Trends In Vegetation Greenness Research Articles

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

IntroductionThe vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.MethodsThis study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.ResultsThe study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Hydrological Factor and Land Use/Land Cover Change Explain the Vegetation Browning in the Dosso Reserve, Niger

The West Sahel is facing significant threats to its vegetation and wildlife due to the land degradation and habitat fragmentation. It is crucial to assess the regional vegetation greenness dynamics in order to comprehensively evaluate the effectiveness of protection in the natural reserves. This study analyzes the vegetation greenness trends and the driving factors in the Dosso Partial Faunal Reserve in Niger and nearby unprotected regions—one of the most important habitats for endemic African fauna—using satellite time series data from 2001 to 2020. An overall vegetation browning trend was observed throughout the entire region with significant spatial variability. Vegetation browning dominated in the Dosso Reserve with 17.7% of the area showing a significant trend, while the area with significant greening was 6.8%. In a comparison, the nearby unprotected regions to the north and the east were found to be dominated by vegetation browning and greening, respectively. These results suggest that the vegetation protection practice was not fully effective throughout the Dosso Reserve. The dominant drivers were also diagnosed using the Random Forest model-based method and the Partial Dependence Plot tool, showing that water availability (expressed as soil moisture) and land use/land cover change were the most critical factors affecting vegetation greenness in the study region. Specifically, soil moisture stress and specific land management practices associated with logging, grazing, and land clearing appeared to dominate vegetation browning in the Dosso Reserve. In contrast, the vegetation greening in the central Dosso Reserve and the nearby unprotected region to the east was probably caused by the increase in shrubland/forest, which was related to the effective implementation of protection. These findings improve our understanding of how regional vegetation greenness dynamics respond to environmental changes in the Dosso Reserve and also highlight the need for more effective conservation planning and implementation to ensure sustainable socio-ecological development in the West Sahel.

Conflicting Changes of Vegetation Greenness Interannual Variability on Half of the Global Vegetated Surface

AbstractChanges in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.

Long-term trends of vegetation greenness under different urban development intensities in 889 global cities

Long-term trends in urban vegetation greenness were systematically analyzed. However, long-term trends in vegetation greenness under different urban development intensities (UDIs) in global cities remain poorly understood. In this study, exurban, suburban, urban and urban core areas were defined according to their UDI in 2001. Subsequently, changes in the UDI and long-term trends in vegetation greenness were analyzed in these four areas in 889 global cities from 2001 to 2019. African and Asian cities have witnessed rapid urbanization, whereas European cities have experienced slow urbanization. The UDI increased significantly in suburban, exurban and urban areas, whereas it remained stable in urban core areas. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) increased in all four areas in European cities but decreased in all four areas of the African cities. The EVI and NDVI decreased in suburban and urban areas due to urbanization, but increased in exurban, and urban core areas. Changes in UDI were an important driver of EVI and NDVI trends in exurban and suburban areas. The results of this study enhance our understanding of long-term trends in vegetation greenness in urban and surrounding areas.

Drought offsets the vegetation greenness-induced gross primary productivity from 1982 to 2018 in China

Recent studies have shown that climate stress may cause asynchronous changes between greenness and productivity, but the mechanism how greenness and productivity respond to drought remains unclear. The significant greening of vegetation in China has attracted worldwide attention; nevertheless, it is unclear whether large-scale greening will translate into productivity in the context of recent droughts. In this study, we used the vegetation index dataset to extract long-term trends in vegetation greenness and productivity from 1982 to 2018, and then chose the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) index to measure how vegetation greenness and productivity change in response to drought. The results show that significant vegetation greening did not cause a simultaneous increase in productivity during 1982–2018. Vegetation greenness and productivity trends were relatively consistent before 2000 and diverged after 2000, especially during the prolonged drought of 2000–2009. The opposite trend between greenness and productivity in humid ecosystems during drought periods mainly caused this trend difference. In humid ecosystems, short-term drought promotes vegetation greening, while long-term drought has no significant impact on greenness. However, the inhibitory effect of drought on productivity deepens with an increase in drought duration. Therefore, prolonged droughts offset vegetation greenness-induced gross primary productivity. We also found that prolonged drought causes a significant decrease in greenness but has little effect on productivity in arid ecosystems, possibly because vegetation productivity is more sensitive to soil water than atmospheric drought. Our results highlight the complexity of vegetation growth in the context of climate change. Furthermore, it is crucial to pay attention to the impact of climate stress on regional carbon sequestration functions, considering the significant carbon sink potential of forest areas in southern China.

