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Remote Sensing of Forest Disturbance

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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: closed (1 July 2017) | Viewed by 106172

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Special Issue Editors

Dr. Sean P. Healey

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Guest Editor

USDA Forest Service, Rocky Mountain Research Station, Ogden, UT, USA
Interests: forest disturbance; forest inventory; remote sensing

Dr. Warren B. Cohen

E-Mail Website
Guest Editor

USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR, USA

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 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

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

Published Papers (14 papers)

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Research

19 pages, 3085 KiB

Open AccessArticle

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

by Alexander J. Hernandez, Sean P. Healey, Hongsheng Huang and R. Douglas Ramsey

Forests 2018, 9(6), 317; https://doi.org/10.3390/f9060317 - 02 Jun 2018

Cited by 8 | Viewed by 4117

Abstract

The water balance in a watershed can be disrupted by forest disturbances such as harvests and fires. Techniques to accurately and efficiently map forest cover changes due to disturbance are evolving quickly, and it is of interest to ask how useful maps of different types of disturbances over time can be in the prediction of water yield. We assessed the benefits of using land cover maps produced at annual vs. five-year intervals in the prediction of monthly streamflows across 10 watersheds contained entirely within the US National Forest System. We found that annually updating land cover maps with forest disturbance data significantly improved water yield predictions using the Soil and Water Assessment Tool (SWAT; p < 0.01 improvement for both the Nash–Sutcliffe efficiency measure and the ratio of the root mean square error to the standard deviation of the measured data). Improvement related to using annually updated land cover maps was directly related to the amount of disturbance observed in a watershed. Our results lay a foundation to apply new high-resolution disturbance datasets in the field of hydrologic modeling to monitor ungauged watersheds and to explore potential water yield changes in watersheds if climate conditions or management practices were to change forest disturbance processes. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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9990 KiB

Open AccessArticle

Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

by Benjamin C. Bright, Andrew T. Hudak, Arjan J. H. Meddens, Todd J. Hawbaker, Jennifer S. Briggs and Robert E. Kennedy

Forests 2017, 8(9), 322; https://doi.org/10.3390/f8090322 - 30 Aug 2017

Cited by 31 | Viewed by 6232

Abstract

Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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8576 KiB

Open AccessFeature PaperArticle

Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts

by Evan B. Brooks, Zhiqiang Yang, Valerie A. Thomas and Randolph H. Wynne

Forests 2017, 8(9), 304; https://doi.org/10.3390/f8090304 - 24 Aug 2017

Cited by 18 | Viewed by 4840

Abstract

Remote detection of forest disturbance remains a key area of interest for scientists and land managers. Subtle disturbances such as drought, disease, insect activity, and thinning harvests have a significant impact on carbon budgeting and forest productivity, but current change detection algorithms struggle to accurately identify them, especially over decadal timeframes. We introduce an algorithm called Edyn, which inputs a time series of residuals from harmonic regression into a control chart to signal low-magnitude, consistent deviations from the curve as disturbances. After signaling, Edyn retrains a new baseline curve. We compared Edyn with its parent algorithm (EWMACD—Exponentially Weighted Moving Average Change Detection) on over 3500 visually interpreted Landsat pixels from across the contiguous USA, with reference data for timing and type of disturbance. For disturbed forested pixels, Edyn had a mean per-pixel commission error of 31.1% and omission error of 70.0%, while commission and omission errors for EWMACD were 39.9% and 65.2%, respectively. Edyn had significantly less overall error than EWMACD (F₁ = 0.19 versus F₁ = 0.13). These patterns generally held for all of the reference data, including a direct comparison to other contemporary change detection algorithms, wherein Edyn and EWMACD were found to have lower omission error rates for a category of subtle changes over long periods. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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10999 KiB

