Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery
Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are impor...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/11/6/695 |
| _version_ | 1797564367485534208 |
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| author | Xiaoxiao Zhu Yongli Zhou Yongjun Yang Huping Hou Shaoliang Zhang Run Liu |
| author_facet | Xiaoxiao Zhu Yongli Zhou Yongjun Yang Huping Hou Shaoliang Zhang Run Liu |
| author_sort | Xiaoxiao Zhu |
| collection | DOAJ |
| description | Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 × 3 window have a relatively high importance score in the random forest model. This indicates that the 3 × 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has an <i>R<sup>2</sup></i> of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has an <i>R<sup>2</sup></i> of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree–shrub–grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047–0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps. |
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| id | doaj.art-6ed4f16484b7499d8801a61c4505098b |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T18:57:18Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-6ed4f16484b7499d8801a61c4505098b2023-11-20T04:41:45ZengMDPI AGForests1999-49072020-06-0111669510.3390/f11060695Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 ImageryXiaoxiao Zhu0Yongli Zhou1Yongjun Yang2Huping Hou3Shaoliang Zhang4Run Liu5School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, ChinaShenhua Zhungeer Energy Company Limited, Ordos 010399, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221008, ChinaForest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 × 3 window have a relatively high importance score in the random forest model. This indicates that the 3 × 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has an <i>R<sup>2</sup></i> of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has an <i>R<sup>2</sup></i> of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree–shrub–grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047–0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps.https://www.mdpi.com/1999-4907/11/6/695forest spatial structureWorldview-2MEA-BP neural networksemi-arid mine dumpsecological restoration |
| spellingShingle | Xiaoxiao Zhu Yongli Zhou Yongjun Yang Huping Hou Shaoliang Zhang Run Liu Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery Forests forest spatial structure Worldview-2 MEA-BP neural network semi-arid mine dumps ecological restoration |
| title | Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery |
| title_full | Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery |
| title_fullStr | Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery |
| title_full_unstemmed | Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery |
| title_short | Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery |
| title_sort | estimation of the restored forest spatial structure in semi arid mine dumps using worldview 2 imagery |
| topic | forest spatial structure Worldview-2 MEA-BP neural network semi-arid mine dumps ecological restoration |
| url | https://www.mdpi.com/1999-4907/11/6/695 |
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