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The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.

Evapotranspiration (E), a pivotal phenomenon inherent to hydrological and thermal dynamics, assumes a position of utmost importance within the intricate framework of the water–energy nexus. However, the quantitative study of E on a large scale for the “Grain for Green” projects under the backdrop of climate change is still lacking. Consequently, this study examined the interannual variations and spatial distribution patterns of E, transpiration (Et), and soil evaporation (Eb) in the Northern Foot of Yinshan Mountain (NFYM) between 2000 and 2020 and quantified the contributions of climate change and vegetation greening to the changes in E, Et, and Eb. Results showed that E (2.47 mm/a, p < 0.01), Et (1.30 mm/a, p < 0.01), and Eb (1.06 mm/a, p < 0.01) all exhibited a significant increasing trend during 2000–2020. Notably, vegetation greening emerged as the predominant impetus underpinning the augmentation of both E and Eb, augmenting their rates by 0.49 mm/a and 0.57 mm/a, respectively. In terms of Et, meteorological factors emerged as the primary catalysts, with temperature (Temp) assuming a predominant role by augmenting Et at a rate of 0.35 mm/a. Temp, Precipitation (Pre), and leaf area index (LAI) collectively dominated the proportional distribution of E, accounting for shares of 32.75%, 28.43%, and 25.01%, respectively. Within the spectrum of predominant drivers influencing Et, Temp exerted the most substantial influence, commanding the largest proportion at 33.83%. For Eb, the preeminent determinants were recognized as LAI and Temp, collectively constituting a substantial portion of the study area, accounting for 32.10% and 29.50%, respectively. The LAI exerted a pronounced direct influence on the Et, with no significant effects on E and bare Eb. Wind speed (WS) had a substantial direct impact on both E and Et. Pre exhibited a strong direct influence on E, Et, and Eb. Relative humidity (RH) significantly affected E directly. Temp primarily influenced Eb indirectly through radiation (Rad). Rad exerted a significant direct inhibitory effect on Eb. These findings significantly advanced our mechanistic understanding of how E and its components in the NFYM respond to climate change and vegetation greening, thus providing a robust basis for formulating strategies related to regional ecological conservation and water resources management, as well as supplying theoretical underpinnings for constructing sustainable vegetation restoration strategies involving water resources in the region.

As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change.

Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future.

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model's reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study's outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management. [ABSTRACT FROM AUTHOR]

In this study, we utilized NDVI data from the moderate resolution imaging spectroradiometer (MODIS) alongside climatic variables obtained from a reanalyzed dataset to analyze Arctic greening during the summer months (June–September) of the last two decades. This investigation entailed a detailed analysis of these changes across various temporal scales. The data indicated a continuous trend of Arctic greening, evidenced by a 1.8% per decade increment in the NDVI. Notably, significant change points were identified in June 2012 and September 2013. A comparative assessment of NDVI pre- and post-these inflection points revealed an elongation of the Arctic greening trend. Furthermore, an anomalous increase in NDVI of 2% per decade was observed, suggesting an acceleration in greening. A comprehensive analysis was conducted to decipher the correlation between NDVI, temperature, and energy budget parameters to elucidate the underlying causes of these change points. Although the correlation between these variables was relatively low throughout the summer months, a distinct pattern emerged when these periods were dissected and examined in the context of the identified change points. Preceding the change point, a strong correlation (approximately 0.6) was observed between all variables; however, this correlation significantly diminished after the change point, dropping to less than half. This shift implies an introduction of additional external factors influencing the Arctic greening trend after the change point. Our findings provide foundational data for estimating the tipping point in Arctic terrestrial ecosystems. This is achieved by integrating the observed NDVI change points with their relationship with climatic variables, which are essential in comprehensively understanding the dynamics of Arctic climate change, particularly with alterations in tundra vegetation.

In recent decades, the vegetation on many coastal dunes has expanded spatially, which is attributed, among other things, to global-scale climate change. The intersite variability in this dune greening has not yet been substantially investigated, nor is it known whether it is consistent with intersite variability in climate change. Therefore, the objectives of this work were firstly to quantify and analyse the change in vegetation cover from multitemporal NDVI time series at a large number (186) of dune fields worldwide, calculated from Landsat satellite imagery available between 1984 and 2021 and secondly, to correlate the identified trends with trends in the main climate variables influencing vegetation growth (temperature, precipitation and wind speed). We show that greening is strongest in cool temperate climates (35° to 66.5° north/south latitudes) and that the rate of greening is accelerating at many sites. We find no dependence between the rate of greening and the local temporal change in temperature, precipitation and/or wind speed. Based on existing literature, sand supply and anthropogenic activities are discussed as possible reasons for the absence of a clear global relationship between variability in dune greening and climate change.

Vegetation primarily draws water from soil moisture (SM), with restoration in drylands often reducing SM storage (SMS). However, anomalies have been detected in the Beijing–Tianjin Sand Source Region (BTSSR) of China via the Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE). This study quantified the sources of increased SMS in drylands to elucidate the effects of vegetation restoration on SMS. The results indicated the following: (1) In vegetated drylands, 46.2% experienced a significant increase in SMS while 53.8% remained stable; both were positively correlated with the normalised difference vegetation index (NDVI). (2) The increase in SMS was accompanied by a decrease in groundwater storage (GWS), as indicated by the significant correlation coefficients of −0.710 and −0.569 for SMS and GWS, respectively. Furthermore, GWS served as the primary source of water for vegetation. (3) The results of the redundancy analysis (RDA) indicated that the initial vegetation, the driver of the observed trend of increased SMS and decreased GWS, accounted for 50.3% of the variability in water storage. Therefore, to sustain dryland ecosystems, we recommend that future vegetation restoration projects give due consideration to the water balance while concurrently strengthening the dynamic monitoring of SMS and GWS.

