Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil
Introduction
Tracking the land uses and land cover changes at a regional scale is of critical importance to analyze the modifications of global biogeochemical cycles and the impacts of environmental policies. Several global land cover maps have been produced from classification of remote sensing data (MODIS land cover product, USGS-IGBP, UMD, GLC2000, GlobCover, etc.). The classification algorithms were an ensemble supervised decision trees, e.g. for MODIS MCD12Q1 product (Friedl, Sulla-Menashe, Tan, Schneider, Ramankutty, Sibley and Huang, 2010), unsupervised classification followed by post-classification refinement, e.g. USGS-IGBP product (Loveland, Reed, Brown, Ohlen, Zhu, Yang and Merchant, 2000), clustered supervised and unsupervised classification, e.g. GlobCover (Bontemps, Defourny, Van Bogaert, Arino, Kalogirou and Ramos Perez, 2011). Such global maps obviously have a small number of classes and have a coarse spatial resolution, and are therefore of limited interest to monitor the area covered by specific crops or plantations. In parallel to the development of these global maps, researchers have used the same satellite image resources to produce maps of crop classes at farm or landscape levels in order to assess regionally and annually the land use changes of the main crops (e.g. Arvor et al., 2011, Brown et al., 2013, Epiphanio et al., 2010, Galford et al., 2010, Wardlow et al., 2007). All these studies have shown the potential of satellite image series to classify different crops and cropping systems, and therefore to assess the consequences of agricultural practices on land use changes. Indeed, the knowledge of the crop, forest or grassland phenology, together with their spectral signature, makes it possible to greatly improve the precision of the determination of subclasses. As a consequence, it is difficult to get a unified methodology and many different methods have been used to classify coarse resolution satellite image time series for the production of crop maps, each method depending on the objective of the study and of the crop type under consideration (García-Mora, Mas, & Hinkley, 2011).
While considerable efforts have been made to follow the deforestation of tropical forests, little has been done to follow the large expansion of forest plantations, while they can represent a significant area compared to deforestation in tropical and subtropical areas (Hansen et al., 2013). For instance, the area of Eucalyptus plantations in Brazil was 3.5 million ha in 2006 and now reaches approximately 5 million hectares (ABRAF, 2012). The Ministry of Agriculture of Brazil expects an increase of forest plantations up to a total of 9 million hectares by 2020 (Ministério da Agricultura website), with probably more than three quarters planted with Eucalyptus species. However, simulated scenarios show that the forest sector will need to reach 13.5 million hectares in 2020 to meet the expected demand for wood (EMBRAPA-Florestas, 2010). For comparison, perennial and annual croplands covered approximately 60 million hectares and native and cultivated pastures 160 million hectares in Brazil ((IBGE, 2006), compiled in Barretto, Berndes, Sparovek, and Wirsenius (2013)). Recent expansion of Eucalyptus plantations is probably the most important agrarian change that have occurred in recent years in some parts of Brazil (Kröger, 2012). Most of commercial Eucalyptus plantations in Brazil are managed to produce pulpwood, charcoal for steel industry, biomass for energy, and timber for solid wood items and different types of reconstituted panels (ABRAF, 2012). Selection of productive hybrids and clones, improved silviculture, and ideal soil and climate conditions of Brazil for eucalypt-based forestry have led to mean productivities of 40–45 m3 ha− 1 yr− 1 in commercial plantations across Brazil (Gonçalves et al., 2013).
There is a critical need to monitor the rapid expansion of short-rotation plantations like Eucalyptus plantations at regional scales. An accurate estimation of recent changes of Eucalyptus areas is a prerequisite to assess, for example, the environmental impact of afforestation or the impact of incentive policies on wood production at a regional scale (Cossalter & Pye-Smith, 2003). Eucalyptus plantations are mostly found in the Minas Gerais and São Paulo states (about half of total Eucalyptus areas in Brazil), but recent expansions mainly occur in the Mato Grosso do Sul and Para states (ABRAF, 2012). New Eucalyptus plantations are mainly established on pastures, or follow the conversion of other forest plantations (mainly pine or low-productive Eucalyptus stands), but few statistical data are available on these land use changes. It is likely that future afforestation rates will also be influenced by availability of suitable land (Piketty, Wichert, Fallot, & Aimola, 2009). Use of high resolution satellite images (e.g. Landsat) to monitor Eucalyptus expansion would be very difficult at large scales for both technical reasons and lack of data in some areas. Coarse resolution satellite images would be more suitable to follow large areas, and a resolution close to 250 m like the one of MODIS sensor onboard Terra satellite (for Red and Near infrared bands) can be sufficient to monitor commercial Eucalyptus plantations (le Maire et al., 2011b, Marsden et al., 2010). Another advantage of MODIS sensor is to have more than 12 years of continuous global data, with a high observation frequency.
Eucalyptus plantations have never been mapped across Brazil using remote sensing data, despite their rapid expansion, their economic importance and their large scale environmental impacts. The specific objectives of this study were 1) to develop a MODIS-based methodology to map Eucalyptus plantations and 2) to provide information on rotation practices through the detection of planting dates after afforestation and successive clearcuts, 3) to evaluate the potential of the method for regional area estimations.
Section snippets
Theoretical background
Classification of vegetation at coarse spatial resolution and decadal temporal scale is associated with two major issues: the classification method itself and the validation of the classification. Classification methods generally rely on the multispectral signals and their changes along the year, processed through classification algorithms. Classifying MODIS pixels at a 250 m spatial resolution is a challenge because spectral information is low at this sensor spatial resolution, and landscape
Choice of the best distance function
The selection of the best distance function was based on the omission–commission graph obtained on the high resolution Landsat classification (Fig. 4a). The classification results are very different depending on the chosen distance function. Following the approach of Boschetti et al. (2004), it was possible to state that some classification methods (i.e. some distance functions) were “dominant” to other ones, and therefore should be preferred. The Boundary Envelope (BE) and the Standardized
Map accuracy assessment
The BE method was shown to be well suited for Eucalyptus classification, both in terms of accuracy and robustness. Validation of such vegetation classification at these scales is a matter of numerous discussions, as emphasized in Strahler et al. (2006). Errors of classification could come from: 1) radiometric errors (sensor, atmospheric corrections, etc.) and percentage of clouds in the time series; 2) the mixed pixel issue and landscape heterogeneity: 3) the misclassification errors due to the
Conclusion
This study mapped the distribution of fast-growing Eucalyptus plantations across a large part of Brazil, using MODIS 250 m NDVI time series. The classification method is based on the NDVI variations during the two first years of a rotation, by computing a matching function based on the Bounding Envelope. Bounding Envelope distance was the best distance function among the seven functions tested. The classification method with BE function was efficient to classify the NDVI time series, robust to
Acknowledgements
This study was conducted in the frame of the SIGMA European Collaborative Project (FP7-ENV-2013 SIGMA — Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM — project no. 603719). The Landsat area includes a JECAM site (Joint Experiment of Crop Assessment and Monitoring). We thank the forest companies Duratex, International Paper of Brasil, and Fibria Celulose for providing the data. We thank NASA for sharing MODIS and Landsat data.
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