Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap the uncertainty related to data source became more important than that of GCMs. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and data source (CHELSA versus Worldclim).
![x3 reunion map km to m x3 reunion map km to m](http://www.xdatabase.de/x3-reunion/de/_bilder/navigation/seite/3.jpg)
We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. However, very few studies consider potential differences related to the source of climate data and/or do not account for spatial information (overlap) in uncertainty assessments. Predictive studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). Newly available high-resolution environmental data and statistical methods enable the development of forecasting models, but the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. The effect of future climate change is poorly studied in the tropics, especially in mountainous areas, yet species living in these environments are predicted to be strongly affected. Results are compared to those achieved by several approaches commonly used to address SITS-based land cover mapping and show that the use of copulas, in combination with the matrix factorization, achieved the highest classification yield compared to competing approaches. Experiments were conducted at a study site located on Reunion Island, using Sentinel-2 SITS data. We will show how the use of particular copulas can improve the accuracy of classification compared to the latest methodologies used for the classification task, such as those using Neural Networks. In this paper, we propose a new approach for Satellite Image Time-Series (SITS) land cover classification, which combines the matrix factorization to reduce the dimensionality of the data and the use of copulas distribution to model the dependencies. Copulas are an excellent statistical tool, able to model joint distributions between even random variables. The classification of this large amount of data requires increasingly precise and fast methods, which must take into account not only the spectral features dependence of each individual image but also that of the temporal ones. Recently, satellite missions, such as Sentinel-2, allow us to capture images in real-time of the Earth’s scenario. Localised Yaki activity is also a negative point to consider, leading some to believe it is used as a Pirate or Yaki safe-haven.A variety of remote sensing applications call for automatic optical classification of satellite images. Increased Kha'ak activity has meant that the Paranid have made no move to reclaim this inhospitable sector and no other race has shown interest as it borders only Paranid space.
![x3 reunion map km to m x3 reunion map km to m](http://blubb.najut.org/x3/xrm/X3map_english_fitted.png)
For a patient player willing to carefully tow their mines out of the dense thicket, this sector makes a good candidate for player complexes, as long as good relations with the Yaki to the south are maintained.
![x3 reunion map km to m x3 reunion map km to m](http://www.xdatabase.de/x3-reunion/en/_bilder/navigation/seite/51.jpg)
![x3 reunion map km to m x3 reunion map km to m](http://www.xdatabase.de/x3-reunion/en/_bilder/navigation/seite/151.jpg)
This sector is extremely crowded with asteroids.