Geomatics in agriculture
A.Y. 2021/2022
Learning objectives
The course provides the basic knowledge on the methodologies to analyze, describe and model spatial data, in order to produce maps of the soil and vegetation properties. Particularly the course consists of two Teaching Units: T.U. 1 "Analysis of spatial variability in agriculture", T. U. 2 "Remote sensing for agriculture". Through hand-on exercises and presentation of case studies, the T.U. 1 introduces the geostatistical tools necessary for describing the spatial variability and for validating the spatial correlation models; moreover, it introduces the use of softwares to analyse the spatial variability and to create interpolation maps. The T.U. 1 teaches the fundamentals of physical principles of remote sensing and of vegetation spectral signatures; it illustrates the available imageries for land monitoring and their application to support agro-practices; it introduces the up-to-date methodologies in remote sensing to estimate vegetation parameters and the specific data handling in time-series analysis; finally it illustrates the 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.
Lesson period: year
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Course syllabus and organization
Single session
Responsible
Lesson period
year
TEACHING UNIT: "Analysis of spatial variability in agriculture":
The lessons and the exercises will be held on the Microsoft Teams platform and can be followed both synchronously on the basis of the first semester schedule, and asynchronously because they will be recorded and left available to students on the same platform.
The program and reference material will not change.
The oral exam will take place using the Microsoft Teams platform. The candidate must deliver a written report before the oral exam. The presentation of the report is mandatory to access the oral exam. The report concerns the exercises carried out using QGIS, and the GSTAT and GeoR softwares
TEACHING UNIT: "Remote sensing for agriculture:
Lessons and practical exercises will be held on Microsoft Teams platform synchronously on the basis of the second semester schedule. Lesson will be recorded and made available on Arial platform as well (https://ariel.unimi.it/) for non-attending students and post lesson consultation purposes.
The pandemic emergence will not affect the academic programme.
The final oral exam will take place using Microsoft Teams platform. It is mandatory to hand in before the oral exam a written report on exercises carried out during laboratories
The lessons and the exercises will be held on the Microsoft Teams platform and can be followed both synchronously on the basis of the first semester schedule, and asynchronously because they will be recorded and left available to students on the same platform.
The program and reference material will not change.
The oral exam will take place using the Microsoft Teams platform. The candidate must deliver a written report before the oral exam. The presentation of the report is mandatory to access the oral exam. The report concerns the exercises carried out using QGIS, and the GSTAT and GeoR softwares
TEACHING UNIT: "Remote sensing for agriculture:
Lessons and practical exercises will be held on Microsoft Teams platform synchronously on the basis of the second semester schedule. Lesson will be recorded and made available on Arial platform as well (https://ariel.unimi.it/) for non-attending students and post lesson consultation purposes.
The pandemic emergence will not affect the academic programme.
The final oral exam will take place using Microsoft Teams platform. It is mandatory to hand in before the oral exam a written report on exercises carried out during laboratories
Course syllabus
TEACHING UNIT: "Analysis of spatial variability in agriculture"
The course takes place in the first semester. It is made of 4 credits (CFU): 2 CFU (16 hours) of lectures; 2 CFU (16 hours) of practical demonstrations and exercises with dedicated software
The lectures introduce 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 in precision agriculture to obtain the prescription maps for irrigation and fertilization.
In particular, the following topics will be focused:
· 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
· presentation of some case studies
The theoretical concepts are consolidated through practical demonstrations and exercises with GIS and dedicated software to represent, analyse and model spatial data.
The program is the same for attending and non-attending students
TEACHING UNIT: "Remote sensing for agriculture":
The course takes place in the second semester. It 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 and intra-farm 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 program is the same for attending and non-attending students.
The course takes place in the first semester. It is made of 4 credits (CFU): 2 CFU (16 hours) of lectures; 2 CFU (16 hours) of practical demonstrations and exercises with dedicated software
The lectures introduce 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 in precision agriculture to obtain the prescription maps for irrigation and fertilization.
In particular, the following topics will be focused:
· 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
· presentation of some case studies
The theoretical concepts are consolidated through practical demonstrations and exercises with GIS and dedicated software to represent, analyse and model spatial data.
The program is the same for attending and non-attending students
TEACHING UNIT: "Remote sensing for agriculture":
The course takes place in the second semester. It 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 and intra-farm 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 program is the same for attending and non-attending students.
