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Russian Boreal Forest Disturbance Maps Derived from Landsat Imagery, 1984-2000

Documentation Revision Date: 2015-11-12

Summary

This data set provides Boreal forest disturbance maps at 30-m resolution for 55 selected sites across Northern Eurasia within the Russian Federation. Disturbance events were derived from selected high-quality multi-year time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images (stacks) over the 1984 to 2000 time period. Forest pixels were classified by year of latest disturbance or as undisturbed.

A time-stamped single-layer disturbance map was produced for each of the 55 Landsat stacks. Across the full extent of Russian forests, 15 classes were mapped: 14 disturbed classes represented by the individual years during which the disturbances were observed, and one undisturbed class. Not all 14 classes were mapped in each stack and the number of classes was determined by stack density.

These maps provide crucial information regarding disturbance history for the selected regions across the Russian boreal forest and are designed to serve as a training and/or validation data set for coarse resolution data products. The overall disturbance classification accuracy was assessed to be good. This data set will benefit subsequent studies on a variety of aspects of the Russian boreal forest, especially in relation to the carbon budget and climate.

There are 55 forest disturbance maps provided in GeoTIFF (.tif) format with this data set. 

Disturbance map for a boreal forest site (Landsat Path 135, Row 21). Inset shows fine resolution of classifications. After Chen et al., 2014.

Citation

Chen, D., T.V. Loboda, S. Channan, and A. Hoffman-Hall. 2015. Russian Boreal Forest Disturbance Maps Derived from Landsat Imagery, 1984-2000. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1294

Table of Contents

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

Data Set Overview

This data set provides boreal forest disturbance maps at 30-m resolution across the Russian Federation for the years 1984 to 2000 derived from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery (stacks). MODIS Vegetation Continuous Fields (MOD44B) data for the year 2000 was used to delineate the forest areas to which subsequent processes were applied.

A time-stamped single-layer disturbance map was produced for each of the 55 Landsat stacks. Across the full extent of Russian boreal forests, 15 classes were mapped: 14 disturbed classes represented by the individual years during which the disturbances were observed, and one undisturbed class. Not all 14 classes were mapped in each stack and the number of classes was determined by stack density.

Data Characteristics

Spatial Coverage

The study area was 55 Landsat scenes located across the boreal forest in Northern Eurasia within the Russian Federation.

Spatial Resolution

30-m resolution for forest disturbance classes within each Landsat scene (approximately 170-km north-south by 185-km east-west)

Temporal Coverage

Data are for the periods 1984-2000

Temporal Resolution

Annual during the growing season, June-August

Study Area (All latitude and longitude given in decimal degrees)

Site Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude
The boreal forest in Northern Eurasia within the Russian Federation. 29.35547 139.0429 66.694742 41.75385

 

Data File Information

There are 55 GeoTIFF (.tif) files at 30-m resolution with this data set.

The files are named by the Landsat path and row number. For example, DSM_114021 where 114 is the path and 021 is the row. The DSM_ prefix denotes these are “disturbance sample maps”.

Mapped Disturbance Classes

Across the full extent of Russian boreal forests, 15 classes were mapped: 14 disturbed classes represented by the individual years during which the disturbances were observed, and one undisturbed class. Not all 14 classes were mapped in each stack and the number of classes was determined by stack density.

Table 1. Mapped forest disturbance classes.

Class Value Class Meaning
0 Unclassified
1 Undisturbed forest
85 Forest disturbed in 1985
86 Forest disturbed in 1986
87 Forest disturbed in 1987
88 Forest disturbed in 1988
89 Forest disturbed in 1989
90 Forest disturbed in 1990
91 Forest disturbed in 1991
92 Forest disturbed in 1992
93 Forest disturbed in 1993
94 Forest disturbed in 1994
95 Forest disturbed in 1995
96 Forest disturbed in 1996
97 Forest disturbed in 1997
98 Forest disturbed in 1998
99 Forest disturbed in 1999
100 Forest disturbed in 2000

 

Spatial Data and Spatial Reference Properties

Table 2. GeoTIFF properties common to all data files.

GeoTIFF Property

Common File Value

file_type

raster

file_format

GTiff

crs_proj4

+proj=utm +zone=XX +datum=WGS84 +units=m +no_defs

The content of the proj4 string varies only by UTM Zone (XX).  The UTM Zone for each file is given in the next table.

map_units

meters

res_x

30

res_y

30

n_bands

1

data_type

Int16

value_range_min

0

 

 

Table 3.  GeoTIFF properties unique to each data file.

