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This paper aims to investigate the possibility of automatic identification of the forest main group species by the use of Airborne LiDAR data (ALS) and multispectral aerial images recorded by unmanned airplanes. The case study is located in South West Romania, in the Mic Mountain area, in the forests belonging to the Production Unit 6 Cuntu, Experimental Base of Carasenbeş. Three research plots with a size of 1 ha (100 x 100 m) each were placed within representative mixed stands of coniferous and deciduous tree species. The tree species within the plots were measured using the FieldMap equipment, the tree heights were recorded by the use of the Vertex IV ultrasound dendrometer, and the diameters were measured with an electronic caliper. Airborne LiDAR data has been processed using specific algorithms, resulting the digital terrain model (DTM), the digital surface model (DSM), and the normalized canopy height model (CHM). Very high spatial resolution images were captured using an eBee RTK device equipped with a CanonS110 RGB camera, in the case of the 2016 flight, or with a CanonS110 NIR camera in the case of the 2017 flight. The Object-Based Image Analysis (OBIA) supervised classification was performed for both RGB and CIR images through several stages, presented as a logical succession of specific processes. The OBIA classification of the RGB image gave better results compared to the OBIA classification of the CIR image, the overall accuracy of the RGB image classification was up to 16.7% higher than in the case of the CIR image. The object-based image analysis gave promising results in terms of automatic identification of the main group trees species, indicating a high level of standardization as well as the ability to be replicated under various conditions (different study areas and / or types of images). [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m³ was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m³). [ABSTRACT FROM AUTHOR]