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.摘要：本研究旨在提出一种全新的咖啡种植区制图方法，该方法在分类流程中纳入多时相数据作为输入参数，具体采用陆地卫星专题制图仪（Landsat TM）归一化植被指数（NDVI）时间序列，并结合面向对象分类方法。本研究采用BFAST算法分析咖啡、牧场与原生植被的时间剖面特征，并结合地理面向对象影像分析（GEOBIA）开展制图工作。本研究从R语言扩展包greenbrown中提取以下多时相变量用于分类：均值、趋势与季节性特征。研究结果表明，咖啡、牧场与原生植被具备各异的时间序列特征，这一发现验证了将此类数据作为制图输入变量的合理性。结合光谱数据与时间变量开展的分类任务，整体准确率高达93%。仅使用多时相数据进行分类时，分类准确率同样超过80%。基于陆地卫星专题制图仪时间序列提取的数据可有效用于咖啡种植区制图，能够降低地物间的混淆程度，提升分类精度，进而可对高空间分辨率快速眼（RapidEye）影像中的地物实现准确识别与制图。