Remote sensing of selective logging in tropical forests: current state and future directions

doi:10.3832/ifor3301-013

Remote sensing of selective logging in tropical forests: current state and future directions

iForest - Biogeosciences and Forestry, Volume 13, Issue 4, Pages 286-300 (2020)
doi: https://doi.org/10.3832/ifor3301-013
Published: Jul 10, 2020 - Copyright © 2020 SISEF

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This paper reviews and discusses the status of remote sensing techniques applied in detecting and monitoring selective logging disturbance in tropical forests. The analyses concentrated on the period 1992-2019. Accurate and precise detection of selectively logged sites in a forest is crucial for analyzing the spatial distribution of forest disturbances and degradation. Remote sensing can be used to monitor selective logging activities and associated forest fires over tropical forests, which otherwise requires labor-intensive and time-consuming field surveys, that are costly and difficult to undertake. The number of studies on remote sensing for selective logging has grown steadily over the years, thus, the need for their review so as to guide forest management practices and current research. A variety of peer reviewed articles are discussed so as to evaluate the applicability and accuracy of different methods in different circumstances. Major challenges with existing approaches are singled out and future needs are discussed.

Keywords

Tropical Forest Disturbance, Selective Logging, Forest Degradation, Forest Canopy Gaps, Disturbance Mapping, Remote Sensing, Forest Monitoring

Authors’ address

(1)

0000-0003-3626-5839
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, Private Bag 3 Wits, 2050 (South Africa)

Corresponding author

Colbert M Jackson
mutisojackson@yahoo.com

Citation

Jackson CM, Adam E (2020). Remote sensing of selective logging in tropical forests: current state and future directions. iForest 13: 286-300. - doi: 10.3832/ifor3301-013

Academic Editor

Emanuele Lingua

Paper history

Received: Nov 25, 2019
Accepted: May 11, 2020

First online: Jul 10, 2020
Publication Date: Aug 31, 2020
Publication Time: 2.00 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sensing of Environment 112: 2729-2740.
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