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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

Monitoring forest land from high altitude and from space

Forest inventory, forest stress, and standardization and calibration studies are presented. These include microscale photointerpretation of forest and nonforest land classes, multiseasonal film densities for automated forest and nonforest land classification, trend and spread of bark beetle infestations from 1968 through 1971, aerial photography for determining optimum levels of stand density to reduce such infestations, use of airborne spectrometers and multispectral scanners for previsual detection of Ponderosa pine trees under stress from insects and diseases, establishment of an earth resources technology satellite test site in the Black Hills and the identification of natural resolution targets, detection of root disease impact on forest stands by sequential orbital and suborbital multispectral photography, and calibration of color aerial photography.

Source record↗
NASA NTRS1993

Satellite inventory of Minnesota forest resources

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Bauer, Marvin E.↗
NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗
NASA NTRS1973

ERTS data user investigation to develop a multistage forest sampling inventory system

The author has identified the following significant results. A system to provide precision annotation of predetermined forest inventory sampling units on the ERTS-1 MSS images was developed. In addition, an annotation system for high altitude U2 photographs was completed. MSS bulk image accuracy is good enough to allow the use of one square mile sampling units. IMANCO image analyzer interpretation work for small scale images demonstrated the need for much additional analyses. Continuing image interpretation work for the next reporting period is concentrated on manual image interpretation work as well as digital interpretation system development using the computer compatible tapes.

Langley, P. G.↗
NASA NTRS2007

Using MODIS and GLAS Data to Develop Timber Volume Estimates in Central Siberia

The boreal forest is the Earth's largest terrestrial biome, covering some 12 million km2 and accounting for about one third of this planet's total forest area. Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. Ground based forest inventories, have much uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse and/or lacking. In addition, many of the forest inventories that do exist for Siberia are now a decade or more old. Thus, available forest inventories fail to capture the current conditions. Changes in forest structure in a particular forest-type and region can change significantly due to changing environment conditions, and natural and anthropogenic disturbance. Remote sensing methods can potentially overcome these problems. Multispectral sensors can be used to provide vegetation cover maps that show a timely and accurate geographic distribution of vegetation types rather than decade old ground based maps. Lidar sensors can be used to directly obtain measurements that can be used to derive critical forest structure information (e.g., height, density, and volume). These in turn can used to estimate biomass components using allometric equations without having to use out dated forest inventory. Finally, remote sensing data is ideally suited to provide a sampling basis for a rigorous statistical estimate of the variance and error bound on forest structure measures. In this study, new remote sensing methods were applied to develop estimates timber volume using NASA's MODerate resolution Imaging Spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) for a 10 deg x 10 deg area in central Siberia. Using MODIS and GLAS data, maps were produced for cover type and timber volume for 2003, and a realistic variance (error bound) for timber volume was calculated for the study area. In this 'study we used only GLAS footprints that had a slope value of less than 10 deg. This was done to avoid large errors due to the effect of slope on the GLAS models. The method requires the integration of new remote sensing methods with available ground studies of forest timber volume conducted in Russian forests. The results were compared to traditional ground forest inventory methods reported in the literature and to ground truth collected in the study area.

Ranson, K. Jon↗
NASA NTRS1973

Automatic photointerpretation for plant species and stress identification (ERTS-A1)

The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained.

Swanlund, G. D.↗
NASA NTRS1973

Automatic interpretation of ERTS data for forest management

Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wild life management, forest inventory and forest condition monitoring. Automatic procedures based on both multi-spectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74% was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 99% was obtained.

Kirvida, L.↗
NASA NTRS2016

Lidar-Based Estimates of Above-Ground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log-linear model result (63.29 +/-1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log-linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon,−40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., N2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10 +/- 0.88 t/ha, and the total biomass for the country, given a total forest area of 688,096 sq km, is 3.65 +/- 0.06 Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152 Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06 Gt) by 19%.

Hybrid 3-Phase sampling↗
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 NTRS1979

Multistage variable probability forest volume inventory

The net board foot volume (Scribner log rule) of the standing Ponderosa pine timber on the Defiance Unit of the Navajo Nation's forested land was estimated using a multistage forest volume inventory scheme with variable sample selection probabilities. The inventory designed to accomplish this task required that both LANDSAT MSS digital data and aircraft acquired data be used to locate one acre ground splits, which were subsequently visited by ground teams conducting detailed tree measurements using an optical dendrometer. The dendrometer measurements were then punched on computer input cards and were entered in a computer program developed by the U.S. Forest Service. The resulting individual tree volume estimates were then expanded through the use of a statistically defined equation to produce the volume estimate for the entire area which includes 192,026 acres and is approximately a 44% the total forested area of the Navajo Nation.

