GV2M: Global Vegetation Monitoring and Modeling
Abstract type : Oral presentation
Session : S4: Agriculture global monitoring
Submitted by : Guerric le Maire
Authors and Speakers : Guerric le Maire
Information about other authors :
Guerric le Maire, UMR Eco&Sols, CIRAD, Montpellier, France
Stéphane Dupuy, UMR TETIS, CIRAD, Montpellier, France
Rodrigo Hakamada, International Paper, Brazil
Yann Nouvellon, UMR Eco&Sols, CIRAD, Montpellier, France
Tracking the land uses and land cover-changes at a regional scale is of critical importance to
analyse the modifications of global biogeochemical cycles and the impacts of environmental
policies. Several global land cover maps have been produced from classification of remotesensing data (MODIS land cover product, USGS-IGBP, UMD, GLC2000, GlobCover, etc.). Such
global maps obviously have a small number of classes and have a coarse spatial resolution,
and are therefore of limited to monitor the area covered by specific crops or plantations.
Classification of specific crops with the same satellite data have been developed in the last
decade to assess regionally and annually the land use change associated with the main crops.
Short rotation Eucalyptus plantations in Brazil are among the more productive forest
ecosystems in the world, with very high photosynthesis, transpiration and primary productivity
compared to natural forest, although they are generally classified as such in global maps
products. Their mean annual increment is 40 m3 ha-1 year-1, ranging between 25 and 60 m3 ha1 year -1. The area of Eucalyptus plantations in Brazil was 3.5 Mha in 2006, now reaches
approximately 5 Mha, and is projected to reach 9 to 13 Mha in 2020, following the global
demand for wood. A precise estimation of recent expansion of Eucalyptus areas and
associated land use change is a prerequisite to assess their environmental impact on regional
carbon and water cycles, and later on climate.
A binary classification of short rotation Eucalyptus plantations using MODIS 16-days 250m
NDVI time series was applied across Brazil. The method was based on the calculation of a
matching function between NDVI time series and 3 years long reference time-series. The
reference time-series was not a single time-series but a bounding envelope. The matching
function was applied to the entire NDVI time series through a moving window. This method was
robust to residual noise on the NDVI time series, and the threshold coefficient for the binary
classification was adjusted using an equilibrate omission-comission criteria. With this method, it
was possible to detect any presence of Eucalyptus between 2003 and 2009 at monthly timesteps, including the periods of bare soils between two rotations (rotations are typically 6-8
years long). The final almost continuous tri-dimensional map (space and time) was validated
with three different datasets:
a local land use map obtained using 4 Landsat images at different dates: comparison
between MODIS classification and Landsat classification gave an average accuracy of 85% and
a user accuracy of 72% for Eucalyptus class. The Landsat classification was also used to
compute the omission-comission Pareto boundary to analyse the resolution effect.
the polygon areas of three companies, which gave the exact position of Eucalyptus
the polygon areas of three companies, which gave the exact position of Eucalyptus
plantations at three different location in Brazil: the producer accuracies was comprised between
58 and 72% depending on the landscape heterogeneity.
a stratified random sampling of 800 locations across Brazil, with sampling based on very
high resolution images visualized in GoogleEarth: the overall accuracy of 87%
All three methods gave high global accuracies statistics, but a global underestimation of
Eucalyptus areas compared to large scales census was observed, and was mainly attributed to
the small isolated Eucalyptus stands and to resolution effects. Different way to improve the
Eucalyptus area estimates are further discussed in this study. The date of afforestation and the
date of clearcut at the end of a rotation and planting date were retrieved from the NDVI timeseries with a precision of ~66 days. Using rotation-wide NDVI time series open the possibility to
monitor the stand biomass and carbon stocks at large scales as already shown locally in other
studies, either through statistical or process-based models. These approaches will be used in
the forthcoming SIGMA European project, with data based on the JECAM “Eucalyptus” site, to
monitor the land use change associated with Eucalyptus expansion in São Paulo region
compared to other land use, like pasture, sugarcane, citrus orchards, etc.
References :
Keywords :
Forest plantations, MODIS NDVI time series, classification
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