Geomatics in Agriculture
A.Y. 2019/2020
Learning objectives
Provide the basic knowledge on the methodologies to analyze, describe and model spatial data, in order to produce maps of soil and vegetation properties. To introduce through hand-on exercises and presentation of case studies, the mathematical and geostatistical tools necessary for describing the spatial variability and for validating the spatial correlation models.
To learn the fundamentals of physical principles of remote sensing and of vegetation spectral signatures. To know the available imageries for land monitoring and their application to support agro-practices.
To introduce to up-to-date methodologies in remote sensing to estimate vegetation parameters and the specific data handling in time-series analysis. To gain insight of possible use of remote sensed data in precision agriculture practice, through case studies and practical examples carried out with specific software tools.
To learn the fundamentals of physical principles of remote sensing and of vegetation spectral signatures. To know the available imageries for land monitoring and their application to support agro-practices.
To introduce to up-to-date methodologies in remote sensing to estimate vegetation parameters and the specific data handling in time-series analysis. To gain insight of possible use of remote sensed data in precision agriculture practice, through case studies and practical examples carried out with specific software tools.
Expected learning outcomes
Knowledge of the fundamental skills to describe, interpret and critically analyze spatial data, in order to produce thematic maps and to assess their accuracy. Ability to use of GIS and software for the analysis and the modeling of spatial data.
Basic knowledge on available platforms and relevant features of remote sensing systems. Ability to retrieve remote sensed data taken on agricultural areas, to identify the proper methodologies to get meaningful information to support agro-practices and to exploit them in decision support systems within precision agriculture workflows.
Basic knowledge on available platforms and relevant features of remote sensing systems. Ability to retrieve remote sensed data taken on agricultural areas, to identify the proper methodologies to get meaningful information to support agro-practices and to exploit them in decision support systems within precision agriculture workflows.
Lesson period: year
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
year
Course syllabus
TEACHING UNIT: "Analysis of spatial variability in agriculture"
The course consists of two parts.
The first introductory part presents the basic mathematical tools, useful in the second part of the course. In particular, the topics introduced in this first part are:
· brief summary of the main features for real functions of one real variable
· vectors, matrices: definitions, matrix algebra and its applications
· vector spaces and their applications: linear functions, time-varying vectors, vector fields, linear algebra and linear systems
· functions of two or more variables: definition, properties, possible display modes (graph, contour curves, chromatic maps), partial and directional derivatives and their use in applications, gradient and its applications, derivative test for unconstrained stationary points, Lagrange multiplier method for constrained maximum and minimum problems.
The second part of the course presents the tools of the statistical and geostatistical analysis to describe and model the spatial variability of quantities of agronomic interest (soil physical and hydrological parameters, crop parameters, etc.), and their applications to obtain the prescription maps for irrigation and fertilization. In particular, the following topics will be introduced:
· statistical and geostatistical analysis of a sample of spatially distributed data
· stationary properties of a quantity with spatial variability
· modeling the spatial variability using variograms
· interpolation of spatially distributed data to produce thematic maps and prescription maps
· applications in agriculture and presentation of some case studies
The theoretical concepts will be consolidated through activities carried out in the computer lab, during which dedicated software will be used to analyse and model spatial data.
TEACHING UNIT: "Remote sensing for agriculture"
The course is made of 4 credits (CFU): 3 CFU corresponding to 24 hours of lectures, including seminars and practical demonstrations with dedicated software; 1 CFU consisting in 16 hours of hands-on computer lab activities.
The lessons focus on concepts and physical principles of remote sensing for agricultural applications. Particular emphasis will be given on near-real-time/seasonal monitoring of crop status and detection of intra-field variability of crop vigor.
The following topics will be presented:
· Physical principles of remote sensing
· Spectral response of vegetation and soils
· Acquisition systems and characteristics of digital images
· Visualization, interpretation and statistical exploration of multispectral digital images
· Computation and interpretation of vegetation indices
· Approaches for creating thematic maps
· Agricultural applications and presentation of case studies
The laboratory deals with open source tools to download, process and exploit multispectral satellite data.
