Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Huuva, Ivan; Persson, Henrik J.; Soja, Maciej J.; Wallerman, Jörgen; Ulander, Lars M. H.; Fransson, Johan E. S.

doi:10.1080/07038992.2020.1838891

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…No clear saturation point in terms of maximum absolute AGB change predicted was observed for the model. This confirms earlier results where AGB decreases of 300 t/ha were predicted using fully polarimetric airborne L-band data [33]. Yet, the variance around the 1:1 line is considerable for all models, and different models may be preferred for the prediction of different types of changes, e.g., the currently investigated models can be suitable to predict larger forest declines, while forest growth may be better captured using a separate set of models.…”

Section: Resultssupporting

confidence: 88%

“…Because of this, the results of this study may not generalize to cases where there are large differences in weather or moisture, or, for example, images acquired in freezing conditions. Indeed, a number of studies have previously noted the strong effect of both moisture and freeze/thaw conditions on backscatter from forests [4,32,33]. In general, SAR backscatter images used for biomass change prediction should be chosen from acquisitions with similar conditions, or differences in backscatter relating to moisture should be somehow corrected for, such as in, for example, Huuva et al [33].…”

Section: Resultsmentioning

confidence: 99%

“…In Swedish boreal forest, Sandberg et al [32] investigated the use of airborne E-SAR P-band SAR for estimating AGB changes. Additionally, Huuva et al [33] and Huuva et al [34] investigated fully polarimetric L-band data for AGB change estimation. The latter two studies used airborne data from the E-SAR and SETHI systems, and PALSAR-2 data, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…RVI has been used extensively to obtain both biomass and vegetation water content, and is correlated with shrubland, crop, and forest vegetation [41,[44][45][46][47]. Among L-band polarization channels, the correlation with both AGB and AGB change in boreal forests has been observed to be the highest for HV (or VH) [33,34]. RVI is essentially the ratio of cross-polarized backscatter power to total backscattered power, and the ratios of polarized channels appear to be less sensitive to topography, viewing geometry, and calibration errors compared to the additive use of polarized channels [4,32,33].…”

Section: Introductionmentioning

confidence: 99%

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Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data

Persson,

Huuva

2024

Remote Sensing

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The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better.

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Section: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data

Persson,

Huuva

2024

Remote Sensing

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“…This method could obtain accurate results, but it is quite time‐consuming, labor‐intensive and limited to the sample plot scale, making it hard to assess biomass over a large area directly. Therefore, many studies have merged data from multiple remote sensing sources to spatially build process‐based mechanistic models (Huuva et al, 2020; Santoro et al, 2021) and data‐driven estimation models (Bennett et al, 2020; Duncanson et al, 2020; Meng et al, 2017; Qiu et al, 2019; Wang et al, 2017; Zeng et al, 2021; Zhang et al, 2019) to estimate above‐ground forest biomass. Previous studies (Khati et al, 2020) have shown that although mechanical models have a relatively high theoretical basis and accuracy, they are not suitable for wide application on a large scale due to the complexity of physical processes and the difficulty of measuring intermediate parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimation of forest above‐ground biomass in Guangxi, China, by integrating forest age and stack learning

Liu

Yue

et al. 2023

Land Degrad Dev

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With the implementation of large‐scale ecological restoration projects, Southwest China has become one of the fastest forest growth areas in the world in terms of vegetation cover and above‐ground biomass (AGB). It is expected to be a potential area for achieving the carbon neutrality target in China. Accurate estimation of forest AGB is becoming an increasingly urgent necessity for carbon neutrality and forest management. However, due to the complex geological background, there is significant uncertainty in estimating forest AGB in the southwestern region, which generally results in underestimating carbon sinks from forest restoration. To address the issue, we propose a method by incorporating forest age information and stack learning technique to estimate forest AGB. Based on remote sensing, forest inventory and in situ forest biomass data, three fundamental methods (Multiple regression, Random forest and Support vector machine) are employed and compared to build the AGB estimation model with vegetation indices, texture feature factors and forest age. Optimal basic models are further enhanced by integration learning to improve the estimation performance and then applied to the study area Guangxi to obtain regional AGB information of different forest types. The results show that: (1) forest age plays a vital role in reducing the uncertainty of AGB estimation. By incorporating forest age information, R2 of AGB estimation is improved by 0.07–0.27 and RMSE is decreased by 16.35%–47.47% for different forest types; (2) with R2 value >0.78, random forest model outperforms support vector machine and multiple linear regression models. Compared with the single optimal model, integration model by stack learning further enhances R2 of estimation by 0.02–0.03 and decreases RMSE by 5.20%–14.89%. (3) The total forest AGB in Guangxi is 988.17 Tg and the average forest AGB level is 73.30 t/ha. Natural broadleaf forest has the highest AGB level (86.75 t/ha), followed by natural coniferous forest (81.19 t/ha), planted coniferous forest (63.23 t/ha) and planted eucalyptus forest (49.71 t/ha). AGB level in karst areas is lower than that in non‐karst areas due to soil and water constraints. The majority of plantation forests in Guangxi is in the early and middle stages of forest succession, with the rapid growth of forest AGB, and has hence significant potential as carbon sinks. This study indicates that stack learning and incorporation of forest age could significantly decrease the uncertainty of forest AGB estimation. Our study helps to provide more accurate AGB information for karst ecological project management and regional carbon neutrality assessment.

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Quantifying the sensitivity of L-Band SAR to a decade of vegetation structure changes in savannas

Wessels

Bouvet

et al. 2023

Remote Sensing of Environment

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Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Cited by 6 publications

References 39 publications

Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data

Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data

Estimation of forest above‐ground biomass in Guangxi, China, by integrating forest age and stack learning

Quantifying the sensitivity of L-Band SAR to a decade of vegetation structure changes in savannas

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