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Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2021-07-29 , DOI: 10.1080/15481603.2021.1952541
Alex Okiemute Onojeghuo 1 , Ajoke Ruth Onojeghuo 1 , Michelle Cotton 1 , Johnathan Potter 1 , Brennan Jones 1
Affiliation  

ABSTARCT

In the Grassland Natural Region (GNR) of southern Alberta, wetlands are relatively small-sized disconnected prairie pothole marshes, swamps, and shallow open water habitats often surrounded by grasslands, parkland forests, agricultural lands, and urban areas. These wetlands are susceptible to climatic variability, resulting in temporally and spatially dynamic habitats that are difficult to map accurately. This study hypothesizes that seasonal synthetic aperture radar (SAR) and optical imagery will capture temporal variations of wetlands in the spring/summer and fall months of 2017, 2018, 2019, and 2020. We propose that these data combined with topographic variability offered by LiDAR-derived topographic wetness index (TWI) shall result in the accurate delineation of the wetlands. Using a combination of open-access government databases, we generated ground and training data to develop the classification models and perform accuracy assessments. The wetland map products’ overall accuracy results ranged from 63.2% to 75.7%. The pixel-based random forest (RF) classified dataset (Dataset 5multi-temporal (2017–2020) S1 SAR (VH) and S2 optical (B8 and B11) bands fused with TWI) had the highest overall accuracy (75.6%). The RF result significantly outperformed similar CART (Classification and Regression Trees) and SVM (Support Vector Machine) classifications, which had overall accuracies of 67.4% and 63.2%, respectively. In addition, the RF optimal wetland product had the best combination of F-score values for wetland and upland classes: 0.61 (marsh), 0.82 (open water), 0.75 (swamp), and 0.8 (uplands). Overall, the methodology adopted in this study is promising for mapping the spatial distribution of wetland habitats across the seasonally dynamic GNR of Alberta.



中文翻译:

艾伯塔草原自然区湿地映射与多时相 sentinel-1 和 -2 图像(2017-2020)和 LiDAR 数据

抽象

在艾伯塔省南部的草原自然区 (GNR),湿地是相对较小的、不连贯的草原坑洼沼泽、沼泽和浅水开阔水域栖息地,通常被草原、绿地森林、农田和城市地区包围。这些湿地易受气候变化的影响,导致难以准确绘制时空动态栖息地。本研究假设季节性合成孔径雷达 (SAR) 和光学图像将捕捉 2017 年、2018 年、2019 年和 2020 年春季/夏季和秋季月份湿地的时间变化。我们建议将这些数据与 LiDAR 提供的地形变化相结合派生的地形湿度指数 (TWI) 将导致对湿地的准确划定。使用开放访问政府数据库的组合,我们生成了地面和训练数据来开发分类模型并进行准确性评估。湿地地图产品的整体精度结果在 63.2% 到 75.7% 之间。基于像素的随机森林 (RF) 分类数据集 (数据集 5多时相 (2017-2020) S1 SAR (VH) 和 S2 光学(B8 和 B11)波段与 TWI 融合)具有最高的整体精度(75.6%)。RF 结果显着优于类似的 CART(分类和回归树)和 SVM(支持向量机)分类,其总体准确率分别为 67.4% 和 63.2%。此外,RF 最佳湿地产品具有湿地和高地类别的 F 值的最佳组合:0.61(沼泽)、0.82(开阔水域)、0.75(沼泽)和 0.8(高地)。总体而言,本研究采用的方法有望用于绘制艾伯塔省季节性动态 GNR 中湿地栖息地的空间分布。

更新日期:2021-07-29
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