Season, Classifier, and Spatial Resolution Impact Honey Mesquite and Yellow Bluestem Detection using an Unmanned Aerial System

Jackson, Matthew N.; Portillo-Quintero, Carlos; Cox, Robert D.; Ritchie, Glen L.; Johnson, Mark D.; Humagain, Kamal; Subedi, Mukti Ram

doi:10.1016/j.rama.2020.06.010

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Previous work in south Texas compared the classification results with different flying altitude over noxious plant honey mesquite and yellow bluestem in south Texas, and the object-based RF classifier outperformed other models tested in the study [18]. The previous study also suggested that flying altitude at approximately 100 m is most suitable for mapping honey mesquite [18]. The highest overall accuracy of mapping mesquite is 87% for flying altitude at 100 m in April, which was lower than this study.…”

Section: Relevent Work Comparisoncontrasting

confidence: 53%

“…The results from this study are comparable to similar studies. Previous work in south Texas compared the classification results with different flying altitude over noxious plant honey mesquite and yellow bluestem in south Texas, and the object-based RF classifier outperformed other models tested in the study [18]. The previous study also suggested that flying altitude at approximately 100 m is most suitable for mapping honey mesquite [18].…”

Section: Relevent Work Comparisonmentioning

confidence: 72%

“…Jackson et al compared the classification results with different flying altitude over noxious plant honey mesquite and yellow bluestem in south Texas, and the Random Forest (RF) classifier outperformed other models tested in the study. His study also suggested that flying altitude at approximately 100 m is most suitable for mapping honey mesquite [18]. The herbicide effect of noxious brush has not been evaluated by UAS equipped multispectral imagery before.…”

Section: Introductionmentioning

confidence: 99%

“…The RF classifier has been previously used on satellite image NDVI and Lidar data to map mesquite in south Texas [15]. Multispectral images and NDVI data have been tested for RF classification on mesquite and yellow bluestem [18]. This earlier work neither compared other band combinations nor evaluated the pixel-based classification method to determine if it was more applicable on brush species.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS)

Shen

Clayton²,

Starek

et al. 2023

Remote Sensing

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Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment.

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Section: Relevent Work Comparisoncontrasting

confidence: 53%

Section: Relevent Work Comparisonmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS)

Shen

Clayton²,

Starek

et al. 2023

Remote Sensing

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“…With the popularization of UAVs, the target detection, attitude control, and recognition detection of UAVs have been widely studied. Jackson et al (2020) conducted plant detection and mapping in Texas by UAVs at altitudes of 30, 60, and 100 m. They found that low spatial resolution (100 m altitude flight, 12 cm pixel resolution) provided less noise and more generalization capability for image classification methods. Yamazaki et al (2020) designed a UAV human search system combined with array microphones to detect the human body and evaluated the accuracy of the method through experiments.…”

Section: Introductionmentioning

confidence: 99%

A Study of Real-Time Recognition of Unmanned Aerial Vehicles in Outdoor Areas Based on a Random Forest Algorithm

Tian

2022

Aviation

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With the widespread use of unmanned aerial vehicles (UAVs) in life, the real-time recognition of UAVs has become an important issue. The authors of this paper mainly studied the application of the random forest (RF) algorithm in the outdoor real-time recognition of UAVs. Mel-Frequency Cepstral Coefficient (MFCC) features were extracted from sound signals firstly, and then the RF method was combined with weighted voting to obtain the improved random forest (IRF) method to identify UAV sounds and environmental sounds. An experimental analysis was conducted. The modeling time of the IRF method increased by 9.52% compared with the RF method, and the recognition rate of the IRF method decreased with the increase of the distance from the microphone; however, the recognition rate of the IRF method was always higher than that of the RF method, and the recognition rate of the IRF method for the mixed samples was always higher than 90%. When the distance was 10 m, the IRF method still had a recognition rate of 91.29%. The experimental results verify the effectiveness of the IRF method for the outdoor real-time recognition of UAVs and its practical application feasibility.

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Evaluating Mesquite Distribution Using Unpiloted Aerial Vehicles and Satellite Imagery

Page

Perotto‐Baldivieso

Ortega-S.

et al. 2022

Rangeland Ecology & Management

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Season, Classifier, and Spatial Resolution Impact Honey Mesquite and Yellow Bluestem Detection using an Unmanned Aerial System

Cited by 5 publications

References 31 publications

Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS)

Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS)

A Study of Real-Time Recognition of Unmanned Aerial Vehicles in Outdoor Areas Based on a Random Forest Algorithm

Evaluating Mesquite Distribution Using Unpiloted Aerial Vehicles and Satellite Imagery

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