructed a decision-making model for the extraction of aquaculture ponds in the coastal waters, and applied this method to the coastal waters of Southeast China. This method combined the image spectral information, spatial features, and morphological operations. The results showed that the total accuracy of this method was 93%, and the Kappa coefficient was 0.86. The overlapping proportions of results between the automated extraction and visual interpretation for test areas were all more than 90%, and the average was 92.5%, which reflected the high precision and reliability of this extraction method. Furthermore, in 2020, the total area of coastal aquaculture ponds in the study area was 6348.51 km(2), which was distributed primarily in the cities of Guangdong and Jiangsu. Kernel density analysis suggested that aquaculture ponds in Guangdong and Jiangsu had the highest degree of concentration, which means that they face higher regulatory pressure in the management of aquaculture ponds than other provinces. Therefore, this method can be used to extract aquaculture ponds in coastal waters of the world, and holds great significance to promote the orderly management and scientific development of fishery aquaculture.

"夜间经济"蕴含巨大的消费潜能和市场空间,夜间经济集聚区作为其载体,其准确识别、合理分类和科学布局是发展夜间经济的切入点和主要抓手,更是夜间经济可持续发展的保障.本文在"点轴发育、定量识别"的认知框架下,在定量表达夜间经济活力测度的基础上,利用焦点统计和ISO聚类分析方法提取和识别夜间经济集聚中心和集聚区,并根据区位熵及其变异系数对识别结果进行类型划分,克服了目前夜间经济实践中存在的集聚区范围划定主观随意、类型标准不一的问题,为夜间经济定量化研究开辟了新的思路.研究表明:①相较于DMPS/OLS及NPP-VIIRS等夜光遥感数据,Luojial-01数据的空间分辨率高,溢出效应低,更适合于"夜间经济区"这种小尺度的精细化研究.②夜间灯光和兴趣点数据是夜间社会活力和功能活力的良好表征,其综合影响可通过夜间经济活力测度来定量表达;③上海推出的12个地标性夜生活集聚区中,有11个被识别,识别率达91.7％;④根据集聚区的功能结构差异,可将其划分为非平衡发展-起步型、平衡发展-起步型、非平衡发展-成熟型、平衡发展-成熟型4种类型,该分类方式具有普适性;⑤在起步阶段,上海中心城区夜间经济集聚区主导功能为购物、餐饮;在成熟阶段,其特色发展方向为住宿、科教文化和体育休闲功能.四大集聚区类型在空间分布上形成明显的圈层结构.

为控制水产养殖塘无序发展带来的负面效应,促进水产养殖业进一步发展,首要解决的就是对其快速、准确识别和提取的问题.水产养殖塘是被复杂道路和堤坝分割的特殊网状水体,单纯的光谱特征或空间纹理特征都不足以对其准确提取,且混合特征规则集对计算机性能要求越发苛刻.鉴于此,以Landsat影像序列为数据源,基于谷歌地球引擎(Google Earth Engine,GEE)平台,提出了一种结合影像光谱信息、空间特征和形态学操作的沿海水产养殖塘自动提取方法.该方法联用了双特征水体光谱指数(改进型组合水体指数(modified combined index for water identification,MCIWI)与改进的归一化差异水体指数(modified normalized difference water index,MNDWI))以突出大面积水体与养殖塘的网格特征,再利用低频滤波空间卷积运算拉伸养殖与非养殖水体之间的差异特征,将水产养殖塘区作为一个整体准确识别和快速提取.研究结果表明:①该方法总精度达到93％,Kappa系数为0.86,典型区域叠加比对检验流程验证,提取结果和实际结果重叠比例均在90％以上,平均重叠比例达92.5％,反映了提取方法的高精度和可靠性;②2020年福建省近岸海域水产养殖塘区总面积为511.73 km2,主要分布在漳州市、福州市和宁德市;③核密度分析结果表明漳州市的水产养殖塘集聚度高,相应其养殖塘管理压力也较大.该方法可以实现近岸海域水产养殖塘的自动化提取,对促进渔业养殖的有序管理和科学发展具有重要的意义.

开展高光谱遥感影像的阴影检测研究有助于去除阴影,并进一步发挥其高光谱分辨率优势.以多角度高光谱影像PROBA/CHRIS为数据源,尝试从增大明亮区植被、阴影区植被、水体区3种典型地物间光谱的差异入手,利用连续投影算法(successive projection algorithm,SPA)选取特征波段,并分析典型地物在CHRIS影像原始波段及归一化差值植被指数上的光谱特征,由此构建该影像的归一化阴影植被指数(normalized shaded vegetation index,NS-VI).基于步长法设置合理阈值,对影像予以分类,并从分类精度及光谱差异增强效果两个角度评价NSVI对CHRIS影像阴影的检测能力.结果表明:B9和B15可作为构建CHRIS影像NSVI的特征波段;基于NSVI阈值法对CHRIS多角度影像予以分类,各角度影像3种地物的分类精度均在94％以上,总Kappa均大于0.89,0°影像的分类效果最佳;经掩模获取分类后3种地物的子影像,子影像光谱均值有差异,但考虑标准差后则发现其光谱重叠现象较为明显,表明NSVI可增强典型地物间的光谱差异,提高了光谱混淆像元间的可分性.通过进一步比较NS-VI与归一化阴影指数和阴影指数的阴影检测效果,亦证明了NSVI的阴影检测能力,说明所构建的NSVI能够应用于PROBA/CHRIS高光谱影像的阴影检测,可为该影像的阴影去除及阴影信息修复等工作提供重要支持.

以2018年底美国加州史上死伤最惨重、也最具破坏性的"坎普"林火(Camp Fire)为研究对象,根据近红外、短波红外和热红外光谱段对林火灾害不同生命周期的敏感度,采用归一化燃烧指数NBR、热红外地表温度LST和归一化植被指数NDVI等模型进行灾时高温火点识别及溯源、灾后植被损失和植被恢复模式评估.结果 表明,dNBR高于0.1的烧伤区域面积占比达66.53％,其中高强度烧伤区超19％;火灾造成平均植被覆盖度下降11.55％.经LST反演识别的7个典型高温火点受灾状况远高于其他区域,NDVI和FVC最高降幅为0.45和49.4％,分别是全区NDVI和FVC降幅的7.4倍和6.9倍,可见热红外LST反演技术在高温火点精确定位和受灾程度定量判定上的高效和准确程度.灾后植被恢复研究表明,林火对高植被覆盖区破坏较为严重.灾后1 a内植被恢复速度较慢.过火区总体植被恢复情况较差,部分区域出现土壤退化的现象.预计植被完全恢复还需要更长的时间.

利用1992—2017年7个时期的遥感数据,基于遥感生态指数(RSEI),对福建紫金矿区开采以来的生态质量进行评价.结果表明:1)RSEI可以较好反映矿区生态状态的时空变化,1992—2017年,紫金矿重点开发区内裸土面积逐渐扩大,RSEI呈现略微的下降趋势,但整个区域的RSEI反而有一定程度的上升,重点开发区外围的生态状况出现好转;2)从面积上看,占主导地位的区域RESI由"中等"向"优良"发展,说明紫金矿业集团在开发矿山的同时,注重生态保护;3)紫金山矿区不同时期裸土面积扩张的原因与矿区开发技术和金铜价格上涨有关,而矿区生态质量的总体上升与企业和地方政府在生态环境保护方面的大量经费投入密不可分.

Fujian Province is the first "National Ecological Civilization Experimental Zone" approved by the State Council of China.As the provincial capital,a good ecological environment for Fuzhou City is always expected.During the progress of urbanization,Fuzhou has witnessed a significant urban thermal environment (UTE) change,leading the city to be reputed by media as the top one of the three new "furnace cities" in China.To investigate the process of the city from a non-furnace city to a top furnace city in China,the dynamics of urban biophysical components of Fuzhou and the associated UTE between 1989 and 2013 have been analyzed by remote sensing technology.The result shows that the urban heat island (UHI) effect in Fuzhou greatly aggravates as the UHI Ratio Index (URI) of the city increases from 0.29 to 0.53 in the past 24 years.The spatiotemporal variation analysis on the basis of the thermal profiles reveals that the spatial structure of the UTE is greatly influenced by the spatial pattern of land cover types.On the whole,the increase and amalgamation of impervious surface patches,reduction and fragmentation of vegetation and water covers,and the blockage of the urban ventilation are the main factors contributing to the formation of the "furnace city" of Fuzhou.The study provides a guidance and support for the mitigation of the UHI effect and the achievement of city's healthy sustainable development.

Land surface temperature (LST), regional evapotranspiration (ET), build-up land information, vegetation information, and other land surface parameters are of great significance for urban scientific planning and urban ecosystem restoration. Taking Fuzhou City as an example and gaining the LST, regional ET, build-up land and vegetation information by Landsat satellite images, this paper finds out the spatial-temporal variation of the LST and ET in study area and conducts quantitative analysis for the relationship of the above land surface parameters. The results show that the distribution of ET in Fuzhou City has obvious zonation and the LST, urban build-up land and vegetation are significant factors affecting the urban heat island and regional ET. There is a clear linear positive correlation between ET and land surface vegetation cover and a significant linear negative correlation between ET and LST as well as between ET and build-up land information.

