Cetak

Remotely sensing the species of individual trees

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This Thesis aimed to accurately map the species of individual trees using multi source remotely sensed data, including aerial photographs, airbone LiDAR and hyperspectral data. The Research in the thesis firstly evaluated the performance of geometric and radiometric metrics from airbone LiDAR data under leaf on and leaft off conditions for individual tree species discrimination. The results emphatized the importance of intensity-related LiDAR metrics for tree species identification under both leaft-on and leaf-off conditions. Then, the thesis examinded whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airbone LiDAR based individual tree species mapping.
This Thesis explored the potential of various remotely sensed datasets for individual tree species mapping. The methodologies and findings in this thesis can be applied in the mapping of other tree species, which encriches the knowledge of species-specific characteristics and related remotely sensed signatures.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

DS 621.3678 YIF r

Penerbit

Netherland : Univerity of Twente., 2020

Deskripsi Fisik

viii, 149 hlm. : illus. ; 24 cm

Bahasa

Inggris

ISBN/ISSN

978-90-365-4953-0

Klasifikasi

621.3678

Tipe Isi

text

Tipe Media

other

Tipe Pembawa

unspecified

Edisi

-

Subjek

Penginderaan Jauh

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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