Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

作者:Mu, XH (Mu, Xihan)[ 1 ] ; Hu, MG (Hu, Maogui)[ 2,3 ] ; Song, WJ (Song, Wanjuan)[ 1 ] ; Ruan, GY (Ruan, Gaiyan)[ 1 ] ; Ge, Y (Ge, Yong)[ 2 ] ; Wang, JF (Wang, Jinfeng)[ 2 ] ; Huang, S (Huang, Shuai)[ 1 ] ; Yan, GJ (Yan, Guangjian)[ 1 ]

REMOTE SENSING

卷: 7  期: 12  页: 16164-16182

DOI: 10.3390/rs71215817

出版年: DEC 2015

摘要

Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases.

通讯作者地址: Hu, MG (通讯作者)

Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, A11,Datun Rd, Beijing 100101, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, Beijing Key Lab Remote Sensing Environm & Digital, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

[ 3 ] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China