Project Details
Description
The project aims to develop remote sensing methods that will be integrated into operational frameworks for monitoring changes in woody vegetation cover in eastern Australia. The focus will primarily be on mapping areas of increasing woody vegetation to complement the existing SLATS data in Queensland (QLD) and New South Wales (NSW) which maps clearing and other types of decreasing woody vegetation cover.
Areas of increasing woody vegetation are commonly referred to as regrowth, and may include (i) regrowth of existing vegetation that has been previously cleared or died, (ii) areas of new planting and growth from seed dispersal, and (iii) encroachment or spreading of woody vegetation from the edges of existing stands. Woody vegetation change also encompasses thickening, where an increase in foliage cover, basal area or stem density is observed in existing forest stands.
Woody vegetation change mapping will be based on dense time-series of ALOS PALSAR, ALOS-2 PALSAR-2, Sentinel-1 and optical data, and using time-series analysis and machine learning methods. Terrestrial and airborne laser scanner data (where available) and in situ (field) data will be used to calibrate/validate algorithms for detecting regrowth and changes in vegetation cover. The key innovation of the project is the development of methods for the integration of multi-sensor earth observation (EO) data for monitoring increases in woody vegetation cover.
Areas of increasing woody vegetation are commonly referred to as regrowth, and may include (i) regrowth of existing vegetation that has been previously cleared or died, (ii) areas of new planting and growth from seed dispersal, and (iii) encroachment or spreading of woody vegetation from the edges of existing stands. Woody vegetation change also encompasses thickening, where an increase in foliage cover, basal area or stem density is observed in existing forest stands.
Woody vegetation change mapping will be based on dense time-series of ALOS PALSAR, ALOS-2 PALSAR-2, Sentinel-1 and optical data, and using time-series analysis and machine learning methods. Terrestrial and airborne laser scanner data (where available) and in situ (field) data will be used to calibrate/validate algorithms for detecting regrowth and changes in vegetation cover. The key innovation of the project is the development of methods for the integration of multi-sensor earth observation (EO) data for monitoring increases in woody vegetation cover.
| Status | Active |
|---|---|
| Effective start/end date | 1/04/24 → 31/03/25 |