Being an important part of the green space system, urban grassland has played a significant role in landscaping environment, regulating microclimate and preventing soil from erosion. Therefore, it is of great importance to monitor the health status of urban grassland timely and efficiently. Remote sensing technique has been widely used for assessing vegetation growth status for decades. Numerous studies have found that red edge indices are closely related to the important biochemical parameters of green plants. Thus, they can be regarded as important indicators for monitoring health status of vegetation. However, there is no explicit conclusion about which index is more suitable for monitoring the health status of urban grasslands among the existing red edge indices. The European Sentinel-2A satellite was successfully launched in late June 2015, aiming to replace and improve the old generation of satellite sensors of high resolution (i.e. Landsat and SPOT), with improved spectral capabilities. The multispectral instrument (MSI) of Sentinel-2 has made available a set of 13 spectral bands ranging from visible (VIS) and near infrared (NIR) to shortwave infrared (SWIR), featuring four bands at 10 m, six bands at 20 m, and three bands at 60 m of spatial resolution. In comparison to the previous sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region centered at 705, 740 and 783 nm, providing an opportunity for assessing red-edge spectral indices for monitoring the health status of urban grasslands. For this reason, the main objective of this paper is to find a red edge index that is more suitable for evaluating the growth status of urban grassland based on Sentinel-2A sensor data. Taking the urban grasslands in Fuzhou and Xiamen cities, Southeastern China, as examples, we firstly investigated the spectral responsive characteristics of grasslands in different health status using Sentinel-2A images dated on June 23, 2016 and August 22, 2016, respectively for Fuzhou and Xiamen. On this basis, six red edge indices related to grassland chlorophyll content were then selected to test their efficiency in detecting grassland health status. These are the red edge position (REP), the terrestrial chlorophyll index (MTCI), the normalized difference red edge index (NDRE1), the novelinverted red-edge chlorophyll index (IRECI), the red-edge chlorophyll index (CIred- edge) and the modified chlorophyll absorption ratio index (MCARI2). Furthermore, independent sample T test and Euclidean distance methods were employed to evaluate the performance of the selected indices in the detection of grassland health status. Results showed that the six red edge indices had different performances. They have different degrees of sensitivity to the changes of grassland health status. In general, the IRECI was the most sensitive to the grassland health status among the six indices in the two study areas. The index can reveal significant differences in the numerical range and mean values between grasslands with different health status. The overall accuracy of the index is greater than 85%with a kappa coefficient exceeding 0.8 both in Fuzhou and Xiamen cases. The NDRE1 and MCARI2 indices ranked the second and third, while the other three indices were unable to effectively detect the health status of the grasslands. Accordingly, the IRECI is the optimal red edge index for evaluating the grassland health status using Sentinel-2A imagery.

以福州城区为例,使用2003,2013年的Landsat卫星影像获得了研究区蒸发散量、建筑用地和植被信息,查明了研究区蒸发散量的时空变化特征,并对上述参数之间的相关关系进行了定量分析.研究结果表明,福州研究区2013年的区域蒸发散量比2003年有较大幅度的降低,其降幅达30.62％.回归分析表明,蒸发散量与建筑用地呈很强的线性负相关关系,而与植被则呈明显的线性正相关关系.显然福州城市的扩展、建筑用地的增加和植被的减少是导致研究区ET降低的最重要原因.

以福建省长汀县侵蚀退化严重的河田盆地为例,重点研究该区水土流失生态修复的主要树种——马尾松林的碳储量动态变化.通过2011年的野外样地调查获得了马尾松林的实测数据,并将其与同期SPOT5影像对应样地改进的归一化植被指数(MNDVI)数据进行回归分析,建立了河田盆地2011年马尾松林碳储量的反演模型.进一步通过不变特征法对所获得的2011年模型进行校正,使其能够推广应用于2004年和2009年的马尾松林碳储量反演,以揭示河田盆地马尾松林碳储量在2004-2011年间的时空变化.研究结果表明,这期间河田盆地马尾松林的总碳储量和碳密度均呈逐步上升的趋势:总碳储量由2004年的9.28×105 t增加到2011年的12.49×105 t,碳密度由27.31×10 4 t/m2增加到35.84×10-4 t/m2,总的说明该区马尾松林的碳汇能力在这期间有了明显的增加,而且在2009-2011年间表现得更为明显.

全球气候的变化已使得人类日益关注森林生态系统的碳储量变化.以福建省长汀县河田盆地为例,开展马尾松林碳储量估算模型的研究.通过2010年的野外样地调查获得了马尾松林的实测数据,并将其与同年的ALOS遥感影像对应样地的植被光谱信息进行比较.通过研究5种遥感植被指数与马尾松林碳储量之间的相关关系,从中选取了基于归一化植被指数(NDVI)的研究区最佳马尾松林碳储量反演模型.精度分析表明,该模型平均相对误差为-1.95％,均方根误差为3.01 t/hm2,因此可以有效地用于反演研究区的马尾松林碳储量.利用该模型反演出河田盆地2010年马尾松林的总碳储量为114.58×104 t,碳密度为34.92 t./hm2.