Modelling the Dynamics of Carbon Storages for Pinus densata Using Landsat Images in Shangri-La Considering Topographic Factors

Abstract

Accurate estimation of forest carbon storage is essential for understanding the dynamics of forest resources and optimizing decisions for forest resource management. In order to explore the changes in the carbon storage of Pinus densata in Shangri-La and the influence of topography on carbon storage, two dynamic models were developed based on the National Forest Inventory (NFI) and Landsat TM/OLI images with a 5-year interval change and annual average change. The three modelling methods used were partial least squares (PLSR), random forest (RF) and gradient boosting regression tree (GBRT). Various spectral and texture features of the images were calculated and filtered before modelling. The terrain niche index (TNI), which is able to reflect the combined effect of elevation and slope, was added to the dynamic model, the optimal model was selected to estimate the carbon storage, and the topographic conditions in areas of change in carbon storage were analyzed. The results showed that: (1) The dynamic model based on 5-year interval change data performs better than the dynamic model with annual average change data, and the RF model has a higher accuracy compared to the PLSR and GBRT models. (2) The addition of TNI improved the accuracy, in which R2 is improved by up to 10.48% at most, RMSE is reduced by up to 7.32% at most, and MAE is reduced by up to 8.89% at most, and the RF model based on the 5-year interval change data has the highest accuracy after adding TNI, with an R2 of 0.87, an RMSE of 3.82 t-C·ha−1, and a MAE of 1.78 t-C·ha−1. (3) The direct estimation results of the dynamic model showed that the carbon storage of Pinus densata in Shangri-La decreased in 1987–1992 and 1997–2002, and increased in 1992–1997, 2002–2007, 2007–2012, and 2012–2017. (4) The trend of increasing or decreasing carbon storage in each period is not exactly the same on the TNI gradient, according to the dominant distribution, as topographic conditions with lower elevations or gentler slopes are favorable for the accumulation of carbon storage, while the decreasing area of carbon storage is more randomly distributed topographically. This study develops a dynamic estimation model of carbon storage considering topographic factors, which provides a solution for the accurate estimation of forest carbon storage in regions with a complex topography.

Tree Crown Affects Biomass Allocation and Its Response to Site Conditions and the Density of Platycladus orientalis Linnaeus Plantation

“…Crown indicators are often used to construct models of tree growth or biomass [104]. It has been observed that the correlation between crown and tree biomass is affected by different site conditions [105,106]. This research also revealed that the relationship between crown indicators and tree organ biomass in P. orientalis plantations varied between site conditions, with the crown's contribution to biomass being notably higher in good soil quality sites than in those with poor soil quality.…”

Estimation of Above-Ground Biomass for Pinus densata Using Multi-Source Time Series in Shangri-La Considering Seasonal Effects

“…Shangri-La has a remarkable monsoon climate, with rainfall concentrated in the months of June to October each year [36]. The average annual precipitation is 268~945 mm, and the annual sunshine is 1742.9~2186.6 h [37]. It is one of only three remaining natural forest areas and a key area for the implementation of the "Tianbao Project" in China.…”

“…Twenty percent (12 groups) of the data were set apart separately (this portion of data was not involved in model training) for model validation; the accuracy evaluation indicators employed were the relative root mean square error (rRMSE) and the prediction accuracy (p) [57]. To ensure that the model results were as objective as possible, each model was fitted 20 times in this study to allow the mean values of evaluation indicators to be used for comparison [37]. The formulas are as follows:…”