Accuracy of tree geometric parameters depending on the LiDAR data density

[EN] The aim of this study was to compare geometric parameters of olive trees (tree height, crown base height, crown diameters, crown area), using LiDAR data of different densities: 0.5, 3.5 and 9 points m(-2). Two strategies were proposed and verified with a focus on raster and raw data analysis. Statistical tests have shown, that for the tree height and crown base height estimation, the choice of strategy was irrelevant, but denser LiDAR data provided more accurate results. The raster analysis strategy applied for sparse and dense LiDAR datasets allowed crown shape to be determined with a similar accuracy which means raster data are useful for estimating other indirect tree parameters. The quality of results was independent from the tree size.The authors appreciate the financial support provided by the Vice-Rectorate for Research of the Universitat Politecnica de Valencia [Grant PAID-06-12-3297; SP20120534].Hadás, E.; Estornell Cremades, J. (2016). Accuracy of tree geometric parameters depending on the LiDAR data density. European Journal of Remote Sensing. 49:73-92. https://doi.org/10.5721/EuJRS20164905S73924

Study of Shrub Cover and Height Using LIDAR Data in a Mediterranean Area

[EN] In this work we studied the height and coverage of shrub vegetation using light detection and ranging (LIDAR) data. The maximum dominant heights of vegetation were measured in the field in 83 stands of a 0.5-m radius, and the data were compared with figures for heights obtained from LIDAR data in concentric areas with different radii. The minimum root mean square error (RMSE) between the field measurements and LIDAR data was found for radii between 1.5 and 2.25 m, RMSE being 0.26 m. When the slopes are low and an accurate digital terrain model is obtained, it was shown that the radius can be reduced. Shrub heights were also studied in plots of 100 m(2). In this case, the 95th percentile of the LIDAR data included in each plot was the best predictor of height with R(2) of 0.71 and a RMSE of 0.13 m. For detecting the presence of shrub vegetation, the highest accuracy was obtained when the canopy height model and a spectral image were combined (overall accuracy of 90%). FOR. SCI. 57(3):171-179.Financial support of this study was provided by Universidad Politècnica de Valencia (PAID-06-08-3297). We thank the city hall of Chiva for their support in the field campaign.Estornell Cremades, J.; Ruiz Fernández, LÁ.; Velázquez Martí, B. (2011). Study of Shrub Cover and Height Using LIDAR Data in a Mediterranean Area. Forest Science. 57(3):171-179. http://hdl.handle.net/10251/47020S17117957

Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

[EN] Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 x 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now.This research was funded by regional government of Spain, Generalitat Valenciana, within the framework of the research project AICO/2020/246 and the APC was also funded by the research project AICO/2020/246.Morell-Monzó, S.; Sebastiá-Frasquet, M.; Estornell Cremades, J. (2021). Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information. Remote Sensing. 13(4):1-18. https://doi.org/10.3390/rs13040681S11813

Assessing the capabilities of high-resolution spectral, altimetric, and textural descriptors for mapping the status of citrus parcels

[EN] Agricultural land abandonment is an increasing phenomenon around the world with relevant environmental and socio-economic implications. In the European Union about 11 % of agricultural land is at high risk of abandonment. The Comunitat Valenciana region (Spain) is the most important citrus producer in Europe suffering from this problem. Identifying the status of citrus crops at the parcel level is essential for policymakers in agriculture. This work assessed the use of WorldView-3 data, Very High-Resolution Airborne Images, and Structure from Motion point clouds to identify the status of citrus parcels using two machine learning algorithms: Random Forest and Support Vector Machines. Different analyses involving combinations of the three data sources were carried out to assess the impact on classification accuracy. The results showed the high potential of airborne imagery (OA ¿ 0.967) and WorldView-3 (OA ¿ 0.936) to detect parcel status using a single image. The SfM data showed a lower potential (OA ¿ 0.825). Adding SfM point cloud to the multispectral information produced small improvements (0.4¿2.0 %) in classification accuracy. The class separability analysis showed the importance of WV-3 SWIR bands to detect abandoned parcels as they produce more spectral separability over the productive parcels in the 1570 nm ¿ 2330 nm spectrum. The results also show the importance of GLCM texture features extracted from sub-metric images due to their ability to model spatial planting patterns typical of fruit cropsThis research was funded by regional government of Spain, Generalitat Valenciana, within the framework of the research project AICO/2020/246. Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Morell-Monzó, S.; Estornell Cremades, J.; Sebastiá-Frasquet, M. (2023). Assessing the capabilities of high-resolution spectral, altimetric, and textural descriptors for mapping the status of citrus parcels. Computers and Electronics in Agriculture. 204:1-11. https://doi.org/10.1016/j.compag.2022.10750411120

Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas

[EN] Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam's Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.Morell-Monzó, S.; Estornell Cremades, J.; Sebastiá-Frasquet, M. (2020). Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sensing. 12(12):1-18. https://doi.org/10.3390/rs12122062S1181212MacDonald, D., Crabtree, J. ., Wiesinger, G., Dax, T., Stamou, N., Fleury, P., … Gibon, A. (2000). Agricultural abandonment in mountain areas of Europe: Environmental consequences and policy response. Journal of Environmental Management, 59(1), 47-69. doi:10.1006/jema.1999.0335Kosmas, C., Kairis, O., Karavitis, C., Acikalin, S., Alcalá, M., Alfama, P., … Solé-Benet, A. (2015). An exploratory analysis of land abandonment drivers in areas prone to desertification. CATENA, 128, 252-261. doi:10.1016/j.catena.2014.02.006Corbelle Rico, E., & Crecente Maseda, R. (2018). Estudio da evolución da superficie agrícola na comarca da Terra Chá a partir de fotografía aérea histórica e mapas de usos, 1956-2004. Recursos Rurais, (4), 57-65. doi:10.15304/rr.id5312Gellrich, M., Baur, P., Koch, B., & Zimmermann, N. E. (2007). Agricultural land abandonment and natural forest re-growth in the Swiss mountains: A spatially explicit economic analysis. Agriculture, Ecosystems & Environment, 118(1-4), 93-108. doi:10.1016/j.agee.2006.05.001Rey Benayas, J. M. (2007). Abandonment of agricultural land: an overview of drivers and consequences. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 2(057). doi:10.1079/pavsnnr20072057Árgyelán, T. (2015). Abandonment phenomenon in Europe. Acta Universitatis Sapientiae, Agriculture and Environment, 7(1), 89-97. doi:10.1515/ausae-2015-0008Citricultura Valenciana: Gestión Integrada de Plagas y Enfermedades en Cítricoshttp://gipcitricos.ivia.es/citricultura-valencianaRounsevell, M. D. A., Reginster, I., Araújo, M. B., Carter, T. R., Dendoncker, N., Ewert, F., … Tuck, G. (2006). A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems & Environment, 114(1), 57-68. doi:10.1016/j.agee.2005.11.027Verburg, P. H., Schulp, C. J. E., Witte, N., & Veldkamp, A. (2006). Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agriculture, Ecosystems & Environment, 114(1), 39-56. doi:10.1016/j.agee.2005.11.024Dubinin, M., Potapov, P., Lushchekina, A., & Radeloff, V. C. (2010). Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing. Remote Sensing of Environment, 114(8), 1638-1648. doi:10.1016/j.rse.2010.02.010Ruiz-Flan˜o, P., Garci´a-Ruiz, J. M., & Ortigosa, L. (1992). Geomorphological evolution of abandoned fields. A case study in the Central Pyrenees. CATENA, 19(3-4), 301-308. doi:10.1016/0341-8162(92)90004-uFischer, J., Hartel, T., & Kuemmerle, T. (2012). Conservation policy in traditional farming landscapes. Conservation Letters, 5(3), 167-175. doi:10.1111/j.1755-263x.2012.00227.xPenov, I. (2004). The Use of Irrigation Water in Bulgaria’s Plovdiv Region During Transition. Environmental Management, 34(2), 304-313. doi:10.1007/s00267-004-0019-8Novara, A., Gristina, L., Sala, G., Galati, A., Crescimanno, M., Cerdà, A., … La Mantia, T. (2017). Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of The Total Environment, 576, 420-429. doi:10.1016/j.scitotenv.2016.10.123Cerdà, A., Ackermann, O., Terol, E., & Rodrigo-Comino, J. (2019). Impact of Farmland Abandonment on Water Resources and Soil Conservation in Citrus Plantations in Eastern Spain. Water, 11(4), 824. doi:10.3390/w11040824Rey Benayas, J. M., & Bullock, J. M. (2012). Restoration of Biodiversity and Ecosystem Services on Agricultural Land. Ecosystems, 15(6), 883-899. doi:10.1007/s10021-012-9552-0Shrivastava, R. J., & Gebelein, J. L. (2007). Land cover classification and economic assessment of citrus groves using remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 61(5), 341-353. doi:10.1016/j.isprsjprs.2006.10.003Löw, F., Prishchepov, A., Waldner, F., Dubovyk, O., Akramkhanov, A., Biradar, C., & Lamers, J. (2018). Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series. Remote Sensing, 10(2), 159. doi:10.3390/rs10020159Alcantara, C., Kuemmerle, T., Prishchepov, A. V., & Radeloff, V. C. (2012). Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sensing of Environment, 124, 334-347. doi:10.1016/j.rse.2012.05.019Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A., & Hostert, P. (2015). Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sensing of Environment, 163, 312-325. doi:10.1016/j.rse.2015.03.028Dara, A., Baumann, M., Kuemmerle, T., Pflugmacher, D., Rabe, A., Griffiths, P., … Hostert, P. (2018). Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series. Remote Sensing of Environment, 213, 49-60. doi:10.1016/j.rse.2018.05.005Müller, D., Leitão, P. J., & Sikor, T. (2013). Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agricultural Systems, 117, 66-77. doi:10.1016/j.agsy.2012.12.010Yin, H., Prishchepov, A. V., Kuemmerle, T., Bleyhl, B., Buchner, J., & Radeloff, V. C. (2018). Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment, 210, 12-24. doi:10.1016/j.rse.2018.02.050Kuemmerle, T., Hostert, P., Radeloff, V. C., van der Linden, S., Perzanowski, K., & Kruhlov, I. (2008). Cross-border Comparison of Post-socialist Farmland Abandonment in the Carpathians. Ecosystems, 11(4), 614-628. doi:10.1007/s10021-008-9146-zGrădinaru, S. R., Kienast, F., & Psomas, A. (2019). Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl. Ecological Indicators, 96, 79-86. doi:10.1016/j.ecolind.2017.06.022Prishchepov, A. V., Radeloff, V. C., Dubinin, M., & Alcantara, C. (2012). The effect of Landsat ETM/ETM + image acquisition dates on the detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment, 126, 195-209. doi:10.1016/j.rse.2012.08.017Baumann, M., Kuemmerle, T., Elbakidze, M., Ozdogan, M., Radeloff, V. C., Keuler, N. S., … Hostert, P. (2011). Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy, 28(3), 552-562. doi:10.1016/j.landusepol.2010.11.003Szostak, M., Hawryło, P., & Piela, D. (2017). Using of Sentinel-2 images for automation of the forest succession detection. European Journal of Remote Sensing, 51(1), 142-149. doi:10.1080/22797254.2017.1412272Kanjir, U., Đurić, N., & Veljanovski, T. (2018). Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring. ISPRS International Journal of Geo-Information, 7(10), 405. doi:10.3390/ijgi7100405Proisy, C., Viennois, G., Sidik, F., Andayani, A., Enright, J. A., Guitet, S., … Suhardjono. (2018). Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia. Marine Pollution Bulletin, 131, 61-71. doi:10.1016/j.marpolbul.2017.05.056Thanh Noi, P., & Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18(2), 18. doi:10.3390/s18010018Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9), 2784-2817. doi:10.1080/01431161.2018.1433343https://rdrr.io/cran/raster/https://cran.r-project.org/web/packages/rgdal/index.htmlHuete, A., Justice, C., & Liu, H. (1994). Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49(3), 224-234. doi:10.1016/0034-4257(94)90018-3Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385-396. doi:10.1016/s0034-4257(01)00318-2Silleos, N. G., Alexandridis, T. K., Gitas, I. Z., & Perakis, K. (2006). Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years. Geocarto International, 21(4), 21-28. doi:10.1080/10106040608542399Huete, A. . (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:10.1016/0034-4257(88)90106-xGitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. doi:10.1016/s0034-4257(96)00072-7Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300. doi:10.1016/j.patrec.2005.08.011Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. doi:10.1007/bf00058655Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222. doi:10.1080/01431160412331269698Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. doi:10.1016/j.rse.2014.02.015Whiteside, T. G., Maier, S. W., & Boggs, G. S. (2014). Area-based and location-based validation of classified image objects. International Journal of Applied Earth Observation and Geoinformation, 28, 117-130. doi:10.1016/j.jag.2013.11.009Morell Monzó, S., & Membrado-Tena, J. C. (2019). Causas y consecuencias del crecimiento urbanístico en el litoral valenciano a través de la evolución de los usos del suelo. El caso de Oliva. Cuadernos de Turismo, (44), 303-326. doi:10.6018/turismo.44.404861Smith, P., House, J. I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., … Pugh, T. A. M. (2015). Global change pressures on soils from land use and management. Global Change Biology, 22(3), 1008-1028. doi:10.1111/gcb.1306

