4.7 Article

Extracting the Forest Type From Remote Sensing Images by Random Forest

Journal

IEEE SENSORS JOURNAL
Volume 21, Issue 16, Pages 17447-17454

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.3045501

Keywords

Forestry; Remote sensing; Random forests; Feature extraction; Vegetation; Sensors; Vegetation mapping; Forest type extraction; RF classification; object oriented; SVM classification

Funding

  1. National Natural Science Foundation of China [31770768]
  2. Fundamental Research Funds for the Central Universities [2572014BB13, 2572017PZ04]
  3. Heilongjiang Province Applied Technology Research and Development Program Major Project [GA18B301, GA20A301]
  4. China State Forestry Administration Forestry Industry Public Welfare Project [201504307]
  5. Science and Technology Project Plan of Heilongjiang Archives Bureau [HDK2018-20]

Ask authors/readers for more resources

Identifying forest types and their distribution using remote sensing imagery is crucial for forest resource monitoring and management. This study proposed a method based on GF-2 remote sensing images and other data sources that can enhance the accuracy of forest type classification.
Identifying the types of forest and the corresponding distribution is of significance in forest resource monitoring and management. Considering the low accuracy of extracting the information of forest types from high-resolution remote sensing images and the lack of an effective identification method. The GF-2 remote sensing image in the Laoshan construction area of the Maoershan Forest Farm, Heilongjiang Province was as the data source, supplemented by aerial RGB images with a resolution of 0.2 m and the second type inventory of forest resources data. Considering the spatial characteristics of the spectrum, texture, vegetation index, terrain, multiscale segmentation was performed, the optimal feature space was constructed, and the number of decision trees was estimated. In this manner, an object-oriented random forest (RF) scheme was established. Comparative experiments were performed using the support vector machine(SVM) classifier. The experimental results indicated that the overall accuracy and kappa coefficient of the proposed method was 83.16% and 79.86%, respectively, higher than those of the SVM classification method. These findings demonstrated that the proposed method can effectively increase the classification accuracy of forest types.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available