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Michigan Technological University (2017)

MODELS OF FOREST INVENTORY FOR ISTANBUL FOREST USING AIRBORNE LiDAR AND SPACEBORNE IMAGERY

Ozkal, Mustafa Kagan

Titre : MODELS OF FOREST INVENTORY FOR ISTANBUL FOREST USING AIRBORNE LiDAR AND SPACEBORNE IMAGERY

Auteur : Ozkal, Mustafa Kagan

Université de soutenance : Michigan Technological University

Grade : Master of Science in Forestry (MS) 2017

Résumé
Active remote sensing technology (LiDAR) and passive remote sensing technology (Pleiades and Göktürk-2 satellites) were used to find a meaningful relationship between ground data and remote sensing instruments for Istanbul Forest, Turkey. Two dominant species in the field, oak (deciduous trees) and maritime pine (coniferous trees), were researched. There were 86 plots total, 41 for maritime pine and 45 for oak. Three diameter at breast height (DBH) thresholds were studied. Trees of any DBH (DBH≥0.1 cm), trees ≥8 cm DBH thresholds and, trees ≥10 cm DBH thresholds. Both satellite image metrics were derived from Grey Level Co-occurrence Measures (GLCM). All metrics derived from satellite images and LiDAR data were incorporated into a hybrid approach. All metrics were separated and compared to each other to investigate how they are functioning separately. Linear regression, randomForest, and randomForest imputation models were used. The best R2 was 0.90 using three remote sensing instruments for tree counts based on the plot level for oak species. The highest % explained variances were 67.15% for tree count based on the plot level for oak species in randomForest model and 55.85% for tree count based on the plot level for oak species in randomForest Imputation. LiDAR data had a better relationship with ground data. Band 2 and band 4 of both satellite images were stronger predictors for deciduous trees. Band 3 and band 4 of both satellite images were used more for coniferous trees. Some of the most useful GLCM options were entropy for deciduous trees and correlation, variance and second moment for coniferous trees.

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Page publiée le 13 novembre 2017