2024
Geographic Information Technologies in Precision Agriculture
Name: Geographic Information Technologies in Precision Agriculture
Code: ERU12714M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Rural Engineering
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
The student should acquire knowledge, skills and competences (at the level of the agricultural parcel):
1. in the field handling of GNSS (intelligent surveys and sampling of soil, plants, etc.);
2. Geographic Information Technology (GIS) and the ability to integrate spatial data in different formats;
3. the ability to structure spatial databases as models of reality;
4. the application of spatial analysis functions in a GIS, from the perspective of support for agronomic
decision making;
5. the use of structured search languages and map algebra;
6. the application of several techniques of remote sensing at different scales for land surface analysis,
namely interpreting and identifying morphological and texture characteristics in the vegetation cover and
estimating parameters at vegetative vigor level).
1. in the field handling of GNSS (intelligent surveys and sampling of soil, plants, etc.);
2. Geographic Information Technology (GIS) and the ability to integrate spatial data in different formats;
3. the ability to structure spatial databases as models of reality;
4. the application of spatial analysis functions in a GIS, from the perspective of support for agronomic
decision making;
5. the use of structured search languages and map algebra;
6. the application of several techniques of remote sensing at different scales for land surface analysis,
namely interpreting and identifying morphological and texture characteristics in the vegetation cover and
estimating parameters at vegetative vigor level).
Contents
1- Operation, type of positioning and errors correction of a GNSS and its applications in the agroforestry
area.
2- Application of GIS techniques: Construction and structuring of spatial databases, as models of reality;
3 - Application and techniques of satellite image processing and analysis: Contrast enhancement of digital
image; Image classification (supervised and unsupervised); Calculation and analysis of vegetation indexes
(linear and orthogonal); Principal component analysis; Multi-resolution segmentation; Object-oriented
classification.
4 - Estimation of agricultural and forestry parameters based on data obtained through remote sensing
(spatial ramps, UAV / DRONE ramps, motorized ramps) using GIS and digital image processing software.
5 - Estimation of soil parameters based on geo-electric sensors (Survey of soil ECa).
6 - Practical work
7- Seminars in TIG.
area.
2- Application of GIS techniques: Construction and structuring of spatial databases, as models of reality;
3 - Application and techniques of satellite image processing and analysis: Contrast enhancement of digital
image; Image classification (supervised and unsupervised); Calculation and analysis of vegetation indexes
(linear and orthogonal); Principal component analysis; Multi-resolution segmentation; Object-oriented
classification.
4 - Estimation of agricultural and forestry parameters based on data obtained through remote sensing
(spatial ramps, UAV / DRONE ramps, motorized ramps) using GIS and digital image processing software.
5 - Estimation of soil parameters based on geo-electric sensors (Survey of soil ECa).
6 - Practical work
7- Seminars in TIG.
Teaching Methods
Work in classroom: The theoretical and practical competences will be acquired in the classroom. The
theoretical component will always be followed by its application in practical situations, through the
resolution of exercises using specific programs and technologies. A work group will integrate the acquired
knowledge given in the curricular unit.
Remote work: Introduction to the fundamental theoretical concepts through made available resources, in
Moodle (lessons, several texts, PowerPoint presentations, Internet pages, etc). Students work will be
guided to reach specific objectives. They will also develop collaborative work in Moodle.
Evaluation: 40% for the theoretical component and 60% the practical component.
theoretical component will always be followed by its application in practical situations, through the
resolution of exercises using specific programs and technologies. A work group will integrate the acquired
knowledge given in the curricular unit.
Remote work: Introduction to the fundamental theoretical concepts through made available resources, in
Moodle (lessons, several texts, PowerPoint presentations, Internet pages, etc). Students work will be
guided to reach specific objectives. They will also develop collaborative work in Moodle.
Evaluation: 40% for the theoretical component and 60% the practical component.
Teaching Staff (2023/2024 )
- Adélia Maria Oliveira Sousa [responsible]
- Ana Cristina Andrade Gonçalves
