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

Remote Sensing

Xihan Mu³  Maogui Hu¹  Wanjuan Song³  Gaiyan Ruan³  Yong Ge¹  Jinfeng Wang¹  Shuai Huang³  Guangjian Yan³  Xin Li²  Yuei-An Liou²  Qinhuo Liu²  Randolph H. Wynne² 

[1] The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, A11, Datun road, Beijing 100101 China;;The State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Beijing Normal University, Beijing 100875, ChinaThe State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Beijing Normal University, Beijing 100875, China;

关键词: validation;    sampling methods;    fractional vegetation cover;    remote sensing product;    scaling bias;    spatial autocorrelation.;   

DOI  :  10.3390/rs71215817

来源: mdpi

【 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.