Consistent response of vegetation dynamics to recent climate change in tropical mountain regions

Global climate change has emerged as a major driver of ecosystem change. Here, we present evidence for globally consistent responses in vegetation dynamics to recent climate change in the world's mountain ecosystems located in the pan‐tropical belt (30°N–30°S). We analyzed decadal‐scale trends and seasonal cycles of vegetation greenness using monthly time series of satellite greenness (Normalized Difference Vegetation Index) and climate data for the period 1982–2006 for 47 mountain protected areas in five biodiversity hotspots. The time series of annual maximum NDVI for each of five continental regions shows mild greening trends followed by reversal to stronger browning trends around the mid‐1990s. During the same period we found increasing trends in temperature but only marginal change in precipitation. The amplitude of the annual greenness cycle increased with time, and was strongly associated with the observed increase in temperature amplitude. We applied dynamic models with time‐dependent regression parameters to study the time evolution of NDVI–climate relationships. We found that the relationship between vegetation greenness and temperature weakened over time or was negative. Such loss of positive temperature sensitivity has been documented in other regions as a response to temperature‐induced moisture stress. We also used dynamic models to extract the trends in vegetation greenness that remain after accounting for the effects of temperature and precipitation. We found residual browning and greening trends in all regions, which indicate that factors other than temperature and precipitation also influence vegetation dynamics. Browning rates became progressively weaker with increase in elevation as indicated by quantile regression models. Tropical mountain vegetation is considered sensitive to climatic changes, so these consistent vegetation responses across widespread regions indicate persistent global‐scale effects of climate warming and associated moisture stresses.     

Spatial heterogeneity of tundra vegetation response to recent temperature changes

The spatial heterogeneity of recent decadal dynamics in vegetation greenness and biomass in response to changes in summer warmth index (SWI) was investigated along spatial gradients on the Arctic Slope of Alaska. Image spatial analysis was used to examine the spatial pattern of greenness dynamics from 1991 to 2000 as indicated by variations of the maximum normalized difference vegetation index (Peak NDVI) and time‐integrated NDVI (TI‐NDVI) along latitudinal gradients. Spatial gradients for both the means and temporal variances of the NDVI indices for 0.1° latitude intervals crossing three bioclimate subzones were analyzed along two north–south Arctic transects. NDVI indices were generally highly variable over the decade, with great heterogeneity across the transects. The greatest variance in TI‐NDVI was found in low shrub vegetation to the south (68.7–68.8°N) and corresponded to high fractional cover of shrub tundra and moist acidic tundra (MAT), while the greatest variance in Peak‐NDVI predominately occurred in areas dominated by wet tundra (WT) and moist nonacidic tundra (MNT). Relatively high NDVI temporal variances were also related to specific transitional areas between dominant vegetation types. The regional temporal variances of NDVI from 1991 to 2000 were largely driven by meso‐scale climate dynamics. The spatial heterogeneity of the NDVI variance was mostly explained by the fractional land cover composition, different responses of each vegetation type to climate change, and patterned ground features. Aboveground plant biomass exhibited similar spatial heterogeneity as TI‐NDVI; however, spatial patterns are slightly different from NDVI because of their nonlinear relationship.     

Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change

Field observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi‐arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large‐scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. The net global figure – considered over the full length of the time series – showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short‐term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long‐term NDVI time series.     

Future changes and driving factors of global peak vegetation growth based on CMIP6 simulations

Accurate detection and attribution of changes in global peak vegetation growth at the annual scale are prerequisites for characterising the productivity of terrestrial ecosystems and developing strategies for the sustainable management of ecosystems. This study examined the long-term global normalised difference vegetation index during the baseline period (1982–2015) and found widespread greening in 70% of global vegetated areas in response to climate warming. However, climate change is not the only cause of global greening. The spatial variability in the response of global vegetation to environmental factors has not been well established. The Cubist model was used to investigate the relationship between peak vegetation growth and environmental variables. The results showed that 64% of the spatial variation in greening/browning can be explained by climate (including precipitation and temperature), followed by atmospheric components of nitrogen deposition and carbon dioxide concentration (17%), terrain properties (12%), and soil properties (7%). By incorporating future climate and atmospheric component projections from the Coupled Model Intercomparison Project Phase 6 into the model, enhanced vegetation greening was predicted globally, particularly in evergreen needle-leaf forests and grasslands, from 2081 to 2100. Many browning changes were predicted in evergreen and deciduous broadleaf forests, mixed forests, and around areas influenced by human land use. Overall, these findings reveal that environmental factors have relevant integrated impacts on vegetation dynamics under climate change and should be considered during the design of local mitigation and adaptation management strategies.     

