Estimación de parámetros de estructura de nogales utilizando láser escáner terrestre

[EN] Juglans regia L. (walnut) is a tree of significant economic importance, usually cultivated for its seed used in the food market, and for its wood used in the furniture industry. The aim of this work was to develop regression models to predict crown parameters for walnut trees using a terrestrial laser scanner. A set of 30 trees was selected and the total height, crown height and crown diameter were measured in the field. The trees were also measured by a laser scanner and algorithms were applied to compute the crown volume, crown diameter, total and crown height. Linear regression models were calculated to estimate walnut tree parameters from TLS data. Good results were obtained with values of R2 between 0.90 and 0.98. In addition, to analyze whether coarser point cloud densities might affect the results, the point clouds for all trees were subsampled using different point densities: points every 0.005 m, 0.01 m, 0.05 m, 0.1 m, 0.25 m, 0.5 m, 1 m, and 2 m. New regression models were calculated to estimate field parameters. For total height and crown volume good estimations were obtained from TLS parameters derived for all subsampled point cloud (0.005 m – 2 m).[ES] Juglans regiaL. (nogal) es un árbol de importancia económica por el fruto que proporciona y por su madera utilizada en la industria del mueble. El objetivo de este trabajo fue calcular modelos de regresión para estimar los pa-rámetros altura total, altura, diámetro y volumen de copa de nogales utilizando datos registrados mediante un escáner láser terrestre. Un conjunto de 30 árboles fueron escaneados y se aplicaron algoritmos para calcular los parámetros anteriores, que también se midieron en campo utilizando técnicas tradicionales. Se obtuvieron buenos resultados, con valores de R2 entre 0,90 y 0,98 para todos los parámetros. Además, para analizar la relación entre la densidad de puntos registrada y la precisión en la estimación de los parámetros de los nogales, las nubes de puntos de todos los árboles fueron sub-muestreadas utilizando diferentes distancias de separación entre puntos: 0,005 m, 0,01 m, 0,05 m, 0,1 m, 0,25 m, 0,5 m, 1 m y 2 m. Se calcularon nuevos modelos de regresión con los datos muestreados obteniéndose buenas estimaciones de los parámetros para todos los conjuntos de datos.The authors appreciate the financial support provided by the regional government of Spain (Conselleria d'Educacio, Cultura i Esport Generalitat Valenciana) in the framework of the Project GV/2014/016.Estornell, 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. (48):67-76. https://doi.org/10.4995/raet.2017.7429SWORD677648Belsley. D.A. 1991. Conditioning Diagnostics: Collinearity and Weak Data in Regression. John Wiley & Sons.Chianucci, F., Puletti, N., Giacomello, E., Cutini, A., & Corona, P. (2015). Estimation of leaf area index in isolated trees with digital photography and its application to urban forestry. Urban Forestry & Urban Greening, 14(2), 377-382. doi:10.1016/j.ufug.2015.04.001Corona, P., Agrimi, M., Baffetta, F., Barbati, A., Chiriacò, M. V., Fattorini, L., … Mattioli, W. (2011). Extending large-scale forest inventories to assess urban forests. Environmental Monitoring and Assessment, 184(3), 1409-1422. doi:10.1007/s10661-011-2050-6Fernández-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. Computers and Electronics in Agriculture, 90, 176-185. doi:10.1016/j.compag.2012.09.017Gil, E., Llorens, J., Llop, J., Fàbregas, X., & Gallart, M. (2013). Use of a Terrestrial LIDAR Sensor for Drift Detection in Vineyard Spraying. Sensors, 13(1), 516-534. doi:10.3390/s130100516Greaves, H. E., Vierling, L. A., Eitel, J. U. H., Boelman, N. T., Magney, T. S., Prager, C. M., & Griffin, K. L. (2015). Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164, 26-35. doi:10.1016/j.rse.2015.02.023Keightley, K. E., & Bawden, G. W. (2010). 3D volumetric modeling of grapevine biomass using Tripod LiDAR. Computers and Electronics in Agriculture, 74(2), 305-312. doi:10.1016/j.compag.2010.09.005Manes, F., Incerti, G., Salvatori, E., Vitale, M., Ricotta, C., & Costanza, R. (2012). Urban ecosystem services: tree diversity and stability of tropospheric ozone removal. Ecological Applications, 22(1), 349-360. doi:10.1890/11-0561.1MAAM. 2015. Encuesta sobre superficies y rendimientos cultivos (ASYRCE). Encuesta de marco de áreas de Espa-a. Ministerio de Agricultura, Alimentación y Medio Ambiente de Espa-a, 44 pp.Rosell, J. R., Llorens, J., Sanz, R., Arnó, J., Ribes-Dasi, M., Masip, J., … Palacín, J. (2009). Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning. Agricultural and Forest Meteorology, 149(9), 1505-1515. doi:10.1016/j.agrformet.2009.04.008Rosell Polo, J. R., Sanz, R., Llorens, J., Arnó, J., Escolà, A., Ribes-Dasi, M., … Palacín, J. (2009). A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosystems Engineering, 102(2), 128-134. doi:10.1016/j.biosystemseng.2008.10.009Rosell, J. R., & Sanz, R. (2012). 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Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia "san Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz; ArgentinaFil: Chesñevar, Carlos Iván. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

