Evaluation of the Accuracy of the Field Quadrat Survey of Alpine Grassland Fractional Vegetation Cover Based on the Satellite Remote Sensing Pixel Scale

Abstract

The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation of the accuracy of the field quadrat survey of FVC based on the satellite remote sensing pixel scale is inadequate, especially in the alpine grassland of the Qinghai-Tibet Plateau. In this paper, five different alpine grasslands were examined, the accuracy of the FVC obtained by the photography method was analyzed, and the influence of the number of samples on the field survey results was studied. First, the results show that the threshold method could accurately extract the vegetation information in the photos and obtain the FVC with high accuracy and little subjective interference. Second, the number of samples measured on the ground was logarithmically related to the accuracy of the FVC of the sample plot (p < 0.001). When the number of samples was larger, the accuracy of the FVC of the sample plot was higher and closer to the real value, and the stability of data also increased with the increase of the number of samples. Third, the average FVC of the measured quadrats on the ground was able to represent the FVC of the sample plot, but on the basis that there were enough measured quadrats. Finally, the results revealed that the degree of fragmentation reflecting the state of ground vegetation affects the acquisition accuracy of FVC. When the degree of fragmentation of the sample plot is higher, the number of samples needed to achieve the accuracy index is higher. Our results suggest that when obtaining the FVC on the satellite remote sensing pixel scale, the number of samples measured on the ground is an important factor affecting the accuracy, which cannot be ignored.

Review of Remote Sensing Applications in Grassland Monitoring

“…Although sampling methods for ground measurements exist, the most commonly used method is the threshold-based photographic method that usually employs digital cameras or spectrometers to shoot the ground. Some studies focused on FVC estimation at nearsurface and proved the validity of the photographic method [24,96,97]. In [24], the excess green index was calculated based on UAV RGB images for each pixel with a threshold to distinguish vegetation and non-vegetation pixels through an iterative algorithm.…”

“…Some studies focused on FVC estimation at nearsurface and proved the validity of the photographic method [24,96,97]. In [24], the excess green index was calculated based on UAV RGB images for each pixel with a threshold to distinguish vegetation and non-vegetation pixels through an iterative algorithm. Then, the effect of the degree of vegetation fragmentation on the number of required sample images for validation was also investigated in [24].…”

“…In [24], the excess green index was calculated based on UAV RGB images for each pixel with a threshold to distinguish vegetation and non-vegetation pixels through an iterative algorithm. Then, the effect of the degree of vegetation fragmentation on the number of required sample images for validation was also investigated in [24]. Xu et al [96] adopted ground-based RGB images, which set thresholds for the pixel difference values between different bands of RGB images to distinguish photosynthetic vegetation and senescent vegetation and then estimated the coverage.…”

“…As a result, several studies [68,154] fused these images from different satellites to complement missing information and to enhance the temporal continuity. In addition, many studies [22,24,63,103] installed multispectral sensors or RGB cameras on UAVs to acquire near-surface multispectral images. Compare with satellite images, these images have a much finer spatial resolution, usually less than 10 m, and they are not affected by cloud cover.…”

Geo-Informatics in Resource Management

“…Ruiz-Lendínez [17] located and mapped abandoned farmlands, applying textural characterization on high spatial resolution aerial imagery. Finally, Chen et al [18] estimated fraction vegetation cover on five grassland types using unmanned aerial images, applying the threshold method. Results were highly reliable, although it was necessary to take into account the heterogeneity of the underlying surface using the sample method.…”