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An approach for detecting five typical vegetation types on the Chinese Loess Plateau using Landsat TM data

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Abstract

Remote sensing can provide large-scale spatial data for the detection of vegetation types. In this study, two shortwave infrared spectral bands (TM5 and TM7) and one visible spectral band (TM3) of Landsat 5 TM data were used to detect five typical vegetation types (communities dominated by Bothriochloa ischaemum, Artemisia gmelinii, Hippophae rhamnoides, Robinia pseudoacacia, and Quercus liaotungensis) using 270 field survey data in the Yanhe watershed on the Loess Plateau. The relationships between 200 field data points and their corresponding radiance reflectance were analyzed, and the equation termed the vegetation type index (VTI) was generated. The VTI values of five vegetation types were calculated, and the accuracy was tested using the remaining 70 field data points. The applicability of VTI was also tested by the distribution of vegetation type of two small watersheds in the Yanhe watershed and field sample data collected from other regions (Ziwuling Region, Huangling County, and Luochuan County) on the Loess Plateau. The results showed that the VTI can effectively detect the five vegetation types with an average accuracy exceeding 80 % and a representativeness above 85 %. As a new approach for monitoring vegetation types using remote sensing at a larger regional scale, VTI can play an important role in the assessment of vegetation restoration and in the investigation of the spatial distribution and community diversity of vegetation on the Loess Plateau.

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Acknowledgments

We would like to thank the National Nature Science Foundation of China (NSFC) projects (41371280; 41030532). We would also like to acknowledge the assistance of the Ansai Ecological Experimental Station for Soil and Water Conservation, CAS.

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Authors and Affiliations

  1. College of Life Science, Guizhou University, Guiyang, 550025, Guizhou Province, China

    Zhi-Jie Wang

  2. Institute of Soil and Water Conservation, Northwest A&F University, 26 Xinong Road, Yangling, 712100, Shaanxi Province, China

    Ju-Ying Jiao & Yuan Su

  3. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, 712100, Shaanxi Province, China

    Zhi-Jie Wang, Ju-Ying Jiao & Bo Lei

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  1. Zhi-Jie Wang
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Correspondence to Ju-Ying Jiao.

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Wang, ZJ., Jiao, JY., Lei, B. et al. An approach for detecting five typical vegetation types on the Chinese Loess Plateau using Landsat TM data. Environ Monit Assess 187, 577 (2015). https://doi.org/10.1007/s10661-015-4799-5

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