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

Autor: Polina Lemenkova
Přispěvatelé: Schmidt United Institute of Physics of the Earth [Moscow] (IPE), Russian Academy of Sciences [Moscow] (RAS)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.2: Compression (Coding)
010504 meteorology & atmospheric sciences
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.8: Scene Analysis
Iceland
Pharmaceutical Science
computer science
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation
01 natural sciences
geography
Arctic
Pharmacology (medical)
mapping
[SDV.BDD]Life Sciences [q-bio]/Development Biology
ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION
[SDV.EE]Life Sciences [q-bio]/Ecology
environment

Data processing
education
geography.geographical_feature_category
[SDE.IE]Environmental Sciences/Environmental Engineering
Hyperspectral imaging
land cover type
ACM: K.: Computing Milieux/K.8: PERSONAL COMPUTING
Spectral bands
computer.file_format
Vegetation
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.1: Digitization and Image Capture
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Landsat TM
[SDE]Environmental Sciences
land cover use
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.3: Enhancement
Raster graphics
ecology
environment
ACM: I.: Computing Methodologies
QGIS
vegetation index
010506 paleontology
NDVI
[SDE.MCG]Environmental Sciences/Global Changes
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences
[SDV.BID]Life Sciences [q-bio]/Biodiversity
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General
Normalized Difference Vegetation Index
Multispectral pattern recognition
[SDV.EE.ECO]Life Sciences [q-bio]/Ecology
environment/Ecosystems

[INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
image analysis
ACM: K.: Computing Milieux/K.3: COMPUTERS AND EDUCATION
cartography
0105 earth and related environmental sciences
Remote sensing
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Glacier
modeling
15. Life on land
data visalization
[SDE.ES]Environmental Sciences/Environmental and Society
ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation
ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING
Complementary and alternative medicine
13. Climate action
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
satellite image
Environmental science
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
computer
Zdroj: Advanced Research in Life Sciences
Advanced Research in Life Sciences, Sciendo, 2020, 4 (1), pp.70-78. ⟨10.2478/arls-2020-0021⟩
ISSN: 2543-8050
DOI: 10.2478/arls-2020-0021⟩
Popis: 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 Eyjafjörđur and the Skagafjörđur. 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.
Databáze: OpenAIRE