105 research outputs found
Influence of Altitude on Tropical Marine Habitat Classification using Fixed-Wing UAV Imagery
No full textUnmanned aerial vehicles (UAVs) are cost-effective remote sensing tools useful for generating very high-resolution (VHR) aerial imagery. Habitat maps generated from UAV imagery are a fundamental component of marine spatial planning, essential for the designation and governance of marine protected areas (MPAs). We investigated whether UAV survey altitude affects habitat classification performance and the classification accuracy of thematic maps from a tropical shallow water environment. We conducted repeated UAV flights at 75, 85, and 110 m, using a fixed-wing UAV on the Turneffe Atoll, Belize. Flights were ground-truthed with snorkel surveys. Images were mosaiced to form orthomosaics and transformed into thematic maps through semi-automatic object-based image analysis (OBIA). Three subset areas (4000 m2, 17000 m2 and 17000 m2) from two cayes on the atoll were selected to investigate the effect of survey altitude. A linear regression demonstrated that for every 1 m increase in survey altitude, there was a ~1% decrease in the overall classification accuracy. A low survey altitude of 75 m produced a higher classification accuracy for thematic maps and increased the representation of mangrove, seagrass, and sand. The variability in classified cover was driven by altitude, although the direction and extent of this relationship was specific to each class. For coral and sea, classified cover decreased with increased altitude. Mangrove classified cover was non-sensitive to altitude changes, demonstrating a lesser need for a consistent survey altitude. Sand and seagrass had a greater sensitivity to altitude, due to classified cover variability between altitudes. Our findings suggest that survey altitude should be minimised when classifying tropical marine environments (coral, seagrass) and, given that most fixed-wing UAVs are restricted to a minimum altitude of 70 m, we recommend an altitude of 75 m. Survey altitude should be a major consideration when targeting habitats with greater sensitivity to altitude variabilit
A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia
No full textThe loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up-to-date information on peat swamp forest loss across large areas, and support spatial explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can ‘see through’ cloud, but experience so far has shown that it doesn't discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel-1 and Sentinel-2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel-based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object-based classification or pixel-based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalize on the potential of big data
Spatial Data and Software
No full textGeo-spatial data are information which can be pinpointed to spatially explicit locations on Earth. Most of the data you sample in ecology are of geo-spatial nature, regardless of whether you recorded the spatial coordinates during data collection or not. A geo-spatial data element consists in principle out of two parts: (1) spatial coordinates in a defined coordinate system, such as latitude and longitude and (2) one or more values such as a label, a physical measurement or a species observation associated with this location
Open data and open source for remote sensing training in ecology
No full textRemote sensing is one of the most important tools in ecology and conservation for an effective monitoring of
ecosystems in space and time. Hence, a proper training is crucial for developing effective conservation practices
based on remote sensing data. In this paper we aim to highlight the potential of open access data and open source
software and the importance of the inter-linkages between these and remote sensing training, with an
interdisciplinary perspective. We will first deal with the importance of open access data and then we provide
several examples of Free and Open Source Software (FOSS) for a deeper and more critical understanding of its
application in remote sensin
- …
