Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods

Schwarz, Markus; Steinmeier, Charlotte; Holecz, F.; Stebler, O.; Wagner, H. Gg.

doi:10.1080/02827580310018023

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…Bark beetle dispersal should be anticipated to protect the remaining standing trees and prevent subsequent indirect economic losses [2,3]. Thus, foresters aim to immediately clear up windthrown areas and plan an adapted reforestation [4]. Since resources of manpower are generally limited, both the extent and degree of the affected forest area and its accessibility must be known shortly after the storm.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

2019

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Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer’s accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user’s accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user’s needs.

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Section: Introductionmentioning

confidence: 99%

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

2019

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“…Generally, CD techniques can be differentiated in pixel-based analysis (PBA) and object-based image analysis (OBIA) [15,17,26]. In OBIA, instead of analyzing single pixels, typically image segmentation is applied first.…”

Section: Introductionmentioning

confidence: 99%

“…Comparison of active and passive data as well as pixel-and object-based approaches for detecting windthrow Schwarz et al [15] Forest disturbance detection Desclée et al [17] Forest cover disturbance detection 32,150 West Siberia, Russia Landsat TM/ETM+ Unsupervised classification of Tasseled Cap Indices to detect changes caused by forest harvesting and windthrow Dyukarev et al [18] Continuous forest disturbance monitoring 3600 Georgia, United States Landsat High temporal forest monitoring with Forest Disturbance Algorithm (CMFDA) Zhu et al [19] Assessing tree damage caused by hurricanes Golf Coast, United States Landsat, MODIS…”

Section: Introductionmentioning

confidence: 99%

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

Einzmann

Immitzer

Böck

et al. 2017

Forests

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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.

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“…Information on the characteristic distribution and legacies of these natural disturbances over time and space has been reported and simulated in the northeast from a few well-studied sites, [4][5][6][7][8][9][10] but the demand for agencies charged with forest management to remotely and repeatedly document the spatial extent and magnitude of such events on a broader scale has been increasing over time. 11,12 Knowledge of the variability found within these patterns is also important to efforts to accurately model carbon balances worldwide.…”

Section: Use Of Waveform Lidar and Hyperspectral Sensors To Assess Sementioning

confidence: 99%

Use of waveform lidar and hyperspectral sensors to assess selected spatial and structural patterns associated with recent and repeat disturbance and the abundance of sugar maple (<italic>Acer saccharum</italic> Marsh.) in a temperate mixed hardwood and conifer forest

Anderson

Ducey

Fast

et al. 2011

J. Appl. Remote Sens

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Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA's Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing the highest levels of sugar maple basal area and biomass. The levels of sugar maple abundance were determined through analysis of the 1997 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) high resolution spectral imagery and inventory of USFS Northern Research Station field plots. We found a relationship between field measurements of stem volume losses and the LVIS metric of mean canopy height (r 2 = 0.66; root mean square errors = 5.7 m 3 /ha, p < 0.0001) in areas that had been subjected to moderate-to-severe ice storm damage, accurately documenting the short-term outcome of a single disturbance event.

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Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods

Cited by 21 publications

References 16 publications

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data

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

Use of waveform lidar and hyperspectral sensors to assess selected spatial and structural patterns associated with recent and repeat disturbance and the abundance of sugar maple (<italic>Acer saccharum</italic> Marsh.) in a temperate mixed hardwood and conifer forest

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