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DOE OSTI2025

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Quantifying Mangrove Canopy Regrowth and Recovery After Hurricane Irma With Large-Scale Repeat Airborne Lidar in the Florida Everglades

Hurricane Irma caused significant damages to mangrove forested wetlands in south Florida, including defoliation, tree snapping, and uprooting. Previous studies have used optical satellite imagery to estimate large-scale forest disturbance and resilience patterns. However, satellite images alone cannot provide measurements of vertical mangrove structure. In this study, we used dense point cloud data collected by NASA Goddard’s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager before (March 2017) and after (December 2017 and March 2020) Hurricane Irma to quantify the recovery, or lack thereof, of the three-dimensional (3D) mangrove forest structure. Recent resilience and vulnerability models developed from Landsat time series following the storm were used to group the lidar data into distinct disturbance-recovery classes. We then analyzed lidar-based forest canopy within each of the recovery classes to test a suite of forest structural characteristics. Our results indicate that 77.0 % of the survey area experienced canopy height loss three months after Hurricane Irma, whereby the majority of canopy height loss occurred in areas with the tallest mangrove forests (i.e., 15–25 m tall). Our analysis shows that the mangrove canopy height in South Florida increased by an average 0.26 m from December 2017 to March 2020, with most of the forest (84.7 % of the survey area) experiencing canopy height regrowth. However, only 38.1 % of the survey area has recovered to pre-storm canopy height. The distribution of canopy height was significantly altered by Hurricane Irma in the low and intermediate resilience classes, but were not significantly different 2.5 years later. Indeed, in areas of low resilience, little to no vertical change has occurred suggesting the absence of canopy regrowth and natural regeneration. Conversely, mangroves in high resilience class, which are dominated by shorter canopies (<5 m), were not heavily damaged by the storm and have maintained the same structural attributes as those before Hurricane Irma. Our findings highlight that hurricane disturbances significantly alter mangrove forest canopy structure, but recovery of vertical structure varies by resilience classes, species composition, and canopy height.

Lidar↗

NASA NTRS2009

Can a Satellite-Derived Estimate of the Fraction of PAR Absorbed by Chlorophyll (FAPAR(sub chl)) Improve Predictions of Light-Use Efficiency and Ecosystem Photosynthesis for a Boreal Aspen Forest?

Gross primary production (GPP) is a key terrestrial ecophysiological process that links atmospheric composition and vegetation processes. Study of GPP is important to global carbon cycles and global warming. One of the most important of these processes, plant photosynthesis, requires solar radiation in the 0.4-0.7 micron range (also known as photosynthetically active radiation or PAR), water, carbon dioxide (CO2), and nutrients. A vegetation canopy is composed primarily of photosynthetically active vegetation (PAV) and non-photosynthetic vegetation (NPV; e.g., senescent foliage, branches and stems). A green leaf is composed of chlorophyll and various proportions of nonphotosynthetic components (e.g., other pigments in the leaf, primary/secondary/tertiary veins, and cell walls). The fraction of PAR absorbed by whole vegetation canopy (FAPAR(sub canopy)) has been widely used in satellite-based Production Efficiency Models to estimate GPP (as a product of FAPAR(sub canopy)x PAR x LUE(sub canopy), where LUE(sub canopy) is light use efficiency at canopy level). However, only the PAR absorbed by chlorophyll (a product of FAPAR(sub chl) x PAR) is used for photosynthesis. Therefore, remote sensing driven biogeochemical models that use FAPAR(sub chl) in estimating GPP (as a product of FAPAR(sub chl x PAR x LUE(sub chl) are more likely to be consistent with plant photosynthesis processes.

