A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm
The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in São Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space–time classifier calibrated with all sites together on years 2009–2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R² = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R² = 0.95 and –1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation.
| Main Authors: | , , , , , , |
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | U30 - Méthodes de recherche, A01 - Agriculture - Considérations générales, canne à sucre, Saccharum officinarum, utilisation des terres, imagerie par satellite, Landsat, plantation, fouille de données, échantillonnage aléatoire, http://aims.fao.org/aos/agrovoc/c_7501, http://aims.fao.org/aos/agrovoc/c_6727, http://aims.fao.org/aos/agrovoc/c_4182, http://aims.fao.org/aos/agrovoc/c_36761, http://aims.fao.org/aos/agrovoc/c_36766, http://aims.fao.org/aos/agrovoc/c_5992, http://aims.fao.org/aos/agrovoc/c_eb9cea5d, http://aims.fao.org/aos/agrovoc/c_24499, http://aims.fao.org/aos/agrovoc/c_1070, |
| Online Access: | http://agritrop.cirad.fr/592792/ http://agritrop.cirad.fr/592792/1/2019Luciano_JAGsugarcaneSP.pdf |
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