Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the ...most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.
This paper reports the development of a ∼30 m resolution two‐dimensional hydrodynamic model of the conterminous U.S. using only publicly available data. The model employs a highly efficient numerical ...solution of the local inertial form of the shallow water equations which simulates fluvial flooding in catchments down to 50 km2 and pluvial flooding in all catchments. Importantly, we use the U.S. Geological Survey (USGS) National Elevation Dataset to determine topography; the U.S. Army Corps of Engineers National Levee Database to explicitly represent known flood defenses; and global regionalized flood frequency analysis to characterize return period flows and rainfalls. We validate these simulations against the complete catalogue of Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) maps and detailed local hydraulic models developed by the USGS. Where the FEMA SFHAs are based on high‐quality local models, the continental‐scale model attains a hit rate of 86%. This correspondence improves in temperate areas and for basins above 400 km2. Against the higher quality USGS data, the average hit rate reaches 92% for the 1 in 100 year flood, and 90% for all flood return periods. Given typical hydraulic modeling uncertainties in the FEMA maps and USGS model outputs (e.g., errors in estimating return period flows), it is probable that the continental‐scale model can replicate both to within error. The results show that continental‐scale models may now offer sufficient rigor to inform some decision‐making needs with dramatically lower cost and greater coverage than approaches based on a patchwork of local studies.
Key Points
A 30 m resolution flood hazard model of the entire conterminous United States is built using publicly available data
Delineations of flood hazard are comprehensively validated against United States government agency benchmarks
Model performance is largely comparable to quality local models, offering cheaper hazard information with complete spatial coverage
To embrace the burgeoning land change science studies that exploit public-domain cloud-computing platforms such as Google Earth Engine (GEE), for the first time, we organized a special issue entitled ...“Remote Sensing of Land Change Science with Google Earth Engine” in the journal “Remote Sensing of Environment”. This paper serves as a summary to a collection of 19 papers that have been published since the inception of the special issue in November 2017. In particular, we summarized their contributions with regard to two perspectives: what new themes of questions are articulated, what contributions have been made. Taking account of the disciplinary difference, we carried out the summary separately in two major science domains: Remote Sensing of Environment (RSE), i.e., naturally-induced land change, and Remote Sensing of Society (RSS), i.e., human-induced land change. Furthermore, we presented a historical review of the developments of GEE-relevant studies published before our special issue. Finally, we provided a future prospect on how GEE will continue to evolve to further the study of land change science.
Leaf Area Index (LAI) is a fundamental vegetation biophysical variable serving as an essential input to many land surface and atmospheric models. Long-term LAI maps are typically generated with ...satellite images at moderate spatial resolution (0.25 to 1 km), such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS). While useful for regional-scale land surface modeling, these moderate resolution products often cannot resolve spatial heterogeneity important for many agricultural and hydrological applications. This paper proposes an approach to map LAI at 30-m resolution based on Landsat images for the Contiguous US (CONUS) consistent with the MODIS product, aimed at multi-scale modeling applications. The algorithm was driven by 1.6 million spatially homogeneous samples derived from MODIS LAI and Landsat surface reflectance products from 2006 to 2018. Based on these samples, we trained separate random forest models to estimate LAI from Landsat surface reflectance for eight biomes of the National Land Cover Database (NLCD). A balanced sample design regarding the saturation status of MODIS LAI and a machine-learning-based noise detection technique were introduced to mitigate the trade-off in estimation accuracy between medium LAI (e.g., 3 to 4, unsaturated) and high LAI (e.g., 4–6, saturated).
This approach was evaluated using ground measurements from 19 National Ecological Observatory Network (NEON) sites and eight independent sites from other sources. These sites comprise a representative sample of forests, grasslands, shrublands, and croplands across the US. For NEON sites, the LAI estimates show an overall Root Mean Squared Error (RMSE) of 0.8 with r2 of 0.88. For the eight independent sites, the Landsat LAI algorithm achieves RMSE between 0.52 and 0.91. The uncertainty in Landsat estimated LAI varies across biomes and locations. The proposed algorithm was implemented on the Google Earth Engine platform, allowing for the rapid generation of long-term high-resolution LAI records from the 1980s using Landsat images (code is available at https://github.com/yanghuikang/Landsat-LAI). Our findings also highlight the importance of sample balance on regression-based modeling in remote sensing applications.
•Map Leaf Area Index from Landsat images for CONUS using machine learning.•Generated 1.6 million samples from MODIS LAI and Landsat surface reflectance data.•Balanced sampling and a novel noise detection technique adopted to reduce bias.•Produce MODIS-consistent LAI estimation with improved spatial resolution.•Enable rapid generation of 30-m LAI back to the 1980s using Google Earth Engine.
