Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method
Implementing forest protection measures, such as afforestation, can be an effective approach toward slowing down the increase of CO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> concentration and attaining carbon neutrality. The estima...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10839140/ |
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| author | Xinyi Liu Li He Zhengwei He Yun Wei |
| author_facet | Xinyi Liu Li He Zhengwei He Yun Wei |
| author_sort | Xinyi Liu |
| collection | DOAJ |
| description | Implementing forest protection measures, such as afforestation, can be an effective approach toward slowing down the increase of CO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> concentration and attaining carbon neutrality. The estimation of forest parameters is of great significance in understanding regional and global climate change patterns, and the Forest Leaf Area Index (LAI) is a crucial parameter. Current LAI products are mostly generated by moderate-resolution remote sensing data which does not meet the precision requirements for mountain forest ecosystems. To overcome this issue, there is an urgent need for higher resolution LAI data. This article proposes a data fusion method to map LAI in Wolong Nature Reserve that utilizes Sentinel-2 reflectance data, solar sensor geometry parameters, and vegetation indices extracted from the Google Earth Engine platform, along with canopy height data derived from canopy height estimation models in previous studies, combined with GLASS LAI V6 to estimate LAI using the random forest algorithm. The resulting LAI distribution map was plotted at a resolution of 20 m. The study demonstrated that incorporating canopy heights into the estimation model led to an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> model accuracy of greater than 0.83. The 20-m resolution LAI map increased spatial details compared to the moderate-resolution LAI map, making it more suitable for mountain forest ecosystems that exhibit significant spatial heterogeneity. |
| format | Article |
| id | doaj-art-14aef7653c1546ffaad42257503ea5f7 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-14aef7653c1546ffaad42257503ea5f72025-02-04T00:00:14ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184510452410.1109/JSTARS.2025.352842910839140Estimation of Forest Leaf Area Index Based on GEE Data Fusion MethodXinyi Liu0https://orcid.org/0000-0002-6712-4599Li He1https://orcid.org/0000-0002-1362-8310Zhengwei He2https://orcid.org/0000-0001-7690-2949Yun Wei3https://orcid.org/0009-0002-5085-8446School of Computer Engineering, Chengdu Technological University, Chengdu, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, ChinaImplementing forest protection measures, such as afforestation, can be an effective approach toward slowing down the increase of CO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> concentration and attaining carbon neutrality. The estimation of forest parameters is of great significance in understanding regional and global climate change patterns, and the Forest Leaf Area Index (LAI) is a crucial parameter. Current LAI products are mostly generated by moderate-resolution remote sensing data which does not meet the precision requirements for mountain forest ecosystems. To overcome this issue, there is an urgent need for higher resolution LAI data. This article proposes a data fusion method to map LAI in Wolong Nature Reserve that utilizes Sentinel-2 reflectance data, solar sensor geometry parameters, and vegetation indices extracted from the Google Earth Engine platform, along with canopy height data derived from canopy height estimation models in previous studies, combined with GLASS LAI V6 to estimate LAI using the random forest algorithm. The resulting LAI distribution map was plotted at a resolution of 20 m. The study demonstrated that incorporating canopy heights into the estimation model led to an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> model accuracy of greater than 0.83. The 20-m resolution LAI map increased spatial details compared to the moderate-resolution LAI map, making it more suitable for mountain forest ecosystems that exhibit significant spatial heterogeneity.https://ieeexplore.ieee.org/document/10839140/Data fusion methodGoogle Earth Engine (GEE)Leaf Area Index (LAI)random forest |
| spellingShingle | Xinyi Liu Li He Zhengwei He Yun Wei Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Data fusion method Google Earth Engine (GEE) Leaf Area Index (LAI) random forest |
| title | Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method |
| title_full | Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method |
| title_fullStr | Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method |
| title_full_unstemmed | Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method |
| title_short | Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method |
| title_sort | estimation of forest leaf area index based on gee data fusion method |
| topic | Data fusion method Google Earth Engine (GEE) Leaf Area Index (LAI) random forest |
| url | https://ieeexplore.ieee.org/document/10839140/ |
| work_keys_str_mv | AT xinyiliu estimationofforestleafareaindexbasedongeedatafusionmethod AT lihe estimationofforestleafareaindexbasedongeedatafusionmethod AT zhengweihe estimationofforestleafareaindexbasedongeedatafusionmethod AT yunwei estimationofforestleafareaindexbasedongeedatafusionmethod |