 

Filename

UTM Zone

min_x

min_y

max_x

max_y

n_cols

n_rows

Value

_range

_max

mapped classes

DSM_114021.tif

53

518775

6100665

721395

6297255

6754

6553

100

2000

DSM_114024.tif

53

388785

5629575

586665

5829165

6596

6653

100

1999, 2000

DSM_115025.tif

53

232755

5471205

452115

5686215

7312

7167

100

1992, 2000

DSM_117020.tif

53

272175

6250215

487395

6457905

7174

6923

100

1999, 2000

DSM_117021.tif

53

220095

6095175

452115

6314715

7734

7318

100

1999, 2000

DSM_117022.tif

52

562155

5948655

779445

6153315

7243

6822

99

1994, 1999

DSM_117023.tif

52

515685

5787945

733875

5995515

7273

6919

100

2000

DSM_121017.tif

52

417825

6710535

637245

6919965

7314

6981

100

2000

DSM_121019.tif

52

317895

6400035

536835

6612735

7298

7090

100

2000

DSM_121022.tif

51

550185

5944515

754185

6143715

6800

6640

100

1990, 2000

DSM_122021.tif

51

492075

6086385

718815

6298725

7558

7078

100

1992, 1999, 2000

DSM_123020.tif

51

439695

6240285

665415

6457095

7524

7227

100

2000

DSM_124017.tif

51

491775

6712665

705375

6922545

7120

6996

99

1999

DSM_124018.tif

51

445425

6560685

668715

6777015

7443

7211

99

1999

DSM_124021.tif

51

292695

6094365

523515

6312615

7694

7275

100

2000

DSM_127018.tif

50

526425

6557805

736215

6765135

6993

6911

100

1998, 2000

DSM_127019.tif

50

478425

6401235

701115

6619815

7423

7286

100

1998, 2000

DSM_130017.tif

50

329355

6707805

540555

6921525

7040

7124

100

2000

DSM_131020.tif

49

414315

6247845

628095

6457935

7126

7003

100

1994, 2000

DSM_133018.tif

49

339525

6555465

554745

6770685

7174

7174

95

1995

DSM_133019.tif

49

283395

6394185

510015

6624315

7554

7671

100

1995, 2000

DSM_135017.tif

48

546135

6707685

772815

6925395

7556

7257

100

2000

DSM_135018.tif

48

502395

6551985

726315

6777615

7464

7521

99

1999

DSM_135019.tif

48

455205

6407265

664305

6607785

6970

6684

100

1994, 2000

DSM_135020.tif

48

402615

6252015

631815

6465315

7640

7110

94

1990, 1992, 1994

DSM_135021.tif

48

360765

6095025

583845

6309315

7436

7143

100

1990, 1992, 1994, 2000

DSM_135022.tif

48

315015

5936355

522705

6146355

6923

7000

100

1990, 2000

DSM_137019.tif

48

278235

6393585

501015

6616395

7426

7427

100

1991, 1995, 1999, 2000

DSM_137020.tif

48

222105

6242085

447315

6468615

7507

7551

100

1991, 1995, 1998, 1999, 2000

DSM_138021.tif

47

448185

6092235

666915

6308715

7291

7216

100

1991, 1992, 1994, 1999, 2000

DSM_140019.tif

47

353775

6402105

564195

6613785

7014

7056

100

1990, 2000

DSM_143017.tif

46

529635

6713775

737505

6924105

6929

7011

100

1992, 2000

DSM_147017.tif

45

519735

6712275

731565

6924105

7061

7061

100

2000

DSM_148019.tif

45

334875

6401595

548595

6615285

7124

7123

99

1990, 1991, 1999

DSM_151015.tif

45

310545

7015785

537315

7234875

7559

7303

99

1999

DSM_152016.tif

44

478215

6866535

691125

7078215

7097

7056

100

2000

DSM_161017.tif

42

323505

6708975

542745

6925695

7308

7224

93

1987, 1988, 1993

DSM_164017.tif

41

400635

6709365

612705

6923805

7069

7148

100

1987, 1989, 1999,  2000

DSM_165018.tif

41

262635

6558915

472815

6772905

7006

7133

99

1988, 1992, 1993, 1999

DSM_165019.tif

40

553815

6406605

769245

6618615

7181

7067

93

1986, 1988, 1993

DSM_165020.tif

40

507195

6251505

726915

6462615

7324

7037

100

1986, 1987, 1988, 1990, 1992, 1993,  2000

DSM_167018.tif

40

423045

6550785

644415

6769035

7379

7275

93

1987, 1988, 1993

DSM_170015.tif

40

336525

7015095

559935

7233405

7447

7277

100

2000

DSM_170017.tif

39

555255

6719595

751965

6922365

6557

6759

88

1988

DSM_170020.tif

39

406035

6247650

615525

6457740

6983

7003

99

1987, 1988, 1989, 1999

DSM_171016.tif

39

512985

6870015

734115

7082715

7371

7090

100

1987, 1988, 1989, 1994, 2000

DSM_171018.tif

39

408825

6556815

621225

6767985

7080

7039

100

1987, 1999, 2000

DSM_173029.tif

37

556665

4845435

741465

5037825

6160

6413

100

1986, 1988, 1989, 1991, 1992, 1998, 1999, 2000

DSM_174017.tif

38

543300

6717750

744060

6921090

6692

6778

89

1987, 1988, 1989

DSM_175015.tif

39

259515

7019175

491115

7241715

7720

7418

100

1987, 1988, 1989, 2000

DSM_176019.tif

38