Terrestrial primary production is a fundamental ecological process and a crucial component in understanding the flow of energy through trophic levels. The global MODIS gross primary production (GPP) ...and net primary production (NPP) products (MOD17) are widely used for monitoring GPP and NPP at coarse resolutions across broad spatial extents. The coarse input datasets and global biome‐level parameters, however, are well‐known limitations to the applicability of the MOD17 product at finer scales. We addressed these limitations and created two improved products for the conterminous United States (CONUS) that capture the spatiotemporal variability in terrestrial production. The MOD17 algorithm was utilized with medium resolution land cover classifications and improved meteorological data specific to CONUS in order to produce: (a) Landsat derived 16‐day GPP and annual NPP at 30 m resolution from 1986 to 2016 (GPPL30 and NPPL30, respectively); and (b) MODIS derived 8‐day GPP and annual NPP at 250 m resolution from 2001 to 2016 (GPPM250 and NPPM250 respectively). Biome‐specific input parameters were optimized based on eddy covariance flux tower‐derived GPP data from the FLUXNET2015 database. We evaluated GPPL30 and GPPM250 products against the standard MODIS GPP product utilizing a select subset of representative flux tower sites, and found improvement across all land cover classes except croplands. We also found consistent interannual variability and trends across NPPL30, NPPM250, and the standard MODIS NPP product. We highlight the application potential of the production products, demonstrating their improved capacity for monitoring terrestrial production at higher levels of spatial detail across broad spatiotemporal scales.
We produced two higher resolution primary production datasets, using better input data than currently existing datasets. These products more closely match the scale of many ecological processes and management activities, and will facilitate better understandings of production dynamics. Our products correspond well with other production datasets at multiple scales. The products fill a critical gap in our ability to monitor and assess terrestrial production dynamics in relation to many ecological processes and land use change. As production is a foundational ecological process and ecosystem service, understanding these dynamics is critical for environmental sustainability.
Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked ...or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales.
We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data.
Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product.
The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.
We model the spatial distribution of snow depth across a wind‐dominated alpine basin using a geostatistical approach with a complex variable mean. Snow depth surveys were conducted at maximum ...accumulation from 1997 through 2003 in the 2.3 km2 Green Lakes Valley watershed in Colorado. We model snow depth as a random function that can be decomposed into a deterministic trend and a stochastic residual. Three snow depth trends were considered, differing in how they model the effect of terrain parameters on snow depth. The terrain parameters considered were elevation, slope, potential radiation, an index of wind sheltering, and an index of wind drifting. When nonlinear interactions between the terrain parameters were included and a multiyear data set was analyzed, all five terrain parameters were found to be statistically significant in predicting snow depth, yet only potential radiation and the index of wind sheltering were found to be statistically significant for all individual years. Of the five terrain parameters considered, the index of wind sheltering was found to have the greatest effect on predicted snow depth. The methodology presented in this paper allows for the characterization of the spatial correlation of model residuals for a variable mean model, incorporates the spatial correlation into the optimization of the deterministic trend, and produces smooth estimate maps that may extrapolate above and below measured values.
Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained ...supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) to monitoring rangeland productivity fail to effectively capture important aspects of this heterogeneity. While field methods can effectively capture high levels of detail at fine spatial and temporal resolutions, they are limited in their applicability and scalability to larger spatial extents and longer time periods. Alternatively, remote sensing based approaches that scale broad spatiotemporal extents simplify important heterogeneity occurring at fine scales. We address these limitations to monitoring rangeland productivity by combining a continuous plant functional type (PFT) fractional cover dataset with a Landsat derived gross primary production (GPP) and net primary production (NPP) model. Integrating the annual PFT dataset with a 16-day Landsat normalized difference vegetation (NDVI) composite dataset enabled us to disaggregate the pixel level NDVI values to the sub-pixel PFTs. These values were incorporated into the productivity algorithm, enabling refined estimations of 16-day GPP and annual NPP for the PFTs that composed each pixel. We demonstrated the results of these methods on a set of representative rangeland sites across the western United States. Partitioning rangeland productivity to sub-pixel PFTs revealed new dynamics and insights to aid the sustainable management of rangelands.
The synthetic steroid mifepristone is reported to have anti-obesity and anti-diabetic effects in mammals on normal and high-fat diets (HFD). We previously reported that mifepristone blocks the ...negative effect on life span caused by mating in female
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Here we asked if mifepristone could protect virgin females from the life span-shortening effect of HFD. Mifepristone was assayed for effects on life span in virgin females, in repeated assays, on regular media and on media supplemented with coconut oil (HFD). The excrement quantification (EX-Q) assay was used to measure food intake of the flies after 12 days mifepristone treatment. In addition, experiments were conducted to compare the effects of mifepristone in virgin and mated females, and to identify candidate mifepristone targets and mechanisms.
Mifepristone increased life span of virgin females on regular media, as well as on media supplemented with either 2.5 or 5% coconut oil. Food intake was not reduced in any assay, and was significantly increased by mifepristone in half of the assays. To ask if mifepristone might rescue virgin females from all life span-shortening stresses, the oxidative stressor paraquat was tested, and mifepristone produced little to no rescue. Analysis of extant metabolomics and transcriptomics data suggested similarities between effects of mifepristone in virgin and mated females, including reduced tryptophan breakdown and similarities to dietary restriction. Bioinformatics analysis identified candidate mifepristone targets, including transcription factors Paired and Extra-extra. In addition to shortening life span, mating also causes midgut hypertrophy and activation of the lipid metabolism regulatory factor SREBP. Mifepristone blocked the increase in midgut size caused by mating, but did not detectably affect midgut size in virgins. Finally, mating increased activity of a SREBP reporter in abdominal tissues, as expected, but reporter activity was not detectably reduced by mifepristone in either mated or virgin females.
Mifepristone increases life span of virgin females on regular and HFD without reducing food intake. Metabolomics and transcriptomics analyses suggest some similar effects of mifepristone between virgin and mated females, however reduced midgut size was observed only in mated females. The results are discussed regarding possible mifepristone mechanisms and targets.
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