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Physical and Environmental Geography Commons™

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Remote Sensing

Classification

Articles 1 - 3 of 3

Full-Text Articles in Physical and Environmental Geography

Techniques For Tree Species Classification With Hyperspectral Imagery At Neon Science Sites, Anthony T. Albanese May 2023

Techniques For Tree Species Classification With Hyperspectral Imagery At Neon Science Sites, Anthony T. Albanese

Theses and Dissertations

Studies three sets of techniques for hyperspectral tree species classification at NEON science sites, aiming to work towards producing a general classification model.

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Identification Of Poverty Areas By Using Machine Learning Classification Methods From Satellite Imagery In Buraydah City, In The Qassim Region Of Saudi Arabia, Amal Alfawzan Jan 2022

Identification Of Poverty Areas By Using Machine Learning Classification Methods From Satellite Imagery In Buraydah City, In The Qassim Region Of Saudi Arabia, Amal Alfawzan

Murray State Theses and Dissertations

Saudi Arabia is a wealthy country with its many resources, but it has seen an increase in poverty recently because of a high rate of population growth with a high rate of unemployment. Some estimate that the number of Saudi Arabians living in poverty is between two and four million. This research aims to develop a way to detect poverty through remote sensing. The study area is Buraydah City, the largest city of the Qassim region, an important agricultural center that plays a significant role in the economy of Saudi Arabia. The research hypothesized that there are poor areas within …

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Using The 500 M Modis Land Cover Product To Derive A Consistent Continental Scale 30 M Landsat Land Cover Classification, Hankui Zhang, David P. Roy Aug 2017

Using The 500 M Modis Land Cover Product To Derive A Consistent Continental Scale 30 M Landsat Land Cover Classification, Hankui Zhang, David P. Roy

GSCE Faculty Publications

Classification is a fundamental process in remote sensing used to relate pixel values to land cover classes present on the surface. Over large areas land cover classification is challenging particularly due to the cost and difficulty of collecting representative training data that enable classifiers to be consistent and locally reliable. A novel methodology to classify large volume Landsat data using high quality training data derived from the 500 m MODIS land cover product is demonstrated and used to generate a 30 m land cover classification for all of North America between 20°N and 50°N. Publically available 30 m global monthly …

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