Assessment of areas of selective logging and burned forests in Mato Grosso State, Brazil, from satellite imagery

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1 XIV WORLD FORESTRY CONGRESS, Durban, South Africa, 7-11 September 2015 Assessment of areas of selective logging and burned forests in Mato Grosso State, Brazil, from satellite imagery Yosio Edemir Shimabukuro 1,2, Jukka Miettinen 1, René Beuchle 1, Rosana Cristina Grecchi 1, Marcela Velasco Gomez 1, Dario Simonetti 1, Frédéric Achard 1 1 European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES), Forest Resources and Climate Unit, Ispra, Italy {yosio.shimabukuro, jukka.miettinen, rene.beuchle, rosana.grecchi, diana.velasco-gomez, dario.simonetti, frederic.achard}@jrc.ec.europa.eu 2 Brazilian National Institute for Space Research - INPE, Brazil yosio@dsr.inpe.br Abstract This paper presents a semi-automated procedure for mapping degraded forest areas in Mato Grosso state, Brazil, from medium (30m) resolution satellite imagery. In the Brazilian Amazon region forest degradation in the context of UNFCCC REDD+ (i.e. considered as long term reduction of Carbon stocks in forests) is mainly driven by logging activities and forest fires. Medium to fine spatial resolution satellite data allow assessing the extent and distribution of deforestation, selective logging and burned forest areas with high reliability. However, it is difficult to obtain the full coverage of a region with cloud free satellite imagery for mapping logging tracks and burned areas. Such dynamic landscape features are detectable on satellite imagery only during a limited period of time in humid tropical regions (a few weeks / months). The proposed method consists in the further development of the approach used in the TREES-3 pan-tropical forest area survey conducted by the European Commission s Joint Research Centre. It is applied to Mato Grosso State, Brazilian Amazon, using Landsat satellite imagery (Thematic Mapper sensor), acquired close to the year The approach is using a sample of sites systematically distributed at confluence geographical points (77 sample sites in Mato Grosso). The semi-automated procedure for analysis of satellite imagery extracted over the sample sites consists into: a) image pre-processing, b) production of fraction images : vegetation, soil and shade, and c) creation of spatially and spectrally homogeneous areas ( objects ). In each of the 77 sample sites, spatial objects are then labelled as burned areas or logged forest areas through visual image interpretation. The resulting maps for the 77 sample sites are then combined with the forest cover maps of year 2010 from the TREES-3 survey. Statistical estimates of forest degradation by fires and selective logging are finally generated. The results show that 18,452 km 2 and 9,435 km 2 of forests appear as burned or affected by selective logging in year 2010, respectively, representing c. 4% and 2% of the total forest area of this state. The future availability of time series of finer spatial resolution data from the Sentinel-2 satellite (10m resolution) is expected to allow improving the assessment of forest degradation processes using such approach. Keywords: Remote sensing, Forest degradation, Selective logging, Forest fires, Sampling approach, medium and high spatial resolution data, Tropical region. Introduction, scope and main objectives The majority of carbon emissions from land cover changes can be attributed to deforestation (Achard et al. 2014). However due to the increasing amount of anthropogenic activities in the tropical belt, the contribution of forest degradation to carbon emissions is becoming more and more significant: in the Brazilian Amazon, emissions from forest degradation may reach up to 40% of the emissions from deforestation (Berenguer et al. 2014). Consequently forest degradation is a key process for REDD+

