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Open AccessArticle

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

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State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3
Henan Aero Geophysical Survey and Remote Sensing Center, Zhengzhou 450053, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1216; https://doi.org/10.3390/rs11101216
Received: 26 March 2019 / Revised: 17 May 2019 / Accepted: 18 May 2019 / Published: 22 May 2019
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. View Full-Text
Keywords: CBERS-04; multi-seasonal images; quasi-circular vegetation patch; random forest; Yellow River Delta CBERS-04; multi-seasonal images; quasi-circular vegetation patch; random forest; Yellow River Delta
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Liu, Q.; Song, H.; Liu, G.; Huang, C.; Li, H. Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest. Remote Sens. 2019, 11, 1216.

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