Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm
The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacki...
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1535 |
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| author | Fugen Jiang Feng Zhao Kaisen Ma Dongsheng Li Hua Sun |
| author_facet | Fugen Jiang Feng Zhao Kaisen Ma Dongsheng Li Hua Sun |
| author_sort | Fugen Jiang |
| collection | DOAJ |
| description | The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), <i>k</i>-nearest neighbor (<i>k</i>NN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an <i>R</i><sup>2</sup> of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, <i>k</i>NN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future. |
| first_indexed | 2024-03-10T12:17:12Z |
| format | Article |
| id | doaj.art-42c95bda00b04a4782248c4fc6a5e2ae |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:17:12Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-42c95bda00b04a4782248c4fc6a5e2ae2023-11-21T15:45:54ZengMDPI AGRemote Sensing2072-42922021-04-01138153510.3390/rs13081535Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking AlgorithmFugen Jiang0Feng Zhao1Kaisen Ma2Dongsheng Li3Hua Sun4Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaState Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming, East China Normal University, Shanghai 200241, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaHebei Academy of Forestry and Grassland Investigation and Planning, Shijiazhuang 050051, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaThe forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), <i>k</i>-nearest neighbor (<i>k</i>NN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an <i>R</i><sup>2</sup> of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, <i>k</i>NN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.https://www.mdpi.com/2072-4292/13/8/1535forest canopy heightICESat-2GEEstacking algorithmplantations |
| spellingShingle | Fugen Jiang Feng Zhao Kaisen Ma Dongsheng Li Hua Sun Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm Remote Sensing forest canopy height ICESat-2 GEE stacking algorithm plantations |
| title | Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm |
| title_full | Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm |
| title_fullStr | Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm |
| title_full_unstemmed | Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm |
| title_short | Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm |
| title_sort | mapping the forest canopy height in northern china by synergizing icesat 2 with sentinel 2 using a stacking algorithm |
| topic | forest canopy height ICESat-2 GEE stacking algorithm plantations |
| url | https://www.mdpi.com/2072-4292/13/8/1535 |
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