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Remote Sensing Data Before and After California Rim and King Forest Fires, 2010-2015

Documentation Revision Date: 2016-08-31

Data Set Version: V1

Summary

This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices. Reflectance measurements for the spectroscopic AVIRIS and MASTER sensors are distributed as multi-band geotiffs for each megafire and acquisition date. Derived operational metric products for each sensor are provided in individual GeoTIFFs. GeoTIFFs produced from LiDAR point data depict first order topographic indices and summary statistics of vertical vegetation structure.

There are a total of 390 GeoTIFF files in this data set (Table 1). Four shapefiles are provided, containing either the fire boundary of the respective fire area or the area plus a 2-km buffer around the fire perimeters. Note that all of the spatial data products provided are clipped to this 2-km buffer area.

Figure 1. NDVI from MASTER surveys of King Fire area on 09/19/2013 and 11/17/2014.

Citation

Stavros, E.N., Z. Tane, V. Kane, S. Veraverbeke, R. McGaughey, J.A. Lutz, C. Ramirez, and D.S. Schimel. 2016. Remote Sensing Data Before and After California Rim and King Forest Fires, 2010-2015. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1288

Table of Contents

  1. Data Set Overview
  2. Data Characteristics
  3. Application and Derivation
  4. Quality Assessment
  5. Data Acquisition, Materials, and Methods
  6. Data Access
  7. References

Data Set Overview

Investigators: Stavros, E.N., Z. Tane, V. Kane, S. Veraverbeke, R. McGaughey, J.A. Lutz, C. Ramirez, and D.S. Schimel.

This data set provides high-resolution surface reflectance, thermal imagery, burn severity metrics, and LiDAR-derived structural measures of forested areas in the Sierra Nevada Mountains, California, USA, collected before and after the August 2013 Rim and September 2014 King mega forest fires. 

Three instruments were used in airborne campaigns before and after each megafire. The instruments include the high spatial (14.8 m) and hyperspectral resolution Airborne Visible Infrared Imager (AVIRIS), high-spatial resolution (35 m) MODIS/ASTER (MASTER) thermal infrared imager, and LiDAR. 

Pre-fire data were paired with post-fire collections to assess pre- and post-fire landscape characteristics and fire severity. Field estimates of fire severity were collected to compare with derived remote sensing indices.

Related Publication: Stavros, E.N., T. Zachary, V. Kane, S. Veraverbeke, R. McGaughey, J. Lutz, C. Ramirez, & D.S. Schimel. 2016. Unprecedented remote sensing data over the King and Rim Megafires in the Sierra Nevada Mountains of California. Ecology. In press.

Data Characteristics

Spatial Coverage

The Rim Fire occurred in the Stanislaus National Forest and Yosemite National Park in the Sierra Nevada Mountains, California, USA, and burned 104,131 ha.

The King Fire occurred in El Dorado County in the Sierra Nevada Mountains, California, USA, and burned 39,545 ha.

Figure 2. Coverage

Figure 2. Representations of the spatial extent of pre- and post-fire airborne campaigns for each instrument over the a) Rim Fire and b) King Fire. Note that all post-fire acquisitions cover the full extent of pre-fire acquisitions plus what is represented for the post-fire acquisition.

Spatial Resolution

Product resolution differs depending upon the data source and processing.

  • AVIRIS = ~14.6 meters
  • MASTER = 35 meters
  • LiDAR = 1, 1.5, or 30 meters (depending upon product derivation)

Temporal Coverage

The data cover the period 2010-07-21 to 2014-11-17.

Temporal Resolution

Fifteen aerial surveys were conducted before and after the August 2013 Rim Fire and Spetember 2014 King Fire.

