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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA

This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF), and Multi-Layer Perceptron (MLP) algorithms for LAI and BAPH modeling. The results showed that RF outperformed MLP by reducing the normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. The final variables selected for modeling of both LAI and BAPH indicated the superiority of Sentinel-2 variables over the Sentinel-1 SAR with minor contributions of site variables (mainly elevation). The red-edge spectral vegetation indices played a significant role in both LAI and BAPH estimation. We attained the lowest nRMSEs of 0.12, and 0.16 for the final LAI model of RS, and BF, respectively using Sentinel-2 and site variables. The lowest nRMSE for both RS and BF BAPH models was 0.12. As RS and BF are the primary host species for a cyclically occurring and most destructive pest of the region, eastern spruce budworm (Choristoneura fumiferana; SBW), these estimations will be useful to evaluate SBW dynamics in the region.

Forest inventory↗
NASA NTRS2017

Climate Limits Across Space and Time on European Forest Structure

The impact climate has on forests has been extensively studied. However, the large scale effect climate has on forest structures, such as average diameters, heights and basal area are understudied in a spatially explicit manner. The limits, tipping points and thresholds that climate places on forest structures dictate the services a forest may provide, the vulnerability of a forest to mortality and the potential value of the timber there within. The majority of current research either investigates climate impacts on forest pools and fluxes, on a tree physiological scale or on case studies that are used to extrapolate results and potential impacts. A spatially explicit study on how climate affects forest structure over a large region would give valuable information to stakeholders who are more concerned with ecosystem services that cannot be described by pools and fluxes but require spatially explicit information - such as biodiversity, habitat suitability, and market values. In this study, we quantified the limits that climate (maximum, minimum temperature and precipitation) places on 3 forest structures, diameter at breast height, height, and basal area throughout Europe. Our results show clear climatic zones of high and low upper limits for each forest structure variable studied. We also spatially analyzed how climate restricts the potential bio-physical upper limits and creates tipping points of each forest structure variable and which climate factors are most limiting. Further, we demonstrated how the climate change has affected 8 individual forests across Europe and then the continent as a whole. We find that diameter, height and basal area are limited by climate in different ways and that areas may have high upper limits in one structure and low upper limits in another limited by different climate variables. We also found that even though individual forests may have increased their potential upper limit forest structure values, European forests as a whole have lost, on average, 5.0%, 1.7% and 6.5% in potential mean forest diameter, height and basal area, respectively.

Moreno, Adam La Salle↗
NASA NTRS2017

Evaluation of Sentinel-1A Data For Above Ground Biomass Estimation in Different Forests in India

Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented.

Data↗
NASA NTRS1988

Parametric analysis of synthetic aperture radar data for the study of forest stand characteristics

A parametric analysis of a Gulf Coast forest stand was performed using multipolarization, multipath airborne SAR data, and forest plot properties. Allometric equations were used to compute the biomass and basal area for the test plots. A multiple regression analysis with stepwise selection of independent variables was performed. It is found that forest stand characteristics such as biomass, basal area, and average tree height are correlated with SAR data.

Wu, Shih-Tseng↗
NASA NTRS2018

Mapping Forest Disturbance Intensity in North and South Carolina Using Annual Landsat Observations and Field Inventory Data

Disturbance and regrowth are vital processes in determining the roles of forest ecosystem in the carbon and biogeochemical cycles. Using time series observations, the vegetation change tracker (VCT) algorithm was designed to map the location, timing, and spectral magnitudes of forest disturbance events. While these spectral disturbance magnitudes are indicative of physical changes in tree cover or biomass, their quantitative relationships have yet to be established. This study focuses on estimating disturbance intensity as measured by percent basal area removal using spectral indices from the VCT algorithm over North and South Carolina. Repeat measurements on Forest Service Forest Inventory Analysis (FIA) ground plots, which provide changes in basal area between multiple dates at precise locations, are used for training and validation of the model. The overall R2 between predicted disturbance intensity and reference data is 0.66, and cross-validation prediction uncertainty is 14% in North Carolina. Possible causes of this uncertainty could be site heterogeneity and the temporal offset between ground measurements and satellite observations. Results show the area of stand clearing disturbances remains relatively stable around 1143 km2 yr−1 in North and South Carolina throughout the period of observations (1985–2015). The average amount of forest area affected by partial disturbance is much higher at 3287 km2 yr−1. The area of partial disturbances has strong inter-annual variability with a high value of 6000 km2 in 2007 and a low value of 1919 km2 in 2013.

