Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China

Abstract

Vegetation in arid and semi-arid regions frequently exists in patches, which can be effectively mapped by remote sensing. However, not all satellite images are suitable to detect the decametric-scale vegetation patches because of low spatial resolution. This study compared the capability of the first Gaofen Satellite (GF-1), the second Gaofen Satellite (GF-2), and China-Brazil Earth Resource Satellite 4 (CBERS-04) panchromatic images for mapping quasi-circular vegetation patches (QVPs) with K-Means (KM) and object-based example-based feature extraction with support vector machine classification (OEFE) in the Yellow River Delta, China. Both approaches provide relatively high classification accuracy with GF-2. For all five images, the root mean square errors (RMSEs) for area, perimeter, and perimeter/area ratio were smaller using the KM than the OEFE, indicating that the results from the KM are more similar to ground truth. Although the mapped results of the QVPs from finer-spatial resolution images appeared more accurate, accuracy improvement in terms of QVP area, perimeter, and perimeter/area ratio was limited, and most of the QVPs detected only by finer-spatial resolution imagery had a more than 40% difference with the actual QVPs in these three parameters. Compared with the KM approach, the OEFE approach performed better for vegetation patch shape description. Coupling the CBERS-04 with the OEFE approach could suitably map the QVPs (overall accuracy 75.3%). This is important for ecological protection managers concerned about cost-effectiveness between image spatial resolution and mapping the QVPs.

Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest

“…Errors in the classification mainly existed between the QVPs and the bare soil. However, as in previous studies [16,73], the detection accuracy of the QVPs was low (best result precision rate = 66.3%, recall rate = 47.5%, F measure = 0.528). This low detection accuracy may be attributed to the omission of the smaller size QVPs (less than 3 × 3 pixels), or the coalescence between the QVPs with other QVPs or other vegetation.…”

“…The lowest detection accuracy was obtained from the late autumn data acquired on the image from the 24 October 2015 (A d = 40.7%, A r = 25.2%, F = 0.311). Through the visual interpretation of the Quickbird image (with a spatial resolution of 0.6 m) carried out with ENVI 5.1 software with the multi-temporal finer spatial resolution images from Google Earth and the multi-temporal CBERS-04 images, we identified 139 QVPs distributed in the study area [16,73], which was used as the correct number of QVPs to calculate the precision rate, recall rate, and F measure for assessing the final detection accuracy of the QVPs. The precision rate, recall rate, and F measure resulting from the use of the five predictive variable datasets are presented in Figure 4, which displays a similar curve to the overall classification accuracies and Kappa coefficients for land cover classification presented in Figure 3.…”

“…When the gaps between QVPs or between QVPs and other strips of vegetation are less than 5 m, or the diameter of a QVP is less than 10 m, the 5 m spatial resolution of CBERS-04 imagery is not enough to detect the QVPs. Therefore, it is necessary to further improve spatial resolution [16,38]. (3) Higher spectral resolution data are applied.…”

“…Accuracy assessment of the classification results was presented in a confusion matrix and measured according to the producer's (PA) and user's accuracy (UA), overall accuracy (OA), and the Kappa coefficient. Next a successive identification was performed, where a majority analysis with a 3 × 3 kernel size was first used to match the salt and pepper noise pixels within a large single class with a large single class, followed by a change in the classification to vector analysis in order to separate the QVPs from the non-QVP vegetation based on the thresholds of area (less than 3000 m 2 ) and perimeter/area (less than 0.54), as determined by previous results [16,28,73]. The precision rate (Ad), recall rate (Ar), and F measure (F) were then used to analyze the final detection accuracy of the QVPs; these factors have often been used for performance measures in pattern recognition, information retrieval, machine learning and binary classification [73,99].…”

“…Compared with banded vegetation patterns, the spotted vegetation pattern has not as yet attracted equivalent attention [2]. Patchy vegetation patterns have been reported in America, Africa, Asia, Australia, and Spain [3][4][5][6][7][8][9][10][11][12][13][14][15][16], where the range of annual precipitation is from 50 to 750 mm, and appears to be unrelated to any particular soil type or plant species [14]. In general, it is considered as the result of plant-oil feedback occurring in water-limited regions [12,[14][15][16].…”