Open Access Te Herenga Waka-Victoria University of Wellington
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Using UAV imagery to perform fine-scale mapping of wetland vegetation

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Version 2 2023-09-22, 02:15
Version 1 2021-12-08, 21:40
posted on 2023-09-22, 02:15 authored by Hipgrave, Patrick

Differentiating between species of plants in aerial imagery is often challenging and, in some cases, can be impossible without significant field data collection. However, remote sensing technology is developing to the point where it is increasingly possible to eliminate the need for extensive fieldwork entirely and conduct non-disruptive monitoring of fragile environments. The increasing availability of UAV platforms with integrated high-resolution cameras and low-cost image processing software is also making remote sensing operations accessible to those outside the scientific community with an interest in environmental monitoring. This project trialled an emerging set of image analysis techniques called ‘object-based image analysis’ to create fine scale maps of a recovering wetland area, based on aerial photographs collected using a consumer-grade UAV (unmanned aerial vehicle). The effects of including additional ancillary data (such as digital surface models (DSMs) and multispectral imagery) in the classification process were also assessed to compare the ability of a standard digital camera to produce high-accuracy classifications to that of a more specialised multispectral sensor. The inclusion of this extra information was found to significantly improve classification accuracy in almost all cases, making a strong argument for the inclusion of ancillary data whenever possible, especially when considering the ease with which ancillary datasets can be produced. The high-resolution (between 2 and 4cm/pixel) imagery provided sufficient detail to observe 28 distinct land cover classes in total, with around 20 classes per image. While the number of classes in the classification scheme may have imposed limits on the overall accuracy of the classified maps, several classes were classified with a high (70% or greater) level of accuracy, including two invasive species, showing that the object-based school of image classification has potential to be a powerful tool for detecting and tracking individual vegetation types.


Copyright Date


Date of Award



Te Herenga Waka—Victoria University of Wellington

Rights License

CC BY-ND 4.0

Degree Discipline

Geographic Information Science

Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level


Degree Name

Master of Science

ANZSRC Type Of Activity code


Victoria University of Wellington Item Type

Awarded Research Masters Thesis



Alternative Title

Wings over Wairio: Using UAV imagery to perform fine-scale mapping of wetland vegetation

Victoria University of Wellington School

School of Geography, Environment and Earth Sciences


de Roiste, Mairead; Hartley, Stephen