Automatic detection of benthos & birds: Microphytobenthos cover and bird number detection on the Galgeplaat mudflat using terrestrial imagery

Automatic detection of benthos & birds: Microphytobenthos cover and bird number detection on the Galgeplaat mudflat using terrestrial imagery

Rammos, P.

Lindenbergh, R. (mentor)

Civil Engineering and Geosciences

Geoscience and Remote Sensing

Msc. Geomatics

2012-09-20

Ecological monitoring, i.e. the process of assessing the quality and health of natural habitats, is required to assess the impact of anthropogenic influence. This process is often inhibited by the expenses involved and a limited accessibility to the study site. The use of remotely sensed data then logically comes to mind as a potential solution. This thesis focuses on the ecological monitoring of an intertidal mudflat located in the Oosterschelde known as the Galgeplaat. It possesses a monitoring platform standing 15 meters tall producing imagery that was previously used for monitoring the morphology of the mudflat. The goal is to examine the potential of using this available terrestrial imagery for ecological monitoring of the mudflat and whether it is possible to do this automatically. Two separate case studies were formulated to investigate this potential; 1. automatic detectionof microphytobenthos and 2. automatic detection of bird numbers. The primary focus in this thesis was put on the microphytobenthos case study which elicited the most interest from involved parties. The main inhibiting factor for microphytobenthos detection was the presence of macroalgae (in particular brown macroalgae) in the images, which possess similar spectral properties to that of microphytobenthos. Two methods were used to detect microphytobenthos: I. maximum likelihood classification combined with the masking of the macroalgae (the undesired target) and II. Kohonen’s self organising maps (SOM). The results of this case study indicated that distinguishment between microphytobenthos and macroalgae was best achieved with the Self organizing map (SOM) approach. For the detection of bird numbers consecutive snapshot images of the camera were used such that the motion of birds could be taken advantage of. Background subtraction using a weighted mean background image and a standard deviation image was the most promising of the methods used to count the birds in the 20 frame video sequences. The video sequences with the least zoom (far scale) produced the most erroneous detections. This probably as a result of the lack of movement visible in the video and the small size of the birds (more interference from noise). The results suggest ecological monitoring, in this case of microphytobenthos cover and bird numbers on the Galgeplaat, is indeed possible by using the available terrestrial imagery from the platform. In the current state of development however, the process cannot be claimed fully automatic yet as some a priori knowledge from the user’s part is still required. Regardless, the use of remotely sensed imagery for ecological monitoring proves promising in comparison with current ecological monitoring for several reasons. Provided that unsuitable images are filtered out (images where raindrops are on the camera lens, or other irreparable images) the problem of limited accessibility to the mudflat is ruled out. This makes it possible to produce high time resolution data. Additionally, no costs are required for lab work or transport to the mudflat for bird counting and the collection of specimen.

image processing
ecology
automatic detection

http://resolver.tudelft.nl/uuid:c7e67b12-6dc1-4b70-b1a9-443ad0730e60

Student theses

master thesis

(c) 2012 Rammos, P.