Apresentação
Público Alvo
Students, researchers and professionals in the fields of Anthropology, Social Sciences and Biological Sciences.
Number of participants: minimum 5 / maximum 20
Price: 90€ (20€ for students and 50€ for CRIA’s members)
Registration: The enrollment form includes a field for participants to write a brief justification of interest in the course (Portuguese or English) and explain which of these situations they are in: 1) I have never used GIS and I’m not familiar with mapping; 2) I have knowledge on some of the basics (what is a vector, raster), 3) I have previously used GIS for basic mapping, but I would like to know more. Students must provide a proof for enrollment in a university to carlos.moreira@cria.org.pt in order to apply for student prices.
Objetivos
Summary
Mapping is an essential tool for describing and understanding how humans and non-humans inhabit and share an environment.
In shared landscapes, mapping, animal observations, habitats and landmarks (human occupation of land; e.g., agricultural fields, fountains) allows us to analyse the spatiotemporal dynamics of human-wildlife coexistence.
This course will provide basic tools for mapping multispecies occurrence and interactions with QGIS, which can be useful for researchers and students undertaking fieldwork. Examples of map construction in the context of anthropology and primatology fieldwork will be used. Participants will also get familiar with remote sensing, including accessing and processing satellite imagery and exploring openly available datasets (e.g., human population density, forest cover, protected areas).
Material: Each participant/student must bring their laptop
Coordination: Amélia Frazão Moreira and Tânia Minhós
Trainer: Elena (Hellen) Bersacola – Postdoctoral researcher associated with the Centre for Ecology and Conservation (University of Exeter). Hellen's research focuses on understanding human-wildlife interactions within complex socio-ecological systems using different techniques including camera traps, interviews, spatiotemporal models and analysis of remote sensing data. During the past eight years Hellen has worked in Guinea-Bissau, West Africa, and currently co-directs the Cantanhez Chimpanzee Project, a multi-institutional network of researchers and conservation practitioners working on primate ecology, health and conservation in Cantanhez National Park, a protected agro-forest landscape in Guinea-Bissau.
Programa
The course will be taught in English, face-to-face, combining the manipulation of mapping tools, using selected case studies for practical exercises.
The contents will be as follows:
- Introduction to mapping: You will be introduced to GIS and its components, including coordinate reference systems, vector and raster layers, different geoprocessing and analysis tools. We will access freely available shapefiles, including country and administrative areas, protected area borders and species geographical ranges.
- Work with vector shapefiles. You will create a map comprising different vector shapefiles including points, lines and polygons, created from GPS data, using analysis tools and a reference map. You will know how to edit and extract data from shapefiles.
- Work with raster data 1: satellite imagery and other raster data for map visualisation. You will familiarise with ESA Sentinel and other freely available data, including forest cover and human population density. You will search, retrieve and create new layers from satellite imagery. You will learn how to create a map showing your study area, including 'true colour' satellite imagery, country and Protected Area borders and other elements (roads, villages).
- Work with raster data 2: extract raster data for statistical analysis. You will be introduced to the use of raster layers in statistical analyses, such as species distribution models, and learn how to extract data from raster layers at pre-determined spatial points to add to your dataset. As an example, you will create a raster of normalised difference vegetation index (NDVI) covering a protected area and extract raster values at survey locations.
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