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes.

Shifts in the trends of vegetation greenness and photosynthesis in different parts of Tibetan Plateau over the past two decades

Ecosystems on the Tibetan Plateau (TP) are fragile and sensitive to climate change and thus provide insight into the climate regulations on vegetation structure and function. In this study, we utilized the Normalized Difference Vegetation Index (NDVI) and solar-induced chlorophyll fluorescence (SIF) to explore vegetation dynamics during the growing season from 2000 to 2021, and further investigated how climate change has influenced them. Based on both NDVI and SIF, significant vegetation greening trends were observed in northeastern TP across the study period due to continuous increases in both temperature and water availability. Browning trends were observed in southwestern TP due to decreasing precipitation and enhanced warming stress during 2000–2007, but the trends reversed after 2007. In the southeastern region, we also detected a turning point of a more prominent greening trend in 2016. Moreover, changes in SIF exhibited greater magnitude compared to NDVI, implying vegetation photosynthesis is more sensitive to climate change than greenness, potentially due to variations in light use efficiency. This study strengthens our understanding of climate regulations on vegetation structure and function under different hydrothermal conditions and highlights the need for additional research on the differences between greenness and photosynthesis.

Different trends of vegetation activity over northern extratropics during two multidecadal warming periods in the 20th century

There have been two periods (1916–1945 and 1982–2014) of rapid and comparable rates (0.054 °C/year vs. 0.055 °C/year) of increase in Global Mean Temperature (GMT). However, little is known about different trends in vegetation activity over the northern extratropics during these two periods, especially for vegetation dynamics in response to the earlier global warming in the 20th century. In this study, we find that although the global warming rates are comparable between the two warming periods, regional warming over the northern extratopics has significant differences. Meanwhile, vegetation greening trends are significantly different, with the latter period being 3–4 times greater than the former. Further analysis of the leading causes reveals that the impacts of the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) on regional climate are critical to these differences. In 1916–1945, the in-phase combination of AMO and PDO offsets each other's impact on the climate factors and weakens the greening trend, while in 1982–2014, the effects of AMO and PDO are synergetic for the out-of-phase combination and enhance the greening trend. Our findings highlight the role of internal climate variability in modulating vegetation dynamics under global warming and suggest that future projections of vegetation change may be improved by considering decadal and multidecadal climate oscillations.

Changes in vegetation greenness related to climatic and non-climatic factors in the Sudano-Sahelian region

The potential drivers of vegetation changes in the Sudano-Sahelian region of Africa remain poorly understood due to complex interactions between climatic and anthropogenic processes. In this study, we analyzed the vegetation greenness trends in relation to rainfall variability that we considered the essence of climatic effects on vegetation in a well-known water-limited environment by using time series of satellite data in the Sudano-Sahelian region during 2001–2020. We quantified in more detail the relative contributions of rainfall variability (climatic factor), land use/land cover (LULC) change, and fire occurrence change (non-climatic factors) to vegetation greenness trends in selected sub-regions. The results showed that vegetation greening was widespread (26.9% of the total study area), while vegetation browning was more clustered in central West Africa (5% of the total study area). About half of the vegetation greening area can be explained by long-term rainfall variability during 2001–2020, but most of the area characterized by a browning trend was unrelated to rainfall variability. An analysis of the relative importance showed that LULC changes had significant local effects on vegetation greenness and that these changes were characterized by a strong spatial heterogeneity in specific sub-regions. Gains in cropland and natural vegetation related to positive land management were probably the dominant drivers of greening in Senegal and Ethiopia. Also, the combined impacts of rainfall variability and LULC changes contributed to greening trends in the arid zone, particularly in Mali and Sudan. In contrast, vegetation browning in central West Africa appeared to be driven by cropland gain and natural vegetation loss associated with extensive agricultural production activities. Furthermore, we found that repeated fires for agricultural expansion in central West Africa intensified vegetation browning. These results advanced our understanding of vegetation dynamics in response to climatic and non-climatic factors in Sudano-Sahelian drylands characterized by increasing pressures on land resources.

Coarse spatial resolution remote sensing data with AVHRR and MODIS miss the greening area compared with the Landsat data in Chinese drylands.