Zhang, Qingyuan↗

NASA NTRS1973

Satellite geological and geophysical remote sensing of Iceland

The author has identified the following significant results. Under a binational, multidisciplinary experiment ERTS-1 imagery is being used to measure and map dynamic natural phenomena in Iceland. A few of the initial results from the project are: (1) a large variety of geological and volcanic features can be studied, particularly on imagery acquired at low sun angle ( 10 deg), which have not been previously recognized; (2) under optimum snow cover conditions, geothermal areas can be discerned by their snowmelt pattern or by warm spring discharge into frozen lakes; (3) a variety of map types at scale of 1:1,000,000 and 1:500,000, can be compiled, made more accurate, or updated (changes in coastline, glaciers, lakes, etc.); (4) the persistence of snow in the highland areas, during the summer months, has important ramifications to rangeland management; (5) false color composites (MSS) permitted the mapping of four types of vegetation; forested, reclaimed, cultivated areas and grasslands, and the mapping of the seasonal change of the vegetation, all of high value to rangeland management when complete, repetitive coverage of Iceland becomes a reality with an operational satellite; and (6) the volcanic eruption on Heimaey was recorded.

Williams, R. S., Jr.↗

NASA NTRS1973

Satellite geological and geophysical remote sensing of Iceland: Preliminary results of geologic, hydrologic, oceanographic, and agricultural studies with ERTS-1 imagery

The author has identified the following significant results. The wide variety of geological and geophysical phenomena which can be observed in Iceland, and particularly their very direct relation to the management of the country's natural resources, has provided great impetus to the use of ERTS-1 imagery to measure and map the dynamic natural phenomena in Iceland. MSS imagery is being used to study a large variety of geological and geophysical eruptive products, geologic structure, volcanic geomorphology, hydrologic, oceanographic, and agricultural phenomena of Iceland. Some of the preliminary results from this research projects are: (1) a large number of geological and volcanic features can be studied from ERTS-1 imagery, particularly imagery acquired at low sun angle, which had not previously been recognized; (2) under optimum conditions the ERTS-1 satellite can discern geothermal areas by their snow melt pattern or warm spring discharge into frozen lakes; (3) various maps at scales of 1:1 million and 1:500,000 can be updated and made more accurate with ERTS-1 imagery; (4) the correlation of water reserves with snowcover can improve the basis for planning electrical production in the management of water resources; (5) false-color composites (MSS) permitted the mapping of four types of vegetation: forested; grasslands, reclaimed, and cultivated areas, and the seasonal change of the vegetation, all of high value to rangeland management.

Williams, R. S., Jr.↗

NASA NTRS1974

The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning

The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is complete on 1:250,000 scale enlargements of ERTS imagery and 1:120,000 hi-flight photography. Maps of geology, soils, vegetation-land use and land resources units were interpreted to show limitations, suitabilities, and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS imagery has shown a number of features not previously mapped in Oregon. A multistage timber inventory of Ochoco National Forest was made, using ERTS images as the first stage. Inventory of forest clear-cutting practices was successfully demonstrated with color composites. Soil tonal differences in fallow fields correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic material classes was successful in separating most major classes around Newberry Caldera, Mt. Washington, and Big Summit Prairie.

Schrumpf, B. J.↗

NASA NTRS2011

Combined Retrievals of Boreal Forest Fire Aerosol Properties with a Polarimeter and Lidar

Absorbing aerosols play an important, but uncertain, role in the global climate. Much of this uncertainty is due to a lack of adequate aerosol measurements. While great strides have been made in observational capability in the previous years and decades, it has become increasingly apparent that this development must continue. Scanning polarimeters have been designed to help resolve this issue by making accurate, multi-spectral, multi-angle polarized observations. This work involves the use of the Research Scanning Polarimeter (RSP). The RSP was designed as the airborne prototype for the Aerosol Polarimetery Sensor (APS), which was due to be launched as part of the (ultimately failed) NASA Glory mission. Field observations with the RSP, however, have established that simultaneous retrievals of aerosol absorption and vertical distribution over bright land surfaces are quite uncertain. We test a merger of RSP and High Spectral Resolution Lidar (HSRL) data with observations of boreal forest fire smoke, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS). During ARCTAS, the RSP and HSRL instruments were mounted on the same aircraft, and validation data were provided by instruments on an aircraft flying a coordinated flight pattern. We found that the lidar data did indeed improve aerosol retrievals using an optimal estimation method, although not primarily because of the constraints imposed on the aerosol vertical distribution. The more useful piece of information from the HSRL was the total column aerosol optical depth, which was used to select the initial value (optimization starting point) of the aerosol number concentration. When ground based sun photometer network climatologies of number concentration were used as an initial value, we found that roughly half of the retrievals had unrealistic sizes and imaginary indices, even though the retrieved spectral optical depths agreed within uncertainties to independent observations. The convergence to an unrealistic local minimum by the optimal estimator is related to the relatively low sensitivity to particles smaller than 0.1 ( m) at large optical thicknesses. Thus, optimization algorithms used for operational aerosol retrievals of the fine mode size distribution, when the total optical depth is large, will require initial values generated from table look-ups that exclude unrealistic size/complex index mixtures. External constraints from lidar on initial values used in the optimal estimation methods will also be valuable in reducing the likelihood of obtaining spurious retrievals.

