Wetland maps currently in use by the Province of Nova Scotia, namely the Department of Natural Resources (DNR) wetland inventory map and the swamp wetland classes of the DNR forest map, need to be updated. In this study, wetlands were mapped in an area southwest of Halifax, Nova Scotia by classifying a combination of multi-date and multi-beam RADARSAT-2 C-band polarimetric SAR (polSAR) images with spring Lidar, and fall QuickBird optical data using the Random Forests (RF) classifier. The resulting map has five wetland classes (open-water/marsh complex, open bog, open fen, shrub/treed fen/bog, swamp), plus lakes and various upland classes. Its accuracy was assessed using data from 156 GPS wetland sites collected in 2012 and compared to the one obtained with the current wetland map of Nova Scotia. The best overall classification was obtained using a combination of Lidar, RADARSAT-2 HH, HV, VH, VV intensity with polarimetric variables, and QuickBird multispectral (89.2%). The classified image was compared to GPS validation sites to assess the mapping accuracy of the wetlands. It was first done considering a group consisting of all wetland classes including lakes. This showed that only 69.9% of the wetland sites were correctly identified when only the QuickBird classified image was used in the classification. With the addition of variables derived from lidar, the number of correctly identified wetlands increased to 88.5%. The accuracy remained the same with the addition of RADARSAT-2 (88.5%). When we tested the accuracy for identifying wetland classes (e.g. marsh complex vs. open bog) instead of grouped wetlands, the resulting wetland map performed best with either QuickBird and Lidar, or QuickBird, Lidar, and RADARSAT-2 (66%). The Province of Nova Scotia’s current wetland inventory and its associated wetland classes (aerial-photo interpreted) were also assessed against the GPS wetland sites. This provincial inventory correctly identified 62.2% of the grouped wetlands and only 18.6% of the wetland classes. The current inventory’s poor performance demonstrates the value of incorporating a combination of new data sources into the provincial wetland mapping.

新斯科舍省目前正在使用的湿地地图，即自然资源部（DNR）湿地清单地图和DNR森林地图的沼泽湿地类别，需要进行更新。在这项研究中，通过对多日期和多波束RADARSAT-2 C波段极化SAR（polSAR）图像与春季激光雷达的组合进行分类，并使用QuickBird秋季的光学数据，对新斯科舍省哈利法克斯西南部的湿地进行了制图随机森林（RF）分类器。生成的地图包含五个湿地类别（开阔水域/沼泽复合体，开阔沼泽，开，灌木/特里芬沼泽/沼泽，沼泽），以及湖泊和各种高地类别。使用2012年收集的156个GPS湿地站点的数据评估了其准确性，并将其与新斯科舍省当前的湿地地图获得的数据进行了比较。使用激光雷达，RADARSAT-2 HH，HV，VH，具有极化变量的VV强度和QuickBird多光谱（89.2％）的组合可获得最佳的总体分类。将分类后的图像与GPS验证站点进行比较，以评估湿地的制图准确性。首先要考虑一个由所有湿地类别（包括湖泊）组成的小组。这表明，当仅使用QuickBird分类图像进行分类时，只有69.9％的湿地被正确识别。加上来自激光雷达的变量，正确识别的湿地数量增加到88.5％。添加RADARSAT-2（88.5％）的准确性保持不变。当我们测试识别湿地类别（例如沼泽地与开阔沼泽）而不是分组湿地的准确性时，使用QuickBird和Lidar或QuickBird，Lidar和RADARSAT-2生成的湿地图效果最佳（66％）。新斯科舍省目前的湿地清单及其相关的湿地类别（航空照片解释）也根据GPS湿地进行了评估。该省级清单正确地确定了62.2％的分组湿地和仅18.6％的湿地类别。当前清单的不良表现证明了将新数据源组合到省级湿地测绘中的价值。分组湿地的2％，仅湿地类别的18.6％。当前清单的不良表现证明了将新数据源组合到省级湿地测绘中的价值。分组湿地的2％，仅湿地类别的18.6％。当前清单的不良表现证明了将新数据源组合到省级湿地测绘中的价值。