Tree extraction and estimation of walnut structure parameters using airborne LiDAR data

[EN] The development of new tools based on remote sensing data in agriculture contributes to cost reduction, increased production, and greater profitability. Airborne LiDAR (Light Detection and Ranging) data show a significant potential for geometrically characterizing tree plantations. This study aims to develop a methodology to extract walnut (Juglans regia L.) crowns under leafless conditions using airborne LiDAR data. An original approach based on the alpha-shape algorithm, identification of local maxima, and k-means algorithms is developed to extract the crowns of walnut trees in a plot located in Viver (Eastern Spain) with 192 trees. In addition, stem diameter and volume, crown diameter, total height, and crown height were estimated from cloud metrics and other 2D parameters such as crown area, and diameter derived from LiDAR data. A correct identification was made of 178 trees (92.7%). For structure parameters, the most accurate results were obtained for crown diameter, stem diameter, and stem volume with coefficient of determination values (R-2) equal to 0.95, 0.87 and 0.83; and RMSE values of 0.43 m (5.70%), 0.02 m (9.35%) and 0.016 m(3) (21.55%), respectively. The models that gave the lowest R-2 values were 0.69 for total height and 0.70 for crown height, with RMSE values of 0.84 m (12.4%) and 0.83 m (14.5%), respectively. A suitable definition of the central and lower parts of tree canopies was observed. Results of this study generate valuable information, which can be applied for improving the management of walnut plantations.Estornell Cremades, J.; Hadas, E.; Marti-Gavila, J.; López- Cortés, I. (2021). Tree extraction and estimation of walnut structure parameters using airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation. 96:1-9. https://doi.org/10.1016/j.jag.2020.102273S199