The warming-wetting climates in Chinese drylands, together with a series of ecological engineering projects, had caused apparent changes to vegetation therein. Regarding the vegetation greening trend, different remote sensing data had yielded distinct findings. It was critical to evaluate vegetation dynamics in Chinese drylands using a series of remote sensing data. By comparing the three most commonly used remote sensing datasets [i.e., MODIS, Advanced Very High Resolution Radiometer (AVHRR), and Landsat], this study comprehensively investigated vegetation dynamics for Chinse drylands. All three remote sensing datasets exhibited evident vegetation greening trends from 2000 to 2020 in Chinese drylands, especially in the Loess Plateau and Northeast China. However, Landsat identified the largest greening areas (89.8%), while AVHRR identified the smallest greening area (58%). The vegetation greening areas identified by Landsat comprise more small patches than those identified by MODIS and AVHRR. The MODIS data exhibited a higher consistency with Landsat than with AVHRR in terms of detecting vegetation greening areas. The three datasets exhibited high consistency in identifying vegetation greening in Northeast China, Loess Plateau, and Xinjiang. The percentage of inconsistent areas among the three datasets was 39.56%. The vegetation greening areas identified by Landsat comprised more small patches. Sensors and the atmospheric effect are the two main reasons responsible for the different outputs from each NDVI product. Ecological engineering projects had a great promotion effect on vegetation greening, which can be detected by the three NDVI datasets in Chinese drylands, thereby combating desertification and reducing dust storms.

Quantifying the nonlinear response of vegetation greening to driving factors in Longnan of China based on machine learning algorithm

The main influencing factors and their nonlinear effects on the changes of vegetation in China’s mountainous areas under the interaction of different factors are not yet clear, and comprehending the evolutionary trends and driving mechanisms of vegetation is crucial to reveal the changes in ecosystem structure and function. In this study, trend analysis (M−K, T-S and EEMD) combined with machine learning algorithm, namely Boosted Regression Tree model (BRT), were used to quantify the trends of nonlinear responses and thresholds for bioclimatic variables, topography, soil properties and anthropogenic factors for vegetation changes in Longnan. The results showed that the trend analysis clearly confirm the increasing trend of vegetation at multiple spatio-temporal scales. The BRT indicated that total precipitation (bio12, 15.22%), land use (LUCC, 12.68%), elevation (DEM, 11.20%), and population density (Pd, 9.20%) were the more important factors of dominant vegetation greening. Bioclimatic variables were found to revealed the effects of climate with vegetation more clearly. In addition, the BRT revealed that the selected factors have the different nonlinear response relationships to vegetation greening trend and specific thresholds. Among them, increasing of cropland, grassland and forestland can promote vegetation greening. However, GDP, Pd, DEM, bio12, mean diurnal range and temperature seasonality (bio2, bio4) exceed the threshold can significantly inhibit vegetation growth. The BRT combined with trend analysis revealed the nonlinear response relationships and thresholds of the drivers behind the vegetation change patterns, which have obvious effects in exploring the driving mechanisms of vegetation changes in mountainous areas. This study provided an important reference for better revealing the interaction mechanisms between vegetation changes and drivers in the semi-humid zone in East Asia even globally.

Climate Change and CO2 Fertilization Have Played Important Roles in the Recent Decadal Vegetation Greening Trend on the Chinese Loess Plateau

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.

Competition between biogeochemical drivers and land-cover changes determines urban greening or browning

Urban vegetation, a harbinger of future global vegetation change, is controlled by complex urban environments. The urban-rural gradient in vegetation greenness trends and their responses to biogeochemical drivers (e.g. elevated atmospheric CO2 concentration and climate warming) and land-cover changes, however, remain unclear. Here we used satellite-derived enhanced vegetation index to examine the greenness trends for 1500-plus cities in China for 2000–2019. We developed a conceptual framework to differentiate between the contributions of four key drivers to the greenness trends: two biogeochemical drivers, a background biogeochemical driver (BBD) and an urban biogeochemical driver (UBD), and two drivers of land-cover changes, urban expansion or densification (UED) and urban green recovery (UGR). We find that the greening trends gradually decreased from urban cores to urban new towns and then to browning trends in urban fringes. The significant greening in urban cores was mainly contributed by BBD (25.6%) and UBD (52.3%). While the minor greening in urban new towns was contributed by both BBD (33.1%) and UBD (24.1%) and weakened by UED (−39.7%). The UED (−64.4%) dominated the browning in urban fringes. These results suggest that biogeochemical drivers and land-cover changes jointly regulated the urban-rural gradient in greenness trends, which contributes to the assessment of future global vegetation change driven by complex environmental changes.

