scholarly journals Assessment of Forest Biomass Estimation from Dry and Wet SAR Acquisitions Collected during the 2019 UAVSAR AM-PM Campaign in Southeastern United States

2020 ◽  
Vol 12 (20) ◽  
pp. 3397
Author(s):  
Unmesh Khati ◽  
Marco Lavalle ◽  
Gustavo H. X. Shiroma ◽  
Victoria Meyer ◽  
Bruce Chapman
Keyword(s):  
United States ◽  
Deciduous Forest ◽  
Cloud Model ◽  
Southeastern United States ◽  
Forest Biomass ◽  
Estimation Accuracy ◽  
Biomass Estimation ◽  
Temperate Deciduous Forest ◽  
Multi Temporal ◽  
L Band

Forest above-ground biomass (AGB) estimation from SAR backscatter is affected by varying imaging and environmental conditions. This paper quantifies and compares the performance of forest biomass estimation from L-band SAR backscatter measured selectively under dry and wet conditions during the 2019 AM-PM NASA airborne campaign. Seven Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images acquired between June and October 2019 over a temperate deciduous forest in Southeastern United States with varying moisture and precipitation conditions are examined in conjunction with LIDAR and field measurements. Biomass is estimated by fitting a 3-parameter modified Water Cloud Model (WCM) to radiometric terrain corrected SAR backscatter. Our experiment is designed to quantify the biomass estimation errors when biomass models are calibrated and validated on varying acquisition conditions (dry or wet). Multi-temporal estimation strategies are also evaluated and compared with single-acquisition estimation approaches. As an outcome, the experiment shows that the WCM model calibrated and validated on single acquisitions adapts to different soil moisture conditions with RMSD up to 18.7 Mg/ha. The AGB estimation performance, however, decreases with RMSD upwards of 30 Mg/ha when the model is cross-validated on moisture and precipitation conditions different than the calibration conditions. Results confirm that calibrating the model over the multi-temporal data using averaged backscatter or weighted combinations of individual AGB estimates, improves the biomass estimation accuracy up to about 20% at L-band. This study helps design biomass cal/val procedures and biomass estimation algorithms for dense time-series to be collected by low-frequency radar missions such as NASA-ISRO SAR (NISAR) and BIOMASS.

scholarly journals Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-band

2021 ◽  
Vol 13 (11) ◽  
pp. 2147
Author(s):  
Xing Peng ◽  
Xinwu Li ◽  
Yanan Du ◽  
Qinghua Xie
Keyword(s):  
Covariance Matrix ◽  
Input Parameter ◽  
Forest Biomass ◽  
Three Dimensional ◽  
Estimation Accuracy ◽  
Biomass Estimation ◽  
Height Estimation ◽  
Matrix Estimation ◽  
Forest Height ◽  
L Band

Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m.

Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States

2017 ◽  
Vol 199 ◽  
pp. 158-171 ◽  
Author(s):  
Mukhtar Ahmed Ajaz Ahmed ◽  
Amr Abd-Elrahman ◽  
Francisco J. Escobedo ◽  
Wendell P. Cropper ◽  
Timothy A. Martin ◽  
...  
Keyword(s):  
United States ◽  
Southeastern United States ◽  
Forest Biomass ◽  
Water Yield ◽  
Spatially Explicit ◽  
Multi Scale ◽  
Spatially Explicit Modeling

scholarly journals Estimation of Above-Ground Biomass over Boreal Forests on Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1550 ◽  
Author(s):  
Martyna Stelmaszczuk-Górska ◽  
Mikhail Urbazaev ◽  
Christiane Schmullius ◽  
Christian Thiel
Keyword(s):  
Boreal Forests ◽  
Learning Algorithm ◽  
Forest Biomass ◽  
Radar Data ◽  
Accurate Estimate ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Multi Temporal ◽  
L Band

The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. In particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha−1). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha−1 to 4.7 t ha−1; and a RMSEcor of 30.2 t ha−1 was calculated.

scholarly journals Aboveground Forest Biomass Estimation Combining L- and P-Band SAR Acquisitions

2018 ◽  
Vol 10 (7) ◽  
pp. 1151 ◽  
Author(s):  
Michael Schlund ◽  
Malcolm Davidson
Keyword(s):  
Aboveground Biomass ◽  
Boreal Forests ◽  
Laser Scanning ◽  
Forest Biomass ◽  
European Space Agency ◽  
Low Frequency ◽  
Biomass Estimation ◽  
Retrieval Performance ◽  
In Situ Data ◽  
L Band

