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Enhancing Landsat image based aboveground biomass estimation of black locust with scale bias-corrected LiDAR AGB map and stratified sampling.

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Title: Enhancing Landsat image based aboveground biomass estimation of black locust with scale bias-corrected LiDAR AGB map and stratified sampling.
Authors: Qin, Shuhong, Wang, Hong, Li, Xiuneng, Gao, Jay, Jin, Jiaxin, Li, Yongtao, Lu, Jinbo, Meng, Pengyu, Sun, Jing, Song, Zhenglin, Donev, Petar, Ma, Zhangfeng
Source: Geo-Spatial Information Science; Oct2024, Vol. 27 Issue 5, p1475-1488, 14p
Subject Terms: BLACK locust, FEATURE extraction, FOREST biomass, LANDSAT satellites, TREE farms
Abstract: There is a growing interest in leveraging LiDAR-generated forest Aboveground Biomass (LG-AGB) data as a reference to retrieve AGB from satellite observations. However, the biases arising from the upscaling process and the impact of the sampling strategy on model accuracy still need to be resolved. In this study, we first corrected the bias arising from upscaling the LG-AGB map to match the spatial resolution of Landsat observations. Subsequently, the stratified random sampling method was used to select training samples from the corrected LG-AGB map (cLG-AGB) for the Random Forest (RF) regression model. The RF model features were extracted from the Landsat observations and auxiliary data. The impact of strata numbers on model accuracy was explored during the sampling process. Finally, independent validation was conducted using in situ measurements. The results indicated that: (1) about 68% of the biases can be corrected in the up-scale transformation; (2) compared to no stratification, a three-strata model achieved a 6.5% improvement in AGB estimation accuracy while requiring a 37.8% reduction in sample size; (3) the black locust forest had a low saturation point at 60.52 ± 4.46 Mg/ha AGB and 72.4% AGB values were underestimated and the remaining were overestimated. In summary, our study provides a framework to harmonize near-surface LiDAR and satellite data for AGB estimation in plantation forest ecosystems with small patch sizes and fragmented distribution. [ABSTRACT FROM AUTHOR]
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  Label: Title
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  Data: Enhancing Landsat image based aboveground biomass estimation of black locust with scale bias-corrected LiDAR AGB map and stratified sampling.
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  Data: <searchLink fieldCode="AR" term="%22Qin%2C+Shuhong%22">Qin, Shuhong</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Hong%22">Wang, Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Xiuneng%22">Li, Xiuneng</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Jay%22">Gao, Jay</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Jiaxin%22">Jin, Jiaxin</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yongtao%22">Li, Yongtao</searchLink><br /><searchLink fieldCode="AR" term="%22Lu%2C+Jinbo%22">Lu, Jinbo</searchLink><br /><searchLink fieldCode="AR" term="%22Meng%2C+Pengyu%22">Meng, Pengyu</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Jing%22">Sun, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Song%2C+Zhenglin%22">Song, Zhenglin</searchLink><br /><searchLink fieldCode="AR" term="%22Donev%2C+Petar%22">Donev, Petar</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhangfeng%22">Ma, Zhangfeng</searchLink>
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  Data: Geo-Spatial Information Science; Oct2024, Vol. 27 Issue 5, p1475-1488, 14p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22BLACK+locust%22">BLACK locust</searchLink><br /><searchLink fieldCode="DE" term="%22FEATURE+extraction%22">FEATURE extraction</searchLink><br /><searchLink fieldCode="DE" term="%22FOREST+biomass%22">FOREST biomass</searchLink><br /><searchLink fieldCode="DE" term="%22LANDSAT+satellites%22">LANDSAT satellites</searchLink><br /><searchLink fieldCode="DE" term="%22TREE+farms%22">TREE farms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: There is a growing interest in leveraging LiDAR-generated forest Aboveground Biomass (LG-AGB) data as a reference to retrieve AGB from satellite observations. However, the biases arising from the upscaling process and the impact of the sampling strategy on model accuracy still need to be resolved. In this study, we first corrected the bias arising from upscaling the LG-AGB map to match the spatial resolution of Landsat observations. Subsequently, the stratified random sampling method was used to select training samples from the corrected LG-AGB map (cLG-AGB) for the Random Forest (RF) regression model. The RF model features were extracted from the Landsat observations and auxiliary data. The impact of strata numbers on model accuracy was explored during the sampling process. Finally, independent validation was conducted using in situ measurements. The results indicated that: (1) about 68% of the biases can be corrected in the up-scale transformation; (2) compared to no stratification, a three-strata model achieved a 6.5% improvement in AGB estimation accuracy while requiring a 37.8% reduction in sample size; (3) the black locust forest had a low saturation point at 60.52 ± 4.46 Mg/ha AGB and 72.4% AGB values were underestimated and the remaining were overestimated. In summary, our study provides a framework to harmonize near-surface LiDAR and satellite data for AGB estimation in plantation forest ecosystems with small patch sizes and fragmented distribution. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/10095020.2023.2249042
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 1475
    Subjects:
      – SubjectFull: BLACK locust
        Type: general
      – SubjectFull: FEATURE extraction
        Type: general
      – SubjectFull: FOREST biomass
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      – SubjectFull: LANDSAT satellites
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      – SubjectFull: TREE farms
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      – TitleFull: Enhancing Landsat image based aboveground biomass estimation of black locust with scale bias-corrected LiDAR AGB map and stratified sampling.
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            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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