E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Remote Sensing of Forest Disturbance"

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

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. 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). 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

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

Published Papers (4 papers)

View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Open AccessArticle Patch-Based Forest Change Detection from Landsat Time Series
Forests 2017, 8(5), 166; doi:10.3390/f8050166
Received: 20 February 2017 / Revised: 28 April 2017 / Accepted: 5 May 2017 / Published: 11 May 2017
PDF Full-text (6489 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In the species-rich and structurally complex forests of the Eastern United States, disturbance events are often partial and therefore difficult to detect using remote sensing methods. Here we present a set of new algorithms, collectively called Vegetation Regeneration and Disturbance Estimates through Time
[...] Read more.
In the species-rich and structurally complex forests of the Eastern United States, disturbance events are often partial and therefore difficult to detect using remote sensing methods. Here we present a set of new algorithms, collectively called Vegetation Regeneration and Disturbance Estimates through Time (VeRDET), which employ a novel patch-based approach to detect periods of vegetation disturbance, stability, and growth from the historical Landsat image records. VeRDET generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization, an algorithm originally designed for signal denoising. This study explores VeRDET’s effectiveness in detecting forest change using four vegetation indices and two parameters controlling the spatial and temporal scales of segmentation within a calibration region. We then evaluate algorithm effectiveness within a 386,000 km2 area in the Eastern United States where VeRDET has overall error of 23% and omission error across disturbances ranging from 22% to 78% depending on agent. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
Figures

Figure 1

Open AccessArticle How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?
Forests 2017, 8(4), 98; doi:10.3390/f8040098
Received: 5 February 2017 / Revised: 16 March 2017 / Accepted: 23 March 2017 / Published: 26 March 2017
Cited by 1 | PDF Full-text (6383 KB) | HTML Full-text | XML Full-text
Abstract
Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping
[...] Read more.
Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
Figures

Figure 1

Open AccessArticle Effects of Burn Severity and Environmental Conditions on Post-Fire Regeneration in Siberian Larch Forest
Forests 2017, 8(3), 76; doi:10.3390/f8030076
Received: 11 January 2017 / Revised: 1 March 2017 / Accepted: 7 March 2017 / Published: 11 March 2017
PDF Full-text (13240 KB) | HTML Full-text | XML Full-text
Abstract
Post-fire forest regeneration is strongly influenced by abiotic and biotic heterogeneity in the pre- and post-fire environments, including fire regimes, species characteristics, landforms, hydrology, regional climate, and soil properties. Assessing these drivers is key to understanding the long-term effects of fire disturbances on
[...] Read more.
Post-fire forest regeneration is strongly influenced by abiotic and biotic heterogeneity in the pre- and post-fire environments, including fire regimes, species characteristics, landforms, hydrology, regional climate, and soil properties. Assessing these drivers is key to understanding the long-term effects of fire disturbances on forest succession. We evaluated multiple factors influencing patterns of variability in a post-fire boreal Larch (Larix sibirica) forest in Siberia. A time-series of remote sensing images was analyzed to estimate post-fire recovery as a response variable across the burned area in 1996. Our results suggested that burn severity and water content were primary controllers of both Larch forest recruitment and green vegetation cover as defined by the forest recovery index (FRI) and the fractional vegetation cover (FVC), respectively. We found a high rate of Larch forest recruitment in sites of moderate burn severity, while a more severe burn was the preferable condition for quick occupation by vegetation that included early seral communities of shrubs, grasses, conifers and broadleaf trees. Sites close to water and that received higher solar energy during the summer months showed a higher rate of both recovery types, defined by the FRI and FVC, dependent on burn severity. In addition to these factors, topographic variables and pre-fire condition were important predictors of post-fire forest patterns. These results have direct implications for the post-fire forest management in the Siberian boreal Larch region. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)
Figures

Figure 1

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
PDF Full-text (15257 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

Journal Contact

MDPI AG
Forests Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Special Issue Edit a special issue Review for Forests
loading...
Back to Top