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NASA NTRS2021

Fairfax County Urban Development: Identifying Urban Heat Mitigation Strategies for Climate Adaptation Planning in Fairfax County, Virginia

Extreme high temperatures lead to increased instances of cardiovascular disease, pulmonary disease, and even death, as well as increased energy consumption and infrastructure costs. People in urbanized areas experience higher temperatures than rural areas due to diminished vegetation and increased impervious surfaces which absorb and radiate heat. Fairfax County, Virginia has embarked on Resilient Fairfax, a program aimed at addressing climate adaptation and resilience. The DEVELOP team partnered with the Fairfax County Office of Environmental and Energy Coordination (OEEC) to assess the extent of the urban heat island effect on the county and its most vulnerable populations. The team used data from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), as well as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) for the years 2013 to 2021 and found that the hottest spots were in densely urbanized areas, with temperatures as much as 47°F above that of undeveloped reference areas. The team used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model and determined that areas with higher tree canopy cover had greater heat mitigation capacity. Estimates from the InVEST model showed that a 4.5% increase in canopy cover across the county could result in a temperature reduction of up to 2.4°F in some areas. The results will allow partners to assess heat distribution across Fairfax County and implement effective mitigation strategies, including locating prime locations for cooling centers and increasing canopy cover.

W. Pierce Holloway↗

NASA NTRS2021

Estimating Bare Earth in Sparse Boreal Forests With WorldView Stereo Imagery

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Christopher S R Neigh↗

NASA NTRSDate not supplied

Assessing Urban Heat in the Cincinnati and Covington Area to Inform Local Decision-Makers and Environmental Justice Initiatives

The Urban Heat Island effect is a phenomenon characterized by urban areas experiencing temperatures that are, on average, warmer than surrounding suburban and rural regions. Urban Heat Islands are fueled by expansive impervious surfaces, vehicle emissions, and insufficient urban green space. Densely populated urban centers like the Cincinnati, Ohio and Covington, Kentucky area can experience negative health impacts due to the Urban Heat Island. NASA DEVELOP partnered with Groundwork USA and Groundwork Ohio River Valley to combine environmental education and outreach with technical capacity building in NASA Earth observations. The DEVELOP team used Landsat 5 TM and ISS ECOSTRESS to calculate daytime and nighttime land surface temperature anomalies. The team found that the Cincinnati and Covington area was 8.32°F warmer during the day and 4.97°F warmer at night compared to non-urban areas. The team also ran the Natural Capital Project InVEST Urban Cooling Model to map a heat mitigation index for the study area. The heat mitigation index models the cooling capacity of each pixel and the effect of green spaces on the pixel’s ability to mitigate heat. Results show which communities are most vulnerable to impacts of increased urban heat. The team also assessed alternative tree canopy scenarios with the InVEST model to better understand the effectiveness of potential heat mitigation strategies. Increasing tree canopy cover by 25% in urbanized land cover types was found to reduce mean temperature across the study area by 0.87°F. Combining these results with work from Groundwork’s Climate Safe Neighborhoods program highlights the relationship between extreme heat and historical-race based housing practices in the region. Results provide partners at Groundwork with refined methodologies to support future education and outreach, as well as increase future capacity to NASA Earth observations.

Celeste Gambino↗

NASA NTRS2022

Yonkers Urban Development II: Leveraging NASA Earth Observations to Support Modeling Urban Cooling Interventions and Urban Heat Vulnerability in Yonkers, New York

