Mapping of Native Plant Species and Noxious Weeds in Typical Area of the Three-River Headwaters Region by Using Worldview-2 Imagery

  • Benlin Wang
  • Ru AnEmail author
  • Yu Zhang
  • Zetian Ai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)


Mapping of native plant species and noxious weeds is critical to monitor grassland degradation in the Three-River Headwaters Region. The grass species in the study area were divided into native plant species and noxious weeds based on the applicability of grazing. Field data of the two kinds of grass species were collected and divided into ten coverage grades with an interval of 10%. The eight original bands were used to derive 37 features by Random Forest (RF) algorithm, including first derivative (FD), vegetation indexes (VI), biochemical indexes (BI), hat transform (KT) and gray level co-occurrence matrix (GLCM). The importance of each feature was calculated and 17 of them were selected by RF, reflecting their superiority in identifying native plant species and noxious weeds. The random forest algorithm was also used in classification of the native plant species with an overall accuracy (OA) of 43.2% (8 bands), 45.9% (37 features) and 51.3% (17 selected features) and an 10% grade expansion accuracy (GEA) of 59.4%, 64.8% and 70.2%, respectively. The noxious weeds with a higher overall accuracy (OA) of 62.1% (8 bands), 64.8% (37 features) and 67.5% (17 selected features) and an 10% grade expansion accuracy (GEA) of 86.4%, 83.7% and 89.1%, respectively. Therefore, the classification of native plant species and noxious weeds coverage grades with the interval of 10% demonstrated the potential of the WorldView-2 data for mapping native plant species and noxious weeds in the typical area of Three-River Headwaters Region.


Coverage Native plant species Noxious weeds Worldview-2 imagery Random Forest algorithm Three-River Headwaters Region Qinghai-Tibet Plateau 



This work is supported by the National Nature Science Foundation of China (No. 41871326; 41271361); Key Project in the National Science & Technology Pillar Program during the Twelfth Five-Year Plan Period (No. 2013BAC03B04); and Jiangsu Province Key R&D Plan (No. BE2017115). We express our heartfelt gratitude for Lijun Huang, Xiaoling Zhou, Yu Zhang, Jietong Liu, and Yinan Wang for their work of field samples collection.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hohai UniversityNanjingChina
  2. 2.Chuzhou UniversityChuzhouChina
  3. 3.Nanjing Urban Planning and Research CenterNanjingChina

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