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint.

Satellite observations have revealed strong land surface “greening” (i.e., increases in vegetation greenness or leaf area index (LAI)) in the Northern Hemisphere over the past few decades. European terrestrial ecosystems are a greening hotspot, but how they respond to land surface greening, climate change, CO2 fertilization, land use and land cover change (LULCC) and other factors is unclear. Here, we assessed how these interacting factors might be combined to alter terrestrial gross primary production (GPP) throughout Europe during the period of 2001 to 2016 using a process-based Farquhar GPP model (i.e., FGM). We found a more productive European terrestrial ecosystem and most of the GPP enhancement in Europe was explained by increases in LAI (62%) and atmospheric CO2 concentration (29%). Spatially, the spatial signature of the LAI and GPP trends both suggested widespread (72–73% of the vegetated area) greening phenomena across Europe, among which 23.7% and 13.3% were statistically significant (p < 0.05). The interannual trend of GPP estimated by the FGM (0.55% yr−1) was reasonable compared with other GPP products (0.47% yr−1 to 0.92% yr−1) and the observed LAI increasing rate (0.62% yr−1). FGM factorial simulations suggested that land surface greening (+35.5 Pg C yr−2, p < 0.01), CO2 fertilization (+16.9 Pg C yr−2, p < 0.01), temperature warming (+3.7 Pg C yr−2, p < 0.05), and enhanced downwards solar radiation (+1.2 Pg C yr−2, p > 0.05) contributed to the GPP enhancement, while the enhanced vapour pressure deficit (−5.6 Tg C yr−2, p < 0.01) had significant negative impacts on GPP, especially in 2006 and 2012, when extreme droughts struck south-eastern Europe. Meanwhile, approximately 1.8% of the total area of Europe experienced LULCC from 2001 to 2016 and LULCC exerted a small but significant (−1.3 Tg C yr−2, p < 0.01) impact on GPP due to decreases in the total number of vegetated pixels (−159 pixels yr−1). Although the LULCC effect was negative, the largest increase occurred in forested land (+0.9% of total area). In addition, the increasing trends for the annual mean LAI (0.01 m2 m−2 yr−1, p < 0.001) and total GPP (22.2 Tg C yr−2, p < 0.001) of forests were more significant and higher than those of other vegetation types, suggesting that European forests may continue to play important roles in combating climate change in the future with long-lasting carbon storage potential. These results provide the first systematic quantitative analysis of the driving force of enhanced gross carbon assimilation by European ecosystems by considering variations in leaf physiological traits with environmental adaptations.

Increasing numbers of observations and research studies have detected widespread vegetation greening across China since the 1980s. The dynamics of vegetation can influence the process of terrestrial evapotranspiration (ET) and its components (vegetation transpiration (Ec), soil evaporation (Es), and intercepted precipitation evaporation (Ei)). However, it is still not clear how the ET components responded to China’s greening. This work investigated the characteristics and dynamics of ET components for different climate zones and moisture regions and the dominant ecosystems over China using PML ET products during 2001–2020. The results showed that ET increased by 9%, Ec and Ec/ET increased by 18.7% and 4.4%, respectively, contributing to more than 90% of the ET increment across China. The increment in Ec generally increased from north to south with the most obvious change of Ec/ET having occurred in the temperate zone and semi-humid regions. Es increased in arid, semi-arid and plateau climate regions but decreased in the remaining climate zones. As a result, Es only decreased by 2.7% on average, while Es/ET decreased by 5.7%. Ei increased by 26.6% across China, while Ei/ET changed slightly due to the little contribution of Ei to ET. The agricultural ecosystem presented the most obvious change of Ec and Es among the dominant ecosystems, and the most obvious change of Ei occurred in the forest ecosystem. Vegetation greening altered biophysical factors that govern heat and vapor exchange in the soil-plant-atmosphere continuum, thus modulating the reallocation of ET components.

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects.

In this study, we utilized NDVI data from the moderate resolution imaging spectroradiometer (MODIS) alongside climatic variables obtained from a reanalyzed dataset to analyze Arctic greening during the summer months (June–September) of the last two decades. This investigation entailed a detailed analysis of these changes across various temporal scales. The data indicated a continuous trend of Arctic greening, evidenced by a 1.8% per decade increment in the NDVI. Notably, significant change points were identified in June 2012 and September 2013. A comparative assessment of NDVI pre- and post-these inflection points revealed an elongation of the Arctic greening trend. Furthermore, an anomalous increase in NDVI of 2% per decade was observed, suggesting an acceleration in greening. A comprehensive analysis was conducted to decipher the correlation between NDVI, temperature, and energy budget parameters to elucidate the underlying causes of these change points. Although the correlation between these variables was relatively low throughout the summer months, a distinct pattern emerged when these periods were dissected and examined in the context of the identified change points. Preceding the change point, a strong correlation (approximately 0.6) was observed between all variables; however, this correlation significantly diminished after the change point, dropping to less than half. This shift implies an introduction of additional external factors influencing the Arctic greening trend after the change point. Our findings provide foundational data for estimating the tipping point in Arctic terrestrial ecosystems. This is achieved by integrating the observed NDVI change points with their relationship with climatic variables, which are essential in comprehensively understanding the dynamics of Arctic climate change, particularly with alterations in tundra vegetation. [ABSTRACT FROM AUTHOR]