Reponses of Land Surface Albedo to Global Vegetation Greening: An Analysis Using GLASS Data

Global vegetation greening during recent decades has been observed from various remote sensing data. The global and regional climate can be altered by an increase in carbon storage, as well as changes in land surface albedo (LSA) and evaporation. However, the LSA changes induced by global vegetation greening are still not clear, and contrasting responses of LSA to vegetation changes were reported in previous studies. In this study, we analyzed the LSA in response to global vegetation greening using the Global Land Surface Satellite (GLASS) data and a vegetation-induced LSA change model. The results showed that vegetation greening trends could be observed worldwide, which resulted in contrasting LSA responses at regional scales (LSA increased as leaf area index (LAI) increased, or LSA decreased as LAI increased). Moreover, these contrasting LSA responses to global vegetation greening were effectively explained by the albedo difference between a vegetation and soil background. The results provide new insights into the relationship between LSA changes and global vegetation dynamics, and can support recommendations for policies of vegetation protection, and large-scale afforestation and deforestation.

Trend of Vegetation and Environmental Factors and Their Feedback in the Karst Regions of Southwestern China

Vegetation plays a vital role in the terrestrial ecosystem. Vegetation variations not only result from climatic and environmental change but also feed back to the climate through biogeochemical and biogeophysical processes. Previous studies have mainly focused on the influence of environmental factors on vegetation changes, but the interactions between vegetation and the prevalent environmental factors in the karst areas of southwestern China have been poorly understood. Based on remote sensing data, this study used trend analysis and Granger causality analysis to investigate vegetation trends, the driving factors, and their interactions during the period 2001 to 2021. In summary, we explored how these factors influenced vegetation growth and how vegetation changes fed back to produce ecosystems and environmental variations in southwestern China during this period. The results showed improvements in water conditions as well as a vegetation greening trend in most of the regions of southwestern China, together with a weakening trend of rocky desertification and an increasing trend of vegetation growth during the period. Both terrestrial water storage and vegetation growth in the energy-limited alpine regions are sensitive to temperature. Natural restoration is suggested in this area. Vegetation growth in the karst areas is sensitive to water stress-related variables due to the particular geological and soil characteristics. The bidirectional causality relationship between vegetation greening and the environment factors in many of these areas indicates that the vegetation changes can also significantly affect water balance and conditions. Ecological engineering projects are suggested in this area. The vegetation growth in the SC Basin, HN, HB, GD, and eastern GX is also sensitive to water stress, while these regions are vulnerable to waterlogging. This study helps to improve our understanding of ecosystem management and promote sustainable development in southwestern China.

Trends in Forest Greening and Its Spatial Correlation with Bioclimatic and Environmental Factors in the Greater Mekong Subregion from 2001 to 2020

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.

Quantifying the direct effects of long-term dynamic land use intensity on vegetation change and its interacted effects with economic development and climate change in jiangsu, China

Vegetation change reflects sensitive responses of ecosystem environment to global climate change as well as land use. It is well known that land use type and its transformation affect vegetation change. However, how the changes in land use intensity (LUI) within different land use types impact vegetation and the interactions with other drivers remain poorly understood. We measured the LUI of Jiangsu Province, China, within the main land use types in 1995, 2000, 2005, 2010, 2015 and 2018 by combining remote sensing-based land use data with representative county scale economic and social indicators. Structural equation models (SEMs) were built to quantify the influences of long term LUI on vegetation change interacting with economic development, climate change and topographical conditions in transformed land, cropland, rural settlements and urbanized land, respectively. Seventy percent of significant vegetation change existed in non-transformed land use types. Although the area with a vegetation greening trend is larger than that with a vegetation browning trend, the vegetation browning areas is prominent in urbanized lands and some croplands in south basins. The constructed SEMs suggested the dominant negative effect of fast economic development regardless of land use types, while LUI played important and different direct and indirect effects on affecting vegetation change significantly interacting with economic development and climate change in different land use types. The LUI increasing led a vegetation greening in cropland, and stronger than climate warming with both positive direct and indirect effects for influencing climate change. The LUI change took negative effects on vegetation change in rural and urban areas, while a positive indirect effect of LUI increasing in urbanized land signaled the positive results of human managements. We then provided some land use-specific suggestions on basin scale for land management in Jiangsu. Our results highlight the necessity of long-term LUI quantification and promote the understanding of its effects on vegetation change interacted with other drivers within different land use types. This can be very helpful for sustainable land use and managements in regions with fast economic development.

