Improving forest type classification using the vegetation local difference index-Reference-Cited by-同舟云学术

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Improving forest type classification using the vegetation local difference index

Published:2015-07-18 Issue:14 Volume:36 Page:3701-3713
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Chen Chenxin¹,Bian Zhao²,Li Shengyang¹,Tang Ping²,Wu Honggan³

Affiliation:

1. Key Laboratory of Space Utilization, Technology and Engineering Centre for Space Utilization, Chinese Academy of Sciences, Beijing, China

2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

3. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China

Funder

the Scientific and Technological Support to Run Special Project of Chinese Academy of Sciences

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2015.1047992

Reference32 articles.

1. Satellite image classification using genetically guided fuzzy clustering with spatial information

2. Tasseled cap transformation for HJ-1A/B charge coupled device images

3. Improving classification of woodland types using modified prior probabilities and Gaussian mixed model in mountainous landscapes

4. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification

5. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data

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2. Distribution modelling of vegetation types based on area frame survey data;Applied Vegetation Science;2019-09-14

3. Analysis of features importance based on different seasonal OLI images by Random Forest;IOP Conference Series: Materials Science and Engineering;2019-08-01

4. Forest Type Classification in Poyang Lake Basin Based on Multi-source Data Fusion;Terrestrial Environmental Sciences;2018-10-10

5. Forest Types Classification Based on Multi-Source Data Fusion;Remote Sensing;2017-11-10

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