Prerequisites for admission
No prior knowledge is required for both attending and non-attending students
Teaching methods
TEACHING UNIT: "Analysis of spatial variability in agriculture":
The course includes lectures, as well as practical demonstration anch exercises with dedicated softwares in PC lab. Moreover, some case studies in the field of precision agriculture are presented
The materials (slides and references) to learn the topics illustrated in class, the tutorials to use the softwares and carry out the exercises are uploaded in Ariel (https://ariel.unimi.it/)
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 agriculture monitoring and precision agriculture purposes using software like SNAP, QGis and R.
All the materials related to the topics showed in the classes (slides, references), tutorials to use the presented software and to carry out the exercises are uploaded in Ariel (https://ariel.unimi.it/).
Attendance is strongly recommended
The course includes lectures, as well as practical demonstration anch exercises with dedicated softwares in PC lab. Moreover, some case studies in the field of precision agriculture are presented
The materials (slides and references) to learn the topics illustrated in class, the tutorials to use the softwares and carry out the exercises are uploaded in Ariel (https://ariel.unimi.it/)
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 agriculture monitoring and precision agriculture purposes using software like SNAP, QGis and R.
All the materials related to the topics showed in the classes (slides, references), tutorials to use the presented software and to carry out the exercises are uploaded in Ariel (https://ariel.unimi.it/).
Attendance is strongly recommended
Teaching Resources
TEACHING UNIT: "Analysis of spatial variability in agriculture":
- Slides displayed during the lessons
- For each of the following texts, the pages and the topics of the course illustrated are indicated:
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
- QGIS, R, GSTAT and GeoR Tutorials
- Tutorial on the practical activities and exercises performed during the classes with QGIS and GSTAT e GeoR
The references are the same for attending and non-attending students
TEACHING UNIT: "Remote sensing for agriculture":
On lectures:
· Slides displayed during the lessons.
· Suggested books:
o Agricoltura Di Precisione - Casa R., 2016, Edagricole
o Principi e metodi di telerilevamento - Brivio P.A., Lechi G., Zilioli E., Ed. Città Studi, 2006
o Elementi di geomatica. - Gomarasca Mario A. Editore: ASITA, 2004
o ASRAR G., (1989). Theory and applications of optical remote sensing - Ed:John Wiley & Sons New York, 1989 XIV, 734 pp.
o RICHARDS, J.A., (1993): Remote Sensing Digital Image Analysis. An Introduction, 2nd Ed., Berlin, Springer-Verlag.
o Lillesand T. & Kiefer R. (2000): Remote sensing and image interpretation - 4. ed
o LIANG S. (2004). Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons, 534 p.
o 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.
· R tutorials:
o https://rcompanion.org/rcompanion/,
o https://geocompr.robinlovelace.net/,
o https://www.r-bloggers.com/,
o http://ww2.coastal.edu/kingw/statistics/R-tutorials/,
o http://zoonek2.free.fr/UNIX/48_R/02.html,
o https://www.dummies.com/programming/r/.
· QGIS tutorials:
o https://www.qgistutorials.com/en/,
o https://docs.qgis.org/testing/en/docs/training_manual/.
· SNAP tutorials:
o https://step.esa.int/main/doc/tutorials/,
o https://step.esa.int/main/doc/tutorials/snap-tutorials/.
The references are the same for attending and non-attending students
- Slides displayed during the lessons
- For each of the following texts, the pages and the topics of the course illustrated are indicated:
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
- QGIS, R, GSTAT and GeoR Tutorials
- Tutorial on the practical activities and exercises performed during the classes with QGIS and GSTAT e GeoR
The references are the same for attending and non-attending students
TEACHING UNIT: "Remote sensing for agriculture":
On lectures:
· Slides displayed during the lessons.
· Suggested books:
o Agricoltura Di Precisione - Casa R., 2016, Edagricole
o Principi e metodi di telerilevamento - Brivio P.A., Lechi G., Zilioli E., Ed. Città Studi, 2006
o Elementi di geomatica. - Gomarasca Mario A. Editore: ASITA, 2004
o ASRAR G., (1989). Theory and applications of optical remote sensing - Ed:John Wiley & Sons New York, 1989 XIV, 734 pp.
o RICHARDS, J.A., (1993): Remote Sensing Digital Image Analysis. An Introduction, 2nd Ed., Berlin, Springer-Verlag.
o Lillesand T. & Kiefer R. (2000): Remote sensing and image interpretation - 4. ed
o LIANG S. (2004). Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons, 534 p.
o 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.