Xin Tao↗
NASA NTRS2009

Estimating Volume, Biomass, and Carbon in Hedmark County, Norway Using a Profiling LiDAR

A profiling airborne LiDAR is used to estimate the forest resources of Hedmark County, Norway, a 27390 square kilometer area in southeastern Norway on the Swedish border. One hundred five profiling flight lines totaling 9166 km were flown over the entire county; east-west. The lines, spaced 3 km apart north-south, duplicate the systematic pattern of the Norwegian Forest Inventory (NFI) ground plot arrangement, enabling the profiler to transit 1290 circular, 250 square meter fixed-area NFI ground plots while collecting the systematic LiDAR sample. Seven hundred sixty-three plots of the 1290 plots were overflown within 17.8 m of plot center. Laser measurements of canopy height and crown density are extracted along fixed-length, 17.8 m segments closest to the center of the ground plot and related to basal area, timber volume and above- and belowground dry biomass. Linear, nonstratified equations that estimate ground-measured total aboveground dry biomass report an R(sup 2) = 0.63, with an regression RMSE = 35.2 t/ha. Nonstratified model results for the other biomass components, volume, and basal area are similar, with R(sup 2) values for all models ranging from 0.58 (belowground biomass, RMSE = 8.6 t/ha) to 0.63. Consistently, the most useful single profiling LiDAR variable is quadratic mean canopy height, h (sup bar)(sub qa). Two-variable models typically include h (sup bar)(sub qa) or mean canopy height, h(sup bar)(sub a), with a canopy density or a canopy height standard deviation measure. Stratification by productivity class did not improve the nonstratified models, nor did stratification by pine/spruce/hardwood. County-wide profiling LiDAR estimates are reported, by land cover type, and compared to NFI estimates.

Nelson, Ross↗
NASA NTRS1986

Analysis of forest structure using thematic mapper simulator data

The potential of Thematic Mapper Simulator (TMS) data for sensing forest structure information has been explored by principal components and feature selection techniques. In a survey of forest structural properties conducted for 123 field sites of the Sequoia National Park, the canopy closure could be well estimated (r = 0.62 to 0.69) by a variety of channel bands and band ratios, without reference to the forest type. Estimation of the basal area was less successful (r = 0.51 or less) on the average, but could be improved for certain forest types when data were stratified by floristic composition. To achieve such a stratification, individual sites were ordinated by a detrended correspondence analysis based on the canopy of dominant species. The analysis of forest structure in the Sequoia data suggests that total basal area can be best predicted in stands of lower density, and in younger even-aged managed stands.

Peterson, D. L.↗
NASA NTRS2014

An Efficient, Multi-Layered Crown Delineation Algorithm for Mapping Individual Tree Structure Across Multiple Ecosystems

Deriving individual tree information from discrete return, small footprint LiDAR data may improve forest above ground biomass estimates, and provide tree-level information that is important in many ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershed based delineation of a CHM, which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data from the Smithsonian Environmental Research Center (SERC), Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithm correctly identified 70% of dominant trees, 58% of co-dominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithm had difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site in Maryland. The ability for the algorithm to reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.

LiDAR↗
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 NTRS1989

Relationships between multipolarized radar backscatter and slash pine stand parameters

Multipolarized L-band (24.5 cm) aircraft radar data was obtained for a primarily forested area in northern Florida. Based on the results of previous studies by Hoffer and Hussin (1989), a swath of medium incidence angle (35-25 deg) data was defined. Three groups of slash pine stands were located in the data: 4- to 17-year-old plantations, 18- to 48-year-old plantations, and 16- to 53-year-old natural stands. Stand data obtained from the forest-products companies operating in the area include age, tree height, diameter-at-breast height, basal area, volume (cords/acre), and density (trees/acre). Each of these stand parameters were compared to each of the four polarizations (HH, VV, VH, and HV) of the radar data for each group of stands. Statistically significant relationships were found between the radar backscatter and the forest stand parameters only for the 4- to 17-year-old slash pine plantation stands. In general, the cross-polarized radar backscatter was more highly correlated with the various stand parameters than the like-polarized backscatter, and the VV-polarized data were more highly correlated than the HH-polarized data.