Principal component analysis applied to remote sensing

[EN] The main objective of this article was to show an application of principal component analysis (PCA) which is used in two science degrees. Particularly, PCA analysis was used to obtain information of the land cover from satellite images. Three Landsat images were selected from two areas which were located in the municipalities of Gandia and Vallat, both in the Valencia province (Spain). In the first study area, just one Landsat image of the 2005 year was used. In the second study area, two Landsat images were used taken in the 1994 and 2000 years to analyse the most significant changes in the land cover. According to the results, the second principal component of the Gandia area image allowed detecting the presence of vegetation. The same component in the Vallat area allowed detecting a forestry area affected by a forest fire. Consequently in this study we confirmed the feasibility of using PCA in remote sensing to extract land use information.[ES] El objetivo principal de este artículo es mostrar una aplicación del análisis de componentes principales (PCA) que se utiliza en dos grados de la ciencia. En particular, se utilizó el análisis de PCA para obtener información de la cobertura del suelo a partir de imágenes de satélite. Tres imágenes Landsat fueron seleccionadas a partir de dos áreas que se encuentran en los municipios de Gandia y Vallat, ambos en la provincia de Valencia (España). En la primera área de estudio, se utilizó una sola imagen Landsat del año 2005. En la segunda área de estudio, se utilizaron dos imágenes Landsat tomadas en los años 1994 y 2000 para analizar los cambios más significativos en la cobertura de la tierra. Según los resultados, el segundo componente principal de la imagen de área Gandia permitió la detección de la presencia de vegetación. El mismo componente en el área de Vallat permitió detectar un área forestal afectada por un incendio forestal. En consecuencia, en este estudio se confirmó la viabilidad del uso de PCA en teledetección para extraer la información territorial.Estornell, J.; Martí-Gavliá, JM.; Sebastiá, MT.; Mengual, J. (2013). Principal component analysis applied to remote sensing. Modelling in Science Education and Learning. 6(2):83-89. doi:10.4995/msel.2013.1905SWORD838962Xiuping Jia, & Richards, J. A. (1999). Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 538-542. doi:10.1109/36.739109J. R. Eastman, M. Filk. Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing. 59(6) 991-996. (1993)

Estimation of Walnut Structure Parameters Using Terrestrial Photogrammetry Based on Structure-from-Motion (SfM)