Fire

Sensor

Before or After Fire

Aerial Survey Dates

Rim

AVIRIS

Before

2013-06-26

 

 

After

2013-11-17

 

 

After

2014-06-03

 

MASTER

Before

2013-06-26

 

 

After

2013-11-17

 

 

After

2014-06-03

 

LiDAR

Before

2010-07-21

 

 

After

2013-11-15

King

AVIRIS

Before

2013-09-19

 

 

After

2014-11-17

 

MASTER

Before

2013-09-19

 

 

Mid-fire

2014-09-19

 

 

After

2014-11-17

 

LiDAR

Before

2013-03-20

 

 

After

2015-01-15

Study area: (all latitue and longitude given in decimal degrees; datum NAD 1983)

Site

Westernmost Longitude

Easternmost Longitude

Northernmost Latitude

Southernmost Latitude

Rim Fire

-120.1570

-119.6191

38.1016

37.6964

King Fire

-120.7268

-120.3529

39.1621

38.7390

 

Data File Information

Data for each combination of fire, sensor, and survey date are available in two-dimensional GeoTIFF format with geographic information embedded.

  • Level 2 (L2) reflectance data from the spectroscopic instruments are stored as multi-band GeoTIFFs. Each L2 AVIRIS GeoTIFF contains 200 bands and each L2 MASTER GeoTIFF contains 34 bands. 
  • L2 data products derived from the three-dimensional LiDAR point clouds are stored as single-band GeoTIFFs.
  • Level 3 (L3) data products, operational metrics produced from the L2 spectroscopic datum, are stored in individual single-band GeoTIFFs. 

GeoTIFF Projection Parameters

NAD83_UTM_zone_10N
Authority: EPSG

Projection: Transverse_Mercator
false_easting: 500000.0
false_northing: 0.0
central_meridian: -123.0
scale_factor: 0.9996
latitude_of_origin: 0.0
Linear Unit: Meter (1.0)

Geographic Coordinate System: GCS_North_American_1983
Angular Unit: Degree (0.0174532925199433)
Prime Meridian: Greenwich (0.0)
Datum: D_North_American_1983
  Spheroid: GRS_1980
    Semimajor Axis: 6378137.0
    Semiminor Axis: 6356752.314140356
    Inverse Flattening: 298.257222101

Perimeter boundaries of the respective fire study areas are provided as shapefiles.

  • Four shapefiles are provided, containing either the fire boundary of the respective fire area or the fire area plus a 2-km buffer around the fire perimeters.
  • Note that all of the spatial data products provided are clipped to this 2-km buffer area.

Table 1. Fifteen aerial surveys were conducted before and after the August 2013 Rim and Spetember 2014 King Fires and data were processed into 390 product files.

Fire

Sensor

Survey Dates

Level 2 File Count

Level 3 File Count

Rim

AVIRIS

2013-06-26

1

28

 

 

2013-11-17

1

31

 

 

2014-06-03

1

31

 

MASTER

2013-06-26

1

21

 

 

2013-11-17

1

24

 

 

2014-06-03

1

24

 

LiDAR

2010-07-21

23

-

 

 

2013-11-15

23

-

King

AVIRIS

2013-09-19

1

28

 

 

2014-11-17

1

31

 

MASTER

2013-09-19

1

21

 

 

2014-09-19

1

24

 

 

2014-11-17

1

24

 

LiDAR

2013-03-20

23

-

 

 

2015-01-15

23

-

 

 

Total files

103

287

 

AVIRIS and MASTER file names are as follows:

FireName_Instrument_YYYYMMDD_L#v#-BandName.tif

Where:
•    FireName is either RimFire or KingFire; 
•    Instrument may be either AVIRIS or MASTER;
•    YYYYMMDD is the date of acquisition; 
•    L# is the data product as either Level 2 (L2) or Level 3 (L3); 
•    v# is the version number; and, 
•    BandName distinguishes the spectral band, operational metric, or structural summary statistic. 

User notes:
Level 2 spectral band data from the AVIRIS sensor (200 bands) have been combined into one GeoTIFF file. Bands 105-114 and 155-168 were excluded.

Level 2 spectral band data from the MASTER sensor, including bands 1 - 25, 28 - 30, 43, 44, 47 - 49, and an LST band have been combined into one GeoTIFF file.

 

Table 2. AVIRIS L2 Bands

NOTE: For the latest calibration files for L0-L2 data please see the AVIRIS site directly.

Band Numbers Bandwidth (µm) Spectral Range (µm)
1-32 0.0094 0.41 - 0.70
33-97 0.0094 0.68 - 1.27
98-162 0.0097 1.25 - 1.86
163-224 0.0097 1.84 - 2.45

 

 

Table 3. MASTER L2 Bands

NOTE: For the most recent calibration information please visit the MASTER website directly.

