| Full text | |
| Author(s): |
dos Santos Luciano, Ana Claudia
;
Araujo Picoli, Michelle Cristina
;
Rocha, Jansle Vieira
;
Duft, Daniel Garbellini
;
Camargo Lamparelli, Rubens Augusto
;
Lima Verde Leal, Manoel Regis
;
Le Maire, Guerric
Total Authors: 7
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION; v. 80, p. 10-pg., 2019-08-01. |
| Abstract | |
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 Sao 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 R-2 = 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 R-2 = 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. (AU) | |
| FAPESP's process: | 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil |
| Grantee: | Rubens Augusto Camargo Lamparelli |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |