An official website of the United States government
Abstract Remote sensing imagery can provide critical information on the magnitude and extent of damage caused by forest pests and pathogens. However, monitoring short‐term changes in deciduous forest condition caused by defoliating insects is challenging and requires approaches that directly account for seasonal vegetation dynamics. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016–2018 gypsy moth (Lymantria dispar) outbreak in southern New England. Our comparisons revealed that most models made reasonable predictions of changes in canopy condition and egg and larval abundances ofL. dispar, indicating a strong correlation between our harmonic‐based estimates of condition change and defoliator activity. The greatest differences in predictive ability were in the spectral domain, with assessments based on Tasseled Cap Greenness, Simple Ratio, and the Enhanced Vegetation Index ranking among the top models, and the commonly used Normalized Difference Vegetation Index consistently exhibiting poorer performance. We also observed notable differences in the magnitude of scores for different baseline periods. Additionally, we found that Landsat‐based condition scores better explained larval abundance than egg mass counts, which have historically been used as a proxy for later‐season larval abundance, indicating that our remote sensing approach may be more accurate and cost‐effective for generating consistent retrospective assessments ofL. disparpopulation abundance in addition to estimates of canopy damage. These findings provide important linkages between spectral changes detected using a harmonic modeling approach and biophysical aspects of defoliator activity, with potential to extend monitoring and prediction to regional or even continental scales.
more »
« less
Surface Flux Equilibrium Theory‐Derived Evapotranspiration Estimate Outperforms ECOSTRESS, MODIS, and SSEBop Products
Abstract Evapotranspiration (ET) is a critical process influencing energy, water, and carbon cycles. Numerous methods have been developed to estimate ET accurately and robustly across diverse scales. Many of these methods are constrained by reliance on remote sensing data, which is prone to gaps, or by the need for model calibration and training. This study evaluates the performance of the calibration‐free surface flux equilibrium theory (SFET) for ET estimation at 33 Ameriflux sites in the continental USA. SFET‐derived ET estimates are intercompared with widely used continental remote sensing products, including ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station, Moderate Resolution Imaging Spectroradiometer, and SSEBop. Results indicate that SFET consistently outperforms these ET products. SFET's performance is found to be better under wet conditions and clear skies, with reduced accuracy under arid and high evaporative stress conditions. Overall, SFET exhibits significant potential for providing accurate, continuous, long‐term ET estimates, paving the way for operational application in uninstrumented regions over large scales.
more »
« less
- Award ID(s):
- 2317819
- PAR ID:
- 10600252
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 52
- Issue:
- 10
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Semi‐arid riparian woodlands face threats from increasing extractive water demand and climate change in dryland landscapes worldwide. Improved landscape‐scale understanding of riparian woodland water use (evapotranspiration, ET) and its sensitivity to climate variables is needed to strategically manage water resources, as well as to create successful ecosystem conservation and restoration plans for potential climate futures. In this work, we assess the spatial and temporal variability of Cottonwood (Populus fremontii)‐Willow (Salix gooddingii) riparian gallery woodland ET and its relationships to vegetation structure and climate variables for 80 km of the San Pedro River corridor in southeastern Arizona, USA, between 2014 and 2019. We use a novel combination of publicly available remote sensing, climate and hydrological datasets: cloud‐based Landsat thermal remote sensing data products for ET (Google Earth Engine EEFlux), Landsat multispectral imagery and field data‐based calibrations to vegetation structure (leaf‐area index, LAI), and open‐source climate and hydrological data. We show that at landscape scales, daily ET rates (6–10 mm day−1) and growing season ET totals (400–1,400 mm) matched rates of published field data, and modelled reach‐scale average LAI (0.80–1.70) matched lower ranges of published field data. Over 6 years, the spatial variability of total growing season ET (CV = 0.18) exceeded that of temporal variability (CV = 0.10), indicating the importance of reach‐scale vegetation and hydrological conditions for controlling ET dynamics. Responses of ET to climate differed between perennial and intermittent‐flow stream reaches. At perennial‐flow reaches, ET correlated significantly with temperature, whilst at intermittent‐flow sites ET correlated significantly with rainfall and stream discharge. Amongst reaches studied in detail, we found positive but differing logarithmic relationships between LAI and ET. By documenting patterns of high spatial variability of ET at basin scales, these results underscore the importance of accurately accounting for differences in woodland vegetation structure and hydrological conditions for assessing water‐use requirements. Results also suggest that the climate sensitivity of ET may be used as a remote indicator of subsurface water resources relative to vegetation demand, and an indicator for informing conservation management priorities.more » « less