[EN] Remote sensing techniques are increasingly used for crop monitoring to improve the profitability of plantations. These studies are mainly based on spectral information recorded by satellites or unmanned aerial vehicles. However, the development of Earth Observation Systems capable of retrieving 3D point clouds at an affordable cost enables the possibility of exploring new approaches in agriculture. In this context, more research is required to analyze the capability of 3D data for inventory, management and prediction of inputs (water, fertilizers and pesticides) and outputs (production, biomass) of fruit plantations. To do this, the complete representation of each tree contribute to extract the main geometric parameters. The objective of this work is to obtain regression models to estimate total height (H-t), crown height (H-c), stem diameter (D-s), crown diameter (D-c), stem volume (V-s) and crown volume (V-c) from 45 walnut specimens. For this, 3D models were computed for these trees by applying ground-based Structure from Motion (SfM). A circular photogrammetric survey of each tree was carried out using a standard digital camera and three-dimensional point clouds were retrieved for each tree. From these data, the tree parameters were computed. Linear regression models were obtained to estimate H-t, H-c, D-s, D-c, V-s and V-c, with R-2 values between 0.89 and 0.99. The results showed accurate fits between field parameters and those derived from 3D point clouds retrieved from SfM technique, indicating the applicability of this cost-effective method to model walnut trees and to extract their accurate parameters without costly field campaigns.Fernández-Sarría, A.; López- Cortés, I.; Marti-Gavila, J.; Estornell Cremades, J. (2022). Estimation of Walnut Structure Parameters Using Terrestrial Photogrammetry Based on Structure-from-Motion (SfM). Journal of the Indian Society of Remote Sensing. 50(10):1931-1944. https://doi.org/10.1007/s12524-022-01576-x193119445010Arnó, J., Masip, J., & Rosell-Polo, J. R. (2015). Influence of the scanned side of the row in terrestrial laser sensor applications in vineyards: Practical consequences. Precision Agriculture, 16(2), 119–128. https://doi.org/10.1007/s11119-014-9364-7Arnó, J., Vallès, J. M., Llorens, J., Sanz, R., Masip, J., Palacín, J., & Rosell-Polo, J. R. (2013). Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precision Agriculture, 14(3), 290–306. https://doi.org/10.1007/s11119-012-9295-0Auat Cheein, F., Guivant, J., Sanz, R., Escolà, A., Yandún, F., Torres-Torriti, M., & Rosell-Polo, J. R. (2015). Real-time approaches for characterization of fully and partially scanned canopies in groves. COMPAG, 118, 361–371. https://doi.org/10.1016/j.compag.2015.09.017Bork, E. W., & Su, J. G. (2007). Integrating LiDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis. Remote Sensing of Environment, 111, 11–24. https://doi.org/10.1016/j.rse.2007.03.011Brede, B., Calders, K., Lau, A., Raumonen, P., Bartholomeus, H., Herold, M., & Kooistra, L. (2019). Non-destructivetree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR. Remote Sensing of Environment, 233, 111355. https://doi.org/10.1016/j.rse.2019.111355Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32, 1309–1332. https://doi.org/10.1109/tro.2016.2624754Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., Culvenor, D., Avitabile, V., Disney, M., Armston, J., & Kaasalainen, M. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6, 198–208. https://doi.org/10.1111/2041-210X.12301Chang, A., Jung, J., Maeda, M. M., & Landivar, J. (2017). Crop height monitoring with digital imagery from unmanned aerial system (UAS). COMPAG, 141, 232–237. https://doi.org/10.1016/j.compag.2017.07.008Cunliffe, A. M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment, 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019Dalla, A., Rex, F., Almeida, D., Sanquetta, C., Silva, C., Moura, M., Wilkinson, B., et al. (2020). Measuring individual tree diameter and height using gatoreyehigh-density UAV-lidar in an integrated crop-livestock-forest system. Remote Sensing, 12, 863. https://doi.org/10.3390/rs12050863De Eugenio, A., Fernández-Landa, A., & Merino-de-Miguel, S. (2018). 3D models from terrestrial photogrammetry in the estimation of forest inventory variables. Revista de Teledetección, 51, 113–124. https://doi.org/10.4995/raet.2018.9174Díaz-Varela, R., de la Rosa, R., León, L., & Zarco-Tejada, P. (2015). High-Resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: Application in breeding trials. Remote Sensing, 7(4), 4213–4232. https://doi.org/10.3390/rs70404213Dong, J., Burnham, J.G., Boots, B., Rains, G., Dellaert, F. (2017). 4D crop monitoring: Spatio-temporal reconstruction for agriculture. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 3878–3885. https://doi.org/10.1109/ICRA.2017.7989447.Ehlert, D., Heisig, M., & Adamek, R. (2010). Suitability of a laser rangefinder to characterize winter wheat. Precision Agriculture, 11, 650–663.Escolà, A., Martínez-Casasnovas, J. A., Rufat, J., Arnó, J., Arbonés, A., Sebé, F., Pascual, M., Gregorio, E., & Rosell-Polo, J. R. (2017). Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precision Agriculture, 18, 111–132. https://doi.org/10.1007/s11119-016-9474-5Estornell, J., Ruiz, L. A., Velázquez-Martí, B., López-Cortés, I., Salazar, D., & Fernández-Sarría, A. (2015). Estimation of pruning biomass of olive trees using airborne discrete-return LiDAR data. Biomass and Bioenergy, 81, 315–321. https://doi.org/10.1016/j.biombioe.2015.07.015Estornell, J., Velázquez-Martí, A., Fernández-Sarría, A., López-Cortés, I., Martí-Gavilá, J., & Salazar, D. (2017). Estimation of structural attributes of walnut trees based on terrestrial laser scanning. Revista de Teledetección, 0(48), 67–76. https://doi.org/10.4995/raet.2017.7429Estornell, J., Velázquez-Martí, B., Fernández-Sarría, A., & Martí, J. (2018). Lidar methods for measurement of trees in urban forests. Journal of Applied Remote Sensing, 12(4), 046009. https://doi.org/10.1117/1.JRS.12.046009Fernández-Sarría, A., López-Cortés, I., Estornell, J., Velázquez-Martí, B., & Salazar, D. (2019). Estimating residual biomass of olive tree crops using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 75, 163–170. https://doi.org/10.1016/j.jag.2018.10.019Fernández-Sarría, A., Martínez, L., Velázquez-Martí, B., Sajdak, M., Estornell, J., & Recio, J. A. (2013). Different methodologies for calculating crown volumes of Platanus hispanica trees using terrestrial laser scanner and a comparison with classical dendrometric measurements. COMPAG, 90, 176–185.Gené-Mola, J., Gregorio López, E., Auat Cheein, F. A., Guevara, J., Llorens Calveras, J., Sanz Cortiella, R., & Rosell Polo, J. R. (2020). Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow. COMPAG, 168, 105121. https://doi.org/10.1016/j.compag.2019.105121Gil, E., Arnó, J., Llorens, J., Sanz, R., Llop, J., Rosell-Polo, J. R., Gallart, M., & Escolà, A. (2014). Advanced technologies for the improvement of spray application techniques in Spanish viticulture: An overview. Sensors, 14, 691–708. https://doi.org/10.3390/s140100691Goesele, M., Snavely, N., Seitz, S.M., Curless, B., Hoppe, H. (2007). Multi-view stereo for community photo collections. In Proceedings of the11th International Conference on Computer Vision, Rio de Janeiro, Brazil pp. 1–8. https://doi.org/10.1109/ICCV.2007.4408933.Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44–56. https://doi.org/10.1007/s11119-013-9335-4Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., Martius, C., Goodman, R., Disney, M., Manuri, S., et al. (2018). Estimation of above ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution, 9(2), 223–234. https://doi.org/10.1111/2041-210X.12904Guerra-Hernández, J., Cosenza, D. N., Estraviz Rodriguez, L. C., Silva, M., Tomé, M., Díaz-Varela, R. A., & González-Ferreiro, E. (2018). Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. International Journal of Remote Sensing, 39(15–16), 5211–5235. https://doi.org/10.1080/01431161.2018.1486519Hadas, E., Borkowski, A., Estornell, J., & Tymkow, P. (2017). Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis. GIScience & Remote Sensing, 54(6), 898–917. https://doi.org/10.1080/15481603.2017.1351148Hargreaves, G. H. (1994). Defining and using reference evapotranspiration. Journal of Irrigation and Drainage Engineering. https://doi.org/10.1061/(ASCE)0733-9437(1994)120:6(1132)Heinzel, J., & Huber, M. O. (2017). Tree stem diameter estimation from volumetric TLS image data. Remote Sensing, 9, 614. https://doi.org/10.3390/rs9060614Jay, S., Rabatel, G., Hadoux, X., Moura, D., & Gorretta, N. (2015). In-field crop row phenotyping from 3D modeling performed using Structure from motion. COMPAG, 110, 70–77.Jenkins, J., Chojnack, D., Heath, L., Birdsey, R. (2004). Comprehensive database of diameter-based biomass regressions for north american tree Species; United States Forest Service, Northeastern Research Station, p. 45.Jiménez-Brenes, F. M., López-Granados, F., Castro, A. I., Torres-Sánchez, J., Serrano, N., & Peña, J. M. (2017). Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling. Plant Methods, 13, 1–15. https://doi.org/10.1186/s13007-017-0205-3Kankare, I., Räty, M., Yu, X., Holopainen, M., Vastaranta, M., Kantola, T., Hyyppä, J., Hyyppä, H., Alho, P., & Viitala, R. (2013). Single tree biomass modelling using airborne laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 85, 66–73. https://doi.org/10.1016/j.isprsjprs.2013.08.008Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). LiDAR remote sensing for ecosystem studies. BioScience, 52, 19–30.Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H., Yu, X., Kaartinen, H., Jaakola, A., Guan, F., Holopainen, M., & Vastaranta, M. (2016). Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 63–77.López-Cortés, I., Martí-Gavilá, J., Estornell, J., Fernández-Sarría, A. (2019). Comparación de parámetros de olivos a partir de UAV y datos LiDAR aéreos. In Proceedings of XVIII Congreso de la Asociación Española de Teledetección, Valladolid, pp. 24–27 September 2019; pp. 439–442.Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94Maltamo, M., Räty, J., Korhonen, L., Kotivuori, E., Kukkonen, M., Peltola, H., Kangas, J., & Packalen, P. (2020). Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images. European Journal of Remote Sensing. https://doi.org/10.1080/22797254.2020.1816142Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & Vintila, R. (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10, 45–62. https://doi.org/10.1007/s11119-008-9091-zMiller, J., Morgenroth, J., & Gomez, C. (2015). 3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates. Urban for Urban Green, 14(4), 932–940. https://doi.org/10.1016/j.ufug.2015.09.001Ministerio de Agricultura, Pesca y Alimentación (2019). Encuesta sobre superficies y rendimientos cultivos (ESYRCE). Encuesta de marco de áreas de España. p. 178. Retrieved 15 Sept 2021 from https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/esyrce/.Miranda-Fuentes, A., Llorens, J., Rodriguez-Lizana, A., Cuenca, A., Gil, E., Blanco-Roldán, G. L., & Gil-Ribes, J. A. (2016). Assessing the optimal liquid volume to be sprayed on isolated olive trees according to their canopy volumes. STOTEN, 568, 296–305. https://doi.org/10.1016/j.scitotenv.2016.06.013Moorthy, I., Miller, J. R., Jimenez Berni, J. A., Zarco-Tejada, P., Hu, B., & Chen, J. (2011). Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agricultural and Forest Meteorology, 151(2), 204–214. https://doi.org/10.1016/j.agrformet.2010.10.005Neuenschwander, A., & Pitts, K. (2019). The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment, 221, 247–259. https://doi.org/10.1016/j.rse.2018.11.005Palacín, J., Pallejà, T., Tresanchez, M., Sanz, R., Llorens, J., Ribes-Dasi, M., Masip, J., Arnó, J., Escolà, A., & Rosell, J. R. (2007). Real-time tree-foliage surface estimation using a ground laser scanner. IEEE Transactions on Instrumentation and Measurement, 56(4), 1377–1383. https://doi.org/10.1109/TIM.2007.900126Pierzchala, M., Giguère, P., & Astrup, R. (2018). Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-slam. COMPAG, 145, 217–225. https://doi.org/10.1016/j.compag.2017.12.034Pitkänen, T. P., Raumonen, P., & Kangas, A. (2019). Measuring stem diameters with TLS in boreal forests complementary fitting procedure. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 294–306. https://doi.org/10.1016/j.isprsjprs.2018.11.027Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy, 31(9), 646–655. https://doi.org/10.1016/j.biombioe.2007.06.022Sanz, R., Llorens, J., Escolà, A., Arnó, J., Planas, S., Román, C., & Rosell-Polo, J. R. (2018). LiDAR and non-LiDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard. Agricultural and Forest Meteorology, 260–261, 229–239. https://doi.org/10.1016/j.agrformet.2018.06.017Schlaegel, B. (1984). Green ash volume and weight tables. US Department of Agriculture, Forest Service, Southern Forest Experiment Station.Sheridan, R. D., Popescu, S. C., Gatziolis, D., Morgan, C. L. S., & Ku, N. W. (2015). Modeling forest aboveground biomass and volume using airborne LiDAR metrics and forest inventory and analysis data in the Pacific Northwest. Remote Sensing, 7, 229–255. https://doi.org/10.3390/rs70100229Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from Internet photo collections. International Journal of Computer Vision, 80(2), 189–210. https://doi.org/10.1007/s11263-007-0107-3Stereńczak, K., Kraszewski, B., Mielcarek, M., Piasecka, N., Lisiewicz, M., & Heurich, M. (2020). Mapping individual trees with airborne laser scanning data in an European lowland forest using a selfcalibration algorithm. International Journal of Applied Earth Observation and Geoinformation, 93, 102191. https://doi.org/10.1016/j.jag.2020.102191Tagarakis, A. C., Koundouras, S., Fountas, S., & Gemtos, T. (2018). Evaluation of the use of LIDAR laser scanner to map pruning wood in vineyards and its potential for management zones delineation. Precision Agriculture, 19, 334–347. https://doi.org/10.1007/s11119-017-9519-4Torres-Sánchez, J., de Castro, A., Peña, J. M., Jiménez-Brenes, F. M., Arquero, O., Lovera, M., et al. (2018). Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis. Biosystems Engineering, 176, 172–184. https://doi.org/10.1016/j.biosystemseng.2018.10.018Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., & Peña, J. M. (2015). High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS ONE, 10(6), e0130479. https://doi.org/10.1371/journal.pone.0130479Valbuena, M. A., Santamaría, J., & Sanz, F. (2016). Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the national forest inventory data. Forest Systems, 25(1), e046. https://doi.org/10.5424/fs/2016251-05790Van der Zande, V., Hoet, W., Jonckheere, I., van Aardt, J., & Coppin, P. (2006). Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agricultural and Forest Meteorology, 141(2–4), 147–160. https://doi.org/10.1016/j.agrformet.2006.09.007Velázquez-Martí, B., Fernández-González, E., López-Cortes, I., & Salazar-Hernández, D. M. (2011). Quantification of the residual biomass obtained from pruning of trees in Mediterranean olive groves. Biomass and Bioenergy, 35(2), 3208–3217. https://doi.org/10.1016/j.biombioe.2011.04.042Velázquez-Martí, B., López-Cortés, I., & Salazar, D. M. (2014). Dendrometric analysis of olive trees for wood biomass quantification in Mediterranean orchards. Agroforestry Systems, 88(5), 755–765. https://doi.org/10.1007/s10457-014-9718-1Walklate, P. J., Cross, J. V., Richardson, G. M., Murray, R. A., & Baker, D. E. (2002). Comparison of different spray volume deposition models using LIDAR measurements of apple orchards. Biosystems Engineering, 82(3), 253–267.Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., & Vopenka, P. (2016). Assessment of forest structure using two UAV Techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7, 62. https://doi.org/10.3390/f7030062Wang, X., Cheng, X., Gong, P., Huang, H., Li, Z., & Li, X. (2011). Earth science applications of ICESat/GLAS: A review. International Journal of Remote Sensing, 32(23), 8837–8864.Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., & Pfeifer, N. (2017). A case study of UAS borne laser scanning for measurement of tree stem diameter. Remote Sensing, 9, 1154. https://doi.org/10.3390/rs9111154Xie, Q., Dash, J., Huete, A., Jiang, A., Yin, G., Ding, Y., Peng, D., Hall, C. C., Brown, L., Shi, Y., et al. (2019). Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 80, 187–195. https://doi.org/10.1016/j.jag.2019.04.019Xing, D., Bergeron, J. C., Solarik, K. A., Tomm, B., Macdonald, S. E., Spence, J. R., & He, F. (2019). Challenges in estimating forest biomass: Use of allometric equations for three boreal tree species. Canadian Journal of Forest Research, 49(12), 1613–1622.Zarco-Tejada, P. J., Diaz-Varela, R., Angileri, V., & Loudjani, P. (2014). Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 255, 89–99.Zhang, Z., Cao, L., & She, G. (2017). Estimating forest structural parameters using canopy metrics derived from airborne LiDAR data in subtropical forests. Remote Sens, 9, 940. https://doi.org/10.3390/rs909094

