Multitemporal variables for the mapping of coffee cultivation areas

Abstract

The objective of this work was to propose a new methodology for mapping coffee cropping areas that includes multitemporal data as input parameters in the classification process, by using the Landsat TM NDVI time series, together with an object-oriented classification approach. The algorithm BFAST was used to analyze coffee, pasture, and native vegetation temporal profiles, allied to a geographic object-based image analysis (GEOBIA) for mapping. The following multitemporal variables derived from the R package greenbrown were used for classification: mean, trend, and seasonality. The results showed that coffee, pasture, and native vegetation have different temporal behaviors, which corroborates the use of these data as input variables for mapping. The classifications using temporal variables, associated with spectral data, achieved high-global accuracy rates with 93% hit. When using only temporal data, ratings also showed a hit percentage above 80% accuracy. Data derived from Landsat TM time series are efficient for mapping coffee cropping areas, reducing confusion between targets and making the classification process more accurate, contributing to a correct characterization and mapping of objects derived from a RapidEye image, with a high spatial solution.

Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

“…Producer Accuracy = Xii X + i × 100% (10) where N = the number of all pixels used for observation, R = the number of rows in the error matrix (number of classes), Xii = Diagonal values of the contingency matrices of row i and column i, X + i = column pixel number i, and Xi + row pixel number i.…”

“…Several techniques for mapping coffee plantations were previously used by researchers. These include: supervised pixel-based classification [7,8], object-based classification [9,10], hybrid classification [11], use of spectral variables [12], topographic variables [7,13,14], tasseled cap, and the correlation of NDVI and precipitation [14]. Generally, the aforementioned research uses the classification technique.…”

“…Jia et al [33] explain that the addition of multi-temporal data derived from medium-resolution images achieved a significant increase in the accuracy of high-resolution image classification, especially in terms of differentiating plantation types. Souza et al [10] mapped coffee plantations by integrating multi-temporal variables extracted from Landsat (30 m) and Rapid-Eye imagery (5 m), achieving significant accuracy compared to when only using Rapid-Eye. Sentinel 2 is a new generation of medium-resolution imagery launched in 2015 and has significant potential for mapping coffee plantations [34].…”

Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System

“…Prediction models have been applied in several areas of knowledge, including agriculture (Mincato et al 2020, Liakos et al 2018, Chlingaryan et al 2018. In coffee culture, several applications of the use of artificial intelligence are found in the literature, of which the quality classification of coffee beans (Oliveira et al 2019), prediction of the degree of roasting of coffee (Leme et al 2019), detection of diseases such as rust using neural networks (Da Silva et al 2017) and decision tree (Meira et al 2009), geographical identification of coffee samples (Borsato et al 2011), mapping of coffee areas (Souza et al 2019, Marujo et al 2017, Souza et al 2016) among other applications. Regarding crop forecasting studies, computational models resulting from the performance of artificial intelligence techniques and data science have great contributions and advantages of being used, as they allow the interconnected study of management, climate and soil variables presenting themselves as useful tools for the study of dynamic and complex environments (Van Keulen & Asseng 2019).…”