· R tutorials:
o https://rcompanion.org/rcompanion/,
o https://geocompr.robinlovelace.net/,
o https://www.r-bloggers.com/,
o http://ww2.coastal.edu/kingw/statistics/R-tutorials/,
o http://zoonek2.free.fr/UNIX/48_R/02.html,
o https://www.dummies.com/programming/r/.
· QGIS tutorials:
o https://www.qgistutorials.com/en/,
o https://docs.qgis.org/testing/en/docs/training_manual/.
· SNAP tutorials:
o https://step.esa.int/main/doc/tutorials/,
o https://step.esa.int/main/doc/tutorials/snap-tutorials/.
The references are the same for attending and non-attending students
Assessment methods and Criteria
The final grade is the average of the marks obtained for the tests of each Teaching Unit.
TEACHING UNIT: "Analysis of spatial variability in agriculture":
The exam consists of a written report in the form of a technical / scientific report and an oral examination. The report concerns the exercises carried out during the class using QGIS and the GSTAT and GeoR softwares for the geostatistical analysis of a sample and the interpolation of the sample measurements
The candidate must deliver the written report before the oral exam. The presentation of the report is mandatory to access the oral exam.
The report will be evaluated on the clear and complete presentation of the methodologies and the results obtained. In particular, the candidate's ability to interpret the results will be considered.
Starting from the discussion of the report, the oral exam includes questions about all the topics of the program, to verify the level of understanding of the topics covered by the exercises and the ability to apply the knowledge acquired.
Grades are expressed in thirties, considering oral exam as well as written report
TEACHING UNIT: "Remote sensing for agriculture":
The exam consists in a written report on laboratory activities and an oral exam.
The report will describe the activities performed during the laboratory regarding the processing and analysing optical multispectral satellite data devoted to agricultural monitoring. 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 report will be evaluated on the basis of its completeness according to given assignments and student's abilities in satellite data interpretation.
The oral exam consists in the discussion of the provided report and includes questions related on lectures topics.
Grades are expressed in thirties, considering oral exam as well as written report
TEACHING UNIT: "Analysis of spatial variability in agriculture":
The exam consists of a written report in the form of a technical / scientific report and an oral examination. The report concerns the exercises carried out during the class using QGIS and the GSTAT and GeoR softwares for the geostatistical analysis of a sample and the interpolation of the sample measurements
The candidate must deliver the written report before the oral exam. The presentation of the report is mandatory to access the oral exam.
The report will be evaluated on the clear and complete presentation of the methodologies and the results obtained. In particular, the candidate's ability to interpret the results will be considered.
Starting from the discussion of the report, the oral exam includes questions about all the topics of the program, to verify the level of understanding of the topics covered by the exercises and the ability to apply the knowledge acquired.
Grades are expressed in thirties, considering oral exam as well as written report
TEACHING UNIT: "Remote sensing for agriculture":
The exam consists in a written report on laboratory activities and an oral exam.
The report will describe the activities performed during the laboratory regarding the processing and analysing optical multispectral satellite data devoted to agricultural monitoring. 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 report will be evaluated on the basis of its completeness according to given assignments and student's abilities in satellite data interpretation.
The oral exam consists in the discussion of the provided report and includes questions related on lectures topics.
Grades are expressed in thirties, considering oral exam as well as written report
Analisi della variabilità spaziale in agricoltura
AGR/08 - AGRICULTURAL HYDRAULICS AND WATERSHED PROTECTION - University credits: 0
ICAR/06 - SURVEYING AND MAPPING - University credits: 0
ICAR/06 - SURVEYING AND MAPPING - University credits: 0
Lessons: 32 hours
Professor:
Ortuani Bianca
Telerilevamento per l'agricoltura
AGR/08 - AGRICULTURAL HYDRAULICS AND WATERSHED PROTECTION - University credits: 0
ICAR/06 - SURVEYING AND MAPPING - University credits: 0
ICAR/06 - SURVEYING AND MAPPING - University credits: 0
Computer room practicals: 16 hours
Lessons: 24 hours
Lessons: 24 hours
Professors:
Boschetti Mirco, Nutini Francesco
Educational website(s)
Professor(s)
Reception:
By appointment (send the request by e-mail)
DiSAA