Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran

作者：Manizheh Rajab Pourrahmati, Nicolas N. Baghdadi, Ali Asghar Darvishsefat, Manouchehr Namiranian, Ibrahim Fayad, Jean‐Stéphane Bailly, Valéry Gond · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2015 · DOI：10.1109/jstars.2015.2478478 · 被引用次数：28 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Advanced Image Fusion Techniques

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 Wextand 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 accur...