SAGA GIS for information extraction on presence and conditions of vegetation of northern coast of Iceland based on the Landsat TM

• Published in: Acta Biologica Marisiensis
• Main Author: Lemenkova, Polina
• Other Authors: Ocean University of China (OUC)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: Iceland; Landsat TM; SAGA GIS; cartography; vegetation index; machine learning; automatization; mapping; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION; ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.3: Relaxation; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.8: Scene Analysis; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.8: Scene Analysis/I.4.8.0: Color; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.8: Scene Analysis/I.4.8.1: Depth cues; ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation; ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING; ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications; ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.5: Implementation; ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.2: Compression (Coding); ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.1: Digitization and Image Capture; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.1: Pixel classification; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.0: Edge and feature detection; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.2: Region growing; partitioning; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [SDE]Environmental Sciences; [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]; [SDE.MCG]Environmental Sciences/Global Changes; [SDE.IE]Environmental Sciences/Environmental Engineering; [SDU]Sciences of the Universe [physics]; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology; [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology; [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology; Arctic; Eyjafjörður; Skagafjörður; ice covered areas
• Online Access: https://hal.archives-ouvertes.fr/hal-02986272; https://hal.archives-ouvertes.fr/hal-02986272/document; https://hal.archives-ouvertes.fr/hal-02986272/file/10.2478_abmj-2020-0007.pdf; https://doi.org/10.2478/abmj-2020-0007

International audience The paper aims to evaluate the presence and condition of vegetation by SAGA GIS. The study area covers northern coasts of Iceland including two fjords, the Eyjafjörður and the Skagafjörður, prosperous agricultural regions. The vegetation coverage in Iceland experience the impact of harsh climate, land use, livestock grazing, glacial ablation and volcanism. The data include the Landsat TM image. The methodology is based on computing raster bands for simulating Tassel Cap Transformation (wetness, greenness and brightness) and Enhanced Vegetation Index (EVI) sensitive to high biomass. The results include modelled three bands of brightness, greenness and wetness. Greenness variation shows the least values in ice-covered areas (-56.98 to-18.69). High values (-23.48 to 9.12) are in the valleys with dense vegetation, correlating with the geomorphology of the river network, the vegetation-free areas and ocean which corresponds to the peak of 30.87 to 41.19. The bell-shaped data distribution shows frequency 43.19-141.74 for vegetation indicating healthy state and canopy density. Maximal values are in ice-covered regions and glaciers (64°N-65°N). Very low values (0 to-20) show desertification and mountainous rocks. Moderate values (20-40) indicate healthy vegetation. The most frequent data:-28,17 to 11,8. The EVI shows data variations (-0.14 to 0.04). The study contributes both to the regional studies of Arctic Iceland and methodological approach of remote sensing data processing by SAGA GIS.