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This study aimed to identify regression and classification models using the most robust VI derived from Sentinel-2/MSI digital images for the detection of areas attacked by Sapajus nigritus in a Pinus taeda L. stand located in Bocaina do Sul, Santa Catarina State. In order to do this, three orbital images of the Sentinel-2/MSI constellation were used in pre-attack, attack and post-attack dates. A total of 46 field plots were allocated, in which tree individuals were classified according to the intensity of the year in: I: no damage, II: moderate, III: severe and VI: dead. Using Principal Component Analysis, vegetation index were selected as regressor variables for the development of regression models by Stepwise, Support Vector Machine (SVM) and Random Forest (RF) to estimate the percentage of attack classes per plot. The best model selection was based on regression model fit statistics. The most robust indexes were MCARI, NDI45 and NDVIRR, which estimated for the classes a percentage of attack of S. nigritus per plot with adjusted R2 of 0.80, 0.46, 0.88 and 0.97; Syx of 13.49, 61.42, 15.11 and 8.69 (% 0.045 ha-1) and RMSE of 1.95, 4.49, 2.11 and 0.32 (% 0.045 ha-1). It was possible to estimate/identify areas attacked by this primate in the evaluated stand, highlighting the Stepwise model, followed by SVM and RF. However, it is recommended to test digital images of high/ultrahigh spatial resolution to corroborate with such methodology.

Estimation of sanity of a stand of Pinus taeda L. after the attack of Sapajus nigritus Kerr (1972) using vegetation index