Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine
The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the diffi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2051 |
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| author | Bo Xie Chunxiang Cao Min Xu Xinwei Yang Robert Shea Duerler Barjeece Bashir Zhibin Huang Kaimin Wang Yiyu Chen Heyi Guo |
| author_facet | Bo Xie Chunxiang Cao Min Xu Xinwei Yang Robert Shea Duerler Barjeece Bashir Zhibin Huang Kaimin Wang Yiyu Chen Heyi Guo |
| author_sort | Bo Xie |
| collection | DOAJ |
| description | The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km<sup>2</sup>. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R<sup>2</sup> = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R<sup>2</sup> = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery. |
| first_indexed | 2024-03-10T03:44:46Z |
| format | Article |
| id | doaj.art-3e7ee64c881c4843a6aa36c67ac1cc12 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:44:46Z |
| publishDate | 2022-04-01 |
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| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3e7ee64c881c4843a6aa36c67ac1cc122023-11-23T09:09:48ZengMDPI AGRemote Sensing2072-42922022-04-01149205110.3390/rs14092051Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth EngineBo Xie0Chunxiang Cao1Min Xu2Xinwei Yang3Robert Shea Duerler4Barjeece Bashir5Zhibin Huang6Kaimin Wang7Yiyu Chen8Heyi Guo9State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaThe large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km<sup>2</sup>. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R<sup>2</sup> = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R<sup>2</sup> = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery.https://www.mdpi.com/2072-4292/14/9/2051forest canopy closureendmembers determinationdimidiate pixel modelspectral vegetation indicesregional scale |
| spellingShingle | Bo Xie Chunxiang Cao Min Xu Xinwei Yang Robert Shea Duerler Barjeece Bashir Zhibin Huang Kaimin Wang Yiyu Chen Heyi Guo Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine Remote Sensing forest canopy closure endmembers determination dimidiate pixel model spectral vegetation indices regional scale |
| title | Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine |
| title_full | Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine |
| title_fullStr | Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine |
| title_full_unstemmed | Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine |
| title_short | Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine |
| title_sort | improved forest canopy closure estimation using multispectral satellite imagery within google earth engine |
| topic | forest canopy closure endmembers determination dimidiate pixel model spectral vegetation indices regional scale |
| url | https://www.mdpi.com/2072-4292/14/9/2051 |
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