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Mapping forest habitats in protected areas by integrating LiDAR and SPOT Multispectral Data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

KNAS (Continuous Habitat Mapping of Protected Areas) is a Metria AB project that produces vegetation and habitat mapping in protected areas in Sweden. Vegetation and habitat mapping is challenging due to its heterogeneity, spatial variability and complex vertical and horizontal structure. Traditionally, multispectral data is used due to its ability to give information about horizontal structure of vegetation. LiDAR data contains information about vertical structure of vegetation, and therefore contributes to improve classification accuracy when used together with spectral data. The objectives of this study are to integrate LiDAR and multispectral data for KNAS and to determine the contribution of LiDAR data to the classification accuracy. To achieve these goals, two object-based classification schemes are proposed and compared: a spectral classification scheme and a spectral-LiDAR classification scheme. Spectral data consists of four SPOT-5 bands acquired in 2005 and 2006. Spectral-LiDAR includes the same four spectral bands from SPOT-5 and nine LiDAR-derived layers produced from NH point cloud data from airborne laser scanning acquired in 2011 and 2012 from The Swedish Mapping, Cadastral and Land Registration Authority. Processing of point cloud data includes: filtering, buffer and tiles creation, height normalization and rasterization. Due to the complexity of KNAS production, classification schemes are based on a simplified KNAS workflow and a selection of KNAS forest classes. Classification schemes include: segmentation, database creation, training and validation areas collection, SVM classification and accuracy assessment. Spectral-LiDAR data fusion is performed during segmentation in eCognition. Results from segmentation are used to build a database with segmented objects, and mean values of spectral or spectral-LiDAR data. Databases are used in Matlab to perform SVM classification with cross validation. Cross validation accuracy, overall accuracy, kappa coefficient, producer’s and user’s accuracy are computed. Training and validation areas are common to both classification schemes. Results show an improvement in overall classification accuracy for spectral-LiDAR classification scheme, compared to spectral classification scheme. Improvements of 21.9 %, 11.0 % and 21.1 % are obtained for the study areas of Linköping, Örnsköldsvik and Vilhelmina respectively. 

Place, publisher, year, edition, pages
2016.
Series
TRITA-GIT EX, 16-006
Keyword [en]
LiDAR, spectral, spectral-LiDAR, SVM classification, forest classification
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-189199OAI: oai:DiVA.org:kth-189199DiVA: diva2:944108
External cooperation
Metria AB
Subject / course
Geoinformatics
Educational program
Degree of Master - Geodesy and Geoinformatics
Presentation
2016-06-17, 3085, Drottning Kristinas Väg 30, Stockholm, 15:00
Supervisors
Examiners
Available from: 2016-06-29 Created: 2016-06-28 Last updated: 2016-06-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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