2 (Reduction of Emissions from Deforestation and forest Degradation) in framework of the UNFCCC (United Nations Framework Convention on Climate Change). Logging activities (other than clear-cuts) are considered as a forest degradation process in relation to REDD+ as it usually leads to a long-term reduction of carbon stocks in tropical forests. Carbon emissions from selectively logged forest have been shown to be on average equivalent to about 12% of those from deforestation (Pearson et al. 2014). In the Amazonian region forest degradation is mainly driven by selective logging activities and forest fires. Medium to high spatial resolution satellite data allows analyzing the extent and distribution of deforestation, selective logging and burned forest areas with high reliability. However, it is difficult to obtain full coverage of satellite imagery for mapping dynamic landscape features, which are detectable only for a limited period of time, in humid tropical regions (Miettinen et al. 2013). Fraction images derived from Landsat Thematic Mapper (TM) sensor have been used for the assessment of tropical forest cover, especially in the Brazilian Amazon: soil fraction images for mapping deforested areas and shade fraction images for mapping burned areas (Souza et al. 2005, Shimabukuro et al. 2009, Shimabukuro et al. 2014). Fraction images derived from Linear Spectral Mixing Model (Shimabukuro and Smith 1991) have the following characteristics: a) vegetation fraction images highlight the forest cover conditions similarly to vegetation indices such as NDVI (Normalized Difference Vegetation Index); b) shade fraction images highlight areas with low reflectance values such as water, shadow and burned areas; and c) soil fraction images highlight areas with high reflectance values such as bare soils and clear cuts. Consequently our hypotheses in this study are that: (i) vegetation fraction images can allow differentiating closed forest from non-forest areas, (ii) shade fraction images can allow identifying burned forest, and (iii) soil fraction images can allow identifying selectively logged forest. In this context the main purpose of our study is to develop a semi-automated procedure based on fraction images from Landsat TM dataset for mapping forest degradation due to selectively logging and fires in the Brazilian Amazon. Methodology/approach This study was performed for the area of Mato Grosso State, in Brazil (Figure 1), with a total area of 903,366 km² (IBGE 2002). Due to variable climate, terrain relief, precipitation patterns and length of the dry season, the State of Mato Grosso has a naturally diverse biodiversity, resulting in many different vegetation types of the Cerrado and Amazon biomes. Furthermore, Mato Grosso, located partly in the arc of deforestation area at the southern extent of the Brazilian Legal Amazon, has one of the highest annual deforestation rates (INPE 2008), with deforestation activities occurring since the late 1980s (INPE 2014) and with intense forest degradation activities due to fire and selective logging (Eva et al. 2012). Forest clearance causes, among others, habitat fragmentation, which leaves the remaining forest more vulnerable to edge effects such as fire (Cochrane and Laurance 2002). In consequence, the fire events are likely to happen more frequently at the border of deforestation fronts. Temporally, the fires in Mato Grosso are highly concentrated in the dry season between June and October, with 80-95% of all yearly fire activity taking place during this period (Anderson et al. submitted). For this study, we consider all available cloud-free Landsat TM images during the dry season of year 2010 (June to October).

3 Fig. 1: Study area: State of Mato Grosso with location of the systematic sample sites The proposed method consists into the further development of the approach used in the TREES-3 pantropical deforestation survey conducted at the European Commission Joint Research Centre. It is based on a systematic sample of sites for which Landsat images are acquired. The semi-automated approach consists into: a) satellite image pre-processing (Bodart et al. 2011), b) production of fraction images of vegetation, soil and shade (Shimabukuro and Smith 1991) and c) creation of spatially and spectrally homogeneous areas ( objects ) (Raši et al. 2011). The extent of forest degradation by fires is estimated using the full dataset of 77 sample sites of the TREES-3 survey (with 10x10km 2 size) distributed systematically at latitude / longitude confluence point in Mato Grosso. Image objects within the sample sites are labelled as burned areas through a visual image interpretation tool (Simonetti et al. 2011). The resulting map is then combined with the land cover dataset of the TREES-3 survey for year 2010 to generate statistical estimates of the burned forest area. The extent of forest degradation by selective logging in Mato Grosso is estimated using a subset of the systematic sample by considering only the sample sites where selective logging takes place. Selective logging areas are assessed in a total of 41 sample sites using a larger site size (20x20km 2 ). Image objects are labelled as selective logging areas through visual interpretation of the soil fraction images. Results The resulting burned forest areas are related either to a deforestation process or to a degradation process: in the case of deforestation the forest cover is first fully logged (clear cut) and then the remaining vegetation is burned for conversion to agricultural land use, whereas in the case of degradation the forest cover is burned through an uncontrolled fire without removal of wood nor conversion to another land use (Figure 2). This makes the use of a time series dataset essential for differentiating between deforestation and forest degradation processes. Deforested areas will appear as non-forest areas (cropland or grassland) in the successive months or years while burned forest (degraded forest) will quickly recover as forest regrowth. The interpretation of selectively logged areas, on the other hand, is mainly based on the detection of logging tracks and reduced canopy cover caused by logging activities. The soil fraction highlights these

4 features and thereby facilitate visual interpretation of selective logging. Figure 3 illustrates the visual interpretation process: selective logging areas can be clearly observed in the derived soil fraction image. Fig. 2: Sample site (20x20 km 2 size with 10x10km 2 box) centered Latitude South 13 - Longitute West 59 : a) Landsat TM bands 5/4/3 composite and b) corresponding shade fraction image highlighting burned areas as bright areas. Fig. 3: Sample site (20x20 km 2 size with 10x10km 2 box) centered Latitude South 12 - Longitute West 54 : a) Landsat TM bands 5/4/3 composite and b) corresponding soil fraction image highlighting the selective logging tracks and openings as bright dots.

