Welcome to visit Scientia Silvae Sinicae,Today is

Scientia Silvae Sinicae ›› 2021, Vol. 57 ›› Issue (11): 105-118.doi: 10.11707/j.1001-7488.20211111

Previous Articles     Next Articles

Extraction of Larch Plantation Based on Multi-Classifier Ensemble

Ting Ma,Chonggui Li*,Fuquan Tang,Jie Lü   

  1. College of Geomatics, Xi'an University of Science and Technology Xi'an 710054
  • Received:2020-09-24 Online:2021-11-25 Published:2022-01-12
  • Contact: Chonggui Li

Abstract:

Objective: This study was implemented to explore the potential of multi-temporal, multi-spectral, and high-spatial-resolution images in larch plantations identification, and to find a method for rapid identification of larch plantations integrated with multiple classifiers by multi-feature combination scheme, so as to provide a reference for remote sensing monitoring and management of larch plantations and forest resource investigation. Method: In this paper, first of all, the key period and parameters of larch identification were determined from multi-temporal Landsat8 OLI images of Mengjiagang forest farm in Heilongjiang Province by analyzing the seasonal and spectral features of larch. Secondly, the feature information extracted from images was screened through the variable importance metric (VIM) to establish multi-feature data sets of different phenological stages. Finally, a classification method with the multi-classifier ensemble was established by using the advantages of random forest (RF), support vector machine (SVM), maximum likelihood classification (MLC), and back-propagation (BP) neural network, to extract larch plantations. Result: Using the classification of multi-classifier integration, the overall accuracy was 93.8%, and the accuracy of extracted larch plantation area was 96.3%. Compared with RF, MLC, SVM and BP, the overall accuracy was improved by 10% on average. Conclusion: This paper found that compared with single-phase images, multi-temporal data contains more phenological phases of larch, reflecting the unique seasonal characteristics of larch, which is more conducive to the extraction of larch. At the same time, the multi-classifier integration strategy combines the advantages of each classifier, which would effectively improve the overall accuracy and achieve high-precision extraction of larch plantation.

Key words: larch plantation, classification of forest types, GF-1, multiple classifier integration

CLC Number: