Mapping Wetland Burned Area from Sentinel-2 across the Southeastern United States and Its Contributions Relative to Landsat-8 (2016–2019)

Vanderhoof, Melanie K.; Hawbaker, Todd J.; Teske, Casey; Noble, Joe; Picotte, Joshua J.

doi:10.3390/fire4030052

Cited by 21 publications

(14 citation statements)

References 84 publications

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“…Ecoregion specific differences between public and private land of average climate variables including annual precipitation (Precip) and maximum vapour pressure deficit (VPDmax), as well as average topographic descriptors including elevation, slope, and folded aspect (Aspect f ). www.publish.csiro.au/wf International Journal of Wildland Fire K intensity or understory fires are common, such as pine plantations, omission errors in the Landsat BA Product are higher (Vanderhoof et al 2021) and therefore FRQ may be underestimated, while TSLB, LFFI, FIL, and FRIc may all be overestimated. Conversely, commission error in the Landsat BA Product is highest in cultivated crops and pasture/hay land cover types (Hawbaker et al 2020a), which may overestimate FRQ and underestimate the remaining metrics.…”

Section: Discussionmentioning

confidence: 99%

Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership

Vanderhoof

Hawbaker

Teske

et al. 2022

Int. J. Wildland Fire

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Background. Remotely sensed burned area products are critical to support fire modelling, policy, and management but often require further processing before use. Aim. We calculated fire history metrics from the Landsat Burned Area Product across the conterminous U.S. (CONUS) including (1) fire frequency, (2) time since last burn (TSLB), (3) year of last burn, (4) longest fire-free interval, (5) average fire interval length, and (6) contemporary fire return interval (cFRI). Methods. Metrics were summarised by ecoregion and land ownership, and related to historical and cheatgrass datasets to demonstrate further applications of the products. Key results. The proportion burned ranged from 0.7% in the Northeast Mixed Woods to 74.1% in the Kansas Flint Hills. The Flint Hills and Temperate Prairies showed the highest burn frequency, while the Flint Hills and the Sierra Nevada and Klamath Mountains showed the shortest TSLB. Compared to private, public land had greater burned area (19 of 31 ecoregions) and shorter cFRI (25 of 31 ecoregions). Conclusions. Contemporary fire history metrics can help characterise recent fire regimes across CONUS. Implications. In regions with frequent fire, comparison of contemporary with target fire regimes or invasive species datasets enables the efficient incorporation of burned area data into decision-making.

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Section: Discussionmentioning

confidence: 99%

Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership

Vanderhoof

Hawbaker

Teske

et al. 2022

Int. J. Wildland Fire

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“…By utilizing visible and infrared bands, the appearance of open areas could be monitored by observing the plant density index extracted using the NDVI [55,56]. At the same time, drought areas were identified by observing the soil moisture index using the NDMI [55,57]. The calculation of the NDVI and NDMI values for each pixel was conducted following Equations ( 4) and ( 5), according to [55][56][57]:…”

Section: Calculation Of Wetness and Vegetation Indicesmentioning

confidence: 99%

“…At the same time, drought areas were identified by observing the soil moisture index using the NDMI [55,57]. The calculation of the NDVI and NDMI values for each pixel was conducted following Equations ( 4) and ( 5), according to [55][56][57]:…”

Section: Calculation Of Wetness and Vegetation Indicesmentioning

confidence: 99%

“…To obtain accurate information regarding these conditions, an analysis of the NDMI and the NDVI was generated using multispectral satellite images. In this study, Landsat-8 images were used as optical images that presented detailed spatial information captured using several types of sensors, including sensors that were sensitive to red, green, and blue light as well as nearinfrared and short-wave infrared bands [41,55,57]. These images presented images of the whole earth's surface were freely accessible and had a sufficient spatial resolution.…”

Section: Wetness and Vegetation Indicesmentioning

confidence: 99%

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The Determination of Priority Areas for the Restoration of Degraded Tropical Peatland Using Hydrological, Topographical, and Remote Sensing Approaches

