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Investigation:

CD-37 (Lefsky / ??)

LBA Dataset ID:

CD37_BIOMASS_LANDSAT_GLAS

Originator(s):

1. HELMER, E.H.
2. LEFSKY, M.A.
      3. ROBERTS, D.A.

Point(s) of Contact:

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

Dataset Abstract:

We estimated the age of humid lowland tropical forests in Rondonia, Brazil, from a somewhat densely spaced time series of Landsat images (1975 to 2003) with an automated procedure, the Threshold Age Mapping Algorithm (TAMA), first described here. We then estimated a landscape-level rate of aboveground woody biomass accumulation of secondary forest by combining forest age mapping with biomass estimates from the Geoscience Laser Altimeter System (GLAS). Though highly variable, the estimated average biomass accumulation rate of 8.4 Mg ha-1 yr-1 agrees well with ground-based studies for young secondary forests in the region. In isolating the lowland forests, we mapped land cover and general types of old-growth forests with decision tree classification of Landsat imagery and elevation data. We then estimated aboveground live biomass for seven classes of old-growth forest. [Helmer et al. 2009]

Beginning Date:

1975-06-19

Ending Date:

2003-05-20

Metadata Last Updated on:

2013-02-13

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-37 Secondary Forest Biomass and Age Class, Rondonia, Brazil:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1145

Documentation/Other Supporting Documents:

LBA-ECO CD-37 Secondary Forest Biomass and Age Class, Rondonia, Brazil:  http://daac.ornl.gov/LBA/guides/CD37_Biomass_Landsat_GLAS.html

Citation Information - Other Details:

Helmer, Eileen H., Michael A. Lefsky, and Dar A. Roberts. 2013. LBA-ECO CD-37 Secondary Forest and Age Class, Rondonia, Brazil. Data set. Available on-line [http://daac.ornl.gov ] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1145

Keywords - Theme:

Parameter Topic Term Source Sensor
BIOMASS BIOSPHERE TERRESTRIAL ECOSYSTEMS ICESAT GLAS (GEOSCIENCE LASER ALTIMETER SYSTEM)
BIOMASS BIOSPHERE TERRESTRIAL ECOSYSTEMS LANDSAT LANDSAT TM
BIOMASS BIOSPHERE TERRESTRIAL ECOSYSTEMS LANDSAT LANDSAT MSS
FORESTS BIOSPHERE TERRESTRIAL ECOSYSTEMS COMPUTER MODEL ANALYSIS

Uncontrolled Theme Keyword(s):  FOREST AGE, GEOSCIENCE LASER ALTIMETER SYSTEM (GLAS), THRESHOLD AGE MAPPING ALGORITHM (TAMA), TROPICAL FOREST

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  RONDONIA -9.34190 -10.77690 -62.89260 -64.38890

Related Publication(s):

Helmer, E.H., M.A. Lefsky, and D.A. Roberts. 2009. Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. Journal of Applied Remote Sensing, Vol.3 033505.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

Data are available in three comma-delimited ASCII files (.csv) and five raster image files (.img)

File #1: GLAS_Secondary_Forest.csv

File #2: TAMA_Model_Checkpoints.csv

File #3: TAMA_Thresholds.csv

File #4: automask_wb181_217.img

File #5: land_cover_forest_formation.img

File #6: sfage_all_auto1_filter_ar01msk_undisteq30.img

File #7: ar_midptage_auto_5x5circle.img

File #8: sfonly_midpointage_c23_auto.img



File contents and organization



ASCII files:



File name: GLAS_Secondary_Forest.csv

Column Heading Units/format Description

1 New_obs Internal observation number

2 Obs Internal observation number

3 Elev_DEM m.a.s.l Elevation in meters above sea level from the SRTM digital elevation model

4 Time UTC Time of GLAS waveform collection

5 Latitude degrees Latitude for GLAS waveform location. Negative values indicate S and positive values N

6 Longitude degrees Longitude for GLAS waveform location. Negative values indicate W and positive values E

7 X_UTM Sample coordinates in UTM

8 Y_UTM Sample coordinates in UTM

9 Elev_GLAS m.a.s.l Ground elevation as estimated from GLAS waveform

10 Period GLAS collection period

11 Age_2003 years Age of forest in 2003 reported in years

12 Height m.a.s.l Forest height in meters

13 Height_ver m.a.s.l Forest height in meters calculated from GLAS waveform using Lefsky's processing algorithm

14 Biomass Mg per ha Biomass in megagrams per hectare (Mg per ha) calculated from forest height

15 Pixels number of pixels of secondary forest in surrounding 5x5 window



Example data records:

