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Boreal forests form the largest and least disturbed forest biome in the northern hemisphere. However, anthropogenic pressure from intensified forest management, eutrophication, and climate change may alter the ecosystem functions of understory vegetation and services boreal forests provide. Swedish forests span long gradients of climate, nitrogen deposition, and management intensity. This makes them ideal to study how the species composition and functions of other, more pristine, boreal forests might change under increased anthropogenic pressure. Moreover, the National Forest Inventory (NFI) has collected systematic data on Swedish forest vegetation since the mid-20th century. We use this data to quantify changes in vegetation types between two periods, 1953–1962 and 2003–2012. The results show changes in forest understory vegetation since the 1950s at scales not previously documented in the boreal biome. The spatial extent of most vegetation types changed significantly. Shade-adapted and nutrient-demanding species (those with high specific leaf area) have become more common at the expense of light-demanding and nutrient-conservative (low specific leaf area) species. The cover of ericaceous dwarf shrubs decreased dramatically. These effects were strongest where anthropogenic impacts were greatest, suggesting links to drivers such as nitrogen deposition and land-use change. These changes may impact ecosystem functions and services via effects on higher trophic levels and faster plant litter decomposition in the expanding vegetation types. This, in turn, may influence nutrient dynamics, and consequently ecosystem productivity and carbon sequestration.

Aim The increasing availability of remote sensing (RS) products from airborne laser scanning (ALS) surveys, synthetic aperture radar acquisitions and multispectral satellite imagery provides unprecedented opportunities for describing the physical structure and seasonal changes of vegetation. However, the added value of these RS products for predicting species distributions and animal habitats beyond land cover maps remains little explored. Here, we aim to assess how metrics derived from different types of high‐resolution (10 m) RS products predict the habitat suitability of wetland birds. Location North‐eastern part of the Netherlands. Methods We built species distribution models (SDMs) with occurrence observations from territory mapping of two selected wetland bird species (great reed warbler and Savi's warbler) and metrics from a Dutch land cover map, country‐wide ALS and Sentinel‐1 and Sentinel‐2 RS products. We then compared model performance, relative variable importance and response curves of the SDMs to assess the contribution and ecological relevance of each RS product and metric. Results Our results showed that ALS and Sentinel metrics improve SDMs with only land cover metrics by 11% and 10% of the Area Under Curve (AUC) for the great reed warbler and the Savi's warbler respectively. Assessments of feature importance revealed that all types of RS products contributed substantially to predicting the habitat suitability of these wetland birds, but that the most important variables vary among species. Main conclusions Our study demonstrates that metrics from different high‐resolution RS products capture complementary ecological information on animal habitats, including aspects such as the proportional cover of habitat types, vegetation density and the horizontal variability of vegetation height. Land cover maps with detailed spatial and thematic information can already achieve high model accuracies, but adding metrics derived from ALS point clouds and Sentinel imagery further improve model accuracy and enhance the understanding of animal–habitat relationships.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The normalized differential open-area index (NDOAI) computed from 8-day VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, the VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, the VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 70%.

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.

Climate variables carry signatures of variability at multiple timescales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified or discussed at the global scale. Here, we set out to gain a global understanding of the relevance of different modes of variability in vegetation greenness and its covariability with climate. We used > 30 years of remote sensing records of the normalized difference vegetation index (NDVI) to characterize biosphere variability across timescales from submonthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere-biosphere covariability at each timescale. Our results show that short-term (intra-annual) and longerterm (interannual and longer) modes of variability make regionally highly important contributions to NDVI variability: short-term oscillations focus in the tropics where they shape 27% of NDVI variability. Longer-term oscillations shape 9% of NDVI variability, dominantly in semiarid shrublands. Assessing dominant timescales of vegetation-climate covariation, a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the tropics. Finally, we find that correlations between variables can differ and even invert signs across timescales. For southern Africa for example, correlation between NDVI and Tair is positive for the seasonal signal but negative for short-term and longer-term oscillations, indicating that both short- and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between timescales exist for 15% of vegetated areas for NDVI with Tair and 27% with Prec, indicating global relevance of scale-specific climate sensitivities. Our analysis provides a detailed picture of vegetation-climate covariability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) nondominant subsignals in climate variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous ecosystem responses on different timescales may depend on climate zone and vegetation type, and they are to date not well understood and do not always correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for comparing remote sensing products.

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05° 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIF(sub clear-inst)) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIF(sub all-daily)) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIF(sub yield), the ratio between OCO-2 SIF and CSIF(sub clear-inst) can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIF(sub all-daily) with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m 2 /year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

Recently, extreme climate variation has been studied in different parts of the world, and the present study aims to study the impacts of climate extremes on vegetation. In this study, we analyzed the spatiotemporal variations of temperature and precipitation extremes during 1960–2015 in the Yangtze River Basin (YRB) using the Mann-Kendall (MK) test with Sen’s slope estimator and kriging interpolation method based on daily precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin). We also analyzed the vegetation dynamics in the YRB during 1982–2015 using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets and investigated the relationship between temperature and precipitation extremes and NDVI using Pearson correlation coefficients. The results showed a pronounced increase in the annual mean maximum temperature (Tnav) and mean minimum temperature (Txav) at the rate of 0.23 °C/10 years and 0.15 °C/10 years, respectively, during 1960–2015. In addition, the occurrence of warm days and warm nights shows increasing trends at the rate of 1.36 days/10 years and 1.70 days/10 years, respectively, while cold days and cold nights decreased at the rate of 1.09 days/10 years and 2.69 days/10 years, respectively, during 1960–2015. The precipitation extremes, such as very wet days (R95, the 95th percentile of daily precipitation events), very wet day precipitation (R95p, the number of days with rainfall above R95), rainstorm (R50, the number of days with rainfall above 50 mm), and maximum 1-day precipitation (RX1day), all show pronounced increasing trends during 1960–2015. In general, annual mean NDVI over the whole YRB increased at the rate of 0.01/10 years during 1982–2015, with an increasing transition around 1994. Spatially, annual mean NDVI increased in the northern, eastern, and parts of southwestern YRB, while it decreased in the YRD and parts of southern YRB during 1982–2015. The correlation coefficients showed that annual mean NDVI was closely correlated with temperature extremes during 1982–2015 and 1995–2015, but no significant correlation with precipitation extremes was observed. However, the decrease in NDVI was correlated with increasing R95p and R95 during 1982–1994.