【作者】 余朝林；

【导师】 杜华强；

【作者基本信息】 浙江农林大学 ， 森林经理学， 2012， 硕士

【摘要】 遥感在森林碳储量定量估算中的作用得到广泛的认可，是陆地植被碳储量定量研究的重要进展，也是目前和未来森林碳估算及其动态变化规律研究的重要手段之一。各种生物量、碳储量遥感估算模型见诸报道，模型的特点是简单，但结构多样，缺陷是模型易受植被类型、光照条件、观察位置、冠层结构等影响，同时对土壤背景等非植被因素也比较敏感。因此，模型的普适性即可移植性差，这也是生物量碳储量遥感估算的一个主要问题。针对该问题，本研究以毛竹(Phyllostachys heterocycla var.pubescens)林为例，以浙江省临安市、安吉县、龙泉市为研究区域，研究毛竹林地上部分生物量遥感估算模型的可移植性。本研究主要包括以下3方面的内容：1、三个研究区遥感数据预处理，包括空间配准、地形校正、大气校正及毛竹林遥感信息的提取。2、选用一元线性模型、一元非线性模型、逐步回归模型、多元线性模型和Erf-BP神经网络模型等5种模型，分别构建三个区域毛竹林碳储量遥感估算模型。3、毛竹林碳储量遥感估算模型的可移植性进行分析与评价，即选择某区域精度较好的模型并分别移植到其他两个区，采用实际地面调查样地对其可移植性进行评价。研究主要得到以下几方面的结论：(1)对三个不同的区域，所设定的5类模型中，Erf-BP神经网络模型精度最高、逐步回归模型和一元非线性模型次之。(2) Erf-BP神经网络模型的可移植性优于逐步回归模型和一元非线性模型，具有较强的可移植性。(3)模型类型和模型自变量对统计模型的可移植性有较大的影响。更多还原

【Abstract】 Quantitative estimation of forest carbon stocks using remote sensing is widely recognized, which isimportant progress of quantitative study on terrestrial vegetation carbon stocks. Remote sensing is oneof the important means of future forest carbon estimation and its dynamic change detection. Researcheson a variety of remote sensing estimation models for biomass carbon stocks have been reported. Thecharacteristics of those models are simple, but structurally diverse. The defects of those models arevulnerable to vegetation type, light conditions, observation position, and canopy structure, while arealso more sensitive to the non-vegetation factors, such as soil background. Therefore, the statisticalmodel with poor transferability is also a major problem in estimation of biomass carbon stocks usingremote sensing. Aimed to this issue and taken Lin’an City, Zhejiang Province, Anji County, LongquanCity as study areas, the transferability of bamboo forest aboveground biomass estimation model basedon remote sensing will be discussed in this study.This research mainly includes the following three aspects.1、Remote sensing data preprocessing for three study areas, including spatial registration, terraincorrection, atmospheric correction, and bamboo forest remote sensing information extraction.2、Linear and nonlinear models, stepwise regression model, multivariate linear model, and Erf-BPneural network model were built to estimate bamboo forest carbon stocks using remote sensing data.3、The transferability of bamboo forest aboveground biomass estimation model was analyzed andevaluated. The models for one study area with better accuracy were transplanted to the other two areas,and results of model transferability were tested using the actual ground survey samples.There are three main conclusions:1、For the three study areas, Erf-BP neural network model had the highest accuracy, followed by thestepwise regression model and nonlinear model.2、The transferability of the Erf-BP neural network model was superior to the stepwise regressionmodel and nonlinear model, and Erf-BP model had strong transferability.3、Model type and independent variables of model had a significant impact on the transferability ofthe statistical model.更多还原