Mapping Lorey's height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images

• Published in: European journal of remote sensing Vol. 51; no. 1; pp. 100 - 115
• Main Authors: Rajab Pourrahmati, Manizheh, Baghdadi, Nicolas, Darvishsefat, Ali A, Namiranian, Manouchehr, Gond, Valery, Bailly, Jean-Stéphane, Zargham, Nosratollah
• Format: Journal Article
• Language: English
• Published: Cagiari Taylor & Francis 01-01-2018; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Applications; artificial neural network (ANN); Artificial neural networks; Computer Science; Data mining; Digital Elevation Models; Ecosystem management; Elevation; Engineering Sciences; Environmental Engineering; Environmental Sciences; Forest ecosystems; Forest management; Forests; Global Changes; ICESat/GLAS; Image Processing; In situ measurement; Iran; Laser altimeters; Lorey's height; Machine Learning; Mapping; Mountainous areas; Neural networks; optical images; Optics; Photonic; Principal components analysis; random forest (RF); Regression analysis; Regression models; Remote sensing; Satellites; Sensing techniques; Statistics; Waveforms; Iran; LOREY'S HEIGHT; OPTICAL IMAGES; ARTIFICIAL NEURAL NETWORK (ANN); IRAN; RANDOM FOREST (RF); ICESAT/GLAS
• ISSN: 2279-7254; 2279-7254
• DOI: 10.1080/22797254.2017.1405717

Lorey's height, representative of mean height in uneven-aged forest stands, is a valuable parameter for forest ecosystem management. While in situ measures provide the most precise information, remote-sensing techniques may provide less expensive but denser and more operational alternative of Lorey's height estimation over highly mountainous areas. This research aims first to evaluate the performances of two nonparametric data mining methods, random forest (RF) and artificial neural network (ANN), for estimation of Lorey's height using ice, cloud and land elevation satellite/geoscience laser altimeter system (ICESat/GLAS) in Hyrcanian forests of Iran and then to provide Lorey's height map using a synergy of ICESat/GLAS and optical images (TM and SPOT). RF and ANN GLAS height models were developed using waveform deterministic metrics, principal components (PCs) from principal component analysis (PCA) and terrain index (TI) extracted from a digital elevation model (DEM). The best result was obtained using an ANN combining first three PCs of PCA and waveform extent ʺW
ext
ʺ (RMSE = 3.4 m, RMSE% = 12.4). In order to map Lorey's height, GLAS-estimated heights were regressed against indices derived from optical images and also topographic information. The best model (RF regression with RMSE = 5.5 m and = 0.59) was applied on the entire study area, and a wall-to-wall height map was generated. This map showed relatively good compatibility with in situ measurements collected in part of the study area.