extracted from Mato Grosso in 1997, making the state the second largest producer ... Three study areas of 900km2 were selected, located in Sinop, Cláudia and.
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Detection of logging in Amazonian transition forests using spectral mixture models A. L. MONTEIRO, C. M. SOUZA JR and P. BARRETO Instituto do Homem e Meio Ambiente da Amazoˆnia (IMAZON), Caixa Postal 5101, Ananindeua, Para´ 67, 113–000 Brazil (Received 18 January 2001; in final form 30 January 2002) Abstract. Techniques to detect the area affected by logging in the Amazon basin have yet to be tested on transitional forest environments, which contribute significantly to the total logging in the region. Logging in transitional forests is selective and leaves small clearing in the forests where timber is temporarily stored. These areas, called log landings, can be detected automatically in soil fraction images, generated through linear mixture modelling. Based on a harvesting radius from these log landings, it is possible to estimate the area affected by logging. This method was tested in Amazonian transition forest, using Landsat TM and ETM satellite sensor data from the years 1992, 1996 and 1999. Additionally, a methodology to record areas of old and repeated logging is described.
1. Introduction Various methodologies have been tested to detect intensive selective logging (38m3 ha−1) in dense forest in the north-eastern Amazon using satellite sensor imagery. Examples include visual interpretation (Watrin and Rocha 1992), supervised classification (Stone and Lefebvre 1998), and soil fraction images obtained through linear mixture modelling (Souza and Barreto 2000). However, these methodologies have not been tested in transition forests, which possess lower densities of commercial tree species (RADAMBRASIL 1981). Approximately 9.8 million m3 of timber (35% of Brazil’s total production) were extracted from Mato Grosso in 1997, making the state the second largest producer of timber in Brazil (Smeraldi and Verı´ssimo 1999). A large part of this logging occurs in transition forests. Logging in this forest type is selective (20 m3 ha−1) and generally unplanned (Tropical Forest Foundation 1997). The environmental impacts associated with this type of logging include: (i) threats of extinction to local forest species; (ii) increased deforestation (Frontpix 1994); and (iii) increases in forest fire susceptibility (Holdsworth and Uhl 1997). Local studies focusing on the specific characteristics of logging centres (i.e. forest type, frontier ages, and harvesting intensity) can assist in estimating the area affected by logging in the Amazon basin. Current estimates vary and are based on either field interviews (10 000–15 000 km2 year−1; Nepstad et al. 1999), or texture analysis (2655–5406 km2 year−1; Janeszek 1999) using Landsat Thematic Mapper (TM) band 5 (1.55–1.75 mm). International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online © 2003 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/01431160210153994
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In this study, spectral mixture models (Souza and Barreto 2000) are used to detect selective logging in Amazonian transition forests. Additionally, the use of a square buffer to estimate the area affected by logging is introduced and a methodology to record old and repeated logging is described. Finally, the results were used to evaluate the relationship between deforestation and logging in the region. 2. Methods 2.1. Study area and data acquisition Three study areas of 900 km2 were selected, located in Sinop, Cla´udia and Marcelaˆndia in the state of Mato Grosso, Brazil (figure 1). The area possesses transition forests on latosol soils over flat to undulating terrain, with an average annual precipitation of 2000 mm (RADAMBRASIL 1981). Two Landsat TM 5 images (bands 1–5 and 7), from May 1992 and July 1996, and one Landsat Enhaced Thematic Mapper (ETM) 7 image (bands 1–5 and 7), from August 1999, were acquired through the Tropical Rain Forest Information Center (TRFIC). The areas were visited over the period 1–3 June 2000. A total of 20 GPS points were collected and used to assess the accuracy of the final map. The scene from August 1999, georeferenced by TRFIC (RMS=0.29 pixel), was used to register the other subsets from 1992 (RMS=0.91 pixel) and 1996 (RMS= 0.87 pixel). The images were processed individually due to a lack of atmospheric calibration data. Figure 2 presents steps used to process the images.
Figure 1. Location of study sites.
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Figure 2. Methodology to map logging activities.
2.2. Mapping selective logging The ISODATA classification algorithm was used to generate a forest map. This task was accomplished by grouping the spectral classes (n=15) generated by ISODATA into thematic classes (forest, non-forest (i.e. pastures, agriculture, secondary growth, urban areas), and water). Isolated pixels and small forest gaps were removed with a ‘clump’ filter. Soil fraction images of the forested areas were then acquired, generated by a mixture model, to enhance areas cleared for temporary log storage (log landings; Souza and Barreto 2000). Log landings were identified from the soil fraction image
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by selecting small areas (1–4 pixels) with soil abundance greater than 20% and more than 100 m from rivers (figures 2 and 3). An estimate of the area affected by logging was based on a harvesting radius extending from each log landing. This radius was estimated through 100 randomly selected measurements between detected log landings in the soil fraction image. A radius of 350 m (half of the maximum distance measured) was used to estimate the
Figure 3. Detection of log landings in Cla´udia-MT (1999). The soil fraction image (c) more clearly highlights log landings compared to a colour composite image (a) or band 3 of Landsat ETM (b). An estimate of the area logged surrounding each landing (radius=350 m) is shown in (d).
