Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Wang, Fugui; Xu, Y. Jun

doi:10.1007/s10661-009-0798-8

Cited by 101 publications

(60 citation statements)

References 39 publications

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“…These techniques are typically based on comparisons between data collected before and after a storm in the fallen area. Wang et al reported on the detection of downed trees in an affected area by comparing changes in optical remote sensing images and standard aerial photographs (4000-5000 m high) before and after a hurricane [6,7]. Szantoi et al employed a Sobel edge detection algorithm combined with spectral information based on color filtering.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

2017

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Surveys of windthrown trees, resulting from hurricanes and other types of natural disasters, are an important component of agricultural insurance, forestry statistics, and ecological monitoring. Aerial images are commonly used to determine the total area or number of downed trees, but conventional methods suffer from two primary issues: misclassification of windthrown trees due to the interference from other objects or artifacts, and poor extraction resolution when trunk diameters are small. The objective of this study is to develop a coarse-to-fine extraction technique for individual windthrown trees that reduces the effects of these common flaws. The developed method was tested using UAV imagery collected over rubber plantations on Hainan Island after the Nesat typhoon in China on 19 October 2011. First, a coarse extraction of the affected area was performed by analyzing the image spectrum and textural characteristics. A thinning algorithm was then used to simplify downed trees into skeletal structures. Finally, fine extraction of individual trees was achieved using a line detection algorithm. The completeness of windthrown trees in the study area was 75.7% and the correctness was 92.5%. While similar values have been reported in other studies, they often include constraints, such as tree height. This technique is proposed to be a more feasible extraction algorithm as it is capable of achieving low commission errors across a broad range of tree heights and sizes. As such, it is a viable option for extraction of windthrown trees with a small trunk diameter.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

2017

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“…Additionally, Lidar has been used to detect three-dimensional variance after hurricanes (Li et al 2008;Hussain et al 2011) and tornadoes (Kashani et al 2014). Texture information derived from imagery (Vijayaraj et al 2008;Radhika et al 2012) has been used to rapidly detect damaged areas, as have band ratios (e.g., Normalized Difference Vegetation Index; NDVI) (Ill et al 1997;Womble et al 2006;Liou et al 2010;Wang and Xu 2010;. Multi-temporal analysis is generally employed for postdisaster events through the application of change detection (Al-Khudhairy et al 2005;Chen & Hutchinson 2007, 2010Butenuth et al 2011) for coregistered pre-and post-event images.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the amount of time required to manually define the debris zone, there is much subjectivity in the delineation, and efforts to quickly establish a debris line or zone result in errors and oversimplification (Friedland et al 2011). Wang andXu (2010), , Thompson et al (2011), andSzantoi et al (2012) investigated detection of hurricane-damaged forest areas and forest debris, but automatic mapping of urban debris zones remains a research gap, particularly for events with widespread impacts.…”

Section: Introductionmentioning

confidence: 99%

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Jiang

Friedland

2015

Geomatics, Natural Hazards and Risk

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Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectralÀtextural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

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“…Satellite image-based remote sensing has become an essential data source for assessing rapid or abrupt changes, being low cost, easily and readily accessible, yet it still provides adequate information (Kennedy et al 2009. A great variety of change detection approaches have been tested (Singh 1989, Coppin et al 2004, Kennedy et al 2009, McRoberts et al 2010, some of which have focused directly on the assessment of wind damage to forests (Wang & Xu 2010). In general, methodological approaches for detecting changes in vegetation can be grouped into two primary categories: trajectory analysis and bi-temporal methods (McRoberts & Walters 2012).…”

Section: Introductionmentioning

confidence: 99%

Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data

Jonikavičius¹,

Mozgeris²

2013

iForest

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© iForest -Biogeosciences and Forestry IntroductionThe assessment of wind-caused forest damage includes some specifics that distinguish it from other tasks of detection and measuring of forest changes, at least in the context of Lithuanian forestry conditions. Fast and accurate determination of affected areas of forest and the severity of the damage are essential to enable forest managers to take immediate action on sanitary felling, timber processing and reforestation. Damage is usually geographically concentrated, some areas being difficult to access and measure in the field; however, all forests in Lithuania are precisely mapped and attributed by stand-wise forest inventories .This paper introduces a method of assessing the forest damage caused by a wind storm on August 8, 2010, in Lithuania. The introduced method may also be useful for solving other remote sensing based change detection tasks. The solution is based on integrating methodological approaches originating from two areas of satellite image usage related to the forest inventory: (i) forest change detection, and (ii) the nearest neighbor technique.The process of analyzing image data from two or more periods for the purpose of mapping cover change is commonly referred to as change detection (McRoberts et al. 2010). The principles of satellite image-based forest change detection were first discussed in detail more than a decade ago (Häme 1991, Olsson 1994, Varjo 1997 and are still a subject of current research and interest (Kennedy et al. 2009, McRoberts et al. 2010. Forests can be considered to grow at a constant rate of change, divided into long-term, short-term and rapid change (Häme 1991). Long-term change is usually connected to the growth of the stand, and even that can be monitored using state-of-the-art remote sensing techniques such as aerial imaging and laser scanning (Hyyppa et al. 2008). Satellite remote sensing can be considered to be a practical technique for long-term change observations, in particular because data with image characteristics suitable for monitoring forest changes are available for most of the Earth's surface for the past 40 years (e.g., the Landsat program -Wulder et al. 2008, McRoberts et al. 2010). Short-term or seasonal changes, usually related to reforestation, defoliation etc. can be monitored by satellite image-based remote sensing (Vogelmann et al. 2009, McRoberts & Walters 2012. The main challenge for foresters, however, is the detection and measuring of rapid changes such as tree felling, wind damage and so on. Satellite image-based remote sensing has become an essential data source for assessing rapid or abrupt changes, being low cost, easily and readily accessible, yet it still provides adequate information

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Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Cited by 101 publications

References 39 publications

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data

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