Linear mixture model classi cation of burned forests in the Eastern Amazon

int. j. remote sensing, 1998, vol. 19, no. 17, 3433± 3440 Linear mixture model classi cation of burned forests in the Eastern Amazon M. A. COCHRANE² and C. M. SOUZA Jr² ² Instituto do Homem e Meio Ambiente da Amazoà nia (IMAZON), Caixa Postal 1015, Bele m, Para 66.000 Brazil Biology Department, Pennsylvania State University, University Park, Pennsylvania 16802, USA (Received 7 October 1997; in nal form 26 June 1998 ) Abstract. A methodology is described for detecting and classifying burned forests in Amazonia. Linear mixture models using three image endmembers (vegetation, soil, shade) were used to separate forest from non-forest. Forested areas were unmixed using vegetation, non-photosynthetic vegetation (NPV) and shade endmembers and reclassi ed as unburned, recently burned and older burned forests. The NPV fraction provided the greatest separability of the forest classes and has potential for subclassi cation of burned areas into damage classes. For 184 km 2 of burned forest, a conservative estimate of 9% (22 metric tons haõ 1 ) of living biomass was lost due to forest res between 1991± 1993. 1. Introduction Currently, it is estimated that 10 000 km 2 of forest in the Brazilian Amazon are selectively logged each year (Verõ  ssimo and Amaral 1996). Evidence of re in selectively logged forests of the eastern Amazon is already common and widespread. In 1995 alone, it is estimated that 21% of land holdings in this region burned, with the area of standing forest a ected by res exceeding new deforestation by three-fold (Alencar et al. 1997 ). However, despite the prevalence of res in these forests, the use of satellite sensor imagery for classi cation of burned forests has met with limited success. The detection of re damage is complicated by the rapid regrowth of ground vegetation after res (Lefebvre and Stone 1994) and the heterogeneity of burned forests (Cochrane and Schulze, in press). Furthermore, classi cation of burned forests has been di cult due to the low temporal (Landsat-TM) or spatial (AVHRR) resolution of current sensors (Setzer et al. 1994). Given the large amount of previously burned forest and logged forest which already exist and the obvious fact that these forests are becoming a larger element of the landscape, the time has come to study these `new landscape elements. In this letter, we provide preliminary results of our use of linear mixture models for classifying burned forests and the potential for quantifying damage levels within these forests. 2. Methods 2.1. Study area This study was conducted within a 1406 km 2 area just south of the logging town of Tailaà ndia, ParaÂ, in Brazil. The landscape in the study region is a mosaic of 0143± 1161/ 98 $12.00 Ñ 1998 Taylor & Francis Ltd

3434 M. A. Cochrane and C. M. Souza Jr pastures, small agricultural plots, natural forest, second growth forest, unburned logged and burned logged forests. The forest of this region is tropical moist evergreen on latosol soils. The region is subject to a strong dry season from June through November and averages between 1500 and 1800 mm annual rainfall (Cochrane and Schulze, in press). 2.2. Ground data and remotely sensed data Bands 1± 5, 7 of two Landsat TM images (18 August 1991, 18 June 1993, path 223/062) with sensor radiometric corrections were acquired from INPE (Brazilian Space Agency). Nearest neighbor resampling was used to register the subsets (1406 km 2 ) of the two images (rms= 0.75 pixels) used in this research. The images were geometrically corrected using di erentially corrected GPS points strati ed throughout the imaged region (12 GCPs; rms= 0.71 pixels). Atmospheric e ects were ignored since no atmospheric calibration data are available for this region. The 1991 image was used as a reference for evidence of pre-existing burned forests. Descriptive ground data of the region s forests were collected from ten 0.5 ha plots within a 100 km 2 subsection of the study area in October 1996 (table 1) and included a full inventory of all trees> 10 cm dbh as well as interviews with landowners (Cochrane and Schulze, in press). In addition, forests along all roads within the study area were visited and investigated for evidence of previous burning. 2.3. Mixing models The images were processed as follows ( gure 1). Endmembers were extracted from the image and selected using principal component axes 1 and 2 (Smith et al. 1985); corresponding to 98% of the multi-spectral data variance. A three-endmember mixing model was then run to separate the proportion of landscape materials (i.e. vegetation and soil) and illumination variability (i.e. shade) found in the study area. Table 1. Field data from the study area describing physical characteristics of classes of burned forests. Unburned Lightly Moderately Heavily (control) burned burned burned forest forest forest forest Ô Ô Ô Sample size (ha) 1.3 1.0 1.3 1.5 Live stem (> 10 cm dbh) Density (#haõ 1 ) 515 344 237 116 Dead stem density (#haõ 1 ) 42 105 147 259 Average canopy cover (%) 87.1Ô 2.9# 61.0 8.8# 33.9 10.0# 13.8 5.1# Living biomass (metric tons haõ 1 )* 242 220 129 47 Dead biomass (metric tons haõ 1 )* 53 50 71 116 Total biomass (metric tons haõ 1 )* 295 270 200 163 #Standard deviation. *Above ground biomass calculated based on diameter of all trees > (Source: Cochrane and Schulze in press). 10 cm dbh.