In particular, the following topics will be covered:
· Search and download of Sentinel-2 satellite data
· Basic tools for the management of satellite data and basic operations for vegetation indices computation
· Interpretation of multispectral and multitemporal satellite data
· A practical case study on the exploitation of satellite data to produce thematic maps to support precision agriculture practices.
The course consists of two parts.
The first introductory part presents the basic mathematical tools, useful in the second part of the course. In particular, the topics introduced in this first part are:
· brief summary of the main features for real functions of one real variable
· vectors, matrices: definitions, matrix algebra and its applications
· vector spaces and their applications: linear functions, time-varying vectors, vector fields, linear algebra and linear systems
· functions of two or more variables: definition, properties, possible display modes (graph, contour curves, chromatic maps), partial and directional derivatives and their use in applications, gradient and its applications, derivative test for unconstrained stationary points, Lagrange multiplier method for constrained maximum and minimum problems.
The second part of the course presents the tools of the statistical and geostatistical analysis to describe and model the spatial variability of quantities of agronomic interest (soil physical and hydrological parameters, crop parameters, etc.), and their applications to obtain the prescription maps for irrigation and fertilization. In particular, the following topics will be introduced:
· statistical and geostatistical analysis of a sample of spatially distributed data
· stationary properties of a quantity with spatial variability
· modeling the spatial variability using variograms
· interpolation of spatially distributed data to produce thematic maps and prescription maps
· applications in agriculture and presentation of some case studies
The theoretical concepts will be consolidated through activities carried out in the computer lab, during which dedicated software will be used to analyse and model spatial data.
TEACHING UNIT: "Remote sensing for agriculture"
The course is made of 4 credits (CFU): 3 CFU corresponding to 24 hours of lectures, including seminars and practical demonstrations with dedicated software; 1 CFU consisting in 16 hours of hands-on computer lab activities.
The lessons focus on concepts and physical principles of remote sensing for agricultural applications. Particular emphasis will be given on near-real-time/seasonal monitoring of crop status and detection of intra-field variability of crop vigor.
The following topics will be presented:
· Physical principles of remote sensing
· Spectral response of vegetation and soils
· Acquisition systems and characteristics of digital images
· Visualization, interpretation and statistical exploration of multispectral digital images
· Computation and interpretation of vegetation indices
· Approaches for creating thematic maps
· Agricultural applications and presentation of case studies
The laboratory deals with open source tools to download, process and exploit multispectral satellite data.
In particular, the following topics will be covered:
· Search and download of Sentinel-2 satellite data
· Basic tools for the management of satellite data and basic operations for vegetation indices computation
· Interpretation of multispectral and multitemporal satellite data
· A practical case study on the exploitation of satellite data to produce thematic maps to support precision agriculture practices.
Prerequisites for admission
No prior knowledge is required
Teaching methods
TEACHING UNIT: "Analysis of spatial variability in agriculture"
The course includes lectures, exercises and computer lab activities. In particular, the exercises are related to calculation examples and the presentation of case studies in the field of precision agriculture
The computer laboratory activity consists in the use of dedicated software to analyse and model spatial data
Attendance is strongly recommended
TEACHING UNIT: "Remote sensing for agriculture"
The classes include lectures, seminars, practical demonstrations and presentation of a case studies on precision agriculture.
The computer laboratory provides students with hands-on experience on exploration, processing and exploitation of remote sensing data for precision agriculture purposes.
Attendance is strongly recommended
The course includes lectures, exercises and computer lab activities. In particular, the exercises are related to calculation examples and the presentation of case studies in the field of precision agriculture
The computer laboratory activity consists in the use of dedicated software to analyse and model spatial data
Attendance is strongly recommended
TEACHING UNIT: "Remote sensing for agriculture"
The classes include lectures, seminars, practical demonstrations and presentation of a case studies on precision agriculture.
The computer laboratory provides students with hands-on experience on exploration, processing and exploitation of remote sensing data for precision agriculture purposes.
