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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
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SmallSmallSmallSmall----scalescalescalescale deforestationdeforestationdeforestationdeforestation
monitoringmonitoringmonitoringmonitoring in Juma REDD in Juma REDD in Juma REDD in Juma REDD projectprojectprojectprojectComparing PRODES, CLASlite and ImageSVM
CIFOR MRV Workshop
8th March 2012, PetrópolisFlorian ReimerSouth Pole Carbon
Study Area
• 5890 km² of which roughly 4720 km²are old-growthrainforest
• Sustainable Development Reserve (RDS)created in 2006
• 2008 start of Bolsa Floresta andCCBS Validation by TÜV Südas REDD project
• Land-use by traditional non-indigenous communities legalized
• 339 families of traditional shiftingcultivators
• Low historic Deforestation pressure insidethe reserve, but high in southern area ofextensive cattle-ranching
Framework of the study
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
PRODES
ProsEasy downloadNeeds only GIS
ConsOnly for Brazilian AmazonMaps nothing smaller 3 haSometimes 1-2 years behind
CLASlite v2.3
ProsSemi-AutomatedNeeds only georeferenced imageWorks for entire Amazon
ConsMaps only DeforestationDegradation not very reliableClosed system, no adjustments
imageSVM
ProsCan map any landcover and forest typeCan be adjusted if erroneousHighest accuracy over all years
ConsNeeds GPS ground-truthing dataNeeds high input in time, expertise andcomputer power
Time Step 1 Time Step 2
Multi-YearLayerstack
e.g.2007
& 2008
Single-Year Layerstacks
Every Image gets classified for Forest / Non-Forest and the mapscompared to get change „Deforestation“
Results
1.1.1.1.
1. Deforestation in Reserve much lower than in Buffer
2. Sharp decline from 2008 to following years – stop of single large cattle ranch clearing
2.2.2.2.
3. Overdetection CLASlite 2008 due to seasonally drying wetlands
4. Underdetection PRODES 2009 due to small-scale deforestation dominance
4.4.4.4.
3.3.3.3.
• 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$.
Conclusion and synthesis I
• Deforestation inside the Juma Reserve has historically been low
• Trend continued during our study period, possibly effect of Reserve, PES & REDD project
• High predicted baseline for future deforestation in the REDD project reflects external pressure from extensive pastures south of the Reserve
• The drop of deforestation in the buffer zone after 2008 related to a slow down in the expansion of a single large-scale land clearing east to the reserve
• External actors (e.g. cattle ranchers of Apuí) do not benefit from the REDD project
Conclusion and synthesis II
• In low-deforestation, small-holder deforestation settings like the Juma Reserve, classification must have adequate minimal-mapping area.
• In the specific case of the Juma Reserve, using ImageSVM instead of PRODES, would have avoided underdetection worth roughly US$ 1 million over four years
• Supervised classifications fulfill the VCS REDD methodology requirement of monitoring various forest types and land uses
• Advanced image classification (SVM) using all satellite bands can better differentiate similar forest types or landcovers than simpler approaches like Maximum Likelihood or using 3 bands only
Thank you for your attention
SmallSmallSmallSmall----scalescalescalescale deforestationdeforestationdeforestationdeforestation
monitoringmonitoringmonitoringmonitoring in Juma REDD in Juma REDD in Juma REDD in Juma REDD projectprojectprojectprojectComparing PRODES, CLASlite and ImageSVM
Florian ReimerSouth Pole Carbon
