Estimation of the crop density of small grains using LiDAR sensors

Saeys, Wouter; Lenaerts, Bart; Craessaerts, Geert; Baerdemaeker, Josse De

doi:10.1016/j.biosystemseng.2008.10.003

Cited by 107 publications

(65 citation statements)

References 4 publications

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“…LiDAR, also referred as laser scanning (LS), has evolved into a state-of-the-art technology for highly accurate 3D data acquisition. By now several studies indicate a high value for 3D vegetation description, such as in agricultural monitoring of trees (Rosell and Sanz, 2012;Seidel et al, 2011), field crops (Höfle, 2013;Saeys et al, 2009;Lumme et al, 2008) or harvest residues (Lenaerts et al, 2012). Harvest residues play an important role in agricultural management, for instance concerning the calculation of biomass and subsequently the need of fertilization as 'humus compensation' (Fink, 1996) or for renewable energy production (Pimentel, 1981).…”

Section: Introductionmentioning

confidence: 99%

Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density

Koenig

Höfle

Müller

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data set for remote sensing, such as deriving soil properties from hyperspectral sensors. In this study full-waveform laser scanning data acquired with a Riegl VZ-400 instrument is used to classify 3D point clouds into post-harvest straw residues and bare soil. A workflow for point cloud based classification is presented using radiometric and geometric point features. A radiometric correction is performed by using a range-correction function f(r), which is derived from lab experiments with a reference target of known reflectance. Thereafter, the corrected signal amplitude and local height features are explored with respect to the target classes. The following procedure includes feature calculation, decision tree analysis, point cloud classification and finally result validation using detailed classified reference RGB images. The classification tree separates the classes of harvest residues and bare soil with an accuracy of 96% by using geometric and radiometric features. The LiDAR-derived harvest residue coverage value of 75% lies in accordance with the image-based reference (coverage of 68%). The results indicate the high potential of radiometric features for natural surface classification, particularly in combination with geometric features.

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Section: Introductionmentioning

confidence: 99%

Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density

Koenig

Höfle

Müller

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…They can be classified into first returns, reflected from the tree tops, intermediate returns, from the leaves or branches, and last returns, reflected from the ground. Aerial laser scanning has many uses: measuring agricultural productivity (Saeys et al 2009), distinguishing faint archaeological evidences (Bennett 2012), forestry practices (Hyyppä et al 2012), advancing the science of geomorphology (Sofia et al 2014), measuring volcano uplift (Whelley et al 2014), glacier decline and snowpack (Abermann et al 2010), and providing data for topographic mapping, to name just a few. Using the LiDAR point cloud data, one can extract specific features, such as dimensions of underground ancient structures or aboveground parameters of individual trees (Popescu et al 2003, Popescu 2007, Edson & Wing 2011, Dalponte et al 2014, and obtain ecosystem level information such as forests biomass or carbon sequestration capacity (Lefsky et al 2005, Popescu 2007, García et al 2010, Lee et al 2013.…”

Section: Introductionmentioning

confidence: 99%

An integrated airborne laser scanning approach to forest management and cultural heritage issues: a case study at Porolissum, Romania

et al. 2017

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Abstract. This paper explores the opportunities that arise where forest ecosystem management and cultural heritage monuments protection converge. The case study area for our analysis was the landscape surrounding the Moigrad-Porolissum Archaeological site. We emphasize that an Airborne Laser Scanning (ALS or LiDAR-Light Detection and Ranging) approach to both forest management and cultural heritage conservation is an outstanding tool, assisting policy-makers and conservationists in decision making for integrated planning and management of the environment. LiDAR-derived surface models enabled a synoptic, never-seen-before view of the ancient Roman frontiers defensive systems while also revealing the present forest road network. The thorough and accurate road inventory data are very useful for updating and modifying forest base maps and registries and also for identifying the priority sectors for archaeological discharge. The ability to identify and determine optimal routes for forest management and to locate previously unmapped ancient archaeological remains aids in reducing costs and creating operational efficiencies as well as in complying with the legislation and avoiding infringements. The potential of LiDAR to demonstrate the long-term and comprehensive human impact on wooded areas is discussed. We identified a significant historical landscape change, consisting of a deforestation period, spanning over more than 160 years, during the Roman Period in Dacia (106-271 AD). The transdisciplinary analysis of the LiDAR data provides the base for combining knowledge from archaeology, forestry and environmental history in order to achieve a thorough analysis of the landscape changes and history. In the "nature versus culture" dichotomy, the landscape, outfield areas and forests are primarily perceived as nature, while in reality they are often heavily marked by human impact. LiDAR offers an efficient method for broadening our knowledge regarding the character and extent of human interaction with landscapes -forested or otherwise.

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“…By contrast, studies show that terrestrial laser scanning (TLS), as an active system, can be applied for agricultural purposes. Investigated plant parameters are plant height (Zhang and Grift, 2012), biomass (Keightley and Bawden, 2010;Ehlert et al, 2009;2008), crop density (Hosoi and Omasa, * Corresponding author: nora.tilly@uni-koeln.de 2012;Saeys et al, 2009), and leaf area index (Gebbers et al, 2011). As mentioned the large height of maize plants causes difficulties for ground based system.…”

Section: Introductionmentioning

confidence: 99%

Terrestrial laser scanning for plant height measurement and biomass estimation of maize

Tilly

Hoffmeister

Schiedung

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estimators. In this contribution, first results of multitemporal surveys on a maize field with a terrestrial laser scanner are shown. The achieved point clouds are interpolated to generate Crop Surface Models (CSM) that represent the top canopy. These CSMs are used for visualizing the spatial distribution of plant height differences within the field and calculating plant height above ground with a high resolution of 1 cm. In addition, manual measurements of plant height were carried out corresponding to each TLS campaign to verify the results. The high coefficient of determination (R² = 0.93) between both measurement methods shows the applicability of the presented approach. The established regression model between CSM-derived plant height and destructively measured biomass shows a varying performance depending on the considered time frame during the growing period. This study shows that TLS is a suitable and promising method for measuring plant height of maize. Moreover, it shows the potential of plant height as a non-destructive estimator for biomass in the early growing period. However, challenges are the non-linear development of plant height and biomass over the whole growing period.

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Estimation of the crop density of small grains using LiDAR sensors

Cited by 107 publications

References 4 publications

Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density

Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density

An integrated airborne laser scanning approach to forest management and cultural heritage issues: a case study at Porolissum, Romania

Terrestrial laser scanning for plant height measurement and biomass estimation of maize

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