1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
2. Key Laboratory of Soil and Water Conservation & Desertification Combating of Ministry of Education, Beijing Forestry University, Beijing 100083, China
3. Jinyun National Positioning Observation and Research Station of Forest Ecosystem in Three Gorges Reservoir Area, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
4. Experimental Center of Desert Forestry, Chinese Academy of Forestry, Dengkou 015200, China
Abstract：Shelterbelt systems are the main type of vegetation in the desert oasis regions, which provide a strong guarantee for wind-break and sand fixation, salt-water regulation and water-heat balance. It is important to investigate the spatial distribution information of shelterbelts. However, precisely mapping shelterbelts systems on a large scale are difficult due to narrow strips, small patches and wide & scattered distribution. This study aims to accurately map shelterbelts using object-oriented extraction based on GF-2 satellite imagery in Dengkou oasis. Firstly, the optimal scale parameter of SF segmentation was determined by local variance (LV) and rate of change (ROC) curve, and then the features space and classifier’s parameters were optimized by Out of bag error (OOB error) and Gini index through Random Forest (RF) algorithm prior to classification. Finally, Random Forest, CART decision tree, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) were compared and validated for shelterbelt systems extraction. The results showed that: (1) the ROC-LV curve method can obtain the possible value of optimal scale parameter more objective and more efficiently than iterating all scale parameter values. (2) OOB error and Gini index through RF algorithm can effectively eliminate the redundant features among spectral, shape and texture. The processing time was sharply reduced and ensuring the accuracy of the classification. (3) The classification results were verified based on the measured data sets, and the results showed that the feature optimization based on the RF algorithm combined with the SVM classifier was the best method for extracting the desert oasis shelterbelt systems, with the highest producer accuracy of 97.14%. Meanwhile, the extracted area of shelterbelt systems was 208.99 km2, which was close to reality (210 km2). The SVM classifier performs better than the other three classifiers while zooming in a small areas; (4) Due to the high resolution of GF-2 images and the near-infrared band, sub-meter information can be obtained through appropriate band fusion. Based on the object-oriented method, a single shelterbelt can be used as the basic unit to explore the attributes and characteristics of the shelterbelts net. For example, the broken shelterbelts information could be extracted. All these conclusions will provideimportant technical support for the shelterbeltextraction in the desert oasis areas.
Key words：Shelterbelt extraction; GF-2 remote-sensing imagery; Random Forest; Object-oriented
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