261885

6401325

475635

6616305

7125

7166

89

1988, 1989

DSM_179015.tif

38

246645

7017225

481515

7243815

7829

7553

100

1988, 2000

DSM_179016.tif

37

501795

6869205

704295

7074045

6750

6828

99

1985, 1986, 1988, 1999

DSM_182020.tif

36

375345

6246765

583185

6457485

6928

7024

95

1988, 1992, 1994, 1995

DSM_183016.tif

36

487590

6863520

697560

7075590

6999

7069

89

1988, 1989

Application and Derivation

These samples provide crucial information regarding disturbance history in selected regions across the Russian boreal forest and are designed to serve as a training and/or validation data set for coarse resolution data products. It can provide insights regarding the timing and locations of fire and logging events that occurred from 1985 to 2000 within the 55 selected Landsat stacks in the Russian boreal forest. Even though it is not a wall-to-wall assessment, this effort represents the first known product with this spatial resolution and geographical span before the year 2000 and could be expanded further using additional stacks. However, the current selection of stacks was considered to be a good representation of the heterogeneity of the Russian boreal forest in terms of both forest composition and disturbance regime.

Although the timing of disturbances are approximated and linked to the dates of available imagery rather than to the actual timing of disturbance, the applications of this data set include:

  • An opportunity to assess variation in temporal changes in disturbance rates at decadal scales across Russia as a whole, and at finer temporal scales in European Russia, where dense stacks of Landsat data are available.
  • The quantification of long-term forest disturbance history within individual Landsat scenes. While we did not provide a differentiation between fire- and logging-driven disturbances, this data set represents the basis for attribution based on extent, shape and possibly additional spectral information in the data.
  • The potential to serve as a data input to studies that require information regarding the disturbance history of the Russian boreal forest.

This data set will benefit subsequent studies on a variety of aspects of the Russian boreal forest, especially in relation to the carbon budget and climate.

Quality Assessment

The results of the disturbance classification algorithm were evaluated through an analyst-driven double-blind validation (Chen et al., 2014). The double-blind method separates the process of mapping and random point generation from analyst-driven assessment by involving a separate set of analysts who have no a-priori knowledge of disturbances in the area and no prior involvement in the processing stream. These analysts are requested to examine the stack of imagery and assign the year of disturbance or undisturbed category to a set of points with no attributive information. No prior information is given to the analyst regarding the number of pixels expected to belong to a particular disturbed or undisturbed category with the varying number of points among image stacks.

The time-stamped sample points were compared with the classification results through the construction of confusion matrices and corresponding statistics including the omission and commission errors, overall accuracy and Kappa coefficient. In addition to the global accuracy assessment (i.e., across the entire Russian boreal forest), the 55 maps were divided into several groups based on either geographical location (i.e., European Russia, Western Siberia and Eastern Siberia) or stack density (i.e., sparse or dense). For each of these groups, the evaluation statistics were also calculated and compared.

The overall accuracy, calculated based on the 55 maps across the entire Russian boreal forest, was 83.98%, along with a Kappa coefficient of 0.83. Overall commission and omission errors of the classification were low. The average omission error for all classes was 11.24%, with the lowest omission error for 1998 disturbances (0.00%). The omission error for the undisturbed class was relatively high at 55.04%. In terms of commission error, the average value was 16.28%, and the range across all classes was relatively narrower than that of the omission error, with the largest and smallest being 30.48% (for 1990 disturbances) and 1.00% (for 1995 disturbances), respectively.