Band Number Band Center (µm) Bandwidth (µm) Spectral Range
1 0.46 0.04 0.440-0.480
2 0.5 0.04 0.480-0.520
3 0.54 0.04 0.520-0.560
4 0.58 0.04 0.560-0.600
5 0.66 0.06 0.630-0.690
6 0.71 0.04 0.690-0.730
7 0.75 0.04 0.730-0.770
8 0.8 0.04 0.780-0.820
9 0.865 0.04 0.845-0.885
10 0.905 0.04 0.885-0.925
11 0.945 0.04 0.925-0.965
12 1.625 0.05 1.600-1.650
13 1.675 0.05 1.650-1.700
14 1.725 0.05 1.700-1.750
15 1.775 0.05 1.750-1.800
16 1.825 0.05 1.800-1.850
17 1.875 0.05 1.850-1.900
18 1.925 0.05 1.900-1.950
19 1.975 0.05 1.950-2.000
20 2.075 0.05 2.050-2.100
21 2.16 0.05 2.135-2.185
22 2.21 0.05 2.185-2.235
23 2.26 0.05 2.235-2.285
24 2.3295 0.065 2.297-2.362
25 2.3945 0.065 2.362-2.427
28 3.45 0.15 3.375-3.525
29 3.6 0.15 3.525-3.675
30 3.75 0.15 3.675-3.825
43 8.7 0.4 8.50-8.90
44 9.1 0.4 8.90-9.30
47 10.625 0.65 10.30-10.95
48 11.3 0.7 10.95-11.65
49 12.05 0.5 11.80-12.30

 

 

Table 4. Level 3 products and BandNames for AVIRIS and MASTER sources as used in the respective GeoTIFF file names provided with this data set. Source products are listed side-by-side to show Level 3 products in common.

 

Product

AVIRIS Level 3 Product

Band Name

MASTER Level 3 Product

Band Name

1

Anthocyanin Reflectance Index 1

ARI1

 

 

2

Anthocyanin Reflectance Index 2

ARI2

 

 

3

Atmospherically Resistant Vegetation Index

ARVI

 

 

4

 

 

Burned Area Index

BAI

5

Carotenoid Reflectance Index 1

CRI1

 

 

6

Carotenoid Reflectance Index 2

CRI2

 

 

7

Char Soil Index

CSI

Char Soil Index

CSI

8

 

 

Char Soil Index Thermal

CSIT

9

differenced Normalized Burn Ratio

dNBR

differenced Normalized Burn Ratio

dNBR

10

Enhanced Vegetation Index

EVI

Enhanced Vegetation Index

EVI

11

 

 

Global Environment Monitoring Index

GEMI

12

 

 

Global Environment Monitoring Index 3

GEMI3

13

 

 

Mid Infrared Burn Index

MIRBI

14

Modified Carotenoid Reflectance Index Green Model

mCRIG

 

 

15

Modified Carotenoid Reflectance Index Red Edge Model

mCRIEE

 

 

16

Modified Chlorophyll Absorption Ratio Index

mCARI

 

 

17

Modified Red Edge Normalized Difference Vegetation Index

mNDVI705

 

 

18

Modified Red Edge Simple Ratio Index

mSR705

 

 

19

Modified Soil Adjusted Vegetation Index

mSAVI

Modified Soil Adjusted Vegetation Index

MSAVI

20

Moisture Stress Index

MSI

 

 

21

 

 

Near-Shortwave Infrared Emissivity version 1

NSEv1

22

 

 

Near-Shortwave Infrared Emissivity version 2

NSEv2

23

 

 

Near-Shortwave Infrared Temperature version 1

NSTv1

24

 

 

Near-Shortwave Infrared Temperature version 2

NSTv2

25

Normalized Burn Ratio

NBR

Normalized Burn Ratio

NBR

26

 

 

Normalized Burn Ratio Thermal

NBRT

27

Normalized Difference Infrared Index

NDII

 

 

28

Normalized Difference Lignin Index

NDLI

 

 

29

Normalized Difference Nitrogen Index

NDNI

 

 