The City of Yonkers, New York, located in Westchester County, is experiencing rising temperatures which are a growing threat to the health and safety of its residents. Furthermore, the risk of heat-related illnesses and mortality disproportionately affects neighborhoods in Yonkers historically subjected to race-based housing segregation. To better understand these inequities, Groundwork Hudson Valley and NASA DEVELOP collaborated for a second term to evaluate community-level heat vulnerability, landcover distribution, street-level thermal comfort, and modeled urban cooling interventions. This team applied 2019 5-year American Community Survey (ACS) data and social and biophysical heat vulnerability variables established by the New York State Department of Health (NYSDOH), along with land surface temperature (LST) data collected from Landsat 8 Thermal Infrared Sensor (TIRS), and ISS ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to identify communities in Yonkers in need of prioritized cooling intervention at the census tract level. Data from the Real-Time Mesoscale Analysis (RTMA) provided relevant meteorological data for the ENVI-met model to conduct street-level thermal observations and model tree canopy cooling interventions in the Yonkers neighborhoods of Kimball and Old 7th Ward. The project results will support the prioritization and equitable distribution of cooling infrastructure in identified neighborhoods. Additionally, Groundwork Hudson Valley will use the analyses as a heat literacy tool to improve advocacy efforts and inform both residents and officials about how investment in deliberate modification to tree canopy cover improves the city’s thermal environment and helps mitigate extreme heat.

Tamara Barbakova↗

NASA NTRS2021

Estimating Bare Earth in Sparse Boreal Forests With WorldView Stereo Imagery

Christopher S. R. Neigh↗

NASA NTRS2024

San Joaquin Valley Health & Air Quality II: Assessing Urban Heat Island Distribution and its Intersections with Air Quality to Understand Converging Vulnerabilities

The city of Stockton, California, located within the San Joaquin Valley (SJV), is a major hub for agricultural production and has endured the continuous threat to community health from nitrogen dioxide (NO 2 ) and increasing temperatures. The convergence of these issues occurs within historically segregated communities that are disproportionately facing health risks related to heat and air quality. Little Manila Rising (LMR), a social and environmental justice (EJ) advocacy non-profit, partnered with NASA DEVELOP for a second term project to evaluate county wide urban heat islands, sociodemographic vulnerability, landcover classification, and the convergence of these variables. We utilized Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) data to produce a land surface temperature (LST) and Normalized Difference Vegetation Index map. They added Centers for Disease Control (CDC) socioeconomic data from 2020 to identify which communities in Stockton were more susceptible to these environmental factors. Additionally, we used NAIP imagery to create a landcover map differentiating developed infrastructure from tree canopy cover. We discovered that south Stockton, where LMR resides, had the worst convergence of heat, air pollution, low canopy coverage and sociodemographic vulnerability compared to northern and rural parts of the city. This was further substantiated by statistical analysis showing a strong positive relationship between areas of high LST and low vegetation. The results provided LMR with compelling evidence to use in their EJ advocacy, and in their efforts to inform state officials of the discriminatory issues they face.

Urban Heat Islands↗

NASA NTRS2022

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

land surface temperature↗

NASA NTRS2023

Youngstown and Warren Disasters: Mapping Flood Susceptibility, Vulnerability, and Risk and Tree Canopy Coverage in Northern Ohio to Inform Stormwater Management and Flood Mitigation Efforts

Both pluvial and fluvial flooding events pose direct challenges on urban infrastructure and communities across the United States. Heavy rainfall events oversaturate the ground, overflow waterbodies, and overwhelm stormwater infrastructure. Vulnerable areas receive heavy damage from flooding events due to physical factors like increased impervious surfaces, poor stormwater systems, and limited greenspaces. These vulnerable neighborhoods are comprised of aging populations, minority communities, and lower income levels. Lack of data in these communities have made it difficult to implement policymaking and flood mitigation strategies. Using the Urban Flood Risk Mitigation model (InVEST) and the PlanetScope satellite constellation, the team visualized historical flooding and tree canopy coverage as a measure of flood susceptibility. The team also used the Arc-Malstrom model to provide further insight into where flooding accumulates via surface elevation depressions in the study area. To validate these models, the team explored the spatial variation of rainfall events using NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG). The resulting maps highlight areas surrounding the cities of Youngstown and Warren as being the most flood susceptible and socially vulnerable, while the city centers contain the lowest tree canopy coverage. The DEVELOP team collaborated with the Environmental Collaborative of Ohio (ECO) to create products for end users within the City of Warren’s Water Pollution Control Department, the Eastgate Regional Council of Governments and the Healthy Community Partnership of Mahoning Valley. These products help identify target areas for preventative flood mitigation measures as well as areas ideal for green infrastructure intervention.

