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Investigation:

CD-34 (Chambers / Higuchi / J. dos Santos / Camargo)

LBA Dataset ID:

CD34_AMAZON_LANDSAT

Originator(s):

1. NEGRON-JUAREZ, R.I.
2. CHAMBERS, J.Q.
3. GUIMARAES, G.
4. ZENG, H.C.
5. RAUPP, C.F.M.
      6. MARRA, D.M.
7. RIBEIRO, G.H.P.M.
8. SAATCHI, S.S.
9. NELSON, B.W.
10. HIGUCHI, N.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Dataset Abstract:

Climate change is expected to increase the intensity of extreme precipitation events in Amazonia that in turn might produce more forest blowdowns associated with convective storms. Yet quantitative tree mortality associated with convective storms has never been reported across Amazonia, representing an important additional source of carbon to the atmosphere. Here we demonstrate that a single squall line (aligned cluster of convective storm cells) propagating across Amazonia in January, 2005, caused widespread forest tree mortality and may have contributed to the elevated mortality observed that year.

Beginning Date:

2004-10-14

Ending Date:

2005-07-29

Metadata Last Updated on:

2013-08-01

Data Status:

Archived

Access Constraints:

PUBLIC

Data Center URL:

http://daac.ornl.gov/

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Access Instructions:

PUBLIC

Data Access:

IMPORTANT: The LBA-ECO Project website is no longer being supported. Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC. Please follow the fair use guidelines found in the dataset documentation when using or citing LBA data.
Datafile(s):

LBA-ECO CD-34 Landsat Fractional Land Cover Analysis, Manaus, Brazil: 2004-2005:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1176

Documentation/Other Supporting Documents:

LBA-ECO CD-34 Landsat Fractional Land Cover Analysis, Manaus, Brazil: 2004-2005:  http://daac.ornl.gov/LBA/guides/CD34_Amazon_Landsat.html

Citation Information - Other Details:

Negron-Juarez, Robinson I, J.Q. Chambers, G. Guimaraes, H.C. Zeng, C.F.M. Raupp, D.M. Marra, G.H.P.M. Ribeiro, S.S. Saatchi, B.W. Nelson, and N. Higuchi. 2013. LBA-ECO CD-34 Landsat Fractional Land Cover Analysis, Manaus, Brazil: 2004-2005. Data set. Available on-line [http://daac.ornl.gov ] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1176

Keywords - Theme:

Parameter Topic Term Source Sensor
BIOMASS SPECTRAL/ENGINEERING LAND USE/LAND COVER LANDSAT-5 (LAND REMOTE-SENSING SATELLITE-5) ANALYSIS
DEFORESTATION SPECTRAL/ENGINEERING LAND USE/LAND COVER LANDSAT-7 ANALYSIS

Uncontrolled Theme Keyword(s):  BLOWDOWN, FOREST DISTURBANCE, MANAUS, MONTE CARLO (MC) SIMULATION MODEL, NON-PHOTOSYNTHETIC VEGETATION, NPV, SPECTRAL MIXTURE ANALYSIS, SQUALL LINE EVENT, TREE MORTALITY

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  AMAZONAS (MANAUS) -2.63000 -2.63000 -60.17000 -60.17000

Related Publication(s):

Negron-Juarez, R.I., J.Q. Chambers, G. Guimaraes, H.C. Zeng, C.F.M. Raupp, D.M. Marra, G.H.P.M. Ribeiro, S.S. Saatchi, B.W. Nelson, and N. Higuchi. 2010. Widespread Amazon forest tree mortality from a single cross-basin squall line event. Geophysical Research Letters 37, L16701, doi:10.1029/2010GL043733

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

The following ENVI images (3) comprise this archive:

20041014_L5p231r062_CRLRSZCAL

20050729_L5p231r062_CRLRSZCAL

sma_dNPV_2005-2004

The images in this data set are in ENVI Standard format. An ENVI image consists of an image file and a corresponding ASCII header file (.hdr). The header file contains information about the structure and geolocation of the image.

