Remote sensing methods to uncover vegetation structure
In this course, we will cover many digital methods to describe and understand vegetation structure with a focus on forest and urban green spaces. We will use a range of tools from traditional, non-remote sensing approaches, to high-resolution laser scanning. We will elaborate into two- and three-dimensional metrics to illustrate vegetation structural complexity and diversity. This course focuses on explaining the pros & cons of different spatial data, including tree location data, satellite image-based rasters, spaceborne lidar footprints, and 3D point clouds acquired by drones.
We will go into the forest and urban green space to collect our own data and use it for practical assignments. The course focuses on live communication and both on individual and work in pairs.
This course focuses on tree-dominated (forest) ecosystems both in natural and cultural context, thus also including urban forests. Nevertheless, it will be useful for individuals studying shrub-dominated ecosystems, gardens and orchards, and coupled vegetation systems at landscape scale.
Syllabus and other information
Syllabus
P000121 Remote sensing methods to uncover vegetation structure, 3.0 Credits
Subjects
Education cycle
Postgraduate levelGrading scale
Language
EnglishPrior knowledge
Admitted as PhD student at SLU or other high education institution. Basic knowledge of R coding language.Objectives
The aim of this course is to master technical skills and uncover practical aspects of studying vegetation structure in tree-dominated ecosystems. Students will elaborate into theoretical concepts and applied aspects of vegetation structure at non-spatial, horizontal, and vertical dimensions. This will be achieved by applying a variety of R data analysis instruments and using open access information and data collected during field sessions.
After completing the course, student should be able to:
• understand the theory of vegetation structure
• describe stand structural diversity using non-spatial data
• apply tree location data to uncover stand spatial patterns
• harness raster data on tree structure at landscape level
• deliver structural characteristics from spaceborne lidar data
• plan and carry out drone lidar acquisitions over forests
• describe vertical stand structure using 3D point clouds.
Content
The course emphasizes the pivotal role of vegetation structure in silviculture, forest and landscape ecology, biodiversity science, and other disciplines. This course introduces a broad range of possible digital and remote sensing methods to study vegetation forest structure. The latter will be considered from different dimensions: horizontal and vertical, but also using conventional non-spatial data. This course focuses on analysing the data from traditional forest experiments or inventories; open-source remote sensing data like satellite imagery and spaceborne lidar footprints; manually collected 3D points clouds using drone equipped with lidar.
The course is organized in two modules. First module introduces the theory of vegetation forest structure and applied implications of it in different disciplines. Students will elaborate into traditional forest inventory data and describe structural heterogeneity in forests. Students will collect tree location data to study spatial patterns in forest stand. This module also focuses on spaceborne optical and lidar data for investigating forest structure at landscape level. Second module will focus on 3D point clouds acquired by airborne remote sensing and how these data can be used to describe forest structure at horizontal and vertical levels. Students will collect their own data in forest and make individual project analysing it.
The course has two field trips and preparation session for pair assignment as mandatory elements. The individual project (module I) and pair assignment (module II) are the base for grades. For pair assignment, students will share study area and jointly discuss workflow, but will use different methods and will be evaluated separately.
The course will use different R packages as main analysing tools for both modules. Students will use GPS-recording tools during the first field trip and drone with mounted lidar during the second trip. Course leader will assist students with primary lidar data processing.
Additional information
The course is developed within international project QuantiFOR, an initiative to share knowledge on quantitative methods in forest science, supported by Swedish Institute.Responsible department
Department of Southern Swedish Forest Research Centre