Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 548207
Title Exploiting low-cost and commonly shared aerial photographs and LiDAR data for detailed vegetation structure mapping of the Wadden Sea Island of Ameland
Author(s) Mücher, C.A.; Kramer, H.; Najafabadi, M.R.; Kooistra, L.; Kuiters, A.T.; Slim, P.A.
Source SDRP Journal of Earth Sciences & Environmental Studies 4 (2019)1. - ISSN 2472-6397 - p. 502 - 515.
DOI https://doi.org/10.25177/JESES.4.1.2
Department(s) Earth Observation and Environmental Informatics
PE&RC
Laboratory of Geo-information Science and Remote Sensing
Vegetation, Forest and Landscape Ecology
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract Regular mapping of vegetation structure is important for biodiversity monitoring, and increasingly for tracking compliance with nature policy mandates. As such, the Netherlands uses vegetation structure mapping to monitor the Natura 2000 site on the Dutch Wadden Sea island of Ameland. Three decades of natural gas extraction here has caused soil subsidence, impacting vegetation and habitats on the island. In the Netherlands, vegetation structure mapping is typically done using conventional techniques, primarily field surveys combined with visual interpretation of aerial photographs. This procedure is time-consuming and often too inconsistent and inefficient for large areas. In the current study we exploited commonly shared and low-cost aerial photographs and LiDAR data for detailed vegetation structure mapping. Aerial photographs are not always easy to use in automatic classification procedures, as they often lack calibrated spectral reflectance values. Furthermore, pre-processing of aerial photographs to render them more attractive may skew the image so that it no longer accurately depicts the original scene anymore. Our aim was to determine if automatic or semi-automatic classification techniques could be applied to these readily available Dutch data to support mapping and monitoring of the vegetation structure of larger areas. We compared the effectiveness of two well-known classification methods, namely rule-based (RB) and random forest (RF). The RF algorithm was applied with its default settings, as supplied by eCognition software. Both classification methods performed well, with overall accuracies of 84.1% (RB) and 86.4% (RF). Each method, however, has its advantages and disadvantages, which are discussed. Overall, RF classification was preferred over RB classification, as it was better able to handle the complexity of the rules needed for distinguishing more classes. Provision of in situ training data, such as vegetation relevés, was not really problem in the Dutch context. Nevertheless, exploitation of new digital aerial photographs produced each year in a semi-automatic process remains a challenge. Commercial high resolution satellite imagery (~0.5 m resolution) is therefore still preferred by us. This latter, unfortunately, is more costly than aerial photographs which, while not always ideal, are readily available at no additional costs for involved organisations
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