Details

Autor(en) / Beteiligte

• Fathololoumi, Solmaz
• Firozjaei, Mohammad Karimi
• Li, Huijie
• Biswas, Asim

Titel

Surface biophysical features fusion in remote sensing for improving land crop/cover classification accuracy

Ist Teil von

• The Science of the total environment, 2022-09, Vol.838, p.156520-156520, Article 156520

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. [Display omitted] •A feature and decision level-based fusion were proposed to land crop/cover classification.•Biophysical characteristics effects on the land crop/cover classification accuracy was assessed.•NDVI was the most important feature in land crop/cover classification.•The efficiency of Sentinel 2 derived features in land crop/cover classification was higher than Sentinel 1.•The use of voting strategy significantly improved the accuracy of the Land crop/cover map.