Knobelspiesse, K.↗

NASA NTRS2019

Developing an Aircraft-Based Angular Distribution Model of Solar Reflection from Wildfire Smoke to Aid Satellite-Based Radiative Flux Estimation

This study examines the angular distribution of scattered solar radiation associated with wildfire smoke aerosols observed over boreal forests in Canada during the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) campaign. First, it estimates smoke radiative parameters (550 nm optical depth of 3.9 and single scattering albedo of 0.90) using quasi-simultaneous multiangular and multispectral airborne measurements by the Cloud Absorption Radiometer (CAR). Next, the paper estimates the broadband top-of-atmosphere radiances that a satellite instrument such as the Clouds and the Earth’s Radiant Energy System (CERES) could have observed, given the narrowband CAR measurements made from an aircraft circling about a kilometer above the smoke layer. This estimation includes both an atmospheric correction that accounts for the atmosphere above the aircraft and a narrowband-to-broadband conversion. The angular distribution of estimated radiances is found to be substantially different than the angular model used in the operational data processing of CERES observations over the same area. This is because the CERES model is a monthly average model that was constructed using observations taken under smoke-free conditions. Finally, a sensitivity analysis shows that the estimated angular distribution remains accurate for a fairly wide range of smoke and underlying surface parameters. Overall, results from this work suggest that airborne CAR measurements can bring some substantial improvements in the accuracy of satellite-based radiative flux estimates.

Varnai, Tamas↗

NASA NTRS1973

Investigation of the detection and monitoring of forest insect infestation in the Sierra Nevada Mountains of California

The author has identified the following significant results. Results of analysis of ERTS-1 color composites made by NASA from MSS bands 4, 5, and 7, frame #1055-18055 at a scale of 1:1,000,000 indicate that forests damaged by insects can be delineated and mapped from areas with no damage; and at this same scale other details detected include timbered and nontimbered areas, pasture and agricultural land, deserts, lakes, mountain meadows, riparian vegetation, rock domes, old burned areas, and major stream courses. Enlargements from the above to a scale of 1:80,000 have improved detectibility to the point that three degrees of timber mortality can be identified and mapped.

Hall, R. C.↗

NASA NTRS1992

Biophysical, morphological, canopy optical property, and productivity data from the Superior National Forest

Described here are the results of a NASA field experiment conducted in the Superior National Forest near Ely, Minnesota, during the summers of 1983 and 1984. The purpose of the experiment was to examine the use of remote sensing to provide measurements of biophysical parameters in the boreal forests. Leaf area index, biomass, net primary productivity, canopy coverage, overstory and understory species composition data are reported for about 60 sites, representing a range of stand density and age for aspen and spruce. Leaf, needle, and bark high-resolution spectral reflectance and transmittance data are reported for the major boreal forest species. Canopy bidirectional reflectance measurements are provided from a helicopter-mounted Barnes Multiband Modular Radiometer (MMR) and the Thematic Mapper Simulator (TMS) on the NASA C-130 aircraft.

Hall, F. G.↗

DOE OSTI2023

Soil organic matter diagenetic state informs boreal forest ecosystem feedbacks to climate change

