forest structural classification and above ground biomass ... · 1,467 tree species 15,706 plots...
TRANSCRIPT
Forest Structural Classification and Above Ground Biomass Estimation for Australia
Professor Richard Lucas1
Jingyi Sun2
Centre for Ecosystem Sciences (CES) School of Biological, Earth and Environmental Sciences (BEES) University of New South Wales (UNSW) Australia Sydney, NSW, Australia 2School of Engineering 1Joint Remote Sensing Research Program (JRSRP)
GFOI R&D and GOFC-GOLD Land Cover Science Meeting, The Hague, The Netherlands 31 October – 4 November, 2016
John Armston1 Peter Scarth2
GEDI Science Team Department of Geographical Sciences 1150 Lefrak Hall, University of Maryland, College Park MD 20742, USA 3University of Queensland, St Lucia Campus, Brisbane, Qld, Australia.
Overview
• Australia’s woody vegetation – Structural classification (height and cover)
– Dominant floristics
• Defining remnant (undisturbed from direct human activity) woody vegetation – National datasets
– State-wide datasets
• TERN Biomass Library – Plot data
– Lidar data
• Identifying reference sites for undisturbed forests.
• Case study: Brigalow Belt Bioregion, Queensland
• Future sensors and updating the biomass library
• GFOI Study Site: Injune Landscape Collaborative Project
Structural Classification of Australia’s Woody Vegetation
Generated using a combination of Spaceborne optical, radar and lidar data
Structural Classification of Australia’s Woody Vegetation
Dominant Forest Types National Forest Inventory Australia
Defining Remnant Forest
• Remnant forests in Australia are defined as those that have remained undisturbed by human activities since European settlement.
• Areas of remnant vegetation mapped in Queensland through reference to historical aerial photography
• For other states, undisturbed (typically remnant) forests need to be inferred from other mapping efforts.
National Datasets: Protected Areas of Australia
Statewide Datasets
South Australia Victoria
Tasmania New South Wales and ACT
Queensland
• Remnant forests in Australia are defined as those that have remained undisturbed by human activities since European settlement.
• Areas of remnant vegetation mapped in Queensland through reference to historical aerial photography
• For other states, undisturbed (typically remnant) forests need to be inferred from other mapping efforts.
Defining Remnant Forest
TERN Biomass Library, Australia
1,073,837 hugs of 839,866 trees 1,467 tree species 15,706 plots from 16,391 observations across 12,663 sites
Australian National Biomass Library (2016).
National Airborne Lidar Validation Datasets
Summary of Biomass Library
Biomass Library Summary
Remnant Forest Plots with LIDAR
A Unique Mosaic Product for Australia
Landsat-derived persistent green, ALOS HH and HV in RGBt
Forest Growth Stage Mapping
Landsat FPC and ALOS PALSAR
L-band HH and HV (RGB)
Differentiation of early regrowth
and remnant forest
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(g0
; d
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; d
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FP
C (
%)
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Characteristics of Remnant (undisturbed forest): Different Regional Ecosystems - Brigalow Belt Bioregion
Regional Ecosystem
Integration of ALOS PALSAR and Landsat-derived FPC
Forest Growth Stage: Brigalow Belt Bioregion
Regional Ecosystem Mapping Growth stage map
Quantifying Relative Rates of Degradation and Regeneration
Regrowth Recovery from Fire
Mature Pine and Eucalyptus
Recovering the Endangered Brigalow
Forest Ecosystems, Queensland, Australia
Regrowth classification Relevance to Vegetation Management Acts, Australia
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TAS
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QLD
NT
NSW
Lidar
P-band
L-band
C-band
Optical
Temporal Analysis of Biomass Library
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Year of Data Collection
Number of satellites supporting regional to global biomass mapping
Optical C-band L-band P-band Spaceborne lidar
Landsat-7 ERS-1 SAR JERS-1 SAR BIOMASS ICESAT GLAS
Landsat-8 ERS-2 SAR ALOS PALSAR ICESAT-2
Sentinel-2 RADARSAT-1 ALOS-2 PALSAR-2 GEDI ON ISS
RADARSAT-2 SAOCOM CONAE
Sentinel-1 NISAR
Freq
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The Injune Landscape Collaborative Project
• RESEARCH OBJECTIVES: – To extend methods development using SAR and sensor synergy for
deforestation and degradation monitoring and retrieving estimates of AGB.
