Vegetation Burn Severity Mapping Using Landsat-8 and WorldView-2

Wu, Zhuoting; Middleton, Barry R.; Hetzler, Robert; Vogel, John M.; Dye, Dennis G.

doi:10.14358/pers.81.2.143

Cited by 42 publications

(18 citation statements)

References 46 publications

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“…At a regional scale, the risk of invasion could be also evaluated by mapping burnt areas, which are more likely to be infested by Hakea sericea [2,12], using remote sensing. WV2 or Sentinel 2B imagery could be used for that purpose [68][69][70].…”

Section: Discussionmentioning

confidence: 99%

Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach

2017

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Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion should be directed at understanding and quantification of their location, extent and evolution, namely the monitoring of the phenomenon. In this sense, the techniques and methods of remote sensing can be very useful. The aim of this paper was to identify and quantify the areas covered by the invasive plant Hakea sericea using high spatial resolution images obtained from aerial platforms (Unmanned Aerial Vehicle: UAV/drone) and orbital platforms (WorldView-2: WV2), following an object-oriented image analysis approach. The results showed that both data were suitable. WV2reached user and producer accuracies greater than 93% (Estimate of Kappa (KHAT): 0.95), while the classifications with the UAV orthophotographs obtained accuracies higher than 75% (KHAT: 0.51). The most suitable data to use as input consisted of using all of the multispectral bands that were available for each image. The addition of textural features did not increase the accuracies for the Hakea sericea class, but it did for the general classification using WV2.

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Section: Discussionmentioning

confidence: 99%

Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach

2017

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“…In addition to NDVI, previous work [44] has proposed the use of the Normalized Burn Ratio (NBR) [26], and the differenced Normalized Burn Ratio (dNBR) [26] for detecting and monitoring post-fire vegetation changes over time. Unfortunately, their use was not possible in this study because they require data from regions of the optical spectrum that are not available in GeoEye images (Table 1).…”

Section: Satellite Datamentioning

confidence: 99%

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

et al. 2016

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Abstract:Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery.

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“…Compared to field efforts required to directly measure the post-fire burn effects, remote sensing based measures are a more affordable option and are especially suitable for monitoring large and topographically complex landscapes (Chuvieco, 2009). Fire severity studies, in particular, have been widely based on the remotely sensed data acquired by sensors onboard the series of Landsat satellites (Brewer et al, 2005;Chu et al, 2016;Cocke et al, 2005;Fang and Yang, 2014;Fernández-Manso and Quintano, 2015;Parker et al, 2015;Quintano et al, 2013;Wu et al, 2015;Veraverbeke and Hook, 2013). The main advantages of Landsat imagery include multispectral sampling (6 reflective bands and one thermal band), moderate spatial resolution and image time series covering > 40 years (Fang and Yang, 2014).…”

Section: Introductionmentioning

confidence: 99%

Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature

Quintano

Fernández–Manso

Roberts

2017

Remote Sensing of Environment

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Forest fires are incidents of great importance in Mediterranean environments. Landsat data have proven to be suitable for evaluating post-fire vegetation damage and determining different levels of burn severity, which is crucial for planning post-fire rehabilitation. This study assessed the utility of combined Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images and Land Surface Temperature (LST) to accurately map burn severity. We studied a large convection-dominated wildfire, which occurred on 19-21 September 2012 in Spain, in a zone dominated by Pinus pinaster Ait. Burn severity degree (low, moderate, and high) was measured 2-3 months after fire in 111 field plots using the Composite Burn Index (CBI). Four fraction images were generated using MESMA from the reflective bands of a post-fire Landsat 7 Enhanced Thematic Mapper (ETM +) image: 1.-char, 2.-green vegetation (GV), 3.-non-photosynthetic vegetation and soil (NPVS) and 4.-shade. The thermal band was converted to LST using a single channel algorithm. Next, Multinomial Logistic Regression (MLR) was used to obtain the probability of each burn severity level from MESMA fraction images and LST. Finally, a burn severity map was generated from the probability images and independently validated using an error matrix, producer and user accuracies per class, and κ statistic. MLR identified the char fraction image and LST as the only significant explanatory variables when burn severity acted as the response variable. Two burn severity degrees (low-moderate and high) were finally considered to build the final burn severity map. In this way, we reached a higher accuracy (κ = 0.79) than using the original three burn severity levels (κ = 0.66). Our study demonstrates the validity of combining fraction images and LST from Landsat data to map burn severity accurately in Mediterranean countries.

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Vegetation Burn Severity Mapping Using Landsat-8 and WorldView-2

Cited by 42 publications

References 46 publications

Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach

Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature

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