Small-scale UAS for geoinformatics applications on an island campus

Starek, Michael J.; Davis, Thomas J.; Prouty, D.; Berryhill, Jacob

doi:10.1109/upinlbs.2014.7033718

Cited by 14 publications

(12 citation statements)

References 3 publications

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“…This can sometimes result in noisier point cloud data over vegetation dependent on weather conditions and vegetation structure. 42 However, SfM computed from hyperspatial resolution imagery collected from a low flying UAS, as done here, can provide upward of two orders of magnitude increase in point density relative to traditional airborne LiDAR collected at higher altitudes above ground. This high point density enables noise to be easily smoothed and provides a high-definition point cloud for reconstructing vegetation structure.…”

Section: Lidar-derived Vegetation Metrics For Unmanned Aircraftmentioning

confidence: 85%

Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Chu

Chen

Landivar

et al. 2016

J. Appl. Remote Sens

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"Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery," J. Appl. Remote Sens. 10(3), 036018 (2016), doi: 10.1117/1.JRS.10.036018. Abstract. This paper explores the potential of using unmanned aircraft system (UAS)-based visible-band images to assess cotton growth. By applying the structure-from-motion algorithm, the cotton plant height (ph) and canopy cover (cc) information were retrieved from the point cloud-based digital surface models (DSMs) and orthomosaic images. Both UAS-based ph and cc follow a sigmoid growth pattern as confirmed by ground-based studies. By applying an empirical model that converts the cotton ph to cc, the estimated cc shows strong correlation (R 2 ¼ 0.990) with the observed cc. An attempt for modeling cotton yield was carried out using the ph and cc information obtained on June 26, 2015, the date when sigmoid growth curves for both ph and cc tended to decline in slope. In a cross-validation test, the correlation between the ground-measured yield and the estimated equivalent derived from the ph and/or cc was compared. Generally, combining ph and cc, the performance of the yield estimation is most comparable against the observed yield. On the other hand, the observed yield and cc-based estimation produce the second strongest correlation, regardless of the complexity of the models.

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Section: Lidar-derived Vegetation Metrics For Unmanned Aircraftmentioning

confidence: 85%

Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Chu

Chen

Landivar

et al. 2016

J. Appl. Remote Sens

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“…This can sometimes result in sparse or noisy point cloud data over vegetation dependent on weather conditions and vegetation structure. 38 This can be negated, as was done here, by flying during low wind conditions and filtering point cloud noise. A major advantage of SfM is the potential of acquiring hyperspatial resolution point cloud data.…”

Section: Discussionmentioning

confidence: 98%

“…When counting aphids, actual counts were only done for leaves with 10 or fewer insects. All estimates above this level were done in ranges with midpoints within these ranges used as the estimated count: 11 to 25 aphids (18 midpoint), 26 to 50 aphids (38), 51 to 100 aphids (75), 101 to 500 aphids (300), 501 to 1000 aphids (750), and greater than 1000 aphids per leaf (1500 was used as the midpoint based on field observations). Plant response was measured on a scale from 1 to 10, with each number representing a percentage of plants in a plot covered in sooty mold with a rating of 1 indicating 0% to 10% sooty mold coverage, and increasing at regular intervals to a rating of 10 indicating 90% to 100% sooty mold coverage.…”

Section: Field Measurement Surveysmentioning

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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“…By applying the calibrated optimization, the SfM algorithm performs 3D reconstruction and generates a densified point cloud from a large number of common features in the images. This is subsequently used to derive a 2D orthomosaic image [32].…”

Section: Canopy Height Model Generationmentioning

confidence: 99%

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

et al. 2017

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Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R 2 = 0.48) and the lodging rate (R 2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.

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Small-scale UAS for geoinformatics applications on an island campus

Cited by 14 publications

References 3 publications

Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

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

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

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