@misc{SmithZottaBoultonetal.2023,
  author    = {Smith, Taylor and Zotta, Ruxandra-Maria and Boulton, Chris A. and Lenton, Timothy M. and Dorigo, Wouter and Boers, Niklas},
  title     = {Reliability of resilience estimation based on multi-instrument time series},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1322},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-58912},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589120},
  pages     = {173 -- 183},
  year      = {2023},
  abstract  = {Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process - rather than actual changes in the dynamical properties of the system - is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.},
  language  = {en}
}
@article{SmithZottaBoultonetal.2023,
  author    = {Smith, Taylor and Zotta, Ruxandra-Maria and Boulton, Chris A. and Lenton, Timothy M. and Dorigo, Wouter and Boers, Niklas},
  title     = {Reliability of resilience estimation based on multi-instrument time series},
  series = {Earth System Dynamics},
  volume    = {14},
  journal   = {Earth System Dynamics},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {2190-4987},
  doi       = {10.5194/esd-14-173-2023},
  pages     = {173 -- 183},
  year      = {2023},
  abstract  = {Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process - rather than actual changes in the dynamical properties of the system - is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.},
  language  = {en}
}
@misc{SmithRheinwaltBookhagen2019,
  author    = {Smith, Taylor and Rheinwalt, Aljoscha and Bookhagen, Bodo},
  title     = {Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {725},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43016},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-430165},
  pages     = {475 -- 489},
  year      = {2019},
  abstract  = {Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m-2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.},
  language  = {en}
}
@article{SmithBookhagenCannon2015,
  author    = {Smith, Taylor and Bookhagen, Bodo and Cannon, Forest},
  title     = {Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia},
  series = {The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union},
  volume    = {9},
  journal   = {The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union},
  number    = {5},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1994-0416},
  doi       = {10.5194/tc-9-1747-2015},
  pages     = {1747 -- 1759},
  year      = {2015},
  abstract  = {Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity. In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10\% of glacier areas, as compared to a similar to 750 glacier control data set, and can reliably classify a given Landsat scene in 3-5 min. The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data.},
  language  = {en}
}
@article{SmithRheinwaltBookhagen2019,
  author    = {Smith, Taylor and Rheinwalt, Aljoscha and Bookhagen, Bodo},
  title     = {Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset},
  series = {Earth Surface Dynamics},
  volume    = {7},
  journal   = {Earth Surface Dynamics},
  publisher = {Copernicus Publ.},
  address   = {G{\"o}ttingen},
  issn      = {2196-6311},
  doi       = {10.5194/esurf-7-475-2019},
  pages     = {475 -- 489},
  year      = {2019},
  abstract  = {Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m-2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.},
  language  = {en}
}
@article{SmithBookhagenRheinwalt2017,
  author    = {Smith, Taylor and Bookhagen, Bodo and Rheinwalt, Aljoscha},
  title     = {Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016},
  series = {The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union},
  volume    = {11},
  journal   = {The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union},
  issn      = {1994-0416},
  doi       = {10.5194/tc-11-2329-2017},
  pages     = {2329 -- 2343},
  year      = {2017},
  abstract  = {High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.},
  language  = {en}
}
@misc{SmithBookhagenRheinwalt2017,
  author    = {Smith, Taylor and Bookhagen, Bodo and Rheinwalt, Aljoscha},
  title     = {Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-403911},
  pages     = {15},
  year      = {2017},
  abstract  = {High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.},
  language  = {en}
}
@misc{AtmaniBookhagenSmith2022,
  author    = {Atmani, Farid and Bookhagen, Bodo and Smith, Taylor},
  title     = {Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1275},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-56991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-569915},
  pages     = {20},
  year      = {2022},
  abstract  = {The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.},
  language  = {en}
}
@article{SmithRheinwaltBookhagen2021,
  author    = {Smith, Taylor and Rheinwalt, Aljoscha and Bookhagen, Bodo},
  title     = {Topography and climate in the upper Indus Basin},
