Tropical forest AGB estimation based on structure parameters extracted by TomoSAR
Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of prac...
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| Published in: | International journal of applied earth observation and geoinformation Vol. 121; p. 103369 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01-07-2023
Elsevier |
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| Abstract | Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that “Model 7” is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability.
•AGB estimation model built by structure parameters perform well in Mondah forest.•Tree density relates with the 30 m power in ground dominant scattering mechanism.•Canopy height and ScRv backscattering power at 35 m and 45 m are vital for forest AGB estimation. |
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| AbstractList | Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that “Model 7” is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability. Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that “Model 7” is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability. •AGB estimation model built by structure parameters perform well in Mondah forest.•Tree density relates with the 30 m power in ground dominant scattering mechanism.•Canopy height and ScRv backscattering power at 35 m and 45 m are vital for forest AGB estimation. |
| ArticleNumber | 103369 |
| Author | Li, Wenmei Zhang, Yu Chen, Huaihuai Chen, Erxue Zhao, Dan Zhang, Jiadong Zhao, Lei |
| Author_xml | – sequence: 1 givenname: Wenmei orcidid: 0000-0002-1108-0507 surname: Li fullname: Li, Wenmei email: liwm@njupt.edu.cn organization: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, No. 9 Wenyuan Road, Qixia District, Nanjing, 210023, Jiangsu, China – sequence: 2 givenname: Yu surname: Zhang fullname: Zhang, Yu organization: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, No. 9 Wenyuan Road, Qixia District, Nanjing, 210023, Jiangsu, China – sequence: 3 givenname: Jiadong surname: Zhang fullname: Zhang, Jiadong organization: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, No. 9 Wenyuan Road, Qixia District, Nanjing, 210023, Jiangsu, China – sequence: 4 givenname: Huaihuai surname: Chen fullname: Chen, Huaihuai organization: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, No. 9 Wenyuan Road, Qixia District, Nanjing, 210023, Jiangsu, China – sequence: 5 givenname: Erxue orcidid: 0000-0001-8172-274X surname: Chen fullname: Chen, Erxue organization: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, No. 1 Dongxiaofu, Haidian District, Beijing, 100091, Beijing, China – sequence: 6 givenname: Lei surname: Zhao fullname: Zhao, Lei email: zhaoleiiam@gmail.com organization: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, No. 1 Dongxiaofu, Haidian District, Beijing, 100091, Beijing, China – sequence: 7 givenname: Dan surname: Zhao fullname: Zhao, Dan organization: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 52 Sanlihe Rd., Xicheng District, Beijing, 100864, Beijing, China |
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| Cites_doi | 10.1109/TGRS.2011.2177843 10.1109/TGRS.2017.2711037 10.54386/jam.v21i2.231 10.1109/TGRS.2015.2488358 10.1109/TGRS.2016.2585741 10.1109/TGRS.2011.2147321 10.1080/22797254.2021.1901063 10.1109/MGRS.2019.2957215 10.1007/s11676-019-00955-4 10.1109/TGRS.2019.2908517 10.1109/MGRS.2019.2963093 10.1109/LGRS.2015.2477858 10.1109/36.134089 10.1109/LGRS.2020.3027439 10.1109/LGRS.2014.2365613 10.1109/JSTARS.2017.2741723 10.1109/TGRS.2020.3020775 10.3390/rs13020186 10.1109/LGRS.2017.2709839 10.1109/TGRS.2011.2159614 10.1109/TGRS.2010.2091278 10.1109/TGRS.2009.2023785 10.1109/TVT.2017.2704610 10.1109/TGRS.2013.2246170 10.1109/36.868873 10.1109/TGRS.2015.2451992 10.1109/TGRS.2021.3138763 10.1109/TGRS.2011.2125972 10.1016/j.isprsjprs.2014.08.014 10.1109/JSTARS.2018.2859050 |
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| Keywords | Structure parameters Tropical forest Canopy height Forest height Tomographic SAR Forest aboveground biomass |
| Language | English |
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