Remote Sensing of Japanese WWII airstrips in the Papua Province Republic of Indonesia

Remote Sensing of Japanese WWII airstrips in the Papua Province Republic of Indonesia: Classification of the area surrounding three WWII airstrips (Mongosah, Otawiri and Sagan)

van der Valk, Dirk (TU Delft Civil Engineering and Geosciences; TU Delft Geoscience and Remote Sensing)

Lindenbergh, Roderik (mentor)
Hanssen, Ramon (mentor)

Delft University of Technology

2017-08-17

In the Second World War Dutch New Guinea was a strategic battle front for both the Japanese and the Allied forces in the Pacific War. A lot of airstrips were constructed and bombed during this time, of which at least three (Mongosah, Otowari and Sagan) have never been visited after the war. This provided a great opportunity to find potential war heritage and airstrip equipment. Later this year an additional research team will go on an in-situ exploration to potentially find those objects. To do so, they needed a classification map giving information on the type and location of the vegetation. This map helps to know where to land with a helicopter, to setup base camp, to find travel ways, etc. Thus, the main objective of this thesis is to check whether it is possible to create a proper classification image with the available data. I used data obtained from the Sentinel 2 Mission (Optical data), the ALOS PALSAR Mission (L-Band Radar data) and the SRTM Mission (Digital elevation data). I pre-processed the data and used the supervised classification method, “Maximum Likelihood Classification” (MLC). I masked clouds via three different cloud masking methods, MLC Method, Threshold Method and Sen2cor (scene classification) Method. I compared the three different methods with each other and there is no significant difference between them. The classifications have been cross-validated with a reference validation dataset and the classified pixels are on average about 90% correctly classified.

Remote Sensing
Most likelyhood classification
Classification

http://resolver.tudelft.nl/uuid:e19a6102-9c06-40e0-a13a-16f7138fbeac

-2.495677, 133.094938

Student theses

master thesis

© 2017 Dirk van der Valk