File Naming

----------------

The files are named with the date, sensor, path/row, and the processing performed. 20050729_L5p231r02_CRLRSZCAL: On the image for July 29, 2005 (20050729) from Landsat 5 (L5) path 231 row 62 (p231r062) the Carlotto haze correction (CRL) was applied, resized with respect to the original image (RSZ ) to have a consistent size of all images, and calibrated (CAL) using invariant targets to get reflectance values. There is also a corresponding ENVI header named 20050729_L5p231r02_CRLRSZCAL.hdr. ENVI header files are ASCII files that describe the structure of the image file.

Data Contents

----------------

Each image matching (20050729_L5p231r02_CRLRSZCAL) is in ENVI format with a corresponding header (20050729_L5p231r02_CRLRSZCAL.hdr). These images have 6 band bands from Landsat: bands 1 ( centered at 0.45-0.52 um), 2 (0.52-0.60 um), 3 (0.63-0.69 u­m), 4 (0.76-0.90 u­m), 5 (1.55-0.75 u­m), and 7 (2.08-2.35 u­m). Landsat band 6 (thermal band 10.4-12.5 u­m) was not used since it has a different pixel size with respect to the others Landsat bands. Each band was labeled with their respective calibration equation. Spectral mixture analysis was applied on these images. sma_dNPV_2005-2004 (*.hdr) contain the difference of NPV on July 29, 2005 and NPV on October 14, 2004.

Data Projection

---------------------

The images are in the UTM projection for the most appropriate UTM zone. The ENVI header file associated with each image contains a map info line that indicates the UTM zone, upper left corner coordinate, and resolution. For example:

map info = {UTM, 1.000, 1.000, 666825.000, 9778885.000, 3.0000000000e+001, 3.0000000000e+001, 20, South, WGS-84, units=Meters}

The file is in UTM, Zone 20 South, in the WGS-84 datum. It has a 30-meter pixel size (3.00e+001) with the upper left corner of the upper left pixel (1,1) at coordinate , 666825.000, 9778885.000

Data Application and Derivation:

The strong relationship observed between Landsat DNPV and field�measured tree mortality enabled us to generate quantitative regional tree mortality

rates across the forested landscape. Squall lines such as the one studied here occur on average 4 times a month in the Amazon Basin and can cause wide-spread tree mortality at spatial scales difficult to capture with plot based field studies.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

Landsat data sensitivity to patches of fallen trees.



To determine the sensitivity of Landsat to patches of fallen trees at the single pixel level, we performed an experiment in 2008 in the National Institute for Amazonian Research (INPA) reserve,north of the city of Manaus. Vegetation in this reserve is predominantly old-growth closed-canopy

upland forest [Chambers et al. 2004]. We randomly selected 13 individual pixels encompassing the full range of variation in the 2008 Landsat dNPV (NPV2008-NPV2007) following the approach described in the main article. In the field each pixel was located using a handheld GPS (60CSx GPS,Garmin Ltd.) and a 30x30m plot was installed at each pixel. The number of trees, both standing and fallen, with diameter at breast height (dbh) greater than 10cm was recorded for each plot. Analysis of the field data from these 13 plots showed that on average, patches of 5-10 fallen trees are the minimum disturbance detectable from Landsat.

Process Description:

Data Acquisition Materials and Methods:

