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USGS2005

Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests

The ponderosa pine forests of the Colorado Front Range, USA, have historically been subjected to wildfires. Recent large burns have increased public interest in fire behavior and effects, and scientific interest in the carbon consequences of wildfires. Remote sensing techniques can provide spatially explicit estimates of stand structural characteristics. Some of these characteristics can be used as inputs to fire behavior models, increasing our understanding of the effect of fuels on fire behavior. Others provide estimates of carbon stocks, allowing us to quantify the carbon consequences of fire. Our objective was to use discrete-return lidar to estimate such variables, including stand height, total aboveground biomass, foliage biomass, basal area, tree density, canopy base height and canopy bulk density. We developed 39 metrics from the lidar data, and used them in limited combinations in regression models, which we fit to field estimates of the stand structural variables. We used an information–theoretic approach to select the best model for each variable, and to select the subset of lidar metrics with most predictive potential. Observed versus predicted values of stand structure variables were highly correlated, with r 2 ranging from 57% to 87%. The most parsimonious linear models for the biomass structure variables, based on a restricted dataset, explained between 35% and 58% of the observed variability. Our results provide us with useful estimates of stand height, total aboveground biomass, foliage biomass and basal area. There is promise for using this sensor to estimate tree density, canopy base height and canopy bulk density, though more research is needed to generate robust relationships. We selected 14 lidar metrics that showed the most potential as predictors of stand structure. We suggest that the focus of future lidar studies should broaden to include low density forests, particularly systems where the vertical structure of the canopy is important, such as fire prone forests.

Colorado↗

USGS2015

The Landscape Evolution Observatory: a large-scale controllable infrastructure to study coupled Earth-surface processes

Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit comprising much of Earth's uplands. The convergent topography of these landscapes generates spatially variable substrate and moisture content, facilitating biological diversity and influencing how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of these significant ecosystem services, refining our understanding of how these functions are affected by landscape evolution, weather variability, and long-term climate change is imperative. In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale controllable infrastructure consisting of three replicated artificial landscapes (each 330 m 2 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are possible at unprecedented scale. The Landscape Evolution Observatory was designed as a community resource to advance understanding of how topography, physical and chemical properties of soil, and biological communities coevolve, and how this coevolution affects water, carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and an extensive network of sensors and samplers, LEO enables an iterative scientific approach that includes numerical model development and virtual experimentation, physical experimentation, data analysis, and model refinement. We plan to engage the broader scientific community through public dissemination of data from LEO, collaborative experimental design, and community-based model development.

Arizona↗

USGS2025

Development of ‘SedCam’— A close-range remote sensing method of estimating suspended-sediment concentration in small rivers

The adaptation of suspended-sediment surrogate technologies continues to rapidly expand across geomorphology and fluvial sediment monitoring efforts. Over a decade of research and development shows increased reliability and accuracy of in-situ surrogates with reduced program cost as compared to traditional sample-based methods, but environmental fouling and probe damage can be problematic. The SedCam technique is a unique non-contact close-range remote sensing method to estimate suspended-sediment concentration from multispectral imagery of a river surface. In contrast to typical airborne- or satellite-based platforms, SedCam uses broadband sensors with lower spectral resolution (three bands covering wavelengths of 340 to 1100 nm) but greater spatial resolution (0.5 mm pixel size; equivalent to medium to coarse sand) and temporal resolution (15-min intervals during daylight hours). This paper summarizes lessons learned from two studies, utilizing three consumer-grade digital cameras (each with different spectral signatures) at two different rivers (each with different sediment characteristics). >90,000 images and 174 concurrent physical samples represent a collective period of 26 months. A subset of these data pairs supports the development of four regression models. Statistical diagnostics show model error can be <40 % when surface point samples are used, with coefficients of determination ≥0.90. This novel approach shows similar accuracy to other surrogate methods such as instream turbidity. Results of this study indicate that optimizing spectra based on expected suspended-sediment concentration increases model performance.

Geomorphology↗

USGS2014

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2014

Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.