Hussin, Yousif Ali↗
NASA NTRS2002

Imaging Laser Altimetry in the Amazon: Mapping Large Areas of Topography, Vegetation Height and Structure, and Biomass

A flight mission of NASA GSFC's Laser Vegetation Imaging Sensor (LVIS) is planned for June-August 2003 in the Amazon region of Brazil. The goal of this flight mission is to map the vegetation height and structure and ground topography of a large area of the Amazon. This data will be used to produce maps of true ground topography, vegetation height, and estimated above-ground biomass and for comparison with and potential calibration of Synthetic Aperture Radar (SAR) data. Approximately 15,000 sq. km covering various regions of the Amazon will be mapped. The LVIS sensor has the unique ability to accurately sense the ground topography beneath even the densest of forest canopies. This is achieved by using a high signal-to-noise laser altimeter to detect the very weak reflection from the ground that is available only through small gaps in between leaves and between tree canopies. Often the amount of ground signal is 1% or less of the total returned echo. Once the ground elevation is identified, that is used as the reference surface from which we measure the vertical height and structure of the vegetation. Test data over tropical forests have shown excellent correlation between LVIS measurements and biomass, basal area, stem density, ground topography, and canopy height. Examples of laser altimetry data over forests and the relationships to biophysical parameters will be shown. Also, recent advances in the LVIS instrument will be discussed.

Blair, J. Bryan↗
NASA NTRS1985

On the use of L-band multipolarization airborne SAR for surveys of crops, vineyards, and orchards in a California irrigated agricultural region

The airborne L-band synthetic aperture radar (SAR) collected multipolarization calibrated image data over an irrigated agricultural test site near Fresno, CA, on March 6, 1984. The conclusions of the study are as follows: (1) the effects of incidence angle on the measured backscattering coefficients could be removed by using a correction factor equal to the secant of the angle raised to the 1.4 power, (2) for this scene and time of year, the various polarization channels were highly correlated such that the use of more than one polarization added little to the ability of the radar to discriminate vegetation type or condition; the exception was barley which separated from vineyards only when a combination of like and cross polarization data were used (polarization was very useful for corn identification in fall crops), (3) an excellent separation between herbaceous vegetation (alfalfa, barley, and oats) or bare fields and trees in orchards existed in brightness was well correlated to alfalfa height or biomass, especially for the HH polarization combination, (5) vineyards exhibited a narrow range of brightnesses with no systematic effects of type or number of stakes nor of number of wires in the trellises nor of the size of the vines, (6) within the orchard classes, areal biomass characterized by basal area differences caused radar image brightness differences for small to medium trees but not for medium to large trees.

Paris, J. F.↗
NASA NTRS1985

Forest discrimination with multipolarization imaging radar

The use of radar polarization diversity for discriminating forest canopy variables on airborne synthetic-aperture radar (SAR) images is evaluated. SAR images were acquired at L-Band (24.6 cm) simultaneously in four linear polarization states (HH, HV, VH, and VV) in South Carolina on March 1, 1984. In order to relate the polarization signatures to biophysical properties, false-color composite images were compared to maps of forest stands in the timber compartment. In decreasing order, the most useful correlative forest data are stand basal area, forest age, site condition index, and forest management type. It is found that multipolarization images discriminate variation in tree density and difference in the amount of understory, but do not discriminate between evergreen and deciduous forest types.

Ford, J. P.↗
NASA NTRS1985

Forest discrimination with multipolarization imaging radar

The relations between polarization signatures and biophysical characteristics through a range of different forest environments were investigated using airborne synthetic-aperture (SAR) images acquired at L-band on March 1, 1984 in South Carolina. SAR data acquired in four linear polarization states with 10-m spatial resolution were encoded as color composite images and compared to US Forest Service forest stand data. The most useful correlative forest data were stand basal area, forest age, site condition index, and forest management type. It is found that the multipolarization images discriminate variation in tree density or difference in the amount of understory, but no evidence has been found for discrimination between evergreen and deciduous forest types.

