Optical image classification converts spectral data into thematic information from the spectral signature of each object in the image. However, spectral separability is influenced by intrinsic characteristics of the targets, as well as the characteristics of the images used. The classification process will present more reliable results when aspects associated with natural environments (climate, soil, relief, water, etc.) and anthropic environments (roads, constructions, urban area) begin to be considered, as they determine and guide land use and land cover (LULC). The objectives of this study are to evaluate the integration of environmental variables with spectral variables and the performance of the Random Forest algorithm in the classification of Landsat-8 OLI images, of a watershed in the Eastern Amazon, Brazil. The classification process used 96 predictive variables, involving spectral, geological, pedological, climatic and topographic data and Euclidean distances. The selection of variables to construct the predictive models was divided into two approaches: (i) data set containing only spectral variables, and (ii) set of environmental variables added to the spectral data. The variables were selected through nonlinear correlation analysis, with the Randomized Dependence Coefficient and the Recursive Feature Elimination (RFE) method, using the Random Forest classifier algorithm. The spectral variables NDVI, bands 2, 4, 5, 6 and 7 of the dry season and band 4 of the rainy season were selected in both approaches (i and ii). The Euclidean distance from the urban area, Arenosol soil class, annual precipitation, precipitation in February and precipitation of the wettest quarter were the variables selected from the auxiliary data set. This study showed that the addition of environmental data to the spectral data reduces the limitation of the latter, regarding the discrimination of the different classes of LULC, in addition to improving the accuracy of the classification. The addition of soil classes to spectral variables provided a reduction in errors for vegetation classification (Evergreen Forest and Cerrado Sensu Stricto), as it was able to inform about nutrient availability and water storage capacity. The study demonstrates that the addition of environmental variables to the spectral variables can be an alternative to improve monitoring in areas of ecotone in Neotropical regions.

光学图像分类将图像中每个对象的光谱特征从光谱数据转换为主题信息。但是，光谱的可分离性受目标的固有特性以及所使用图像的特性的影响。当开始考虑与自然环境（气候，土壤，地形，水等）和人类环境（道路，建筑，市区）相关的方面时，分类过程将提供更可靠的结果，因为它们可以确定并指导土地使用和土地覆被（LULC）。这项研究的目的是评估环境变量与光谱变量的集成以及随机森林算法在巴西东部亚马逊流域的Landsat-8 OLI图像分类中的性能。分类过程使用了96个预测变量，涉及光谱，地质，儿童学，气候和地形数据以及欧几里得距离。选择用于构建预测模型的变量分为两种方法：（i）仅包含光谱变量的数据集，以及（ii）添加到光谱数据中的环境变量集。使用随机森林分类器算法，通过随机相关系数和递归特征消除（RFE）方法，通过非线性相关分析选择变量。在两种方法（i和ii）中均选择了光谱变量NDVI，干旱季节的频带2、4、5、6和7和雨季的频带4。距市区的欧几里得距离，槟榔土壤等级，年降水量，从辅助数据集中选择的变量是2月的降水量和最湿季的降水量。这项研究表明，将环境数据添加到光谱数据中，不仅可以改善光谱分类的准确性，还可以减少光谱数据的局限性（就区分LULC的不同类别而言）。将土壤类别添加到光谱变量中可以减少植被分类（常绿森林和塞拉多森苏斯特里克托）的错误，因为它能够告知养分利用率和储水量。研究表明，将环境变量添加到光谱变量中可以作为改进对新热带地区过渡带地区监测的一种替代方法。