Terrestrial laser scanning for crop monitoring. Capturing 3D data of plant height for estimating biomass at field scale

Terrestrial laser scanning (TLS) is a young remote sensing method, but the trustworthiness of such measurements offers great potential for accurate surveying. TLS allows non-experts to rapidly acquire 3D data of high density. Generally, this acquisition of accurate geoinformation is increasingly desired in various fields, however this study focuses on the application of TLS for crop monitoring. The increasing cost and efficiency pressure on agriculture induced the emergence of site specific crop management, which requires a comprehensive knowledge about the plant development. An important parameter to evaluate this development or rather the actual plant status is the amount of plant biomass, which is however directly only determinable with destructive sampling. With the aim of avoiding destructive measurements, interest is increasingly directed towards non-contact remote sensing surveys. Nowadays, different approaches address biomass estimations based on other parameters, such as vegetation indices (VIs) from spectral data or plant height. Since the plants are not taken it is feasible to perform several measurements across a field and across the growing season. Hence, the change of spatial and temporal patterns can be monitored. This study applies TLS for objectively measuring and monitoring plant height as estimator for biomass at field scale. Overall 35 TLS campaigns were carried out at three sites over four growing seasons. In each campaign a 3D point cloud, covering the surface of the field, was obtained and interpolated to a crop surface model (CSM). A CSM represents the crop canopy in a very high spatial resolution on a specific date. By subtracting a digital terrain model (DTM) of the bare ground from each CSM, plant heights were calculated pixel-wise. Manual measurements aligned well with the TLS data and demonstrated the main benefit of CSMs: the highly detailed acquisition of the entire crop surface. The plant height data were used to estimate biomass with empirically developed biomass regression models (BRMs). Validation analyses against destructive measurements were carried out to confirm the results. The spatial and temporal transferability of crop-specific BRMs was shown. In one case study, the estimations from plant height and six VIs were compared and the benefit of fusing both parameters was investigated. The analyses were based on the TLS-derived CSMs and spectral data measured with a field spectrometer. The important role of plant height as a robust estimator was shown in contrast to a varying performance of BRMs based on the VIs. A major benefit through the fusion of both parameters in multivariate BRMs could not be concluded in this study. Nevertheless, further research should address this fusion, with regard to the capability of VIs to assess information about the vegetation cover or biochemical and biophysical parameters

Measuring surface moisture on a sandy beach based on corrected intensity data of a mobile terrestrial LiDAR

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 x 200 m per minute)

Implementing Unmanned Aerial Systems Within a Field-Based Maize (Zea mays L.) Breeding Program: Improving Yield Prediction and Understanding Temporal QTL Expression of Plant Height

Unmanned aerial system (UAS) technologies are becoming common place within field-based agriculture programs allowing breeders to evaluate greater numbers of genotypes, reducing resource inputs and maintaining unbiased data collection. A comprehensive evaluation was conducted focused on the implementation of UAS technologies within a field-based maize breeding program using the plant height phenotype as a proof of concept in implementation and validation. A robust data processing pipeline was developed to extract height measurements from RGB structure from motion (SfM) point clouds. The 95th percentile (P95) height estimates exceeded 70% correlation to manual ground truth measurements across diverse germplasm groups of hybrid (F1) and inbred lines. Sigmoidal functions were developed to model the overall growth and trajectory of hybrids (R^2 : >98%; RMSE: 99%; RMSE: 1.5- fold similar to functional growth parameters. A ~4-fold improvement in indirect selection of hybrid grain yield was achieved using functional growth parameters compared to conventional manual, terminal plant height (PHTTRML). We expanded our implementation of UAS phenotyping to evaluate three inbred line mapping populations aimed at studying functional QTL and temporal QTL expression. Functional growth parameters identified 34 associations explaining 3 to 15% genetic variation. Height was estimated at one-day intervals to 85 DAS using the Weibull function, identifying 58 unique temporal peak QTL locations. Temporal QTL demonstrated all of the identified significant QTL had dynamic expression patterns. In all, UAS technologies improved phenotypic selection accuracy and have capacity to monitor traits on a temporal scale furthering our understanding of crop development and biological trajectories

