68 research outputs found
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. 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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)
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
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). 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Remote Sensing Temporal Reconstruction of the Flooded Area in "Tablas de Daimiel" Inland Wetland 2000-2021
[EN] Tablas de Daimiel National Park (TDNP) is a unique inland wetland located in the Mancha plain (Spain). It is recognized at the international level, and it is protected by different figures, such as Biosphere Reserve. However, this ecosystem is endangered due to aquifer overexploitation, and it is at risk of losing its protection figures. The objective of our study is to analyze the evolution of the flooded area between the year 2000 and 2021 by Landsat (5, 7 and 8) and Sentinel-2 images, and to assess the TDNP state through an anomaly analysis of the total water body surface. Several water indices were tested, but the NDWI index for Sentinel-2 (threshold -0.20), the MNDWI for Landsat-5 (threshold -0.15), and the MNDWI for Landsat-8 (threshold -0.25) showed the highest accuracy to calculate the flooded surface inside the protected area's limits. During the period 2015-2021, we compared the performance of Landsat-8 and Sentinel-2 and an R2 value of 0.87 was obtained for this analysis, indicating a high correspondence between both sensors. Our results indicate a high variability of the flooded areas during the analyzed period with significant peaks, the most notorious in the second quarter of 2010. Minimum flooded areas were observed with negative precipitation index anomalies since fourth quarter of 2004 to fourth quarter of 2009. This period corresponds to a severe drought that affected this region and caused important deterioration. No significant correlation was observed between water surface anomalies and precipitation anomalies, and the significant correlation with flow and piezometric anomalies was moderate. This can be explained because of the complexity of water uses in this wetland, which includes illegal wells and the geological heterogeneity.Pena-Regueiro, J.; Estornell Cremades, J.; Aguilar-Maldonado, J.; Sebastiá-Frasquet, M. (2023). Remote Sensing Temporal Reconstruction of the Flooded Area in "Tablas de Daimiel" Inland Wetland 2000-2021. Sensors. 23(8). https://doi.org/10.3390/s2308409623
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
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