Crop height determination with UAS point clouds

Grenzdörffer, Görres

doi:10.5194/isprsarchives-xl-1-135-2014

Cited by 58 publications

(46 citation statements)

References 7 publications

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“…2017, 9, 239 10 of 14 acquisition and processing cost of the proposed method is lower [24,35]. The crop height can also be extracted by the soil point method, which extracts the crop height from DSM using DEM of soil points in the vegetated imagery [22,23]. Compared with the soil point method, the proposed method can be used in high density crop areas where there is little soil background.…”

Section: Crop Mapping Without Crop Heightmentioning

confidence: 99%

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Yang

Song

et al. 2017

Remote Sensing

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Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively.

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Section: Crop Mapping Without Crop Heightmentioning

confidence: 99%

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Yang

Song

et al. 2017

Remote Sensing

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“…The DTM was reconstructed from the flowering RGB orthomosaic (captured 2-weeks prior) and CSM data sets. 26 The first step includes the ground class segmentation in the RGB orthomosaic [ Fig. 2(b)].…”

Section: Data Processing Workflow: Crop Surface Model Orthomosaic Gementioning

confidence: 99%

“…[18][19][20][21][22][23][24] Application and process involved in plant height measurement conducted using UAS platforms were discussed by few researchers. [25][26][27] The process involves (i) collecting aerial data imagery from a camera mounted onboard in UAS, (ii) generating ultrahigh resolution crop surface models (CSMs), and (iii) determining plant height from the CSM, 28 herein, defined as CSM-estimated plant height. However, studies validating CSM-estimated plant height via ground-truthing measurements to predict field crop yields are scarce in the scientific literature.…”

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confidence: 99%

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Varela

Assefa

Prasad

et al. 2017

J. Appl. Remote Sens

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, "Spatio-temporal evaluation of plant height in corn via unmanned aerial systems," J. Appl. Remote Sens. 11(3), 036013 (2017), doi: 10.1117/1.JRS.11.036013. Abstract. Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log-log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Studies have also applied this approach to derive crop height from UAS-SfM derived point cloud data. [14][15][16] The combination of vegetation indices and UAS-based canopy surface models have been shown to estimate biomass in barley and corn more accurately than vegetation indices alone. 15,16 The results from these studies suggest that crop height metrics can be used as a complement to NDVI and potentially as a surrogate for assessing crop health under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

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"Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment," J. Appl. Remote Sens. 11(2), 026035 (2017), doi: 10.1117/1.JRS.11.026035. Abstract. A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

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Crop height determination with UAS point clouds

Cited by 58 publications

References 7 publications

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

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