We employed a method similar to Chambers et al. [2007] to investigate forest disturbance and tree mortality produced by this squall line. Landsat images from Brazil\'s National Institute for Space Research (INPE) covering the Manaus area (scene P231 R062, 3.4 by 10^4 Km^2) collected on 10 July 2001 (Landsat 7, L7, for calibration), 14 October 2004 (Landsat 5, L5, previous to disturbance) and 29 July 2005 (Landsat 5, for disturbance evaluation) were used in this work. These images are available in encoded radiance values and must be converted to reflectance values. All images were georeferenced (400 control points per image) with respect to the NASA Geocover data (https://zulu.ssc.nasa.gov/mrsid/). The Carlotto technique [Carlotto, 1999] which accounts for correction due to haze and smoke contamination was applied over the images, as needed. The 2001 L7 image was used as a reference image which was atmospherically corrected and converted to reflectance using the Atmospheric CORrection Now (ACORN) software (ImSpec LLC, Boulder, CO). L5 images were radiometrically calibrated band by band with respect to the L7 image using invariant targets. Spectral mixture analysis (SMA) [Adams et al., 1995] based on scene-derived endmembers of green vegetation (GV; photosynthetically active vegetation), nonphotosynthetic vegetation (NPV; wood, dead vegetation and surface litter), soil, and shade were obtained using a pixel purity index (PPI) algorithm. PPI and SMA are tools available in the Environment for Visualizing Images (ENVI, ITT industries, Inc, Boulder CO, USA) software. As SMA deals with the spectral signature of each pixel (Landsat bands used were 1,2,3,4,5 and 7) this approach also allowed us to separate landuse areas from natural forest disturbances since the first have exposed soil while the second are covered by damaged vegetation [Souza et al., 2005]. A visual quality control attending both the shape and direction of blowdown patches was performed to validate the results.

To quantify landscape-scale forest disturbance associated with this squall line event we combined field measured tree mortality, remote sensing data and modeling. First, SMA was applied to the Landsat images collected on the 14th of October, 2004 and the 29th of July, 2005 to determine per pixel fractional abundance of GV, NPV, soil, and shade. Changes in NPV (delta NPV) provide a quantitative measure of changes in forest structure from tree mortality (increase in tree mortality) and were calculated by subtracting the 2004 NPV image from the 2005 NPV image. Second, over one of the blowdown areas (centered at 2.6 degrees S, 60.3 degrees W) we established five sites each containing six 20m by 20m forest inventory plots (N = 30) randomly distributed across the entire delta NPV disturbance gradient. To estimate tree mortality and biomass loss we use two approaches: (i) Landsat derived delta NPV was combined with an aboveground biomass distribution map [Saatchi et al., 2007], and (ii) a Monte Carlo (MC) simulation model which used a distribution function for stem density and tree biomass generated from permanent forest inventory plots.

In our MC model, the number of trees (Ntrees) affected by the squall line was calculated as Ntrees = rho times Mortality times A, where rho is the stem density per pixel. Mortality was obtained from linear regressions between delta NPV and field measured tree mortality, and A is the pixel area. Biomass loss was calculated as: Biomass loss = The sum from i=1 to Ntrees of Mi, where Ntrees is the total number of dead trees in each pixel, and Mi is the biomass for each tree. Rho and Mi were randomly selected from their respective distribution functions obtained from permanent inventory plots across the Amazon forest.

References:

Adams, J. B., et al. (1995), Classification of multispectral images based on fractions of endmembers: Application to landcover change in the Brazilian Amazon, Remote Sens. Environ., 52, 137-154, doi:10.1016/0034-4257(94)00098-8.



Carlotto, M. J. (1999), Reducing the effects of space-varying, wavelength dependent scattering in multispectral imagery, Int. J. Remote Sens., 20, 3333-3344, doi:10.1080/014311699211363.



Chambers, J. Q., et al. (2004), Respiration from a tropical forest ecosystem: partitioning of sources and low carbon use efficiency. Ecology Applications, 14, Supplement: LBA Experiment, S72-S88, doi: 10.1890/01-6012



Chambers, J. Q., J. I. Fisher, H. Zeng, E. L. Chapman, D. B. Baker, and G. Hurtt (2007), Hurricane Katrina\'s carbon footprint on U.S. Gulf coast forest, Science, 318, 1107, doi:10.1126/science.1148913.



Saatchi, S. S., R. A. Houghton, R. C. Alvala, J. V. Soares, and Y. Yu (2007), Distribution of aboveground live biomass in the Amazon basin, Global Change Biol., 13, 816-837, doi:10.1111/j.1365-2486.2007.01323.x.



Souza, C. M., D. A. Roberts, and M. A. Cochrane (2005), Combining spectral and spatial information to map canopy damage from selective logging and forest fires, Remote Sens. Environ., 98, 329-343, doi:10.1016/j.rse.2005.07.013.

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