New_obs,Obs, Elev_DEM,Time,Latitude,Longitude,X_UTM,Y_UTM,Elev_GLAS,Period,Age_2003,Height,Height_GLAS,Biomass,Pixels

2649,35948,160.03,121950602.5,-9.642592,-63.07054,492315.174,8934143.378,168.809,L2A,0.458,12.729,0.2,121.4,25

6707,101748,159.11,153142560.3,-9.46336,-63.411322,454902.903,8953933.546,151.054,L3A,1.417,41.364,0.2,414.4,25

6835,101903,139.4,153142564.2,-9.704703,-63.444692,451274.241,8927245.183,140.554,L3A,1.417,6.329,0.2,55.9,25

11588,180753,0,185542976,-9.668608,-63.071218,492241.388,8931266.996,143.067,L3D,2.5,0,0.2,-8.8,25

15410,233786,145.85,204094343.6,-9.692295,-63.328121,464060.229,8928631.59,146.179,L3F,3.083,2.705,0.2,18.8,25

18274,270155,152.95,227779315.9,-9.727236,-62.915366,509337.494,8924784.646,157.228,L3H,3.833,2.877,0.2,20.6,25

28907,527931,182.84,162202587.2,-10.178309,-63.769567,415757.187,8874813.644,183.58,L3B,1.833,4.381,0.2,36,25

6836,101904,138.03,153142564.2,-9.706257,-63.444911,451250.442,8927073.334,139.64,L3A,1.417,10.105,0.2,94.6,24



File name: TAMA_Model_Checkpoints.csv

Column Heading Units/format Description

1 X_UTM Sample coordinates in UTM

2 Y_UTM Sample coordinates in UTM

3 Period GLAS sampling period

4 Age_2003 years Age of forest in 2003 reported in years

5 Height meters Forest height reported in meters

6 Biomass Mg Biomass of forest calculated from estimated height

7 Age_biomass years Age of forest based on calculated biomass

8 Age_total years Total age of forest calculated by adding column 4 and column 8

9 Notes Notes on forest dynamics from images



Missing values represented as -9999



Example data records:

X_UTM,Y_UTM,Period,Age_2003,Height,Biomass,Age_biomass,Age_total,Notes

492315.174,8934143.378,L2A,0.458,12.7,121.4,11.238,11.696,-9999

454902.903,8953933.546,L3A,1.417,41.4,414.37,3.786,5.202,logged only; not cleared

451274.241,8927245.183,L3A,1.417,6.3,55.92,9.143,10.56,-9999

492241.388,8931266.996,L3D,2.5,0,-8.84,5.762,8.262,cleared after 2003 (based on 2005 image)

464060.229,8928631.59,L3F,3.083,2.7,18.84,4.405,7.488,-9999

509337.494,8924784.646,L3H,3.833,2.9,20.6,9,12.833,cleared after 2003 (based on 2005 image)

415757.187,8874813.644,L3B,1.833,4.4,35.99,1.143,2.976,-9999

451250.442,8927073.334,L3A,1.417,10.1,94.55,10.5,11.917,-9999

450754.584,8923454.324,L3A,1.417,18,174.83,14.595,16.012,-9999

488505.731,8959698.844,L3C,2.083,12.9,122.77,4,6.083,-9999

457142.487,8930535.823,L3E,2.833,11.1,104.24,8.25,11.083,-9999

505024.014,8906252.339,L3I,4.417,13.9,133.76,11.571,15.988,-9999

498427.073,8889735.131,L2A,0.458,7.7,70.19,3.762,4.22,-9999

412204.167,8879790.995,L3C,2.083,5.5,47.12,1.286,3.369,-9999



File name: TAMA_thresholds.csv

Column Heading Description

1 Image_year Image year (yyyy)

2 Wetness_MSS Minimum TM Wetness for forest (rescaled to 8 bits) or maximum value in the MSS red band (band 2)

3 Greenness_NDVI Maximum TM Greenness for old forest (rescaled to 8 bits) or maximum NDVI for MSS (rescaled to 8 bits)



Example data records:

Image_year,Wetness_MSS,Greenness_NDVI

1975,36,174

1984,134,163

1986,168,176

1990,172,126

1992,161,120

1993,168,171

1995,172,128

1998,168,134

2000,190,132

2001,164,150

2003,188,115



Raster files:

All five raster image files are organized in a raster_images directory, with subdirectories forest_age, forest_type, minimum_forest_mask.

For each raster image (*.img) an accompanying text header file (*.img.txt) describes the image contents and format.



forest_age (3 images):

ar_midptage_auto_5x5circle.img

This is the file that was used to extract secondary forest age in 2003 for all GLAS shots in which the 5x5-pixel window surrounding the shot center is at least 92% (23 of 25 pixels) secondary forest. Each pixel is secondary forest age, in years, calculated from sfonly_midpointage_c23_auto.img as the average age of all pixels in a circular 5x5 window surrounding the given pixel.



sfage_all_auto1_filter_ar01msk_undisteq30.img

This is the map of forest age class output from applying the Threshold Age Mapping Algorithm (TAMA). As mentioned in Helmer et al. (2009), the algorithm applies only to lowland forests. Hill and submontane old-growth forests, for example, are misclassified as older secondary forest in this file.



The classes in this file are as follows:



Number Name Age Class

1 nonfor03 Non forest in 2003

2 A01-03 0-2 yr

3 B00-01 2-3 yr

4 E98-00 3-5 yr

5 F95-98 5-8 yr

6 G93-95 8-10 yr

7 H92-93 10-11 yr

8 I90-92 11-13 yr

9 J86-90 13-17 yr

10 K84-86 17-19 yr

11 L75-84 19-28 yr

30 lowl_og_forwetl Lowland old-growth forest and swamp



sfonly_midpointage_c23_auto.img

In this file, each age class from sfage_all_auto1_filter_ar01msk_undisteq30.img has been replaced with the value of the midpoint of its age class, in yr.





forest_type (1 image):

land_cover_forest_formation.img

This is the classification of forest formation and land cover.



Original file name: ar_age_classific_ar01masked_altsubm_manual3_altcolor3.img



Class Class Name

0 Background and unaged SF at scene edges

1 Cleared or logged 2000-2003

2 Secondary forest

3 Cleared or logged 2000-2003

4 Cleared or logged 2000-2003

5 Secondary forest

6 Secondary forest

7 Secondary forest

8 Secondary forest

9 Secondary forest

10 Secondary forest

11 Secondary forest

12 Secondary forest

13 Floodplain forest

14 Floodplain forest and swamp

15 Lowland old growth forest

16 Bare and urban

17 Pasture and agriculture

18 Woody agriculture

19 Hill and submontane savanna

20 Hill and submontane woody savanna and cliff vegetation (shadowed)

21 Hill and submontane woody savanna and cliff vegetation (sunlit)

22 Hill and submontane forest (shadowed), seasonal to semi-deciduous

23 Hill and submontane forest (sunlit), seasonal to semi-deciduous

24 Hill and submontane forest and gallery forest, evergreen to seasonal

25 Sandy swamp, dense

26 Sandy swamp, open

27 Topographic shadow

28 Water



minimum_forest_mask (1 image):

automask_wb181_217.img

This is the minimum forest mask derived from fitting a histogram to the distribution of values for the wetness-brightness difference index (after the WBDI was rescaled to 8 bits).

