Abstract
High-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial resolution CBERS-04 satellite images to map QVPs in the Yellow River Delta, China, using the Random Forest (RF) classifier. The classification accuracies corresponding to individual and multi-season combined images were compared to understand the seasonal effect and the importance of optimal image timing and acquisition frequency for QVP mapping. For classification based on single season imagery, the early spring March imagery, with an overall accuracy (OA) of 98.1%, was proven to be more adequate than the other four individual seasonal images. The early spring (March) and winter (December) combined dataset produced the most accurate QVP detection results, with a precision rate of 66.3%, a recall rate of 43.9%, and an F measure of 0.528. For larger study areas, the gain in accuracy should be balanced against the increase in processing time and space when including the derived spectral indices in the RF classification model. Future research should focus on applying higher resolution imagery to QVP mapping.
Highlights
In arid and semi-arid regions of the world, vegetation usually appears as patches due to the environmental constraints such as a limited water supply, soil salinization or local microtopography
A total of 332 ggeeoorreferenced data from three ffiield surveys carried out in October 2013 [76], August 2017 [77], and May 2018 were used to identify the quasi-circular vegetation patch (QVP) in the China-Brazil Earth Resource Satellite (CBERS)-04 imagery for inclusion in the referenccee ddaattaasseett..TThheeccoollelecctitoionnoof fgegoeroerfeefreernecnecdedadtatwa ewreereequeaqlulyaldlyivdidiveiddiendtoinatotraaintrinaigndinagtadseatta(tsheet raensdpeacntiivnedlyep) aendeanntivnadleidpeantidoenndtavtaalsideta(ttiohne dnautmasbeetr(tohfetnhuemQbVePrso,fbtahreeQsoViPl,sa,nbdartehseowil,aatnerdatrheeawwaeterer a5r1e6apwixeerles,551366 ppiixxeellss, 5a3n6dp3i5x8elpsi,xaenlsd, r3e5s8pepcitxievlesl,yr)efsopretchtievRelFy)clfaosrsitfihceaRtioFnclaansdsifaiccautiroancyanasdseascscmureancty. assessment
Three bands of pan-sharpened 5 m multispectral imagery, and nine spectral indices were used as predictive variables for the Random Forest (RF) classification of the QVPs (Table 2), which can be derived from three multispectral bands (Near infrared, Red, and Green) of CBERS-04, and have repeatedly been demonstrated to improve classification accuracy due to sensitivity to vegetation characteristics, insensitivity to soil effect, and the ability to capture both land surface features and the seasonal changes in the growth of vegetation [47,64,81,82,83,84]
Summary
In arid and semi-arid regions of the world, vegetation usually appears as patches due to the environmental constraints such as a limited water supply, soil salinization or local microtopography. Fassnacht et al [36] stated that the selection of the classifier itself was usually of low significance if the remotely sensed data met the demands of the classifier and study subject Among these temporal series approaches, the RF classifier has been the most often used in recent years, and was demonstrated to produce higher classification accuracy with multi-seasonal images for a range of applications, including tree species detection [38,68,69], identification of crop types [48,66,70], and land cover classification [56,58,59].
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