Large-scale forest clear-cut identification is one of the major application of remote sensing techniques. ALOS-2/PALSAR-2 is the latest SAR satellite providing multi-polarized L-band SAR data. With increasing deforestation, it is important to assess the potential of SAR data for identifying clear-cuts in forest regions. In this research work, multi-temporal ALOS-2/PALSAR-2 SAR data and supplementary Landsat-8 optical data sets are acquired over Indian tropical forest, and SAR parameters are analysed over a progressively clear-cut Teak plantation. Sensitivity of the SAR parameters to progressive clear-cuts is estimated and found that the cross-polarized backscatter ${\sigma }_{HV}^0$ and entropy parameter H are most sensitive to both partial and complete clear-cut in forest compartments. An entropy thresholding based classification is carried out to identify clear-cut regions with a good accuracy. The study highlights the utility of SAR parameters to monitor forest clear-cuts for better forest management.

大规模森林砍伐识别是遥感技术的主要应用之一。ALOS-2 / PALSAR-2是最新的SAR卫星，可提供多极化L波段SAR数据。随着森林砍伐的增加，评估SAR数据对识别森林地区的明确砍伐潜力具有重要意义。在这项研究工作中，在印度热带森林上采集了多时相的ALOS-2 / PALSAR-2 SAR数据和补充的Landsat-8光学数据集，并在逐步采伐的柚木人工林上分析了SAR参数。估计SAR参数对渐进清晰的敏感度，发现交叉极化的反向散射${\sigma }_{H\mathrm{伏特}}^0$和熵参数H对林区的部分和全部砍伐最敏感。进行基于熵阈值的分类，以高精度识别清晰区域。这项研究强调了SAR参数在监测森林砍伐情况以更好地管理森林方面的实用性。