Gross Primary Productivity (GPP), a critical metric quantifying the total carbon dioxide assimilated by vegetation through photosynthesis, plays a pivotal role in terrestrial ecosystem carbon cycle studies. However, accurately estimating GPP at large scales remains subject to significant uncertainties. This study evaluates four widely used remote sensing-based GPP products (rEC-LUE, MODIS, VPM, GOSIF) across China using eddy covariance data from 66 flux towers. Methodologies include Getis-Ord Gi* hotspot analysis, Sen's slope estimation, Reduced Major Axis (RMA) regression, and partial correlation analysis to assess their spatiotemporal consistency and climatic response patterns. The results indicated that: (1) At the national scale, VPM exhibited the best performance (R-2 = 0.74). Ecosystem-level evaluations revealed that VPM achieved the highest accuracy for grassland (R-2 = 0.78) and cropland (R-2 = 0.87), while GOSIF performed best for forest (R-2 = 0.78). All four products performed well for the wetland (R-2 > 0.72). (2) At the site scale, GOSIF showed better agreement with eddy covariance data for most forest and grassland sites, whereas VPM excelled for cropland sites. All products exhibited limited capability in reproducing the interannual variability of site-level GPP. (3) VPM and GOSIF maintained high spatiotemporal consistency across diverse scales and hydrothermal conditions. (4) All products consistently identified precipitation as the dominant driver of GPP variations in northeastern China and the northern Tibetan Plateau. This study can enhance our understanding of vegetation carbon sequestration dynamics in China and provide theoretical support for the development of environmental policies. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This study estimated aboveground carbon stock (AGC) using field data and integrated multi-source remote sensing imagery to understand the effects of Pantana phyllostachysae Chao (P. phyllostachysae) stress. AGC remote sensing inversion was performed while accounting for P. phyllostachysae stress, and changes were analyzed. Results indicate: (1) Carbon content coefficients of Moso bamboo leaves, branches, and culms under pest stress ranged from 0.422 to 0.543 g/g, decreasing with increased stress. (2) A random forest model using multi-source data demonstrated the best performance (R2 = 0.688), estimating average AGC at 28.427 t/ha and total carbon sequestration at 913.902 MtC (Million tons of Carbon). (3) Increased pest stress resulted in gradual reductions in AGC. (4) Pest stress is estimated to result in a carbon sequestration loss of 77.443 MtC. The AGC estimation model indicates that P. phyllostachysae significantly reduces AGC, providing crucial data for understanding carbon cycling and enhancing carbon sink management in Moso bamboo forests.

To address gaps in understanding how external stresses influence remote-sensing inversion of vegetation biochemical components, a P-PROSAIL model incorporating stress factors was developed, with Shunchang County and Yanping District in Fujian Province as the study areas. The model's effectiveness was assessed, yielding R² values of 0.7133, 0.7066, 0.6441, 0.6392, 0.6057, 0.7038, 0.5323, and 0.5149 for leaf area index (LAI), canopy dry matter content (CDMC), canopy cellulose content (CCC), canopy lignin content (CLC), canopy protein content (CPC), canopy nitrogen content (CNC), canopy tannin content (CTC), and canopy flavonoid content (CFC), respectively. While CDMC and most other components showed stable inversions, CTC and CFC exhibited uncertainties due to pest stress. This study clarified the internal and external change characteristics and mechanisms of Moso bamboo forests under Pantana phyllostachysae stress, providing empirical support for the ecological health of bamboo forests.

Leaf area index (LAI) serves as a crucial indicator for assessing vegetation growth status, and unmanned aerial vehicle (UAV) optical remote sensing technology provides an effective approach for forest pest-related research. This study investigated the feasibility of LAI estimation in Moso bamboo (Phyllostachys pubescens) forests with different damage levels using UAV data while simultaneously exploring the scale effects of various spatial resolutions. Through image resampling using 10 distinct spatial resolutions and field data classification based on Pantana phyllostachysae Chao pest severity (healthy and mild damaged as Scheme 1, moderate damaged and severe damaged as Scheme 2, and all as Scheme 3), three machine learning algorithms (SVM, RF, and XGBoost) were employed to establish LAI estimation models for both single and mixed damage levels. Comparative analysis was conducted across different schemes, algorithms, and spatial resolutions to identify optimal estimation models. The results showed that (1) XGBoost-based regression models achieved superior performance across all schemes, with optimal model accuracy consistently observed at 3 m spatial resolutions; (2) minimal scale effects occurred at a 3 m resolution for Schemes 1 and 2, while Scheme 3 showed lowest scale effects at 1.5 m followed by 3 m resolutions; (3) Scheme 3 exhibited significant advantages in mixed damaged bamboo forest inversion with robust performance across all damage levels, whereas Schemes 1 and 2 demonstrated higher accuracy for single damaged scenarios compared to mixed damaged. This research validates the feasibility of incorporating pest stress factors into LAI estimation through different pest damage models, offering novel perspectives and technical support for parameter inversion in Moso bamboo forests.

