地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (11): 1667-1678.doi: 10.12082/dqxxkx.2018.180196

• 遥感科学与应用技术 • 上一篇    下一篇

基于实测光谱模拟Landsat-8 OLI数据估算非光合 植被覆盖度

王光镇(), 王静璞*(), 韩柳, 柴国奇, 王周龙   

  1. 鲁东大学资源与环境工程学院,烟台 264025
  • 收稿日期:2018-04-19 修回日期:2018-08-25 出版日期:2018-11-20 发布日期:2018-11-28
  • 通讯作者: 王静璞 E-mail:wang1636@sina.cn;wangjp@ldu.edu.cn
  • 作者简介:

    作者简介:王光镇(1991-),男,硕士生,主要从事草原植被遥感监测研究。E-mail: wang1636@sina.cn

  • 基金资助:
    国家自然科学基金青年科学基金项目(41701005);国家自然科学基金重点项目(41330746);山东省自然科学基金培养项目(ZR2017PD006)

Estimating Fractional Cover of Non-photosynthetic Vegetation Using Field Spectral to Simulate Landsat-8 OLI

WANG Guangzhen(), WANG Jingpu*(), HAN Liu, CHAI Guoqi, WANG Zhoulong   

  1. College of Resource and Environment Engineering, Ludong University, Yantai 264025, China
  • Received:2018-04-19 Revised:2018-08-25 Online:2018-11-20 Published:2018-11-28
  • Contact: WANG Jingpu E-mail:wang1636@sina.cn;wangjp@ldu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No.41701005;Key Program of National Natural Science Foundation of China, No.41330746;Shandong Provincial Natural Science Foundation, China, No.ZR2017PD006.

摘要:

定量的估算非光合植被覆盖度(Fractional Cover of Non-photosynthetic Vegetation, fNPV)对草原生态系统碳储存、植被生产力、土壤侵蚀和火灾监测均具有重要的意义。本文以锡林郭勒草原实测高光谱和样方盖度为数据源,利用NPV(Non-Photosynthetic Vegetation)、PV(Photosynthetic Vegetation)、BS(Bare Soil)的平均光谱通过线性光谱混合模型模拟得到混合场景光谱,寻找区分NPV/PV/BS的敏感性波段,然后分别评价不同多光谱指数与fNPV的相关性。最后利用野外混合场景实验验证光谱指数估算fNPV的有效性。在此基础上,探讨基于OLI数据的NDVI(Normalized Difference Vegetation Index)-DFI(Dead Fuel Index)特征空间是否满足三元线性混合模型的基本假设。结果表明:短波红外(SWIR)波段是区分NPV/PV/BS的敏感性波段,以此为基础构建的OLI-DFI指数具备有效区分NPV/PV/BS的潜力。在模拟混合场景条件下,OLI-DFI和MODIS-DFI指数均与fNPV呈显著相关,决定性系数R2分别为0.84和0.94,均方根误差RMSE分别为0.09和0.05,而NDI和NDSVI指数与fNPV相关性很低。与模拟混合场景相比,在野外混合场景下OLI-DFI和MODIS-DFI指数估算fNPV的有效性均有一定程度的降低,R2分别为0.65和0.75,RMSE分别为0.14和0.12。基于OLI数据构建的NDVI-DFI特征空间满足三元线性混合模型的基本假设,可有效的估算fNPV

关键词: Landsat-8 OLI, 非光合植被, DFI指数, 混合场景, NDVI-DFI特征空间

Abstract:

Quantitative estimation fractional cover of non-photosynthetic vegetation (fNPV) is critical for grassland ecosystem carbon storage, vegetation productivity, soil wind erosion. Remote sensing is an important tool for estimating the fractional cover of non-photosynthetic vegetation as a key descriptor of grassland ecosystem function. Developing tools that allow for monitoring of non-photosynthetic vegetation in space and time is a key step needed to improve management of grassland. In this paper, the data source of the ground-based measured endmember spactrum, mixed scenario spactrum and coverage information is presented. Firstly, we used the mean spectrum of NPV, PV and BS three components to simulate the mixed spectral through a linear spectral mixture model, and then the correlation between different multispectral indices and fNPV was evaluated. On this basis, explore the NDVI-DFI feature space correspond to the fundamental assumption of ternary linear mixed model. Finally, the validity of the estimate fNPV of multispectral index is verified by the field mixed scenario. The results show that the SWIR band is the sensitivity of the NPV, PV and BS, based on the improved OLI-DFI index are effective to distinguish the NPV, PV and BS potential. In the simulate mixed scenario, OLI-DFI and MODIS-DFI index correlated with fNPV, the coefficient of determination (R2) was 0.84 and 0.94, and root mean square error(RMSE) was 0.09 and 0.05(n=66, p<0.001). However, the NDI and NDSVI index correlation with fNPV is very low. Additionally, compared to simulate the mixing, in the field mixed scenario, OLI-DFI and MODIS- DFI index for estimating the effectiveness of the fNPV all have a certain degree of decline, R2 was 0.65 and 0.75, RMSE was 0.14 and 0.12, respectively. Based on the OLI data to construct the NDVI-DFI feature space to satisfy the fundamental assumption of ternary linear mixed model, which can effectively estimate the fNPV. The research will provide theoretical basis for the estimation of fNPV multispectral remote sensing.

Key words: Landsat-8 OLI, non-photosynthetic vegetation, dead fuel index, mixed scenario, NDVI-DFI feature space