Managing nature-based solutions in fire-prone ecosystems: Competing management objectives in California forests evaluated at a landscape scale

California’s cap-and-trade compliance offset market incentivizes forest managers to maintain elevated carbon stocks. It provides these incentives without enforcing standardized fire mitigation practices despite many projects being located in fire prone regions. Here, we evaluated the difference between management actions in California forests that participated in the carbon offset market versus those that engaged with state programs to reduce wildfire risk via fuel reduction treatments. Using remotely sensed data from the California Forest Observatory and the Moderate Resolution Imaging Spectroradiometer, we compared the vertical forest structure and vegetation canopy trends on forest offsets with forests that are receiving fuel treatment. We found California forests managed for carbon under the Improved Forest Management (IFM) program by the California Air Resources Board had higher levels of biomass than forests managed for fire risk reduction as indicated by 2016 lidar-estimated fuel loads. In addition, IFM-participating forests did not reduce their fuel loads between 2016 and 2020, whereas lands receiving grants for fuel management did, indicating that on average, the IFM projects were not engaging in fuel reduction efforts. However, despite the differences in fuel management between IFM projects and active fuel treatments, we found that both types of management saw a declining trend in vegetation greenness between 2015 and 2021. While declining greenness is expected of active fuel treatments associated with vegetation removal, such a trend in the case of IFM indicates additional wildfire risk. Managing forests for long-term carbon storage and sequestration requires consideration of fire risk mitigation. Given the little evidence of fuel reduction in the first decade of IFM projects implementation we question whether the century-long duration of carbon stocks in these offsets is realistic. We recommend that policymakers reevaluate the incentives directed at carbon stock preservation or expansion to better encompass the growing wildfire risk in California.

Optimality principles explaining divergent responses of alpine vegetation to environmental change.

Recent increases in vegetation greenness over much of the world reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (Fmax ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year-1 in drier regions and a browning of 0.12 ± 0.08% year-1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in Fmax with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year-1 ) and browning (0.07 ± 0.06% year-1 ). We also predicted the observed reduced sensitivities of Fmax to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces Fmax in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO2 stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

Quantifying the impacts of opencast mining on vegetation dynamics over eastern India using the long-term Landsat-series satellite dataset

Enhanced spatio-temporal and up-to-date information on vegetation dynamics at various spatial scales are imperative in understanding the human, biosphere, and atmosphere interactions. Thus, the present study attempts to derive the vegetation greenness trends with the medium spatial resolution (30 m) satellite data at the regional scale with the support of Google Earth Engine (GEE) cloud platform. The long-term Landsat series satellite dataset was employed to characterize vegetation greenness trends using the Mann-Kendall test over the mining-dominated regions of Eastern India (Jharkhand and Odisha states) for two study periods, viz. earlier (1988–2004) and later (2000−2020). The key findings revealed that ∼1285 km2 (2.97%) and 1688 km2 (3.91%) areas over Jharkhand state and ∼ 5213 km2 (5.68%) and 2940 km2 (3.20%) areas over Odisha state showed the negative vegetation greenness trend (indicative of decreasing vegetation activity) during 1988–2004 and 2000–2020, respectively. It was observed that the major anthropogenic activities, particularly opencast mining, are the major factor for vegetation degradation in Jharkhand and Odisha states, contributing to ∼3–5.7% vegetation degradation during the study periods. The negative vegetation greenness trend patches were mainly observed in mining sites, settlement encroachments, construction sites, roadways, logging sites, etc. The drastic rise in the intensity of mining activities in the last two decades (2000–2020) has led to massive vegetation destruction compared to the earlier period (1988–2004). Furthermore, the key climatic parameters (i.e., precipitation, temperature, downward radiation, and soil moisture) have less control over the long-term vegetation greenness trends in the mining-dominated regions (∼ 27%) in contrast to forest regions (∼ 47%). The findings of the study shall be helpful to the policy-makers, stakeholders, environmentalists, and government bodies to formulate and implement various sustainable development programs in the mining-dominated regions to ensure ecological conservation and enhance ecological services.