The course is divided into three parts:
(1) Basic statistical data.
(2) Forest inventory, techniques and tools: GIS, GNSS remote sensing.
(3) Principles of remote sensing.
Classroom exercises in computer lab (Excel, GIS software); exercises in the forests.
Kohl M. et al 2006. sampling methods, remote sensing and GIS multiresource inventories. Springler.
Kangas & Maltamo 2008 Forest inventory Springler.
Knolewdge acquired: Forest resource inventory and monitoring ; Remote sensing ; Classification techniques.
Sampling strategies and methods; Classification techniques for territorial units; Active and passive remote sensing systems.
Skills acquired (at the end of the course):
Selection of the most appropriate sampling method; sample size, estimate of the accuracy, sampling schemes (areal and linear) on GIS software. Classifications of multispectral remote sensing images.
Courses to be used as requirements (required and/or recommended):
Courses required: none
Courses recommended: GIS, statistics, forest mensuration
Total hours of the course (including the time spent in attending lectures, seminars, private study, examinations, etc...): 150
Hours reserved to private study and other indivual formative activities: 98
Contact hours for: Lectures (hours): 33
Contact hours for: Laboratory (hours): 0
Contact hours for: Laboratory-field/practice (hours): 13
Seminars (hours): 6
Intermediate examinations: 0
Frequency of lectures, practice and lab: highly recommended.
Teaching tools: Statistics in Microsoft Excel, GIS software, GPS
Type of Assessment
Oral for evaluating the theoretical knowledge and the result of the field work
Credit 1: Introduction to survey sampling of forest and environmental resources. Inventories and environmental monitoring. Historical evolution and presentation of inventories in Italy and abroad.
The National Forest Inventory.
Credit 2: Phases and procedures for planning a statistically sound sampling. Staistics and estimators.
Credit 3: Methods of inventory used in Italy. Recall of dendrometry and biometrics. Integration of the phase of relief on the ground with the sampling phase. Exemplification for detection of forest variables.
Credit 4: Integration of 'ground truth' data (sampling) with information derived from remote sensing or geographic information systems and GNSS. Introduction to spatial analysis. Examples for the detection of forest variables.
Credit 5: Exercises and lectures in the field
Credit 6: Exercises and lectures in the field