Estimation of Above Ground Biomass Using Support Vector Machines and ALOS/PALSAR data

L-band Synthetic aperture radar (SAR) data has been extensively used for forest aboveground biomass (AGB) estimation due to its higher saturation level. However, SAR backscatter is highly influenced by the topography characteristics along with the bio-geophysical properties of vegetation and underneath soil characteristics. This has limited the accuracy of directly relating the SAR backscatter with above ground biomass in highly undulated terrain. In this study, it has been observed that terrain degree of slope and aspect plays a vital role in influencing the SAR backscatter in addition with AGB. Because of this, the degree of slope and aspect along with SAR backscatter in HH (transmit and receive polarizations are horizontal) and HV (transmit horizontal and receive vertical) polarizations have been considered as inputs for Support Vector Machine (SVM) to improve the biomass retrieval accuracy. Our results demonstrate that the accuracy of AGB estimation over hilly terrain can be significantly improved by considering topographical characteristics in addition to L-band backscatter.

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Benchmarking the Retrieval of Biomass in Boreal Forests Using P-Band SAR Backscatter with Multi-Temporal C- and L-Band Observations

Remote Sensing ◽

10.3390/rs11141695 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1695 ◽

Cited By ~ 3

Author(s):

Cartus ◽

Santoro ◽

Wegmüller ◽

Rommen

Keyword(s):

Boreal Forests ◽

Forest Site ◽

Above Ground Biomass ◽

Retrieval Accuracy ◽

Ground Biomass ◽

Multi Temporal ◽

L Band ◽

Semi Empirical ◽

Relative Root ◽

Band Image

The planned launch of a spaceborne P-band radar mission and the availability of C- and L-band data from several spaceborne missions suggest investigating the complementarity of C-, L-, and P-band backscatter with respect to the retrieval of forest above-ground biomass. Existing studies on the retrieval of biomass with multi-frequency backscatter relied on single observations of the backscatter and were thus not able to demonstrate the potential of multi-temporal C- and L-band data that are now available from spaceborne missions. Based on spaceborne C- and L-band and airborne P-band images acquired over a forest site in southern Sweden, we investigated whether C- and L-band backscatter may complement retrievals of above-ground biomass from P-band. To this end, a retrieval framework was adopted that utilizes a semi-empirical model for C- and L-bands and an empirical parametric model for P-band. Estimates of above-ground biomass were validated with the aid of 20 m-diameter plots and a LiDAR-derived biomass map with 100 m × 100 m pixel size. The highest retrieval accuracy when not combining frequencies was obtained for P-band with a relative root mean square error (RMSE) of 30% at the hectare scale. The retrieval with multi-temporal L- and C-bands produced errors of the order of 40% and 50%, respectively. The P-band retrieval could be improved for 4% when using P-, L-, and C-bands jointly. The combination of C- and L-bands allowed for retrieval accuracies close to those achieved with P-band. A crucial requirement for achieving an error of 30% with C- and L-bands was the use of multi-temporal observations, which was highlighted by the fact that the retrieval with the best individual L-band image was associated with an error of 61%. The results of this study reconfirmed that P-band is the most suited frequency for the retrieval of above-ground biomass of boreal forests based on backscatter, but also highlight the potential of multi-temporal C- and L-band imagery for mapping above-ground biomass, for instance in areas where the planned ESA BIOMASS P-band mission will not be allowed to acquire data.

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Optimal Support Vector Machines for forest above-ground biomass estimation from multisource remote sensing data

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6352721 ◽

2012 ◽

Cited By ~ 4

Author(s):

Ying Guo ◽

Zengyuan Li ◽

Xu Zhang ◽

Er-xue Chen ◽

Lina Bai ◽

...

Keyword(s):

Remote Sensing ◽

Support Vector Machines ◽

Remote Sensing Data ◽

Support Vector ◽

Biomass Estimation ◽

Above Ground Biomass ◽

Sensing Data ◽

Ground Biomass ◽

Vector Machines

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An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure

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

10.1109/jstars.2010.2086436 ◽

2010 ◽

Vol 3 (4) ◽

pp. 576-593 ◽

Cited By ~ 169

Author(s):

Richard Lucas ◽

John Armston ◽

Russell Fairfax ◽

Rod Fensham ◽

Arnon Accad ◽

...

Keyword(s):

Vegetation Structure ◽

Above Ground Biomass ◽

Alos Palsar ◽

Moisture Condition ◽

Surface Moisture ◽

Ground Biomass ◽

L Band

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The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass

Frontiers in Earth Science ◽

10.3389/feart.2021.752254 ◽

2021 ◽

Vol 9 ◽

Author(s):

Unmesh Khati ◽

Marco Lavalle ◽

Gulab Singh

Keyword(s):

Time Series ◽

Soil Moisture ◽

Field Data ◽

Large Scale ◽

Test Site ◽

Above Ground Biomass ◽

Retrieval Accuracy ◽

Ground Biomass ◽

L Band ◽

Field Plots

Physics-based algorithms estimating large-scale forest above-ground biomass (AGB) from synthetic aperture radar (SAR) data generally use airborne laser scanning (ALS) or grid of national forest inventory (NFI) to reduce uncertainties in the model calibration. This study assesses the potential of multitemporal L-band ALOS-2/PALSAR-2 data to improve forest AGB estimation using the three-parameter water cloud model (WCM) trained with field data from relatively small (0.1 ha) plots. The major objective is to assess the impact of the high uncertainties in field inventory data due to relatively smaller plot size and temporal gap between acquisitions and ground truth on the AGB estimation. This study analyzes a time series of twenty-three ALOS-2 dual-polarized images spanning 5 years acquired under different weather and soil moisture conditions over a subtropical forest test site in India. The WCM model is trained and validated on individual acquisitions to retrieve forest AGB. The accuracy of the generated AGB products is quantified using the root mean square error (RMSE). Further, we use a multitemporal AGB retrieval approach to improve the accuracy of the estimated AGB. Changes in precipitation and soil moisture affect the AGB retrieval accuracy from individual acquisitions; however, using multitemporal data, these effects are mitigated. Using a multitemporal AGB retrieval strategy, the accuracy improves by 15% (55 Mg/ha RMSE) for all field plots and by 21% (39 Mg/ha RMSE) for forests with AGB less than 100 Mg/ha. The analysis shows that any ten multitemporal acquisitions spanning 5 years are sufficient for improving AGB retrieval accuracy over the considered test site. Furthermore, we use allometry from colocated field plots and Global Ecosystem Dynamics Investigation (GEDI) L2A height metrics to produce GEDI-derived AGB estimates. Despite the limited co-location of GEDI and field data over our study area, within the period of interest, the preliminary analysis shows the potential of jointly using the GEDI-derived AGB and multi-temporal ALOS-2 data for large-scale AGB retrieval.

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Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

Remote Sensing ◽

10.3390/rs10091355 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1355 ◽

Cited By ~ 4

Author(s):

Luciana Pereira ◽

Luiz Furtado ◽

Evlyn Novo ◽

Sidnei Sant’Anna ◽

Veraldo Liesenberg ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Floodplain Forest ◽

Single Frequency ◽

Above Ground Biomass ◽

Alos Palsar ◽

Area Index ◽

Ground Biomass ◽

L Band ◽

Sar Data

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

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