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

Characterizing beach changes using high-frequency Sentinel-2 derived shorelines on the Valencian coast (Spanish Mediterranean)

[EN] Shoreline position can be efficiently extracted with subpixel accuracy frommid-resolution satellite imagery using tools as SHOREX. However, it is necessary to develop procedures for deriving descriptors of the beach morphology and its changes in order to become truly useful data for characterizing the coastal dynamism. A new approach is proposed based on a spatiotemporal model of the beach widths. Divided into 80 m analysis segments, it offers a robust and detailed characterization of the beach state along large micro-tidal regions, with continuous information through time and space. Geographical and temporal differences can be recognized andmeasured, making it possible to study the beach response both to general factors (as wave conditions) and to punctual anthropic actions (as small sand nourishments). Widths were defined throughout two and a half years from 60 shorelines (3.04 m RMSE) covering 50 km of the Gulf of Valencia. Important width contrasts appeared along the study site associated with sediment imbalances motivated by sediment traps and other anthropic actions. Segments too narrow for maintaining the recreational function were located and mapped (16% narrower than 30 m). Short-term width changes appeared linked to storm events, with fast retreatments and slow recoveries. Punctually, even small-magnitude nourishments created perceptible changes in width (12,830 m(3) were associated with a 4 m increase). This novel description of the beach state and its changes from Satellite-Derived Shorelines is useful for coastal management, especially considering the global coverage of these free satellite images. It may improve the comprehension of coastal processes as well as monitor human interventions on the coast, helping in the decision making. (c) 2019 Elsevier B.V. All rights reserved.This study is supported by the contract of C. Cabezas-Rabadan (FPU15/04501) from the Spanish Ministry of Education, Culture and Sports, and by the project RESETOCOAST (CGL2015-69906-R) from the Spanish Ministry of Economy and Competitiveness.Cabezas-Rabadán, C.; Pardo Pascual, JE.; Palomar-Vázquez, J.; Fernández-Sarría, A. (2019). Characterizing beach changes using high-frequency Sentinel-2 derived shorelines on the Valencian coast (Spanish Mediterranean). The Science of The Total Environment. 691:216-231. https://doi.org/10.1016/j.scitotenv.2019.07.084S21623169

Lidar methods for measurement of trees in urban forests

[EN] This study compares the estimations of biophysical parameters of Platanus hispanica urban trees, namely total height, crown height, crown volume, and the amount of residual biomass from pruning, obtained by terrestrial laser scanner (TLS), airborne laser scanner (ALS)of low density (0.7 points · m¿2), and measured by standard field methods. Regression models were calculated to obtain the relationships among parameters retrieved by all techniques, testing all possible combinations (manual-TLS, manual-ALS, TLS-ALS, and vice versa). The most accurate fits were found for vegetation attributes (stem and crown diameter) estimated by TLS and ALS data with R2 between 0.84 and 0.96, respectively. The least accurate models were found when crown height and pruning biomass were estimated from ALS data (R2 ¿ 0.68 and R2 ¿ 0.59, respectively). The methods reported in this research might be of interest for the management of urban forests to study residual biomass calculation, sink CO2, the influence of humidity and of shadow areas whatever the information capture system used, whether it is derived from ALS, TLS, or classical dendrometry measurements.Estornell Cremades, J.; Velázquez Martí, B.; Fernández-Sarría, A.; Marti-Gavila, J. (2018). Lidar methods for measurement of trees in urban forests. Journal of Applied Remote Sensing. 12(4):046009-1-046009-17. doi:10.1117/1.JRS.12.046009S046009-1046009-1712

Daily Concentrations of PM2.5 in the Valencian Community Using Random Forest for the Period 2008–2018

Fine particulate matter (PM2.5) is a global problem that affects the population health and contributes to climate change. Remote sensing provides useful information for the development of air quality models. This work aims to obtain a daily model of PM2.5 levels in the Valencian Community with a resolution of 1 km for the period 2008–2018. MODIS-MAIAC images, meteorological parameters of the MERRA-2 project, land cover information and ground level measurements of PM2.5 levels were analysed with Random Forest. The verification of the model was carried out using cross-validation repeated ten times, and an evaluation of a test set with 20% of the collected information. The final model was used to generate maps of the daily concentrations of PM2.5 for the area of the Valencian Community throughout the study period.Centro de Investigaciones del Medioambient