Paulinia↗

USGS2018

A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m 2 , 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2021

Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements

Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different EOS requires compensating for atmospheric and view angle effects before integrating comparable surface reflectance (SR) values. The objectives of this study were i) to assess how different atmospheric constituents affect the atmospheric correction results in Sentinel-2 and WorldView-3 imagery, ii) to establish a relationship with field spectra measurements, and iii) to develop an empirical approach to ensure that SR values extracted from different EOS can be normalized for use in monitoring vegetation and land cover status. We compared surface reflectance values derived from Sentinel-2 images corrected with Sen2Cor, MODTRAN or FLAASH atmospheric correction approaches for the visible-to-near infrared regions. Additionally, this information was compared to SR values extracted from WorldView-3 imagery acquired from the same dates and location (Central Spain) and corrected with MODTRAN and FLAASH approaches. Assessment of the atmospheric correction was conducted by comparing satellite image SR with ground-truth spectra acquired with a FieldSpec hand-held spectroradiometer. The results emphasized the importance of using common atmospheric parameters collected from ancillary data sources (i.e. MODIS Atmosphere & Land products) to ensure a reliable SR comparison. When compared to field-collected spectral data, SR from corrected Sentinel-2 push-broom imagery showed a reliable match (<4% difference in the visible bands and <0.52% difference in the near infrared bands). However, SR imagery from the pointable WorldView-3 instrument showed significant deviation, likely resulting from the effects of steep off-nadir acquisition angles (24.6° to 39.1°) combined with surface anisotropy. The magnitude and sign of the deviation in SR differed depending on the vegetation type, wavelength and sun-surface-sensor geometry. Therefore, it was necessary to account for angular effects to ensure reliable comparisons of imagery from the different EOS. In this study, an empirical angular correction approach was developed based on calibrating each WorldView-3 band against the ground-truth spectra. This correction allowed for the accurate signal normalization of WorldView-3 and Sentinel-2 imagery SR in the visible-to-near infrared regions.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2009

An improved procedure for detection and enumeration of walrus signatures in airborne thermal imagery

In recent years, application of remote sensing to marine mammal surveys has been a promising area of investigation for wildlife managers and researchers. In April 2006, the United States and Russia conducted an aerial survey of Pacific walrus (Odobenus rosmarus divergens) using thermal infrared sensors to detect groups of animals resting on pack ice in the Bering Sea. The goal of this survey was to estimate the size of the Pacific walrus population. An initial analysis of the U.S. data using previously-established methods resulted in lower detectability of walrus groups in the imagery and higher variability in calibration models than was expected based on pilot studies. This paper describes an improved procedure for detection and enumeration of walrus groups in airborne thermal imagery. Thermal images were first subdivided into smaller 200 x 200 pixel "tiles." We calculated three statistics to represent characteristics of walrus signatures from the temperature histogram for each the. Tiles that exhibited one or more of these characteristics were examined further to determine if walrus signatures were present. We used cluster analysis on tiles that contained walrus signatures to determine which pixels belonged to each group. We then calculated a thermal index value for each walrus group in the imagery and used generalized linear models to estimate detection functions (the probability of a group having a positive index value) and calibration functions (the size of a group as a function of its index value) based on counts from matched digital aerial photographs. The new method described here improved our ability to detect walrus groups at both 2 m and 4 m spatial resolution. In addition, the resulting calibration models have lower variance than the original method. We anticipate that the use of this new procedure will greatly improve the quality of the population estimate derived from these data. This procedure may also have broader applicability to thermal infrared surveys of other wildlife species. Published by Elsevier B.V.

International Journal of Applied Earth Observation↗

USGS2015

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover ( r 2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass ( r 2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

Maryland↗

USGS2020

Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements

Surface soil temperature is the soil temperature from the surface to 10 cm in depth. Surface soil temperature plays a significant role in agricultural drought monitoring, ecosystem energy transfer modeling, and global carbon cycle evaluation. Studies have been proposed to estimate surface soil temperature, but surface soil temperature monitoring within forested areas still poses a significant challenge. In this study, we proposed a surface soil temperature retrieval method using combined satellite observations and in-situ measurements for the Great Dismal Swamp (GDS). The GDS is a U.S. protected area managed and protected by the U.S. Fish and Wildlife Service. It is located along the boundary of Virginia and North Carolina, with maple gum, Atlantic white cedar, and pine pocosin as the main forest cover types. Ground-based surface soil temperature measurements were collected for these forest types from May 2015 to April 2017. Both the Land Remote Sensing Satellite (Landsat) Thermal Infrared Sensor (TIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) carry two thermal infrared (TIR) channels. The TIR channels with similar corresponding wavelengths were first fused using an improved fusing model to generate high resolution TIR measurements. Then the enterprise algorithm was applied to calculate land surface temperature (LST) from the fused TIR bands. An improved soil temperature retrieval method was applied to generate surface soil temperature based on LST and vegetation index (VI) within the study area for the three forest types. In-situ measurements were used to build the surface soil temperature retrieval method, and results were then validated. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated separately as VIs in the model to monitor surface soil temperature. The R 2 for retrieved surface soil temperature through satellite observations was 0.76, and the RMSE was 1.96 ℃ ">℃ ℃ when NDVI was integrated in the model; the R 2 was 0.78, and the RMSE was 1.85 ℃ ">℃ ℃ when EVI was used.