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects. [ABSTRACT FROM AUTHOR]

Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future. [ABSTRACT FROM AUTHOR]

As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change. [ABSTRACT FROM AUTHOR]

In urban settings, trees and greenery play a vital role in environmental well-being and community vitality. This study explores the impact of Miami-Dade County’s tree-planting initiative on urban greenness and considers the influence of climate dynamics. Using Landsat data from 2006 to 2019, we find stable overall greenness, with 5.64% of the Census blocks exhibiting significant changes. Seasonal analysis reveals winter as prominent, with 61.47% of Census blocks showing increased greenness. Temperature and precipitation, especially post-2010, correlate with greenness changes. Despite a reported increase in tree cover from 14% to 20%, our findings show only 5–6% of Census blocks with statistically significant changes, highlighting the complexity of achieving substantial improvements in green canopy coverage. The study raises questions about the efficacy of large-scale tree-planting initiatives in densely urbanized areas when human factors are not well understood. Implications for urban planning stress the importance of preserving green spaces and informed decision-making for enhancing vegetation cover in Miami-Dade County, emphasizing the need to consider local conditions, seasonal variations, policies, and human factors in urban greening efforts.

Vegetation is an essential component of terrestrial ecosystems and supplies multiple ecosystem benefits and services. Several indices have been used to monitor changes in vegetation communities using remotely-sensed data. However, only a few studies have conducted a comparative analysis of different indices concerning vegetation greenness variation. Additionally, there have been oversights in assessing the change in greenness of evergreen woody species. In this study, we used the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of terrestrial vegetation (NIRv), and the leaf area index (LAI) data derived from MODIS data to examine spatial and temporal change in vegetation greenness in the growing season (May–September) and then evaluated the evergreen vegetation greenness change using winter (December–February) greenness using trend analysis and consistency assessment methods between 2000 and 2022 on the Tibetan Plateau, China. The results found that vegetation greenness increased in 80% of pixels during the growing season (northeastern, central-eastern, and northwestern regions). Nevertheless, a decline in the southwestern and central-southern areas was identified. Similar trends in greenness were also observed in winter in about 80% of pixels. Consistency analyses based on the four indexes showed that vegetation growth was enhanced by 29% and 30% of pixels in the growing season and winter, respectively. Further, there was relatively strong consistency among the different vegetation indexes, particularly between the NIRv and EVI. The LAI was less consistent with the other indexes. These findings emphasize the importance of selecting an appropriate index when monitoring long-term temporal trends over large spatial scales.

Evapotranspiration (E), a pivotal phenomenon inherent to hydrological and thermal dynamics, assumes a position of utmost importance within the intricate framework of the water–energy nexus. However, the quantitative study of E on a large scale for the "Grain for Green" projects under the backdrop of climate change is still lacking. Consequently, this study examined the interannual variations and spatial distribution patterns of E, transpiration (Et), and soil evaporation (Eb) in the Northern Foot of Yinshan Mountain (NFYM) between 2000 and 2020 and quantified the contributions of climate change and vegetation greening to the changes in E, Et, and Eb. Results showed that E (2.47 mm/a, p < 0.01), Et (1.30 mm/a, p < 0.01), and Eb (1.06 mm/a, p < 0.01) all exhibited a significant increasing trend during 2000–2020. Notably, vegetation greening emerged as the predominant impetus underpinning the augmentation of both E and Eb, augmenting their rates by 0.49 mm/a and 0.57 mm/a, respectively. In terms of Et, meteorological factors emerged as the primary catalysts, with temperature (Temp) assuming a predominant role by augmenting Et at a rate of 0.35 mm/a. Temp, Precipitation (Pre), and leaf area index (LAI) collectively dominated the proportional distribution of E, accounting for shares of 32.75%, 28.43%, and 25.01%, respectively. Within the spectrum of predominant drivers influencing Et, Temp exerted the most substantial influence, commanding the largest proportion at 33.83%. For Eb, the preeminent determinants were recognized as LAI and Temp, collectively constituting a substantial portion of the study area, accounting for 32.10% and 29.50%, respectively. The LAI exerted a pronounced direct influence on the Et, with no significant effects on E and bare Eb. Wind speed (WS) had a substantial direct impact on both E and Et. Pre exhibited a strong direct influence on E, Et, and Eb. Relative humidity (RH) significantly affected E directly. Temp primarily influenced Eb indirectly through radiation (Rad). Rad exerted a significant direct inhibitory effect on Eb. These findings significantly advanced our mechanistic understanding of how E and its components in the NFYM respond to climate change and vegetation greening, thus providing a robust basis for formulating strategies related to regional ecological conservation and water resources management, as well as supplying theoretical underpinnings for constructing sustainable vegetation restoration strategies involving water resources in the region. [ABSTRACT FROM AUTHOR]