Experiencias de innovación docente y uso de nuevas tecnologías en la enseñanza del tratamiento de la imagen digital en Geomática

[ES] La asignatura de Tratamiento de la imagen digital se encuentra en segundo de Grado en Ingeniería en Geomática y Topografía. Sus seis créditos se reparten en tres de teoría de aula y tres de prácticas de laboratorio. En este trabajo se muestra cómo se ha organizado la docencia y las metodologías empleadas en la asignatura buscando el aprendizaje autónomo y significativo del alumno, intentando aumentar su motivación e implicación. Se pretende cambiar la tendencia pasiva del alumno hacia un aprendizaje activo dentro y fuera del aula que le permita llegar a las clases con el material trabajado y dispuesto a resolver las cuestiones que se planteen y a obtener e interpretar resultados. Se diseñaron clases prácticas basadas en los contenidos teóricos, con videos de 10 minutos (Polimedias) sobre cómo ejecutar las prácticas y con un test que permite evaluar los resultados y recoger los comentarios que se derivan del desarrollo de las prácticas. Sin embargo, es necesario avanzar en el diseño de actividades encaminadas hacia la docencia inversa (flip teaching), es decir hacia clases en las que el alumno debata y resuelva las dudas y cuestiones que se le planteen y que le permitan abandonar la posición de alumno pasivo.Porres De La Haza, MJ.; Fernández-Sarría, A.; Recio Recio, JA. (2014). Experiencias de innovación docente y uso de nuevas tecnologías en la enseñanza del tratamiento de la imagen digital en Geomática. Editorial Universitat Politècnica de València. 897-904. http://hdl.handle.net/10251/168756S89790

Monitoring the response of mediterranean beaches to storms and anthropogenic actions using Landsat imagery

[EN] Large-scale and continuous monitoring of morphological changes on beaches is of great interest for coastal management. Shoreline positions were extracted with the system SHOREX on multiple dates on three beaches of the Gulf of Valencia from Landsat 5, 7 and 8 images from the period 1984-2014. These data made it possible to analyze the evolution of the beaches over three decades, as well as their short-term changes. In this way, the capacity of the shorelines to represent the response of the beaches to coastal storms and anthropogenic actions was evaluated. The shorelines obtained from SHOREX show great potential for monitoring and surveillance of the state of the beaches, while the analysis of their changes provides key information on the nature of the beaches.[ES] La monitorización a gran escala y de forma continua de los cambios morfológicos en playas presenta un gran interés para la gestión costera. La posición de la línea de costa ha sido definida en tres playas del golfo de Valencia en múltiples fechas durante el periodo 1984-2014 partiendo de las imágenes Landsat 5, 7 y 8 y el sistema para la extracción de líneas de costa SHOREX. Estos datos han permitido analizar la evolución de las playas durante tres décadas, así como sus cambios a corto plazo. De este modo, se ha evaluado la capacidad de las líneas para representar la respuesta de las playas ante fenómenos de temporales costeros y actuaciones antrópicas en el medio costero. Las líneas obtenidas de SHOREX muestran un gran potencial para el seguimiento y la vigilancia del estado de las playas, a la vez que el análisis de sus cambios suministra información clave de la naturaleza de las playas.Este trabajo se ha beneficiado del contrato de investigación FPU15 otorgado por el Ministerio de educación, ciencia y deporte al primer autor, así como por fondos del proyecto RESETOCOAST (CGL2015-69906-R) del Programa Retos-2015 del Ministerio de Economía, Industria y Competitividad.Cabezas-Rabadán, C.; Pardo Pascual, JE.; Almonacid-Caballer, J.; Palomar-Vázquez, J.; Fernández-Sarría, A. (2019). Monitorización de la respuesta de playas mediterráneas a temporales y actuaciones antrópicas mediante imágenes Landsat. GeoFocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica. (23):119-139. https://doi.org/10.21138/GF.640S1191392

Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery

[EN] This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (-0.75 +/- 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (+/- 6.55 m) and 5.50 m (+/- 4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (+/- 5.79 m).The authors appreciate the financial support provided by the Spanish Ministry of Economy and Competitiveness in the framework of project CGL2015-69906-R. This study is part of the Ph.D. dissertation of the second author, which is supported by a grant from the Spanish Ministry of Education, Culture and Sports (I+D+i 2013-2016). The authors are extremely grateful to different reviewers and editors of this work because their observations and suggestions have improved the final article a lot.Pardo Pascual, JE.; Sánchez García, E.; Almonacid-Caballer, J.; Palomar-Vázquez, J.; Priego De Los Santos, E.; Fernández-Sarría, A.; Balaguer-Beser, Á. (2018). Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery. Remote Sensing. 10(2). https://doi.org/10.3390/rs10020326S10

Use of Gabor Filters for texture classification of digital images

In this article various methodologies, based on the use of Gabor filters, are described and analysed for the extraction of texture features and the subsequent classification of aerial and satellite digital images. Images of urban, forest and agricultural areas were used, where the complexity of the terrain and the differences in vegetation density require the consideration of the existing texture features as a base for elaborating land use cartography. The use of Gabor filters is driven by the potential they have to isolate texture according to particular frequencies and orientations. The parameters that define a Gabor filter are its frequency,... (Ver más) standard deviation and orientation. By varying these parameters, a filter bank is obtained that covers the frequency domain almost completely. Several alternatives have been studied for the application of Gabor filters: (a) the use of complete filter banks; (b) the sum of the filters of equal frequency; and (c) the selection of those filters that minimise, a priori, the classification error. From the application of filters in each of the three methods, a group of images is obtained that allow for the numeric quantification of textures in the image. The evaluation of the classification results shows that combining these textural variables with the multispectral information permits us to characterize the existing regions in the territory with more precision, using supervised digital image classification techniques

Análisis de imágenes mediante texturas: Aplicación a la clasificación de unidades de vegetación

Este trabajo detalla el proceso realizado sobre imágenes pancromáticas digitalizadas a 60cm y 2m por píxel, con el fin de analizar las diferentes posibilidades de trabajo de las variables texturales en la caracterización de unidades de vegetación, sea natural o sean cultivos. Se ha trabajado sobre dos ámbitos geográficos diferentes para resaltar las diferencias de resultados entre la vegetación influenciada por la acción humana y aquella que lo está en menor medida. Sobre las imágenes digitalizadas se han extraído las zonas de ensayo sobre las que se han calculado variables de textura a partir de la matriz de coocurrencias de niveles de gris, con un vecindario óptimo de 21x21 píxeles y a partir de filtros de energía de 7x7. Sobre todas esas variables se han aplicado clasificaciones supervisadas, trabajando con distintos tipos de clase en cada caso y analizando los resultados obtenidos según las propias características de densidad y distribución de la vegetación

Clasificación de entornos forestales mediterráneos mediante técnicas de análisis de texturas

Dada la variabilidad estructural característica de numerosas superficies forestales, la información espectral es, a menudo, insuficiente para la clasificación de unidades, requiriéndose información sobre su estructura espacial, la cual puede obtenerse mediante técnicas de análisis de texturas. En este trabajo se emplean datos provenientes de ortofotografías aéreas y de satélites de alta resolución (QuickBird) para la extracción de variables de textura sobre zonas de montaña en diversos ámbitos mediterráneos (Menorca, Castellón y Valencia), se estudian y evalúan varios métodos de análisis de texturas y se comparan los resultados con los obtenidos por métodos de clasificación exclusivamente espectrales. Los métodos evaluados son diversos: estadísticos, como los basados en la matriz de coocurrencias de niveles de gris; métodos basados en filtros de energía y de Gabor; y aquellos en los que previamente se descompone la imagen mediante la transformada de wavelets. Los resultados muestran un gran potencial de las técnicas basadas en texturas en los problemas de clasificación forestal. Además, este tipo de variables, que proporcionan información sobre la distribución espacial y estructural de las coberturas de vegetación, empleadas de forma conjunta con las variables espectrales, muestran un incremento significativo de la fiabilidad en las clasificaciones