A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images

Li, Linyuan; Mu, Xihan; Macfarlane, Craig; Song, Wanjuan; Chen, Jun; Yan, Kai; Yan, Guangjian

doi:10.1016/j.agrformet.2018.07.028

Cited by 37 publications

(17 citation statements)

References 31 publications

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“…The UVC estimates derived using the BAPS algorithm agreed with the reference values measured with downward-looking photography, with an RMSE of 0.1144 and bias of 0.0405. This finding verified the applicability of the HAGFVC algorithm embedded in the BAPS method for forest scenes, although the HAGFVC algorithm was originally designed for the application to crops [31]. The BAPS-derived UVC estimates were relatively higher than the reference UVC, with a slight overestimation for large overstory tree cover (Figure 9).…”

Section: Discussionsupporting

confidence: 58%

“…To quantify the OCC and UVC, we proposed the BAPS method to process images mainly using back-projecting 3D SfM point cloud onto superpixel-segmented RGB images. Only the original images that cover the plot with sufficient proportion of the image area (over 50% in this study) and visually sufficient projected points were used for the estimation of OCC and UVC.The workflow consisted of two major steps: (1) determining crown pixels through back-projecting 3D crown point cloud onto superpixel-segmented UAV-based RGB images and then calculating the OCC; (2) locating forest floor pixels by morphologically dilating crown areas and using an advanced image segmentation method named half-Gaussian fitting method (HAGFVC) [31] to separate green vegetation pixels from non-vegetation pixels and calculate the UVC, where we assume that the extracted forest floor areas can represent the situation of the entire forest. The implemented steps are described hereafter and are illustrated in Figure 4.…”

Section: Quantification Of Occ and Uvc Using Bapsmentioning

confidence: 99%

“…We masked the dilated overstory pixels and extracted the forest floor pixels in Figure 6c. The extracted forest floor pixels underwent pixel classification using an advanced histogrambased method, HAGFVC [31]. HAGFVC aims at estimating green cover from close-range UAV-based RGB images and was designed to address the mixed pixel problem in UAV-based images.…”

Section: Calculation Of Uvcmentioning

confidence: 99%

“…After separating pure and mixed pixels, HAGFVC used an automatic thresholding method to classify mixed pixels as vegetation or background at the equivalent rates of misclassification [34]. The extracted forest floor pixels were classified into green vegetation or non-vegetation classes ( Figure 6d) as follows: The extracted forest floor pixels underwent pixel classification using an advanced histogram-based method, HAGFVC [31]. HAGFVC aims at estimating green cover from close-range UAV-based RGB images and was designed to address the mixed pixel problem in UAV-based images.…”

Section: Calculation Of Uvcmentioning

confidence: 99%

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Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

et al. 2020

Remote Sensing

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Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

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Section: Discussionsupporting

confidence: 58%

Section: Quantification Of Occ and Uvc Using Bapsmentioning

confidence: 99%

Section: Calculation Of Uvcmentioning

confidence: 99%

Section: Calculation Of Uvcmentioning

confidence: 99%

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Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

et al. 2020

Remote Sensing

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“…One of the specific techniques for managing the water in the crop and in the soil is the use of images [14]. The images of the green cover of the crop or the canopy give an idea of the amount of water the crop is losing by evapotranspiration [15]. The percentage of the green cover of the crop (PGC) is related to the crop evaporation model [16].…”

Section: Introductionmentioning

confidence: 99%

Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture

Mateo-Aroca

Garcı́a-Mateos

Ruiz-Canales

et al. 2019

Water

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Due to the limitations of drones and satellites to obtain aerial images of the crops in real time, the time to flight delay, the problems caused by adverse weather conditions and other issues, the use of fixed cameras placed on the regions of interest is essential to get closer, periodic and on-demand images. Water management in agriculture is one of the most important applications of these images. Top view images of a crop can be processed for determining the percentage of green cover (PGC), and 2D images from different viewing angles can be applied for obtaining 3D models of the crops. In both cases, the obtained data can be managed for calculating several parameters such as crop evapotranspiration, water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant. For this purpose, a remote image capture system has been developed for an application in lettuce crops. The system consists of several capture nodes and a local processing base station which includes image processing algorithms to obtain key features for decision-making in irrigation and harvesting strategies. Placing multiple image capture nodes allows obtaining different observation zones that are representative of the entire crop. The nodes have been designed to have autonomous power supply and wireless connection with the base station. This station carries out irrigation and harvesting decisions using the results of the processing of the images captured by the nodes and the information of other local sensors. The wireless connection is made using the ZigBee communication architecture, supported by XBee hardware. The two main benefits of this choice are its low energy consumption and the long range of the connection.

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Segmentation of vegetation and microplots in aerial agriculture images: A survey

Mardanisamani

Eramian

2022

The Plant Phenome Journal

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Because of the increasing global population, changing climate, and consumer demands for safe, environmentally friendly, and high‐quality food, plant breeders strive for higher yield cultivars by monitoring specific plant phenotypes. Developing new crop cultivars and monitoring through current methods is time‐consuming, sometimes subjective, and based on subsampling of microplots. High‐throughput phenotyping using unmanned aerial vehicle‐acquired aerial orthomosaic images of breeding trials improves and simplifies this labor‐intensive process. To perform per‐microplot phenotype analysis from such imagery, it is necessary to identify and localize individual microplots in the orthomosaics. This paper reviews the key concepts of recent studies and possible future developments regarding vegetation segmentation and microplot segmentation. The studies are presented in two main categories: (a) general vegetation segmentation using vegetation‐index‐based thresholding, learning‐based, and deep‐learning‐based methods; and (b) microplot segmentation based on machine learning and image processing methods. In this study, we performed a literature review to extract the algorithms that have been developed in vegetation and microplots segmentation studies. Based on our search criteria, we retrieved 92 relevant studies from five electronic databases. We investigated these selected studies carefully, summarized the methods, and provided some suggestions for future research.

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A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images

Cited by 37 publications

References 31 publications

Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture

Segmentation of vegetation and microplots in aerial agriculture images: A survey

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