Measuring Vegetation Lab
A.Y. 2024/2025
Learning objectives
To become familiar with the type of data (and derived indexes) useful to measure vegetation and its condition, how to collect such data, the available databases and technologies/tools employed to get them.
To practice on data acquisition for an area of interest using specific databases (satellite data) or multi-spectral cameras mounted on drones (only for areas of interest easily accessible and in close proximity, e.g. within the Lombardy region).
To learn how to interpret data and derived indexes in order to understand the ongoing phenomena (land use changes, growth stage of the vegetation, yield, health/nutritional status) and causes thereof.
To practice on data acquisition for an area of interest using specific databases (satellite data) or multi-spectral cameras mounted on drones (only for areas of interest easily accessible and in close proximity, e.g. within the Lombardy region).
To learn how to interpret data and derived indexes in order to understand the ongoing phenomena (land use changes, growth stage of the vegetation, yield, health/nutritional status) and causes thereof.
Expected learning outcomes
A student is expected to be able to:
1) identify the type of data required (wavelength and resolution) for the area of interest, how to obtain them, the type of tools/database needed, how to process them in order to get the derived indexes, and to store them in a reasonable timeframe;
2) correctly interpret the ongoing phenomena concerning vegetation cover on the basis of primary data and derived indexes in the area of interest.
3) present the results achieved for one or more area(s) of interest in written and oral form in a synthetic and complete manner
1) identify the type of data required (wavelength and resolution) for the area of interest, how to obtain them, the type of tools/database needed, how to process them in order to get the derived indexes, and to store them in a reasonable timeframe;
2) correctly interpret the ongoing phenomena concerning vegetation cover on the basis of primary data and derived indexes in the area of interest.
3) present the results achieved for one or more area(s) of interest in written and oral form in a synthetic and complete manner
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The introductory lectures will focus on the physical basis of remote sensing, the type of data, their resolutions (spatial, temporal, spectral and radiometric), data repositories and historical series, the indexes used to describe vegetation status (NDVI [Normalized Difference Vegetation Index], LAI [Leaf Area Index]) their interpretation and representation using QGIS and the Google Earth Engine platform.
Examples on how to use remote sensing data to highlight patterns and trends in land use change, health, growth and yield of vegetation cover, the effect of traumatic events (storms, floods, fires ) or anthropic disturbances.
Each student, either alone or in small groups, needs to identify an area of interest, gather the data of adequate spatial and temporal resolution, and calculate the relative indexes and their change over time. The selected areas of interest must undergo some type of change over time (e.g. due to some agricultural development or construction project) or exceptional events. The areas can be selected by the students on the basis of direct or indirect knowledge.
Depending on the availability of a drone with a multi spectral camera, a field trip shall be organized for direct data acquisition.
Examples on how to use remote sensing data to highlight patterns and trends in land use change, health, growth and yield of vegetation cover, the effect of traumatic events (storms, floods, fires ) or anthropic disturbances.
Each student, either alone or in small groups, needs to identify an area of interest, gather the data of adequate spatial and temporal resolution, and calculate the relative indexes and their change over time. The selected areas of interest must undergo some type of change over time (e.g. due to some agricultural development or construction project) or exceptional events. The areas can be selected by the students on the basis of direct or indirect knowledge.
Depending on the availability of a drone with a multi spectral camera, a field trip shall be organized for direct data acquisition.
Prerequisites for admission
A basic understanding of remote sensing, plants and photosynthesis. A basic knowledge of GIS operations is useful.
Teaching methods
The introductory lectures will be delivered in traditional format using powerpoint and practicing with live examples to learn how to acquire and interpret remote sensing data (from satellite or drones). The students are then asked to identify, in agreement with the instructors, one or more areas of interest for which they will gather satellite data and, when possible, their own data with a multi spectral camera mounted on the drone made available by the lab course.
Teaching Resources
Lecture slides employed during the lab course will be made available as ppt or pdf files through the MyAriel website, as well as additional material (data, original literature, notes and bibliographic material) in their own file format.
Assessment methods and Criteria
The exam shall focus on the material produced by the student at the end of the lab in the form of a written report (issue tackled, aim, methods employed, major achievements) and the related oral presentation.
BIO/04 - PLANT PHYSIOLOGY - University credits: 3
Lessons: 24 hours
Professors:
Fugazza Davide, Morandini Piero Angelo
Shifts:
Professor:
Morandini Piero Angelo
Lezioni in codocenza
Professor:
Fugazza DavideProfessor(s)
Reception:
Please, contact me by email to fix an appointment
via Celoria 10, building 22120, floor -1