Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105330
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Lin, H | - |
dc.creator | Zhao, W | - |
dc.creator | Long, J | - |
dc.creator | Liu, Z | - |
dc.creator | Yang, P | - |
dc.creator | Zhang, T | - |
dc.creator | Ye, Z | - |
dc.creator | Wang, Q | - |
dc.creator | Matinfar, HR | - |
dc.date.accessioned | 2024-04-12T06:51:43Z | - |
dc.date.available | 2024-04-12T06:51:43Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105330 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Lin H, Zhao W, Long J, Liu Z, Yang P, Zhang T, Ye Z, Wang Q, Matinfar HR. Mapping Forest Growing Stem Volume Using Novel Feature Evaluation Criteria Based on Spectral Saturation in Planted Chinese Fir Forest. Remote Sensing. 2023; 15(2):402 is available at https://doi.org/10.3390/rs15020402. | en_US |
dc.subject | Feature evaluation criterion | en_US |
dc.subject | Forest growing stem volume | en_US |
dc.subject | Kriging spherical model | en_US |
dc.subject | Quadratic model | en_US |
dc.subject | Spectral saturation | en_US |
dc.title | Mapping forest growing stem volume using novel feature evaluation criteria based on spectral saturation in planted Chinese fir forest | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.3390/rs15020402 | - |
dcterms.abstract | Forest growing stem volume (GSV) is regarded as one of the most important parameters for the quality evaluation and dynamic monitoring of forest resources. The accuracy of mapping forest GSV is highly related to the employed models and involved remote sensing features, and the criteria of feature evaluation severely affect the performance of the employed models. However, due to the linear or nonlinear relationships between remote sensing features and GSV, widely used evaluation criteria inadequately express the complex sensitivity between forest GSV and spectral features, especially the saturation levels of features in a planted forest. In this study, novel feature evaluation criteria were constructed based on the Pearson correlations and optical saturation levels of the alternative remote sensing features extracted from two common optical remote sensing image sets (GF-1 and Sentinel-2). Initially, the spectral saturation level of each feature was quantified using the kriging spherical model and the quadratic model. Then, optimal feature sets were obtained with the proposed criteria and the linear stepwise regression model. Finally, four widely used machine learning models—support vector machine (SVM), multiple linear stepwise regression (MLR), random forest (RF) and K-neighborhood (KNN)—were employed to map forest GSV in a planted Chinese fir forest. The results showed that the proposed feature evaluation criteria could effectively improve the accuracy of estimating forest GSV and that the systematic distribution of errors between the predicted and ground measurements in the range of forest GSV was less than 300 m3/hm2. After using the proposed feature evaluation criteria, the highest accuracy of mapping GSV was obtained with the RF model for GF-1 images (R2 = 0.49, rRMSE = 28.67%) and the SVM model for Sentinel-2 images (R2 = 0.52, rRMSE = 26.65%), and the decreased rRMSE values ranged from 1.1 to 6.2 for GF-1 images (28.67% to 33.08%) and from 2.3 to 6.8 for Sentinel-2 images (26.85% to 33.28%). It was concluded that the sensitivity of the optimal feature set and the accuracy of the estimated GSV could be improved using the proposed evaluation criteria (less than 300 m3/hm2). However, these criteria were barely able to improve mapping accuracy for a forest with a high GSV (larger than 300 m3/hm2). | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, Jan. 2023, v. 15, no. 2, 402 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2023-01 | - |
dc.identifier.scopus | 2-s2.0-85146589855 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.artn | 402 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Hunan Provincial Natural Science Foundation of China; Excellent Youth Project of the Scientific Research Foundation of the Hunan Provincial Department of Education; Postgraduate scientific research Innovative project of Hunan province | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
remotesensing-15-00402.pdf | 4.76 MB | Adobe PDF | View/Open |
Page views
8
Citations as of Jul 7, 2024
Downloads
1
Citations as of Jul 7, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.