The fate of soil organic carbon (SOC) in boreal forests is dependent on the integrative ecosystem response to climate change. For example, boreal forest productivity is often nitrogen (N) limited, and climate warming can enhance N cycling and primary productivity. However, the net effect of this feedback on the SOC reservoir and its longevity with climate change remain unclear due to difficulty in detecting small differences between large and variable carbon (C) fluxes needed to determine net changes in soil reservoirs. The diagenetic state of SOC – resulting from the physicochemical and biological transformations that alter the original biomolecular composition of detrital inputs to soil over time – is useful for tracing the net response of SOC at the timescales relevant to climate change not usually discernible from fluxes and stocks alone. Here, we test two hypotheses using a mesic boreal forest climate transect: (1) the SOC diagenetic state is maintained across this climosequence, and (2) the maintenance of the SOC diagenetic state is a consequence of coupled soil C and N cycling, signifying the role of enhanced N cycling supporting SOC inputs that maintain SOC stocks within the warmer-climate forests. Shifts in nonvascular to vascular plant inputs with climate observed in these and other boreal forests highlighted the need to carefully separate biogeochemical indicators of SOC source from those signifying diagenetic alteration. We thus evaluated and applied lignin biomarkers to assess the diagenetic alteration of SOC in these boreal forest organic soils and directly compared the lignin diagenetic state with that of soil organic nitrogen (SON) assessed through amino acid composition. The lignin diagenetic state remained constant across the climate transect, indicating a balance between the input and removal of lignin in these mesic boreal forests. When combined with previous knowledge of these forest ecosystems, including the diagenetic state of SON and direct measures of C fluxes and stocks, the results indicate a coupled increase in C and N cycling with climate warming that supports forest productivity and maintains SOC stocks. This balance could markedly shift as other factors begin to limit forest productivity (e.g., trace nutrients, water) with further climate change or affect forest nutrient allocation (e.g., forest age or compositional change). Further application of the approach presented here could be used to detect the limits of this and other ecosystem–climate feedbacks, by providing a tractable and parameterizable index of the lignin state across large spatial scales, necessary for ecosystem-scale parameterizations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Improving Post-Hurricane Katrina Forest Management with MODIS Time Series Products

Hurricane damage to forests can be severe, causing millions of dollars of timber damage and loss. To help mitigate loss, state agencies require information on location, intensity, and extent of damaged forests. NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data products offers a potential means for state agencies to monitor hurricane-induced forest damage and recovery across a broad region. In response, a project was conducted to produce and assess 250 meter forest disturbance and recovery maps for areas in southern Mississippi impacted by Hurricane Katrina. The products and capabilities from the project were compiled to aid work of the Mississippi Institute for Forest Inventory (MIFI). A series of NDVI change detection products were computed to assess hurricane induced damage and recovery. Hurricane-induced forest damage maps were derived by computing percent change between MODIS MOD13 16-day composited NDVI pre-hurricane "baseline" products (2003 and 2004) and post-hurricane NDVI products (2005). Recovery products were then computed in which post storm 2006, 2007, 2008 and 2009 NDVI data was each singularly compared to the historical baseline NDVI. All percent NDVI change considered the 16-day composite period of August 29 to September 13 for each year in the study. This provided percent change in the maximum NDVI for the 2 week period just after the hurricane event and for each subsequent anniversary through 2009, resulting in forest disturbance products for 2005 and recovery products for the following 4 years. These disturbance and recovery products were produced for the Mississippi Institute for Forest Inventory's (MIFI) Southeast Inventory District and also for the entire hurricane impact zone. MIFI forest inventory products were used as ground truth information for the project. Each NDVI percent change product was classified into 6 categories of forest disturbance intensity. Stand age and stand type raster data, also provided by MIFI, were used along with the forest disturbance/recovery products to create forest damage stratification products integrating 3 stand type classes, 6 stand age classes, and 6 forest disturbance intensity classes. This stratification product will be used to aid MIFI timber inventory planning and to prepare for damage assessments due to future hurricane events. Validation of MODIS percent NDVI change products was performed by comparing the MODIS percent NDVI change products to those from Landsat data for the same time and MIFI inventory district area.

