基于森林资源二类调查的县域森林碳汇及其价值估算研究一 以湖北省当阳市为例

ResearchonCountyForestCarbonSinkandItsValueEstimationBasedontheSecond

摘要:

摘要：森林质量评价服务于森林经营决策、监督管理和验收的全部流程。为克服地面调查林分质量中存在的成本高、时效性差等缺点，基于遥感数据形成一套林分质量评价体系，以提高评价效率，推动森林质量精准提升进程。本研究以新密市2018年生长季哨兵二号数据为基础，辅以数字高程影像，构建了基于遥感影像森林质量评价方法。首先选取样本小班，然后基于哨兵影像提取植被指数、纹理指数和干扰指数三类与森林质量密切相关的指标，借助熵值法计算各指标权重，构建出遥感估算林分质量模型。并以基于当年小班调查数据评价的林分质量结果作为参照，检验模型可信度。最后将模型用于量化全市森林质量并提出后续经营对策。结果发现，遥感评价指标中二阶矩、对比度和方差三个指标的权重相对较大，遥感评价林分质量结果与小班数据所得林分质量线性拟合的决定系数R2可达0.9861。结果表明：基于遥感数据的林分质量评价方法是可行的，该结果为大尺度区域范围森林质量评价提供了参考。

Abstract:

Abstract： Forest stand quality evaluation serves the entire process of forest management decision-making, supervision and management, and acceptance. In order to overcome the shortcomings of high cost and poor timeliness in the forest stand quality of the ground survey, a stand quality evaluation system was formed based on remote sensing data to improve the evaluation efficiency and promote the precise improvement of forest quality. This study is based on the Sentinel-2 data of the 2018 growing season in Xinmi, supplemented by digital elevation images. First, ground samples are selected. Then, the vegetation index, the texture index, and the disturbance index, three types of indicators closely related to forest quality, were extracted based on the Sentinel-2 images. The entropy method is used to calculate the weight of each indicator, and a remote sensing model for estimating forest stand quality is constructed. The stand quality results evaluated based on the survey data of samples were used as references to test the credibility of the model. Finally, the model is used to quantify Xinmi’s forest quality and propose follow-up management strategies. The weights of the second moment, contrast, and variance of the remote sensing evaluation indicators are relatively large. The stand quality results of the remote sensing evaluation are linearly fitted with the stand quality obtained from the samples, and the coefficient of determination R2 is 0.9861. The stand quality evaluation method based on remote sensing data is feasible, and in the stand quality evaluation system of remote sensing estimation, the three indexes of the second moment, contrast, and variance are the best for the classification of forest stand quality in Xinmi.