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area affected by logging. Finally, a square buffer was applied to log landing centroids to estimate the area affected by logging. 2.3. T emporal logging analysis The classified images from 1992 and 1996 were combined to identify classes of old and repeated logging. The classes were defined as: $ $
Old logging: forest areas logged in the 1992 Landsat image, but not in 1996. Repeated logging: forest areas logged in both 1992 and 1996.
Through this process, logged areas in the 1996 image were classified into three types (i.e. recent, old and repeated). This newly classified 1996 image was then combined with the 1999 image to identify old and repeated logging over the extended period. The classification criteria above were used, with the additional condition for repeated logging, including logging detected in 1992 and/or 1996 and in 1999 (figure 2). 3. Results Significant forest cover changes occurred in the study areas between 1992 and 1999 (figure 4). Changes were most drastic in Marcelaˆndia, where 50% of the native forest (from 66 692 to 32 821 ha) was lost. In Cla´udia and Sinop, losses were approximately 38% and 28%, respectively (table 1). Sinop reported the lowest area affected by logging (10 731 ha), followed by Marcelaˆndia (19 391 ha) and Cla´udia (25 276 ha). Repeated logging, which significantly degrades forests (Gerwing and Souza 2000), predominated in Marcelaˆndia, corresponding to 18% of the total area affected by logging in 1999 (table 1). In Cla´udia and Sinop the area of repeated logging was approximately 10% and 5% respectively, of all logged areas (table 1). Three temporal logging patterns were identified (table 1). In Sinop, a large decrease occurred in the area newly logged on each date, falling from 8538 ha in 1992 to 2023 ha in 1999. In Cla´udia logging rates remained relatively stable over the seven year period, while in Marcelaˆndia logging rates nearly doubled between 1992 and 1996, and later fell sharply in 1999. Deforestation was associated primarily with unlogged forests in the three study sites (table 2). For example, of the total deforestation in Sinop from 1992 to 1999 (17 805 ha), 71% (12 750 ha) was directly converted from unlogged forests (table 2). In Cla´udia deforestation of unlogged areas accounted for 81% (8940/10 922 ha) of the total area deforested, while in Marcelaˆndia 68% (14 170/20 692 ha) of deforested areas were unlogged (table 2). 4. Accuracy considerations The accuracy of the above methodology to estimate the total area affected by logging (recent, old and repeated) was tested with field data. Of the three areas, Marcelaˆndia reported the greatest accuracy (80%), followed by Cla´udia (73%) and Sinop (69%). Inaccuracy is primarily due to the rapid re-growth of vegetation in log landings, erasing the logging ‘scar’, and thus causing logged forest to be misclassified as forest. Detection of intensive logging in dense forest requires image acquisition to be no more than two years apart (Stone and Lefebvre 1998). A more robust temporal analysis is currently being conducted with images from 1987 to 2000 to evaluate the appropriate temporal frequency to detect logging in transition forests.
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Figure 4. Changes in forest cover in the study areas over the years 1992, 1996 and 1999.
5. Conclusions Soil fraction images are useful to detect logging in the transition forests of Mato Grosso. In addition, the method presented here to estimate the harvesting radius surrounding log landings can be an alternative to direct field measurements. The radius defined for logging centres in this study (350 m) is larger than in the Paragominas region (180 m, Souza and Barreto 2000). This fact could be associated with a lower density of commercially valuable trees in transition forests
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Table 1. Changes in forest cover in the study areas. Area (ha) 1992
Area (ha) 1996
Area (ha) 1999
Sinop Forest Non-forest Water Log landings Logging* Recent logginga Old loggingb Repeated logging (2×)c Repeated logging (3×)d
67 184.66 12 722.27 1502.13 52.85 8538.09 8538.09 – – –
56 529.41 21 091.18 558.49 43.47 11 777.45 4131.61 6648.27 997.57 –
48 022.70 30 525.09 701.65 19.31 10 731.25 2023.28 8139.33 275.66 292.98
Cla´udia Forest Non-forest Water Log landings Logging* Recent logginga Old loggingb Repeated logging (2×)c Repeated logging (3×)d
65 279.79 13 173.03 1507.32 90.36 9949.50 9949.50 – – –
50 826.69 18 805.05 1621.44 97.02 18 649.80 9783.00 7638.12 1228.68 –
39 906.72 23 985.27 751.14 80.19 25 276.68 8239.32 14 470.11 1733.04 834.21
Marcelaˆndia Forest Non-forest Water Log landings Logging* Recent logginga Old loggingb Repeated logging (2×)c Repeated logging (3×)d
66 962.68 16 039.86 1209.07 34.53 5753.85 5753.85 – – –
40 742.18 30 927.72 1079.79 108.93 17 141.38 13 878.58 2176.43 1086.37 –
32 821.49 36 696.90 1062.30 27.32 19 391.99 4050.89 11 806.21 2955.86 579.03
*Total logged area=a+b+c+d
(20 m3 ha−1) than in dense forests (38 m3 ha−1). This variation shows the importance of considering the specific characteristics of logging centres in the estimate of the total area affected by logging in the Amazon. Recording old logging may help to reduce the differences between estimates obtained through field interviews and satellite sensor images because old logged areas are prone to be misclassified as intact forest. In addition, mapping repeated logging could be useful to identify highly degraded forest, as proposed by Gerwing and Souza (2000). Finally, the square buffer is more suitable to represent the square pattern ‘stand’ used in the logging activity. It was found that a large part of deforestation occurred directly from unlogged forests. Based on field knowledge of deforestation patterns this result is unlikely, since logged forests persist at least a year before deforestation. It is believed that a more robust temporal evaluation will reveal a logging dynamic following the pattern of deforestation in the region and increase classification accuracy.