30

Normalized Difference Vegetation Index

NDVI

Normalized Difference Vegetation Index

NDVI

31

 

 

Normalized Difference Vegetation Index Thermal

NDVIT

32

Photochemical Reflectance Index

PRI

 

 

33

Plant Senescence Reflectance Index

PSRI

 

 

34

Red Edge Normalized Difference Vegetation Index

NDVI705

 

 

35

Red Edge Position Index

REPI

 

 

36

Red Green Ration Index

RGRI

 

 

37

Relative Burn Ratio

RBR

Relative Burn Ratio

RBR

38

Relative difference Normalized Burn Ratio

RdNBR

Relative difference Normalized Burn Ratio

RdNBR

39

 

 

Shortwave Middle Infrared Index

SMI

40

Simple Ratio Index

SR

 

 

41

 

 

Soil Adjusted Vegetation Index

SAVI

42

 

 

Soil Adjusted Vegetation Index Thermal

SAVIT

43

Structure Insensitive Pigment Index

SIPI

 

 

44

 

 

Vegetation Index 3

VI3

45

 

 

Vegetation Index 6 Thermal

VI6T

46

Vogelmann Red Edge Index 1

VOG1

 

 

47

Water Band Index

WBI

 

 

LiDAR file names are as follows:

FireName_Instrument_YYYYMMDD_L#v#-BandName.tif

Where:
•    FireName is either RimFire or KingFire; 
•    Instrument is LiDAR;
•    YYYYMMDD is the date of acquisition; 
•    L# is the data product as either Level 2 (L2) or Level 3 (L3); 
•    v# is the version number; and, 
•    BandName distinguishes the spectral band, operational metric, or structural summary statistic. 

For LiDAR metrics, there are 1, 1.5, and 30-m resolution products. The resolution is part of the BandName. 30-m resolution GeoTIFFs span the full extent of coverage.  


Table 5. Level 2 products and BandNames for the LiDAR source as used in the respective GeoTIFF files names provided with this data set.

Product

LIDAR Level 2 Product

Band Name

1

Canopy height model

Canopy_height_model_1meter

2

Intensity

Intensity_1p5meter

3

Count of all returns - 30 meter

Count_all_returns_30meter

4

Count of first returns - 30 meter

Count_first_returns_30meter

5

Canopy cover >2 m from first returns

Cover_first_returns_above_2m_30meter

6

Canopy cover >2 m from all returns

Cover_all_returns_above_2m_30meter

7

Canopy cover 2-8 m from all returns

Cover_all_returns_above_2to8m_30meter

8

Canopy cover 8-16 m from all returns

Cover_all_returns_above_8to16m_30meter

9

Canopy cover 16-32 m from all returns

Cover_all_returns_above_16to32m_30meter

10

Canopy cover >32 m from all returns

Cover_all_returns_above_32m_30meter

11

Mean height for all returns above 2 m

Height_mean_all_returns_above_2m_30meter

12

Height of 25th percentile for returns above 2 m

Height_P25_all_returns_above_2m_30meter

13

Height of 50th percentile for returns above 2 m

Height_P50_all_returns_above_2m_30meter

14

Height of 75th percentile for returns above 2 m

Height_P75_all_returns_above_2m_30meter

15

Height of 95th percentile for returns above 2 m

Height_P95_all_returns_above_2m_30meter

16

Standard deviation of all height returns above 2 m

Height_stdev_all_returns_above_2m_30meter

17

Rumple – rugosity of canopy surface

Rumple_30meter

18

Aspect

Topography_aspect_30meter

19

Elevation

Topography_elevation_30meter

20

Slope

Topography_slope_30meter

21

SRI – topographic Solar Radiation Index

Topography_sri_30meter

22

Intensity 95%

Intensity_95percent_30meter

23

Intensity mean

Intensity_mean_30meter

24

Canopy cover >2 m from first returns

Cover_1st_returns_above_2m_30meter (RimFire files only.)

Table 6. Rim Fire GeoTIFF file spatial data properties.