-
Abstract The National Ecological Observatory Network Terrestrial Observation System (NEON TOS) produces open‐access data products that allow data users to investigate the impact of change drivers on key “sentinel” taxa and soils. The spatial and temporal sampling strategy that coordinates implementation of these protocols enables integration across TOS products and with products generated by NEON aquatic, remote sensing, and terrestrial instrument subsystems. Here, we illustrate the plots and sampling units that make up the physical foundation of a NEON TOS site, and we describe the scales (subplot, plot, airshed, and site) at which sampling is spatially colocated across protocols and subsystems. We also describe how moderate resolution imaging spectroradiometer‐enhanced vegetation index (MODIS‐EVI) phenology data are used to temporally coordinate TOS sampling within and across years at the continental scale of the observatory. Individually, TOS protocols produce data products that provide insight into populations, communities, and ecosystem processes. Within the spatial and temporal framework that guides cross‐protocol implementation, the ability to draw inference across data products is enhanced. To illustrate this point, we develop an example using R software that links two TOS data products collected with different temporal frequencies at both plot and site spatial scales. A thorough understanding of how TOS protocols are integrated with each other in space and time, and with other NEON subsystems, is necessary to leverage NEON data products to maximum effect. For example, a researcher must understand the spatial and temporal scales at which soil biogeochemistry data, soil microbe biomass data, and plant litter production and chemistry data may be combined to quantify soil nutrient stocks and fluxes across NEON sites. We present clear links among TOS protocols and across NEON subsystems that will enhance the utility of NEON TOS data products for the data user community.more » « less
-
Abstract. Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.more » « less
-
Cumulative impact assessments (CIAs) for new Arctic oilfields have not adequately addressed the potential landscape impacts of climate change or the indirect impacts of infrastructure in areas with ice-rich permafrost (IRP) (e.g., Raynolds et al. 2020). The main goals of this paper are: (1) trace the history of remote sensing for assessing past cumulative impacts in the Prudhoe Bay Oilfield (PBO), Alaska; (2) discuss some promising new remote-sensing and modeling tools; and (3) point toward improved capability to predict future changes. We first define IRP and cumulative impacts (CIs) and distinguish direct impacts (footprint) of infrastructure from the indirect impacts that follow construction. Aerial photographs (U.S. Navy 1948–1949) provided images of PBO landscapes before development occurred. The oil industry initiated annual high-resolution aerial-photograph missions of the PBO in 1968. In the same year, the International Biological Program (IBP) Tundra Biome started geoecological investigations that used these images to map landforms, soils, and vegetation of the PBO (Walker et al. 1980). The maps were later adapted to GIS approaches in three highly impacted 25-km2 areas of the PBO, which included several years of changes to tundra areas adjacent to infrastructure (Walker et al. 1987). The National Research Council later updated these three landscape-scale maps to 2001 and contracted the oil companies and Quantum Spatial Inc. to produce a regional-scale historical analysis of the network of roads, pipelines and other forms of infrastructure in all the North Slope Oilfields (NRC 2003). The regional- and landscape-scale maps used for NRC analysis were updated again in 2010 when unexpected rapid expansion of ice-wedge thermokarst was detected (Raynolds et al. 2014, 2016). Up to this time, CIAs of the PBO relied on aerial photographs and maps produced by the oil industry. The spatial resolution of available satellite-based remote-sensing data was insufficient to discern the details of periglacial landforms (e.g., ice-wedge polygons and nonsorted circles) or of roads, pipelines, or changes to land surfaces adjacent to infrastructure. Industry-sponsored studies that used remote-sensing products included studies of oil-pipeline spills, reserve-pits leaks (e.g., Jorgenson et al. 1995), off-road vehicles trails, and recovery following removal of gravel pads. Highlighted studies for this talk include a new NSF project that is part of the NSF Navigating the New Arctic initiative that is using integrated ground-based studies, advanced remote-sensing tools, and improved modeling approaches to examine climate- and infrastructure-related changes (Walker et al. 2022, Bergstedt 2022). Other projects that use PBO datasets for calibration, include an analysis of long-term impacts from a catastrophic flood (Shur et al. 2016, Zwieback et al. 2021) and studies that are using massive amounts of high-resolution imagery and pattern-recognition tools to detect and map ice-wedge polygons, water bodies, and infrastructure across the circumpolar Arctic (Bartsch et al. 2020; Witherrana et al. 2021). These tools combined with improved modeling approaches that bridge the gap between regional and engineering scales (e.g., Deimling et al. 2021) promise to greatly improve our ability to predict and monitor future infrastructure and landscape changes in areas with IRP.more » « less
