Application of landsat 8 satellite imagery data for mapping vegetation and land cover classes – a case study at UiTM Perlis / Nurul Hidayatul Fatini Musa

• मुख्य लेखक: Musa, Nurul Hidayatul Fatini
• स्वरूप: थीसिस
• भाषा: अंग्रेज़ी
• प्रकाशित: 2020
• विषय: Aerial geography; Geographic information systems
• ऑनलाइन पहुंच: https://ir.uitm.edu.my/id/eprint/36920/1/36920.pdf

The lack of research study done within Universiti Teknologi Mara (UiTM) Perlis Branch especially regarding to the trees distribution caused the advent of this research to be carried out. This research aims to study the distribution of trees by identifying trees species, and examine the applicability of Landsat 8 satellite imagery data in monitoring the trees distribution at UiTM Perlis. It specifically focuses the sampling data collection at Hutan Semarak and Tasik Ilham. The methods involve are optimum index factor (OIF), normalized difference vegetation index (NVDI), classification and accuracy assessment to validate the data. All the method has been processes by using ArcGIS and ERDAS Imagine software. The distribution pattern of each trees species was random distribution. The selected species of trees that have been identified within UiTM Perlis including Hevea sp., Microcos sp., Cerbera sp., Diptrocarpus sp., Pterocarpus sp., Polyalthia sp., Cassia sp., Cocos sp., Roystonea sp., Peltophorum sp., and Casuarina sp., and other land cover classes including building, court, field, lake, parking and road. The best band combination is (2-5-7) with the highest value of OIF is 112312.709 and the lowest value of OIF value is (2-3-4) at 814.258. NDVI red with range of 0.010 – 0.503 is more sensitive towards trees compared to green NDVI with range of 0.015 – 0.444. The Minimum Distance Classifier has produced the highest value of overall accuracy assessment and overall kappa statistics which is 73.33% and 0.7030. Compared to the Spectral Correlation Mapper Classifier recorded value the lowest value of overall accuracy assessment and overall kappa statistics which is 70.00% and 0.6674. From the overall result, Landsat 8 has potential in mapping trees distribution within the study area.