Abstract
The seasonal effect on land cover classification has been widely recognized. It is important to use the imagery acquired at key points of vegetation phenological development to obtain a higher classification accuracy for land cover. This study compared the effect of seasons on landscape classification and the quasi-circular vegetation patches (QVPs) detection from four fused Gaofen 1 images acquired in the different seasons by using the pixel-based random forest (RF) and object-based support vector machine (SVM) methods over the Yellow River Delta, China. The results from this study demonstrated that the seasonal effect on classifying landscapes and detecting the QVPs is significant, especially for the pixel-based RF method. The object-based SVM method was more appropriate for classifying landscape from the non-growing season images, while the pixel-based RF approach was more suitable for classifying the growing-season images. The spring data (April imagery; overall accuracy = 99.8%) and the winter data (February imagery; F measure = 65.9%) yielded the best results for landscape classification and QVP detection, respectively, by using the object-based SVM approach. Therefore, in practice, we recommend the use of February to April imagery with the object-based SVM approach to map the QVPs in the future.
Highlights
Vegetation patchiness can contribute to an increase in plant biomass and biodiversity, and provide habitat for various animals, and impede and alter surface runoff, and improve local soil physicochemical properties, and suggest key indicators for assessing vegetation pattern formation, ecosystem function, evolution, resilience, and degradation in the arid and semi-arid areas around the world [1]-[5]
Analogous to arid and semi-arid areas around the world, vegetation pattern in the Yellow River Delta (YRD), China is marked by the bare soils interspersed with the quasi-circular vegetation patches (QVPs) [6], which was first discovered from high spatial resolution imagery in 2011
The individual 2 m resolution pan-sharpened Gaofen 1 (GF-1) multispectral images were classified into three landscape types: vegetated area, water, and others using the objectbased support vector machine (SVM) approach
Summary
Vegetation patchiness can contribute to an increase in plant biomass and biodiversity, and provide habitat for various animals, and impede and alter surface runoff, and improve local soil physicochemical properties, and suggest key indicators for assessing vegetation pattern formation, ecosystem function, evolution, resilience, and degradation in the arid and semi-arid areas around the world [1]-[5]. Analogous to arid and semi-arid areas around the world, vegetation pattern in the Yellow River Delta (YRD), China is marked by the bare soils interspersed with the quasi-circular vegetation patches (QVPs) [6], which was first discovered from high spatial resolution imagery in 2011.
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