|Responsible unit:||Department of Geosciences, Environment and Spatial Plannings|
|Course/CS Responsible:||Master's degree in Remote Sensing|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|M:DR||0||The study plan from 2018||1||-||3||21||81|
|Maria Joana Afonso Pereira Fernandes|
|Neftali Sillero Pablos|
It is intended that:
1) Students acquire training on the main applications of Remote Sensing (RS) in the area of Biology.
2) Gain experience in manipulating data from the satellites most used in biology, using different types of software, in particular open source software.
To this end, several examples of applications will be presented:
- Biogeographic analysis of the Iberian herpetofauna.
- Analysis of the vertical distribution of the herpetofauna with altimetry RS data.
- Water detection.
- Detection of environmental pollution.
- Landscape connectivity analysis.
- Studies of home ranges.
It is intended that students:
1) To know the techniques used in the collection of the variables with biological importance.
2) To be able to Incorporate RS variables into biology studies.
3) To know and are able to apply the main statistical tools for analysis of RS data in Biology.
4) To be able to apply RS techniques to various applications in Biology
1) Main RS data and sensors with use in Biology: vegetation and water indices; vegetation mapping, surface temperature, altimetry.
2) Incorporation of RS variables into biological studies: the importance of the study scale and spatial resolution.
3) Essential RS variables for biodiversity.
4) Main statistical tools for data analysis of RS: GLM and ecological modeling.
5) Main applications: biogeography, ecotoxicology, landscape connectivity, spatial ecology.
Chuvieco, E. (2000). Fundamentos de Teledetección Espacial, 3ª ed. revisada. (E. Rialp, Ed.), Madrid.
Kerr, J. T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18(6), 299–305.
Sillero, N. (2008): Mortality Levels of Amphibians on Spanish Country Roads: Descriptive and Spatial Analysis. Amphibia-Reptilia 29: 337-347.
Gonzalez, RC; Woods, RE (2008) Digital Image Processing, 3nd ed., Prentice Hall, Upper Saddle River, NJ.
Sillero, N.; Brito, J.C.; Skidmore, A.; Toxopeus, A.G. (2009): Biogeographical Patterns Derived from Remote Sensing Variables: the Amphibians and Reptiles of the Iberian Peninsula. Amphibia-Reptilia 30: 185-206.Blaschke, T (2010) Object based image analysis for repote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1), 2-16.
The way the various topics are presented and discussed in the theoretical lectures aims to stimulate the students and teach the relevant topics of the program.
In the practical lectures, the students are invited to think about the various problems, helping to consolidate the knowledge acquired in the theoretical lectures. The practical exercises solved in the computer aim to train the students to solve the most typical problems in remote sensing, related with the topics of the program.
Lectures are given based on Power Point presentations and practival exercises using R statistical software. Examples with different outputs and data from the teacher's own research will be used.
|Frequência das aulas||21,00|
|Trabalho de investigação||15,00|
The assessment is done through a practical exame (PE) usin the R software. The final mark will be: FM = PE.