U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia.

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery.

Reprint of: Synthesising the effects of land use on natural and managed landscapes.

To properly manage our natural and managed landscapes, and to restore or repair degraded areas, it is important to know the changes that have taken place over time, particularly with respect to land use and its cumulative effect on ecological function. In common with many places in the world, where the industrial revolution resulted in profound changes to land use and management, Australia's landscapes have been transformed in the last 200 years. Initially the VAST (Vegetation Assets, States and Transitions) system was developed to describe and map changes in vegetation over time through a series of condition states or classes; here we describe an enhancement to the VAST method which will enable identification of the factors contributing to those changes in state as a result of changes in management practice.

Synthesising the effects of land use on natural and managed landscapes.

To properly manage our natural and managed landscapes, and to restore or repair degraded areas, it is important to know the changes that have taken place over time, particularly with respect to land use and its cumulative effect on ecological function. In common with many places in the world, where the industrial revolution resulted in profound changes to land use and management, Australia's landscapes have been transformed in the last 200 years. Initially the VAST (Vegetation Assets, States and Transitions) system was developed to describe and map changes in vegetation over time through a series of condition states or classes; here we describe an enhancement to the VAST method which will enable identification of the factors contributing to those changes in state as a result of changes in management practice.

Describing and mapping human-induced vegetation change in the Australian landscape.

Australian reporting requirements for native vegetation require improved spatial and temporal information on the anthropogenic effects on vegetation. This includes better linkage of information on vegetation type (e.g.