应用遥感数据识别意大利沿海松林灾害级别

Classification of Damage Level for Italian Coast Forestry Using Remote Sensing Data

分析了 L andsat TM和 ERS- 1SAR数据用于识别意大利沿海松林污染灾害级别的潜在能力。结果显示出夏季获取的 TM数据在森林灾害制图上远比冬季获取的 TM数据和 SAR影像有效 ,但 SAR影像的贡献也是不可忽略的。文中给出了应用人工神经元网络 B- P模型得到的对意大利沿海松林污染灾害级别划分的结果和应用

The potential of applying Landsat TM and ERS 1 SAR data to classify the damage levels of Italian coast forestry was analyzed. The result indicates that TM data acquired in summer is more effective than that obtained in winter and ERS 1 SAR data on forestry damage mapping. But the contribution of ERS 1 SAR data for this study is not neglected. The B P (Back propagation) model of artificial neural network was applied to identify different levels of forestry damage. The evaluation for the classified precision with FINDKAPPA program is provided and the map of forestry damage levels for study area is also provided.