North Carolina, Virginia↗

USGS2022

Achieving sub-nanoTesla precision in multirotor UAV aeromagnetic surveys

An uncrewed aerial vehicle (UAV) multirotor aeromagnetic system using a 5-m sling load for a magnetic sensor system is described and characterized. Four magnetic surveys with identical flight lines were completed, at two nominal altitudes of 25 and 40 m. The surveys were used to assess the repeatability of data collected with the described UAV aeromagnetic system, and comparison with a ground survey was used to assess the precision. The 5-m sling is designed to reduce magnetic interference from the UAV. A magnetic compensation model was developed for this particular UAV aeromagnetic system. This custom compensation model reduces the noise in the collected data by a factor of five over the uncompensated data, and the 5-m sling further reduces the noise by an estimated factor of four over a similar system with a 3-m sling. The precision of the UAV aeromagnetic system was then estimated to be sub-nT, with 50% of the noise component <0.3 nT, and 90% <0.6 nT.

Journal of Applied Geophysics↗

USGS2004

Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis

The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992-93, 1995) and SPOT Vegetation (1998-2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroundings. We then summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analysed using Landsat TM imagery and extensive ground knowledge to assess the results. This technique identified anomalies that can be linked to numerous anthropogenic impacts including agricultural and urban expansion, maintenance of protected areas and increased fallow. Local variance analysis is a reliable method for assessing vegetation degradation resulting from human pressures or increased land productivity from natural resource management practices. ?? 2004 Published by Elsevier Ltd.

Journal of Arid Environments↗

USGS2008

Multiyear riparian evapotranspiration and groundwater use for a semiarid watershed

Riparian evapotranspiration (ET) is a major component of the surface and subsurface water balance for many semiarid watersheds. Measurement or model-based estimates of ET are often made on a local scale, but spatially distributed estimates are needed to determine ET over catchments. In this paper, we document the ET that was quantified over 3 years using eddy covariance for three riparian ecosystems along the Upper San Pedro River of southeastern Arizona, USA, and we use a water balance equation to determine annual groundwater use. Riparian evapotranspiration and groundwater use for the watershed were then determined by using a calibrated, empirical model that uses 16-day, 250-1000 m remote-sensing products for the years of 2001-2005. The inputs for the model were derived entirely from the NASA MODIS sensor and consisted of the Enhanced Vegetation Index and land surface temperature. The scaling model was validated using subsets of the entire dataset (omitting different sites or years) and its capable performance for well-watered sites (MAD=0.32 mm day-1, R2=0.93) gave us confidence in using it to determine ET over the watershed. Three years of eddy covariance data for the riparian sites reveal that ET and groundwater use increased as woody plant density increased. Groundwater use was less variable at the woodland site, which had the greatest density of phreatophytes. Annual riparian groundwater use within the watershed was nearly constant over the study period despite an on-going drought. For the San Pedro alone, the amounts determined in this paper are within the range of most recently reported values that were derived using an entirely different approach. However, because of our larger estimates for groundwater use for the main tributary of the San Pedro, the watershed totals were higher. The approach presented here can provide riparian ET and groundwater use amounts that reflect real natural variability in phreatophyte withdrawals and improve the accuracy of a watershed's water budget. ?? 2008 Elsevier Ltd. All rights reserved.