Remote Sensing L etters 3435 Figure 1. Flow chart of the image processing methodology. The mixing model was solved by applying a least-square estimator (Shimabukuro and Smith 1991), with an unconstrained solution (Schanzer 1993), given by for n DNb= F i DN i,b +e b (1) i= 1 n F i = 1 i= 1 where DNb is the Digital Number in band b; Fi the fraction of endmember i; DNi,b the relative Digital Number of endmember i, in band b; and e b is the error in band b. The shade fraction image was then classi ed to create a binary image of forest (pixel value= 1) and non-forest classes (pixel value= 0) as suggested by Shimabukuro et al. (1997). The binary image was ltered using a median lter to remove `saltand-pepper noise (Gonzales and Woods 1993) and the resulting image mask was multiplied by the original image to remove unforested areas while retaining the pixel values of forested areas. A second mixing model was created using a band 4/band 5 scatterplot of the forest masked image to select forest endmembers (vegetation, shade and nonphotosynthetic vegetation (NPV, (Roberts et al. 1993)) and unmix the forested pixels. For this model, the shade endmember was set to zero in all bands in order to create an envelope for the majority of pixels in the data set. Classi cation of the 1993 image was performed to separate unburned forest, recently burned forest (<1 year-old), and older burned forest (1 to 2 years-oldð as determined by comparison with the 1991 image and ground information), using the NPV fraction image. Pixel values of known areas were extracted from the fraction image for each of the above classes and were plotted in a vegetation-npv-shade mixing space diagram to evaluate the degree of separability of these forest classes.

3436 M. A. Cochrane and C. M. Souza Jr 3. Results Analyses of the fraction images derived from the vegetation-npv-shade mixing model showed that neither vegetation nor shade fractions allowed for separation of unburned, recently burned, and older burned forests. These forest classes concentrate between 20% and 40% in the shade fraction and show considerable overlap (40% to 60%) in the vegetation fraction. However, the NPV fraction image ( gure 2 (a)) provided good separability of unburned forest (0± 20%) and recently burned forest (20± 60%). As expected, older burned forest overlaps with both unburned forest and recently burned forest, concentrating between 15 and 25% of NPV ( gure 3). Classi cation of the image ( gure 2 (b)) shows that 371 km 2, of the 1406 km 2 comprising the study area, were cleared by 1993. Within the remaining 1035 km 2 of forest, at least 184 km 2 (18%) burned during the time interval between the 1991 and 1993 images. Burned forests were subclassed into recent burns (22 km 2 ) and older burns (162 km 2 ). 4. Classi cation evaluation Variability in re spread and intensity leads to the formation of a heterogeneous landscape consisting of unburned `forest islands and burned forest patches impacted to di ering degrees (Cochrane and Schulze, in press). Burn variability and vegetation regrowth a ected the classi cation of burned forests. Estimated accuracy for the classi cation of unburned, recently burned and older burned forests, was 92%, 93% and 71% respectively (table 2). Burned forest classi cation accuracy may be arti - cially low due to the inclusion of small unburned forest patches in the test elds. 5. Discussion This methodology shows the potential of mixture models for classi cation of re impacted forests in Landsat-TM images of Amazonia. With this methodology it is possible to detect both recent and older burns (>1 year-old) using the forest NPV fraction image. Extreme site heterogeneity made unsupervised classi cations untenable due to spectral mixture of the land-cover classes. Maximum likelihood classi cation confused much of the recently burned forest with non-forest classes and was unable to classify older burned forests. Though the information content present in older burns is limited, the information present in more recent burns ( gure 3 (a)) is rich and may be su cient for subclassi cation into various levels of re damage. Our initial results show that there are three cluster regions, obtained using a k-means algorithm, in the vegetation- NPV-shade mixing space which may correlate with re intensity ( gure 3 (c) table 3). We postulate that cluster 1 can be associated with lightly burned forests (58% Table 2. Error matrix of the forest classi cation. Older Class Unburned burned Recently accuracy Classes forest forest burned forest Non-forest (%) Unburned forest 2005 148 25 0 92 Older burned forest 534 1575 10 88 71 Recently burned forest 0 72 986 3 93 Overall accuracy=4566/5446=84%

Remote Sensing L etters 3437 Figure 2. (a) NPV fraction image obtained with the mixing model of the forested portions of the study area (1406 km 2 ); and (b) classi cation of the forested areas based on the NPV fraction image.

3438 M. A. Cochrane and C. M. Souza Jr Figure 3. NPV-vegetation-shade mixing space diagram showing the distribution of forest classes; (a) Recently Burned Forest and Unburned Forest, (b) includes Older Burned Forest, and (c) cluster centroid location of recently burned forest classes.

