文献类型：期刊文献

中文题名：基于高分2号遥感数据估测中亚热带天然林木本植物物种多样性

英文题名：Predict Tree Species Diversity from GF-2 Satellite Data in a Subtropical Forest of China

作者：刘鲁霞[1] 庞勇[1] 任海保[2] 李增元[1]

第一作者：刘鲁霞

通信作者：Pang, Yong

机构：[1]中国林业科学研究院资源信息研究所;[2]中国科学院植物研究所植被与环境变化国家重点实验室

年份：2019

期号：2

起止页码：61-74

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;EI(收录号：20192206992351);Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家自然科学基金面上项目(31570546;41371074);国家自然科学基金青年项目(41801330)

语种：中文

中文关键词：物种多样性;高分2号遥感数据;纹理特征;植被指数

外文关键词：tree species diversity;GF-2 remote sensing data;texture features;spectral indices

分类号：S718.5

摘要：【目的】探索高分2号遥感数据与中亚热带天然林木本植物物种Shannon-Wiener多样性指数、Simpson多样性指数和Pielou均匀性指数之间的关系,为森林经营管理和保护策略提供参考。【方法】提取高分2号多光谱数据的原始波段、植被指数、纹理特征和全色波段纹理特征,使用随机森林算法筛选变量并对3种多样性指数进行建模,设置不同纹理提取窗口来寻找最优窗口。【结果】基于随机森林算法的RFE冗余变量去除方法可从众多遥感变量中快速选择对模型精度具有显著贡献的少量变量。多光谱数据3×3窗口纹理特征、全色数据7×7窗口纹理特征和植被指数结合的特征集对3种多样性指数具有较好估测结果,其决定系数(R^2)和均方根误差(RMSE)分别为0.47和0.300(Shannon-Wiener多样性指数)、0.53和0.042 (Simpson多样性指数)、0.61和0.051 (Pielou均匀性指数)。植被指数中类胡萝卜素反射率指数与3种多样性指数具有显著相关关系。【结论】高分2号遥感数据中的植被指数和纹理特征可有效估测研究区森林木本植物物种多样性。类胡萝卜素反射率指数可体现森林中类胡萝卜素相对于叶绿素的含量,在秋冬季节作为反映常绿树种和落叶树种分布的指数,对森林木本植物物种多样性估测具有最大贡献。使用星载遥感数据预测的多样性和均匀性指数分布可有效监测森林木本植物物种多样性变化。
【Objective】 In this paper, we explored the possibility of defining relationships between remote sensing features that come from GF-2 and species diversity indices for subtropical forest in Gutian Mountain of Zhejiang Province, China.【Method】 We extracted the reflectance values, spectral indices, texture from GF-2 data. Random forest models were used to select variables and estimate species diversity indices. We compared the texture values that come from different window size to find the best window size for species diversity estimation.【Result】 Based on the random forest(RF),recursive feature elimination(RFE)was used to find small subsets of features with high discrimination levels on data sets, which provide good performance for species diversity modeling. For the multispectral(MSS)data, the best window size is 3×3, and for the panchromatic(Pan)data, the best window size is 7×7. Both texture features and spectral indices were selected for species diversity modeling and the Carotenoid reflectance index provided the best performance.The determinate coefficient and RMSE for three species diversity are 0.47 and 0.300(Shannon-Wiener diversity index),0.53 and 0.042(Simpson diversity index),0.61 and 0.051(Pielou evenness index),respectively.【Conclusion】 The result demonstrated that the GF-2 data can be used to model tree species diversity effectively. Predictive map derived from the presented method ology can help evaluate spatial aspects and monitor tree species diversity of the studied forest and facilitate the evaluation of forest management and conservation strategies.