Using 2-stage sampling approach to estimate forest loss uncertainty for the Emission Reduction Program in Cameroon
Abstract
Using 2-stage sampling approach to estimate forest loss uncertainty for the Emission Reduction Program in Cameroon
Context/Purpose The Paris Agreement calls on developed and developing countries to further REDD+ actions, including providing performance-based finance. Cameroon is advancing from readiness towards results-based payments and has elaborated an emission reduction project idea note, which was submitted and approved by the FCPF Carbon Fund. The ERP area is in the South Cameroon plateau straddling the bimodal and monomodal rainfall agro-ecological zones and covering an area of 93.328 km² corresponding to about 20% of the total area of Cameroon and representative of the range of tropical moist forest. In light of the challenges of designing and operationalizing such a program, the country is actively working on consolidating a methodology to provide reliable statistics related to historic forest loss in order to establish the program forest reference (emission) level and build a robust monitoring system. This study presents a 2-stage sampling approach to estimate forest loss in the ER Program area for the period 2000 to 2015. Methods A 2-stage sampling approach is used to estimate forest loss uncertainty in the ERP area. The first stage consists of mapping forest cover and forest loss based on the interpretation of Landsat images for the period 2000 to 2015. A reference map is created using a combination of a semi-automated classification procedure and manual enhancements to ensure the greatest possible accuracy[1]. The second stage consists of the validation of map statistics by stratified random sampling method based on the interpretation of segments of high resolution satellite data: SENTINEL, SPOT and Landsat[2]. 100 × 100 m segments or sampling units representing about 0.01% of the study area are visually interpreted by a team of photo-interpreters independently from the map production team to produce a reference dataset. The sampling unit outputs are used to estimate the proportion forest cover and forest cover loss using regression estimators[3]. Results The use of the 2-stage approach is expected to provide robust statistics of forest cover loss with more precision as to the uncertainties. Expected results with Sentinel and Spot data using regression estimator should provide uncertainty, also confirm whether the two-stage sampling approach was justified for collecting a reliable forest cover reference dataset for estimating proportion forest cover area and net proportion forest loss. Furthermore, it brings out the proportion of intact forests between 2000-2015, deforested and / or degraded areas and their degree of confidence. Interpretation: The precise uncertainties will give more credibility to the forest cover loss statistics and hence to the established reference levels. Conclusion The work is current and uses new data and will provide new information regarding forest cover loss and uncertainty. [1] Potapov et al. 2012: Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data. Remote Sensing of Environment 122 (2012) 106-116. [2] Landsat was for the years in which high resolution SPOT and/or SENTINEL data were unavailable [3] Sannier et al. (2013): Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon. Remote Sensing of Environment 151 (2014) 138-148.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC44A..10N
- Keywords:
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- 1632 Land cover change;
- GLOBAL CHANGEDE: 1640 Remote sensing;
- GLOBAL CHANGEDE: 6309 Decision making under uncertainty;
- POLICY SCIENCESDE: 6610 Funding;
- PUBLIC ISSUES