Search USGS⌕ Search

SEARCH · Search USGS

Results for “Journal of Remote Sensing”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9Linked to original sources
USGS2009

Distributed geospatial model sharing based on open interoperability standards

Numerous geospatial computational models have been developed based on sound principles and published in journals or presented in conferences. However modelers have made few advances in the development of computable modules that facilitate sharing during model development or utilization. Constraints hampering development of model sharing technology includes limitations on computing, storage, and connectivity; traditional stand-alone and closed network systems cannot fully support sharing and integrating geospatial models. To address this need, we have identified methods for sharing geospatial computational models using Service Oriented Architecture (SOA) techniques and open geospatial standards. The service-oriented model sharing service is accessible using any tools or systems compliant with open geospatial standards, making it possible to utilize vast scientific resources available from around the world to solve highly sophisticated application problems. The methods also allow model services to be empowered by diverse computational devices and technologies, such as portable devices and GRID computing infrastructures. Based on the generic and abstract operations and data structures required for Web Processing Service (WPS) standards, we developed an interactive interface for model sharing to help reduce interoperability problems for model use. Geospatial computational models are shared on model services, where the computational processes provided by models can be accessed through tools and systems compliant with WPS. We developed a platform to help modelers publish individual models in a simplified and efficient way. Finally, we illustrate our technique using wetland hydrological models we developed for the prairie pothole region of North America.

Journal of Remote Sensing↗
USGS2019

Optical characterization of two cyanobacteria genera, Aphanizomenon and Microcystis, with hyperspectral microscopy.

Cyanobacterial blooms are a nuisance and a potential hazard in freshwater systems worldwide. Remote sensing has been used to detect cyanobacterial blooms, but few studies have distinguished between genera of cyanobacteria. Because some genera are more likely to be toxic than others, this is a useful distinction.In this study, hyperspectral imaging reflectance microscopy was used to examine cyanobacteria from Upper Klamath Lake, Oregon, USA, at high spatial and spectral resolution to determine if two species found commonly in the lake, Aphanizomenon flos-aquae and Microcystis aeruginosa, can be separated spectrally. Of the analytical methods applied, a spectral shape algorithm applied to the derivative was found to be most successful in classifying these species in microscope scenes. Further work is required to determine if the spectral characterization of cyanobacterial genera can be scaled up to other remote sensing applications.

Oregon↗
USGS2007

FFT-enhanced IHS transform method for fusing high-resolution satellite images

Existing image fusion techniques such as the intensity-hue-saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. ?? 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2009

GIS applications for military operations in coastal zones

In order to successfully support current and future US military operations in coastal zones, geospatial information must be rapidly integrated and analyzed to meet ongoing force structure evolution and new mission directives. Coastal zones in a military-operational environment are complex regions that include sea, land and air features that demand high-volume databases of extreme detail within relatively narrow geographic corridors. Static products in the form of analog maps at varying scales traditionally have been used by military commanders and their operational planners. The rapidly changing battlefield of 21st Century warfare, however, demands dynamic mapping solutions. Commercial geographic information system (GIS) software for military-specific applications is now being developed and employed with digital databases to provide customized digital maps of variable scale, content and symbolization tailored to unique demands of military units. Research conducted by the Center for Remote Sensing and Mapping Science at the University of Georgia demonstrated the utility of GIS-based analysis and digital map creation when developing large-scale (1:10,000) products from littoral warfare databases. The methodology employed-selection of data sources (including high resolution commercial images and Lidar), establishment of analysis/modeling parameters, conduct of vehicle mobility analysis, development of models and generation of products (such as a continuous sea-land DEM and geo-visualization of changing shorelines with tidal levels)-is discussed. Based on observations and identified needs from the National Geospatial-Intelligence Agency, formerly the National Imagery and Mapping Agency, and the Department of Defense, prototype GIS models for military operations in sea, land and air environments were created from multiple data sets of a study area at US Marine Corps Base Camp Lejeune, North Carolina. Results of these models, along with methodologies for developing large-scale littoral warfare databases, aid the National Geospatial-Intelligence Agency in meeting littoral warfare analysis, modeling and map generation requirements for US military organizations. ?? 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2010

Irrigated areas of India derived using MODIS 500 m time series for the years 2001-2003

