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

2019 ◽  
Vol 11 (14) ◽  
pp. 1695 ◽  
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.

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

2019 ◽  
Vol 41 (2) ◽  
pp. 95-104 ◽  
Author(s):  
Thota Sivasankar ◽  
Junaid Mushtaq Lone ◽  
Sarma K. K. ◽  
Abdul Qadir ◽  
Raju P.L. N.
Keyword(s):  
Vital Role ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Hilly Terrain ◽  
Retrieval Accuracy ◽  
Ground Biomass ◽  
Vector Machines ◽  
L Band ◽  
Forest Aboveground Biomass

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.  

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

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.

scholarly journals Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1424 ◽  
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Nathan Torbick ◽  
Mark Ducey
Keyword(s):  
Optical Sensors ◽  
Forest Canopy ◽  
Alpha Angle ◽  
Ground Truth ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Truth Data ◽  
Ground Biomass ◽  
Multi Temporal ◽  
L Band

Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it a valuable tool for mapping forest Above Ground Biomass (AGB). This paper studies the ability of the single- and multi-temporal C-band Sentinel-1 and polarimetric L-band PALSAR-2 data to estimate live AGB based on ground truth data collected in New England, USA in 2017. Comparisons of results using the Simple Water Cloud Model (SWCM) on both VH and VV polarizations show that C-band reaches saturation much faster than the L-band due to its limited forest canopy penetration. The exhaustive search multiple linear regression model over the many polarimetric parameters from PALSAR-2 data shows that the combination of polarimetric parameters could slightly improve the AGB estimation, with an adjusted R2 as high as 0.43 and RMSE of around 70 Mg/ha when decomposed Pv component and Alpha angle are used. Additionally, the single- and multi-temporal C-band Sentinel-1 data are compared, which demonstrates that the multi-temporal Sentinel-1 significantly improves the AGB estimation, but still has a much lower adjusted R2 due to the limitations of the short wavelength. Finally, a site-level comparison between paired control and treatment sites shows that the L-band aligns better with the ground truth than the C-band, showing the high potential of the models to be applied to relative biomass change detection.

scholarly journals Estimation of Biomass Carbon Stocks over Peat Swamp Forests using Multi-Temporal and Multi-Polratizations SAR Data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 551-556 ◽  
Author(s):  
A. Wijaya ◽  
V. Liesenberg ◽  
A. Susanti ◽  
O. Karyanto ◽  
L. V. Verchot
Keyword(s):  
Forest Structure ◽  
Strong Correlation ◽  
Carbon Stocks ◽  
Above Ground Biomass ◽  
Scattering Mechanism ◽  
Ground Biomass ◽  
Peat Swamp ◽  
Multi Temporal ◽  
L Band ◽  
Sar Data

The capability of L-band radar backscatter to penetrate through the forest canopy is useful for mapping the forest structure, including above ground biomass (AGB) estimation. Recent studies confirmed that the empirical AGB models generated from the L-band radar backscatter can provide favourable estimation results, especially if the data has dual-polarization configuration. Using dual polarimetry SAR data the backscatter signal is more sensitive to forest biomass and forest structure because of tree trunk scattering, thus showing better discriminations of different forest successional stages. These SAR approaches, however, need to be further studied for the application in tropical peatlands ecosystem We aims at estimating forest carbon stocks and stand biophysical properties using combination of multi-temporal and multi-polarizations (quad-polarimetric) L-band SAR data and focuses on tropical peat swamp forest over Kampar Peninsula at Riau Province, Sumatra, Indonesia which is one of the most peat abundant region in the country. <br><br> Applying radar backscattering (Sigma nought) to model the biomass we found that co-polarizations (HH and VV) band are more sensitive than cross-polarization channels (HV and VH). Individual HH polarization channel from April 2010 explained > 86% of AGB. Whereas VV polarization showed strong correlation coefficients with LAI, tree height, tree diameter and basal area. Surprisingly, polarimetric anisotropy feature from April 2007 SAR data show relatively high correlations with almost all forest biophysical parameters. Polarimetric anisotropy, which explains the ratio between the second and the first dominant scattering mechanism from a target has reduced at some extent the randomness of scattering mechanism, thus improve the predictability of this particular feature in estimating the forest properties. These results may be influenced by local seasonal variations of the forest as well as moisture, but available quad-pol SAR data were unable to show these patterns, since all the SAR data were acquired during the rainy season. <br><br> The results of multi-regression analysis in predicting above ground biomass shows that ALOS PALSAR data acquired in 2010 has outperformed other time series data. This is probably due to the fact that land cover change in the area from 2007 – 2009 was highly dynamic, converting natural forests into rubber and Acacia plantations, thus SAR data of 2010 which was acquired in between of two field campaigns has provided significant results (F = 40.7, P < 0.005). In general, we found that polarimetric features have improved the models performance in estimating AGB. Surprising results come from single HH polarization band from April 2010 that has a strong correlation with AGB (r = 0.863). Also, HH polarization band of 2009 SAR image resulted in a moderate correlation with AGB (r = 0.440).

scholarly journals Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales

2019 ◽  
Vol 77 ◽  
pp. 151-161 ◽  
Author(s):  
Cristina Vittucci ◽  
Gaia Vaglio Laurin ◽  
Gianluca Tramontana ◽  
Paolo Ferrazzoli ◽  
Leila Guerriero ◽  
...  
Keyword(s):  
Optical Depth ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
L Band

scholarly journals An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa

2018 ◽  
Vol 15 (14) ◽  
pp. 4627-4645 ◽  
Author(s):  
Nemesio J. Rodríguez-Fernández ◽  
Arnaud Mialon ◽  
Stephane Mermoz ◽  
Alexandre Bouvet ◽  
Philippe Richaume ◽  
...  
Keyword(s):  
Optical Depth ◽  
Temporal Frequency ◽  
Tree Height ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Data Sets ◽  
Above Ground Biomass ◽  
Data Set ◽  
Ground Biomass ◽  
L Band

Abstract. The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse ( ∼ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.

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