Open AccessFeature PaperArticle

Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators

by Martin Enrique Romero-Sanchez and Raul Ponce-Hernandez

Forests 2017, 8(9), 302; https://doi.org/10.3390/f8090302 - 24 Aug 2017

Cited by 32 | Viewed by 8301

Abstract

Assessing and monitoring forest degradation under national Monitoring, Verification and Reporting (MRV) systems in developing countries have been difficult to implement due to the lack of adequate technical and operational capacities. This study aims at providing methodological options for monitoring forest degradation in developing countries by using freely available remote sensing, forest inventory and ancillary data. We propose using Canopy Cover to separate, through a time series analysis approach using Landsat Imagery, forest areas with changes over time from sectors that report a “stable condition”. Above ground Biomass and Net Primary Productivity derived from remote sensing data were used to define thresholds for areas considered degraded. The approach was tested in a semi-deciduous tropical forest in the Southeast of Mexico. The results showed that higher rates of forest degradation, 1596 to 2865 ha year⁻¹, occur in areas with high population densities. The results also showed that 43% of the forests of the study area remain with no evident signs of degradation, as determined by the indicators used. The approach and procedures followed allowed for the identification and mapping of the temporal and spatial distribution of forest degradation, based on the indicators selected, and they are expected to serve as the basis for operations of the Reduction of Emissions from Deforestation and Forest Degradation (REDD+) initiative in Mexico and other developing countries, provided appropriate adaptations of the methodology are made to the conditions of the area in turn. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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12216 KiB

Open AccessArticle

Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry

by Robert Treuhaft, Yang Lei, Fabio Gonçalves, Michael Keller, João Roberto dos Santos, Maxim Neumann and André Almeida

Forests 2017, 8(8), 277; https://doi.org/10.3390/f8080277 - 31 Jul 2017

Cited by 30 | Viewed by 6297

Abstract

Changes in tropical-forest structure and aboveground biomass (AGB) contribute directly to atmospheric changes in CO

_{2}

, which, in turn, bear on global climate. This paper demonstrates the capability of radar-interferometric phase-height time series at X-band (wavelength = 3 cm) to monitor changes [...] Read more.

Changes in tropical-forest structure and aboveground biomass (AGB) contribute directly to atmospheric changes in CO

_{2}

, which, in turn, bear on global climate. This paper demonstrates the capability of radar-interferometric phase-height time series at X-band (wavelength = 3 cm) to monitor changes in vertical structure and AGB, with sub-hectare and monthly spatial and temporal resolution, respectively. The phase-height observation is described, with a focus on how it is related to vegetation-density, radar-power vertical profiles, and mean canopy heights, which are, in turn, related to AGB. The study site covers

18 \times 60

km in the Tapajós National Forest in the Brazilian Amazon. Phase-heights over Tapajós were measured by DLR’s TanDEM-X radar interferometer 32 times in a 3.2 year period from 2011–2014. Fieldwork was done on 78 secondary and primary forest plots. In the absence of disturbance, rates of change of phase-height for the 78 plots were estimated by fitting the phase-heights to time with a linear model. Phase-height time series for the disturbed plots were fit to the logistic function to track jumps in phase-height. The epochs of clearing for the disturbed plots were identified with ≈1-month accuracy. The size of the phase-height change due to disturbance was estimated with ≈2-m accuracy. The monthly time resolution will facilitate REDD+ monitoring. Phase-height rates of change were shown to correlate with LiDAR RH90 height rates taken over a subset of the TanDEM-X data’s time span (2012–2013). The average rate of change of phase-height across all 78 plots was 0.5 m-yr

^{- 1}

with a standard deviation of 0.6 m-yr

^{- 1}

. For 42 secondary forest plots, the average rate of change of phase-height was 0.8 m-yr

^{- 1}

with a standard deviation of 0.6 m-yr

^{- 1}

. For 36 primary forest plots, the average phase-height rate was 0.1 m-yr

^{- 1}

with a standard deviation of 0.5 m-yr

^{- 1}

. A method for converting phase-height rates to AGB-rates of change was developed using previously measured phase-heights and field-estimated AGB. For all 78 plots, the average AGB-rate was 1.7 Mg-ha