 

GeoTIFFs grouped by Sensor and Product Level

Spatial Data Properties

AVIRIS L2

AVIRIS L3

LIDAR 1-meter*

LIDAR 30-meter

MASTER L2

MASTER L3

Upper

38.0971

38.0971

37.9832

37.9362

38.1016

38.1016

Lower

37.697

37.697

37.6571

37.6591

37.6966

37.6966

Left

-120.02

-120.02

-119.92

-119.86

-120.16

-120.16

Right

-119.62

-119.62

-119.57

-119.63

-119.62

-119.62

Columns

2282

2282

30181

669

1311

1311

Rows

2926

2926

35191

1003

1242

1242

Cell Size

14.8

14.8

1

30

35

35

Bands

224

1

1

1

34

1

NoData Value

-1.7e+308

-9999

-9999

-9999

-1.7e+308

-9999

Compression Type

LZW

LZW

LZW

LZW

LZW

LZW

*Note: canopy height model intensity GeoTIFFs are 1-meter resolution. King Fire intensity is 1.5-meter.

Table 7. King Fire GeoTIFF file spatial data properties.

 

GeoTIFFs grouped by Sensor and Product Level

Spatial Data Properties

AVIRIS L2

AVIRIS L3

LIDAR 1-meter

LIDAR 1.5-meter

LIDAR 30-meter

MASTER L2

MASTER L3

Upper

39.1763

39.1763

39.2192

39.219

39.2191

39.1621

39.1621

Lower

38.6536

38.6536

38.8968

38.8984

38.897

38.7392

38.7392

Left

-120.81

-120.81

-120.68

-120.68

-120.68

-120.73

-120.73

Right

-120.16

-120.16

-120.38

-120.44

-120.38

-120.35

-120.35

Columns

3792

3792

25101

13360

836

892

892

Rows

3872

3872

35121

23380

1170

1318

1318

Cell Size

14.6

14.6

1

1.5

30

35

35

Bands

200

1

1

1

1

34

1

NoData Value

-1.7e+308

-9999

-9999

-9999

-9999

-1.7e+308

-9999

Compression Type

LZW

LZW

LZW

LZW

LZW

LZW

LZW

 

Shapefiles

Four shapefiles are provided, containing either (1) the perimeter boundaries of the respective fire areas or (2) the fire areas plus a 2-km buffer around the fire perimeters. There are no additional attributes provided.

Note that all of the spatial data products provided are clipped to this 2-km buffer area.

Fire perimeters:

  • KingFirePerimeter.shp (KingFirePerimeter.zip)
  • RimFirePerimeter.shp (RimFirePerimeter.zip)

Fire perimeters plus 2-km buffer:

  • KingFireBuffer.shp (KingFireBuffer.zip)
  • RimFireBuffer.shp (RimFireBuffer.zip)

 

Application and Derivation

Megafire frequency is predicted to increase in the future, necessitating a greater understanding of pre- and post-fire landscape characteristics. This data set provides unique before and after conditions that when combined with other data can be used to investigate megafire drivers and relationship to forest management practices with respect to forest structure, type, biomass, and condition, at unprecedented spatial resolution.

This data set includes high-resolution GeoTIFF products containing useful metrics for severity analyses of the August 2013 Rim and September 2014 King megafires in the Sierra Nevada Mountains of California.

Quality Assessment

Both AVIRIS and MASTER products have been validated in distinct studies as part of the pre-Hyperspectral Infrared Imager airborne campaign. The airborne campaign provides the basis for radiance measurement calibration and algorithm refinement for deriving level 2 reflectance, emissivity, and land surface temperature from radiance measurements. Level 2 products have been validated in other studies (Green et al., 1998; Hook et al., 2001).

To demonstrate the full utility of the indices derived from the level 2 products, in situ fire severity estimates represented by the Geo Composite Burn Index (GeoCBI) were gathered for the two fires (Van Wagtendonk et al., 2004). Each plot contained the full range of variation in fire severity found within the mixed conifer fuel type of the Sierra Nevadas. The plots were at least 200 m apart and chosen in relatively homogeneous areas of fuel type and fire severity. 

