Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data

Rupasinghe, Prabha Amali; Milas, Anita Šimić; Arend, Kristin K.; Simonson, Martin A.; Mayer, Christine M.; Mackey, Scudder D.

doi:10.1080/01431161.2018.1539267

Cited by 20 publications

(8 citation statements)

References 44 publications

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“…Radial basis function (RBF) was used with the SVM classifier as it performed better than other kernels [67,68]. Several studies suggested that SVM classifies image accurately with a small number of training samples [69][70][71]. The kNN classifier is a widely used object-based classification algorithm and one of the simplest ML classifiers.…”

Section: Classificationmentioning

confidence: 99%

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

et al. 2019

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Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.

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Section: Classificationmentioning

confidence: 99%

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

et al. 2019

Self Cite

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“…Several studies suggested that SVM is the best classifier when using a small number of training samples (e.g. Foody and Mathur 2004;Mountrakis, Im, and Ogole 2011;Rupasinghe et al 2018), as in this case study. For example, Miranda et al (2020) successfully apply the SVM algorithm to carry out spatio-temporal monitoring of the vegetation covers of ice-free areas in Antarctica with UAV and satellite imagery, demonstrating that they can provide information that quantitatively describes the evolution of these ecosystems.…”

Section: Image Classification Performancementioning

confidence: 83%

High-spatial resolution UAV multispectral data complementing satellite imagery to characterize a chinstrap penguin colony ecosystem on deception island (Antarctica)

Román

Navarro

Caballero

et al. 2022

GIScience & Remote Sensing

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“…Thirteen papers focused on case studies mapping wetlands from coarse to fine scales. Rupasinghe et al [82] and Castellanos-Galindo et al [80] each highlighted the use of green vegetation indices (e.g., Visible Atmospherically Resistant Index (VARI) and Green Leaf Index) developed from RGB imagery along with DSM to classify general shoreline land cover and wetland habitat. Two early examples of UAVs for wetland mapping [15,61] proved that the spectral and spatial resolution provided by an RGB orthophoto was sufficient to identify key wetland vegetation features.…”

Section: Vegetation Inventoriesmentioning

confidence: 99%

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

Dronova

Kislik

Dinh

et al. 2021

Drones

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Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs.

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Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data

Cited by 20 publications

References 44 publications

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

High-spatial resolution UAV multispectral data complementing satellite imagery to characterize a chinstrap penguin colony ecosystem on deception island (Antarctica)

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

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