The values of overall accuracy and Kappa coefficient were similar for all the maps across the entire Russian forests as well as across three major geographic regions and between dense and sparse stacks of imagery. The lowest Kappa (0.76) and overall accuracy (79.01%) were found in Western Siberia—a region dominated by wetlands. The highest Kappa (0.85) and overall accuracy (86.69%) were found in Eastern Siberia. A small difference in Kappa and overall accuracy was also registered between sparse and dense stacks of imagery. It appeared that the confusion between specific time-stamps of disturbances was slightly higher in dense stacks, whereas a smaller number of selections in sparse stacks resulted in a clearer identification of the time of disturbance.

The classifier performance was less desirable in the identification of the undisturbed class (Error of Omission: 55.04%). This may have multiple causes. First, because the number of validation points selected for each class was similar (approximately 100), and because the undisturbed class appeared in almost all classified images, the number of validation points selected from the undisturbed class in each image was small (two points). Such a small number of validation points for this particular class resulted in the high sensitivity of the undisturbed class to errors.  Another potential reason involved the capability of the analysts to correctly identify disturbances. There was large inter-annual variation in the beginning and end dates for the growing season. It is possible that the presence of phenological variations confused the analysts and caused them to identify forests as disturbed when this was not the case. Also, the analysts may have been less likely to differentiate between inter-annual and seasonal variability in forest conditions and natural or anthropogenic disturbances. As a result, the analysts might have mistakenly identified the undisturbed pixels as part of the disturbed classes, which inflated the Error of Omission for the undisturbed class. Also, forests affected by insect infestation may also have shown signs of depressed growth over a period of time, but never resulted in stand mortality, thus contributing to the misclassification by the analysts.

The accuracy of these disturbance maps was also examined according to stratification by geographic region and stack density. The results suggested that regardless of the grouping criteria (i.e., geographical location or stack density), the accuracy of the classification algorithm was generally consistent.

Acquisition Materials and Methods

Study area

The selected study area was the boreal forest in Northern Eurasia within the Russian Federation (European Russia, Western Siberia and Eastern Siberia).

  • About 300 tree species are distributed across this vast area; the dominant species include Larix sibirica (Siberian or Russian Larch), L. gmelinii (Dahurian Larch), Pinus sylvestris (Scots Pine), P. sibirica (Siberian Pine), Picea abies (European or Norway Spruce), P. obovata (Siberian Spruce) and Betula spp. (Birch) (Tishkov 2002).
  • The climate in the region is highly or extremely continental, characterized by very cold winters and warm summers (Shahgedanova 2002). Precipitation in the region is light or modest (Shahgedanova 2002).
  • As with other boreal regions, wildfire is the most important disturbance agent in the Northern Eurasian boreal forest. An average area of 20,000–30,000 km2 is estimated as burned each year across the region (Goldammer and Furyaev 1996).
  • In addition to wildfire, another major disturbance agent in the boreal forest of Northern Eurasia is logging.

Measurement Methods

Image Selection

This data set was derived from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery spanning the period between 1984 and 2000. MODIS Vegetation Continuous Fields (MOD44B) data for the year 2000 were used to delineate the forest areas to which subsequent processes were applied. The Vegetation Continuous Fields product was used to focus the distribution of the Landsat stacks within forested areas (defined here as all pixels with percent tree cover greater than 10%) and thus excluded large zones of croplands and grasslands in southern Russia and tundra in the north.

Preference was given to sites with Landsat images (stacks) containing the largest number of cloud-free images during the growing season (1 June through 31 August) and with near-annual observations rather than to those with the greatest number of images available since many images came from the growing season of the same year.

Selected Landsat images (stacks) for the 55 sites included 241 terrain-corrected L1T images acquired during the 1 June to 31 August growing seasons of 1984-2000. Sites were distributed across the full extent of Russian forests. European Russia, Western Siberia and Eastern Siberia had 16, 7, and 32 sites, respectively.

 

 

Distribution of the 55 stacks

Figure 2. Distribution of the sites for which disturbance maps were generated. Red, blue and green represent sites in European Russia (16), Western Siberia (7), and Eastern Siberia (32), respectively. Letters in the polygons indicate the number of Landsat images in each time series stack, with ‘D’ and ‘S’ representing dense and sparse stacks. Note that the number of years (classes) of disturbed pixels was determined, in part, by the availability of Landsat images.

