The Amazon is the largest expanse of tropical rainforest globally and deforestation resulting from land use changes poses a major concern for sustainable resource management. Synthetic aperture radar (SAR) data have all-weather and all-day capability, and thus are well-suited for mapping land use land cover (LULC) in tropical regions, which are seasonally influenced by cloud cover. Understanding modified land uses and drivers of deforestation is fundamental for the development of policies and measures to reduce emissions and for developing forest reference levels. Sentinel-1 C-band SAR data present unprecedented potential since the observations are free and openly available, providing for the first regular and standardize SAR data. This study analyzes the applicability of Sentinel-1 data for LULC classification as an effort to differentiate modified land uses, which is a current need for early-warning deforestation systems. The study area covers a deforestation frontier in the Peruvian Amazon where the landscape is characterized by a mosaic of LULC types. Collect Earth Online is used for reference LULC data collection, and seven classes are defined for this study: forest, secondary vegetation, agriculture, pasture, urban, mining, water. Amplitude γ^o time-series spanning 2017–2019 are analyzed along with statistical metrics for each class, and a classification decision tree is developed in Google Earth Engine. Overall accuracy obtained is considered low (52%). Results show high user's accuracy for forest and water classification, a lot of confusion between agriculture, secondary vegetation, and forest, and the use of the polarization ratio VV/VH is suggested to be useful for pasture classification. The orientation of streets in a urban environment is confirmed to have high influence on backscattering response. This study provides information for future research on LULC and the identification of drivers in deforestation monitoring systems that could result in additional actionable information for decision-making.

亚马逊是全球最大的热带雨林，土地用途变化导致的森林砍伐是可持续资源管理的主要关注点。合成孔径雷达（SAR）数据具有全天候和全天能力，因此非常适合绘制受云层覆盖季节影响的热带地区的土地利用土地覆盖（LULC）。了解修改后的土地用​​途和毁林动因对于制定减少排放的政策和措施以及发展森林参考水平至关重要。Sentinel-1 C波段SAR数据具有空前的潜力，因为这些观测是免费开放的，可提供第一个常规和标准化的SAR数据。这项研究分析了Sentinel-1数据在土地利用，土地利用变化和土地分类中的适用性，以此来区分改良后的土地用​​途，这是目前对早期森林砍伐系统的需求。研究区域覆盖秘鲁亚马逊地区的森林砍伐地区，该地区的景观以LULC类型的马赛克为特征。在线收集地球用于LULC数据参考，本研究定义了七个类别：森林，次生植被，农业，牧场，城市，采矿，水。振幅 次生植被，农业，牧场，城市，采矿，水。振幅 次生植被，农业，牧场，城市，采矿，水。振幅γ ^Ø时间序列跨越2017至2019年是具有用于每个类别的统计指标一起分析和分类决策树在谷歌地球引擎开发。总体获得的准确性被认为很低（52％）。结果表明，用户对森林和水的分类准确率很高，农业，次生植被和森林之间存在很多混乱，建议使用极化比VV / VH对牧场分类有用。确认城市环境中街道的方向对后向散射响应有很大影响。这项研究为将来的LULC研究和森林砍伐监测系统中的驱动因素识别提供了信息，这些信息可能会为决策提供更多可操作的信息。