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Table 2. Quantification of area deforested based on forest type. Deforestation (ha) 1996–1999
Total (ha)
7037.16 1307.16 – – 9.27
5713.38 1316.61 1761.84 646.20 13.50
12 750.54 2623.77 1761.84 646.20 22.77
8353.59
9451.53
17 805.12
5060.07 660.06 – – 1.53
3880.89 378.09 824.58 116.01 1.44
8940.96 1038.15 824.58 116.01 2.97
5721.66
5201.01
10 922.67
11 049.39 3828.78 – – 22.32
3121.20 1751.67 562.95 337.41 19.17
14 170.59 5580.45 562.95 337.41 41.49
14 900.49
5792.40
20 692.89
1992–1996 Sinop Forest Recent logging Old logging Repeated logging Log landings Total* Cla´udia Forest Recent logging Old logging Repeated logging Log landings Total* Marcelaˆndia Forest Recent logging Old logging Repeated logging Log landings Total*
*In table 1 total deforestation=new non-forest area—old non-forest area +/− water change.
Acknowledgments The authors thank colleagues at Imazon for their comments. This study was made possible through financial support from the ‘Programa de Pesquisa Dirigida’ (PPG-7/MCT/FINEP) and Ford Foundation. References F, 1994, Projeto Fronteiras Parque Indı´gena do Xingu. Projeto Piloto de Apoio a` Fiscalizac¸a˜o e Controle das Fronteiras do Parque Indı´gena do Xingu. Relato´rio- 94/057, Instituto Socioambiental, Sa˜o Paulo, SP (in Portuguese). G, J. J., and S, J. M., 2000, Ecological aspects of forest degradation by logging and fire in eastern Amazonia. Book of abstracts, First L BA Scientific Conference, Bele´m, PA, 295, 193. H, A. R., and U, C., 1997, Fire in Amazonian selectively logged rain forest and the potential for fire reduction. Ecological Applications, 7, 713–725. J, D., 1999, Detection and measurement of Amazon tropical forest logging using remote sensing data. MA thesis, Departament of Geography, Michigan State University. N, D. C., V, J. A., A, A., N, C., L, E., L, P., S, P., P, C., M, P., M, E., C, M., and B, V., 1999, Large-scale impoverishment of Amazonian forests by logging and fire. Nature, 398, 504–508.
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R, 1981, Departamento de Produc¸a˜o Mineral. Projeto RADAMBRASIL, L evantamento de Recursos Naturais. Folha SC.21 Juruena. Rio de Janeiro, 20, 39–40 (in Portuguese). S, R., and V, J. A., 1999. Hitting the target: timber consumption in the Brazilian domestic market and promotion of forest certification. Sa˜o Paulo: Friends of the Earth—Programa Amazo¸nia; Piracicaba, SP: Imaflora; Bele´m, PA: Imazon, 41, 19–20. S, C., and B, P., 2000, An alternative approach for detecting and monitoring selectively logged forests in the Amazon. International Journal of Remote Sensing, 21, 173–179. S, T. A., and L, P. A., 1998, Using multi-temporal satellite data to evaluate selective logging in Para, Brazil. International Journal of Remote Sensing, 13, 2517–2526. T F F and I, 1997, Avaliac¸a˜o das atividades florestais em dois po´los madeireiros no Mato Grosso: Juara e Marcelaˆndia. Relato´rio-97/045, Bele´m, PA (in Portuguese). W, O. S., and R, A. M. A., 1992, Levantamento da vegetac¸a˜o natural e do uso da terra no Municı´pio de Paragominas (PA) utilizando imagens TM/Landsat. Boletim de Pesquisa, 124, EMBRAPA/CPATU, Bele´m, PA, 40 (in Portuguese).
