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Forests
Special Issues
Satellite Time Series Analysis for Forest Mapping and Change Detection
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Satellite Time Series Analysis for Forest Mapping and Change Detection

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1339

Special Issue Editors

Dr. Chao Ding

E-Mail Website
Guest Editor
Center for Territorial Spatial Planning and Real Estate Studies, Beijing Normal University, Zhuhai 519087, China
Interests: vegetation phenology; change detection; land degradation; satellite time series reconstruction; land cover mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Ling Wu

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: forest disturbances detection; attribution of forest disturbances; classification of tree species; land use/cover change detection; data fusion
Dr. Kai Yan

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: vegetation parameters production; radiative transfer modeling; leaf area index (LAI); fraction of absorbed photosynthetically active radiation (fPAR); vegetation dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate forest mapping and data on changing information are essential for comprehending forest dynamics under the impacts of climate variability/change and human activities. Earth observation satellite has substantially facilitated studies on forest dynamics across spatio-temporal scales. Nevertheless, challenges remain partially due to the intricate nature of forest ecosystems and issues regarding satellite data quality. We invite researchers to share their insights on novel methods and applications related to forest mapping and change detection with satellite time series data in this Special Issue.

We welcome submissions related to the following topics (but not limited to):

  • Methods for forest mapping and tree species classification;
  • Methods for detecting changes within forests (disturbances, long-term trends, and phenology);
  • Influences of satellite data quality (e.g., data gaps, noise, and terrain shadows) on change detection;
  • Detection of afforestation/deforestation;
  • Forest disturbance and resilience;
  • Forest degradation and mortality;
  • Long-term changes in key forest variables (e.g., leaf area index, gross primary production, and phenology).

Dr. Chao Ding
Dr. Ling Wu
Dr. Kai Yan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • forest mapping
  • change detection
  • remote sensing time series disturbance
  • trend
  • vegetation phenology
  • land cover change

Published Papers (1 paper)

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Research

21 pages, 11328 KiB  
Article
Detection of Forest Disturbances with Different Intensities Using Landsat Time Series Based on Adaptive Exponentially Weighted Moving Average Charts
by Tingwei Zhang, Ling Wu, Xiangnan Liu, Meiling Liu, Chen Chen, Baowen Yang, Yuqi Xu and Suchang Zhang
Forests 2024, 15(1), 19; https://doi.org/10.3390/f15010019 - 20 Dec 2023
Cited by 1 | Viewed by 860
Abstract
Forest disturbance detection is important for revealing ecological changes. Long-time series remote sensing analysis methods have emerged as the primary approach for detecting large-scale forest disturbances. Many of the existing change detection algorithms focus primarily on identifying high-intensity forest disturbances, such as harvesting [...] Read more.
Forest disturbance detection is important for revealing ecological changes. Long-time series remote sensing analysis methods have emerged as the primary approach for detecting large-scale forest disturbances. Many of the existing change detection algorithms focus primarily on identifying high-intensity forest disturbances, such as harvesting and fires, with only a limited capacity to detect both high-intensity and low-intensity forest disturbances. This study proposes an online continuous change detection algorithm for the detection of multi-intensity forest disturbances such as forest harvest, fire, selective harvest, and insects. To initiate the proposed algorithm, the time series of the Normalized Difference Vegetation Index (NDVI) is fitted into a harmonic regression model, which is then followed by the computation of residuals. Next, the residual time series is entered into the adaptive exponentially weighted moving average (AEWMA) chart. This chart adaptively adjusts the smoothing coefficients to identify both high-intensity and low-intensity disturbances. When the chart value consistently deviates from the control limit, the forest pixel is classified as disturbed. With an overall spatial accuracy of 85.2%, including 86.1% producer’s accuracy and 84% user’s accuracy, along with a temporal accuracy of 96.7%, the algorithm enables precise and timely detection of forest disturbances with multiple intensities. This method provides a robust solution for detecting multi-intensity disturbances in forested regions. Full article
(This article belongs to the Special Issue Satellite Time Series Analysis for Forest Mapping and Change Detection)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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