In recent years, the conversion of saline-alkali land to rice fields has become the most dominant land use change feature in western Jilin, leading to significant surface greening. Saline–alkali land and paddy fields have distinct surface biophysical properties; however, there is a lack of systematic assessment of the moderating effect of planting rice on saline–alkali land on regional climate by changing surface properties. In this paper, multiscale data on the surface temperature of saline–alkali land and paddy fields were obtained using 1 km MODIS product, 30 m Landsat 8 satellite imagery and centimeter-scale UAV imagery in Da’an City, western Jilin as the study area, and the various characteristics of the surface temperature of saline-alkali land and paddy fields in different months of the year and at different times of the day were analyzed. Furthermore, the effect of rice cultivation in saline–alkali land on the local surface temperature was assessed using a space-for-time approach. The results based on satellite observations including both MODIS and Landsat showed that the surface temperature of saline–alkali land was significantly higher than that of paddy fields during the crop growing season, especially in July and August. The high temporal resolution MODIS LST data also indicated the paddy fields cool the daytime surface temperature, while warming the nighttime surface temperature, which was in contrast for saline–alkali land during the growing season. High-resolution UAV observations in July confirmed that the cooling effect of paddy fields was most significant at the middle of day. From the biophysical perspective, the reclamation of saline–alkali land into paddy fields leads to an increase in leaf area index, followed by a significant increase in evapotranspiration. Meanwhile, rice cultivation in saline–alkali land reduces surface albedo and increases surface net radiation. The trade-off relationship between the two determines the seasonal difference in the surface temperature response of saline–alkali land for rice cultivation. At the same time, the daily cycle of crop evapotranspiration and the thermal insulation effect of paddy fields at night are the main reasons for the intraday difference in surface temperature between saline–alkali land and paddy field. Based on the multiscale assessment of the impact of rice cultivation in saline-alkali land on surface temperature, this study provides a scientific basis for predicting future regional climate change and comprehensively understanding the ecological and environmental benefits of saline–alkali land development.

Vegetation primarily draws water from soil moisture (SM), with restoration in drylands often reducing SM storage (SMS). However, anomalies have been detected in the Beijing–Tianjin Sand Source Region (BTSSR) of China via the Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE). This study quantified the sources of increased SMS in drylands to elucidate the effects of vegetation restoration on SMS. The results indicated the following: (1) In vegetated drylands, 46.2% experienced a significant increase in SMS while 53.8% remained stable; both were positively correlated with the normalised difference vegetation index (NDVI). (2) The increase in SMS was accompanied by a decrease in groundwater storage (GWS), as indicated by the significant correlation coefficients of −0.710 and −0.569 for SMS and GWS, respectively. Furthermore, GWS served as the primary source of water for vegetation. (3) The results of the redundancy analysis (RDA) indicated that the initial vegetation, the driver of the observed trend of increased SMS and decreased GWS, accounted for 50.3% of the variability in water storage. Therefore, to sustain dryland ecosystems, we recommend that future vegetation restoration projects give due consideration to the water balance while concurrently strengthening the dynamic monitoring of SMS and GWS. [ABSTRACT FROM AUTHOR]

Accurate detection and quantification of regional vegetation trends are essential for understanding the dynamics of landscape ecology and vegetation distribution. We applied a comprehensive trend analysis to satellite data to describe geospatial changes in vegetation along the Pacific slope of Peru and northern Chile, from sea level to the continental divide, a region characterised by biologically unique and highly sensitive arid and semi-arid environments. Our statistical analyses show broad regional patterns of positive trends in EVI, called “greening”, alongside patterns of “browning”, where trends are negative between 2000 and 2020. The coastal plain and foothills, up 1000 m, contain notable greening of the coastal Lomas and newly irrigated agricultural lands occurring alongside browning trends related to changes in land use practices and urban development. Strikingly, the precordilleras show a distinct ‘greening strip’, which extends from approximately 6°S to 22°S, with an altitudinal trend, ascending from the tropical lowlands (170–780 m) in northern Peru to the subtropics (1000–2800 m) in central Peru and temperate zone (2600–4300 m) in southern Peru and northern Chile. We find that the geographical characteristics of the greening strip do not match climate zones previously established by Köppen and Geiger. Greening and browning trends in the coastal deserts and the high Andes lie within well defined climatic and life zones, producing variable but identifiable trends. However, the distinct Pacific slope greening presents an unexpected distribution with respect to the regional Köppen–Geiger climate and life zones. This work provides insights on understanding the effects of climate change on Peru’s diverse ecosystems in highly sensitive, biologically unique arid and semi-arid environments on the Pacific slope.

In urban settings, trees and greenery play a vital role in environmental well-being and community vitality. This study explores the impact of Miami-Dade County's tree-planting initiative on urban greenness and considers the influence of climate dynamics. Using Landsat data from 2006 to 2019, we find stable overall greenness, with 5.64% of the Census blocks exhibiting significant changes. Seasonal analysis reveals winter as prominent, with 61.47% of Census blocks showing increased greenness. Temperature and precipitation, especially post-2010, correlate with greenness changes. Despite a reported increase in tree cover from 14% to 20%, our findings show only 5–6% of Census blocks with statistically significant changes, highlighting the complexity of achieving substantial improvements in green canopy coverage. The study raises questions about the efficacy of large-scale tree-planting initiatives in densely urbanized areas when human factors are not well understood. Implications for urban planning stress the importance of preserving green spaces and informed decision-making for enhancing vegetation cover in Miami-Dade County, emphasizing the need to consider local conditions, seasonal variations, policies, and human factors in urban greening efforts. [ABSTRACT FROM AUTHOR]

