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Special Issue "Remote Sensing of Forest Disturbance"

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A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: 2 June 2017

Special Issue Editors

Guest Editor
Dr. Sean P. Healey

USDA Forest Service, Rocky Mountain Research Station, Ogden, UT, USA
Website | E-Mail
Guest Editor
Dr. Warren B. Cohen

USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA
Website | E-Mail

Special Issue Information

Dear Colleagues,

Disturbances, such as fire and harvest, shape forests, both at the stand and ecosystem levels. Rates and severity of disturbance affect many ecological functions, including carbon storage, habitat value, and hydrology. At the same time, much of what we do as land managers involves disturbance, whether the goal is resource extraction, manipulation of stand structure and composition, or simply conversion to another land use. Remote sensing methods are well suited to the challenge of studying disturbance impacts because they: (1) provide near real-time coverage; (2) capture both rare and transitory events unlikely to be picked up by ground samples; and (3) offer spatially explicit historical perspective, going back 45 years in some cases.

Exciting advances are occurring in the field of remotely sensed forest disturbance detection, involving: sensor fusion; new and increasingly institutionalized applications; characterization of type and magnitude of change; improvement to computing and data system resources; and more sophisticated time series analysis. This Special Issue of Forests will highlight both new techniques and new applications. Research may take place anywhere in the world, using any combination of sensors, but must represent fundamental advances in how remotely sensed data are used. Application of established methods in new areas is not within the issue’s scope. All manuscripts must address validation and uncertainty. Submissions are welcomed until 2 June, 2017.

Dr. Sean P. Healey
Dr. Warren B. Cohen
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 1200 CHF (Swiss Francs).

Keywords

  • Remote Sensing
  • Disturbance
  • Fire
  • REDD+
  • Time Series Analysis
  • Data Systems
  • Deforestation
  • Degradation

Published Papers (1 paper)

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Research

Open AccessArticle Windthrow Detection in European Forests with Very High-Resolution Optical Data
Forests 2017, 8(1), 21; doi:10.3390/f8010021
Received: 14 October 2016 / Revised: 16 December 2016 / Accepted: 31 December 2016 / Published: 6 January 2017
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Abstract
With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage,
[...] Read more.
With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
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