Forest Structural Classification and Above Ground Biomass Estimation for Australia

Transcription

1 Forest Structural Classification and Above Ground Biomass Estimation for Australia Professor Richard Lucas 1 Jingyi Sun 2 Centre for Ecosystem Sciences (CES) School of Biological, Earth and Environmental Sciences (BEES) University of New South Wales (UNSW) Australia Sydney, NSW, Australia 2 School of Engineering 1 Joint Remote Sensing Research Program (JRSRP) John Armston 1 Peter Scarth 2 GEDI Science Team Department of Geographical Sciences 1150 Lefrak Hall, University of Maryland, College Park MD 20742, USA 3 University of Queensland, St Lucia Campus, Brisbane, Qld, Australia. GFOI R&D and GOFC-GOLD Land Cover Science Meeting, The Hague, The Netherlands 31 October 4 November, 2016

2 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

3 Structural Classification of Australia s Woody Vegetation Generated using a combination of Spaceborne optical, radar and lidar data

4 Structural Classification of Australia s Woody Vegetation

5 Dominant Forest Types National Forest Inventory Australia

6 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.

7 National Datasets: Protected Areas of Australia

8 Statewide Datasets South Australia Victoria Queensland Tasmania New South Wales and ACT

9 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.

10 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).

11 National Airborne Lidar Validation Datasets

12 Summary of Biomass Library

13 Biomass Library Summary

14 Remnant Forest Plots with LIDAR

15 Landsat-derived persistent green, ALOS HH and HV in RGBt A Unique Mosaic Product for Australia

16 Forest Growth Stage Mapping Differentiation of early regrowth and remnant forest Landsat FPC and ALOS PALSAR L-band HH and HV (RGB)

17 HH (g 0 ; db) HV (g 0 ; db) FPC (%) Characteristics of Remnant (undisturbed forest): Different Regional Ecosystems - Brigalow Belt Bioregion a b Regional Ecosystem

18 Integration of ALOS PALSAR and Landsat-derived FPC Forest Growth Stage: Brigalow Belt Bioregion Regional Ecosystem Mapping Growth stage map

19 Quantifying Relative Rates of Degradation and Regeneration Regrowth Recovery from Fire Mature Pine and Eucalyptus

20 Recovering the Endangered Brigalow Forest Ecosystems, Queensland, Australia Relevance to Vegetation Management Acts, Australia Regrowth classification

21 Frequency Pre Temporal Analysis of Biomass Library Year of Data Collection WA VIC TAS SA QLD NT NSW Lidar P-band L-band C-band Optical 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

22 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.

23 Quantifying Tree Level Change: Injune, Qld Tree species dynamics detected a) in the field and b) using time-series of LIDAR data

24 Datasets requested through GFOI

25 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).

26 Hyper-temporal Landsat FPC (actual, simulated) 5 th Aug th Aug st Aug th Aug th Sep th Sep nd Sep th Sep th Oct 2007

27 Hyper-temporal Landsat FPC (actual, simulated) 5 th Aug th Aug st Aug th Aug th Sep th Sep nd Sep th Sep th Aug 2007

28 CARL Framework Implementation 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

29 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 nontropical, but not applied yet to tropical countries.

30 37 th 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.