– Use time-series to better understand and quantify ecosystem response to natural and human drivers.
• OUTCOMES: – Optimised algorithm for retrieving AGB using multi-sensor data.
• Report on sensor synergy for improved AGB estimates.
– Algorithm for deforestation monitoring using time-series, multi-sensor data.
– Forest degradation mapping method using time-series, multi-sensor data.
– Report on sensor interoperability and complementarity for deforestation monitoring and degradation assessment, and landscape response natural and anthropogenic induced change.
Quantifying Tree Level Change: Injune, Qld
Tree species dynamics detected a) in the field and b) using time-series of LIDAR data
2006
2000
Datasets requested through GFOI
Forest Disturbance Monitoring: ALOS PALSAR Correlation (August – October, 2007)
Yang et al. (2016). Observation of vegetation vertical structure and disturbance using L-band InSAR over the Injune region in Australia (in preparation).
5th Aug 2007 13th Aug 2007 21st Aug 2007 21th Aug 2007 6th Sep 2007
14th Sep 2007 22nd Sep 2007 30th Sep 2007
8th Oct 2007
Hyper-temporal Landsat FPC (actual, simulated)
Hyper-temporal Landsat FPC (actual, simulated)
5th Aug 2007 13th Aug 2007 21st Aug 2007 21th Aug 2007 6th Sep 2007
14th Sep 2007 22nd Sep 2007 30th Sep 2007
5th Aug 2007
CARL framework • CARL Level 4 - Pre-operational • Table 1:
• Demonstration in a larger-scale environment. • Representative model or prototype method (near the desired performance),
which is well beyond that of level 3, is tested in a larger-scale environment. • End-to-end processing demonstrated. • Data processing methods have been (partially documented) in peer review
publications (submission for December 2016). • Methods have NOT been assessed for applicability in different forest
monitoring contexts.
• Table 2 • Prototype is available and used by different (1-2) experts, sources of
uncertainties are known and can be quantified. • Data are available for large area/national demonstrations in different tropical
country conditions • Work mostly done in research environment • Training materials/tutorials and guidance documents NOT YET developed
NOR tested in countries
• Briefly discuss the contribution of your work in the context of CARL
• Feedback on v1 of CARL framework • Assets • Limitations • Suggested modifications (will be developed further on Day 2)
CARL Framework Implementation
• Contribution to CARL • Provides national methods for:
• Quantifying vegetation height and cover • Generating open data bases of structural measures and biomass. • Discriminating and mapping relative stages of degradation and regeneration.
• CARL Framework (Version 1) Feedback • Assets
• Capacity to understand/undertake programming in development or implement programed software.
• Capacity to integrate data from different sources • Understanding of the information content of different data sources • A strong and understandable validation dataset to quantify uncertainty.
• Limitations • Historical data (e.g., ICESAT) • Relatively complex algorithms in combination.
• Suggested modifications • Methods that work well in some countries (e.g., Australia), including non-
tropical, but not applied yet to tropical countries.
CARL Framework Implementation
37th ISRSE, Tshwane (Pretoria), South Africa • As part of the 37th International Symposium on Remote Sensing of the
Environment (ISRSE) in Tshwane (Pretoria), South Africa (8-12th May, 2017), two special sessions (6 presentations each) may be of interest to you: – Session 1: Remote Sensing in Support of Ecosystem Restoration: Often, remote sensing
technologies have informed on the loss or degradation of ecosystems but there is considerable potential to also use these data to plan and monitor the restoration of previously lost or disturbed ecosystems. This session aims to highlight this potential, with particular (but not exclusive) emphasis on projects/ideas that focus on restoring significant ecosystems across large areas.
– Session 2: Interoperability for quantifying forest structure and biomass: This session aligns
with that on ecosystem restoration in that it asks how remote sensing data (optical, radar and/or lidar) have or can be used to quantify the structure (e.g., height and vertical distribution of plant material, cover) and biomass of vegetated ecosystems including and relative to the ‘undisturbed state’. Though this approach, relative states of degradation and regeneration can potentially be mapped and described across large areas, with this assisting future conservation and restoration planning.
• From these sessions, we anticipate a Special Issue of Remote Sensing for Ecosystem
Restoration for Remote Sensing in Ecology and Conservation, to be published in late 2017/early 2018.