Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

8th International Symposium of the Digital Earth

地球科学;计算机科学

Hashim, M.^1 ; Pour, A.B.^1 ; Onn, C.H.^1

Institute of Geospatial Science and Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia^1

关键词: Change vector analysis;    Cloud contamination;    Enhanced thematic mappers;    Humid tropical climates;    Optical remote sensing;    Remote sensing technology;    Satellite remotely sensed data;    Vegetation fractions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012010/pdf
DOI  :  10.1088/1755-1315/18/1/012010

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper+(ETM+) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM+dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate.