School of Environmental Sciences, University of Guelph, Canada.
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran.
Sci Total Environ. 2022 Sep 10;838(Pt 3):156520. doi: 10.1016/j.scitotenv.2022.156520. Epub 2022 Jun 6.
Preparing up-to-date land crop/cover maps is important to study in order to achieve food security. Therefore, the aim of this study was to evaluate the impact of surface biophysical features in the land crop/cover classification accuracy and introduce a new fusion-based method with more accurate results for land crop/cover classification. For this purpose, multi-temporal images from Sentinel 1 and 2, and an actual land crop map prepared by Agriculture and Agri-Food Canada (AAFC) in 2019 were used for 3 test sites in Ontario, Canada. Firstly, surface biophysical features maps were prepared based on spectral indices from Sentinel 2 including Normalized Difference Vegetation Index (NDVI), Index-based Built-up Index (IBI), Wetness, Albedo, and Brightness and co-polarization (VV) and cross-polarization (VH) from Sentinel 1 for different dates. Then, different scenarios were generated; these included single surface biophysical features as well as a combination of several surface biophysical features. Secondly, land crop/cover maps were prepared for each scenario based on the Random Forest (RF). In the third step, based on the voting strategy, classification maps from different scenarios were combined. Finally, the accuracy of the land crop/cover maps obtained from each of the scenario was evaluated. The results showed that the average overall accuracy of land crop/cover maps obtained from individual scenario (one feature) including NDVI, IBI, Wetness, Albedo, Brightness, VV and VH were 66%, 68%, 63%, 60%, 57%, 62% and 58%, respectively, which by the surface biophysical features fusion, the overall accuracy of land crop/cover maps increased to 83%. Also, by combining the classification results obtained from different scenarios based on voting strategy, the overall accuracy increased to 89%. The results of this study indicate that the feature level-based fusion of surface biophysical features and decision level based fusion of land crop/cover maps obtained from various scenarios increases the accuracy of land crop/cover classification.
为了实现粮食安全，制作最新的土地作物/覆盖图以进行研究非常重要。因此，本研究的目的是评估地表生物物理特征对土地作物/覆盖分类精度的影响，并引入一种新的融合方法，以获得更准确的土地作物/覆盖分类结果。为此，使用了来自 Sentinel-1 和 2 的多时相图像，以及加拿大农业和农业食品部（AAFC）在 2019 年制作的实际土地作物图，用于加拿大安大略省的 3 个测试点。首先，根据 Sentinel-2 的光谱指数（包括归一化植被指数（NDVI）、基于指数的建成区指数（IBI）、湿度、反照率和亮度以及 Sentinel-1 的同极化（VV）和交叉极化（VH）），为不同日期制作了地表生物物理特征图。然后，生成了不同的情景，这些情景包括单一地表生物物理特征以及几个地表生物物理特征的组合。其次，基于随机森林（RF），为每个情景制作了土地作物/覆盖图。在第三步中，根据投票策略，对不同情景的分类图进行了组合。最后，评估了每个情景获得的土地作物/覆盖图的准确性。结果表明，从单个情景（一个特征）获得的土地作物/覆盖图的平均总体精度，包括 NDVI、IBI、湿度、反照率、亮度、VV 和 VH，分别为 66%、68%、63%、60%、57%、62%和 58%，而通过地表生物物理特征融合，土地作物/覆盖图的总体精度提高到 83%。此外，通过基于投票策略结合不同情景获得的分类结果，总体精度提高到 89%。本研究的结果表明，基于特征级别的地表生物物理特征融合和基于决策级别的来自不同情景的土地作物/覆盖图融合提高了土地作物/覆盖分类的准确性。
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