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

Pourrahmati Manizheh Rajab, Baghdadi Nicolas, Darvishsefat Ali Asghar, Namiranian Manouchehr, Fayad Ibrahim, Bailly Jean Stéphane, Gond Valéry. 2015. Capability of GLAS/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (11) : 5246-5261.

https://doi.org/10.1109/JSTARS.2015.2478478

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Quartile : Q1, Sujet : ENGINEERING, ELECTRICAL & ELECTRONIC / Quartile : Q2, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q2, Sujet : REMOTE SENSING / Quartile : Q2, Sujet : GEOGRAPHY, PHYSICAL

Résumé : The importance of measuring forest biophysical properties for ecosystem health monitoring and forest management encourages researchers to find precise, yet low-cost methods especially in mountainous and large areas. In the present study, geoscience laser altimeter system (GLAS) on board Ice, Cloud, and land Elevation Satellite (ICESat) was used to estimate three biophysical characteristics of forests located in the 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 sets of variables including waveform metrics, principal components (PCs) produced from principal component analysis (PCA) and wavelet coefficients (WCs) generated from wavelet transformation (WT). To validate and compare models, statistical criteria were calculated based on a fivefold cross validation. Best model concerning the maximum height was an MLR (RMSE = 5.0 m) which combined two metrics extracted from waveforms (waveform extent “ Wext” and height at 50% of waveform energy “ H50”), and one from digital elevation model (terrain index, TI). The mean absolute percentage error (MAPE) of maximum height estimates was 16.4%. For Lorey's height, a simple MLR (including Wext and TI) represented the highest performance (RMSE = 5.1 m, MAPE = 24.0%). Generally, MLR models had a better performance when compared to the RF models. In addition, the accuracy of height estimations using waveform metrics was greater than those based on PCs or WCs. Concerning forest volume, regression models estimating 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).

Mots-clés Agrovoc : forêt tropicale, télédétection, satellite, ressource forestière, écosystème forestier, région d'altitude, hauteur, mesure (activité), protection de la forêt, gestion des ressources naturelles, modèle de croissance forestière, Houppier, dendrométrie