InVEST Urban Flood Risk Mitigation Model↗

NASA NTRS2020

Rocky Mountain Disasters - Using NASA Earth Observations to Monitor Post-Fire Vegetation Recovery in the Colorado Front Range

Forest composition and structure in the Colorado Front Range has been altered by changing wildfire regimes. In particular, increased moderate- and high-severity fire significantly reduces forest cover following fire and often results in reduced seedling regeneration. Reduced tree canopy regrowth has chronic effects on upland ecological function and downstream water quality. This project partnered with the US Forest Service to estimate long-term vegetation recovery following four Colorado Front Range fires between 1996 and 2002—the Bobcat, Buffalo Creek, Hayman, and High Meadows fires—using Landsat 5 Thematic Mapper (TM),Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI). The random forest algorithm was applied to produce maps of percent forest canopy cover for coniferous trees, deciduous trees, and all trees using time-series variables for pre- and post-fire as inputs. Similarly, maps of post-fire seedling regeneration were produced using random forest for coniferous trees,deciduous trees, and all trees using ecological drivers (soil, climate, fire, and topography) and pre-fire remote sensing predictors. Relationships between ecological drivers of post-fire vegetation trajectories were also evaluated. Additional analyses were conducted to (1) assess whether seedlings could be detected by Landsat or synthetic aperture radar (SAR) time-series analysis (2) assess pre-fire and post-fire Landsat variables against pre-fire and post-fire tree cover estimates to evaluate whether magnitude of forest change can be detected. Understanding variables that influence vegetative recovery, vegetation type conversion, and watershed characteristics will aid forest restoration efforts and water quality management.

Eric Jensen↗

NASA NTRS2024

San José Urban Development: Quantifying Canopy Cover and Land Surface Temperature in San José to Identify Future Tree Planting Sites

The urban heat island effect refers to the phenomenon of substantially increased temperatures in urban areas compared to their surrounding suburban or rural counterparts. The City of San José’s Department of Parks, Recreation and Neighborhood Services (PRNS) and Department of Transportation (DOT) work to mitigate the UHI effect through urban forestry initiatives. The PRNS and DOT partnered with NASA DEVELOP to identify areas in need of tree plantings. We examined land surface temperature (LST) throughout the city using data from the Thermal Infrared Sensor (TIRS) and TIRS-2 on NASA’s Landsat 8 and 9 satellites from 2013 to 2024. We also assessed canopy cover in parks using LiDAR data collected in 2020 for the United States Geological Survey’s 3D Elevation Program, and measured vegetation greenness using the Normalized Difference Vegetation Index (NDVI) with PlanetScope imagery from 2018 to 2024. We evaluated social and environmental factors that influence the distribution of heat event impacts by creating a heat vulnerability index. We found that heat is concentrated in urban areas and that poor vegetation health is associated with high LST. We also found that socially vulnerable communities are disproportionately located in areas of high environmental risk. These analyses allow the partners to prioritize tree plantings in parks near areas of high social and environmental risk. We determined that Earth observations can be used to inform urban forestry decision making, but because methodologies for using LiDAR to assess canopy cover vary greatly, it is difficult to make comparisons across different canopy cover assessments.

land surface temperature↗

NASA NTRS1999

Intercomparison of Satellite-Derived Snow-Cover Maps

In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40%. The highest percent change (less than 100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the percent change ranged from 0 to 40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.

Hall, Dorothy K.↗

NASA NTRS1987

Detection of soil erosion with Thematic Mapper (TM) satellite data within Pinyon-Juniper woodlands

Pinyon-Juniper woodlands dominate approximately 24.3 million hectares (60 million acres) in the western United States. The overall objective was to test the sensitivity of the LANDSAT Thematic Mapper (TM) spectral data for detecting varying degrees of soil erosion within the Pinyon-Juniper woodlands. A second objective was to assess the potential of the spectral data for assigning the Universal Soil Loss Equation (USLE) crop management (C) factor values to varying cover types within the woodland. Thematic Mapper digital data for June 2, 1984 on channels 2, 3, 4, and 5 were used. Digital data analysis was performed using the ELAS software package. Best results were achieved using CLUS, an unsupervised clustering algorithm. Fifteen of the 40 Pinyon-Juniper signatures were identified as being relatively pure Pinyon-Juniper woodland. Final analysis resulted in the grouping of the 15 signatures into three major groups. Ten study sites were selected from each of the three groups and located on the ground. At each site the following field measurements were taken: percent tree canopy and percent understory cover, soil texture, total soil loss, and soil erosion rate estimates. A technique for measuring soil erosion within Pinyon-Juniper woodlands was developed. A theoretical model of site degradation after Pinyon-Juniper invasion is presented.