Bioclimatic analysis in a region of southern Italy (Calabria)

In this study, an analysis of precipitation and temperature data has been performed over 67 series observed in a region of southern Italy (Calabria). At first, to detect possible trends in the time series, an analysis was performed with the Mann–Kendall non-parametric test applied at monthly and seasonal scale. An additional investigation, useful for checking the climate change effects on vegetation, has also been included analysing bioclimatic indicators. In particular, Emberger, Rivas-Martinez and De Martonne indices were calculated by using monthly temperature and precipitation data in the period 1916–2010. The spatial pattern of the indices has been evaluated and, in order to link the vegetation and the indices, different indices maps have been intersected with the land cover data, given by the Corine Land Cover map. Moreover, the temporal evolution of the indices and of the vegetation has been analysed. Results suggest that climate change may be responsible for the forest cover change, but, given also the good relationship between the various types of bioclimate and forest formations, human activities must be considered.     

Human modification of the landscape and surface climate in the next fifty years

Human modification of the landscape potentially affects exchanges of energy and water between the terrestrial biosphere and the atmosphere. This study develops a possible scenario for land cover in the year 2050 based on results from the IMAGE 2 (Integrated Model to Assess the Greenhouse Effect) model, which projects land‐cover changes in response to demographic and economic activity. We use the land‐cover scenario as a surface boundary condition in a biophysically‐based land‐surface model coupled to a general circulation model for a 15‐years simulation with prescribed sea surface temperature and compare with a control run using current land cover. To assess the sensitivity of climate to anthropogenic land‐cover change relative to the sensitivity to decadal‐scale interannual variations in vegetation density, we also carry out two additional simulations using observed normalized difference vegetation index (NDVI) from relatively low (1982–83) and high (1989–90) years to describe the seasonal phenology of the vegetation. In the past several centuries, large‐scale land‐cover change occurred primarily in temperate latitudes through conversion of forests and grassland to highly productive cropland and pasture. Several studies in the literature indicate that past changes in surface climate resulting from this conversion had a cooling effect owing to changes in vegetation morphology (increased albedo). In contrast, this study indicates that future land‐cover change, likely to occur predominantly in the tropics and subtropics, has a warming effect governed by physiological rather than morphological mechanisms. The physiological mechanism is to reduce carbon assimilation and consequently latent relative to sensible heat flux resulting in surface temperature increases up to 2 °C and drier hydrologic conditions in locations where land cover was altered in the experiment. In addition, in contrast to an observed decrease in diurnal temperature range (DTR) over land expected with greenhouse warming, results here suggest that future land‐cover conversion in tropics could increase the DTR resulting from decreased evaporative cooling during the daytime. For grid cells with altered land cover, the sensitivity of surface temperature to future anthropogenic land‐cover change is generally within the range induced by decadal‐scale interannual variability in vegetation density in temperate latitudes but up to 1.5 °C warmer in the tropics.     

Zones of influence for soil organic matter dynamics: A conceptual framework for data and models

Soil organic matter (SOM) is an indicator of sustainable land management as stated in the global indicator framework of the United Nations Sustainable Development Goals (SDG Indicator 15.3.1). Improved forecasting of future changes in SOM is needed to support the development of more sustainable land management under a changing climate. Current models fail to reproduce historical trends in SOM both within and during transition between ecosystems. More realistic spatio‐temporal SOM dynamics require inclusion of the recent paradigm shift from SOM recalcitrance as an ‘intrinsic property’ to SOM persistence as an ‘ecosystem interaction’. We present a soil profile, or pedon‐explicit, ecosystem‐scale framework for data and models of SOM distribution and dynamics which can better represent land use transitions. Ecosystem‐scale drivers are integrated with pedon‐scale processes in two zones of influence. In the upper vegetation zone, SOM is affected primarily by plant inputs (above‐ and belowground), climate, microbial activity and physical aggregation and is prone to destabilization. In the lower mineral matrix zone, SOM inputs from the vegetation zone are controlled primarily by mineral phase and chemical interactions, resulting in more favourable conditions for SOM persistence. Vegetation zone boundary conditions vary spatially at landscape scales (vegetation cover) and temporally at decadal scales (climate). Mineral matrix zone boundary conditions vary spatially at landscape scales (geology, topography) but change only slowly. The thicknesses of the two zones and their transport connectivity are dynamic and affected by plant cover, land use practices, climate and feedbacks from current SOM stock in each layer. Using this framework, we identify several areas where greater knowledge is needed to advance the emerging paradigm of SOM dynamics—improved representation of plant‐derived carbon inputs, contributions of soil biota to SOM storage and effect of dynamic soil structure on SOM storage—and how this can be combined with robust and efficient soil monitoring.     

