File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

TitleUsing high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem
Authors
KeywordsHigh spatial resolution
Error matrix
Frequency distributions
Random Forests
Scale effect
Spectral library
Issue Date2017
Citation
Remote Sensing of Environment, 2017, v. 191, p. 95-109 How to Cite?
Abstract© 2017 Elsevier Inc. As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.
Persistent Identifierhttp://hdl.handle.net/10722/266781
ISSN
2021 Impact Factor: 13.850
2020 SCImago Journal Rankings: 3.611
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMeng, Ran-
dc.contributor.authorWu, Jin-
dc.contributor.authorSchwager, Kathy L.-
dc.contributor.authorZhao, Feng-
dc.contributor.authorDennison, Philip E.-
dc.contributor.authorCook, Bruce D.-
dc.contributor.authorBrewster, Kristen-
dc.contributor.authorGreen, Timothy M.-
dc.contributor.authorSerbin, Shawn P.-
dc.date.accessioned2019-01-31T07:19:34Z-
dc.date.available2019-01-31T07:19:34Z-
dc.date.issued2017-
dc.identifier.citationRemote Sensing of Environment, 2017, v. 191, p. 95-109-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/266781-
dc.description.abstract© 2017 Elsevier Inc. As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.-
dc.languageeng-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectHigh spatial resolution-
dc.subjectError matrix-
dc.subjectFrequency distributions-
dc.subjectRandom Forests-
dc.subjectScale effect-
dc.subjectSpectral library-
dc.titleUsing high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2017.01.016-
dc.identifier.scopuseid_2-s2.0-85009863176-
dc.identifier.volume191-
dc.identifier.spage95-
dc.identifier.epage109-
dc.identifier.isiWOS:000397360500008-
dc.identifier.issnl0034-4257-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats