基于GF-2影像的西北干旱荒漠低扰动区植被覆盖度提取方法研究  

Remote Sensing Estimation Methods for Determining FVC in Northwest Desert Arid Low Disturbance Areas based on GF-2 Imagery

作　　者：薛心悦 郭小平[1] 薛东明 马原 杨帆[1] XUE Xinyue;GUO Xiaoping;XUE Dongming;MA Yuan;YANG Fan(School of Soil and Water Conservation,Beijing Forestry University,Beijing 100083,China;Seabuckthorn Development and Management Centre of the Ministry of Water Resources(Soil and Water Conservation Plant Development and Manage-ment Centre of the Ministry of Water Resources),Beijing 100038,China)

机构地区：[1]北京林业大学水土保持学院,北京100083 [2]水利部沙棘开发管理中心(水利部水土保持植物开发管理中心),北京100038

出　　处：《Journal of Resources and Ecology》2023年第4期833-846,共14页资源与生态学报（英文版）

基　　金：The National Key Research and Development Program of China(2017YFC0504406)。

摘　　要：植被覆盖度是地表植被的重要指示性指标,对营建植被恢复措施具有十分重要的参考价值。在植被覆盖度相关研究中,针对提取方法的研究引起了众多关注。研究表明,植被覆盖度提取方法普适性较差,而现有的相关研究多是分布在湿润、半湿润和半干旱的农用、林用地上,在植被稀疏且以灌草为主的干旱区较为稀缺。为了探究不同方法在西北干旱荒漠低扰动区估测植被覆盖度的精度及适用性,本文基于GF-2多光谱-全色融合影像,提取能有效排除土壤、气象等信息以获取纯净植被信息的6种植被指数(NDVI、SAVI、MSAVI、ARVI、EVI、MVI),分别建立以单一植被指数为自变量的像元二分模型和以多种植被指数为自变量的参数回归模型(岭回归、主成分回归)及非参数回归模型(随机森林回归)。并引入SSE、R2、RMSE三个统计量验证模型精度及五折交叉验证法探测模型是否存在过拟合现象。经过这些方法筛选模型后,应用所选模型反演研究区植被覆盖度。结果表明:(1)多种模型中,EVI指数构建的像元二分模型和随机森林回归模型更适用于研究区的植被覆盖度提取。在应用两模型对整个研究区的植被覆盖度进行反演后结果表现出显著的相关性,进一步验证了这一结论。(2)像元二分模型中的纯裸土、植被像元数值(VISoil和VIVeg)的取值会明显影响模型精度,在实际研究中不应当盲目采用经验值。(3)植被覆盖结果与研究区山脉轮廓近似,表明盖度分布可能受地形因素影响较大,考虑可以将这一方面内容引入到后续的研究中。Fractional vegetation cover(FVC)is a vital indicator of surface vegetation.Studies of regional vegetation cover are helpful for understanding the status of the regional ecological environment and can provide important references for the formulation of ecological restoration plans and the evaluation of restoration effects.In vegetation cover related research,studies on extraction methods have attracted much attention.Studies have shown that the universality of vegetation cover extraction methods is poor,as well as the existing studies were mostly conducted on agricultural and forest land in wet,semi-humid and semi-arid areas,while few have investigated arid areas with sparse vegetation that is mainly shrubs and grass.To investigate the accuracy and applicability of different methods for estimating vegetation cover in the near-natural zone of the northwest arid desert,this study extracted six vege-tation indices(NDVI,SAVI,MSAVI,ARVI,EVI,and MVI),which could effectively exclude soil and meteorological information to obtain pure vegetation information based on GF-2 multispectral-panchromatic fusion images.Two types of models were then established,including the single VI models(DP model)and multi-VI models(R model,RF model and PCA model),three statistics(SSE,R2,RMSE)were introduced to validate model accuracy and four-fold cross-validation was used to probe the models for overfitting.After filtering the models through these methods,the selected model was applied to invert the vegetation coverage in the study area.The results show three key aspects of this system.(1)Among the various models,the DP model constructed using the EVI and the RF model are more suitable for FVC extraction in the study area.This conclusion was further verified by the sig-nificant correlation between the inversion results of the FVC for the entire study area by applying these two models.(2)The values of pure bare soil and vegetation pixels(VIs and VIv)in the DP model will obviously affect the accu-racy of the model.Thus,the empirical values shou

关 键 词：植被覆盖度 高分二号 随机森林模型 回归模型 

分 类 号：Q948[生物学—植物学] TP751[自动化与计算机技术—检测技术与自动化装置]