Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS

• Main Author: Lemenkova, Polina
• Other Authors: Ocean University of China (OUC), Ufa State Petroleum Technological University, F. U. Enikeev
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2015
• Subjects: Supervised classification; Modeling & Simulation; Supervised classification method; Landsat TM and ETM data; Landsat 7 ETM+; Image classification; Image analysis and processing; Image analysis applications; Landsat TM; remote sensing; спутниковые снимки; Landsat; modeling Ключевые слова: Ландсат; классификация; моделирование; 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.4.6: 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.4.6.1: Pixel classification; 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.4.6: Segmentation/I.4.6.2: Region growing; partitioning; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.2: Compression (Coding); ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS; ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING; ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION; ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.3: Picture/Image Generation; ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.4: Graphics Utilities; ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling; ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.6: Methodology and Techniques; 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.4.0: General/I.4.0.0: Image displays; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.1: Image processing software; 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.4.4: Restoration; ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation; [INFO]Computer Science [cs]; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; [SDE.IE]Environmental Sciences/Environmental Engineering; [SCCO.COMP]Cognitive science/Computer science; [SCCO]Cognitive science; [SDU]Sciences of the Universe [physics]; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; Yamal Peninsula
• Online Access: https://hal.archives-ouvertes.fr/hal-02000251; https://hal.archives-ouvertes.fr/hal-02000251/document; https://hal.archives-ouvertes.fr/hal-02000251/file/Lemenkova_Bashkortostan2015.pdf; https://doi.org/10.6084/m9.figshare.7211639

International audience The emphasis of this article is placed on the technical application of the remote sensing tools and methods for studies of vegetation coverage in northern ecosystems. The study area is located in Yamal peninsula, the Russian Federation. Landsat imagery covering study area in 1988, 2001 and 2011 has been analyzed using ILWIS GIS. The image processing was performed using semi-automated method of image interpretation. The remote sensing data classification from ILWIS menu enabled to map vegetation coverage over research area, which helped to identify land cover types and distribution in Yamal. Results show that Landsat TM imagery with 30 m mesh spacing is useful for landscape mapping and the interpretation of the vegetation cover types. Цель данной работы состоит в технической апробации данных дистанционного зондирования Земли (спутниковые снимки Ландсат) для картографирования почвенно-растительного покрова северных экосистем. Область исслtдований-п-ов Ямал, Россия. Космические снимки Ландсат, покрывающие нужный участок территории в 1988, 2001 и 2011 г.г., были классифицированны и проанализированны в ГИС ИЛВИС (ILWIS GIS). Обработка снимков была осуществлена в полуавтоматическом режиме методами ГИС. Встроенный режим обработки и распознавания растровых изображений в ILWIS GIS позволил провести картографирование почвенно-растительного покрова и распространение ландшафтов на п-ве Ямал. Результаты показывают, изменение ландшафтов за исследуемый участок времени. Работа проиллюстрировала успешное применение спутниковых снимков Ландсат с 30-м разрешением для мелкомасштабного ландшафтного ГИС-картографирования.