The vegetative cover in and surrounding the Rohingya refugee camps in Ukhiya-Teknaf is highly vulnerable since millions of refugees moved into the area, which led to severe environmental degradation. In this research, we used a supervised image classification technique to quantify the vegetative cover changes both in Ukhiya-Teknaf and thirty-four refugee camps in three time-steps: one pre-refugee crisis (January 2017), and two post-refugee crisis (March 2018, and February 2019), in order to identify the factors behind the decline in vegetative cover. The vegetative cover vulnerability of the thirty-four refugee camps was assessed using the Per Capita Greening Area (PCGA) datasets and K-means classification techniques. The satellite-based monitoring result affirms a massive loss of vegetative cover, approximately 5482.2 hectares (14%), in Ukhiya-Teknaf and 1502.56 hectares (79.57%) among the thirty-four refugee camps, between 2017 and 2019. K-means classification revealed that the vegetative cover in about 82% of the refugee camps is highly vulnerable. In the end, a recommendation as to establishing the studied region as an ecological park is proposed and some guidelines discussed. This could protect and reserve forests from further deforestation in the area, and foster future discussion among policymakers and researchers.

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change’s negative effects and maintain water resources’ sustainability in arid and semi-arid regions.

Global dryland areas are vulnerable to climate change and anthropogenic activities, making it essential to understand the primary drivers and quantify their effects on vegetation growth. In this study, we used the Time Series Segmented Residual Trends (TSS-RESTREND) method to attribute changes in vegetation to CO2, land use, climate change, and climate variability in Chinese and American dryland areas. Our analysis showed that both Chinese and American drylands have undergone a greening trend over the past four decades, with Chinese greening likely linked to climatic warming and humidification of Northwest China. Climate change was the dominant factor driving vegetation change in China, accounting for 48.3%, while CO2 fertilization was the dominant factor in American drylands, accounting for 47.9%. However, land use was the primary factor resulting in desertification in both regions. Regional analysis revealed the importance of understanding the drivers of vegetation change and land degradation in Chinese and American drylands to prevent desertification. These findings highlight the need for sustainable management practices that consider the complex interplay of climate change, land use, and vegetation growth in dryland areas.

The vegetation of the Qinghai–Tibet Plateau (QTP) is vital to the global climate change and ecological security of China. However, the impact of climate variation on the spatial pattern and zonal distribution of vegetation in the QTP remains unclear. Accordingly, we used multisource remote-sensing vegetation indices (GIMMS-LAI, GIMMS NDVI, GLOBMAP LAI, MODIS EVI, MODIS NDVI, and MODIS NIRv), climate data, a digital elevation model, and the moving window method to investigate the changes in vegetation greenness and its response to climate variations in the QTP from 2001 to 2016. Results showed that the vegetation was greening in the QTP, which might be attributed to the increases in temperature and radiation. By contrast, the browning of vegetation may be caused by drought. Notably, the spatial patterns of vegetation greenness and its variations were linearly correlated with climate at low altitudes, and vegetation greenness was non-linearly correlated with climate at high altitudes. The Northwestern QTP needs to be focused on in regard to positive and decreased VGEG (vegetation greenness along the elevation gradient). The significantly positive VGEG was up to (34.37 ± 2.21) % of the QTP, which indicated a homogenization of vegetation greenness on elevation. This study will help us to understand the spatial distribution of vegetation greenness and VGEG in the QTP under global warming, and it will benefit ecological environment management, policymaking, and future climate and carbon sink (source) prediction.

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening.

Citrus greening is a severe disease significantly affecting citrus production in the United States because the disease is not curable with currently available technologies. For this reason, monitoring citrus disease in orchards is critical to eradicate and replace infected trees before the spread of the disease. In this study, the canopy shape and vegetation indices of infected and healthy orange trees were compared to better understand their significant characteristics using unmanned aerial vehicle (UAV)-based multispectral images. Individual citrus trees were identified using thresholding and morphological filtering. The UAV-based phenotypes of each tree, such as tree height, crown diameter, and canopy volume, were calculated and evaluated with the corresponding ground measurements. The vegetation indices of infected and healthy trees were also compared to investigate their spectral differences. The results showed that correlation coefficients of tree height and crown diameter between the UAV-based and ground measurements were 0.7 and 0.8, respectively. The UAV-based canopy volume was also highly correlated with the ground measurements (R2 > 0.9). Four vegetation indices—normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), modified soil adjusted vegetation index (MSAVI), and chlorophyll index (CI)—were significantly higher in healthy trees than diseased trees. The RedEdge-related vegetation indices showed more capability for citrus disease monitoring. Additionally, the experimental results showed that the UAV-based flush ratio and canopy volume can be valuable indicators to differentiate trees with citrus greening disease.

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint. [ABSTRACT FROM AUTHOR]

The Yarlung Zangbo River basin is an important alley to transport moisture from the Indian Ocean to the inner Tibetan Plateau. With a wide range of elevations from 147 m to over 7000 m above sea level (a.s.l.), ecosystems respond differently to climate change at various elevations. However, the pattern of elevation-dependent vegetation change and how it responds to recent warming have been rarely reported. Here, we investigated the pattern of vegetation greening at different elevations in this river basin using SPOT normalized difference vegetation index (NDVI) data during 1999–2013, and examined its relationship with elevation-dependent changes in temperature and precipitation. The annual NDVI has increased by 8.83% from 1999 to 2013. In particular, the NDVI increased more apparently at lower elevations, but remained relatively stable or even decreased at high elevations. It seems that rising temperature has driven the basin-wide vegetation greening, but the greening rate is in contrast to the pattern of elevation-dependent warming (EDW) with more significant temperature increase at higher elevations. It appears that decreasing precipitation does not reverse the overall increasing trend in NDVI, but relatively limited precipitation (4000 m).

The Sahel, a semi-arid climatic zone with highly seasonal and erratic rainfall, experienced severe droughts in the 1970s and 1980s. Based on remote sensing vegetation indices since early 1980, a clear greening trend is found, which can be attributed to the recovery of contemporaneous precipitation. Here, we present an analysis using long-term leaf area index (LAI), precipitation, and sea surface temperature (SST) records to investigate their trends and relationships. LAI and precipitation show a significant positive trend between 1982 and 2016, at 1.72 × 10 −3 yr −1 (p < 0.01) and 4.63 mm yr−1 (p < 0.01), respectively. However, a piecewise linear regression approach indicates that the trends in both LAI and precipitation are not continuous throughout the 35 year period. In fact, both the greening and wetting of the Sahel have been leveled off (pause of rapid growth) since about 1999. The trends of LAI and precipitation between 1982 and 1999 and 1999–2016 are 4.25 × 10 − 3 yr −1 to − 0.27 × 10 −3 yr −1, and 9.72 mm yr −1 to 2.17 mm yr −1, respectively. These declines in trends are further investigated using an SST index, which is composed of the SSTs of the Mediterranean Sea, the subtropical North Atlantic, and the global tropical oceans. Causality analysis based on information flow theory affirms this precipitation stabilization between 2003 and 2014. Our results highlight that both the greening and the wetting of the Sahel have been leveled off, a feature that was previously hidden in the apparent long-lasting greening and wetting records since the extreme low values in the 1980s.

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.

Accurate detection and quantification of regional vegetation trends are essential for understanding the dynamics of landscape ecology and vegetation distribution. We applied a comprehensive trend analysis to satellite data to describe geospatial changes in vegetation along the Pacific slope of Peru and northern Chile, from sea level to the continental divide, a region characterised by biologically unique and highly sensitive arid and semi-arid environments. Our statistical analyses show broad regional patterns of positive trends in EVI, called "greening", alongside patterns of "browning", where trends are negative between 2000 and 2020. The coastal plain and foothills, up 1000 m, contain notable greening of the coastal Lomas and newly irrigated agricultural lands occurring alongside browning trends related to changes in land use practices and urban development. Strikingly, the precordilleras show a distinct 'greening strip', which extends from approximately 6°S to 22°S, with an altitudinal trend, ascending from the tropical lowlands (170–780 m) in northern Peru to the subtropics (1000–2800 m) in central Peru and temperate zone (2600–4300 m) in southern Peru and northern Chile. We find that the geographical characteristics of the greening strip do not match climate zones previously established by Köppen and Geiger. Greening and browning trends in the coastal deserts and the high Andes lie within well defined climatic and life zones, producing variable but identifiable trends. However, the distinct Pacific slope greening presents an unexpected distribution with respect to the regional Köppen–Geiger climate and life zones. This work provides insights on understanding the effects of climate change on Peru's diverse ecosystems in highly sensitive, biologically unique arid and semi-arid environments on the Pacific slope. [ABSTRACT FROM AUTHOR]

Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a−1 to 0.0152 a−1. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology.

The “warm-humid” climate change across the Tibetan Plateau (TP) has promoted grassland growth and an overall greening trend has been observed by remote sensing products. Many of the current generations of Earth System Models (ESMs) incorporate advanced process-based vegetation growth in the land surface module that can simulate vegetation growth, but the evaluation of their performance has not received much attention, especially over hot spots where projections of the future climate and vegetation growth are greatly needed. In this study, we compare the leaf area index (LAI) simulations of 35 ESMs that participated in CMIP6 to a remote-sensing-derived LAI product (GLASS LAI). The results show that about 40% of the models overestimated the Tibetan Plateau’s greening, 48% of the models underestimated the greening, and 11% of the models showed a declining LAI trend. The CMIP6 models generally produced poor simulations of the spatial distribution of LAI trend, and overestimated the LAI trend of alpine vegetation, grassland, and forest, but underestimated meadow and shrub. Compared with other vegetation types, simulations of the forest LAI trend were the worst, the declining trend in forest pixels on the TP was generally underestimated, and the greening of the meadow was underestimated as well. However, the greening of the grassland, was greatly overestimated. For the Tibetan Plateau’s averaged LAI, more than 70% of the models overestimated this during the growing seasons of 1981–2014. Similar to the forest LAI trend, the performance of the forest LAI simulation was the worst among the different vegetation types, and the forest LAI was underestimated as well.