The overarching goal of this research was to develop methods and protocols for mapping irrigated areas using a Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m time series, to generate irrigated area statistics, and to compare these with ground- and census-based statistics. The primary mega-file data-cube (MFDC), comparable to a hyper-spectral data cube, used in this study consisted of 952 bands of data in a single file that were derived from MODIS 500 m, 7-band reflectance data acquired every 8-days during 2001-2003. The methods consisted of (a) segmenting the 952-band MFDC based not only on elevation-precipitation-temperature zones but on major and minor irrigated command area boundaries obtained from India's Central Board of Irrigation and Power (CBIP), (b) developing a large ideal spectral data bank (ISDB) of irrigated areas for India, (c) adopting quantitative spectral matching techniques (SMTs) such as the spectral correlation similarity (SCS) R 2 -value, (d) establishing a comprehensive set of protocols for class identification and labeling, and (e) comparing the results with the National Census data of India and field-plot data gathered during this project for determining accuracies, uncertainties and errors. The study produced irrigated area maps and statistics of India at the national and the subnational (e.g., state, district) levels based on MODIS data from 2001-2003. The Total Area Available for Irrigation (TAAI) and Annualized Irrigated Areas (AIAs) were 113 and 147 million hectares (MHa), respectively. The TAAI does not consider the intensity of irrigation, and its nearest equivalent is the net irrigated areas in the Indian National Statistics. The AIA considers intensity of irrigation and is the equivalent of "irrigated potential utilized (IPU)" reported by India's Ministry of Water Resources (MoWR). The field-plot data collected during this project showed that the accuracy of TAAI classes was 88% with a 12% error of omission and 32% of error of commission. Comparisons between the AIA and IPU produced an R 2 -value of 0.84. However, AIA was consistently higher than IPU. The causes for differences were both in traditional approaches and remote sensing. The causes of uncertainties unique to traditional approaches were (a) inadequate accounting of minor irrigation (groundwater, small reservoirs and tanks), (b) unwillingness to share irrigated area statistics by the individual Indian states because of their stakes, (c) absence of comprehensive statistical analyses of reported data, and (d) subjectivity involved in observation-based data collection process. The causes of uncertainties unique to remote sensing approaches were (a) irrigated area fraction estimate and related sub-pixel area computations and (b) resolution of the imagery. The causes of uncertainties common in both traditional and remote sensing approaches were definitions and methodological issues. ?? 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2023

Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m 3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).

Journal of Applied Remote Sensing↗
USGS2013

Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields

Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.

Wyoming↗
USGS2015

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Madre de Dios↗
USGS2014

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

California↗
USGS2018

Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000 km 2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

Arizona↗
USGS2012

Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States

Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation ( R 2 =0.94, N =24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement ( R 2 =0.98, N =24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C 3 versus C 4 plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C 3 versus C 4 based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.

Journal of Applied Remote Sensing↗
USGS2018

Full-range, solar-reflected hyperspectral microscopy to support earth remote sensing research

Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz–tungsten–halogen lamps for illumination. The combined sensors provide a means to collect <10,000 s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as “microscenes,” for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors.

Journal of Applied Remote Sensing↗
USGS2010

Using the Sonoran and Libyan Desert test sites to monitor the temporal stability of reflective solar bands for Landsat 7 enhanced thematic mapper plus and Terra moderate resolution imaging spectroradiometer sensors

Remote sensing imagery is effective for monitoring environmental and climatic changes because of the extent of the global coverage and long time scale of the observations. Radiometric calibration of remote sensing sensors is essential for quantitative & qualitative science and applications. Pseudo-invariant ground targets have been extensively used to monitor the long-term radiometric calibration stability of remote sensing sensors. This paper focuses on the use of the Sonoran Desert site to monitor the radiometric stability of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The results are compared with the widely used Libya 4 Desert site in an attempt to evaluate the suitability of the Sonoran Desert site for sensor inter-comparison and calibration stability monitoring. Since the overpass times of ETM+ and MODIS differ by about 30 minutes, the impacts due to different view geometries or test site Bi-directional Reflectance Distribution Function (BRDF) are also presented. In general, the long-term drifts in the visible bands are relatively large compared to the drift in the near-infrared bands of both sensors. The lifetime Top-of-Atmosphere (TOA) reflectance trends from both sensors over 10 years are extremely stable, changing by no more than 0.1% per year (except ETM+ Band 1 and MODIS Band 3) over the two sites used for the study. The use of a semi-empirical BRDF model can reduce the impacts due to view geometries, thus enabling a better estimate of sensor temporal drifts.