Journal of Arid Environments↗

USGS2012

A simple method for estimating basin-scale groundwater discharge by vegetation in the basin and range province of Arizona using remote sensing information and geographic information systems

Groundwater is a vital water resource in the arid to semi-arid southwestern United States. Accurate accounting of inflows to and outflows from the groundwater system is necessary to effectively manage this shared resource, including the important outflow component of groundwater discharge by vegetation. A simple method for estimating basin-scale groundwater discharge by vegetation is presented that uses remote sensing data from satellites, geographic information systems (GIS) land cover and stream location information, and a regression equation developed within the Southern Arizona study area relating the Enhanced Vegetation Index from the MODIS sensors on the Terra satellite to measured evapotranspiration. Results computed for 16-day composited satellite passes over the study area during the 2000 through 2007 time period demonstrate a sinusoidal pattern of annual groundwater discharge by vegetation with median values ranging from around 0.3 mm per day in the cooler winter months to around 1.5 mm per day during summer. Maximum estimated annual volume of groundwater discharge by vegetation was between 1.4 and 1.9 billion m 3 per year with an annual average of 1.6 billion m 3 . A simplified accounting of the contribution of precipitation to vegetation greenness was developed whereby monthly precipitation data were subtracted from computed vegetation discharge values, resulting in estimates of minimum groundwater discharge by vegetation. Basin-scale estimates of minimum and maximum groundwater discharge by vegetation produced by this simple method are useful bounding values for groundwater budgets and groundwater flow models, and the method may be applicable to other areas with similar vegetation types.

Arizona↗

USGS2015

Wide-area ratios of evapotranspiration to precipitation in monsoon-dependent semiarid vegetation communities

Evapotranspiration (ET) and the ratio of ET to precipitation (PPT) are important factors in the water budget of semiarid rangelands and are in part determined by the dominant plant communities. Our goal was to see if landscape changes such as tree or shrub encroachment and replacement of native grasses by invasive grasses impacted ET and ET/PPT and therefore watershed hydrology in this biome. We determined ET and ET/PPT for shrublands, grasslands and mesquite savannas in southern Arizona at five moisture flux towers and determined the environmental factors controlling ET in each plant community. We then scaled ET over areas of 4–36 km 2 , representing homogeneous patches of each plant community, using the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite. Over wide areas, estimated ET/PPT projected from MODIS EVI ranged from 0.71 for a sparsely-vegetated shrub site to 1.00 for grasslands and mesquite savannas. The results did not support hypotheses that encroachment of mesquites into grasslands or that replacement of native grasses with introduced Eragrostis lehmanniana (lehmann lovegrass) have increased rangeland ET.

Arizona↗

USGS2014

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs.

California;Nevada↗

USGS2017

Benefits of the fire mitigation ecosystem service in the Great Dismal Swamp National Wildlife Refuge, Virginia, USA

The Great Dismal Swamp (GDS) National Wildlife Refuge delivers multiple ecosystem services, including air quality and human health via fire mitigation. Our analysis estimates benefits of this service through its potential to reduce catastrophic wildfire related impacts on the health of nearby human populations. We used a combination of high-frequency satellite data, ground sensors, and air quality indices to determine periods of public exposure to dense emissions from a wildfire within the GDS. We examined emergency department (ED) visitation in seven Virginia counties during these periods, applied measures of cumulative Relative Risk to derive the effects of wildfire smoke exposure on ED visitation rates, and estimated economic losses using regional Cost of Illness values established within the US Environmental Protection Agency BenMAP framework. Our results estimated the value of one avoided catastrophic wildfire in the refuge to be \$3.69 million (2015 USD), or \$306 per hectare of burn. Reducing the frequency or severity of extensive, deep burning peatland wildfire events has additional benefits not included in this estimate, including avoided costs related to fire suppression during a burn, carbon dioxide emissions, impacts to wildlife, and negative outcomes associated with recreation and regional tourism. We suggest the societal value of the public health benefits alone provides a significant incentive for refuge mangers to implement strategies that will reduce the severity of catastrophic wildfires.

North Carolina, Virginia↗

USGS2023

A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT ( A ustralia-wide M achine L earning ET for T rees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R 2 of 0.94 (RMSE 6.63 mm period −1 ) and 0.92 (RMSE 6.89 mm period −1 ), respectively. In comparison, correlation between in-situ ET and a national ET product returned R 2 of 0.44 (RMSE 34.08 mm period −1 ) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.

The Journal of Environmental Management↗