Use of satellite data for the monitoring of species on Saba and St. Eustatius.


On 10 October 2010 Bonaire, Saba and St. Eustatius became ‘special municipalities’ of the Netherlands, making the Dutch government responsible for the implementation and adherence to several international conventions that apply to these islands (e.g. Convention of Biological Diversity, Ramsar convention), including the protection of nature.

Knowledge on the whereabouts of endangered and key species or habitats is essential to ensure their protection against the negative effects of activities such as uncontrolled socio-economic developments (e.g. construction works, harbour expansion, expansion of residential areas) and natural phenomena (e.g. hurricanes, Sea Level Rise). This necessitates early identification of risk locations where future expected activities may collide with species/habitat presence. To determine these whereabouts, monitoring is necessary. Monitoring in the field, however, is often costly and time-consuming. A more effective and quicker approach is desired to obtain a realistic overview of key habitat distributions and associated key species.

At the request of the Dutch Ministry of Economic Affairs the present study examines the possibility to identify the different land cover types (natural and artificial) on Very High Resolution1 satellite images of the Caribbean islands Bonaire, Saba and St. Eustatius, using remote sensing1 analysis. In addition, the possibility to link key species with specific land cover types was assessed by identifying the species’ habitat requirements. Linking species habitat requirements with associated land cover types allows for the identification of their potential occurrence on the islands. It was expected that with niche-modelling potential distribution maps could be developed for different species and habitats. Such maps are valuable to determine risk locations where species/habitat occurrence and planned activities may conflict in the future. This would allow for the proper and early implementation of protective measures.

Worldview-2 satellite images of Saba and St. Eustatius (acquired on 3 December 2010 and 18 February 2011, respectively) were analysed. Analysis of the satellite image of Bonaire was not possible, due to time constraints. From the results of Saba and St. Eustatius it can be concluded that identification of land cover types using satellite images is possible. At present, the results are limited due to a) heterogeneous land cover types and b) the lack of ecological knowledge (e.g. baseline studies).

The identification of artificial features1 (e.g. infrastructure) is not a problem. The challenges encountered are mainly related to the largely mixed heterogeneous vegetation found on Saba and St. Eustatius. Due to the high level of mixing, spectral overlap between different vegetation types is high. Consequently, separating the different vegetation types is difficult. Corrections can be made based on visual interpretation and expertise in the field. This requires time and expert knowledge of the different vegetation types. In addition, both Saba and St. Eustatius exhibit strong differences in altitude, resulting in numerous shadowed areas that impede the identification of the land cover types underneath. Such terrain effect can be corrected using a Digital Elevation Model (DEM). Unfortunately, a sufficiently good DEM (with a high spatial accuracy of around 1 meter) was not yet available2.

Analysis of satellite images resulted in land cover maps with good fit to the distribution of the different land cover types on Saba and St. Eustatius. The produced land cover maps (Figures 4 to 7) give a coarse representation of the distribution of Forest, Shrub, Pasture and Artificial surface on the islands. In addition, it was possible to identify the extent and location of invasive vegetation (e.g. Corallita and other species), although identification to species-level was not possible. At present, these maps provide insufficient detail for biodiversity monitoring, because of the lack of connection with species. They could, however, be used to monitor different land cover development (e.g. forestation, artificial surfaces, shrub and pastures) on the long term (e.g. in years) or to gain a quick overview on the location of invasive vegetation. A distinctive land cover classification based on the available satellite images during the present study, however, was only achieved for the coarser vegetation types.

Ecoprofiles were developed for various species and habitats, describing their habitat requirements. With sufficient detail, these requirements link the species to habitats and thereby allow for the creation of species specific maps. The level of available data on habitat requirements varies per species. Overall knowledge on habitat requirements is generally not sufficient, associating species with multiple habitat types, and making it difficult to pinpoint essential habitat types. The amount of knowledge on habitat requirements has direct influence on the success of niche modelling. This illustrates the necessity of detailed knowledge on species biology, ecology and life history characteristics even when using advanced techniques such as remote sensing.

The production of maps through niche-modelling meant to show the expected geographical distribution of species was not possible due to the limited level of detail within the identified land cover types, and the restricted data on the habitat requirements of the species occurring on Saba and St. Eustatius, in combination with time constraints. Before such maps can be developed several issues need to be solved first. These include specific knowledge on species biology, ecology and life history characteristics of the target species (baseline studies); the collection of more training samples (ground truthing data) in the field; a high quality DEM of Saba and St. Eustatius (and Bonaire as well). This will lead to further adaptation of the chosen classification scheme and aid in separating spectral overlap between the different vegetation types.

This research is part of the Wageningen University BO research program (BO-11-011.05-019) and was financed by the Dutch Ministry of Economic Affairs (EZ) under project number 4308701012. 

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