^{- 1}

-yr

^{- 1}

with a standard deviation of 4.0 Mg-ha

^{- 1}

-yr

^{- 1}

. The secondary-plot average AGB-rate was 2.1 Mg-ha

^{- 1}

-yr

^{- 1}

, with a standard deviation of 2.4 Mg-ha

^{- 1}

-yr

^{- 1}

. For primary plots, the AGB average rate was 1.1 Mg-ha

^{- 1}

-yr

^{- 1}

with a standard deviation of 5.2 Mg-ha

^{- 1}

-yr

^{- 1}

. Given the standard deviations and the number of plots in each category, rates in secondary forests and all forests were significantly different from zero; rates in primary forests were consistent with zero. AGB-rates were compared to change models for Tapajós and to LiDAR-based change measurements in other tropical forests. Strategies for improving AGB dynamical monitoring with X-band interferometry are discussed. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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9076 KiB

Open AccessArticle

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

by Valerie J. Pasquarella, Bethany A. Bradley and Curtis E. Woodcock

Forests 2017, 8(8), 275; https://doi.org/10.3390/f8080275 - 31 Jul 2017

Cited by 44 | Viewed by 9307

Abstract

Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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2431 KiB

Open AccessArticle

Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume

by Sarah Yoga, Jean Bégin, Benoît St-Onge and Demetrios Gatziolis

Forests 2017, 8(7), 253; https://doi.org/10.3390/f8070253 - 15 Jul 2017

Cited by 9 | Viewed by 5185

Abstract

Recent increases in forest diseases have produced significant mortality in boreal forests. These disturbances influence merchantable volume predictions as they affect the distribution of live and dead trees. In this study, we assessed the use of lidar, alone or combined with multispectral imagery, to classify trees and predict the merchantable volumes of 61 balsam fir plots in a boreal forest in eastern Canada. We delineated single trees on a canopy height model. The number of detected trees represented 92% of field trees. Using lidar intensity and image pixel metrics, trees were classified as live or dead with an overall accuracy of 89% and a kappa coefficient of 0.78. Plots were classified according to their class of mortality (low/high) using a 10.5% threshold. Lidar returns associated with dead trees were clipped. Before clipping, the root mean square errors were of 22.7 m³ ha⁻¹ in the low mortality plots and of 39 m³ ha⁻¹ in the high mortality plots. After clipping, they decreased to 20.9 m³ ha⁻¹ and 32.3 m³ ha⁻¹ respectively. Our study suggests that lidar and multispectral imagery can be used to accurately filter dead balsam fir trees and decrease the merchantable volume prediction error by 17.2% in high mortality plots and by 7.9% in low mortality plots. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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3852 KiB

Open AccessFeature PaperArticle

Using Intra-Annual Landsat Time Series for Attributing Forest Disturbance Agents in Central Europe

by Julian Oeser, Dirk Pflugmacher, Cornelius Senf, Marco Heurich and Patrick Hostert

Forests 2017, 8(7), 251; https://doi.org/10.3390/f8070251 - 14 Jul 2017

Cited by 41 | Viewed by 7234

Abstract

The attribution of forest disturbances to disturbance agents is a critical challenge for remote sensing-based forest monitoring, promising important insights into drivers and impacts of forest disturbances. Previous studies have used spectral-temporal metrics derived from annual Landsat time series to identify disturbance agents. Here, we extend this approach to new predictors derived from intra-annual time series and test it at three sites in Central Europe, including managed and protected forests. The two newly tested predictors are: (1) intra-annual timing of disturbance events and (2) temporal proximity to windstorms based on prior knowledge. We estimated the intra-annual timing of disturbances using a breakpoint detection algorithm and all available Landsat observations between 1984 and 2016. Using spectral, temporal, and topography-related metrics, we then mapped four disturbance classes: windthrow, cleared windthrow, bark beetles, and other harvest. Disturbance agents were identified with overall accuracies of 76–86%. Temporal proximity to storm events was among the most important predictors, while intra-annual timing itself was less important. Moreover, elevation information was very effective for discriminating disturbance agents. Our results demonstrate the potential of incorporating dense, intra-annual Landsat time series information and prior knowledge of disturbance events for monitoring forest ecosystem change at the disturbance agent level. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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35762 KiB