Anticipating the spatio‐temporal response of plant diversity and vegetation structure to climate and land use change in a protected area

Vegetation is a key driver of ecosystem functioning (e.g. productivity and stability) and of the maintenance of biodiversity (e.g. creating habitats for other species groups). While vegetation sensitivity to climate change has been widely investigated, its spatio‐temporally response to the dual effects of land management and climate change has been ignored at landscape scale. Here we use a dynamic vegetation model called FATE‐HD, which describes the dominant vegetation dynamics and associated functional diversity, in order to anticipate vegetation response to climate and land‐use changes in both short and long‐term perspectives. Using three contrasted management scenarios for the Ecrins National Park (French Alps) developed in collaboration with the park managers, and one regional climate change scenario, we tracked the dynamics of vegetation structure (forest expansion) and functional diversity over 100 yr of climate change and a further 400 additional years of stabilization. As expected, we observed a slow upward shift in forest cover distribution, which appears to be severely impacted by pasture management (i.e. maintenance or abandonment). The time lag before observing changes in vegetation cover was the result of demographic and seed dispersal processes. However, plant diversity response to environmental changes was rapid. After land abandonment, local diversity increased and spatial turnover was reduced, whereas local diversity decreased following land use intensification. Interestingly, in the long term, as both climate and management scenarios interacted, the regional diversity declined. Our innovative spatio‐temporally explicit framework demonstrates that the vegetation may have contrasting responses to changes in the short and the long term. Moreover, climate and land‐abandonment interact extensively leading to a decrease in both regional diversity and turnover in the long term. Based on our simulations we therefore suggest a continuing moderate intensity pasturing to maintain high levels of plant diversity in this system.     

Projecting future fire activity in Amazonia

Fires are major disturbances for ecosystems in Amazonia. They affect vegetation succession, alter nutrients and carbon cycling, and modify the composition of the atmosphere. Fires in this region are strongly related to land‐use, land‐cover and climate conditions. Because these factors are all expected to change in the future, it is reasonable to expect that fire activity will also change. Models are needed to quantitatively estimate the magnitude of these potential changes. Here we present a new fire model developed by relating satellite information on fires to corresponding statistics on climate, land‐use and land‐cover. The model is first shown to reproduce the main contemporary large‐scale features of fire patterns in Amazonia. To estimate potential changes in fire activity in the future, we then applied the model to two alternative scenarios of development of the region. We find that in both scenarios, substantial changes in the frequency and spatial patterns of fires are expected unless steps are taken to mitigate fire activity.     

Spatial co‐localisation of extreme weather events: a clear and present danger

Extreme weather events have become a dominant feature of the narrative surrounding changes in global climate with large impacts on ecosystem stability, functioning and resilience; however, understanding of their risk of co‐occurrence at the regional scale is lacking. Based on the UK Met Office’s long‐term temperature and rainfall records, we present the first evidence demonstrating significant increases in the magnitude, direction of change and spatial co‐localisation of extreme weather events since 1961. Combining this new understanding with land‐use data sets allowed us to assess the likely consequences on future agricultural production and conservation priority areas. All land‐uses are impacted by the increasing risk of at least one extreme event and conservation areas were identified as the hotspots of risk for the co‐occurrence of multiple event types. Our findings provide a basis to regionally guide land‐use optimisation, land management practices and regulatory actions preserving ecosystem services against multiple climate threats.     