Satellite observations have revealed strong land surface "greening" (i.e., increases in vegetation greenness or leaf area index (LAI)) in the Northern Hemisphere over the past few decades. European terrestrial ecosystems are a greening hotspot, but how they respond to land surface greening, climate change, CO2 fertilization, land use and land cover change (LULCC) and other factors is unclear. Here, we assessed how these interacting factors might be combined to alter terrestrial gross primary production (GPP) throughout Europe during the period of 2001 to 2016 using a process-based Farquhar GPP model (i.e., FGM). We found a more productive European terrestrial ecosystem and most of the GPP enhancement in Europe was explained by increases in LAI (62%) and atmospheric CO2 concentration (29%). Spatially, the spatial signature of the LAI and GPP trends both suggested widespread (72–73% of the vegetated area) greening phenomena across Europe, among which 23.7% and 13.3% were statistically significant (p < 0.05). The interannual trend of GPP estimated by the FGM (0.55% yr−1) was reasonable compared with other GPP products (0.47% yr−1 to 0.92% yr−1) and the observed LAI increasing rate (0.62% yr−1). FGM factorial simulations suggested that land surface greening (+35.5 Pg C yr−2, p < 0.01), CO2 fertilization (+16.9 Pg C yr−2, p < 0.01), temperature warming (+3.7 Pg C yr−2, p < 0.05), and enhanced downwards solar radiation (+1.2 Pg C yr−2, p > 0.05) contributed to the GPP enhancement, while the enhanced vapour pressure deficit (−5.6 Tg C yr−2, p < 0.01) had significant negative impacts on GPP, especially in 2006 and 2012, when extreme droughts struck south-eastern Europe. Meanwhile, approximately 1.8% of the total area of Europe experienced LULCC from 2001 to 2016 and LULCC exerted a small but significant (−1.3 Tg C yr−2, p < 0.01) impact on GPP due to decreases in the total number of vegetated pixels (−159 pixels yr−1). Although the LULCC effect was negative, the largest increase occurred in forested land (+0.9% of total area). In addition, the increasing trends for the annual mean LAI (0.01 m2 m−2 yr−1, p < 0.001) and total GPP (22.2 Tg C yr−2, p < 0.001) of forests were more significant and higher than those of other vegetation types, suggesting that European forests may continue to play important roles in combating climate change in the future with long-lasting carbon storage potential. These results provide the first systematic quantitative analysis of the driving force of enhanced gross carbon assimilation by European ecosystems by considering variations in leaf physiological traits with environmental adaptations. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Global dryland areas are vulnerable to climate change and anthropogenic activities, making it essential to understand the primary drivers and quantify their effects on vegetation growth. In this study, we used the Time Series Segmented Residual Trends (TSS-RESTREND) method to attribute changes in vegetation to CO 2 , land use, climate change, and climate variability in Chinese and American dryland areas. Our analysis showed that both Chinese and American drylands have undergone a greening trend over the past four decades, with Chinese greening likely linked to climatic warming and humidification of Northwest China. Climate change was the dominant factor driving vegetation change in China, accounting for 48.3%, while CO 2 fertilization was the dominant factor in American drylands, accounting for 47.9%. However, land use was the primary factor resulting in desertification in both regions. Regional analysis revealed the importance of understanding the drivers of vegetation change and land degradation in Chinese and American drylands to prevent desertification. These findings highlight the need for sustainable management practices that consider the complex interplay of climate change, land use, and vegetation growth in dryland areas. [ABSTRACT FROM AUTHOR]

Arid ecosystems have exhibited greening trends in recent decades. There is no consensus on how underlying surface changes influence streamflow across vegetation gradients. We investigated this issue for the four typical arid mountain basins using a 30-year runoff database and the Budyko framework to quantify the contributions of climate and underlying surface changes to streamflow variations during summer periods. Results showed that in the poor vegetated basins, i.e., Heizi Basin and Kuche Basin, the underlying surface change has increased summer streamflow by 14.01 and 35.67 mm, respectively; climate contributed only −7.32 and 1.86 mm to summer streamflow changes, respectively. Comparatively, in the well-vegetated basins, i.e., Huangshui Basin and Kaidu Basin, climate change dominated summer streamflow variations by increasing 21.50 and 24.65 mm, respectively; the underlying surface change only increased summer streamflow by 3.72 and 1.56 mm, respectively. Additionally, the decomposition results were extended to monthly scale (from June to September) to reveal the effects of climate and underlying surface changes on monthly streamflow. This study deepens our knowledge of runoff responses, which can provide important references to support water resources management in other regions that receive water from mountains.

The Qinghai–Tibet Plateau (QTP) is ecologically fragile and is especially sensitive to climate change. Previous studies have shown that the vegetation on the QTP is undergoing overall greening with variations along altitudinal gradients. However, the mechanisms that cause the differences in the spatiotemporal patterns of vegetation greening among different types of terrain and vegetation have not received sufficient attention. Therefore, in this study, we used a Landsat NDVI time-series for the period 1992–2020 and climate data to observe the effects of terrain and vegetation types on the spatiotemporal patterns in vegetation greening on the QTP and to analyze the factors driving this greening using the geographical detector and the velocity of the vertical movement of vegetation greenness isolines. The results showed the following: (1) The vertical movement of the vegetation greenness isolines was affected by the temperature and precipitation at all elevations. The precipitation had a more substantial effect than the temperature below 3000 m. In contrast, above 3000 m, the temperature had a greater effect than the precipitation. (2) The velocity of the vertical movement of the vegetation greenness isolines of woody plants was higher than that of herbaceous plants. (3) The influence of slope on the vertical movement of vegetation greenness isolines was more significant than that of the aspect. The results of this study provided details of the spatiotemporal differences in vegetation greening between different types of terrain and vegetation at a 30-m scale as well as of the underlying factors driving this greening. These results will help to support ecological protection policies on the QTP.

