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

Abstract

Abstract: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.

Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site

“…Comparing to existing study about ESU sampling methods [14,20,25], our method can make an appropriate compromise between spatiotemporal representativeness and implementation cost, so it is particularly useful for heterogenous and traffic-inconvenient regions, e.g., mountainous regions.…”

“…Existing researches on field sampling design generally focused on capturing the landscape spatial heterogeneity by distributing the ESUs across the entire study area [16,[18][19][20][21]. Two key issues, which are the main scientific questions addressed in this paper, were often ignored in traditional ESU sampling schemes: First, the spatial heterogeneity changes over time.…”

“…Stratified sampling strategies firstly select auxiliary variables which can be easily generated from remote sensing observations (vegetation indices, generally) to represent the target variables (e.g., leaf area index, fractional vegetation cover and chlorophyll content), then subdivide each auxiliary variable into several strata, and finally sample the plots randomly within each stratum. Stratified sampling strategies are assumed to be capable of optimally capturing the variability across the site extent [13,[19][20][21]. The conditioned Latin hypercube (CLH) sampling is among the most appealing stratified sampling strategies [22].…”

Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data

“…Sampling procedure along the diagonal lines has been widely acknowledged to be effective for ground cover measurement of sample plots, wherein vegetation is not distributed in parallel rows [44,45]. In particular, field surveyors were well trained and measurements of each transect for the first 20 sample plots were cross-validated to guarantee reliability of the field reference data.…”

Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs

“…Determining FVC from digital photographs is often simpler, faster and more economical than measuring LAI [1,15,17]. FVC values derived from ground and near-ground remotely sensed images for validation of FVC estimated from satellite images is vital in ensuring the quality of FVC estimates derived from satellite images [9,[21][22][23][24][25]. However, there are often significant problems with current FVC estimation methodologies.…”