Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland

• Published in: Advanced Research in Life Sciences
• Main Author: Lemenkova, Polina
• Other Authors: Schmidt United Institute of Physics of the Earth Moscow (IPE), Russian Academy of Sciences Moscow (RAS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: Landsat TM; QGIS; NDVI; Vegetation Index; Cartography; ACM: I.: Computing Methodologies; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation; ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION; ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING; ACM: K.: Computing Milieux/K.3: COMPUTERS AND EDUCATION; ACM: K.: Computing Milieux/K.8: PERSONAL COMPUTING; 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.4.1: Digitization and Image Capture; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.3: Enhancement; 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.4.8: Scene Analysis; [SDE]Environmental Sciences; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; [SDE.ES]Environmental Sciences/Environmental and Society; [SDE.MCG]Environmental Sciences/Global Changes; [SDE.IE]Environmental Sciences/Environmental Engineering; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]; [SDV.BDD]Life Sciences [q-bio]/Development Biology; [SDV.BID]Life Sciences [q-bio]/Biodiversity; [SDV.EE]Life Sciences [q-bio]/Ecology; environment; [SDV.EE.ECO]Life Sciences [q-bio]/Ecology; environment/Ecosystems; [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]; [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; Arctic; Eyjafjörður; Skagafjörður; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-03030414; https://hal.archives-ouvertes.fr/hal-03030414/document; https://hal.archives-ouvertes.fr/hal-03030414/file/25438050%20-%20Advanced%20Research%20in%20Life%20Sciences%20Hyperspectral%20Vegetation%20Indices.pdf; https://doi.org/10.2478/arls-2020-0021

International audience The vegetation indices (VIs) derived from the hyperspectral reflectance of vegetation are presented in this study for monitoring live green vegetation in the northern ecosystems of Iceland, along the fjords of Eyjafjörður and the Skagafjörður. The comparative analysis of the following VIs was performed: the NDVI, RVI, NRVI, TVI, CTVI, TTVI and SAVI. The methodology is based on the raster calculator band in a QGIS. The dataset includes a Landsat TM scene of 2013, UTM Zone 53, WGS84 captured from the GloVis. The computed bands include the NIR and R spectral bands and their combinations according to the algorithms of each of the seven VIs. The hyperspectral reflectance and crop canopy computations were applied to generate various scales of VIs and demonstrated following data range: NDVI: -0.91 to 0.65, RVI: 0.22 to 19.65, NRVI: 0.63 to 0.90, TVI: 0 to 1.12, CTVI: -0.64 to 1.07, TTVI: 0.70 to 1.18 and SAVI: -1.36 to 0.99 (roughly to 1.00). Of these, the RVI, NRVI, TVI and TTVI are adjusted to the positive values while the NDVI, CTVI and SAVI do include the negative diapason in the dataset due to the computing algorithm. The algorithms of the seven VIs are described and visualized in form of maps based on the multispectral remote sensing Landsat TM imagery identifying vegetated areas, their health condition and distribution of green areas against the bare soils, rocks, ocean water, lakes and ice-covered glaciers. The paper contributes both to the technical presentation of the QGIS functionality for the Landsat TM data processing by a raster calculator, and to the regional geographic studies of Iceland and Arctic ecosystems.