  series = {The science of the total environment : an international journal for scientific research into the environment and its relationship with man},
  volume    = {786},
  journal   = {The science of the total environment : an international journal for scientific research into the environment and its relationship with man},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0048-9697},
  doi       = {10.1016/j.scitotenv.2021.147363},
  pages     = {11},
  year      = {2021},
  abstract  = {The Upper Indus Basin (UIB), which covers a wide range of climatic and topographic settings, provides an ideal venue to explore the relationship between climate and topography. While the distribution of snow and glaciers is spatially and temporally heterogeneous, there exist regions with similar elevation-snow relationships. In this work, we construct elevation-binned snow-cover statistics to analyze 3415 watersheds and 7357 glaciers in the UIB region. We group both glaciers and watersheds using a hierarchical clustering approach and find that (1) watershed clusters mirror large-scale moisture transport patterns and (2) are highly dependent on median watershed elevation. (3) Glacier clusters are spatially heterogeneous and are less strongly controlled by elevation, but rather by local topographic parameters that modify solar insolation. Our clustering approach allows us to clearly define self-similar snow-topographic regions. Eastern watersheds in the UIB show a steep snow cover-elevation relationship whereas watersheds in the central and western UIB have moderately sloped relationships, but cluster in distinct groups. We highlight this snow-cover-topographic transition zone and argue that these watersheds have different hydrologic responses than other regions. Our hierarchical clustering approach provides a potential new framework to use in defining climatic zones in the cyrosphere based on empirical data.},
  language  = {en}
}
@article{SmithBookhagen2021,
  author    = {Smith, Taylor and Bookhagen, Bodo},
  title     = {Climatic and biotic controls on topographic asymmetry at the global scale},
  series = {Journal of geophysical research : JGR, Earth surface},
  volume    = {126},
  journal   = {Journal of geophysical research : JGR, Earth surface},
  number    = {1},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-9003},
  doi       = {10.1029/2020JF005692},
  pages     = {24},
  year      = {2021},
  abstract  = {Insolation differences play a primary role in controlling microclimate and vegetation cover, which together influence the development of topography. Topographic asymmetry (TA), or slope differences between terrain aspects, has been well documented in small-scale, field-based, and modeling studies. Here we combine a suite of environmental (e.g., vegetation, temperature, solar insolation) and topographic (e.g., elevation, drainage network) data to explore the driving mechanisms and markers of TA on a global scale. Using a novel empirical TA analysis method, we find that (1) steeper terrain has higher TA magnitudes, (2) globally, pole-facing terrain is on average steeper than equator-facing terrain, especially in mid-latitude, tectonically quiescent, and vegetated landscapes, and (3) high-elevation and low-temperature regions tend to have terrain steepened toward the equator. We further show that there are distinct differences in climate and vegetation cover across terrain aspects, and that TA is reflected in the size and form of fluvial drainage networks. Our work supports the argument that insolation asymmetries engender differences in local microclimates and vegetation on opposing terrain aspects, which broadly encourage the development of asymmetric topography across a range of lithologic, tectonic, geomorphic, and climatic settings.},
  language  = {en}
}
@article{AtmaniBookhagenSmith2022,
  author    = {Atmani, Farid and Bookhagen, Bodo and Smith, Taylor},
  title     = {Measuring vegetation heights and their seasonal changes in the Western Namibian Savanna using spaceborne lidars},
  series = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  volume    = {14},
  journal   = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  number    = {12},
  edition   = {12},
  publisher = {MDPI},
  address   = {Basel, Schweiz},
  issn      = {2072-4292},
  doi       = {10.3390/rs14122928},
  pages     = {1 -- 20},
  year      = {2022},
  abstract  = {The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.},
  language  = {en}
}
@misc{SmithBookhagen2020,
  author    = {Smith, Taylor and Bookhagen, Bodo},
  title     = {Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1020},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-48417},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-484176},
  pages     = {15},
  year      = {2020},
  abstract  = {High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987-2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987-1997, to much more positive trends across large regions of HMA during the periods 1997-2007 and 2007-2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.},