Field sites were classified by GeoCBI fire severity classification to investigate differences in the spectral signatures (Figure 3). Field metrics were regressed against the mean index value of the pixels containing each field plot. The results are consistent with those derived from other broadband spectroscopic sensors (e.g., Landsat and MODIS) in that they indicate a drop in the near-infrared (NIR, 0.85-0.88 μm) as vegetation is removed and an increase in shortwave infrared (SWIR, 2.1-2.3 μm) as char and soil exposure remains.

Figure 3. Severity classes

Figure 3. In situ fire severity classifications according to the Geo-Composite Burn Index (GeoCBI).

Level 2 LiDAR products were created using the well-established FUSION software (McGaughey, 2014), thus other studies are relied upon to demonstrate their utility and accuracy.

Data Acquisition, Materials, and Methods

There were three instruments used in the airborne campaigns imaging before and after each megafire: AVIRIS, MASTER, and LiDAR. Details of the data collection and processing are available in the related publication Stavros et al., 2016.

Figure 4. Data process

Figure 4. Processing stream for each airborne instrument: AVIRIS, MASTER, and LiDAR

AVIRIS Products:

AVIRIS is a spectroscopic imager that continually samples the visual to shortwave infrared wavelengths with high spectral resolution. AVIRIS collects 224 contiguous 0.01 μm channels spanning from 0.35 μm to 2.5 μm with a 14.8 m spatial resolution.

•    For the Rim Fire, the pre-fire AVIRIS data were acquired on June 26, 2013 covering approximately 64% of the fire area and the post-fire were acquired on June 3, 2014. 
•    The swath width of AVIRIS is narrower than from MASTER, consequently less of the Rim Fire area was imaged before the fire. 
•    For the King Fire, AVIRIS data cover 100% of the fire with a 2 km buffer both before and after the fire. 

Level 2, georectified and atmospherically-corrected surface reflectance and calculated Level 3 operational metrics are provided as rasters for the fire areas with a 2 km buffer. Products were derived by processing and mosaicking the Level 2 and Level 3 products as follows: 

•    For each flightline, the Atmosphere Removal Algorithm (ATREM) was used to convert radiance measurements to surface reflectance. Images were then manually georeferenced using 1 m digital orthophoto quarter quadrangle images, resampled to the same resolution as AVIRIS. 
•    Before mosaicking, flightlines were normalized to each other to reduce differences in the bidirectional reflectance distribution function, a step necessary to minimize the alternating North-South flight path for each line. The normalization used a modified version of the Canty normalization algorithm. The modified approach considers normalization by band rather than considering all 224 bands together. The advantage of implementing the modified approach was to reduce errors in the normalizations. 
•    Lastly flightlines were then mosaicked together to provide rasters of Level 2 surface reflectance (excluding high water absorption bands 105 to 114 and 155 to 168) and Level 3 operational metrics. 

Operational metrics were selected that are most widely accepted and correlated with physiological phenomenon over a broad set of species. 
•    The bands used in the calculation for each metric (Table 2) were selected automatically from ENVI v5.0, which uses the scientific literature to define the optimal wavelengths for metric calculation. 
•    Differenced metrics (e.g., differenced Normalized Burn Ratio – dNBR) were determined based on the difference of the acquisition for which the metric is derived (e.g., November 17) and June 26, 2013 acquisition for the Rim Fire or the September 9, 2013 acquisition for the King Fire.

MASTER Products:

MASTER is a multi-spectral imager operating in the visible-shortwave infrared and thermal infrared (TIR) regions from 0.4 -13 um collected across 50 channels and allowing calculation of land surface temperature and emissivity from the multi-band thermal data. 
•    The airborne MASTER data extends over approximately 96% of the Rim Fire perimeter and approximately 92% of the perimeter with a 2 km buffer in the months preceding ignition (June 26, 2013) and to full extent of this boundary after the fire. 
•    For the King Fire, MASTER data cover 100% of the fire with a 2 km buffer in single flightline. 

Data were downloaded as geolocated, calibrated radiances (Level 1B) for the visual (VIR), shortwave (SWIR), and middle infrared (MIR) regions and as land surface temperature (LST) and emissivity in the TIR (Level 2). 
•    The atmospheric radiative model MODTRAN was used to process Level 1B radiance to surface reflectance in the 25 bands from VIR to SWIR. 
•    Because the MIR is less sensitive to aerosols, the same methodology was employed as that proposed by Kaufman and Remer, to convert from radiance to reflectance. Both VISWIR and MIR were topographically corrected by implementing the modified c-correction method using the cosine of incidence. 
•    Lastly, in the TIR, we download Level 2 data directly, which uses a land surface temperature and emissivity separation algorithm. 

