Assessment of herbaceous vegetation classification using orthophotos produced from the image acquired with unmanned aerial systems

Sudeera WICKRAMARATHNA; John  GOETZ III; Jon SOUDER; Benjamin PROTZMAN; Brian  SHEPARD; Sorin HERBAN; Francisco MAURO; Hailemariam TEMESGEN; Bogdan M. STRIMBU

doi:10.15835/nbha51313227

Authors

Sudeera WICKRAMARATHNA Oregon State University, College of Forestry, 3100 Jefferson Way, Corvallis OR 97333 (US)
John GOETZ III Clean Water Services, 2550 SW Hillsboro Hwy, Hillsboro OR 97123 (US)
Jon SOUDER Oregon State University, College of Forestry, 3100 Jefferson Way, Corvallis OR 97333 (US)
Benjamin PROTZMAN Clean Water Services, 2550 SW Hillsboro Hwy, Hillsboro OR 97123 (US)
Brian SHEPARD Clean Water Services, 2550 SW Hillsboro Hwy, Hillsboro OR 97123 (US)
Sorin HERBAN Polytechnic University Timisoara, Faculty of Civil Engineering, 2A Traian Lalescu, 300223 Timisoara (RO) https://orcid.org/0000-0001-7814-5574
Francisco MAURO Universidad de Valladolid, Department of Plant Production, Campus Duques de Soria sn, 42005, Soria (ES)
Hailemariam TEMESGEN Oregon State University, College of Forestry, 3100 Jefferson Way, Corvallis OR 97333 (US)
Bogdan M. STRIMBU Oregon State University, College of Forestry, 3100 Jefferson Way, Corvallis OR 97333; National Institute of Research and Development for Biological Sciences, 296 Independenței Bd. District 6, 060031, Bucharest (US) https://orcid.org/0000-0002-9068-9010

DOI:

https://doi.org/10.15835/nbha51313227

Keywords:

image classification, Image enhancement, maximum likelihood classification, reed canary grass, Phalaris arundinacea, simple random sampling

Abstract

Arguably the most popular remote-sensing products are classified images. However, there are no definitive procedures to assess classification accuracy that simultaneously consider resources available and field efforts. The explosive usage of unmanned aerial systems (UAS) in land surveys adds new challenges to classification assessment, as orthorectified images usually contain significant artifacts. This study aims to identify the optimal ratio between training and validation sample size within a supervised classification approach applied to UAS orthophotos. As a case study, we used a wetland area west of Portland, OR, USA, treated with various glyphosate formulations to control Phalaris arundinacea, commonly known as reed canary grass. A completely randomized design with five replications and six glyphosate formulations was used to assess P. arundinacea vigor following repeated herbicide applications. The change in P. arundinacea vitality was monitored with high-resolution four-band imagery acquired with a SlantRange 3PX camera installed on a DJI Matrice 210. The orthophotos created from images were produced with Pix4D, which was subsequently preprocessed with ERDAS Imagine 2020 to reduce the noise, shadows, and artifacts. All images were classified with the maximum likelihood classification algorithm. Simple random and stratified random sampling methods were applied to collect training and validation samples, evaluating eight ratios of training to validation samples to assess their classification accuracy. We found that increasing the training-to-validation sample size ratio enhances accuracy, with the 3:1 ratio being the most reliable in classifying P. arundinacea vigor. Our study provides evidence that image preprocessing and enhancement are essential for UAS-based imagery.

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