Evaluating Airborne Multispectral Digital Video to Differentiate Giant Salvinia from Other Features in Northeast Texas

Fletcher, Reginald S.; Everitt, J. H.; Elder, Howard S.

doi:10.3390/rs2102413

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have also utilized leaf water content for invasive species detection [14], along with plant thermal emissivity [15,16]. However, limitations in these methods become evident when spectral signatures of the target invasive plants are difficult to distinguish from their background (e.g., [17][18][19][20][21]). To address the challenge of spectral confusion, recent studies have attempted to identify invasive species with phenology, capitalizing on the hypothesis that seasonal or annual growth patterns (hence intra-or inter-annual spectral variation) of the invasive plants differ from those of native plants.…”

Section: Introductionmentioning

confidence: 99%

Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network

Dutta

Chen

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Invasive plants are a major agent threatening biodiversity conservation and directly affecting our living environment. This study aims to evaluate the potential of deep learning, one of the fastest-growing trends in machine learning, to detect plant invasion in urban parks using high-resolution (0.1 m) aerial image time series. Capitalizing on a state-of-the-art, popular architecture residual neural network (ResNet), we examined key challenges applying deep learning to detect plant invasion: relatively limited training sample size (invasion often confirmed in the field) and high forest contextual variation in space (from one invaded park to another) and over time (caused by varying stages of invasion and the difference in illumination condition). To do so, our evaluations focused on a widespread exotic plant, autumn olive (Elaeagnus umbellate), that has invaded 20 urban parks across Mecklenburg County (1410 km2) in North Carolina, USA. The results demonstrate a promising spatial and temporal generalization capacity of deep learning to detect urban invasive plants. In particular, the performance of ResNet was consistently over 96.2% using training samples from 8 (out of 20) or more parks. The model trained by samples from only four parks still achieved an accuracy of 77.4%. ResNet was further found tolerant of high contextual variation caused by autumn olive’s progressive invasion and the difference in illumination condition over the years. Our findings shed light on prioritized mitigation actions for effectively managing urban invasive plants.

show abstract

Section: Introductionmentioning

confidence: 99%

Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network

Dutta

Chen

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This ensues their aggressive and competitive behavior, prolific seed production, and seed longevity. 1,2 Invasive weeds are capable of moving from small, manageable infestation to larger areas reaching levels where control is either economically prohibitive and/or cause significant ecological damage. 3 Because of their rapid spreading potential and threat to biodiversity and ecosystem processes, invasive plant species have been a long-standing concern to natural resource managers, ecologists, and biological conservationists.…”

Section: Introductionmentioning

confidence: 99%

Detection of two intermixed invasive woody species using color infrared aerial imagery and the support vector machine classifier

Mırık

Chaudhuri

Surber

et al. 2013

J. Appl. Remote Sens

View full text Add to dashboard Cite

Abstract. Both the evergreen redberry juniper (Juniperus pinchotii Sudw.) and deciduous honey mesquite (Prosopis glandulosa Torr.) are destructive and aggressive invaders that affect rangelands and grasslands of the southern Great Plains of the United States. However, their current spatial extent and future expansion trends are unknown. This study was aimed at: (1) exploring the utility of aerial imagery for detecting and mapping intermixed redberry juniper and honey mesquite while both are in full foliage using the support vector machine classifier at two sites in north central Texas and, (2) assessing and comparing the mapping accuracies between sites. Accuracy assessments revealed that the overall accuracies were 90% with the associated kappa coefficient of 0.86% and 89% with the associated kappa coefficient of 0.85 for sites 1 and 2, respectively. Z-statistics (0.102 < 1.96) used to compare the classification results for both sites indicated an insignificant difference between classifications at 95% probability level. In most instances, juniper and mesquite were identified correctly with <7% being mistaken for the other woody species. These results indicated that assessment of the current infestation extent and severity of these two woody species in a spatial context is possible using aerial remote sensing imagery.

show abstract

“…Therefore, remote sensing has received considerable attention as a rapid and inexpensive method for quantifying vegetation distribution on large land areas [28,29]. A wide range of sensor systems including aerial photographs, airborne and satellite multispectral and hyperspectral sensors have been employed for separately mapping rangeland woody and grass components [2,30,31]. Successful separation of these components is usually linked to differences in reflectance properties because of phenological differences at the time the image was obtained [2,27,32,33].…”

Section: Introductionmentioning

confidence: 99%

Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland

Mırık¹,

Ansley²

2012

Remote Sensing

View full text Add to dashboard Cite

Woody plant encroachment into grasslands and rangelands is a world-wide phenomenon but detailed descriptions of changes in geographical distribution and infilling rates have not been well documented at large land scales. Remote sensing with either aerial or satellite images may provide a rapid means for accomplishing this task. Our objective was to compare the accuracy and utility of two types of images with contrasting spatial resolutions (1-m aerial and 30-m satellite) for classifying woody and herbaceous canopy cover and determining woody infilling rates in a large area of rangeland (800 km 2 ) in northTexas that has been invaded by honey mesquite (Prosopis glandulosa). Accuracy assessment revealed that the overall accuracies for the classification of four land cover types (mesquite, grass, bare ground and other) were 94 and 87% with kappa coefficients of 0.89 and 0.77 for the 1-m and 30-m images, respectively. Over the entire area, the 30-m image over-estimated mesquite canopy cover by 9 percentage units (10 vs. 19%) and underestimated grass canopy cover by the same amount when compared to the 1-m image. The 30-m resolution image typically overestimated mesquite canopy cover within 225 4-ha sub-cells that contained a range of mesquite covers (1-70%) when compared to the 1-m image classification and was not suitable for quantifying infilling rates of this native invasive species. Documenting woody and non-woody canopy cover on large land areas is important for developing integrated, regional-scale management strategies for rangeland and grassland regions that have been invaded by woody plants. OPEN ACCESSRemote Sens. 2012Sens. , 4 1948

show abstract

Evaluating Airborne Multispectral Digital Video to Differentiate Giant Salvinia from Other Features in Northeast Texas

Cited by 7 publications

References 14 publications

Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network

Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network

Detection of two intermixed invasive woody species using color infrared aerial imagery and the support vector machine classifier

Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland

Contact Info

Product

Resources

About