5 Fig. 4: Full Landsat TM scene: (a) bands 5/4/3 composite and (b) corresponding map of burned areas in bright blue with sub-panels showing 4 sample sites (South 11 - West 51, South 11 - West 52, South 12 - West 51, South 12 - West 52 ) sites (Landsat imagery and corresponding burned areas in magenta). Tables 1 and 2 describe the land cover classes from TREES-3 project and burned areas estimated for these classes (Miettinen et al. Submitted). Note that from the total of burned areas in Mato Grosso, around one third took place in forest areas (Tree cover and Tree cover mosaic). This is an alarmingly high percentage in a state with around 50% forest cover, taking into account the naturally very low fire frequencies in humid tropical forests. Table 1: Land cover distribution in Mato Grosso in year km 2 % Tree cover Tree cover mosaic Other wooded land Other land cover Water Table 2: Distribution of burned area in Mato Grosso in year 2010 (in thousand km 2 )

6 1000 km 2 Tree cover 15.7 Tree cover mosaic 2.7 Other wooded land 6.6 Other land cover 33.7 Total 58.7 The results show that 18,452 km 2 and 9,435 km 2 of forest (Tree cover and Tree cover mosaic classes) appear as degraded by fire and selective logging, respectively in year 2010, representing circa 4% and 2% of the total forest area (Figure 5). Fig. 5: Distribution of burned forest and selective logging areas by sample sites. Estimates of selective logging are restricted to 10x10km 2 in order to be consistent with the burned forest estimates Discussion A forest and non-forest mask is essential for developing a procedure for detecting and mapping forest degradation. With this mask, the detected changes can be automatically put into the context of forest disturbance. Using Landsat TM/ETM+ data, vegetation clearance and burned areas are easy to detect and map with semi-automated methods (Shimabukuro et al. 2009, 2012, 2014). However, it is important

7 to note that forest degradation due to fires (burned forest) can only be mapped reliably using a timeseries of images because burned areas can also be related to a deforestation process when burning is used to clear the remaining vegetation (Shimabukuro et al. 2014). We also show in this paper that forest degradation due to selective logging can be detected and mapped using medium resolution soil fraction images. Due to its effects on forest carbon content, it would be essential to include forest degradation activities in REDD+ initiatives. The main benefit of the forest degradation monitoring approach presented in this paper is the capability to discriminate selectively logged forest from burned forest. This is of high importance for estimating the proportions and the total carbon emissions from different types of forest degradation. Conclusions/outlook The proposed method presents great potential for assessing forest degradation, regionally and globally, from sensors with fine spatial resolution and high temporal frequency (Shimabukuro et al. 2014). Thereby, the future availability of 5-days temporal resolution of 10m spatial resolution data from Sentinel-2 satellites is expected to enable efficient implementation of the monitoring approach presented in this paper and considerably improve assessment and monitoring capabilities of forest degradation processes, consequently facilitating the implementation of actions in the framework of REDD+. References Achard F, Beuchle R, Mayaux P, Stibig H-J, Bodart C, Brink A, Carboni S, Desclée B, Donnay F, H. D. Eva HD, A. Lupi A, R. Raši R, R. Seliger R, and D. Simonetti D Determination of tropical deforestation rates and related carbon losses from 1990 to Global Change Biology, 20, , DOI: /gcb Anderson LO, Aragão LEOC, Gloor M, Arai E, Adami M, Saatchi S, Malhi Y, Shimabukuro YE, Barlow JB, Berenguer E, Duarte V. Submitted drought impact on burnt area and carbon emissions in southern Amazonia. Submitted to PLOS ONE. Berenguer E, Ferreira J, Gardner TA, Aragão LEOC, Camargo PB, Cerri CE, Durigan M, Oliveira RC, Vieira ICG, Barlow J A large-scale field assessment of carbon stocks in human-modified tropical forests. Global Change Biology, 20, , doi: /gcb Bodart C, Eva H, Beuchle R, Raši R, Simonetti D, Stibig H-J, Brink A, Lindquist E, Achard F Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS J. Photogramm., 66, , Cochrane MA, Laurance WF Fire as a large-scale edge effect in Amazonian forests. Journal of Tropical Ecology, 18(03): , Eva HD, Achard F, Beuchle R, de Miranda E, Carboni S, Seliger R, Vollmar M, Holler WA, Oshiro OT, Barrena Arroyo V, Gallego J Forest Cover Changes in Tropical South and Central America from 1990 to 2005 and Related Carbon Emissions and Removals. Remote Sensing, 4, INPE Monitoramento da cobertura florestal da Amazonia por Satélites. Sistemas PRODES, DETER, DEGRAD e QUEIMADAS INPE,São José dos Campos, 47 pp.

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