Price, Kevin Paul↗

NASA NTRS2000

Using Airborne Laser Altimetry to Detect Topographic Change at Long Valley Caldera California

The topography of the Long Valley caldera, California, was sampled using airborne laser altimetry in 1993, 1995, and 1997 to test the feasibility of using airborne laser altimetry for monitoring deformation of volcanic origin. Results show the laser altimeters are able to resolve subtle topographic features such as a gradual slope and to detect small transient changes in lake elevation. Crossover and repeat pass analyses of laser tracks indicate decimeter-level vertical precision is obtained over flat and low-sloped terrain for altimeter systems performing waveform digitization. Comparisons with complementary, ground-based CPS data at a site close to Bishop airport indicate that the laser and GPS-derived elevations agree to within the error inherent in the measurement and that horizontal locations agree to within the radius of the laser footprint. A comparison of the data at two sites, one where no change and the other where the maximum amount of vertical uplift is expected, indicates approximately 10 cm of relative uplift occurred 1993-1997, in line with predictions from continuous CPS measurements in the region. Extensive terrain mapping flights during the 1995 and 1997 missions demonstrate some of the unique abilities of laser altimetry; the straightforward creation of high resolution, high accuracy digital elevation models of overflown terrain, and the ability to determine ground topography in the presence of significant ground cover such as dense tree canopies. These capabilities make laser altimetry an attractive technique for quantifying topographic change of volcanic origin, especially in forested regions of the world where other remote sensing instruments have difficulty detecting the underlying topography.

Hofton, M. A.↗

NASA NTRS1999

Using Airborne Laser Altimetry to Detect Topographic Change at Long Valley Caldera, California

The topography of the Long Valley caldera, California, was sampled using airborne laser altimetry in 1993, 1995, and 1997 to test the feasibility of using airborne laser altimetry for monitoring deformation of volcanic origin. Results show the laser altimeters are able to resolve subtle topographic features such as a gradual slope and to detect small transient changes in lake elevation. Crossover and repeat pass analyses of laser tracks indicate decimeter-level vertical precision is obtained over flat and low-sloped terrain for altimeter systems performing waveform digitization. Comparisons with complementary, ground-based GPS data at a site close to Bishop airport indicate that the laser and GPS-derived elevations agree to within the error inherent in the measurement and that horizontal locations agree to within the radius of the laser footprint. A comparison of the data at two sites, one where no change and the other where the maximum amount of vertical uplift is expected, indicates approximately 10 cm of relative uplift occurred 1993-1997, in line with predictions from continuous GPS measurements in the region. Extensive terrain mapping flights during the 1995 and 1997 missions demonstrate some of the unique abilities of laser altimetry; the straightforward creation of high resolution, high accuracy digital elevation models of overflown terrain, and the ability to determine ground topography in the presence of significant ground cover such as dense tree canopies. These capabilities make laser altimetry an attractive technique for quantifying topographic change of volcanic origin, especially in forested regions of the world where other remote sensing instruments have difficulty detecting the underlying topography.

Hofton, M. A.↗

NASA NTRS2011

Canopy Spectral Invariants. Part 1: A New Concept in Remote Sensing of Vegetation

The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral reflectance become wavelength independent and determine two canopy structure specific variables the recollision and escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to incident solar radiation at the leaf and the canopy scale. They are sensitive to important structural features of the canopy such as forest cover, tree density, leaf area index, crown geometry, forest type and stand age. This paper presents the mathematical basis of the concept which is linked to eigenvalues and eigenvectors of the three-dimensional radiative transfer equation.