Satellite‐based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air‐photo studies have documented recent changes in high‐latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24‐year (1986–2010) Landsat time series in a latitudinal transect across the boreal forest‐tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last 40 years. Using a per‐pixel (30 m) trend analysis, 30% of the observable (cloud‐free) land area experienced a significant (P < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak‐summer conditions. The average NDVI trend (0.007 yr^?1) corresponds to a leaf‐area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer. Across the entire transect, the area‐averaged LAI increase was ~0.2 during 1986–2010. A higher area‐averaged LAI change (~0.3) within the shrub‐tundra portion of the transect represents a 20–60% relative increase in LAI during the last two decades. Our Landsat‐based analysis subdivides the overall high‐latitude greening trend into changes in peak‐summer greenness by cover type. Different responses within and among shrub, graminoid, and tree‐dominated cover types in this study indicate important fine‐scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low‐biomass vegetation types over multi‐decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.     

Extensive land cover change across Arctic–Boreal Northwestern North America from disturbance and climate forcing

A multitude of disturbance agents, such as wildfires, land use, and climate‐driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic–Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high‐latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large‐scale land cover changes in the Arctic–Boreal region (ABR) have been poorly characterized. Here, we use 31 years (1984–2014) of moderate spatial resolution (30 m) satellite imagery over a region spanning 4.7 × 10⁶ km² in Alaska and northwestern Canada to characterize regional‐scale ABR land cover changes. We find that 13.6 ± 1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance‐driven decreases in Evergreen Forest area (?14.7 ± 3.0% relative to 1984) and increases in Deciduous Forest area (+14.8 ± 5.2%) in the Boreal biome; and (b) climate‐driven expansion of Herbaceous and Shrub vegetation (+7.4 ± 2.0%) in the Arctic biome. By using time series of 30 m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate‐induced growth that are not observable in coarse spatial resolution (e.g., 500 m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO₂ seasonality and ecosystem productivity at northern high‐latitudes and signal continental‐scale shifts in the structure and function of northern high‐latitude ecosystems in response to climate change.     

Defining the importance of including transient ecosystem responses to simulate C‐cycle dynamics in a global change model

The ability of plant species to migrate is one of the critical issues in assessing accurately the future response of the terrestrial biosphere to climate change. This ability is confined by both natural and human‐induced changes in land cover. In this paper we present land‐cover and Carbon (C) cycle models designed to simulate the biospheric consequences of different types of land‐cover changes. These models, imbedded in the larger integrated assessment model IMAGE 2, were used to demonstrate the importance of considering spatial aspects for global C‐cycle modelling. A gradual‐migration, an unlimited‐migration and a no‐migration case were compared to show the range of possible consequences. Major differences between these cases were simulated for land‐cover patterns and the carbon budget. A large geographical variation in the biospheric response was also simulated. The strongest response was simulated in high‐latitude regions, especially for the migration cases in which land‐cover changes were permitted. In low‐latitudes regions the differences between the migration cases were smaller, mainly due to the effects of land‐use changes. The geographical variation among, and the different responses, the migration cases clearly demonstrate how essential it is to assess biospheric responses to climate change and land use simultaneously. Moreover, it also shows the urgent need for enhanced understanding of spatial and temporal dynamics of the biospheric responses.     

Changes in habitat suitability influence non‐breeding distribution of waterbirds in central Europe

Waterbird species have different requirements with respect to their non‐breeding areas, aiming to survive and gain condition during the non‐breeding period. Selection of non‐breeding areas could change over time and space driven by climate change and species habitat requirements. To help explain the mechanism shaping non‐breeding area selection, we provide site‐specific analyses of distributional changes in wintering waterbirds in central Europe, located at the centre of their flyways. We use wintering waterbirds as a highly dynamic model group monitored over a long‐time scale of 50 years (1966–2015). We identified species habitat requirements and changes in habitat use at the level of 733 individual non‐breeding (specifically wintering) sites for 12 waterbird species using citizen‐science monitoring data. We calculated site‐specific mean numbers and estimated site‐specific trends in numbers. The site‐specific approach revealed a general effect of mean winter temperature of site (seven of 12 species), wetland type (all species) and land cover (all species) on site‐specific numbers. We found increasing site‐specific trends in numbers in the northern and/or eastern part of the study area (Mute Swan Cygnus olor, Eurasian Teal Anas crecca, Common Pochard Aythya ferina, Great Cormorant Phalacrocorax carbo and Eurasian Coot Fulica atra). Common Merganser Mergus merganser, Great Cormorant, Grey Heron Ardea cinerea, Common Pochard, Eurasian Coot and Common Moorhen Galinulla chloropus increased their site‐specific numbers on standing industrial waters with traditionally low fish stock. The site‐specific dynamics of bird numbers helped us to identify general preference for sites reducing winter harshness (warmer areas, running waters and more wetlands in the site vicinity), as well as indicating climate‐driven changes in spatial use of wintering sites (northern/north‐eastern range changes and changes in preference for industrial waters). This fine‐scale (site‐specific) approach can reveal large‐scale range and distribution shifts driven by climate and environmental changes regardless of the availability of large‐scale datasets.     

