@conference {cMarcello25, title = {Comparative Analysis of Tree Segmentation Techniques on High- and Low-Density LiDAR data}, booktitle = {International Conference on Advanced Remote Sensing (ICARS 2025)}, year = {2025}, month = {03/2025}, publisher = {MDPI}, organization = {MDPI}, address = {Basel, Switzerland}, abstract = {

LiDAR systems are powerful tools for sustainable forest management and ecological research, offering the capability to extract detailed information about canopy structure, tree height, biomass, carbon storage, and biodiversity. Unmanned Aerial Systems (ULSs) mounted on drones provide high spatial resolution, density, and flexibility for capturing detailed forest metrics. Their ability to fly at low altitudes enables the collection of fine-scale details. Conversely, Airborne Laser Scanning (ALS) systems, mounted on aircraft, are ideal for large-scale assessments, despite providing a lower point-cloud density compared to ULSs. This study evaluates individual tree segmentation algorithms in a coniferous forest ecosystem using two data sources: (1) high-density ULS LiDAR data collected with the Zenmuse L1 sensor on a DJI Matrice 300RTK drone, and (2) lower-density ALS LiDAR data from the third coverage of Spain{\textquoteright}s PNOA-LiDAR project (Plan Nacional de Ortofotograf{\'\i}a A{\'e}rea). The general methodology for processing LiDAR data involves preliminary steps to generate Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs). Subsequently, segmentation techniques are applied to assist tree-level forest analysis. Segmentation is critical for understanding forest structure; however, selecting the most suitable segmentation technique remains an active area of research. To address this issue, a comparative assessment of four commonly used segmentation algorithms (Watershed, Dalponte2016, Silva2016 and Li2012) was conducted using CHMs and normalized 3D point clouds derived from both high- and low-density LiDAR data. Ground-truth reference data were generated by the manual segmentation of individual trees in a representative plot. The results revealed that the Li2012 algorithm demonstrated the best performance in properly segmenting trees in both types of datasets.

}, keywords = {ALS, CHM, forest, LiDAR, tree segmentation, ULS}, url = {https://sciforum.net/paper/view/21619}, author = {Javier Marcello and Mar{\'\i}a Sp{\'\i}nola and Laia Albors and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} } @conference {cAlbors24, title = {An{\'a}lisis multiresoluci{\'o}n espacial del estado de conservaci{\'o}n de un bosque de laurisilvas con sensores pasivos y activos de teledetecci{\'o}n}, booktitle = {XX Congreso de la Asociaci{\'o}n Espa{\~n}ola de Teledetecci{\'o}n}, year = {2024}, month = {06/2024}, publisher = {Universidad de C{\'a}diz}, organization = {Universidad de C{\'a}diz}, address = {C{\'a}diz, Espa{\~n}a}, abstract = {

La conservaci{\'o}n de las zonas naturales es esencial para mantener el equilibrio ecol{\'o}gico de nuestro planeta y preservar la biodiversidad. Estas {\'a}reas desempe{\~n}an un papel fundamental en la regulaci{\'o}n del clima y la provisi{\'o}n de h{\'a}bitats para innumerables especies. En este contexto, la teledetecci{\'o}n se ha convertido en una herramienta valiosa para la gesti{\'o}n sostenible de estos espacios. Espec{\'\i}ficamente, este trabajo aborda la evaluaci{\'o}n del estado de conservaci{\'o}n de un bosque de laurisilvas a partir de datos multiplataforma (sat{\'e}lite, avi{\'o}n y dron) de alta y muy alta resoluci{\'o}n. Se ha realizado un detallado estudio y evaluaci{\'o}n de {\'\i}ndices de vegetaci{\'o}n, se han aplicado los correspondientes procesados, usando modelos avanzados de correcci{\'o}n y clasificaci{\'o}n y se ha generado informaci{\'o}n relativa al estado de salud del ecosistema, a la estructura del dosel arb{\'o}reo y a la distribuci{\'o}n de los sistemas naturales de vegetaci{\'o}n predominantes (bosques de laurisilva y de fayal-brezal). Los resultados de este trabajo est{\'a}n siendo utilizados para el seguimiento ecol{\'o}gico del Parque y para la identificaci{\'o}n de las zonas de desvitalizaci{\'o}n y de p{\'e}rdida de vigor vegetal.

}, keywords = {dron, {\'\i}ndices de vegetaci{\'o}n, laurisilva, LiDAR, salud vegetal, WorldView-2}, url = {http://eo.csic.es/images/yootheme/aet24/Libro\%20ACTAS\%20XX_AET.pdf}, author = {Laia Albors and Mar{\'\i}a Sp{\'\i}nola and Javier Marcello and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} } @article {aMarcello24, title = {Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data}, journal = {Drones}, volume = {8}, year = {2024}, month = {12/2024}, abstract = {

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91\% and an IoU of 83\%.

}, keywords = {CHM, Dalponte, DEM, forest, Li, LiDAR, Silva, tree segmentation, ULS, watershed}, issn = {2504-446X}, doi = {10.3390/drones8120772}, url = {https://www.mdpi.com/2504-446X/8/12/772}, author = {Javier Marcello and Mar{\'\i}a Sp{\'\i}nola and Laia Albors and Ferran Marques and Dionisio Rodr{\'\i}guez-Esparrag{\'o}n and Francisco Eugenio} }