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Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming years. Adequate mapping of regions with climate-sensitive vegetation and its associated time lag is required for appropriate mitigation planning to avoid potential negative ecological impacts towards vegetation. In this study, ENSO and climate indicator time series data, for example, Multivariate ENSO Index (MEI) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data for rainfall were linked with long-term time series vegetation proxies from remote sensing (RS proxies). ENSO- and rainfall-sensitive areas were identified from each RS proxy using the bivariate Granger test, and the areas identified by multiple RS proxies were taken to identify climate-sensitive regions in Indonesia. Of the biome types in Indonesia, savanna was the most sensitive, with approximately 53% of the total savanna area in Indonesia shown to be sensitive to ENSO and rainfall by two or more RS proxies. Rolling correlation analysis also found that the ENSO effect on the vegetation region after rainfall was positively correlated with the RS proxies with a time lag of +5 months. Therefore, rainfall can be taken as a proxy of the effects of ENSO on the temporal dynamics of sensitive vegetation regions in Indonesia.

Climate change is expected to impact the functioning of the entire Earth system. However, detecting changes in ecosystem dynamics and attributing such change to anthropogenic climate change has proved difficult. Here we analyse the vegetation dynamics of 100 sites representative of the diversity of terrestrial ecosystem types using remote-sensing data spanning the past 40 years and a dynamic model of plant growth, forced by climate reanalysis data. We detect a change in vegetation activity for all ecosystem types and find these changes can be attributed to trends in climate-system parameters. Ecosystems in dry and warm locations responded primarily to changes in soil moisture, whereas ecosystems in cooler locations responded primarily to changes in temperature. We find that the effects of CO2 fertilization on vegetation are limited, potentially due to masking by other environmental drivers. Observed trend switching is widespread and dominated by shifts from greening to browning, suggesting many of the ecosystems studied are accumulating less carbon. Our study reveals a clear fingerprint of climate change in the change exhibited by terrestrial ecosystems over recent decades.An analysis fusing satellite data with a process-based model of plant growth attributes changes in vegetation activity across terrestrial ecosystems to climatic changes.

Solar-induced chlorophyll fluorescence (SIF) is a crucial proxy of photosynthetic processes in vegetation. In recent decades, advancements in remote sensing technology have facilitated long-term global SIF monitoring, significantly enhancing our understanding of vegetation dynamics on a global scale. Despite this progress, current SIF datasets face major challenges, including temporal inconsistencies among various satellite-derived products and a lack of long-term, high-resolution observations. In this study, we developed a \"Long-term Harmonized SIF\" (LHSIF) dataset spanning 1995 to 2024 with a fine spatial resolution of 0.05° by coordinating SIF satellite observations from GOME, SCIAMACHY, GOME-2, and OCO-2. Light use efficiency (LUE)-based spatial downscaling models were employed for each SIF product to generate fine-resolution global SIF maps. The long-term dataset was constructed using temporally corrected GOME-2A SIF (TCSIF) as a benchmark and was combined with a cumulative distribution function (CDF) normalization method for far-red SIF harmonization across satellite sensors from GOME, SCIAMACHY, and OCO-2. The resulting harmonized dataset shows a 49 % reduction in inter-sensor differences compared to the uncorrected data and exhibits a stable interannual increase of 0.31 ± 0.07 % yr.sup.-1 . This result strongly aligns with the growth rate of gross primary production (GPP, 0.47 ± 0.03 % yr.sup.-1) and is consistent with ground-based SIF observations (R0.60). Therefore, the long-term harmonized SIF dataset with a fine 0.05° resolution is valuable for estimating global photosynthesis over extended periods. The LHSIF dataset is available at

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

The karst region in southwestern China is particularly prominent and has become a core issue constraining ecological environment restoration and sustainable development in this area. This study utilized long-term remote sensing data to reveal the spatial pattern evolution characteristics of rocky desertification in the region in 2000, 2010, and 2020. Meanwhile, it analyzed the dynamic trend of vegetation coverage recovery in the area from 2000 to 2020, as well as the analysis of related factors. The results showed that the spatial distribution of the Normalized Difference Vegetation Index (NDVI) remained highly clustered, though the clustering gradually weakened over time. When NDVI exceeded 0.6, the probability of rocky desertification reversal increased. Currently, a core contradiction of “quantity increases but quality stagnates” exists in regional vegetation cover, characterized by a continuous rise in NDVI mean values coexisting with reduced spatial clustering. This phenomenon reflects the evolution of vegetation patterns under the combined effects of ecological engineering interventions, adjustments in human-land relationships, and constraints of karst landforms. Through factor analysis, slope and humidity were identified as key factors influencing vegetation restoration. The findings provide an important theoretical foundation and practical reference for targeted rocky desertification management, optimization of ecological restoration projects, and coordinated human-land development in karst regions.