  language  = {en}
}
@article{SmithBookhagen2020,
  author    = {Smith, Taylor and Bookhagen, Bodo},
  title     = {Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data},
  series = {Frontiers in Earth Science},
  volume    = {8},
  journal   = {Frontiers in Earth Science},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2296-6463},
  doi       = {10.3389/feart.2020.559175},
  pages     = {13},
  year      = {2020},
  abstract  = {High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987-2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987-1997, to much more positive trends across large regions of HMA during the periods 1997-2007 and 2007-2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.},
  language  = {en}
}
@article{SmithBookhagenRheinwalt2017,
  author    = {Smith, Taylor and Bookhagen, Bodo and Rheinwalt, Aljoscha},
  title     = {identified with an automated snowmelt detection algorithm, 1987-2016},
  series = {The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union},
  volume    = {11},
  journal   = {The Cryosphere : TC ; an interactive open access journal of the European Geosciences Union},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1994-0416},
  doi       = {10.5194/tc-11-2329-2017},
  pages     = {2329 -- 2343},
  year      = {2017},
  abstract  = {High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n\&\#8201;=\&\#8201;2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade\&\#8722;1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.},
  language  = {en}
}
@misc{SmithBookhagenCannon2015,
  author    = {Smith, Taylor and Bookhagen, Bodo and Cannon, Forest},
  title     = {Improving semi-automated glacier mapping with a multi-method approach},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {510},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-40847},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408471},
  pages     = {13},
  year      = {2015},
  abstract  = {Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity. In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10\% of glacier areas, as compared to a similar to 750 glacier control data set, and can reliably classify a given Landsat scene in 3-5 min. The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data.},
  language  = {en}
}
@article{SmithBookhagen2016,
  author    = {Smith, Taylor and Bookhagen, Bodo},
  title     = {Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia},
  series = {Remote sensing of environment : an interdisciplinary journal},
  volume    = {181},
  journal   = {Remote sensing of environment : an interdisciplinary journal},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0034-4257},
  doi       = {10.1016/j.rse.2016.03.037},
  pages     = {174 -- 185},
  year      = {2016},
  abstract  = {Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite - sensors the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987-2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E, 2002-2011), AMSR2 (2012-2015), and the Global Precipitation Measurement (GPM, 2014-2015) - in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multiyear snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (<5\%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8\%), followed by maximal multi-year snow depth (7.82\%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64\%), 25th percentile DJF 10-m wind speed (5.44\%), and hillslope angle (5.24\%). Elevation, relief, and terrain aspect show very low influence on SWE variability (<1\%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. (C) 2016 Elsevier Inc. All rights reserved.},
  language  = {en}
}
@misc{SmithBookhagen2018,
  author    = {Smith, Taylor and Bookhagen, Bodo},
  title     = {Using passive microwave data to understand spatio-temporal trends and dynamics in snow-water storage in High Mountain Asia},
  series = {active and passive microwave remote sensing for environmental monitoring II},
  volume    = {10788},
  journal   = {active and passive microwave remote sensing for environmental monitoring II},
  publisher = {SPIE-INT Soc Optical Engineering},
  address   = {Bellingham},
  isbn      = {978-1-5106-2160-2},
  issn      = {0277-786X},
  doi       = {10.1117/12.2323827},
  pages     = {8},
  year      = {2018},
