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www.le.ac.uk. Geography Department. Monitoring Tropical forests with L-band radar: lessons from Indonesian Peat Swamps Matt Waldram, Sue Page, Kevin Tansey. Why do we need to monitor forests? Conservation of forest carbon (e.g. UN REDD+) becoming a reality?

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  1. www.le.ac.uk Geography Department Monitoring Tropical forests with L-band radar:lessons from Indonesian Peat SwampsMatt Waldram, Sue Page, Kevin Tansey

  2. Why do we need to monitor forests? • Conservation of forest carbon (e.g. UN REDD+) becoming a reality? • Need for frequent estimates of forest/carbon loss to support MRV activities. • Radar remote sensing has many advantages particularly in tropical areas and numbers of radar (and other) satellites looks set to increase. www.le.ac.uk

  3. Methods • ALOS PALSAR L-Band Synthetic Aperture Radar in Fine Beam Dual Polarisation mode (no clouds!). • Dual channel FBD (3, with interferometric coherence) & 25m pixel size (2*9 multilooks). • Field data includes plot based estimates of above ground biomass, dead fuel load and water table measurements (largely collected by KFCP REDD demonstration project). www.le.ac.uk

  4. Study area 1: Ex Mega Rice Project and surrounding areas in Central Kalimantan, Indonesia. • Covered by 8 (3 strips) PALSAR scenes & ~12 dates over 4 years. • Very seasonal with long dry season. Mode of deforestation is fire. • 28,500km2 3.76% of Borneo by area (roughly) .

  5. Study area 2: Kamp peninsula and surrounding areas Riau, Indonesia. • Covered by 5 PALSAR scenes (2 strips) & 10 dates over 4 years. • Aseasonal with brief dry season. • Mode of deforestation is clear felling for plantations.

  6. Much effort had gone in to producing backscatter : biomass calibration curves. In order to provide direct estimates of biomass (and hence carbon). • Most radar/biomass studies done using one time period (mapping) or two separate time periods for (change detection). • But I am interested in monitoring, i.e. having multiple time periods (i.e. time as a continuous variable). • How does the radar backscatter change with • time in response to disturbance? From Mitchard ET, Saatchi SS, Woodhouse IH, et al. Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes. Geophysical Research Letters. 2009;36(23)

  7. So when temporal backscatter variation is included the picture becomes much more complicated! • Signal over intact forest is quite stable (saturation of radar signal). • What is driving signal in change in degraded areas? Is it noise or is there information contained within the temporal behaviour?

  8. Peatland water table measured via a network of dipwells (data courtesy of KFCP project). • Non-linear relationships exist between water table depth and radar backscatter and coherence but only in low biomass (degraded) areas. • Changes in backscatter caused by increasing dielectric of wet peat (<0m) and specular reflection from standing water (>0m).

  9. Actual relationship probably between surface soil moisture and radar variables. • Surface soil moisture linearly related to water table at depths down to ~-30cm at which point they become decoupled. • This correlation allows us to model water table depth and to predict water table depth from PALSAR signals (within limits). R2=0.5 for excluded points

  10. What temporal signal does forest disturbance produce? • In Kalimantan forest loss occurs via fire. • This produces a very different signal response to other modes of deforestation, e.g. clear cut logging in kampar.

  11. Temporal backscatter signatures of two modes of forest loss, (4 year PALSAR time series) Kalimantan (fire) Kampar (plantation logging) HH HV

  12. Long term changes in radar signal in response to disturbance. • Generated using landsat derived burn history map and extracting radar signal over burn scars of different age.

  13. We need statistical techniques to map spatial and temporal patterns of radar signal variation. • Generally I have found the literature to be lacking, particularly when it is considered. • Need to develop new techniques able to be applied to large data sets. • One technique I have looked at is empirical orthogonal functions (EOFs).

  14. Red=EOF1, 63.5% R(EOF1),G(EOF2),B(EOF3) = 94.4% Green=EOF2, 22.7% Blue=EOF3, 8.2%

  15. Red=EOF1, 60.1% R(EOF1),G(EOF2),B(EOF3) =92.0% Green=EOF2, 18.4% Blue=EOF3, 13.5%

  16. Conclusions & thoughts….. • Looking at a single data PALSAR scene could be very misleading! • Including temporal trends can be very confusing but information is contained with in temporal variation. • Temporal patterns in the radar signal are effected by: • Mode of forest loss (fire, clear cutting plantations) • Moisture regime/rainfall seasonality • Structure of vegetation type. • Bottom up approach in contrast to top down approach (e.g. GEO FCT).

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