Knyazikhin, Yuri↗

NASA NTRSDate not supplied

San José Urban Development: Quantifying Canopy Cover and Land Surface Temperature in San José to Identify Future Tree Planting Sites

Explore the source record for details and available documents.

Shilpa Kannan↗

NASA NTRSDate not supplied

CropManage Application for Vineyard Irrigation Decision-Support

CropManage is a free web-application developed by U.C. Cooperative Extension to support evapotranspiration based irrigation scheduling and nutrient management for major specialty crops. Prescribed phenology curves are used to develop daily estimates of canopy cover within a given field, based on days since planting (annual crops) or budbreak (trees, vines). These curves are modulated by a MaxCan parameter representing seasonal maximum canopy cover. Crop development observations can be used to adjust for such factors as weather anomalies or non-standard agronomic practice, as needed. Canopy cover is converted to crop coefficient and combined with reference evapotranspiration to derive daily water consumption. Guidance on crop water requirement is then conveyed to users in terms of system runtime issued on-demand for a given date, largely based on total evapotranspiration since last irrigation event. In this study, CropManage was adapted to vineyards by adding modules accounting for early-season soil moisture depletion and cover crop presence. A crop stress parameter was added to accommodate deficit irrigation practice, allowing the user to specify percentage departure from full water requirement along with start/stop dates. An initial verification exercise was performed on three winegrape vineyards located in California’s Central Coast (2020), North Coast (2020) and Central Valley (2019). Daily crop evapotranspiration was monitored by eddy-covariance fluxtowers. MaxCan was measured by ground and satellite observation. Stress regime was specified by grower practice where available, otherwise stress levels were inferred from applied water records. Mean absolute error and mean bias error of modeled cumulative evapotranspiration were computed with respect to the eddy covariance measurements collected throughout the growing season. Results indicate the modified CropManage water management module performs reasonably well for winegrape. Additional effort is planned to modify the nutrient module for vineyard use.

CropManage↗

NASA NTRSDate not supplied

Assessing Alaskan boreal forest landcover affected by climate-wildfire interactions from ground truth surveys and NASA airborne remote sensing

Alaska’s boreal forest is facing unprecedented challenges under rapid climate warming (increasingly severe fires, droughts, pest/disease outbreaks) that may destabilize its function as a global carbon sink. Forests near Fairbanks may be especially vulnerable, impacting air quality and ecosystem services. We combined GT (ground truthing) with Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) images collected by the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) program (2017-2019) to assess landcover change at five recently burned sites (2001-2019) of different fire severities and moisture regimes within 30 miles of Fairbanks. GT included tree seedling counts, understory % cover and >50% leaf canopy color assessment. 36 circular plots (1/30 ha radius) including 6 moderate to severely burned plots were selected across sites. 31 additional sites including 12 burned sites were geotagged in photos. AVIRIS images were processed from 29 spectral bands selected to identify changes in chlorophyll and water content. Images were segmented into natural boundaries (polygons) using ENVI 5.5 software. A spectral library of 8 AVIRIS bands with high between-class/low within-class variation was used in two random forest models to predict vegetation classes (model 1: 12 classes, model 2: 14 classes) in each AVIRIS scene, using 20% of the data as training data. Model 2 classified 20% more polygons overall, but only 42% of GT/geotagged polygons were correctly classified by both models. More forest sites were correctly classified (63%) than open vegetation (32%) or post-fire sites (46%). 50% of aspen forest and post-fire polygons were misclassified as shrubland. GT revealed that post-fire plots supported 134,000 (± 48,000) tree seedlings and saplings ha-1 (0.2 - 4 m height, 64% deciduous) versus 2500 (± 2100) shrubs ha-1 (1-6 m height). > 50% canopy browning was observed in conifer forest (8 plots) with no signs of insect infestation. Canopy herbivory > 50% (leaf miner, leaf beetle) and moose herbivory of tree bark was seen across aspen sites. Our study suggests: 1) low canopy vegetation presents challenges for improved landcover classification, and 2) aspen forest should be differentiated in vegetation maps which would aid in tracking herbivory.

Alaska↗