  abstract  = {High Mountain Asia provides water for more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow - the vast majority of which is not monitored by sparse weather networks. We leverage passive microwave data from the SSMI series of satellites (SSMI, SSMI/S, 1987-2016), reprocessed to 3.125 km resolution, to examine trends in the volume and spatial distribution of snow-water equivalent (SWE) in the Indus Basin. We find that the majority of the Indus has seen an increase in snow-water storage. There exists a strong elevation-trend relationship, where high-elevation zones have more positive SWE trends. Negative trends are confined to the Himalayan foreland and deeply-incised valleys which run into the Upper Indus. This implies a temperature-dependent cutoff below which precipitation increases are not translated into increased SWE. Earlier snowmelt or a higher percentage of liquid precipitation could both explain this cutoff.(1) Earlier work 2 found a negative snow-water storage trend for the entire Indus catchment over the time period 1987-2009 (-4 x 10(-3) mm/yr). In this study based on an additional seven years of data, the average trend reverses to 1.4 x 10(-3). This implies that the decade since the mid-2000s was likely wetter, and positively impacted long-term SWE trends. This conclusion is supported by an analysis of snowmelt onset and end dates which found that while long-term trends are negative, more recent (since 2005) trends are positive (moving later in the year).(3)},
  language  = {en}
}
@article{SmithBookhagen2018,
  author    = {Smith, Taylor and Bookhagen, Bodo},
  title     = {Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009)},
  series = {Science Advances},
  volume    = {4},
  journal   = {Science Advances},
  number    = {1},
  publisher = {American Assoc. for the Advancement of Science},
  address   = {Washington},
  issn      = {2375-2548},
  doi       = {10.1126/sciadv.1701550},
  pages     = {8},
  year      = {2018},
  abstract  = {Snow meltwaters account for most of the yearly water budgets of many catchments in High Mountain Asia (HMA). We examine trends in snow water equivalent (SWE) using passive microwave data (1987 to 2009). We find an overall decrease in SWE in HMA, despite regions of increased SWE in the Pamir, Kunlun Shan, Eastern Himalaya, and Eastern Tien Shan. Although the average decline in annual SWE across HMA (contributing area, 2641 x 10(3) km(2)) is low (average, -0.3\%), annual SWE losses conceal distinct seasonal and spatial heterogeneities across the study region. For example, the Tien Shan has seen both strong increases in winter SWE and sharp declines in spring and summer SWE. In the majority of catchments, the most negative SWE trends are found in mid-elevation zones, which often correspond to the regions of highest snow-water storage and are somewhat distinct from glaciated areas. Negative changes in SWE storage in these mid-elevation zones have strong implications for downstream water availability.},
  language  = {en}
}
@phdthesis{Smith2018,
  author    = {Smith, Taylor},
  title     = {Decadal changes in the snow regime of High Mountain Asia, 1987-2016},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407120},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 142},
  year      = {2018},
  abstract  = {More than a billion people rely on water from rivers sourced in High Mountain Asia (HMA), a significant portion of which is derived from snow and glacier melt. Rural communities are heavily dependent on the consistency of runoff, and are highly vulnerable to shifts in their local environment brought on by climate change. Despite this dependence, the impacts of climate change in HMA remain poorly constrained due to poor process understanding, complex terrain, and insufficiently dense in-situ measurements. HMA's glaciers contain more frozen water than any region outside of the poles. Their extensive retreat is a highly visible and much studied marker of regional and global climate change. However, in many catchments, snow and snowmelt represent a much larger fraction of the yearly water budget than glacial meltwaters. Despite their importance, climate-related changes in HMA's snow resources have not been well studied. Changes in the volume and distribution of snowpack have complex and extensive impacts on both local and global climates. Eurasian snow cover has been shown to impact the strength and direction of the Indian Summer Monsoon -- which is responsible for much of the precipitation over the Indian Subcontinent -- by modulating earth-surface heating. Shifts in the timing of snowmelt have been shown to limit the productivity of major rangelands, reduce streamflow, modify sediment transport, and impact the spread of vector-borne diseases. However, a large-scale regional study of climate impacts on snow resources had yet to be undertaken. Passive Microwave (PM) remote sensing is a well-established empirical method of studying snow resources over large areas. Since 1987, there have been consistent daily global PM measurements which can be used to derive an estimate of snow depth, and hence snow-water equivalent (SWE) -- the amount of water stored in snowpack. The SWE estimation algorithms were originally developed for flat and even terrain -- such as the Russian and Canadian Arctic -- and have rarely been used in complex terrain such as HMA. This dissertation first examines factors present in HMA that could impact the reliability of SWE estimates. Forest cover, absolute snow depth, long-term average wind speeds, and hillslope angle were found to be the