Capability of GLAS/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran

International audienceThe importance of measuring biophysical properties of forest for ecosystem health monitoring and forest management encourages researchers to find precise, yet low cost methods especially in mountainous and large area. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat was used to estimate three biophysical characteristics of forests located in north of Iran: 1) maximum canopy height (Hmax), 2) Lorey's height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR) and also Random Forest (RF) regressions were developed using different set of variables: waveform metrics, Principal Components (PCs) produced from Principal Component Analysis (PCA) and Wavelet Coefficients (WCs) generated from wavelet transformation. To validate and compare different models, statistical criteria were calculated based on a five-fold cross validation. The best model concerning the maximum canopy height was an MLR with an RMSE of 5.0 m which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from the Digital Elevation Model (Terrain Index: TI). The mean absolute error (MAPE) of maximum canopy height estimates is about 16.4%. For Lorey's height, a simple MLR model including two metrics (Wext and TI) represents the highest performance (RMSE=5.1 m, MAPE=24.0%). Totally, MLR models showed better performance rather than RF models, and accuracy of height estimations using waveform metrics was greater than those based on PCs or WCs. Concerning forest volume, employing regression models to estimate volume directly from GLAS data led to a better result (RMSE=128.8 m3/ha) rather than volume-HLorey relationship (RMSE=167.8 m3/ha)

Lorey's height regression for ICESAT-GLAS waveforms in hyrcanian deciduous forests of Iran

IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015International audienceSince Lidar technology provides the most direct measurements of 3D of phenomena, it plays a critical role in a variety of applications. Forest canopy height as a main factor in forest biomass estimation is costly and time consuming to be measured on the ground. This study aims to estimate Lorey's height “Hlorey” using GLAS data based on regression models. Different metrics like waveform extent “Wext”, trail-edge extent “Htrail” and lead-edge extent “Hlead” were extracted from waveforms and a terrain index “TI” was also calculated using a digital elevation model. Hlorey estimated using multiple regression models were compared to field measurements data. A 5-fold cross validation method was used to validate the results. Best model with lowest AIC (297.440) was resulted using combination of Wext and TI (R_a^2=0.72; RMSE= 5.04m). The results show capability of ICESat-GLAS to estimate Lorey's height in sloped area with a simple regression model. It is prospected to reach better result using other statistical methods and also improvement of processing techniques for LiDAR waveforms in the case of sloped terrai