Journal of Guangxi Normal University(Natural Science Edition) ›› 2026, Vol. 44 ›› Issue (1): 227-236.doi: 10.16088/j.issn.1001-6600.2025012201

• Ecology and Environmental Science Research • Previous Articles    

Optimizing Accuracy of Random Forest Interpretation Based on Terrain Data

HE Wenmin1,2,3, LIU Xuanyuan1,2,3, ZHOU Qihai1,2,3, ZHANG Mingxia1,2,3 *   

  1. 1. Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, Guilin Guangxi 541006, China;
    2. Guangxi Key Laboratory of Rare and Endangered Animal Ecology (Guangxi Normal University), Guilin Guangxi 541006, China;
    3. The Chongzuo White-headed Langur Field Observation and Research Station of Guangxi, Chongzuo Guangxi 532204, China
  • Received:2025-01-22 Revised:2025-04-17 Online:2026-01-05 Published:2026-01-26

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Abstract: Land use data can provide important basis for many scientific researches, and remote sensing images are widely used as data source for land use type interpretation. In order to improve the accuracy of remote sensing image classification, it is necessary to involve some other digital information. The terrain in the karst area of Guangxi is complex, together with quick expansion of plantation in recent years, which brings difficulties to the interpretation of remote sensing images. Based on the random forest algorithm, this study conducted land use interpretation of remote sensing images in Chongzuo, Guangxi. Two classification procedures were run: the first one only used remote sensing image data for classification, and altitude and slope data were added in the second procedure. Results show that the classification accuracy of using remote sensing images alone is 0.849, and after adding altitude and slope data, the overall accuracy improved to 0.961. The discrimination among land use types such as natural forests, artificial forests and farmland is largely increased, which is particularly useful in rugged landforms such as karst. This research provides a better solution for land use montoring.

Key words: karst landform, remote sensing image interpretation, terrain factor, land use

CLC Number:  TP18; TP751; P237
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