Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry

In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for

Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications

Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications

HEIGHT, DIAMETER AND TREE CANOPY COVER ESTIMATION BASED ON UNMANNED AERIAL VEHICLE (UAV) IMAGERY WITH VARIOUS ACQUISITION HEIGHT

The forest inventory technique by applying remote sensing technology has become a new breakthrough in technological developments in forest inventory activities. Unmanned Aerial Vehicle (UAV) imagery with camera sensor is one of the inventory tools that produce data with high spatial resolution. The level of spatial resolution of the image is strongly influenced by the flying height of the UAV for a certain camera’s focus. In addition, flight height also affects the acquisition time and accuracy of inventory results, although there is still little research on this matter. The study aims to (a)evaluate the effect of various flying heights on the accuracy of tree height measurements through UAV imagery for every stand age class, (b).estimate the trees diameter and canopy cover for every stand age class. Stand height was estimated using Digital Surface Models (DSM), Digital Terrain Models (DTM) and Orthophoto. DSM and DTM were built by converting orthophoto to pointclouds using the PIX4Dmapper based on Structure From Motion (SFM) on the photogrammetric method to reconstruct topography automatically. Meanwhile, the tree cover canopy was estimated using the All Return Canopy Index (ARCI) formula. The results show that the flight height of 100 meters produces a stronger correlation than the flying height of 80 meters and 120 meters in estimating tree height, based on the high coefficient of determination (R2) and the low root mean square error (RMSE) value. In addition, tree canopy estimation analysis using ARCI has a maximum difference of 9.8% with orthophoto visual delineation.  Key words: canopy height model (CHM), digital surface models (DSM), digital terrain models (DTM), forest inventory, UAV imag

Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry

In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for.BMEL, 2814903915, Verbundprojekt: Entwicklung einer flugrobotergestützten Expertenplattform für einen präzisen Pflanzenschutz im Erwerbsobstbau (Corona-PRO) - Teilprojekt

Low-budget topographic surveying comes of age: Structure from motion photogrammetry in geography and the geosciences

This is the author accepted manuscript. The final version is available from SAGE Publicarions via the DOI in this record

Use of Remote Imagery and Object-based Image Methods to Count Plants in an Open-field Container Nursery

In general, the nursery industry lacks an automated inventory control system. Object-based image analysis (OBIA) software and aerial images could be used to count plants in nurseries. The objectives of this research were: 1) to evaluate the effect of an unmanned aerial vehicle (UAV) flight altitude and plant canopy separation of container-grown plants on count accuracy using aerial images and 2) to evaluate the effect of plant canopy shape, presence of flowers, and plant status (living and dead) on counting accuracy of container-grown plants using remote sensing images. Images were analyzed using Feature Analyst® (FA) and an algorithm trained using MATLAB®. Total count error, false positives and unidentified plants were recorded from output images using FA; only total count error was reported for the MATLAB algorithm. For objective 1, images were taken at 6, 12 and 22 m above the ground using a UAV. Plants were placed on black fabric and gravel, and spaced as follows: 5 cm between canopy edges, canopy edges touching, and 5 cm of canopy edge overlap. In general, when both methods were considered, total count error was smaller [ranging from -5 (undercount) to 4 (over count)] when plants were fully separated with the exception of images taken at 22 m. FA showed a smaller total count error (-2) than MATLAB (-5) when plants were placed on black fabric than those placed on gravel. For objective 2, the plan was to continue using the UAV, however, due to the unexpected disruption of the GPS-based navigation by heightened solar flare activity in 2013, a boom lift that could provide images on a more reliable basis was used. When images obtained using a boom lift were analyzed using FA there was no difference between variables measured when an algorithm trained with an image displaying regular or irregular plant canopy shape was applied to images displaying both plant canopy shapes even though the canopy shape of `Sea Green\u27 juniper is less compact than `Plumosa Compacta\u27. There was a significant difference in all variables measured between images of flowering and non-flowering plants, when non-flowering `samples\u27 were used to train the counting algorithm and analyzed with FA. No dead plants were counted as living and vice versa, when data were analyzed using FA. When the algorithm trained in MATLAB was applied, there was no significant difference in total count errors when plant canopy shape and presence of flowers were evaluated. Based on the combined results from these separate experiments, FA and MATLAB algorithms appear to be fairly robust when used to count container-grown plants from images taken at the heights specified

REVISÃO SISTEMÁTICA DA LITERATURA SOBRE DETECÇÃO DE ÁRVORES UTILIZANDO DADOS DE SENSORIAMENTO REMOTO

O conhecimento do cenário atual referente formas de detecção de árvores por meio de sensoriamento remoto em plantios florestais é essencial para orientar novos estudos relacionados ao tema. Com as publicações científicas dos principais autores e periódicos do assunto dos últimos 20 anos, espera-se neste estudo delinear os principais tipos de dados, espécies e algoritmos estudados recentemente. Com o objetivo de apresentar o desenvolvimento de pesquisas em situações especificas com determinadas espécies, idades, espaçamento, tipos de dados e algoritmos, visando a contagem de árvores. Após reunir a literatura disponível nas bases selecionadas, Web of Science e a Scopus, foi realizada uma análise bibliométrica. Para a mesma utilizou-se o pacote bibliometrix desenvolvido para a linguagem R, para a filtragem de documentos e representação gráfica dos resultados. Foram encontrados resultados classificados em aceitáveis ou não aceitáveis para estimativas florestais, apresentando uma variada gama de alternativas para obtenção do número de árvores de um povoamento florestal, devido as diferenças entre os tipos de dados e algoritmos utilizados. Conclui-se que atualmente não existe uma metodologia padrão para a detecção de árvores e dificilmente será aplicável um método genérico para a contagem, visto a heterogeneidade das florestas e diferentes tratamentos de dados. Foi ainda observadas limitações quanto ao desenvolvimento de metodologias satisfatórias para plantios de Pinus taeda, regularmente espaçados e com idades próximas ao corte comercial