Attendance is strongly recommended
Teaching Resources
TEACHING UNIT: "Analysis of spatial variability in agriculture"
For the introductory part, relative to the mathematical topics:
Stewart, "Calculus", Brooks/Cole Pub Co
Greenwell, Ritchey, Lial, "Calculus for the Life Sciences", Pearson
Stewart, Day, "Biocalcucus: Calculus for the Life Sciences", Brooks/Cole Pub Co
Robert A. Adams and Christopher Essex, "Calculus: A Complete Course", 9th Edition, Pearson
For the part relative to geostatistics:
Webster&Oliver, "Geostatistics for Environmental Scientists", Wiley
Oliver, "Geostatistical applications for Precision Agriculture", Springer
Marsily, "Quantitative Hydrogeology", Academic Press Inc
Isaaks&Srivastava, "An Introduction to Applied Geostatistics", Oxford University Press
TEACHING UNIT: "Remote sensing for agriculture"
On lectures:
Slides displayed during the lessons.
Principi e metodi di telerilevamento - Brivio P.A., Lechi G., Zilioli E., Ed. Città Studi, 2006
Elementi di geomatica. - Gomarasca Mario A. Editore: ASITA, 2004
ASRAR G., (1989). Theory and applications of optical remote sensing - Ed:John Wiley & Sons New York, 1989 XIV, 734 pp.
RICHARDS, J.A., (1993): Remote Sensing Digital Image Analysis. An Introduction, 2nd Ed., Berlin, Springer-Verlag.
Lillesand T. & Kiefer R. (2000): Remote sensing and image interpretation - 4. ed
LIANG S. (2004). Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons, 534 p.
Jensen J. R. (2006). Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall, 608 p.
On lab:
Tutorial on the activities performed during the classes and a template of the technical report.
Agricoltura Di Precisione - Casa R., 2016, Edagricole
R tutorials: (https://rcompanion.org/rcompanion/, https://geocompr.robinlovelace.net/, https://www.r-bloggers.com/, http://ww2.coastal.edu/kingw/statistics/R-tutorials/, http://zoonek2.free.fr/UNIX/48_R/02.html, https://cran.r-project.org/doc/contrib/Lemon-kickstart/)
QGIS tutorials: (https://www.qgistutorials.com/en/, https://docs.qgis.org/testing/en/docs/training_manual/)
SNAP tutorials: (https://step.esa.int/main/doc/tutorials/, https://step.esa.int/main/doc/tutorials/snap-tutorials/)
For the introductory part, relative to the mathematical topics:
Stewart, "Calculus", Brooks/Cole Pub Co
Greenwell, Ritchey, Lial, "Calculus for the Life Sciences", Pearson
Stewart, Day, "Biocalcucus: Calculus for the Life Sciences", Brooks/Cole Pub Co
Robert A. Adams and Christopher Essex, "Calculus: A Complete Course", 9th Edition, Pearson
For the part relative to geostatistics:
Webster&Oliver, "Geostatistics for Environmental Scientists", Wiley
Oliver, "Geostatistical applications for Precision Agriculture", Springer
Marsily, "Quantitative Hydrogeology", Academic Press Inc
Isaaks&Srivastava, "An Introduction to Applied Geostatistics", Oxford University Press
TEACHING UNIT: "Remote sensing for agriculture"
On lectures:
Slides displayed during the lessons.
Principi e metodi di telerilevamento - Brivio P.A., Lechi G., Zilioli E., Ed. Città Studi, 2006
Elementi di geomatica. - Gomarasca Mario A. Editore: ASITA, 2004
ASRAR G., (1989). Theory and applications of optical remote sensing - Ed:John Wiley & Sons New York, 1989 XIV, 734 pp.
RICHARDS, J.A., (1993): Remote Sensing Digital Image Analysis. An Introduction, 2nd Ed., Berlin, Springer-Verlag.
Lillesand T. & Kiefer R. (2000): Remote sensing and image interpretation - 4. ed
LIANG S. (2004). Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons, 534 p.
Jensen J. R. (2006). Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall, 608 p.
On lab:
Tutorial on the activities performed during the classes and a template of the technical report.