Ford, J. P.↗
NASA NTRS1987

Multipolarization SAR data for surface feature delineation and forest vegetation characterization

This paper presents the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation and forest vegetation characterization. Three channels of radioed data (VV/HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The radioed data are linearly stretched to yield a digital number within a range of 0 to 255. The techniques for reducing SAR speckle noise and for measuring the degree of separation are discussed. For surface feature delineation, the results indicate that cross polarization as well as cross polarization radioed data better delineate those surface features that are difficult to separate by like polarization data. The results suggest using a median value filtering technique to reduce within-plot data fluctuation to increase the separability measure. For forest vegetation characterization, the results indicate that multipolarization SAR data may be used to estimate forest properties such as total-tree biomass, basal area, and tree height.

Wu, Shih-Tseng↗
NASA NTRS1987

Parametric analysis of synthetic aperture radar data for characterization of deciduous forest stands

The SAR sensor parameters that affect the estimation of deciduous forest stand characteristics were examined using data sets for the Gulf Coastal Plain region, acquired by the NASA/JPL multipolarization airborne SAR. In the regression analysis, the mean digital-number values of the three polarization data are used as the independent variables to estimate the average tree height (HT), basal area (BA), and total-tree biomass (TBM). The following results were obtained: (1) in the case of simple regression and using 28 plots, vertical-vertical (VV) polarization yielded the largest correlation coefficients (r) in estimating HT, BA, and TBM; (2) in the case of multiple regression, the horizontal-horizontal (HH) and VV polarization combination yielded the largest r value in estimating HT, while the VH and HH polarization combination yielded the largest r values in estimating BA and TBM. With the addition of a third polarization, the increase in r values is insignificant.

Wu, Shih-Tseng↗
NASA NTRS1991

Estimating slash pine biomass using radar backscatter

L-band HV multiple-incidence-angle aircraft synthetic aperture radar (SAR) data were analyzed in relation to average stand biomass, basal area, and tree height for 55 slash pine plantations located in northern Florida. This information was used to develop a system of equations to predict average stand biomass as a function of L-band (24.5-cm) radar backscatter. The system of equations developed in this study using three-stage least-squares and combinatorial screening accounted for 97 percent of the variability observed in average stand biomass per hectare. When applied to an independent data set, the biomass equations had an average bias of less than 1 percent with a standard error of approximately 3 percent. These results indicate that future Shuttle Imaging Radar Systems (e.g., SIR-C, which will have cross-polarized radar sensors) should be able to obtain better estimates of forest biomass than were obtained with previous satellite radar missions, which utilized only HH-polarized SAR data.

Hussin, Yousif Ali↗
NASA NTRS1993

Relating P-band AIRSAR backscatter to forest stand parameters

As part of research on forest ecosystems, the Jet Propulsion Laboratory (JPL) and collaborating research teams have conducted multi-season airborne synthetic aperture radar (AIRSAR) experiments in three forest ecosystems including temperate pine forest (Duke, Forest, North Carolina), boreal forest (Bonanza Creek Experimental Forest, Alaska), and northern mixed hardwood-conifer forest (Michigan Biological Station, Michigan). The major research goals were to improve understanding of the relationships between radar backscatter and phenological variables (e.g. stand density, tree size, etc.), to improve radar backscatter models of tree canopy properties, and to develop a radar-based scheme for monitoring forest phenological changes. In September 1989, AIRSAR backscatter data were acquired over the Duke Forest. As the aboveground biomass of the loblolly pine forest stands at Duke Forest increased, the SAR backscatter at C-, L-, and P-bands increased and saturated at different biomass levels for the C-band, L-band, and P-band data. We only use the P-band backscatter data and ground measurements here to study the relationships between the backscatter and stand density, the backscatter and mean trunk dbh (diameter at breast height) of trees in the stands, and the backscatter and stand basal area.

Wang, Yong↗