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Linking GIS and Remote Sensing Data to Study Vegetation Patterns
- Source :
- New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training, New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training, Kazan National Research Technical University n.a. A.N. Tupolev KNITU-KAI, Nov 2015, Naberezhnye Chelny (Tatarstan), Russia. pp.174-178, ⟨10.6084/m9.figshare.7210388⟩
- Publication Year :
- 2015
- Publisher :
- HAL CCSD, 2015.
-
Abstract
- International audience; The paper studies changes in land cover types in tundra landscapes during the past two decades. The study area is located in the Yamal Peninsula, north-central Russia. The main objective of this research is to analyse changes in vegetation distribution and land cover types over the area of Yamal Peninsula. Methodology of the work aims at technical application of the remote sensing and GIS tools for studies and includes georeferencing, creation of color composites, supervised classification. The research data includes Landsat scenes. The research method consists in Landsat image processing (Fig.1), georeferencing via the Google Earth and spatial analysis performed in ILIWIS GIS. The choice of Landsat scenes for land cover mapping is explained by their well-known advantages of application in geosciences and cartography. The final outcomes show changes in the vegetation coverage and land cover classes in Bovanenkovo region of the Yamal Peninsula, which happened during the past two decades. The results are received by comparing and analyzing of two classified maps covering the same geographic region with time span of 23 years: in 1988 and 2011. The changes mostly concern types of land covers and overall increase of shrubland and willows. It can be explained by the complex environmental changes in Arctic regions, which leads to ―greenness‖ processes, or unnatural increase of willows.
- Subjects :
- land cover analysis
satellite inages
Image classification
[SDE.MCG]Environmental Sciences/Global Changes
SIG et modélisation spatiale
[SCCO.COMP]Cognitive science/Computer science
[SHS]Humanities and Social Sciences
[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing
remote sensing
[SCCO]Cognitive science
Landsat TM and ETM data
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY]
[INFO]Computer Science [cs]
[INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
[STAT.CO]Statistics [stat]/Computation [stat.CO]
GIS Analysis
SIG Systèmes d'information géographique
Spatial analysis
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
[SHS.GEO]Humanities and Social Sciences/Geography
GIS
[STAT]Statistics [stat]
SIG et aménagement
[SHS.ENVIR]Humanities and Social Sciences/Environmental studies
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
Landsat TM
[SDE]Environmental Sciences
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Satellite image interpretation
Spatial analysis statistics
land cover types
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training, New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training, Kazan National Research Technical University n.a. A.N. Tupolev KNITU-KAI, Nov 2015, Naberezhnye Chelny (Tatarstan), Russia. pp.174-178, ⟨10.6084/m9.figshare.7210388⟩
- Accession number :
- edsair.doi.dedup.....259282d2be401f864892e016a09f3514
- Full Text :
- https://doi.org/10.6084/m9.figshare.7210388⟩