Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

Shanxi Province holds an important strategic position in the overall ecological pattern of the Yellow River Basin. To investigate the changes of the ecological environment in the Shanxi section of the Yellow River Basin from 2000 to 2020, we selected MODIS remote sensing image data to determine the remote sensing ecological index (RSEI) based on the principal component analysis of greenness, humidity, dryness, and heat. Then, we analyzed the spatial and temporal variations of ecological quality in this region to explore the influencing factors. We further used the CA-Markov model to simulate and predict the ecological environment under different development scenarios in the Shanxi section of the Yellow River Basin in 2030. The results showed that RSEI had good applicability in the Shanxi section of the Yellow River Basin which could be used to monitor and evaluate the spatiotemporal variations in its ecological environment. From 2000 to 2020, the Shanxi section of the Yellow River Basin was dominated by low quality habitat areas, in which the ecological environment quality continued to improve from 2000 to 2010 and decreased from 2010 to 2020. The high quality habitat areas mainly located on the mountainous areas with superior natural conditions and rich biodiversity, while the low ecological quality areas were mainly in the Taiyuan Basin and the northern part of the study area, where the mining industry developed well. Climate factors were negatively correlated with ecological environment quality in the northern and central parts of the study area, and positively correlated with that in the mountainous area. Under all three development scenarios, the area of cultivated land, forest, water and construction land increased in 2030 compared to that in 2020. Compared to the natural development scenario and the cultivated land protection scenario, the ecological constraint scenario with RSEI as the limiting factor had the highest area of new forest and the lowest expansion rate of cultivated land and construction land. The results would provide a reference for land space planning and ecological environment protection in the Shanxi section of the Yellow River Basin.
山西省在黄河流域总体生态格局中具有重要的战略地位。为深入研究2000—2020年黄河流域山西段生态环境的变化,选用MODIS遥感影像数据,基于绿度、湿度、干度和热度的主成分分析确定遥感生态指数(RSEI),对该区域生态环境质量的时空变化进行分析并探讨影响因素;同时,利用CA-Markov模型对2030年黄河流域山西段不同发展情景下生态环境进行模拟和预测。结果表明: RSEI在黄河流域山西段具有较好的适用性,可用于监测和评估其生态环境的时空变化特征。2000—2020年,黄河流域山西段以低生境质量区为主,其中,2000—2010年生态环境质量持续改善,而2010—2020年则有所退化;高生境质量区集中于山区,其自然条件优越、生物多样性丰富,低生态质量区主要分布在城市群集中的太原盆地及研究区北部采矿业发达的地区;在研究区的北部和中部,气候因子与生态环境质量呈负相关关系,而在高山区域二者呈正相关关系。3种发展情景下,2030年研究区的耕地、林地、水体和建设用地面积均较2020年有所增加;相较于自然发展情景和耕地保护情景,在以RSEI为限制因子的生态约束情景中,新增林地面积最多,而耕地和建设用地的扩张速率最低。研究结果可为黄河流域山西段的国土空间规划及生态环境保护提供参考。.