The objective of this study was to deeply understand the adaptation mechanism of the functional traits of Moso bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) leaves to the environment under different Pantana phyllostachysae Chao damage levels, analyzing the changes in the relationship between specific leaf area (SLA) and leaf dry matter content (LDMC). We combined different machine learning models (decision tree, RF, XGBoost, and CatBoost regression models), and used different canopy heights and different levels of infestation, to analyze the changes in the relationship between the two under different levels of infestation based on the results of the best estimation model. The results showed the following: (1) The SLA of Ph. pubescens showed a decreasing trend with the increase om insect pest degree, and LDMC showed an inverse trend. (2) The SLA of bamboo leaves was negatively correlated with the LDMC under different insect pest degrees; the correlation of the data under the healthy class was higher than that of other insect pest levels, and at the same time better than that of the full sample, which laterally confirmed the effect of insect pest stress on the functional traits of Ph. pubescens leaves. (3) When modeling under different infestation levels, the CatBoost model was used for heavy damage and the RF model was used for the rest of the cases; the decision tree regression model was used when modeling different canopy heights. The findings contribute certain insights into the nuanced responses and adaptive mechanisms of Ph. pubescens forests to environmental fluctuations. Moreover, these results furnish a robust scientific foundation, essential for ensuring the enduring sustainability of Ph. pubescens forest ecosystems.

To evaluate the spatiotemporal changes in the ecological environment of eastern Ukraine since the Russia-Ukraine conflict, this study used MODIS images from March to September 2020 and 2022 to calculate the Remote Sensing???Based Ecological Index. In 2022, compared with 2020, conflict zones exhibited reduced improvement and increased slight degradation, whereas nonconflict areas showed marginal enhancement. Through propensity score matching, the research confirmed the causal relationship between conflict and ecological trends. Pathway analysis revealed that the conflict contributed to 0.016 units increase in ecological quality while reducing the improvement rate by 0.042 units. This study provides empirical support for understanding the correlation between conflicts and specific environmental factors, offering technical references for ecological quality assessments in other conflict areas and future evaluations by the Ukrainian government.

农民是农村环境治理的主力军,提高其环境治理的参与意愿是建立农村环境现代化治理体系的重要基础.基于福建省福州市、三明市、宁德市、龙岩市、南平市55个村庄的286份抽样调查数据,研究农户参与环境治理的路径:正式网络、非正式网络对农户参与环境治理意愿的影响.正式网络和非正式网络均对农户参与环境治理的意愿有较强的影响,网络越发达,农户越愿意参与环境治理;在正式网络方面,农户环境治理意愿的最大影响因素来自村庄治理参与意愿与家乡建设参与意愿两方面;在非正式网络方面,邻里关系、公益活动参与意愿均能够显著提高农户参与环境治理的意愿,尤其是邻里关系;正式网络与非正式网络在对农户参与环境治理意愿的影响中互有中介效应.为此,应建立健全社会协同、公众参与的治理体系,发挥社会网络对农户参与农村环境治理的积极影响.

采用2014-2021年福州新区的地表温度、归一化差值植被指数(NDVI)和土地利用类型等数据,从乡镇单元尺度分析热环境的空间分布情况,并探究植被和土地利用对热环境空间分布的影响.结果表明:2014-2021年福州新区地表温度在空间上有明显的空间自相关性,空间集聚特征显著,新区热岛比例指数(URI)呈现出下降趋势,表明在此期间热岛效应状况总体有所缓解;植被覆盖率(FVC)影响热环境的空间分布,可将福州新区的"热点"划分为FVC、NDVI均较低的乡镇和FVC较高、NDVI较低的乡镇两类;土地利用类型、土地利用程度及周边环境亦影响热环境的空间分布.

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.