New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Figures are re-used under an Attribution 4.0 International (CC BY 4.0) license, or are not copyrighted.Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water

The acquisition of Hyperspectral Digital Surface Models of crops from UAV snapshot cameras

This thesis develops a new approach to capture information about agricultural crops by utilizing advances in the field of robotics, sensor technology, computer vision and photogrammetry: Hyperspectral digital surface models (HS DSMs) generated with UAV snapshot cameras are a representation of a surface in 3D space linked with hyperspectral information emitted and reflected by the objects covered by that surface. The overall research aim of this thesis is to evaluate if HS DSMs are suited for supporting a site-specific crop management. Based on six research studies, three research objectives are discussed for this evaluation. Firstly the influences of environmental effects, the sensing system and data processing of the spectral data within HS DSMs are discussed. Secondly, the comparability of HS DSMs to data from other remote sensing methods is investigated and thirdly their potential to support site-specific crop management is evaluated. Most data within this thesis was acquired at a plant experimental-plot experiment in Klein-Altendorf, Germany, with six different barley varieties and two different fertilizer treatments in the growing seasons of 2013 and 2014. In total, 22 measurement campaigns were carried out in the context of this thesis. HS DSMs acquired with the hyperspectral snapshot cameras Cubert UHD 185-Firefly show great potential for practical applications. The combination of UAVs and the UHD allowed data to be captured at a high spatial, spectral and temporal resolution. The spatial resolution allowed detection of small-scale heterogeneities within the plant population. Additionally, with the spectral and 3D information contained in HS DSMs, plant parameters such as chlorophyll, biomass and plant height could be estimated within individual, and across different growing stages. The techniques developed in this thesis therefore offer a significant contribution towards increasing cropping efficiency through the support of site-specific management

Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging

Data collection with unmanned aerial vehicles (UAVs) fills a gap on the observational scale in re-mote sensing by delivering high spatial and temporal resolution data that is required in crop growth monitoring. The latter is part of precision agriculture that facilitates detection and quan-tification of within-field variability to support agricultural management decisions such as effective fertilizer application. Biophysical parameters such as plant height and biomass are monitored to describe crop growth and serve as an indicator for the final crop yield. Multi-temporal crop surface models (CSMs) provide spatial information on plant height and plant growth. This study aims to examine whether (1) UAV-based CSMs are suitable for plant height modelling, (2) the derived plant height can be used for biomass estimation, and (3) the combination of plant height and vegetation indices has an added value for biomass estimation. To achieve these objectives, UAV-flight campaigns were carried out with a red-green-blue (RGB) camera over controlled field experiments on three study sites, two for summer barley in Western Germany and one for rice in Northeast China. High-resolution, multi-temporal CSMs were derived from the images by using computer vision software following the structure from motion (SfM) approach. The results show that plant height and plant growth can be accurately modelled with UAV-based CSMs from RGB imaging. To maximise the CSMs’ quality, accurate flight planning and well-considered data collection is necessary. Furthermore, biomass is successfully estimated from the derived plant height, with the restriction that results are based on a single-year dataset and thus require further validation. Nevertheless, plant height shows robust estimates in comparison with various vegetation indices. As for biomass estimation in early growth stages additional po-tential is found in exploiting visible band vegetation indices from UAV-based red-green-blue (RGB) imaging. However, the results are limited due to the use of uncalibrated images. Combining visible band vegetation indices and plant height does not significantly improve the performance of the biomass models. This study demonstrates that UAV-based RGB imaging delivers valuable data for productive crop monitoring. The demonstrated results for plant height and biomass estimation open new possi-bilities in precision agriculture by capturing in-field variability