Agricoltura Di Precisione - Casa R., 2016, Edagricole
R tutorials: (https://rcompanion.org/rcompanion/, https://geocompr.robinlovelace.net/, https://www.r-bloggers.com/, http://ww2.coastal.edu/kingw/statistics/R-tutorials/, http://zoonek2.free.fr/UNIX/48_R/02.html, https://cran.r-project.org/doc/contrib/Lemon-kickstart/)
QGIS tutorials: (https://www.qgistutorials.com/en/, https://docs.qgis.org/testing/en/docs/training_manual/)
SNAP tutorials: (https://step.esa.int/main/doc/tutorials/, https://step.esa.int/main/doc/tutorials/snap-tutorials/)
Assessment methods and Criteria
TEACHING UNIT: "Analysis of spatial variability in agriculture"
The exam consists of a written test, a written report on the laboratory activity and an oral exam.
The written test focuses on the introductory part of the course, i.e. on mathematics topics, and consists of open answer exercises. The duration of the written test is 90 minutes and the written test is considered passed with a grade greater than or equal to 18/30.
After passing the written test, but before the oral exam, the candidate must deliver a written report on the laboratory activity.
The passing of the written test and the delivery of the aforementioned report are both necessary conditions to access the oral exam.
The oral exam includes, in addition to the discussion of the report, questions related to all the lectures topics.
For attending students only, an intermediate written test (approximately mid-November) is scheduled which, if passed, will replace the written exam only for the first two exam sessions (January and February exam sessions).
Students who, despite having passed the written test in November, do not pass the exam by February of the current academic year, have to repeat the written test.
TEACHING UNIT: "Remote sensing for agriculture"
The exam consists in a written report on laboratory activities and an oral exam.
The written document will have the structure of a technical / scientific report and should be delivered before the oral test. This is mandatory for oral test admission.
The oral exam consists in the discussion of the provided report and includes questions related on lectures topics.
The exam consists of a written test, a written report on the laboratory activity and an oral exam.
The written test focuses on the introductory part of the course, i.e. on mathematics topics, and consists of open answer exercises. The duration of the written test is 90 minutes and the written test is considered passed with a grade greater than or equal to 18/30.
After passing the written test, but before the oral exam, the candidate must deliver a written report on the laboratory activity.
The passing of the written test and the delivery of the aforementioned report are both necessary conditions to access the oral exam.
The oral exam includes, in addition to the discussion of the report, questions related to all the lectures topics.
For attending students only, an intermediate written test (approximately mid-November) is scheduled which, if passed, will replace the written exam only for the first two exam sessions (January and February exam sessions).
Students who, despite having passed the written test in November, do not pass the exam by February of the current academic year, have to repeat the written test.
TEACHING UNIT: "Remote sensing for agriculture"
The exam consists in a written report on laboratory activities and an oral exam.
The written document will have the structure of a technical / scientific report and should be delivered before the oral test. This is mandatory for oral test admission.
The oral exam consists in the discussion of the provided report and includes questions related on lectures topics.
Analisi della variabilità spaziale in agricoltura
AGR/08 - AGRICULTURAL HYDRAULICS AND WATERSHED PROTECTION
ICAR/06 - SURVEYING AND MAPPING
ICAR/06 - SURVEYING AND MAPPING
Computer room practicals: 16 hours
Practicals: 16 hours
Lessons: 16 hours
Practicals: 16 hours
Lessons: 16 hours
Professors:
Morando Paola, Ortuani Bianca
Shifts:
Telerilevamento per l'agricoltura
AGR/08 - AGRICULTURAL HYDRAULICS AND WATERSHED PROTECTION
ICAR/06 - SURVEYING AND MAPPING
ICAR/06 - SURVEYING AND MAPPING
Computer room practicals: 16 hours
Lessons: 24 hours
Lessons: 24 hours
Professors:
Boschetti Mirco, Nutini Francesco
Shifts:
-
Professors:
Boschetti Mirco, Nutini FrancescoProfessor(s)
Reception:
By appointment (send the request by e-mail)
DiSAA