Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments
Forest canopy height is one of the critical parameters for carbon sink estimation. Although spaceborne lidar data can obtain relatively high precision canopy height on discrete light spots, to obtain continuous canopy height, the integration of optical remote sensing image data is required to achiev...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2275 |
| _version_ | 1797601860075388928 |
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| author | Xiang Huang Feng Cheng Jinliang Wang Bangjin Yi Yinli Bao |
| author_facet | Xiang Huang Feng Cheng Jinliang Wang Bangjin Yi Yinli Bao |
| author_sort | Xiang Huang |
| collection | DOAJ |
| description | Forest canopy height is one of the critical parameters for carbon sink estimation. Although spaceborne lidar data can obtain relatively high precision canopy height on discrete light spots, to obtain continuous canopy height, the integration of optical remote sensing image data is required to achieve “from discrete to continuous” extrapolation based on different prediction models (parametric model and non-parametric model). This study focuses on the Shangri-La area and seeks to assess the practical applicability of two predictive models under complex mountainous conditions, using a combination of active and passive remote sensing data from ICESat-2 and Sentinel-2. The research aims to enhance our understanding of the effectiveness of these models in addressing the unique challenges presented by mountainous terrain, including rugged topography, variable vegetation cover, and extreme weather conditions. Through this work, we hope to contribute to the development of improved geospatial prediction algorithms for mountainous regions worldwide. The results show the following: (1) the fitting effect of the selected parametric model (empirical function regression) is poor in the area of <i>Quercus acutissima</i> and <i>Pinus yunnanensis</i>; (2) evaluation of the importance of each explanatory variable in the non-parametric model (random forest regression) shows that topographic and meteorological factors play a dominant role in canopy height inversion; (3) when random forest regression is applied to the inversion of canopy height, there is often a problem of error accumulation, which is of particular concern to the <i>Quercus acutissima</i> and <i>Pinus yunnanensis</i>; (4) the random forest regression with the optimal features has relatively higher precision by comparing the inversion accuracy of canopy height data of the empirical function regression, random forest regression with all features, and random forest regression with the optimal features in the study area, i.e., R<sup>2</sup> (coefficient of determination) = 0.865 and RMSE (root mean square error) = 3.184 m. In contrast, the poor estimation results reflected by the empirical function regression, mainly resulting from the lack of consideration of topographic and meteorological factors, are not applicable to the inversion of canopy height under complex topographic conditions. |
| first_indexed | 2024-03-11T04:08:30Z |
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| id | doaj.art-dccecdf76ee8433994b153720b2d1164 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:30Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-dccecdf76ee8433994b153720b2d11642023-11-17T23:37:55ZengMDPI AGRemote Sensing2072-42922023-04-01159227510.3390/rs15092275Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous EnvironmentsXiang Huang0Feng Cheng1Jinliang Wang2Bangjin Yi3Yinli Bao4Faculty of Geography, Yunnan Normal University, Kunming 650500, ChinaFaculty of Geography, Yunnan Normal University, Kunming 650500, ChinaFaculty of Geography, Yunnan Normal University, Kunming 650500, ChinaYunnan Institute of Geological Sciences, Kunming 650051, ChinaKunming Surveying and Mapping Management Center, Kunming 650500, ChinaForest canopy height is one of the critical parameters for carbon sink estimation. Although spaceborne lidar data can obtain relatively high precision canopy height on discrete light spots, to obtain continuous canopy height, the integration of optical remote sensing image data is required to achieve “from discrete to continuous” extrapolation based on different prediction models (parametric model and non-parametric model). This study focuses on the Shangri-La area and seeks to assess the practical applicability of two predictive models under complex mountainous conditions, using a combination of active and passive remote sensing data from ICESat-2 and Sentinel-2. The research aims to enhance our understanding of the effectiveness of these models in addressing the unique challenges presented by mountainous terrain, including rugged topography, variable vegetation cover, and extreme weather conditions. Through this work, we hope to contribute to the development of improved geospatial prediction algorithms for mountainous regions worldwide. The results show the following: (1) the fitting effect of the selected parametric model (empirical function regression) is poor in the area of <i>Quercus acutissima</i> and <i>Pinus yunnanensis</i>; (2) evaluation of the importance of each explanatory variable in the non-parametric model (random forest regression) shows that topographic and meteorological factors play a dominant role in canopy height inversion; (3) when random forest regression is applied to the inversion of canopy height, there is often a problem of error accumulation, which is of particular concern to the <i>Quercus acutissima</i> and <i>Pinus yunnanensis</i>; (4) the random forest regression with the optimal features has relatively higher precision by comparing the inversion accuracy of canopy height data of the empirical function regression, random forest regression with all features, and random forest regression with the optimal features in the study area, i.e., R<sup>2</sup> (coefficient of determination) = 0.865 and RMSE (root mean square error) = 3.184 m. In contrast, the poor estimation results reflected by the empirical function regression, mainly resulting from the lack of consideration of topographic and meteorological factors, are not applicable to the inversion of canopy height under complex topographic conditions.https://www.mdpi.com/2072-4292/15/9/2275inversion of canopy heightICESat-2 (Ice, Cloud, and Land Elevation Satellite)mountainous regionsmultisource data fusion |
| spellingShingle | Xiang Huang Feng Cheng Jinliang Wang Bangjin Yi Yinli Bao Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments Remote Sensing inversion of canopy height ICESat-2 (Ice, Cloud, and Land Elevation Satellite) mountainous regions multisource data fusion |
| title | Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments |
| title_full | Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments |
| title_fullStr | Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments |
| title_full_unstemmed | Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments |
| title_short | Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments |
| title_sort | comparative study on remote sensing methods for forest height mapping in complex mountainous environments |
| topic | inversion of canopy height ICESat-2 (Ice, Cloud, and Land Elevation Satellite) mountainous regions multisource data fusion |
| url | https://www.mdpi.com/2072-4292/15/9/2275 |
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