Data Application and Derivation:

TAMA is simple, fast, and self-calibrating. By not using between-date band or index differences or trends, it requires neither image normalization nor atmospheric correction. In addition, it uses an approach to map forest cover for the self-calibrations that is novel to forest mapping with satellite imagery; it maps humid secondary forest that is difficult to distinguish

from old-growth forest in single-date imagery; it does not assume that forest age equals time since disturbance; and it incorporates Landsat Multispectral Scanner (MSS) imagery. Variations on the work that we present here can be applied to other forested landscapes

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

The map of forest age has an overall accuracy of 88%. The Kappa coefficient of agreement of 0.62 is generally considered good. The forest age mapping

algorithm mapped some of the land cleared land from 2000 to 2003 as secondary forest. Some of this land had dense vegetation or large amounts of slash in the fine resolution imagery. Some of it also was clearly pasture or woody agriculture in later fine resolution imagery from 2005. To assure that the algorithm was accurately aging secondary forest younger than 3 yr that was not recently cleared, we excluded recently cleared land from the error assessment of lowland forest age.



The final map of land cover and major forest types, which resulted from the two

decision tree classifications and the editing, has an overall classification accuracy of 69% and a Kappa coefficient of agreement of 0.56 for the 14 classes, which is also considered good. Although overall accuracy was good, it reflects the fact that the error matrix merges secondary forest with lands cleared in the last three years, because, similar to the age mapping algorithm, about one-third of the land that was cleared in the last three years

was classified as secondary forest. Woody agriculture also showed confusion with secondary forest. Remaining errors included confusion between the following classes: pasture vs. savanna; secondary forest vs. semi-evergreen to deciduous woody savanna to forest; and forested wetlands vs. other classes. Other old-growth forest classes had users and producers accuracies ranging from 52 to 98%.



Currently TAMA exists as an algorithm that was implemented within different software rather than as a self-contained computer program.

Process Description:

Data Acquisition Materials and Methods:

The study area extends over one Landsat scene in the Brazilian state of Rondonia, namely World Reference System 2, Path/Row 232/067 (10.1 deg S, 63.7 deg W). Forests in the study area are tropical and include broadleaf seasonal evergreen, semi-evergreen, and deciduous forests, forested wetlands, savannas, and woody savannas. Elevation in study area ranges from river levels of 70 m above sea level in lowlands in the north to about 1100 m on the Serra dos Pacaas Novos. Annual rainfall ranges from 1900 to 2700 mm per year, with a wet season from November to April and a dry season from June to August (Culf et al 1998, Roberts et al. 2002). Major soil types include Latosols, Podzolic soils, Lithosols and Terras Roxas Estruradas (in U.S. soil taxonomy, Oxisols, Ultisols, Entisols and Alfisols).



Landsat imagery for the study included a previously assembled time series of Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) scenes dated from 1975 through 2000 (Roberts et al 2002). We coregistered additional images to this time series, from 1986, 2001, and 2003, to within < 0.5 pixels root mean square error, with nearest neighbor resampling. The data set (or image stack, or image cube) has a pixel size of 30 meters. The image data underwent no normalization or atmospheric correction. Fine resolution reference data included three pan-sharpened IKONOS images dated from 2002 and Quickbird images, dated from 2002 to 2005, that were viewable on Google Earth (http://earth.google.com).



We first mapped forest age from the 11-image sequence of Landsat images with the

Threshold Age Mapping Algorithm (TAMA). After applying TAMA, we isolated the lowland forests by separately mapping land cover and oldgrowth forest types with two decision tree classifications of the Landsat imagery and topographic data. An added benefit of the resulting land-cover and forest-type map was that

it allowed us to estimate AGLB of the different old-growth forest types with coincident GLAS data. We combined the age map from TAMA with the land-cover and forest-type map by replacing all lands mapped as secondary forest in the land-cover and forest-type map with secondary forest age from the age map. We extracted forest age or type for each GLAS waveform from the combined map. For more details on the TAMA classification process see Helmer et al. 2009.

References:

Culf A.D., G. Fisch, J. Lean, and J. Polcher.1998. A comparison of Amazonian climate data with general circulation model simulations, J. Clim. 11, 2674-2773 [doi:10.1175/1520-0442(1998)011<2764:ACOACD>2.0.CO;2].



Roberts,D.A., I. Numata, K. Holmes, G. Batista, T. Krug, A. Monteiro, B. Powell, and O. A. Chadwick. 2002. Large area mapping of land-cover change in Rondonia using multitemporal spectral mixture analysis and decision tree classifiers, J. Geophys. Res. Atmos. 107, 8073 [doi:10.1029/2001JD000374].

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