Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion a...
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/23/5581 |
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| author | Xiaoping Wang Jingming Shi Chenfeng Wang Chao Gao Fei Zhang |
| author_facet | Xiaoping Wang Jingming Shi Chenfeng Wang Chao Gao Fei Zhang |
| author_sort | Xiaoping Wang |
| collection | DOAJ |
| description | The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the <i>NIR</i> reflectance. The relationship between forest stands and red edge is the most significant, and <i>r</i> is 0.53, and the relationship between Simple Ratio Index (<i>SR</i>), near-infrared reflectance of vegetation (<i>NIR<sub>v</sub></i>), normalized difference vegetation index (<i>NDVI</i>), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that <i>SR</i>, <i>NDVI</i> and red edge (<i>B</i><sub>5</sub>) could explain 98% of all spectral information. <i>SR</i>, <i>NDVI</i> and red edge (<i>B</i><sub>5</sub>) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (<i>R</i><sup>2</sup> = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (<i>R</i><sup>2</sup> = 0.63) was higher than that of the linear regression model (<i>R</i><sup>2</sup> = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau. |
| first_indexed | 2024-03-09T01:42:54Z |
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| id | doaj.art-4d3414a848444bc889c08873d32a369f |
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| spelling | doaj.art-4d3414a848444bc889c08873d32a369f2023-12-08T15:25:07ZengMDPI AGRemote Sensing2072-42922023-11-011523558110.3390/rs15235581Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess PlateauXiaoping Wang0Jingming Shi1Chenfeng Wang2Chao Gao3Fei Zhang4State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, ChinaShandong Cartographic Institute, Jinan 250002, ChinaState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, ChinaState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaThe accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the <i>NIR</i> reflectance. The relationship between forest stands and red edge is the most significant, and <i>r</i> is 0.53, and the relationship between Simple Ratio Index (<i>SR</i>), near-infrared reflectance of vegetation (<i>NIR<sub>v</sub></i>), normalized difference vegetation index (<i>NDVI</i>), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that <i>SR</i>, <i>NDVI</i> and red edge (<i>B</i><sub>5</sub>) could explain 98% of all spectral information. <i>SR</i>, <i>NDVI</i> and red edge (<i>B</i><sub>5</sub>) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (<i>R</i><sup>2</sup> = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (<i>R</i><sup>2</sup> = 0.63) was higher than that of the linear regression model (<i>R</i><sup>2</sup> = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau.https://www.mdpi.com/2072-4292/15/23/5581forest standremote sensingLoess Plateauvegetation index (VI) |
| spellingShingle | Xiaoping Wang Jingming Shi Chenfeng Wang Chao Gao Fei Zhang Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau Remote Sensing forest stand remote sensing Loess Plateau vegetation index (VI) |
| title | Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau |
| title_full | Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau |
| title_fullStr | Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau |
| title_full_unstemmed | Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau |
| title_short | Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau |
| title_sort | remote sensing inversion and mapping of typical forest stand age in the loess plateau |
| topic | forest stand remote sensing Loess Plateau vegetation index (VI) |
| url | https://www.mdpi.com/2072-4292/15/23/5581 |
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