Remote Sensing L etters 3439 Table 3. Clusters de ned for recently burned forests using fraction values of NPV and vegetation; cluster 1 may be associated with lightly burned forest, cluster 2 with moderately burned forest and cluster 3 with heavily burned forest. Normalized Endmember percent endmember percent Cluster number NPV Vegetation NPV Vegetation 1 32 44 42 58 2 46 28 57 37 3 85 4 95 5 vegetation), cluster 2 with moderately burned forest (37%) and cluster 3 with heavily burned forest (5%). The vegetated percentage of these clusters roughly correlates with canopy cover in the ground data (table 1). In older burns, post- re vegetative regrowth reduces exposed NPV ( gure 3 (b)) thereby making subclassi cation of these burned forests impossible. Based on data (table 1) from Cochrane and Schulze (in press), we estimate that the 184 km 2 of forest a ected by re in the study area lost a minimum of 9% (22 metric tons haõ 1 ) of its living biomass in the period between the 1991 and 1993 images. This estimate assumes that all forests in the study area are tropical moist forest of roughly similar stature (borne out by extensive eld work) and that only light burns occurred. This is a very conservative estimate since, due to post- re forest regrowth, it underestimates the total area burned. If the structural assumption is not valid (i.e. burned forests had greater logging damage), then regrowth would result in many smaller trees (greater mortality rate) and more available fuels (greater re intensity) resulting in the removal of an even greater percentage of the remaining biomass (Cochrane and Schulze in press). Estimates of damage levels are very crucial since, in areas of severe burning or recurrent re, loss of living biomass can exceed 75%. It is hoped that the new generation of higher spatial resolution satellites will allow for better subclassi cation of recent burns, therefore yielding more accurate estimates of re-induced reductions in living biomass. In principle, the methodology described here makes it possible to create a time series of images that will allow multi-temporal analyses of re impact in Amazonian forests. Furthermore, burned regions may be tracked over time to determine how rapidly vegetative regrowth obscures re damage and whether or not these areas are frequently reburning as reported by Cochrane and Schulze (in press). Our results indicate that a two-year time interval between Landsat-TM image acquisitions would be su cient to detect burned forests. Therefore, this methodology may bene t agencies working with monitoring and law enforcement issues. 6. Conclusion The methodology described in this letter can be used to digitally classify burned forests in Landsat-TM images of Amazonian forests. The NPV fraction present in a forested pixel provides a physically meaningful measurement of canopy openness and has the potential to provide more information on the e ects of re upon forest structure and biomass. The potential for extending this methodology to make such estimates and other multitemporal analyses are currently being pursued.

3440 Remote Sensing L etters Acknowledgments This research was funded by a grant from the PPG7-`Programa de Pesquisa Dirigida (MMA/MCT/ FINEP) to the Instituto do Homem e Meio Ambiente da Amazoà nia (IMAZON). References Alencar, A. A., Nepstad, D., Mendonza, E., Brown, I. F., and Lefebvre, P., 1997, Fires in Amazonia in 1994 and 1995: Four case studies along the arc of deforestation. World Bank, unpublished report. Cochrane, M. A., and Schulze, M., (in press), Fire as a recurrent event in tropical forests of the eastern Amazon: E ects on forest structure, biomass, and species composition. Biotropica. Gonzales, R. C., and Woods, R. E., 1992, Digital Image Processing (Reading, MA: Addison Wesley). Lefebvre, P. A., and Stone, T. A., 1994, Monitoring selective logging in eastern Brazilian Amazonia using multi-temporal Landsat Thematic Mapper imagery. In Proceedings of the ISPRS Commission VII Symposium: Resource and Environmental Monitoring; 1994; Rio de Janeiro, Brazil (SaÄ o Jose dos Campos SP, INPE-Instituto Nacional de Pesquisa Espaciais), 30, pp. 288± 291. Roberts, D. A., Adams, B. J., and Smith, M. O., 1993, Green vegetation, nonphotosynthetic vegetation, and soil in AVIRIS data. Remote Sensing of Environment, 14, 255± 269. Schanzer, D. L., 1993, Comments on the least-squared mixing models to generate fraction images derived for remote sensing multispectral data. IEEE T ransactions on Geoscience and Remote Sensing, 3, 747. Setzer, A. W., Pereira, M. P., and Pereira Jr., A. C., 1994, Satellite studies of biomass burning in AmazoniaÐ some practical aspects. Remote Sensing Reviews, 10, 91± 103. Shimabukuro, Y. E., Mello, E. K., Moreira, J. C., and Duarte V., 1997, SegmentacË aä o e classi cacë aä o da imagem de sombra do modelo de mistura para mapear des orestamento na Amazoà nia. SaÄ o Jose dos Campos, INPE-Instituto Nacional de Pesquisa Espaciais. Divisao de Ensino e DocumentacË aä o, (in Portuguese). Shimabukuro, Y. E., and Smith, J. A., 1991, The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE T ransactions of Geoscience and Remote Sensing 29, 16± 20. Smith, M. O., Johnson, P. E., and Adams, J. B., 1985, Quantitative determination of mineral types and abundances from re ectance spectra using principal components analysis. Journal of Geophysical Research, 90, 797± 804. Veri ssimo, A., and Amaral, P., 1996, ExploracË aä o madeireira na Amazoà nia: situacë aä o atual e perspectivas. Certi cacë aä o Florestal, 3, 9± 16 (in Portuguese).