Anderson, J. E.↗
NASA NTRS2012

Contrasting Patterns of Damage and Recovery in Logged Amazon Forests From Small Footprint LiDAR Data

Tropical forests ecosystems respond dynamically to climate variability and disturbances on time scales of minutes to millennia. To date, our knowledge of disturbance and recovery processes in tropical forests is derived almost exclusively from networks of forest inventory plots. These plots typically sample small areas (less than or equal to 1 ha) in conservation units that are protected from logging and fire. Amazon forests with frequent disturbances from human activity remain under-studied. Ongoing negotiations on REDD+ (Reducing Emissions from Deforestation and Forest Degradation plus enhancing forest carbon stocks) have placed additional emphasis on identifying degraded forests and quantifying changing carbon stocks in both degraded and intact tropical forests. We evaluated patterns of forest disturbance and recovery at four -1000 ha sites in the Brazilian Amazon using small footprint LiDAR data and coincident field measurements. Large area coverage with airborne LiDAR data in 2011-2012 included logged and unmanaged areas in Cotriguacu (Mato Grosso), Fiona do Jamari (Rondonia), and Floresta Estadual do Antimary (Acre), and unmanaged forest within Reserva Ducke (Amazonas). Logging infrastructure (skid trails, log decks, and roads) was identified using LiDAR returns from understory vegetation and validated based on field data. At each logged site, canopy gaps from logging activity and LiDAR metrics of canopy heights were used to quantify differences in forest structure between logged and unlogged areas. Contrasting patterns of harvesting operations and canopy damages at the three logged sites reflect different levels of pre-harvest planning (i.e., informal logging compared to state or national logging concessions), harvest intensity, and site conditions. Finally, we used multi-temporal LiDAR data from two sites, Reserva Ducke (2009, 2012) and Antimary (2010, 2011), to evaluate gap phase dynamics in unmanaged forest areas. The rates and patterns of canopy gap formation at these sites illustrate potential issues for separating logging damages from natural forest disturbances over longer time scales. Multi-temporal airborne LiDAR data and coincident field measurements provide complementary perspectives on disturbance and recovery processes in intact and degraded Amazon forests. Compared to forest inventory plots, the large size of each individual site permitted analyses of landscape-scale processes that would require extremely high investments to study using traditional forest inventory methods.

Morton, D. C.↗
NASA NTRS1992

RGB-NDVI colour composites for visualizing forest change dynamics

The study presents a simple and logical technique to display and quantify forest change using three dates of satellite imagery. The normalized difference vegetation index (NDVI) was computed for each date of imagery to define high and low vegetation biomass. Color composites were generated by combining each date of NDVI with either the red, green, or blue (RGB) image planes in an image display monitor. Harvest and regeneration areas were quantified by applying a modified parallelepiped classification creating an RGB-NDVI image with 27 classes that were grouped into nine major forest change categories. Aerial photographs and stand history maps are compared with the forest changes indicated by the RGB-NDVI image. The utility of the RGB-NDVI technique for supporting forest inventories and updating forest resource information systems are presented and discussed.

Sader, S. A.↗
NASA NTRS2018

Mapping Pine Plantations in the Southeastern U.S. Using Structural, Spectral, and Temporal Remote Sensing Data

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest coverin the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations.Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.

Fagan, M. E.↗
NASA NTRS2018

Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r(sup 2) = 0.91), BA (r(sup 2) = 0.79), and AGB (r(sup 2) = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.

aboveground biomass↗
NASA NTRS1984

Improvements in forest classification and inventory using remotely sensed data

A Forest Classification and Inventory System (Focis) has been developed for large area forest inventories on the basis of Landsat and digital terrain data. It is a potential advantage of Focis that it can provide timely inventories at a reduced cost which are easily updated. The Klamath National Forest in Northern California was employed as test area for the initial development of Focis. Focis is constantly being changed and improved. Two recent additions to the inventory system include a spatial filtering algorithm which improves the spatial coherence in the final classified image, and a modification to the classification procedure designed to reduce the adverse effects of local topography on classification accuracy. Attention is given to a Focis overview, spatial filtering, the interface with the forest service geographic information system, and efforts to reduce the influence of topography.

Woodcock, C. E.↗
NASA NTRS1979

Multistage variable probability forest volume inventory

An inventory scheme based on the use of computer processed LANDSAT MSS data was developed. Output from the inventory scheme provides an estimate of the standing net saw timber volume of a major timber species on a selected forested area of the Navajo Nation. Such estimates are based on the values of parameters currently used for scaled sawlog conversion to mill output. The multistage variable probability sampling appears capable of producing estimates which compare favorably with those produced using conventional techniques. In addition, the reduction in time, manpower, and overall costs lend it to numerous applications.

Anderson, J. E.↗
NASA NTRS2018

Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar

Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world's biodiversity hotspots, Madagascar's lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model's ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with r(squared) values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m(squared)/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and -1 t Cha(exp -1)year(exp -1), consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests.

Forest Productivity↗
NASA NTRS1976

The ten-ecosystem study investigation plan

With the continental United States divided into ten forest and grassland ecosystems, the Ten Ecosystem Study (TES) is designed to investigate the feasibility and applicability of state-of-the-art automatic data processing remote sensing technology to inventory forest, grassland, and water resources by using Land Satellite data. The study will serve as a prelude to a possible future nationwide remote sensing application to inventory forest and rangeland renewable resources. This plan describes project design and phases, the ten ecosystem, data utilization and output, personnel organization, resource requirements, and schedules and milestones.

Kan, E. P.↗