Open AccessArticle

Assessment of Forest Degradation in Vietnam Using Landsat Time Series Data

by James E. Vogelmann, Phung Van Khoa, Do Xuan Lan, Jacob Shermeyer, Hua Shi, Michael C. Wimberly, Hoang Tat Duong and Le Van Huong

Forests 2017, 8(7), 238; https://doi.org/10.3390/f8070238 - 04 Jul 2017

Cited by 23 | Viewed by 8934

Abstract

Landsat time series data were used to characterize forest degradation in Lam Dong Province, Vietnam. We conducted three types of image change analyses using Landsat time series data to characterize the land cover changes. Our analyses concentrated on the timeframe of 1973–2014, with much emphasis on the latter part of that range. We conducted a field trip through Lam Dong Province to develop a better understanding of the ground conditions of the region, during which we obtained many photographs of representative forest sites with Global Positioning System locations to assist us in our image interpretations. High-resolution Google Earth imagery and Landsat data of the region were used to validate results. In general, our analyses indicated that many land-use changes have occurred throughout Lam Dong Province, including gradual forest to non-forest transitions. Recent changes are most marked along the relatively narrow interfaces between agricultural and forest areas that occur towards the boundaries of the province. One important observation is that the most highly protected national reserves in the region have not changed much over the entire Landsat timeframe (1972–present). Spectral changes within these regions have not occurred at the same levels as those areas adjacent to the reserves. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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2132 KiB

Open AccessArticle

Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar

by Katsuto Shimizu, Oumer S. Ahmed, Raul Ponce-Hernandez, Tetsuji Ota, Zar Chi Win, Nobuya Mizoue and Shigejiro Yoshida

Forests 2017, 8(6), 218; https://doi.org/10.3390/f8060218 - 19 Jun 2017

Cited by 40 | Viewed by 5703

Abstract

In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include “logging”, “plantation”, “shifting cultivation”, “urban expansion”, “water invasion”, “recovery”, “other change”, and “no change”. The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments. Full article

(This article belongs to the Special Issue Remote Sensing of Forest Disturbance)

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Open AccessArticle

Patch-Based Forest Change Detection from Landsat Time Series

by M. Joseph Hughes, S. Douglas Kaylor and Daniel J. Hayes

Forests 2017, 8(5), 166; https://doi.org/10.3390/f8050166 - 11 May 2017

Cited by 47 | Viewed by 8447

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 (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)

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6383 KiB

Open AccessArticle

by Warren B. Cohen, Sean P. Healey, Zhiqiang Yang, Stephen V. Stehman, C. Kenneth Brewer, Evan B. Brooks, Noel Gorelick, Chengqaun Huang, M. Joseph Hughes, Robert E. Kennedy, Thomas R. Loveland, Gretchen G. Moisen, Todd A. Schroeder, James E. Vogelmann, Curtis E. Woodcock, Limin Yang and Zhe Zhu

Forests 2017, 8(4), 98; https://doi.org/10.3390/f8040098 - 26 Mar 2017

Cited by 140 | Viewed by 12458

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 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)

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Open AccessArticle

Effects of Burn Severity and Environmental Conditions on Post-Fire Regeneration in Siberian Larch Forest

by Thuan Chu, Xulin Guo and Kazuo Takeda

Forests 2017, 8(3), 76; https://doi.org/10.3390/f8030076 - 11 Mar 2017

Cited by 36 | Viewed by 8372

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 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)

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Open AccessArticle

Windthrow Detection in European Forests with Very High-Resolution Optical Data

by Kathrin Einzmann, Markus Immitzer, Sebastian Böck, Oliver Bauer, Andreas Schmitt and Clement Atzberger

Forests 2017, 8(1), 21; https://doi.org/10.3390/f8010021 - 06 Jan 2017

Cited by 48 | Viewed by 8995

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, 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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