strongest controls on SWE measurement reliability. While forest density and snow depth are factors accounted for in modern SWE retrieval algorithms, wind speed and hillslope angle are not. Despite uncertainty in absolute SWE measurements and differences in the magnitude of SWE retrievals between sensors, single-instrument SWE time series were found to be internally consistent and suitable for trend analysis. Building on this finding, this dissertation tracks changes in SWE across HMA using a statistical decomposition technique. An aggregate decrease in SWE was found (10.6 mm/yr), despite large spatial and seasonal heterogeneities. Winter SWE increased in almost half of HMA, despite general negative trends throughout the rest of the year. The elevation distribution of these negative trends indicates that while changes in SWE have likely impacted glaciers in the region, climate change impacts on these two pieces of the cryosphere are somewhat distinct. Following the discussion of relative changes in SWE, this dissertation explores changes in the timing of the snowmelt season in HMA using a newly developed algorithm. The algorithm is shown to accurately track the onset and end of the snowmelt season (70\% within 5 days of a control dataset, 89\% within 10). Using a 29-year time series, changes in the onset, end, and duration of snowmelt are examined. While nearly the entirety of HMA has experienced an earlier end to the snowmelt season, large regions of HMA have seen a later start to the snowmelt season. Snowmelt periods have also decreased in almost all of HMA, indicating that the snowmelt season is generally shortening and ending earlier across HMA. By examining shifts in both the spatio-temporal distribution of SWE and the timing of the snowmelt season across HMA, we provide a detailed accounting of changes in HMA's snow resources. The overall trend in HMA is towards less SWE storage and a shorter snowmelt season. However, long-term and regional trends conceal distinct seasonal, temporal, and spatial heterogeneity, indicating that changes in snow resources are strongly controlled by local climate and topography, and that inter-annual variability plays a significant role in HMA's snow regime.},
  language  = {en}
}
@misc{HeringHauptfleischJagoetal.2022,
  author    = {Hering, Robert and Hauptfleisch, Morgan and Jago, Mark and Smith, Taylor and Kramer-Schadt, Stephanie and Stiegler, Jonas and Blaum, Niels},
  title     = {Don't stop me now: Managed fence gaps could allow migratory ungulates to track dynamic resources and reduce fence related energy loss},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1278},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-57008},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-570087},
  pages     = {18},
  year      = {2022},
  abstract  = {In semi-arid environments characterized by erratic rainfall and scattered primary production, migratory movements are a key survival strategy of large herbivores to track resources over vast areas. Veterinary Cordon Fences (VCFs), intended to reduce wildlife-livestock disease transmission, fragment large parts of southern Africa and have limited the movements of large wild mammals for over 60 years. Consequently, wildlife-fence interactions are frequent and often result in perforations of the fence, mainly caused by elephants. Yet, we lack knowledge about at which times fences act as barriers, how fences directly alter the energy expenditure of native herbivores, and what the consequences of impermeability are. We studied 2-year ungulate movements in three common antelopes (springbok, kudu, eland) across a perforated part of Namibia's VCF separating a wildlife reserve and Etosha National Park using GPS telemetry, accelerometer measurements, and satellite imagery. We identified 2905 fence interaction events which we used to evaluate critical times of encounters and direct fence effects on energy expenditure. Using vegetation type-specific greenness dynamics, we quantified what animals gained in terms of high quality food resources from crossing the VCF. Our results show that the perforation of the VCF sustains herbivore-vegetation interactions in the savanna with its scattered resources. Fence permeability led to peaks in crossing numbers during the first flush of woody plants before the rain started. Kudu and eland often showed increased energy expenditure when crossing the fence. Energy expenditure was lowered during the frequent interactions of ungulates standing at the fence. We found no alteration of energy expenditure when springbok immediately found and crossed fence breaches. Our results indicate that constantly open gaps did not affect energy expenditure, while gaps with obstacles increased motion. Closing gaps may have confused ungulates and modified their intended movements. While browsing, sedentary kudu's use of space was less affected by the VCF; migratory, mixed-feeding springbok, and eland benefited from gaps by gaining forage quality and quantity after crossing. This highlights the importance of access to vast areas to allow ungulates to track vital vegetation patches.},