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area’s vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection.

The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4% of the parcels had to be excluded, although these represent less than 3% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution.

Rain Use Efficiency (RUE), defined as Aboveground Net Primary Production (ANPP) divided by rainfall, is increasingly used to diagnose land degradation. Yet, the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region. The debate is fueled by methodological issues, especially when using satellite remote sensing data to estimate ANPP, and by differences in the ecological interpretation. An alternative method which solves part of these issues relies on the residuals of ANPP regressed against rainfall (“ANPP residuals”). In this paper, we use long-term field observations of herbaceous vegetation mass collected in the Gourma region in Mali together with remote sensing data (GIMMS-3g Normalized Difference Vegetation Index) to estimate ANPP, RUE, and the ANPP residuals, over the period 1984–2010. The residuals as well as RUE do not reveal any trend over time over the Gourma region, implying that vegetation is resilient over that period, when data are aggregated at the Gourma scale. We find no conflict between field-derived and satellite-derived results in terms of trends. The nature (linearity) of the ANPP/rainfall relationship is investigated and is found to have no impact on the RUE and residuals interpretation. However, at odds with a stable RUE, an increased run-off coefficient has been observed in the area over the same period, pointing towards land degradation. The divergence of these two indicators of ecosystem resilience (stable RUE) and land degradation (increasing run-off coefficient) is referred to as the “second Sahelian paradox”. When shallow soils and deep soils are examined separately, high resilience is diagnosed on the deep soil sites. However, some of the shallow soils show signs of degradation, being characterized by decreasing vegetation cover and increasing run-off coefficient. Such results show that contrasted changes may co-exist within a region where a strong overall re-greening pattern is observed, highlighting that both the scale of observations and the scale of the processes have to be considered when performing assessments of vegetation changes and land degradation.

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a−1 to 0.0152 a−1. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology. [ABSTRACT FROM AUTHOR]

In recent years, the conversion of saline-alkali land to rice fields has become the most dominant land use change feature in western Jilin, leading to significant surface greening. Saline–alkali land and paddy fields have distinct surface biophysical properties; however, there is a lack of systematic assessment of the moderating effect of planting rice on saline–alkali land on regional climate by changing surface properties. In this paper, multiscale data on the surface temperature of saline–alkali land and paddy fields were obtained using 1 km MODIS product, 30 m Landsat 8 satellite imagery and centimeter-scale UAV imagery in Da'an City, western Jilin as the study area, and the various characteristics of the surface temperature of saline-alkali land and paddy fields in different months of the year and at different times of the day were analyzed. Furthermore, the effect of rice cultivation in saline–alkali land on the local surface temperature was assessed using a space-for-time approach. The results based on satellite observations including both MODIS and Landsat showed that the surface temperature of saline–alkali land was significantly higher than that of paddy fields during the crop growing season, especially in July and August. The high temporal resolution MODIS LST data also indicated the paddy fields cool the daytime surface temperature, while warming the nighttime surface temperature, which was in contrast for saline–alkali land during the growing season. High-resolution UAV observations in July confirmed that the cooling effect of paddy fields was most significant at the middle of day. From the biophysical perspective, the reclamation of saline–alkali land into paddy fields leads to an increase in leaf area index, followed by a significant increase in evapotranspiration. Meanwhile, rice cultivation in saline–alkali land reduces surface albedo and increases surface net radiation. The trade-off relationship between the two determines the seasonal difference in the surface temperature response of saline–alkali land for rice cultivation. At the same time, the daily cycle of crop evapotranspiration and the thermal insulation effect of paddy fields at night are the main reasons for the intraday difference in surface temperature between saline–alkali land and paddy field. Based on the multiscale assessment of the impact of rice cultivation in saline-alkali land on surface temperature, this study provides a scientific basis for predicting future regional climate change and comprehensively understanding the ecological and environmental benefits of saline–alkali land development. [ABSTRACT FROM AUTHOR]

The "warm-humid" climate change across the Tibetan Plateau (TP) has promoted grassland growth and an overall greening trend has been observed by remote sensing products. Many of the current generations of Earth System Models (ESMs) incorporate advanced process-based vegetation growth in the land surface module that can simulate vegetation growth, but the evaluation of their performance has not received much attention, especially over hot spots where projections of the future climate and vegetation growth are greatly needed. In this study, we compare the leaf area index (LAI) simulations of 35 ESMs that participated in CMIP6 to a remote-sensing-derived LAI product (GLASS LAI). The results show that about 40% of the models overestimated the Tibetan Plateau's greening, 48% of the models underestimated the greening, and 11% of the models showed a declining LAI trend. The CMIP6 models generally produced poor simulations of the spatial distribution of LAI trend, and overestimated the LAI trend of alpine vegetation, grassland, and forest, but underestimated meadow and shrub. Compared with other vegetation types, simulations of the forest LAI trend were the worst, the declining trend in forest pixels on the TP was generally underestimated, and the greening of the meadow was underestimated as well. However, the greening of the grassland, was greatly overestimated. For the Tibetan Plateau's averaged LAI, more than 70% of the models overestimated this during the growing seasons of 1981–2014. Similar to the forest LAI trend, the performance of the forest LAI simulation was the worst among the different vegetation types, and the forest LAI was underestimated as well. [ABSTRACT FROM AUTHOR]