While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use of L- and P-band airborne SAR and in situ data measured in the field together with laser scanning data acquired over one hemi-boreal (Remningstorp) and one boreal (Krycklan) forest study area in Sweden. We fit statistical models to different combinations of topographic-corrected SAR backscatter and forest heights estimated from PolInSAR for the biomass estimation, and evaluate retrieval performance in terms of R2 and using 10-fold cross-validation. The study shows that specific combinations of radar observables from L- and P-band lead to biomass predictions that are more accurate in comparison with single-band retrievals. The correlations and accuracies between the combinations of SAR features and aboveground biomass are consistent across the two study areas, whereas the retrieval performance varied for individual bands. P-band-based retrievals were more accurate than L-band for the hemi-boreal Remningstorp site and less accurate than L-band for the boreal Krycklan site. The aboveground biomass levels as well as the ground topography differ between the two sites. The results suggest that P-band is more sensitive to higher biomass and L-band to lower biomass forests. The forest height from PolInSAR improved the results at L-band in the higher biomass substantially, whereas no improvement was observed at P-band in both study areas. These results are relevant in the context of combining information over boreal forests from future low-frequency SAR missions such as the European Space Agency (ESA) BIOMASS mission, which will operate at P-band, and future L-band missions planned by several space agencies.

scholarly journals Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1446 ◽  
Author(s):  
Anh Le ◽  
David Paull ◽  
Amy Griffin
Keyword(s):  
Deciduous Forest ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Large Field ◽  
Tropical Deciduous Forest ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Aster Gdem ◽  
Understory Biomass ◽  
Understory Trees

Research on the contribution of understory components to the total above ground biomass (AGB) has to date received very little attention because most prior biomass estimation studies have ignored small regenerating trees beneath the main canopy with the assumption that their contribution to biomass is generally negligible. Only a few biomass studies have emphasized a considerable contribution to biomass of understory components in forest ecosystems. However, this study of native, tropical, deciduous forest biomass in the Central Highlands of Vietnam was able to explore the contribution of small regenerating trees to total biomass by exploiting a large field inventory of hundreds to thousands of individually-counted small regenerating trees per hectare. Thus, this study investigated the influence of small regenerating tree biomass on models of the relationship between total AGB and remote sensing data. These analyses were trained with and without topographic variables derived from ASTER-GDEM. Our results demonstrate that the inclusion of small regenerating understory trees (R2 = 0.42, NRMSE or %RMSE = 30.5%) provides a quantifiable improvement in total estimated AGB compared to using only large woody canopy trees (R2 = 0.21, NRMSE or %RMSE = 36.6%) when correlating field-based biomass measurements with optical image-derived variables. All analyses show that the inclusion of terrain factors made an important contribution to biomass modeling. This study suggests that for young, open forests where there are many small regenerating trees, the contribution of understory biomass should be taken into consideration to improve total AGB estimation.

scholarly journals Biogenic isoprene emission: Model evaluation in a southeastern United States bottomland deciduous forest

1997 ◽  
Vol 102 (D15) ◽  
pp. 18889-18901 ◽  
Author(s):  
Christopher D. Geron ◽  
Dalin Nie ◽  
Robert R. Arnts ◽  
Thomas D. Sharkey ◽  
Eric L. Singsaas ◽  
...  
Keyword(s):  
United States ◽  
Model Evaluation ◽  
Deciduous Forest ◽  
Southeastern United States ◽  
Emission Model ◽  
Isoprene Emission

scholarly journals POLINSAR BASED SCATTERING INFORMATION RETRIEVAL FOR FOREST ABOVEGROUND BIOMASS ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1913-1920
Author(s):  
N. Agrawal ◽  
S. Kumar ◽  
V. A. Tolpekin
Keyword(s):  
Aboveground Biomass ◽  
Cloud Model ◽  
Accuracy Assessment ◽  
Forest Biomass ◽  
Orientation Angle ◽  
Coefficient Of Determination ◽  
Biomass Estimation ◽  
Biophysical Parameters ◽  
Empirical Modelling ◽  
Semi Empirical

<p><strong>Abstract.</strong> Forests play a crucial role in storing carbon and are of paramount importance in maintaining global carbon cycle. Assessment of forest biomass at regional and global level is vital for understanding and monitoring health of both tree species and entire cover. Changes in forest biomass are caused by human activities, natural factors and variations in climate. Forest biomass measurement is necessary for gauging the changes in forest ecosystems. Remote sensing is indispensable for mapping forest biophysical parameters. Microwaves are capable of collecting data even in case of cloud cover as the microwaves are of long wavelength. Microwaves help in retrieving scattering information of target. The goal of this research was to map aboveground biomass (AGB) over Barkot forest range in Dehradun, India. The current work focuses on the retrieval of PolInSAR based scattering information for the estimation of aboveground biomass. Radarsat-2 fully Polarimetric C-band data was used for the estimation of AGB in Barkot forest area. A semi-empirical model, which is Extended Water Cloud Model (EWCM) was utilized for AGB estimation. EWCM considers ground-stem interactions. Due to overestimation of volume scattering, polarization orientation angle shift correction was implemented on the PolInSAR pair. Field biomass data was utilized for accuracy assessment. The results show that coefficient of determination (R<sup>2</sup>) value of 0.47, Root Mean Square Error (RMSE) of 56.18 (t&amp;thinsp;ha<sup>&amp;minus;1</sup>) and accuracy of 72% was obtained between modelled biomass against field measured biomass. Hence, it can be inferred from the obtained results that PolInSAR technique, in combination with semi-empirical modelling approach, can be implemented for estimating forest biomass.</p>

Sign in / Sign up

Export Citation Format

Share Document