Degraded rangeland dominated by unpalatable forbs exhibits large-scale spatial heterogeneity

The spatial heterogeneity of vegetation and soil increases in response to land degradation caused by grazing mainly at a large spatial scale. This increase has been frequently associated with shrub invasion, but shrub invasion does not necessarily accompany land degradation. Instead, dominance by unpalatable forbs has been reported in some regions, but the spatial heterogeneity of such degraded rangeland has not been studied. We investigated the spatial heterogeneity of rangeland dominated by unpalatable forbs at a large spatial scale using Mongolian rangeland as an example. Spatial heterogeneity of the total vegetation cover and community heterogeneity were analyzed for three levels of land degradation. We found that the least-degraded site had homogeneous total vegetation cover and community, that the site with intermediate degradation exhibited low heterogeneity of the total vegetation cover but significant community type variation, and that the most degrade sites exhibited a periodic pattern of total vegetation cover as a result of a mixture of dense and sparse patches of unpalatable forbs. These different responses can be used to assess land degradation levels and may have potential to monitor land degradation at a large scale by satellite images.     

Agriculture rivals biomes in predicting global species richness

Species–area relationships (SARs) provide an avenue to model patterns of species richness and have recently been shown to vary substantially across regions of different climate, vegetation, and land cover. Given that a large proportion of the globe has been converted to agriculture, and considering the large variety in agricultural management practices, a key question is whether global SARs vary across gradients of agricultural intensity. We developed SARs for mammals that account for geographic variation in biomes, land cover and a range of land‐use intensity indicators representing inputs (e.g. fertilizer, irrigation), outputs (e.g. yields) and system‐level measures of intensity (e.g. human appropriation of net primary productivity – HANPP). We systematically compared the resulting SARs in terms of their predictive ability. Our global SAR with a universal slope was significantly improved by the inclusion of any one of the three variable types: biomes, land cover, and land‐use intensity. The latter, in the form of human appropriation of net primary productivity (HANPP), performed as well as biomes and land‐cover in predicting species richness. Other land‐use intensity indicators had a lower predictive ability. Our main finding that land‐use intensity performs as well as biomes and land cover in predicting species richness emphasizes that human factors are on a par with environmental factors in predicting global patterns of biodiversity. While our broad‐scale study cannot establish causality, human activity is known to drive species richness at a local scale, and our findings suggest that this may hold true at a global scale. The ability of land‐use intensity to explain variation in SARs at a global scale had not previously been assessed. Our study suggests that the inclusion of land‐use intensity in SAR models allows us to better predict and understand species richness patterns.     

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: ?6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: ?47.9%, ?41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

中国遥感干旱指数时空特征及其对气候和地表覆盖变化的响应

干旱严重影响植被生长,威胁粮食安全,基于遥感计算的植被状态指数（Vegetation Condition Index,VCI）、温度状态指数（Temperature Condition Index,TCI）和植被健康指数（Vegetation Health Index,VHI）是常用的干旱指数,被广泛应用于干旱监测。为了探究近年来我国干旱特征及其对气候和地表覆盖变化的响应,分析了2003-2016年期间VCI、TCI和VHI的时空变化特征;采用最小二乘（OLS）和偏相关分析方法分析了这些指数对气候和地表覆盖变化的响应。基于上述干旱指数计算的干旱频率表明,中温带中部和南温带等地区干旱发生频率高,干旱指数变化趋势表明在2003-2016年期间中国大部分地区干旱缓解,但在中温带、南温带和高原气候区等局部地区干旱加剧;总体而言,干旱指数随着年平均温度的上升和年降水量的降低而减小,VHI与温度和降水量的相关性在不同气候区的一致性优于VCI和TCI;裸土的减少和植被的增加导致干旱指数增大,树木转变为低矮植被干旱指数降低。