Tropical Geography ›› 2019, Vol. 39 ›› Issue (4): 512-520.doi: 10.13284/j.cnki.rddl.003147

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Remote Sensing Identification of Grassland Shrubs Using Low-Altitude Unmanned Aerial Vehicles

Zhang Shun1,2, Zhao Yujin2, Bai Yongfei2, Yang Long 3 and Sun Zhongyu3   

  1. (1. Shandong University of Science and Technology, Qingdao 266590, China; 2. Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China; 3. Guangzhou Institute of Geography, Guangzhou 510070, China)
  • Online:2019-07-10 Published:2019-07-10

Abstract:

Satellite remote sensing imagery, when used in shrub monitoring, cannot accurately identify individual plants or young shrubs due to limitations in spatial resolution. However, using low-altitude unmanned aerial vehicles (UAVs) for remote sensing has the potential to identify such plants while yielding ultrahigh-resolution images. Therefore, we carried out shrub identification based on remote sensing with a low-altitude UAV, combined with a field investigation, at a shrub encroachment in Xilinhot, Inner Mongolia. An analysis of variance of the normalized differential vegetation index for shrubland, grassland, and bare land determined that the segmentation threshold of the bare and vegetated lands was -0.08. The vegetation covered area was extracted using this threshold and four object-oriented machine learning classification classifiers, i.e., Decision Tree (DT), Bayes, k-nearest neighbor (KNN), and Support Vector Machine (SVM), were used to identify the shrubs. Results showed that the tool used to evaluate the optimal segmentation scale, i.e., estimate the scale parameter, could rapidly determine the segmentation parameters, obtain shrub and grass images, and provide the basis for studying the former. We effectively avoided blind selection and increased the number of calculations by using the feature selection and optimization tool to select 18 object features with the highest degree of discrimination. A comparison of results from the different classifiers revealed that the Bayes classifier had the highest classification accuracy, with an overall accuracy and Kappa coefficient of 92% and 0.83, respectively. Its classification results matched the image features well and identified individual shrubs precisely. According to Bayes classification, shrubs covered 14.74% of the study area, and the average crown was 0.6 m2. The results of the remote sensing experiments were almost the same as those from ground-based measurements in the field. A sensitivity experiment on the sample size showed that: 1) SVM obtained a high classification accuracy with relatively few counts in the training sample but was sensitive to parameter settings, which may lead to abnormal classification results. 2) the Bayes classifier had a high precision with stable number of samples, if sufficient training samples were provided. 3) the overall classification accuracy of the DT classifier varied with the number of samples, and 4) the overall accuracy of the KNN classifier was the lowest among the four classifiers. Due to their different algorithmic features and different sensitivities to the number of training samples, the selection of a suitable classifier depends on the surface object being observed and spatial resolution of the image, along with the scope of the study area.

Key words: shrub encroachment, low-altitude UAV, remote sensing identification, object-oriented, machine learning