Cahyono

Aditya

Istarno

2022

Land

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Degraded peatland is caused by forest clearing and the construction of artificial water networks. When water management is not implemented across land uses in the entire peatland landscape, then it will be a big issue that causes a water deficit and leads to increasing droughts and fires. Effective restoration must first identify the part of Peatland Hydrological system Units (PHUs) with insufficient water storage and resources. This study used intercorrelated factors of water balance, deficit months, NDMI-NDVI indices, dry periods, recurrent fires, peat depth, and water loss conditions, as the evaluation parameters, within individual sub-PHUs to determine the most degraded areas that require intervention and restoration. Sub-PHU was determined based on the peat hydrological unity concept by identifying streamline, outlet channels, peat-depth, slopes, and network connectivity. Global hydrological data using TerraClimate and CHIRPS, combined with field observations, were used to validate and calculate each sub-PHU’s water balance and dry periods. Soil moisture (NDMI), vegetation density (NDVI), and fire frequency were extracted from multispectral satellite images (e.g., Landsat 8, MODIS-Terra, and MODIS-Aqua). Each parameter was ranked by the score for each sub-PHU. The parameters that can be ranked are only the ordinal type of number. The lowest ranks indicated the most degraded sub-PHUs requiring peat rewetting interventions.

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“…Sentinel-2(MSI), provided by the European Space Agency, is a high-resolution multispectral imaging satellite, and its unique three red-edge band data (B5, B6, B7) cover the 703-783 nm band, making it is very effective for monitoring vegetation growth [31][32][33]. In addition, the revisit period of the A and B satellites is shortened to 5 days, which is very suitable for capturing a time-series observation of post-fire vegetation regeneration [34,35].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Effects of Post-Fire Climate and Burn Severity on the Early-Term Regeneration of Forest and Shrub Communities in the San Gabriel Mountains of California from Sentinel-2(MSI) Images

Liu

Chen

et al. 2022

Forests

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Studying the early changes in post-fire vegetation communities may improve the overall resilience of forests. The necessity for doing so was demonstrated by the Bobcat Fire, which seriously threatened the central San Gabriel Mountains and the Angeles National Forest in California. This study aimed to monitor and quantify the effects of climatological and topographic conditions along with burn severity on early (within 1 year) post-fire forests and shrubs community regeneration. In this study, we used Sentinel-2(MSI) intensive time-series imagery (July 2020–October 2021) to make a confusion matrix combined with 389 vegetation sample points on Google Earth Pro. The overall accuracy (OA) and the Kappa coefficient, calculated from the confusion matrix, were used as evaluation parameters to validate the classification results. With multiple linear regression models and Environmental Systems Research Institute (ESRI) historical images, we analyzed the effects of climate and slope aspects on the regeneration of post-fire forest and shrub communities. We also quantitatively analyzed the regeneration rates based on five burn severity types. The results show that the normalized burning rate (NBR) was the most accurate vegetation classification indicator in this study (OA: 92.3–99.5%, Kappa: 0.88–0.98). The vegetation classification accuracy based on SVM is about 6.6% higher than K-Means. The overall accuracy of the burn area is 94.87%. Post-fire climate factors had a significant impact on the regeneration of the two vegetation communities (R2: 0.42–0.88); the optimal regeneration slope was 15–35°; and the fire severity changed the original competition relationship and regeneration rate. The results provide four main insights into the regeneration of post-fire vegetation communities: (1) climate factors in the first regenerating season have important impacts on the regeneration of forest and shrub communities; (2) daytime duration and rainfall are the most significant factors for forests and shrubs regeneration; (3) tolerable low burn severity promotes forests regeneration; and (4) forests have a certain ability to resist fires, while shrubs can better tolerate high-intensity fire ecology. This study could support the implementation of strategies for regionalized forest management and the targeted enhancement of post-fire vegetation community resilience.

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Mapping Wetland Burned Area from Sentinel-2 across the Southeastern United States and Its Contributions Relative to Landsat-8 (2016–2019)

Cited by 21 publications

References 84 publications

Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership

Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership

The Determination of Priority Areas for the Restoration of Degraded Tropical Peatland Using Hydrological, Topographical, and Remote Sensing Approaches

Evaluating Effects of Post-Fire Climate and Burn Severity on the Early-Term Regeneration of Forest and Shrub Communities in the San Gabriel Mountains of California from Sentinel-2(MSI) Images

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