  language  = {en}
}
@article{HeringHauptfleischJagoetal.2022,
  author    = {Hering, Robert and Hauptfleisch, Morgan and Jago, Mark and Smith, Taylor and Kramer-Schadt, Stephanie and Stiegler, Jonas and Blaum, Niels},
  title     = {Don't stop me now: Managed fence gaps could allow migratory ungulates to track dynamic resources and reduce fence related energy loss},
  series = {Frontiers in Ecology and Evolution},
  journal   = {Frontiers in Ecology and Evolution},
  publisher = {Frontiers},
  address   = {Lausanne, Schweiz},
  issn      = {2296-701X},
  doi       = {10.3389/fevo.2022.907079},
  pages     = {1 -- 18},
  year      = {2022},
  abstract  = {In semi-arid environments characterized by erratic rainfall and scattered primary production, migratory movements are a key survival strategy of large herbivores to track resources over vast areas. Veterinary Cordon Fences (VCFs), intended to reduce wildlife-livestock disease transmission, fragment large parts of southern Africa and have limited the movements of large wild mammals for over 60 years. Consequently, wildlife-fence interactions are frequent and often result in perforations of the fence, mainly caused by elephants. Yet, we lack knowledge about at which times fences act as barriers, how fences directly alter the energy expenditure of native herbivores, and what the consequences of impermeability are. We studied 2-year ungulate movements in three common antelopes (springbok, kudu, eland) across a perforated part of Namibia's VCF separating a wildlife reserve and Etosha National Park using GPS telemetry, accelerometer measurements, and satellite imagery. We identified 2905 fence interaction events which we used to evaluate critical times of encounters and direct fence effects on energy expenditure. Using vegetation type-specific greenness dynamics, we quantified what animals gained in terms of high quality food resources from crossing the VCF. Our results show that the perforation of the VCF sustains herbivore-vegetation interactions in the savanna with its scattered resources. Fence permeability led to peaks in crossing numbers during the first flush of woody plants before the rain started. Kudu and eland often showed increased energy expenditure when crossing the fence. Energy expenditure was lowered during the frequent interactions of ungulates standing at the fence. We found no alteration of energy expenditure when springbok immediately found and crossed fence breaches. Our results indicate that constantly open gaps did not affect energy expenditure, while gaps with obstacles increased motion. Closing gaps may have confused ungulates and modified their intended movements. While browsing, sedentary kudu's use of space was less affected by the VCF; migratory, mixed-feeding springbok, and eland benefited from gaps by gaining forage quality and quantity after crossing. This highlights the importance of access to vast areas to allow ungulates to track vital vegetation patches.},
  language  = {en}
}
@article{SmithTraxlBoers2022,
  author    = {Smith, Taylor and Traxl, Dominik and Boers, Niklas},
  title     = {Empirical evidence for recent global shifts in vegetation resilience},
  series = {Nature climate change},
  volume    = {12},
  journal   = {Nature climate change},
  number    = {5},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {1758-678X},
  doi       = {10.1038/s41558-022-01352-2},
  pages     = {477 -- 484},
  year      = {2022},
  abstract  = {The authors demonstrate that a vegetation system's ability to recover from disturbances-its resilience-can be estimated from its natural variability. Global patterns of resilience loss and gains since the early 1990s reveal shifts towards widespread resilience loss since the early 2000s. The character and health of ecosystems worldwide is tightly coupled to changes in Earth's climate. Theory suggests that ecosystem resilience-the ability of ecosystems to resist and recover from external shocks such as droughts and fires-can be inferred from their natural variability. Here, we quantify vegetation resilience globally with complementary metrics based on two independent long-term satellite records. We first empirically confirm that the recovery rates from large perturbations can be closely approximated from internal vegetation variability across vegetation types and climate zones. On the basis of this empirical relationship, we quantify vegetation resilience continuously and globally from 1992 to 2017. Long-term vegetation resilience trends are spatially heterogeneous, with overall increasing resilience in the tropics and decreasing resilience at higher latitudes. Shorter-term trends, however, reveal a marked shift towards a global decline in vegetation resilience since the early 2000s, particularly in the equatorial rainforest belt.},
  language  = {en}
}