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dc.contributor.advisorHancock, Stevenen
dc.contributor.authorFeng, Zijianen
dc.date.accessioned2021-05-13T18:04:21Z
dc.date.available2021-05-13T18:04:21Z
dc.date.issued2020-08-13
dc.identifier.urihttps://hdl.handle.net/1842/37629
dc.identifier.urihttp://dx.doi.org/10.7488/era/910
dc.description.abstractRemote sensing has been applied to many scientific research in recent decades and Lidar is one of the most popular active remote sensing instrument. There are many kinds of Lidar data such as terrestrial laser scanning (TLS) data and airborne laser scanning (ALS) data. TLS data is potential for forest management such as measuring vegetation profiles, or foliage density, height and composition. In recent years, TLS data has been used to produce high resolution (10cm) 3D vegetation maps and calibrate and validate larger scale measurements. However, compared to other remote sensing method it can not measure a large area at once. In this research, we are aimed to assess the distribution of PAI based on 3D forest structure. We will use open-source programs such as voxelTLS, mpLidar and readRXP which provided by Hancock et al. (2017) (https://bitbucket.org/StevenHancock). During the processing, we can use CloudCompare to display the points cloud of forest structure so that we can have an overall impression. Based on the output file, we will classify the PAI with the forest structure and calculate average PAI value in each layer. In addition, we will use the PAI changes to analyse the destruction level caused by forest fire and give the preliminary estimating. This project is a computing work project without fieldworken
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.subjectTLS, Forest Fire, PAI Distribution, Forest Structure.en
dc.titleAssess the change in 3D PAI distribution by fire using TLS dataen
dc.typeThesis or Dissertationen
dc.relation.referencesHancock et al. (2017) (https://bitbucket.org/StevenHancock)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc Master of Scienceen


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