Special Issue "Mapping Forest Dynamics Using Multi-Source Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (10 December 2019).

Special Issue Editors

Dr. Gang Chen
Guest Editor
Laboratory for Remote Sensing and Environmental Change, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28277, USA
Interests: remote sensing; forest disturbances; GEOBIA; spatial ecology
Special Issues and Collections in MDPI journals
Dr. Kaiguang Zhao
Guest Editor
Ohio Agricultural Research and Development Center, School of Environment and Natural Resources, The Ohio State University, Wooster, OH 44691, USA
Interests: carbon monitoring; ecosystem structure and functioning; land dynamics; lidar; ecological modeling; spatial analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Forest ecosystems are increasingly affected by a variety of environmental and anthropogenic disturbances, such as fire, drought, insect and disease outbreaks, logging, and urban development. Although almost none of those disturbances are new, a growing number of studies confirmed that their intensity and frequency have substantially increased over the past decades. Consequently, a prior disturbance regime is likely to influence the response of a forest ecosystem to a new disturbance, resulting in complex, interacting disturbances. While single sensors in remote sensing often face challenges to capture such disturbances and the process of post-disturbance recovery, a growing fleet of sensors with diverse spatial, temporal, spectral and radiometric resolutions has significantly augmented our earth observation capabilities.

This Special Issue aims to review and synthesize the latest, leading-edge advances in mapping forest dynamics using multi-source remote sensing. Original research articles are solicited over a wide range of topics which may focus on, but are not limited to:

  • Mapping large-scale disturbances causing extensive tree damage (e.g., changes in tree structure, canopy cover, biomass and carbon storage)
  • Monitoring stresses affecting forest health (e.g., photosynthesis and phenology)
  • Assessing causes of disturbances/stresses
  • Forest recovery mapping and analysis
  • Integrating a new generation of sensors for tracking forest dynamics
  • New strategies or algorithms to synergize multi-source data

Dr. Gang Chen
Dr. Kaiguang Zhao
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 papers will be 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2200 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.


  • Forest disturbances
  • Causes of disturbances or stresses
  • Post-disturbance recovery
  • Multi-source data integration

Published Papers (1 paper)

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Open AccessArticle
Tracking Reforestation in the Loess Plateau, China after the “Grain for Green” Project through Integrating PALSAR and Landsat Imagery
Remote Sens. 2019, 11(22), 2685; https://doi.org/10.3390/rs11222685 - 17 Nov 2019
Cited by 1
An unprecedented reforestation process happened in the Loess Plateau, China due to the ecological restoration project ‘Grain for Green Project’, which has affected regional carbon and water cycles as well as brought climate feedbacks. Accurately mapping the area and spatial distribution of emerged [...] Read more.
An unprecedented reforestation process happened in the Loess Plateau, China due to the ecological restoration project ‘Grain for Green Project’, which has affected regional carbon and water cycles as well as brought climate feedbacks. Accurately mapping the area and spatial distribution of emerged forests in the Loess Plateau over time is essential for forest management but a very challenging task. Here we investigated the changes of forests in the Loess Plateau after the forest reconstruction project. First, we used a pixel and rule-based algorithm to identify and map the annual forests from 2007 to 2017 in the Loess Plateau by integrating 30 m Landsat data and 25 m resolution PALSAR data in this study. Then, we carried out the accuracy assessment and comparison with several existing forest products. The overall accuracy (OA) and Kappa coefficient of the resultant map, were about 91% and 0.77 in 2010, higher than those of the other forest products (FROM-GLC, GlobeLand30, GLCF-VCF, JAXA, and OU-FDL) with OA ranging from 83.57% to 87.96% and Kappa coefficients from 0.52 to 0.68. Based on the annual forest maps, we found forest area in the Loess Plateau has increased by around 15,000 km2 from 2007 to 2017. This study clearly demonstrates the advantages of data fusion between PALSAR and Landsat images for monitoring forest cover dynamics in the Loess Plateau, and the resultant forest maps with lower uncertainty would contribute to the regional forest management. Full article
(This article belongs to the Special Issue Mapping Forest Dynamics Using Multi-Source Remote Sensing)
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