Climate change and human activities have already caused degradation in a large fraction of vegetation on the Qinghai‐Tibetan Plateau (QTP). Many studies report that climate variability instead of overgrazing has been the primary cause for large‐scale vegetation cover changes on the QTP, for example, Lehnert et al., 2016, https://doi.org/10.1038/srep24367. However, it remains unclear how human activities (mainly livestock grazing) regulate vegetation dynamics under climate change. This paper takes the AVHRR/GIMMS Normalized Difference Vegetation Index (NDVI) as an indicator to analyze the growth status of vegetation zones in the QTP, which has highly sensitive to climate change. The spatiotemporal dynamics of vegetation growth between 1981 and 2015 were analyzed. The dual effects of climate change and human activities were examined by correlation analyses of data from 87 meteorological stations and economic statistical data of the QTP. Results show that: (a) The vegetation in central and southwestern QTP with high altitudes was improving due to the warm‐humid climate trend. An increase in temperature and a reduction in the harsh frigid climate at high altitudes due to global warming has resulted in expansions of the vegetated areas, with the NDVI showing a concordant increase. (b) The degraded areas were mainly confined to the northern and eastern QTP, which have high human and livestock population densities. In comparison to gently changing climate regimes, anthropogenic activities such as chronic concentration of population and livestock in the valleys with a less harsh climate exerts a much stronger pressure on vegetation. The study indicates that the anthropogenic pressures are much more intensive than the impact of climate change and are critical for the conservation and sustainable management of the QTP vegetation. Plain Language Summary Vegetation dynamics and its type are considered to be critical indicators of different climate regimes and have received significant attention from ecologists and climatologists. However, studies on the shift in vegetation toward higher altitudes and higher latitudes with climate warming from the vegetation zone redistribution perspective are relatively scarce. Our results suggest that the degraded areas of vegetation were mainly confined to the northern and eastern Qinghai‐Tibetan Plateau (QTP), which have high human and livestock population densities. In comparison to gently changing climate regimes, anthropogenic activities such as chronic concentration of population and livestock in the relatively less harsh valleys exerts a much stronger pressure on vegetation. Anthropogenic pressures were therefore found to be far more intensive than the impact of climate change and they were the big threats to the sustainability of the QTP. Key Points The vegetation with high altitudes was improving due to the warm‐humid climate trend The degraded vegetation areas were mainly confined to high human and livestock population densities Anthropogenic activities such as chronic concentration of population and livestock exerts a much stronger pressure on vegetation

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass) and carbon (Cmass) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R 2 = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R 2 = 0.07–0.73; %RMSE = 7–38) and multiple (R 2 = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

The dynamics of the ecosystem represented by vegetation under the influence of human activities have become an important issue in the study of the regional ecological environment. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. However, the spatiotemporal patterns and evolutionary trends of anthropogenic impacts on vegetation dynamics in Xinjiang are still unclear. In this study, the anthropogenic impacts on vegetation dynamics were quantitatively assessed by combining the improved normalized difference vegetation index (NDVI) prediction model and the residual analysis method in Xinjiang, China. The human driving factors were analyzed with the support of a stepwise multiple regression model for vegetation changes at the county scale. Based on trend analysis and the Hurst exponent, the spatiotemporal characteristics and evolutionary trends of the impact of human activities on vegetation change were discussed. The results show that (1) the NDVI values in Xinjiang showed a gradually increasing trend at a rate of 0.005/10 years from 1982 to 2018, and the vegetation dynamics mainly showed significant improvements (57.09% of the vegetated areas), especially for crops. (2) The anthropogenic effects of vegetation changes in Xinjiang mainly included positive impact increases (43.22% of the vegetated areas) from 2000 to 2018. Human activities promoted the increase in the NDVI of various vegetation types. Both the positive and negative impacts of human activities increased over the study period, and the growth rate of the positive influence (0.08%/10 years) was higher than that of the negative influence (0.04%/10 years). (3) The cultivated area, GDP of primary industry, and population are the main anthropogenic factors causing the increase in NDVI, which dominate the vegetation greening in 30.34%, 29.22%, and 28.09% of the counties in Xinjiang, respectively. The animal husbandry population, agricultural population, and livestock number are the main anthropogenic factors causing the decrease in NDVI, which dominate the vegetation degradation in 23.60%, 21.35%, and 17.98% of the counties in Xinjiang, respectively. (4) The evolutionary trend of the anthropogenic impact on vegetation dynamics in Xinjiang will be dominated by anti-persistence (53.84% of the vegetated areas), thereby mainly showing that the positive impacts continued to increase (22.56% of the vegetated areas), especially for crops, shrubs, grasslands, and alpine vegetation. Our results are helpful in understanding the characteristics and evolutionary trends of vegetation changes in arid areas caused by human activities and are of significance as a reference for policymakers to appropriately adjust policy guidance in a timely manner to promote the protection and sustainable development of fragile ecosystems.