Understanding the influences of climate change and human activities on vegetation change is the foundation for effective ecosystem management. Based on the 250 m MODIS-NDVI data from 2002 to 2020, we employed Theil-Sen Median trend analysis and the Mann-Kendall test to quantify vegetation change in Hunan Province. By combining with meteorological, nighttime light index, land cover and other data, residual analysis and correlation analysis, we examined the impacts of human activities and climate change on vegetation dynamics at both the pixel level and the county level. The results showed that the normalized difference vegetation index (NDVI) in Hunan Province exhibited a spatial pattern of \"overall improvement with localized degradation\" during 2002-2020. Approximately 64.9% of the study area experienced significant vegetation improvement, mainly occurring in the western and central-southern parts of Hunan Province. 1.4% of the study area experienced significant vegetation degradation, mostly in the newly developed urban areas and the farmland in the Dongting Lake Plain. Human activities and climate change jointly promoted vegetation improvement in 67.9% of the study area. Human activities and climate contributed to 96% and 4% of the NDVI change, respectively. At the county level, human activities contributed to over 80% of the NDVI change in each district or county. The impacts of human activities on vegetation change exhibited significant spatial heterogeneity. Urban expansion led to vegetation degradation in the newly developed areas, while vegetation growth appeared in the old developed urban areas. The ecological restoration projects promoted vegetation restoration in the western part of Hunan Province. This study could help us better understand the spatiotemporal variations of vegetation and their responses to climate change and human activities, which would offer scientific basis for effective ecological restoration policy.
研究气候变化和人类活动对植被变化的影响是有效生态系统管理的基础。本研究基于2002—2020年250 m MODIS-NDVI数据,采用Theil-Sen Median斜率估计和Mann-Kendall趋势分析从像元尺度量化了湖南省植被动态演变趋势;结合气象、夜间灯光指数、土地覆盖等数据,采用残差分析和相关分析等方法,从像元和县域两个尺度揭示了人类活动和气候变化对植被动态演变的影响。结果表明: 2002—2020年,湖南省归一化植被指数(NDVI)动态演变呈“整体改善、局部退化”的空间格局,显著改善的区域占研究区总面积的64.9%,主要分布于湖南省西部和中南部;显著退化的区域占研究区总面积的1.4%,主要分布于城市化区域和洞庭湖平原的耕地区域。人类活动和气候变化共同促进研究区67.9%的植被改善;人类活动和气候变化单独对植被NDVI动态演变的贡献率分别为96%、4%;人类活动对所有区县植被演变的贡献率均超过80%。人类活动对植被演变的影响存在显著空间异质性。城市扩张导致新城区植被退化,但老城区出现植被恢复的现象;生态工程则促进了湖南省西部植被恢复。本研究结果有助于深入认识湖南省植被演变时空格局及其对气候变化和不同人类活动的响应,可为制定有效的生态恢复策略提供科学依据。.

Understanding the spatiotemporal variations and driving factors of regional vegetation coverage is crucial for developing scientific plans for ecological environment protection and maintaining regional ecological balance. Based on the Google Earth Engine (GEE) platform and using Landsat Collection 2 data, we investigated the spatiotemporal variation and driving factors of vegetation coverage in Shanxi Province, China, from 1990 to 2020, by employing methods such as pixel-based binary model, trend analysis, zonal statistics, and geodetector. The results showed that vegetation coverage in Shanxi Province showed a fluctuating upward trend from 1990 to 2020. Vegetation coverage in 44.4% of this region had been significantly improved, and the area with significant degradation accounted for 7.4%. Vegetation coverage in Shanxi Province was positively correlated with elevation, slope, and mountain terrain relief. The area proportion of vegetation coverage growth was the highest in the plateau and hilly regions. Factor detection results showed that land use type, landform type, annual average precipitation, and soil type were the main influencing factors of the spatial differentiation of vegetation coverage in Shanxi Province. Results of the interaction detection showed that the interaction between driving factors all showed enhancement. The interaction between natural factors showed a downward trend, while the interaction results of social factors showed an upward trend, reflecting that the impacts of human activities on vegetation coverage in Shanxi Province were gradually increasing.
探究区域植被覆盖度的时空变化特征及其驱动因子,对于科学制定区域生态环境保护方案、维护区域生态平衡具有重要指导意义。本研究基于Google Earth Engine(GEE)平台,使用Landsat Collection 2数据,结合自然和社会经济数据,借助像元二分模型、趋势分析、分区统计和地理探测器等方法,探究山西省1990—2020年间植被覆盖度时空变化特征及其驱动因子。结果表明: 1990—2020年,山西省植被覆盖度呈波动上升趋势,44.4%区域的植被覆盖得到显著改善,显著退化区域占7.4%。山西省植被覆盖度与高程、坡度和山地地势起伏呈正相关。台地和丘陵地区植被覆盖度增长面积比例最高。因子探测结果表明,土地利用类型、地貌类型、年平均降水量、土壤类型是山西省植被覆盖空间分异的主要影响因素。交互探测发现,驱动因子间的交互作用均表现为增强。研究期间,自然因子间的交互结果呈下降趋势,而社会因子间的交互结果呈增强趋势,反映出人类活动对山西省植被覆盖的影响逐步增大。.