Lewis, Mark David↗

NASA NTRS1999

Mapping Vegetation Structure in Kakadu National Park: An AIRSAR and GIS Application in Conservation

Airborne Synthetic Aperture Radar (AIRSAR) data were used to map vegetation structure in Kakadu National Park Australia as part of the PACRIM project. SAR data were co-registered with Landsat TM, aerial photos, and map data in a geographic information system for a small test area consisting of mangrove, floodplain grasslands, lowland tropical evergreen forest and upland mixed deciduous and evergreen tropical forest near the South Alligator River. Landsat (Thematic Mapper) TM very clearly showed the floristic composition and burn scars from the previous years fires and the AIRSAR data provided a profile of vegetation structure. Extensive field data on vegetation species composition and structure were collected across a series of transects in cooperation with a survey of avifauna in an effort to link the habitat edge structure with bird species responses. A test site was found that contained two types of habitat edges: 1) A structure specific edge - characterized by the appearance of a very strong structural change in the forest canopy occurring in the absence of a substantial turnover in floristics. 2) Floristic edge - a sharp transition in vegetation genetic composition with a mixed set of structural changes. Specific polarization combinations were selected that were highly correlated to a set of desired structural parameters found in the field data. Classification routines were employed to group radar pixels into 3 structural classes based on: the Surface Area to Volume ratio (SA/V) of the stems, the SA/V of the branches, and the leaf area index of the canopy. Separate canopy structure maps were then entered into the GIS and bird responses were observed relative to the classes and their boundaries. Follow-on work will consist of extending this approach to neighboring areas, generating structure maps, predicting bird responses across the edges, and make accuracy assessments.

Imhoff, Marc L.↗

NASA NTRSDate not supplied

Using NASA Earth Observations to Identify Wildfire Impacts on Hydrologic Functions and Recovery in the Gila National Forest

Wildfires can dramatically influence both abiotic and biotic components of a landscape, including soil stability and chemistry; vegetation health, density, and composition; and water flow and quality. In addition to the immediate hazards posed by wildfires, subsequent hazards such as flooding and debris flow can impact the area. In New Mexico’s Gila National Forest, wildfire events have occurred with increasing frequency and severity over recent years, notably including the Whitewater Baldy Complex Fire (2012) and Silver Fire (2013), which burned over 290,000 acres and 138,698 acres, respectively, according to the Burned Area Emergency Response Team Executive Summary reports. Following a significant wildfire, land and resource management decisions are made, including when and where to focus vegetation and stream restoration efforts. This research is the result of a collaboration between the NASA DEVELOP National Program and the US Forest Service, which aimed to utilize NASA Earth observations to help inform those critical land management decisions. The project had two main objectives: 1) assess the efficacy of post-fire vegetation restoration efforts and 2) analyze the impact of burn events on the hydrology at the watershed-scale. To accomplish this first goal, data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) were used to study the vegetation health over time of treated and non-treated burn areas. To accomplish the latter, various aspects of stream behavior were examined in relation to burn events, including the runoff coefficient and “flashiness” of streams based on USGS stream gauge data and Integrated Multi-satelllite Retrievals for GPM (IMERG) precipitation data. This will allow land managers to better understand the complex ecological relationship between the cascading effects after wildfire and the greater forest ecosystem.

Madeline Allen↗

NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗

NASA NTRS1978

Improving forest cover classification accuracy from Landsat by incorporating topographic information

The paper shows that accuracies of computer classification of species-specific forest cover types from Landsat imagery can be improved by 27% or more through the incorporation of topographic information from digital terrain tapes registered to multidate Landsat imagery. The topographic information improves classification accuracies because many common forest tree species have preferred elevation ranges and slope aspects. These preferences allow the separation of forest cover types which have similar spectral signatures but different species compositions. It is noted that the development of a classification system which uses prior probabilities and sets of prior probabilities conditioned by one or two external variables represents a significant increase in classification power.

Strahler, A. H.↗

NASA NTRS1984

Development and evaluation of techniques for using combined microwave and optical image data for vegetation studies

Techniques for using combined image data from the Shuttle Imaging Radar (SIR-B) and the LANDSAT Thematic Mapper (TM) or Multispectral Scanner (MSS) for studies of irrigated crops, and boreal and deciduous forests are developed and evaluated. The effects of the structure and composition of crop canopies and soil surfaces on multiangle L-band HH (Horizontal polarization for transmission and reception) backscattering and on optical reflectance (in TM or MSS bands viewed at the nadir) are investigated. The relative accuracy of digital, calibrated SIR-B image data and LANDSAT TM or MSS image data is evaluated. Textural information extraction-techniques are developed for radar and optical image analysis.

Paris, J. F.↗

NASA NTRS2001

Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques

Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

Guild, Liane S.↗