Web-based estimates of food productivity for enhanced food security.
In the face of global climate change and population increase our ability to monitor the Earth’s natural resources has become more and more pressing. The promise of timely and easy access to geo-information is an essential building stone in this endeavour. Geo Data Infrastructure (GDI) technologies try to sell us the idea that data, and no longer our time, is a consumable. In reality however, we still consume a significant portion of our time and energy on understanding the messages hidden in the terabytes of freely accessible data. This module highlights new technologies that may well change this situation ushering in a new era of distributed data access, processing, and analysis aiming to generate more detailed, accurate, spatially explicit, and timely production forecasts of food and fibre crops at regional scales. It also tries to stimulate participants to engage in rural development oriented research for enhanced food security.
The course is part of the internet based programme ‘GIS and Earth Observation for Environmental modeling and Natural resource management’ (iGEON). The course is an elective course for the double degree: Master of Science in Geographical Information Science and Earth Observation, University of Twente, Faculty ITC, the Netherlands and Master of Science in Geographical Information Science of Lund University, Sweden. The course is also given as a single subject course. The language of instruction is English. The course is fully based on distance learning, with all material distributed over the Internet. It is flexible in the sense that students can study full time (100%), half time (50%), or with a 25% study tempo.
After the course the student is able to use basic computer scripting languages (python, ingrid) to perform various scientific interrogations on remote data collections. Presented analytical procedures cover, amongst others:
From data to information:
- Spatially and temporally aggregate coarse resolution satellite observations (MODIS, SEVIRI, etc.) using web-processing services technology
- Generate descriptive statistics for a variable offered by a remote data server
- Regional assessment of food/fiber production levels:
- Compare and select for given objectives suitable approaches, methods, and materials for the quantification of food productivity at a regional scale
From qualitative to quantitative:
- Apply vegetation growth models on remote data collections
Best practices in open-source software usage and referencing remote data collections in scientific publications or educational materials are demonstrated as well.
At the end of the course the student is able to:
- Monitor drought and insect induced hazards on food and fiber production levels.
- Understand how such information may contribute to enhanced food security.
- Estimate the start, end, and length of a growing season using optical satellite data.
- Estimate agricultural production using qualitative remote sensing techniques.
- Apply quantitative remote sensing techniques for estimating environmental parameter values required by crop growth simulation models.
- Calibrate such models to estimate crop production more realistically.
- Prepare projections of agricultural production for the future based on long-range weather forecasts and climate change scenarios.
- Approximate the final outcome for multiple land use systems for a whole region in a case study setup.
Teaching consists of:
- Lecture 20 hours
- Supervised practical 20 hours
- Group assignment (e.g. workshop, project) 10 hours
- Individual assignment (including Thesis, IFA) 46 hours
- Self study (including unsupervised practicals) 40 hours
- Overhead (e.g. QH, exam, opening) 8 hours
Students are graded for the course with a grade between 10 and 100. 60 is the pass mark.
In order to pass the course the student is required to have passed the exam, all compulsory exercises, and to have participated in all compulsory course elements.
Examination is through a summative assessment (examination) of theory and formative assessment of practical work through individual final assignment and various online tests.
The following are required for admission to the course: Basic admission requirements of Faculty ITC, and basic GIS and remote sensing corresponding to iGEON compulsory courses of semester 1 and 2 (35 ECTS).