Understanding the vegetation dynamics and their drivers in Nepal has significant scientific reference value for implementing sustainable ecological policies. This study provides a comprehensive analysis of the spatio-temporal variations in vegetation cover in Nepal from 2003 to 2022 using MODIS NDVI data and explores the effects of climatic factors and anthropogenic activities on vegetation. Mann-Kendall test was used to assess the significant trend in NDVI and was integrated with the Hurst exponent to predict future trends. The driving factors of NDVI dynamics were analyzed using Pearson\'s correlation, partial derivative, and residual analysis methods. The results indicate that over the last 20 years, Nepal has experienced an increasing trend in NDVI at 0.0013 year-1, with 80% of the surface area (vegetation cover) showing an increasing vegetation trend (~ 28% with a significant increase in vegetation). Temperature influenced vegetation dynamics in the higher elevation areas, while precipitation and human interventions influenced the lower elevation areas. The Hurst exponent analysis predicts an improvement in the vegetation cover (greening) for a larger area compared to vegetation degradation (browning). A significantly increased area of NDVI residuals indicates a positive anthropogenic influence on vegetation cover. Anthropogenic activities have a higher relative contribution to NDVI variation followed by temperature and then precipitation. The results of residual trend and Hurst analysis in different regions of Nepal help identify degraded areas, both in the present and future. This information can assist relevant authorities in implementing appropriate policies for a sustainable ecological environment.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash-Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT (R = 0.822, α < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

Photosynthesis in vegetation is one of the key processes in maintaining regional ecological balance and climate stability, and it is of significant importance for understanding the health of regional ecosystems and addressing climate change. Based on 2001-2021 Global OCO-2 Solar-Induced Fluorescence (GOSIF) dataset, this study analyzed spatiotemporal variations in Asian vegetation photosynthesis and its response to climate and human activities. Results show the following: (1) From 2001 to 2021, the overall photosynthetic activity of vegetation in the Asian region has shown an upward trend, exhibiting a stable distribution pattern with higher values in the eastern and southern regions and lower values in the central, western, and northern regions. In specific regions such as the Turgen Plateau in northwestern Kazakhstan, Cambodia, Laos, and northeastern Syria, photosynthesis significantly declined. (2) Meteorological factors influencing photosynthesis exhibit differences based on latitude and vertical zones. In low-latitude regions, temperature is the primary driver, while in mid-latitude areas, solar radiation and precipitation are crucial. High-latitude regions are primarily influenced by temperature, and high-altitude areas depend on precipitation and solar radiation. (3) Human activities (56.44%) have a slightly greater impact on the dynamics of Asian vegetation photosynthesis compared to climate change (43.56%). This research deepens our comprehension of the mechanisms behind the fluctuations in Asian vegetation photosynthesis, offering valuable perspectives for initiatives in environmental conservation, sustainability, and climate research.

Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.

The concentration of chlorophyll-a (Chl-a) in seawater reflects phytoplankton growth and water eutrophication, which are usually assessed for evaluation of primary productivity and carbon source/sink of coral reefs. However, the precise delineation of Chl-a concentration in coral reefs remains a challenge when ocean satellites with low spatial resolution are utilized. In this study, a remote sensing inversion model for Chl-a was developed in fringing reefs (R2 = 0.76, RMSE =0.41 μg/L, MRE = 14 %) and atolls (R2 = 0.79, RMSE =0.02 μg/L, MRE = 8 %), utilizing reflectance data from the sensitive band of the Landsat-8 Operational Land Imagers (OLI) with a spatial resolution of 30 m. The aforementioned model was utilized to invert high-resolution distribution maps of Chl-a concentration in six major coral reef regions of the South China Sea from 2013 to 2022 and subsequently used to analyze the variations in Chl-a concentration and its influencing factors. The results indicate a Chl-a concentration gradient among coral reefs Daya Bay, Weizhou Island, Luhuitou, Xuwen, Huangyan Island, and Xisha Island in that order. The Chl-a concentration in coral reefs exhibited an overall increasing trend, with significant seasonal fluctuations, characterized by higher concentrations during winter and spring and lower concentrations during summer and autumn. The concentration of Chl-a in coral reefs was positively correlated with the average wind speed.

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.