Image Pre-Processing and Masking

All L1T Landsat images were converted to surface reflectance using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS, V1.1.1) (Masek et al., 2006, Feng et al., 2013). The image stacks were clipped to their common extent to eliminate the variation in exact scene coverage between different images. Although preference was given to near-cloud-free imagery, considerable amounts of cloud and cloud shadow remained. To ensure high confidence disturbances mapping, liberal masks were designed to overestimate cloud and cloud shadow presence, particularly at the edges of clouds and cloud shadows. The masked-out pixels in the Landsat images were removed from the subsequent processes.

Disturbance Mapping

The method described in Healey, et al. (2005), which is based on the disturbance index (DI), was adopted as the core algorithm to detect disturbed pixels from the multiple Landsat images (stack) at a site.  Multiple DI images from the growing season of the same year were composited to create a single annual DI image. DI image stacks were subsequently used to map and time-stamp forest disturbances. Difference DI (ΔDI) was calculated for each two adjacent years. Based on the ΔDI and additional criteria (Chen et al., 2014) a pixel was identified as a disturbed pixel and marked by the year in which it was discovered to be disturbed (i.e., the latter year in each image pair). If a pixel was disturbed multiple times as recorded by the temporal DI stack, the latest disturbance event in each stack was given precedence.

Following this protocol, a time-stamped single-layer disturbance map was produced for each site from the original Landsat images. Across the full extent of Russian forests, 15 classes were mapped: 14 disturbed classes represented by the individual years during which the disturbances were observed, and one undisturbed class. However, it is important to note that not all 14 disturbed classes were mapped for a given site and the number of classes was determined, in part, by the availability of Landsat images.

 

Data Access

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

Russian Boreal Forest Disturbance Maps Derived from Landsat Imagery, 1984-2000

Contact for Data Center Access Information:

References

Crist, E.P. A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 1985, 17, 301–306.

Crist, E.P. and R.C. Cicone. A physically-based transformation of thematic mapper data—The TM tasseled cap. IEEE Trans. Geosci. Remote Sens. 1984, 3, 256–263.

Feng, M., J.O. Sexton, C. Huang, J.G. Masek, E.F. Vermote, F. Gao, R. Narasimhan, S. Channan, R.E. Wolfe, and J.R. Townshend. Global surface reflectance products from Landsat: Assessment using coincident MODIS observations. Remote Sens. Environ. 2013, 134, 276–293.

Goldammer, J.G.; Furyaev, V.V. Fire in Ecosystems of Boreal Eurasia: Ecological Impacts and Links to the Global System. In Fire in Ecosystems of Boreal Eurasia; Goldammer, J.G., Furyaev, V.V., Eds.; Kluwer Academic Publishers: Dordrecht, the Netherlands, 1996; Volume 48, pp. 1–20.

Hansen, M.; DeFries, R.; Townshend, J.R.; Carroll, M.; Dimiceli, C.; Sohlberg, R. Percent Tree Cover, Collection 4, Vegetation Continuous Fields MOD44B. Available online: http://glcf.umd.edu/ data/vcf/ (accessed on 4 May 2014).

Healey, S.P.; Cohen, W.B.; Yang, Z.; Krankina, O.N. Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection. Remote Sens. Environ. 2005, 97, 301–310.

Jones, J.W.; Starbuck, M.J.; Jenkerson, C.B. Landsat Surface Reflectance Quality Assurance Extraction (Version 1.7); U.S. Geological Survey: Reston, VA, USA, 2013; Chapter C7, p. 9.

Kauth, R.J.; Thomas, G.S. The Tasseled Cap—A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA, 29 June–1 July 1976; pp. 41–51.

Loboda, T.V.; Zhang, Z.; O’Neal, K.J.; Sun, G.; Csiszar, I.A.; Shugart, H.H.; Sherman, N.J. Reconstructing disturbance history using satellite-based assessment of the distribution of land cover in the Russian far east. Remote Sens. Environ. 2012, 118, 241–248.

Masek, J.G.; Vermote, E.F.; Saleous, N.E.; Wolfe, R.; Hall, F.G.; Huemmrich, K.F.; Feng, G.; Kutler, J.; Teng-Kui, L. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 2006, 3, 68–72.

Shahgedanova, M. Climate at Present and in the Historical Past. In The Physical Geography of Northern Eurasia; Shahgedanova, M., Ed.; Oxford University Press: New York, NY, USA, 2002; pp. 70–102.

Tishkov, A. Boreal Forests. In The Physical Geography of Northern Eurasia; Shahgedanova, M., Ed.; Oxford University Press: New York, NY, USA, 2002; pp. 216–233.