Journal of Applied Remote Sensing↗
USGS2022

Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

Idaho, Oregon↗
USGS2016

Evaluating lidar point densities for effective estimation of aboveground biomass

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m 2 , corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m 2 . Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m 2 , and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m 2 , our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.

International Journal of Advanced Remote Sensing a↗
USGS2016

MODIS derived vegetation index for drought detection on the San Carlos Apache Reservation

A variety of vegetation indices derived from remotely sensed data have been used to assess vegetation conditions, enabling the identification of drought occurrences as well as the evaluation of drought impacts. Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite data were used to compute the Modified Soil Adjusted Vegetation Index II (MSAVI 2 ) of four dominant vegetation types over a 13-year period (2002 &ndash; 2014) on the San Carlos Apache Reservation in Arizona, US. MSAVI 2 anomalies were used to identify adverse impacts of drought on vegetation, characterized as mean MSAVI 2 below the 13-year average. In terms of interannual variability, we found similar responses between grassland and shrubland, and between woodland and forest vegetation types. We compared MSAVI 2 for specific vegetation types with precipitation data at the same time step, and found a lag time of roughly two months for the peak MSAVI 2 values following precipitation in a given year. All vegetation types responded to summer monsoon rainfall, while shrubland and annual herbaceous vegetation also displayed a brief spring growing season following winter snowmelt. MSAVI 2 values of shrublands corresponded well with precipitation variability both for summer rainfall and winter snowfall, and can be potentially used as a drought indicator on the San Carlos Apache Reservation given its wide geographic distribution. We demonstrated that moderate temporal frequency satellite-based MSAVI 2 can provide drought monitoring to inform land management decisions, especially on vegetated tribal land areas where in situ precipitation data are limited.

International Journal of Advanced Remote Sensing a↗
USGS2010

Modeling the human invader in the United States

Modern biogeographers recognize that humans are seen as constituents of ecosystems, drivers of significant change, and perhaps, the most invasive species on earth. We found it instructive to model humans as invasive organisms with the same environmental factors. We present a preliminary model of the spread of modern humans in the conterminous United States between 1992 and 2001 based on a subset of National Land Cover Data (NLCD), a time series LANDSAT product. We relied on the commonly used Maxent model, a species-environmental matching model, to map urbanization. Results: Urban areas represented 5.1% of the lower 48 states in 2001, an increase of 7.5% (18,112 km 2 ) in the nine year period. At this rate, an area the size of Massachusetts is converted to urban land use every ten years. We used accepted models commonly used for mapping plant and animal distributions and found that climatic and environmental factors can strongly predict our spread (i.e., the conversion of forests, shrub/grass, and wetland areas into urban areas), with a 92.5% success rate (Area Under the Curve). Adding a roads layer in the model improved predictions to a 95.5% success rate. 8.8% of the 1-km 2 > cells in the conterminous U.S. now have a major road in them. In 2001, 0.8% of 1-km 2 > cells in the U.S. had an urbanness value of > 800, (>89% of a 1-km 2 > cell is urban), while we predict that 24.5% of 1-km 2 > cells in the conterminous U.S. will be > 800 eventually. Main conclusion: Humans have a highly predictable pattern of urbanization based on climatic and topographic variables. Conservation strategies may benefit from that predictability.

Journal of Applied Remote Sensing↗
USGS2011

Use of waveform lidar and hyperspectral sensors to assess selected spatial and structural patterns associated with recent and repeat disturbance and the abundance of sugar maple (Acer saccharum Marsh.) in a temperate mixed hardwood and conifer forest

Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA's Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing the highest levels of sugar maple basal area and biomass. The levels of sugar maple abundance were determined through analysis of the 1997 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) high resolution spectral imagery and inventory of USFS Northern Research Station field plots. We found a relationship between field measurements of stem volume losses and the LVIS metric of mean canopy height (r 2 = 0.66; root mean square errors = 5.7 m 3 /ha, p < 0.0001) in areas that had been subjected to moderate-to-severe ice storm damage, accurately documenting the short-term outcome of a single disturbance event.

Journal of Applied Remote Sensing↗