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

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

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

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

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

Mechanized methods for harvesting residual biomass from Mediterranean fruit tree cultivations

[EN] This study evaluates the technology and work systems used in order to harvest residual biomass from pruning in the specific conditions of Mediterranean fruit orchards (narrow distances between crop-rows). Harvesting has been divided into several types of operations pruning, biomass alignment between crop tracks, biomass concentration in piles, chipping and bundling - which have been analyzed in five Mediterranean cultivations for three years. Altogether, three types of pruning have been analyzed: Manual, previous mechanical followed by manual, and fully mechanical; Two types of alignment: Manual and mechanical; Three concentration systems: Manual, tractor with a rake and a forwarder; Four chipping work organization systems: chipper driven inside orchard and manually fed by operators, mobile chipper driven inside orchard with pick-up header, mobile chipper fed by means of mechanical crane, chipper mounted on a truck fed by means of mechanical crane, which was working in a fixed position in a border of the plot after wood concentration. Also two bundling organization systems were checked: bundler machine working in a fixed position after wood concentration and working inside the plot driven among the crops. Previous concentration of the materials was the best alternative for their chipping or bundling in the studied conditions. Regression models have been calculated to predict the time of work of machinery and labor for each alternative. These equations were used to implement logistic planning as the Borvemar model, which defines a logistics network for supplying bio-energy systems.The research shown in this paper was developed by the project AGL2007-62328 funded by the Ministry of Education and Science of Spain, and FEDER funds of European Union.Velázquez Martí, B.; Fernández-González, E.; Callejón-Ferre, ÁJ.; Estornell Cremades, J. (2012). Mechanized methods for harvesting residual biomass from Mediterranean fruit tree cultivations. Scientia Agricola. 69(3):180-188. https://doi.org/10.1590/S0103-90162012000300002S18018869

Teledetección. Nuevas plataformas y sensores aplicados a la gestión del agua, la agricultura y el medio ambiente

Este libro recoge las comunicaciones presentadas al XVII Congreso de la Asociación Española de Teledetección (AET), celebrado del 3 al 7 de octubre de 2017 en el auditorio y palacio de congresos de Murcia y organizado por el Grupo de Sistemas de Información Geográfica y Teledetección del Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA),con el soporte de la AET,el Instituto Geográfico Nacional (IGN), las universidades politécnicas de Cartagena y Valencia, la Confederación Hidrográfica del Segura, el ayuntamiento de Murcia,las empresas Gade Eventos y Geodim y la Universidad Católica de San Antonio El lema elegido para el Congreso ha sido "Nuevas plataformas y sensores de teledetección" aplicados a la gestión del agua,la agricultura y el medio ambiente, con la intención de promover el encuentro entre las comunidades académicas, científicas e industriales en el área de la teledetección, destacando las nuevas plataformas de bajo coste y los logros conseguidos en la generación y difusión de productos útiles para la sociedadRuiz Fernández, LÁ.; Estornell Cremades, J.; Erena Arrabal, M. (2017). Teledetección. Nuevas plataformas y sensores aplicados a la gestión del agua, la agricultura y el medio ambiente. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/90688EDITORIA

Classification of UAV-based photogrammetric point clouds of riverine species using machine learning algorithms: a case study in the Palancia river, Spain

[EN] The management of riverine areas is fundamental due to their great environmental importance. The fast changes that occur in these areas due to river mechanics and human pressure makes it necessary to obtain data with high temporal and spatial resolution. This study proposes a workflow to map riverine species using Unmanned Aerial Vehicle (UAV) imagery. Based on RGB point clouds, our work derived simple geometric and spectral metrics to classify an area of the public hydraulic domain of the river Palancia (Spain) in five different classes: Tamarix gallica L. (French tamarisk), Pinus halepensis Miller (Aleppo pine), Arundo donax L. (giant reed), other riverine species and ground. A total of six Machine Learning (ML) methods were evaluated: Decision Trees, Extra Trees, Multilayer Perceptron, K-Nearest Neighbors, Random Forest and Ridge. The method chosen to carry out the classification was Random Forest, which obtained a mean score cross-validation close to 0.8. Subsequently, an object-based reclassification was done to improve this result, obtaining an overall accuracy of 83.6%, and individually a producer¿s accuracy of 73.8% for giant reed, 87.7% for Aleppo pine, 82.8% for French tamarisk, 93.5% for ground and 80.1% for other riverine species. Results were promising, proving the feasibility of using this cost-effective method for periodic monitoring of riverine species. In addition, the proposed workflow is easily transferable to other tasks beyond riverine species classification (e.g., green areas detection, land cover classification) opening new opportunities in the use of UAVs equipped with consumer cameras for environmental applications.Carbonell-Rivera, JP.; Estornell Cremades, J.; Ruiz Fernández, LÁ.; Torralba, J.; Crespo-Peremarch, P. (2020). Classification of UAV-based photogrammetric point clouds of riverine species using machine learning algorithms: a case study in the Palancia river, Spain. ISPRS. 659-666. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-659-2020S65966