Small-scale deforestation monitoring in Juma REDD project

1,039 views

Published on

Effectively monitoring deforestation is a crucial component for the success of REDD (Reducing Emissions from Deforestation and forest Degradation). In this presentation, Florian Reimer from South Pole Carbon compared techniques for monitoring small-scale deforestation in Juma reserve, exploring three classification methods: PRODES, CLASlite and ImageSVM. He found that using one model over another could avoid underdetection worth roughly US$1 million over four years, a compelling argument for careful selection of techniques depending on the characteristics of the region.

Florian Reimer gave this presentation on 8 March 2012 at a workshop organised by CIFOR, ‘Measurement, Reporting and Verification in Latin American REDD+ Projects’, held in Petropolis, Brazil. Credible baseline setting and accurate and transparent Measurement, Reporting and Verification (MRV) of results are key conditions for successful REDD+ projects. The workshop aimed to explore important advances, challenges, pitfalls, and innovations in REDD+ methods — thereby moving towards overcoming barriers to meeting MRV requirements at REDD+ project sites in two of the Amazon’s most important REDD+ candidate countries, Peru and Brazil. For further information about the workshop, please contact Shijo Joseph via s.joseph (at) cgiar.org

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,039
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
17
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Small-scale deforestation monitoring in Juma REDD project

  1. 1. Small- Small-scale deforestation monitoring in Juma REDD project Comparing PRODES, CLASlite and ImageSVMCIFOR MRV Workshop8th March 2012, PetrópolisFlorian ReimerSouth Pole Carbon
  2. 2. Study Area • 5890 km² of which roughly 4720 km² are old-growthrainforest • Sustainable Development Reserve (RDS) created in 2006 • 2008 start of Bolsa Floresta and CCBS Validation by TÜV Süd as REDD project • Land-use by traditional non-indigenous communities legalized • 339 families of traditional shifting cultivators • Low historic Deforestation pressure inside the reserve, but high in southern area of extensive cattle-ranching
  3. 3. Framework of the study
  4. 4. Comparisson of Small-scale Deforestation Classifiers• PRODES (Projeto Desmatamento) of INPE operational Wall-to-Wall Mapping since 1988. Landsat-Pixels (30m) aggregated to 60m Pixels. Minimal-Mapping Area > 3 ha.• CLASlite 2.3 developed by Carnegie Institute for Science, Standford University, Asner et al. 2009. Minimal-Mapping Area < 1 ha.• imageSVM – Support Vector Machine developed by Rabe, van der Linden, & Hostert of TU Berlin Geomatics. Minimal-Mapping Area < 1 ha.• Data Basis: Landsat 5 TM images 1997, 2006, 2007, 2008, 2009, 2010• Training & Validation data: SPOT 2,4, & 5, HRC CBERS2 & Field data• SPOT images, ENVI & ArcGIS Licenses provided by
  5. 5. imageSVM Pros Can map any landcover and forest type Can be adjusted if erroneous Highest accuracy over all years Cons Needs GPS ground-truthing data Needs high input in time, expertise and computer power PRODESCLASlite v2.3 ProsPros Easy downloadSemi-Automated Needs only GISNeeds only georeferenced imageWorks for entire Amazon Cons Only for Brazilian AmazonCons Maps nothing smaller 3 haMaps only Deforestation Sometimes 1-2 years behindDegradation not very reliableClosed system, no adjustments
  6. 6. Time Step 1 Time Step 2 Single-Year Layerstacks Multi-Year Layerstack e.g. 2007 & 2008
  7. 7. Every Image gets classified for Forest / Non-Forest and the mapscompared to get change „Deforestation“
  8. 8. Results 3. 1. 2. 4.1. Deforestation in Reserve much lower than in Buffer2. Sharp decline from 2008 to following years – stop of single large cattle ranch clearing3. Overdetection CLASlite 2008 due to seasonally drying wetlands4. Underdetection PRODES 2009 due to small-scale deforestation dominance
  9. 9. • PRODES detected only 33 % of the deforestation detected by ImageSVM in the Juma reserve over the observation period (214 ha vs. 655 ha).• The average margin of error of ImageSVM ‘New Deforestation Class’ was +/- 8.7 % (average accuracy 91.3 %)• 655 ha – 214 ha – (655 ha*8.7%) = 384.1 ha; Multiply emission factor 529.43 tCO2 / ha = 203,354 tCO2• Multiply with a price of 5 US$ / tCO2 = 1,016,505 US$.
  10. 10. Conclusion and synthesis I• Deforestation inside the Juma Reserve has historically been low• Trend continued during our study period, possibly effect of Reserve, PES &REDD project• High predicted baseline for future deforestation in the REDD project reflectsexternal pressure from extensive pastures south of the Reserve• The drop of deforestation in the buffer zone after 2008 related to a slowdown in the expansion of a single large-scale land clearing east to the reserve• External actors (e.g. cattle ranchers of Apuí) do not benefit from the REDDproject
  11. 11. Conclusion and synthesis II• In low-deforestation, small-holder deforestation settings like theJuma Reserve, classification must have adequate minimal-mappingarea.• In the specific case of the Juma Reserve, using ImageSVM insteadof PRODES, would have avoided underdetection worth roughly US$1 million over four years• Supervised classifications fulfill the VCS REDD methodologyrequirement of monitoring various forest types and land uses• Advanced image classification (SVM) using all satellite bands canbetter differentiate similar forest types or landcovers than simplerapproaches like Maximum Likelihood or using 3 bands only
  12. 12. Small-Small-scale deforestationmonitoring in Juma REDD projectComparing PRODES, CLASlite and ImageSVM Thank you for your attention Florian Reimer South Pole Carbon

×