Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany.

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Forest Structure Dynamics Following Wildfire and Prescribed Burning in the New Jersey Pine Barrens

The American Midland Naturalist ◽

10.2307/2424750 ◽

1981 ◽

Vol 105 (2) ◽

pp. 321 ◽

Cited By ~ 23

Author(s):

Ralph E. J. Boerner

Keyword(s):

New Jersey ◽

Forest Structure ◽

Prescribed Burning ◽

Pine Barrens ◽

Structure Dynamics

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Gray-headed Lemur (Eulemur cinereiceps) Abundance and Forest Structure Dynamics at Manombo, Madagascar

Biotropica ◽

10.1111/j.1744-7429.2010.00705.x ◽

2010 ◽

Vol 43 (3) ◽

pp. 371-379 ◽

Cited By ~ 8

Author(s):

Steig E. Johnson ◽

Christina Ingraldi ◽

Fidimalala B. Ralainasolo ◽

Hubert E. Andriamaharoa ◽

Reza Ludovic ◽

...

Keyword(s):

Forest Structure ◽

Structure Dynamics ◽

Eulemur Cinereiceps

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Forest structure dependency analysis of L-band SAR backscatter

PeerJ ◽

10.7717/peerj.10055 ◽

2020 ◽

Vol 8 ◽

pp. e10055

Author(s):

Yongjie Ji ◽

Jimao Huang ◽

Yilin Ju ◽

Shipeng Guo ◽

Cairong Yue

Keyword(s):

Synthetic Aperture Radar ◽

Linear Correlation ◽

Forest Structure ◽

Forest Biomass ◽

Synthetic Aperture ◽

Forest Species ◽

Canopy Density ◽

Forest Height ◽

L Band ◽

Aperture Radar

Forest structure plays an important role in forest biomass inversion using synthetic aperture radar (SAR) backscatter. Synthetic aperture radar (SAR) sensors with long-wavelength have the potentiality to provide reliable and timely forest biomass inversion for their ability of deep penetration into the forest. L-band SAR backscatter shows useful for forest above-ground biomass (AGB) estimation. However, the way that forest structure mediating the biomass-backscatter affects the improvement of the related biomass estimation accuracy. In this paper, we have investigated L-band SAR backscatter sensitivity to forests with different mean canopy density, mean tree height and mean DBH (diameter at breast height) at the sub-compartment level. The forest species effects on their relationship were also considered in this study. The linear correlation coefficient R, non-linear correlation parameter, Maximal Information Coefficient (MIC), and the determination coefficient R2 from linear function, Logarithmic function and Quadratic function were used in this study to analyze forest structural properties effects on L-band SAR backscatter. The HV channel, which is more sensitive than HH to forest structure parameters, was chosen as the representative of SAR backscatter. 6037 sub-compartment were involved in the analysis. Canopy density showed a great influence on L-band backscatter than mean forest height and DBH. All of the R between canopy density and L-band backscatter were greater than 0.7 during the forest growth cycle. The sensitivity of L-band backscatter to mean forest height depends on forest canopy density. When canopy density was lower than 0.4, R values between mean forest height are smaller than 0.5. In contrast, the values of R were greater than 0.8 if canopy density was higher than 0.4. The sensitivity SAR backscatter to DBH fluctuated with canopy density, but it only showed obvious sensitivity when canopy density equals to 0.6, where both the linear and non-liner correlation values are higher than others. However, their effects on L-bang HV backscatter are affected by forest species, the effects on three forest structural parameters depend on tree species.

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Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

Remote Sensing ◽

10.3390/rs11161934 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1934 ◽

Cited By ~ 4

Author(s):

Ibrahim El Moussawi ◽

Dinh Ho Tong Minh ◽

Nicolas Baghdadi ◽

Chadi Abdallah ◽

Jalal Jomaah ◽

...

Keyword(s):

Forest Management ◽

Tropical Forest ◽

Forest Structure ◽

Vertical Structure ◽

Three Dimensional ◽

Region Of Interest ◽

Scientific Basis ◽

Coefficient Of Determination ◽

Phase Measurements ◽

L Band

Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities.

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Forest Structure Characterization From SAR Tomography at L-Band

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2018.2859050 ◽

2018 ◽

Vol 11 (10) ◽

pp. 3402-3414 ◽

Cited By ~ 12

Author(s):

Marivi Tello ◽

Victor Cazcarra-Bes ◽

Matteo Pardini ◽

Konstantinos Papathanassiou

Keyword(s):

Forest Structure ◽

Structure Characterization ◽

L Band

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Definition of Tomographic SAR Configurations for Forest Structure Applications at L-Band

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2020.3027439 ◽

2020 ◽

pp. 1-5

Author(s):

Victor Cazcarra-Bes ◽

Matteo Pardini ◽

Konstantinos Papathanassiou

Keyword(s):

Forest Structure ◽

L Band ◽

Definition Of

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Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

Remote Sensing ◽

10.3390/rs12101649 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1649 ◽

Cited By ~ 1

Author(s):

Kotaro Iizuka ◽

Yuichi S. Hayakawa ◽

Takuro Ogura ◽

Yasutaka Nakata ◽

Yoshiko Kosugi ◽

...

Keyword(s):

Forest Structure ◽

Canopy Cover ◽

Remote Sensing Data ◽

Tree Height ◽

Synthetic Aperture ◽

Stem Volume ◽

Structure Parameters ◽

L Band ◽

Canopy Size ◽

Resolution Data

The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry.

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