Real-Time Tree-Foliage Surface Estimation Using a Ground Laser Scanner

Palacín, Jordi; Pallejà, Tomàs; Tresánchez, Marcel; Sanz, Ricardo; Llorens, Jordi; Ribes-Dasi, Manel; Masip, Joan; Arnò, Jaume; Escolà, Alexandre; Rosell, Joan Ramón

doi:10.1109/tim.2007.900126

Cited by 68 publications

(47 citation statements)

References 15 publications

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“…Another possibility is the use of ground-based sensors to get information about the geometry and/or structure of the canopy (López-Lozano et al 2009;Rosell et al 2009b;Llorens et al 2011). Specifically, laser sensors have been tested in fruit orchards (apple and pear) (Walklate et al 2002;Palacín et al 2007;Rosell et al 2009a;Sanz et al 2011), in citrus (Wei and Salyani 2004;Lee and Ehsani 2009) and in grapevine, in which in addition to laser sensors (Arnó et al 2006;Rosell et al 2009b;Llorens et al 2011), radiometric sensors mounted on tractors were used (Goutouly et al 2006;Drissi et al 2009;Mazzetto et al 2010). As an alternative to optical sensors, ultrasonic sensors (US) have been used to estimate LAI in cereals (Scotford and Miller 2004) and measure canopy volume in different crops: fruit trees (Giles et al 1988;Solanelles et al 2006;Escolà et al 2011), grapevines (Gil et al 2007;Llorens et al 2011) and citrus (Tumbo et al 2002;Schumann and Zaman 2005;Zaman and Schumann 2005).…”

Section: Introductionmentioning

confidence: 99%

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

et al. 2012

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Estimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to\ud the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractormounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate\ud the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R2 = 0.81) between canopy volume and the measured values of LAI for 1 m long sections.\ud Nevertheless, the best estimation of the LAI was given by the TAI (R2 = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.Peer ReviewedPostprint (published version

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Section: Introductionmentioning

confidence: 99%

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

et al. 2012

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“…In this way, high efficiency and low production costs can be achieved for pesticide spray applications with minimum environmental impact [7,8,9,10,11,12,13,14]. …”

Section: Introductionmentioning

confidence: 99%

Evaluation of Object Surface Edge Profiles Detected with a 2-D Laser Scanning Sensor

Yan

Wang

Zhu

et al. 2018

Sensors

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Canopy edge profile detection is a critical component of plant recognition in variable-rate spray control systems. The accuracy of a high-speed 270° radial laser sensor was evaluated in detecting the surface edge profiles of six complex-shaped objects. These objects were toy balls with a pink smooth surface, light brown rectangular cardboard boxes, black and red texture surfaced basketballs, white smooth cylinders, and two different sized artificial plants. Evaluations included reconstructed three-dimensional (3-D) images for the object surfaces with the data acquired from the laser sensor at four different detection heights (0.25, 0.50, 0.75, and 1.00 m) above each object, five sensor travel speeds (1.6, 2.4, 3.2, 4.0, and 4.8 km h−1), and 8 to 15 horizontal distances to the sensor ranging from 0 to 3.5 m. Edge profiles of the six objects detected with the laser sensor were compared with images taken with a digital camera. The edge similarity score (ESS) was significantly affected by the horizontal distances of the objects, and the influence became weaker when the objects were placed closer to each other. The detection heights and travel speeds also influenced the ESS slightly. The overall average ESS ranged from 0.38 to 0.95 for all the objects under all the test conditions, thereby providing baseline information for the integration of the laser sensor into future development of greenhouse variable-rate spray systems to improve pesticide, irrigation, and nutrition application efficiencies through watering booms.

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“…Laser scanners mounted onto various mobile terrestrial platforms have been increasingly described these years, and the subject of tree properties investigation based on mobile laser scanning (MLS) has been increasingly exploited. The associated application cases involve individual tree recognition [22], assessment of the influence of foliation on laser pulse echoes [23], real-time tree-foliage surface estimation [24], multi-echoes-based crown reconstruction [25], etc. As regards mobile hyperspectral imaging, its implementation tends to be concisely mentioned in the specs of mobile mapping systems [20], and little research on tree spectral reflectance properties has been reported.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Tree Spectral Reflectance Characteristics Using a Mobile Terrestrial Line Spectrometer and Laser Scanner

Lin

Puttonen

Hyyppä

2013

Sensors

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In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt.

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Real-Time Tree-Foliage Surface Estimation Using a Ground Laser Scanner

Cited by 68 publications

References 15 publications

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

Evaluation of Object Surface Edge Profiles Detected with a 2-D Laser Scanning Sensor

Investigation of Tree Spectral Reflectance Characteristics Using a Mobile Terrestrial Line Spectrometer and Laser Scanner

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