An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China-Reference-Cited by-同舟云学术

An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China

Published:2023-11-10 Issue:1 Volume:60 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Ma Ye¹²^ORCID,Zhen Zhen¹³^ORCID,Li Fengri¹,Feng Fujuan²⁴,Zhao Yinghui¹²^ORCID

Affiliation:

1. School of Forestry, Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, P. R. China

2. Northeast Asia Biodiversity Research Center, Northeast Forestry University, Harbin, China

3. Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea

4. College of Life Science, Northeast Forestry University, Harbin, China

Funder

Science and Technology Basic Resources Investigation Program of China

National Forestry and Grassland Data Center-Heilongjiang platform

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2023.2271246

Reference60 articles.

1. Experimental Approach to the Selection of the Components in the Minimum Noise Fraction

2. The Correlation Coefficient: An Overview

3. Specification, evaluation, and interpretation of structural equation models

4. Random forest in remote sensing: A review of applications and future directions

5. The probability of wind damage in forestry under a changed wind climate

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