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Forest cover trends from time series Landsat data for the Australian continent
Affiliation:1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing 100101, China;2. Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA;3. Google Earth Engine, Mountain View, CA 94043, USA;4. Department of Earth System Science, Tsinghua University, Beijing 100084, China;5. U. S. Geological Survey, Reston, VA 20192, USA;1. CSIRO, Waite Campus, Urrbrae, South Australia 5064, Australia;2. School of Commerce, University of South Australia, Adelaide, South Australia 5002, Australia;3. School of Life and Environmental Sciences, Deakin University, Burwood 3125, Australia;4. School of Public Policy, University of California Riverside, Riverside, CA 92521, USA;5. Center for Food and Resources, University of Adelaide, South Australia 5064, Australia;6. School of Biological Sciences, The University of Queensland, St Lucia, Queensland 4072, Australia;7. ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Queensland 4072, Australia
Abstract:In perennial and natural vegetation systems, monitoring changes in vegetation over time is of fundamental interest for identifying and quantifying impacts of management and natural processes. Subtle changes in vegetation cover can be identified by calculating the trends of a vegetation density index over time. In this paper, we apply such an index-trends approach, which has been developed and applied to time series Landsat imagery in rangeland and woodland environments, to continental-scale monitoring of disturbances within forested regions of Australia. This paper describes the operational methods used for the generation of National Forest Trend (NFT) information, which is a time-series summary providing visual indication of within-forest vegetation changes (disturbance and recovery) over time at 25 m resolution. This result is based on a national archive of calibrated Landsat TM/ETM+ data from 1989 to 2006 produced for Australia's National Carbon Accounting System (NCAS). The NCAS was designed in 1999 initially to provide consistent fine-scale classifications for monitoring forest cover extent and changes (i.e. land use change) over the Australian continent using time series Landsat imagery. NFT information identifies more subtle changes within forested areas and provides a capacity to identify processes affecting forests which are of primary interest to ecologists and land managers. The NFT product relies on the identification of an appropriate Landsat-based vegetation cover index (defined as a linear combination of spectral image bands) that is sensitive to changes in forest density. The time series of index values at a location, derived from calibrated imagery, represents a consistent surrogate to track density changes. To produce the trends summary information, statistical summaries of the index response over time (such as slope and quadratic curvature) are calculated. These calculated index responses of woody vegetation cover are then displayed as maps where the different colours indicate the approximate timing, direction (decline or increase), magnitude and spatial extent of the changes in vegetation cover. These trend images provide a self-contained and easily interpretable summary of vegetation change at scales that are relevant for natural resource management (NRM) and environmental reporting.
Keywords:Vegetation density index  Forest monitoring  Remote sensing  Landsat time series  Natural resource management  Data visualisation
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