Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series-Reference-Cited by-同舟云学术

Online Forest Disturbance Detection at the Sub-Annual Scale Using Spatial Context From Sparse Landsat Time Series

Published:2022 Issue: Volume:60 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wu Ling¹^ORCID,Liu Xiangnan¹^ORCID,Liu Meiling¹,Yang Jinghui¹^ORCID,Zhu Lihong¹,Zhou Botian²

Affiliation:

1. School of Information Engineering, China University of Geosciences, Beijing, China

2. Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09690492.pdf?arnumber=9690492

Reference42 articles.

1. Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data

2. Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

3. Shewhart and EWMA t control charts for short production runs

4. Short-run statistical process control:Q-chart enhancements and alternative methods

5. On t and EWMA t charts for monitoring changes in the process mean

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