Using the topographically, atmospherically-corrected, and georectified Level 2 data, indices were calculated for each acquisition date. These indices were calculated assuming optimal bands at distinguishing all char, green vegetation, substrate, and non-photosynthetic vegetation. 
•    Indices were determined based on literature and are provided as Level 3 data products. 
•    Differenced metrics were determined based on the difference of the acquisition for which the metric is derived and June 26, 2013 acquisition for the Rim Fire or the September 9, 2013 acquisition for the King Fire.

LiDAR Products:

LiDAR data can be used to develop high-resolution ground models and to study vegetation structure based on the height of returns from foliage and the intensity of returns. 

Rim Fire
Two sets of airborne LiDAR data were collected over areas burned by the Rim Fire. 
•    The 2010 LiDAR acquisition (10,895 ha) was collected to enable studies of the interaction of fire with forests for the mixed-severity fires that had previously burned the area, approximately half of this area was subsequently burned by the Rim Fire. 
•    The 2013 post-fire LiDAR acquisition includes the entire area of the Rim Fire, the 2010 LiDAR acquisition, and a 2 km buffer around the area of the Rim Fire. 
•    The 2010 and 2013 LiDAR acquisitions were acquired using commercial, small footprint, discrete return instruments. 
•    One-meter resolution digital terrain models (DTM) were produced from each acquisition by its vendor. 

King Fire
Two sets of airborne LiDAR data were collected over areas burned by the King Fire.
•    In 2012, the King Fire LiDAR data covers 34% of the eventual fire area. 
•    In 2015, coverage was 100% with a 2 km buffer after the fire. 
•    The 2012 and 2015 LiDAR acquisitions were acquired using commercial, small-footprint, discrete return instruments. 
•    One-meter resolution digital terrain models (DTM) were produced from each acquisition by its vendor.

Processing:
THE USFS FUSION software package, version 3 (McGaughey, 2014) was used to produce raster data files of metrics from each vendor's LiDAR return data. The metrics from the two acquisitions were processed so that their grid arrays align. Metrics available are listed in Table 8. All canopy metrics are normalized to the height above ground.

LiDAR data layers available include:
•    Return height metrics, calculated for returns >2 m in height to separate measurements of the tree canopy from ground and shrub returns. 
•    Canopy cover metrics were calculated as the proportion of returns within a height stratum, such as 2 to 8 m, divided by all returns in that stratum and below. 
•    The canopy surface models were calculated using the height of the highest return for each 1-1.5 m grid cell. The intensity image was produced using the maximum intensity for each 1-1.5 m grid cell. The topographic metrics were calculated from the 1 m digital terrain model.

Data Access

These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Remote Sensing Data Before and After California Rim and King Forest Fires, 2010-2015

Contact for Data Center Access Information:

References

Green, R. O., M.L. Eastwood, C.M. Sarture, T.G. Chrien, M. Aronsson, B.J. Chippendale, J.A. Faust, B.E. Pavri, C.J. Chovit, M. Solies, M.R. Olah, & O. Williams. 1998. Imaging spectroscopy and the Airborne Visisble/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 65, 225-248.

Hook, S.J., Myers, J.J.J., Thome, K.J., Fitzgerald, M. & Kayle, A. B. 2001. The MODIS/ASTER airborne simulator (MASTER) -- a new instrument for earth science studies. Remote Sens. Environ. 76, 93-102.

McGaughey, R.J. 2014. FUSION/LDV: Software for LiDAR data analysis and visualization.

Stavros, E.N., T. Zachary, V. Kane, S. Veraverbeke, R. McGaughey, J. Lutz, C. Ramirez, & D.S. Schimel. 2016. Unprecedented remote sensing data over the King and Rim Megafires in the Sierra Nevada Mountains of California. Ecology. In press.

Van Wagtendonk, J.W., Root, R.R. & Key, C.H. 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sens. Environ. 92, 397-408.