The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). This dataset includes all of the attribute information for each point, including if the location of the point intersects a treatment type or combination of treatments as well as a KML file showing the location of each point.

Spatially-referenced data used in the study "Evaluating the Factors Responsible for Post-Fire Water Quality Response in Forests of the Western USA"

Spatially-referenced data used in the study "Evaluating the Factors Responsible for Post-Fire Water Quality Response in Forests of the Western USA"

This dataset provides annual forest fire burned area and daily hotspot products developed using data acquired from the Advanced Very-High-Resolution Radiometer (AVHRR) instruments carried aboard two NOAA polar-orbiting satellites (NOAA-11 and NOAA-14). The fire products were generated over 12 fire seasons (1st May - 31st October) from 1989-2000 across North America at 1-km resolution and subset to the ABoVE spatial domain of Alaska and Canada.

■ 상품 설명 및 특징 - '2024 산림 빅데이터 플랫폼 데이터 기반 분석과제 수행' 사업 중 '산불 예방 계획 수립 방안 분석' 과제의 결과로 도출된 분석용 데이터셋 - 제공되는 데이터는 2023년 1월부터 12월까지의 산불 발생 데이터를 기반으로 산불 발생 요인과 결합한 데이터 - 제공되는 데이터는 웨슬리퀘스트의 데이터 전처리 기술로 정제 가공한 데이터 ■ 기간 및 범위 - 2023년 1월 ~ 2023년 12월 (일단위) - 강원특별자치도, 경상북도, 경기도, 경상남도, 충청남도 5개 지역 ■ 활용 예제 - 본 데이터 상품을 활용하여 사용자는 다음과 같은 정보를 확인할 수 있습니다. 1) 최근 10년간 발생한 산불에 대한 정보 (일시, 피해면적, 진화시간 등) 2) 최근 10년간 발생한 산불의 발생 요인에 대한 정보 (기상, 지형, 인위적 요인 등) [원본 데이터](https://www.bigdata-forest.kr/product/WSQ000301)는 로그인 후 구매하여 다운로드 하십시오.

Wildfire can impact soil-physical and soil-hydraulic properties, with major implications for hydrologic and ecologic response. The durations of these soil impacts are poorly characterized for some forested environments. This dataset sheds light on the first four years of recovery of soil-physical properties of bulk density, loss on ignition (measure of soil organic matter), ground cover, and soil particle size distribution and of soil-hydraulic properties of sorptivity and field-saturated hydraulic conductivity. The dataset also includes a simple infiltration model used to examine infiltration as the sites recover from fire. Sample locations within the 2013 Black Forest Fire study area are: BF1, UTM-Easting (m) 532027, UTM-Northing (m) 4323210, Approximate elevation (± 5m) 2288; BF2, UTM-Easting (m) 532139, UTM-Northing (m) 4323273, Approximate elevation (± 5m) 2288; BF3, UTM-Easting (m) 532015, UTM-Northing (m) 4323416, Approximate elevation (± 5m) 2285; BF4, UTM-Easting (m) 532166, UTM-Northing (m) 4323576, Approximate elevation (± 5m) 2292; BF5, UTM-Easting (m) 532370, UTM-Northing (m) 4323194, Approximate elevation (± 5m) 2293; BF6, UTM-Easting (m) 532712, UTM-Northing (m) 4323283, Approximate elevation (± 5m) 2300; UTM is Universal Transverse Mercator, Zone 13, NAD83 datum, GRS80 geodetic reference system.

Wildfire can impact soil-physical and soil-hydraulic properties, with major implications for hydrologic and ecologic response. The durations of these soil impacts are poorly characterized for some forested environments. This dataset sheds light on the first four years of recovery of soil-physical properties of bulk density, loss on ignition (measure of soil organic matter), ground cover, and soil particle size distribution and of soil-hydraulic properties of sorptivity and field-saturated hydraulic conductivity. The dataset also includes a simple infiltration model used to examine infiltration as the sites recover from fire. Sample locations within the 2013 Black Forest Fire study area are: BF1, UTM-Easting (m) 532027, UTM-Northing (m) 4323210, Approximate elevation (± 5m) 2288; BF2, UTM-Easting (m) 532139, UTM-Northing (m) 4323273, Approximate elevation (± 5m) 2288; BF3, UTM-Easting (m) 532015, UTM-Northing (m) 4323416, Approximate elevation (± 5m) 2285; BF4, UTM-Easting (m) 532166, UTM-Northing (m) 4323576, Approximate elevation (± 5m) 2292; BF5, UTM-Easting (m) 532370, UTM-Northing (m) 4323194, Approximate elevation (± 5m) 2293; BF6, UTM-Easting (m) 532712, UTM-Northing (m) 4323283, Approximate elevation (± 5m) 2300; UTM is Universal Transverse Mercator, Zone 13, NAD83 datum, GRS80 geodetic reference system.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. This dataset is comprised of two different zip files. Zip File 1: The data within this zip file are composed of two wildland fire datasets. (1) A merged dataset consisting of 40 different wildfire and prescribed fire layers. The original 40 layers were all freely obtained from the internet or provided to the authors free of charge with permission to use them. The merged layers were altered to contain a consistent set of attributes including names, IDs, and dates. This raw merged dataset contains all original polygons many of which are duplicates of the same fire. This dataset also contains all the errors, inconsistencies, and other issues that caused some of the data to be excluded from the combined dataset. Care should be used when working with this dataset as individual records may contain errors that can be more easily identified in the combined dataset. (2) A combined wildland fire polygon dataset composed of both wildfires and prescribed fires ranging in years from mid 1800s to the present that was created by merging and dissolving fire information from 40 different original wildfire datasets to create one of the most comprehensive wildfire datasets available. Attributes describing fires that were reported in the various sources are also merged, including fire names, fire codes, fire IDs, fire dates, fire causes. Zip File 2: The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. This dataset is comprised of two different zip files. Zip File 1: The data within this zip file are composed of two wildland fire datasets. (1) A merged dataset consisting of 40 different wildfire and prescribed fire layers. The original 40 layers were all freely obtained from the internet or provided to the authors free of charge with permission to use them. The merged layers were altered to contain a consistent set of attributes including names, IDs, and dates. This raw merged dataset contains all original polygons many of which are duplicates of the same fire. This dataset also contains all the errors, inconsistencies, and other issues that caused some of the data to be excluded from the combined dataset. Care should be used when working with this dataset as individual records may contain errors that can be more easily identified in the combined dataset. (2) A combined wildland fire polygon dataset composed of both wildfires and prescribed fires ranging in years from mid 1800s to the present that was created by merging and dissolving fire information from 40 different original wildfire datasets to create one of the most comprehensive wildfire datasets available. Attributes describing fires that were reported in the various sources are also merged, including fire names, fire codes, fire IDs, fire dates, fire causes. Zip File 2: The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.