Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds

Souza, Carlos Eduardo Alves de; Lamparelli, Rubens Augusto Camargo; Rocha, Jansle Vieira; Magalhães, Paulo Sérgio Graziano

doi:10.1080/01431161.2017.1285082

Cited by 80 publications

(54 citation statements)

References 30 publications

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“…The CHM is one of the most common methodologies designed to extract the vegetation canopy height parameter from point clouds. These technologies have been primarily used to estimate biomass of forest [48][49][50], shrub [51,52], and crop [37,38,46,53]. However, the accuracy of inversion results of the CHM algorithm is closely related to the number of point clouds.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, UAV with an optical camera has been used to accurately estimate vegetation biophysical characteristics, such as canopy height, vertical structure using a combination of structure from motion (SfM) algorithm, and a canopy height model (CHM) [37,38,45,46], which can be used to improve the accuracy of biomass estimation. The SfM is a computer technique that can generate 3D geometry by automatically extracting the corresponding feature points from unordered overlapped UAV RGB images and optimizing the 3D location of corresponding features based on the principles of photogrammetry [47].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

et al. 2018

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“…A number of UAV-based SfM studies have focused on a single UAV campaign to monitor crop height. Such studies examined a variety of agricultural crops, including maize [47], sorghum [47,48], alfalfa [49], vineyards [50,51], sugarcane [52] and olive tree plantations [53]. Despite positive results, a single crop height extraction during the growing season is generally not able to provide the information needed to inform precision agricultural management.…”

Section: Introductionmentioning

confidence: 99%

Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

et al. 2018

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Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R 2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R 2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.

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“…Moreover, the development of advanced surveying instrument and aerial platforms, i.e., UAVs with the advantages of lowcost and time conservation, has made it easier to obtain DEM of terrain with different surface coverage. Many studies of DEMs generation with both UAV photogrammetry (De Souza et al, 2017;Rock et al, 2011) and contour interpolation (Ardiansyah and Yokoyama, 2002;Arun, 2013) can be found in literature and accuracy assessment of the achieved DEMs has been comprehensively investigated. The accuracy assessment process was mainly performed on distributed points, where using check points as the evaluation criterion of result (Uysal et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Cross Validation on the Equality of Uav-Based and Contour-Based Dems

Ma¹,

Xu²,

Wu³

et al. 2018

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

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ABSTRACT：Unmanned Aerial Vehicles (UAV) have been widely used for Digital Elevation Model (DEM) generation in geographic applications. This paper proposes a novel framework of generating DEM from UAV images. It starts with the generation of the point clouds by image matching, where the flight control data are used as reference for searching for the corresponding images, leading to a significant time saving. Besides, a set of ground control points (GCP) obtained from field surveying are used to transform the point clouds to the user's coordinate system. Following that, we use a multi-feature based supervised classification method for discriminating non-ground points from ground ones. In the end, we generate DEM by constructing triangular irregular networks and rasterization. The experiments are conducted in the east of Jilin province in China, which has been suffered from soil erosion for several years. The quality of UAV based DEM (UAV-DEM) is compared with that generated from contour interpolation (Contour-DEM). The comparison shows a higher resolution, as well as higher accuracy of UAV-DEMs, which contains more geographic information. In addition, the RMSE errors of the UAV-DEMs generated from point clouds with and without GCPs are ±0.5m and ±20m, respectively.

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Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds

Cited by 80 publications

References 30 publications

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

Cross Validation on the Equality of Uav-Based and Contour-Based Dems

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