A machine learning assessment of multi-resolution remote sensing data for Natura 2000 dune habitat classification

A machine learning assessment of multi-resolution remote sensing data for Natura 2000 dune habitat classification

Günes, Merve (TU Delft Civil Engineering and Geosciences)

Lhermitte, S.L.M. (mentor)
Smal, I.V. (mentor)
de Vries, S. (graduation committee)
Eleveld, Marieke (mentor)

Delft University of Technology

Geoscience and Remote Sensing

2020-10-02

Coastal dunes habitats are in the transition zone of marine and terrestrial ecosystems and therefore have high biodiversity in terms of animal and vegetation species. These species provide important ecosystem services to the human kind. Due to the development and operation of Maasvlakte 2, an increase of nitrogen emission is expected in the province Zuid-Holland of the Netherlands. When this is taken up by the vegetation and soil in the dunes, encroachment by shrubs will dominate. This will lead to a decrease in species diversity of dune habitats in protected areas of the Natura 2000 network, which is an unfavourable situation conforming to the Nature Protection Law (Wet Natuurbescherming). The main loss is expected to be in grey dunes and humid dune slacks of the Natura 2000 areas nearby Maasvlakte 2. This research focuses on the classification of grey dunes, white dunes, humid dune slacks, woody vegetation, water and their sub-classes in the Natura 2000 areas Solleveld & Kapittelduinen, Voornes Duin and Goeree & Kwade Hoek. Remote sensing in combination with machine learning have been proven to be useful in the monitoring of dune habitats. Open access satellite data provide high temporal and geographic coverage, but are often limited in their spatial resolution. Contrarily, high spatial resolution products are limited in their spectral resolution. In this thesis, the effects of spatial resolution, spectral features and feature combinations on the classification of dune habitats using aerial images and a Landsat-7 image (2012) are analysed. The classifications are performed with a Random Forest classifier, using both a pixel-based and region-based approach. In contrast to the traditional pixel-based method, the region-based method incorporates spectral and statistical information of neighbouring pixels. The best performance is achieved with the region-based approach on a 4m resolution aerial image with an accuracy of 87%. The 10m region-based classification is the second best with an accuracy of 83%. The misclassified pixels are mostly classified as the other sub-class of the same main-class. The region-based method should not be applied to satellite images with low spatial resolution as it causes more loss of detail. More spectral bands contribute to a better classification performance, but they contribute less than the spectral bands that were already used. Also, Landsat-7 images were used to create yearly habitat maps for 2012 and 2019 to subsequently analyse the habitat changes between those years, although the accuracies of the Landsat-7 maps are an issue. The main changes in habitat are found in the woody vegetation (shrubs, trees) and humid dune slacks. The woody vegetation decreased in area, causing an increase in grey dunes area in Voornes Duin and Goeree & Kwade Hoek, whereas the opposite has happened in Solleveld & Kapittelduinen. Loss of area in humid dune slacks is detected in all three regions of interest. The local dune manager confirmed that the detected changes in woody vegetation are mostly correct and caused by dune management activities. The perceived changes in humid dune slacks are less reliable. Finally, a modified training dataset is used to classify Sentinel-2 images in 2019. These results compared reasonably well with the few locations we visited during our fieldwork campaign.

airborne
spaceborne
optical remote sensing
coastal dune habitats
nitrogen emission
machine learning
Random Forest
region-based
multi-resolution
Landsat-7
Sentinel-2

http://resolver.tudelft.nl/uuid:5e1043a1-c383-4fbd-a3af-338bad36b121

51.9595317, 3.985464

Student theses

master thesis

© 2020 Merve Günes