Application of airborne LiDA R data in viewshed analysis

Revista oficial de la Asociación Española de Teledetección[EN] The environmental impact assessment and landscape analysis of any work or activity over the territory requires a study of the visual impact what can be done from the application of viewshed analysis. The accuracy of these results depends largely on the parameters for calculating them, accuracy and spatial resolution of initial elevation data and digital models derived. In this study viewshed analysis in 4 areas of the town of Gandia with different characteristics (urban, forest, beach, mixed) were analyzed from 4 types of geographic information: a) Digital Elevation Model (DEM) and b) Digital Surface Model (DSM) derived from LiDAR data with density of 1 point/m2; c) DTM from a photogrammetric flight with a pixel size of 5×5 m; d) Overlay cadastral cartography with the previous DTM. For the validation of the results, 120 checking points were used to calculate the overall accuracy and kappa index. The results showed a high overall accuracy for the viewsheds calculated from the DSM derived from LiDAR data being the overall accuracy and index kappa 90% and 0.80, respectively. The conclusions drawn from this study indicated that the use of this source of information showed a good performance for the generation of viewshed analysis.[ES] Los estudios de impacto ambiental o paisajismo de cualquier obra o actuación en el territorio requieren de un estudio del impacto visual de las mismas a partir de la generación de cuencas visuales. La exactitud de estos resultados depende en gran medida del tipo datos de elevaciones iniciales, de los modelos digitales que se deriven y de los parámetros del cálculo de las cuencas visuales. En este estudio se analizaron cuencas visuales generadas en cuatro zonas del municipio de Gandia con características diferenciadas (urbana, forestal, playa, mixta) a partir de cuatro tipos de información cartográfica: a) Modelo Digital del Terreno (MDT) y b) Modelo Digital de Superficie (MDS) calculados a partir de datos LiDAR con una densidad media de 1 punto/m2; c) MDT derivado de un vuelo fotogramétrico a escala 1/5000; d) Superposición cartografía catastral con elevaciones de edificios y el MDT anterior. Para la validación de las mismas se utilizaron 120 puntos de muestreo (60 visibles y 60 no visibles) con los que se calculó la fiabilidad global e índice kappa. Los resultados obtenidos muestran una fiabilidad global muy alta en las cuencas visuales calculadas a partir del MDS derivado de los datos LiDAR siendo la fiabilidad global e índice kappa del 90% y 0,80, respectivamente. La conclusión que se desprenden de este estudio indica que la utilización del MDS derivado de los datos LiDAR de baja densidad genera resultados satisfactorios en la generación de cuencas visuales para los estudios de paisajismo o impacto ambiental.Pellicer, I.; Estornell Cremades, J.; Martí, J. (2014). Aplicación de datos LiDAR aéreo para el cálculo de cuencas visuales. Revista de Teledetección. (41):9-18. doi:10.4995/raet.2014.2293SWORD9184

Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics

[EN] LiDAR full-waveform (LFW) pulse density is not homogeneous along study areas due to overlap between contiguous flight stripes and, to a lesser extent, variations in height, velocity and altitude of the platform. As a result, LFW-derived metrics extracted at the same spot but at different pulse densities differ, which is called ¿side-lap effect¿. Moreover, this effect is reflected in forest stand estimates, since they are predicted from LFW-derived metrics. This study was undertaken to analyze LFW-derived metric variations according to pulse density, voxel size and value assignation method in order to reduce the side-lap effect. Thirty LiDAR samples with a minimum density of 16 pulses.m¿2 were selected from the testing area and randomly reduced to 2 pulses.m¿2 with an interval of 1 pulse.m¿2, then metrics were extracted and compared for each sample and pulse density at different voxel sizes and assignation values. Results show that LFW-derived metric variations as a function of pulse density follow a negative exponential model similar to the exponential semivariogram curve, increasing sharply until they reach a certain pulse density, where they become stable. This value represents the minimum pulse density (MPD) in the study area to optimally minimize the side-lap effect. This effect can also be reduced with pulse densities lower than the MPD modifying LFW parameters (i.e. voxel size and assignation value). Results show that LFW-derived metrics are not equally influenced by pulse density, such as number of peaks (NP) and ROUGHness of the outermost canopy (ROUGH) that may be discarded for further analyses at large voxel sizes, given that they are highly influenced by pulse density. In addition, side-lap effect can be reduced by either increasing pulse density or voxel size, or modifying the assignation value. In practice, this leads to a proper estimate of forest stand variables using LFW data.This research has been funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R. The authors also thank the Bureau of Land Management and the Panther Creek Remote Sensing and Research Cooperative Program for the data provided.Crespo-Peremarch, P.; Ruiz Fernández, LÁ.; Balaguer-Beser, Á.; Estornell Cremades, J. (2018). Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics. ISPRS Journal of Photogrammetry and Remote Sensing. 146:453-464. https://doi.org/10.1016/j.isprsjprs.2018.10.012S45346414