Individual Tree Species Classification in a Mining Area of the Yellow River Basin Using UAV-Based LiDAR, Hyperspectral, and RGB Data.
Saved in:
| Title: | Individual Tree Species Classification in a Mining Area of the Yellow River Basin Using UAV-Based LiDAR, Hyperspectral, and RGB Data. |
|---|---|
| Authors: | Wang, Guo1,2 (AUTHOR), Nie, Sheng2,3 (AUTHOR) niesheng@aircas.ac.cn, Xi, Xiaohuan2,3 (AUTHOR), Wang, Cheng1,2,3 (AUTHOR), Wang, Hongtao2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1361. 32p. |
| Subjects: | Vegetation classification, Machine learning, Visible spectra, Spectral imaging, Mineral industries, Remote sensing, Boosting algorithms |
| Geographic Terms: | Yellow River (China) |
| Abstract: | Highlights: What are the main findings? In the ecologically fragile and structurally complex mining landscapes of the Yellow River Basin, the fusion of UAV-based LiDAR, hyperspectral, and RGB data enabled the extraction of 278 complementary features for each individual tree. These features comprehensively captured biochemical, structural, and textural characteristics, facilitating the discrimination of spectrally similar species within heterogeneous tree–shrub–grass mosaics. Through rigorous statistical evaluation using 5 × 5 repeated cross-validation combined with Friedman and Nemenyi tests, XGBoost was identified as the optimal classifier for this challenging mining environment. It achieved superior performance (Overall Accuracy = 0.897, Kappa = 0.811), demonstrating high stability and computational efficiency compared with linear, instance-based, and single-tree models. Feature importance analysis further indicated that blue-edge spectral bands sensitive to stress-induced pigment variation, red-edge vegetation indices, and LiDAR-derived canopy height were the dominant contributors to accurate species discrimination. These results confirm that the integration of biochemical information with three-dimensional structural attributes is essential for reliable classification in mining-disturbed ecosystems. What are the implications of the main findings? The XGBoost-based framework establishes a reproducible pipeline for individual tree species mapping, supported by statistical significance testing (Friedman and Nemenyi tests). However, due to severe class imbalance (e.g., only 27 samples for Ligustrum quihoui) and other limitations (single site, minimal LiDAR exploitation), the framework currently achieves reliable discrimination only for dominant and moderately represented classes; high-precision mapping of rare species remains an unresolved challenge. In addition, the resulting wall-to-wall species distribution map supports evidence-based and spatially targeted restoration strategies. It enables precision revegetation planning, identification of areas where restoration progress has stagnated, and long-term adaptive management through repeatable and cost-effective UAV surveys. Consequently, the framework directly contributes to the sustainable ecological rehabilitation of mining-impacted regions. The Yellow River Basin contains abundant coal resources; however, its ecological environment is inherently fragile, and vegetation degradation has been further intensified by extensive mining activities. Accurate classification of individual tree species in mining-affected areas is therefore essential for assessing ecological conditions and establishing a scientific foundation for targeted restoration and sustainable management. To address this need, an evaluated machine learning framework was developed and evaluated for individual tree species classification in a coal mining area of the Yellow River Basin using integrated unmanned aerial vehicle (UAV) data. A comprehensive feature set was constructed by extracting 278 attributes per tree. These attributes included 224 spectral bands and 29 hyperspectral indices derived from hyperspectral imagery, 24 textural metrics obtained from RGB orthophotos, and one canopy height feature generated from a LiDAR-derived model. Based on ground-truth data from 1095 individual trees, seven machine learning algorithms were trained and systematically compared: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and XGBoost. Statistical significance testing using 5 × 5 repeated cross-validation, together with the Friedman test and post hoc Nemenyi test, and additional model stability analysis consistently identified XGBoost as the optimal classifier. On an independent test set, XGBoost achieved high accuracy (Overall Accuracy = 0.897, Kappa = 0.811) with an efficient training time of 2.36 s. Further analysis demonstrated the critical and complementary roles of hyperspectral and structural features in species discrimination. The optimized model was subsequently applied to generate a detailed wall-to-wall tree species map across the entire mining area. Overall, this study presents a statistically informed comparison of classifiers for multi-source feature-based species discrimination and delivers an evaluated and practical pipeline for effective vegetation monitoring. The proposed framework provides a scientific tool for assessing and managing ecological recovery in complex mining environments, particularly within ecologically sensitive regions such as the Yellow River Basin. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193715392 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Individual Tree Species Classification in a Mining Area of the Yellow River Basin Using UAV-Based LiDAR, Hyperspectral, and RGB Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Guo%22">Wang, Guo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nie%2C+Sheng%22">Nie, Sheng</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> niesheng@aircas.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Xi%2C+Xiaohuan%22">Xi, Xiaohuan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Cheng%22">Wang, Cheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Hongtao%22">Wang, Hongtao</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1361. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vegetation+classification%22">Vegetation classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Visible+spectra%22">Visible spectra</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+imaging%22">Spectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Mineral+industries%22">Mineral industries</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Yellow+River+%28China%29%22">Yellow River (China)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? In the ecologically fragile and structurally complex mining landscapes of the Yellow River Basin, the fusion of UAV-based LiDAR, hyperspectral, and RGB data enabled the extraction of 278 complementary features for each individual tree. These features comprehensively captured biochemical, structural, and textural characteristics, facilitating the discrimination of spectrally similar species within heterogeneous tree–shrub–grass mosaics. Through rigorous statistical evaluation using 5 × 5 repeated cross-validation combined with Friedman and Nemenyi tests, XGBoost was identified as the optimal classifier for this challenging mining environment. It achieved superior performance (Overall Accuracy = 0.897, Kappa = 0.811), demonstrating high stability and computational efficiency compared with linear, instance-based, and single-tree models. Feature importance analysis further indicated that blue-edge spectral bands sensitive to stress-induced pigment variation, red-edge vegetation indices, and LiDAR-derived canopy height were the dominant contributors to accurate species discrimination. These results confirm that the integration of biochemical information with three-dimensional structural attributes is essential for reliable classification in mining-disturbed ecosystems. What are the implications of the main findings? The XGBoost-based framework establishes a reproducible pipeline for individual tree species mapping, supported by statistical significance testing (Friedman and Nemenyi tests). However, due to severe class imbalance (e.g., only 27 samples for Ligustrum quihoui) and other limitations (single site, minimal LiDAR exploitation), the framework currently achieves reliable discrimination only for dominant and moderately represented classes; high-precision mapping of rare species remains an unresolved challenge. In addition, the resulting wall-to-wall species distribution map supports evidence-based and spatially targeted restoration strategies. It enables precision revegetation planning, identification of areas where restoration progress has stagnated, and long-term adaptive management through repeatable and cost-effective UAV surveys. Consequently, the framework directly contributes to the sustainable ecological rehabilitation of mining-impacted regions. The Yellow River Basin contains abundant coal resources; however, its ecological environment is inherently fragile, and vegetation degradation has been further intensified by extensive mining activities. Accurate classification of individual tree species in mining-affected areas is therefore essential for assessing ecological conditions and establishing a scientific foundation for targeted restoration and sustainable management. To address this need, an evaluated machine learning framework was developed and evaluated for individual tree species classification in a coal mining area of the Yellow River Basin using integrated unmanned aerial vehicle (UAV) data. A comprehensive feature set was constructed by extracting 278 attributes per tree. These attributes included 224 spectral bands and 29 hyperspectral indices derived from hyperspectral imagery, 24 textural metrics obtained from RGB orthophotos, and one canopy height feature generated from a LiDAR-derived model. Based on ground-truth data from 1095 individual trees, seven machine learning algorithms were trained and systematically compared: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and XGBoost. Statistical significance testing using 5 × 5 repeated cross-validation, together with the Friedman test and post hoc Nemenyi test, and additional model stability analysis consistently identified XGBoost as the optimal classifier. On an independent test set, XGBoost achieved high accuracy (Overall Accuracy = 0.897, Kappa = 0.811) with an efficient training time of 2.36 s. Further analysis demonstrated the critical and complementary roles of hyperspectral and structural features in species discrimination. The optimized model was subsequently applied to generate a detailed wall-to-wall tree species map across the entire mining area. Overall, this study presents a statistically informed comparison of classifiers for multi-source feature-based species discrimination and delivers an evaluated and practical pipeline for effective vegetation monitoring. The proposed framework provides a scientific tool for assessing and managing ecological recovery in complex mining environments, particularly within ecologically sensitive regions such as the Yellow River Basin. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193715392 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091361 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 1361 Subjects: – SubjectFull: Vegetation classification Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Visible spectra Type: general – SubjectFull: Spectral imaging Type: general – SubjectFull: Mineral industries Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Yellow River (China) Type: general Titles: – TitleFull: Individual Tree Species Classification in a Mining Area of the Yellow River Basin Using UAV-Based LiDAR, Hyperspectral, and RGB Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Guo – PersonEntity: Name: NameFull: Nie, Sheng – PersonEntity: Name: NameFull: Xi, Xiaohuan – PersonEntity: Name: NameFull: Wang, Cheng – PersonEntity: Name: NameFull: Wang, Hongtao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
