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Light detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15 • were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them. Index Terms-Airborne laser scanning, ground filtering algorithms, Mediterranean forest, open-source software. I. INTRODUCTION A IRBORNE light detection and ranging (LiDAR) has gradually become a common tool for collecting elevation information of surface targets with high precision and great density by calculating the time of flight taken for laser pulse travel between the LiDAR sensor and the target [1], [2]. Compared to the traditional photogrammetric method, the accuracies of the LiDAR measurements, approximately 0.15 m in altimetry and 1 m in planimetry under best conditions [3],

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Airborne Laser Scanning (ALS) is capable of estimating a variety of forest parameters using different metrics extracted from the normalized heights of the point cloud using a Digital Elevation Model (DEM). In this study, six interpolation routines were tested over a range of land cover and terrain roughness in order to generate a collection of DEMs with spatial resolution of 1 and 2 m. The accuracy of the DEMs was assessed twice, first using a test sample extracted from the ALS point cloud, second using a set of 55 ground control points collected with a high precision Global Positioning System (GPS). The effects of terrain slope, land cover, ground point density and pulse penetration on the interpolation error were examined stratifying the study area with these variables. In addition, a Classification and Regression Tree (CART) analysis allowed the development of a prediction uncertainty map to identify in which areas DEMs and Airborne Light Detection and Ranging (LiDAR) derived products may be of low quality. The Triangulated Irregular Network (TIN) to raster interpolation method produced the best result in the validation process with the training data set while the Inverse Distance Weighted (IDW) routine was the best in the validation with GPS (RMSE of 2.68 cm and RMSE of 37.10 cm, respectively). OPEN ACCESS Remote Sens. 2015, 7 8632

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