Postgraduate Program in Environmental Sciences, State University of Pará (UEPA), 66095-100 Belém, Brazil.
Operations and Management Center of the Amazon Protection System (CENSIPAM), 66617-420 Belém, Brazil.
Sensors (Basel). 2019 Mar 6;19(5):1140. doi: 10.3390/s19051140.
In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.
在热带地区，如亚马逊地区，由于全年云层覆盖高，光学传感器的使用受到限制。作为替代方案，可以使用合成孔径雷达 (SAR) 产品，单独或与光学图像结合，来监测热带地区。在这个意义上，我们旨在使用 Sentinel 系列产品为热带地区选择最佳的土地利用和土地覆盖 (LULC) 分类方法。我们选择巴西贝伦市作为研究区域。选择了来自 Sentinel-1 (S-1) 和 Sentinel-2 (S-2) 的接近日期的图像，对其进行预处理、分割，并通过随机森林 (RF) 分类器集成以开发机器学习 LULC 分类。我们还结合了纹理图像分析 (S-1) 和植被指数 (S-2)。总共进行了六次 LULC 分类。结果表明，S-1 和 S-2 数据集成 (91.07%) 的总体精度 (OA) 最高，其次是仅 S-2 (89.53%) 和 S-2 与辐射指标 (89.45%)。仅 S-1 数据的结果最差 (56.01)。对于我们的分析，光学产品在堆叠中的集成增加了所有分类中的 OA。然而，由于 S-1 产品对热带地区的重要性，我们建议开展更多的研究。
