Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables
Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/15/2/394 |
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| author | Dongyang Han Jialong Zhang Dongfan Xu Yi Liao Rui Bao Shuxian Wang Shaozhi Chen |
| author_facet | Dongyang Han Jialong Zhang Dongfan Xu Yi Liao Rui Bao Shuxian Wang Shaozhi Chen |
| author_sort | Dongyang Han |
| collection | DOAJ |
| description | Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate <i>Pinus densata</i>’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that <i>Pinus densata</i>’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm<sup>−2</sup> to 25.313 t·hm<sup>−2</sup>. We concluded that a dynamic model based on variability accurately reflects <i>Pinus densata</i>’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models. |
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| format | Article |
| id | doaj.art-1362f12e4dea4bf39e253c0d7f39e558 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
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| publishDate | 2024-02-01 |
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| spelling | doaj.art-1362f12e4dea4bf39e253c0d7f39e5582024-02-23T15:17:07ZengMDPI AGForests1999-49072024-02-0115239410.3390/f15020394Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic VariablesDongyang Han0Jialong Zhang1Dongfan Xu2Yi Liao3Rui Bao4Shuxian Wang5Shaozhi Chen6Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, ChinaForestry College, Southwest Forestry University, Kunming 650224, ChinaMinistry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Shanghai Institute of EcoChongming (SIEC), Fudan University, Shanghai 200433, ChinaCollege of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Xianyang 712100, ChinaInstitute of Southwest Survey and Planning, National Forestry and Grassland Administration, Kunming 650021, ChinaRemote Sensing Center of Yunnan Province, Kunming 650034, ChinaChinese Academy of Forestry, Beijing 100091, ChinaForest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate <i>Pinus densata</i>’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that <i>Pinus densata</i>’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm<sup>−2</sup> to 25.313 t·hm<sup>−2</sup>. We concluded that a dynamic model based on variability accurately reflects <i>Pinus densata</i>’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.https://www.mdpi.com/1999-4907/15/2/394Landsat<i>Pinus</i> <i>densata</i>topographic informationsoil thicknessmultilevel nonlinear mixed-effects model |
| spellingShingle | Dongyang Han Jialong Zhang Dongfan Xu Yi Liao Rui Bao Shuxian Wang Shaozhi Chen Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables Forests Landsat <i>Pinus</i> <i>densata</i> topographic information soil thickness multilevel nonlinear mixed-effects model |
| title | Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables |
| title_full | Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables |
| title_fullStr | Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables |
| title_full_unstemmed | Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables |
| title_short | Improving <i>Pinus densata</i> Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables |
| title_sort | improving i pinus densata i carbon stock estimations through remote sensing in shangri la a nonlinear mixed effects model integrating soil thickness and topographic variables |
| topic | Landsat <i>Pinus</i> <i>densata</i> topographic information soil thickness multilevel nonlinear mixed-effects model |
| url | https://www.mdpi.com/1999-4907/15/2/394 |
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