University of Twente Student Theses


Tree species classification using uav-rgb images and machine learning algorithms in a mixed temperate forest: a case study of Haagse Bos, Netherlands

Eshetae, Meron Awoke (2020) Tree species classification using uav-rgb images and machine learning algorithms in a mixed temperate forest: a case study of Haagse Bos, Netherlands.

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Abstract:Acquiring reliable and accurate information on tree species is of great importance for effective forest monitoring including assessing biodiversity and ecosystem services, building resilience to climate change, and conserving endangered or critical tree species. In view of this, this study aimed at classifying and mapping tree species using UAV-RGB images and machine learning algorithms in a mixed temperate forest, Haagse Bos, Netherlands. For this purpose, the UAV-RGB images captured in September 2019 (leaf-on season) and February 2020 (leaf-off season) were used. A combination of leaf-on and leaf-off season UAV-RGB images were also applied to classify tree species. The object-based image analysis in conjunction with the Support Vector Machine (SVM), K-nearest neighbour (KNN) and Random Forest (RF) classifiers were used to separate seven tree species, three from the broadleaved and four from the coniferous ones. The UAV-RGB image captured in the leaf-on season were used to compare all the three classifiers, and to assess the tree crown segmentation accuracy in the young and mature mixed forest stands using a single Orthophoto and combinations of canopy height model (CHM) and Orthophoto. The accuracy of the multi-resolution segmentation (MRS) algorithm in segmenting tree crown was assessed using three evaluation performance metrics: over segmentation, under segmentation and total segmentation error. Regarding the tree species classification, comparison of classifiers were made based on the overall accuracy and kappa coefficient which were determined from the confusion matrix developed from the 5-fold cross validation. The best classifier was subsequently applied in the leaf-off and combinations of seasons of UAV-RGB images for classifying tree species. Results showed that a single Orthophoto and combinations of Orthophoto and CHM in mature (young) forest stands produced an overall segmentation accuracy of 82 % (73%) and 83% (76%), respectively. The UAV-derived CHM improved the tree crown segmentation of young forest stand by 3%, but it slightly reduced the segmentation accuracy of the mature forest stand by 1%. Among the classifiers, the SVM classifier outperformed the RF and KNN and produced an overall accuracy of 78.94% and a kappa coefficient of 0.75. All the classifiers except KNN produced low values of producer and user accuracies for classifying all coniferous tree species as compared to the broadleaved tree species. The combinations of UAV-RGB images improved the leaf-on and leaf-off season tree species classification by 3.7% and by 11.3 %, respectively. Overall, applying cost-effective UAV-RGB images acquired at different seasons improves the tree species classification in a mixed temperate forest as compared to using a single season UAV-RGB image. This study suggests to use SVM classifier in the study area to classify tree species for assessing the above ground biomass at species level and for utilizing the natural resource in sustainable manner.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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