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Mapping Phragmites cover using WorldView 2/3 and Sentinel 2 images at Lake Erie Wetlands, Canada
Biological Invasions ( IF 2.9 ) Pub Date : 2021-01-03 , DOI: 10.1007/s10530-020-02432-0
Prabha Amali Rupasinghe , Patricia Chow-Fraser

Phragmites australis (Cav.) Trin. ex Steudel subspecies australis is an aggressive plant invader in North American wetlands. Remote sensing provides cost-effective methods to track its spread given its widespread distribution. We classified Phragmites in three Lake Erie wetlands (two in Long Point Wetland Complex (LP) and one in Rondeau Bay Marsh (RBM)), using commercial, high-resolution (WorldView2/3: WV2 for RBM, WV3 for LP) and free, moderate-resolution (Sentinel 2; S2) satellite images. For image classification, we used mixture-tuned match filtering (MTMF) and then either maximum likelihood (ML) or support vector machines (SVM) classification methods. Using WV2/3 images with ML classification, we obtained higher overall accuracy for both LP sites (93.1%) compared with the RBM site (86.4%); both Phragmites users’ and producers’ accuracies were also higher for LP (89.3% and 92.7%, respectively) compared with RBM (84.3% and 88.4%, respectively). S2 images with SVM classification provided similar overall accuracies for LP (74.7%) and for the RBM (74.3%); Phragmites users’ and producers’ accuracies for LP were 85.3% and 76.3%, and for the RBM, 69.1% and 79.2%, respectively. Using WV2/3, we could quantify small patches (percentage cover ≥ 20%; shoots ≥ 1 m tall; stem counts > 25) with accuracy > 80%, whereas parallel effort with S2 images only accurately quantified high density (> 60% cover), mature shoots (> 1 m tall; Stem counts > 100). By simultaneously mapping young or sparsely distributed Phragmites shoots and dense mature stands accurately, we show our approach can be used for routine mapping and regular updating purposes, especially for post-treatment effectiveness monitoring.



中文翻译:

在加拿大伊利湖湿地使用WorldView 2/3和Sentinel 2图像绘制芦苇覆盖图

芦苇(Trav。)前Steudel亚种芦苇在北美湿地的积极的植物入侵者。鉴于遥感的广泛分布,遥感提供了经济有效的方法来追踪其扩散。我们将芦苇分类在三个伊利湖湿地中(两个在长角湿地综合体(LP)中,两个在朗多湾沼泽(RBM)中)使用商业高分辨率(WorldView2 / 3:WV2用于RBM,WV3用于LP)和免费,中等分辨率(前哨2; S2)卫星图像。对于图像分类,我们使用了混合调谐匹配滤波(MTMF),然后使用最大似然(ML)或支持向量机(SVM)分类方法。使用带有ML分类的WV2 / 3图像,与RBM站点(86.4%)相比,我们获得了两个LP站点(93.1%)更高的总体准确性。既芦苇用户和生产者的精度与RBM(分别为84.3%和88.4%,)相比也有所提高对LP(89.3%和92.7%,分别地)。具有SVM分类的S2图像对LP(74.7%)和RBM(74.3%)提供了相似的总体准确性;LP的芦苇使用者和生产者准确度分别为85.3%和76.3%,RBM的准确度分别为69.1%和79.2%。使用WV2 / 3,我们可以量化精度> 80%的小斑块(覆盖率≥20%;枝条≥1 m;茎数> 25),而与S2图像并行工作只能准确量化高密度(覆盖率> 60%) ),成熟芽(> 1 m高;茎数> 100)。通过同时准确地绘制年轻或稀疏分布的芦苇枝和茂密的成熟林分,我们证明了我们的方法可用于常规作图和定期更新,特别是用于后期效果监测。

更新日期:2021-01-03
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