PROJET QUANTIFICATION DES STOCKS DE CARBONE ET DES ÉMISSIONS EN REPUBLIQUE CONGO

PROJET QUANTIFICATION DES STOCKS DE CARBONE ET DES ÉMISSIONS EN REPUBLIQUE CONGO Ifo Suspense Averti, PhD Ecole Normale Supérieure Université Marien N Gouabi Brazzaville Congo

La République du Congo, à cheval sur l équateur et au coeur du deuxième massif forestier mondial, s étend sur 34 millions d ha : 2/3 de formations forestières (soit 22,5 millions d ha) et 1/3 de savanes (soit 11,5 millions d ha). La forêt est présente au Sud et surtout au Nord Atelier scientifique régional sur les équations allométriques 2-5 avril 2013 à Yaoundé, Cameroun 2

Ce projet a commencé au Congo en juin 2010 et avait 3 objectifs: Objectif 1 Le développement des méthodes de détections et mesure de dégradation et la quantification des émissions des gaz a effet de serre en relations aux changements des couvertures forestière 4

Image Processing Techniques for Detecting and Mapping of Forest Degradation Imazon has developed ImgTools a software for processing Landsat images to enhance, detect and map forest degradataion. These tecniques were sucessuly tested in some forest areas of the Republic of Congo. These results are demonstrated in this poster. The next steps are to train remote sensign tecnicians from Republic of Congo and from the organizations collaborating in this project to estimate forest degradation over the entire country using the very large Landsat dataset built for the years 2000, 2005 and 2010. The image processing steps are illuatrated below. (2) Spectral Mixture Analysis (3) Build Masks (a) Water mask (1) Atmospheric Correction (a) Haze corection (b) Radiometric Calibration (c) Atmospheric correction (b) Cloud and shade masks ImgTools Interface (5) Classification Haze, smoke and thin clouds are normalized for the entire Landsat scene. (a) NDFI slicer (b) Change detection DN data conversion to radiance. (c) Classification Atmospheric removal is performed using ENVI-Flash. This proceedure converts to data surface reflectance Remarks Forest Regeneration Degradation Baseline 2005 Water Cloud Deforestation s 2006 Deforestation 2008 Deforestation 2010 ImgTools automates SMA which uses as input reflectance images and endemmbers of Green Vegetation, Non-Photosynthetic Vegetation (NPV) Soil and Shade empirically defined from the Landsat images. The SMA results are fractions of endmembers which give the abundace of them at the subpixel scale. Once SMA Fractions and NDFI are calculated using ImgTools, the next step is to perform image classification. For the reference data (i.e., first year) we use NDFI slicer to create a forest reference map that will monitoried in the following years. The classes obtained with NDFI slicers are: old deforestation, regeneration, forest degradation and forest. Water and cloud masks obtained in Step 3 are applied to the NDFI slicer results. Then, we perform a forest change detection technique using as input the forest reference map (from NDFI slicer) and a tempora l NDFI change. Threshold values for NDFI change are applied to detect forest areas that were converted by deforestation and forest degradation. We have also applied a decision tree classificaiton algorithm to generate the same classfication legend to compare with the change detection results. In order to reduce classification ambiguity and errors, we produce masks of water bodies and clouds which are applied to classifciation products afterwards. These masks are based on subpixel fractions obtained with SMA. (4) Calculate NDFI NDFI enhances the degradation signal caused by selective logging and burning and is obtained from the SMA fractions as indicated below. The appliaction of ImgTools to the Landstat ETM+ data set provided to Imazon shows potential to use these tecniques to map and monitor forest changes associated with deforestation and forest degadation in the Republic of Congo. The next steps for this projet include: 1) Train tecnicians to use ImgTools; 2) Apply ImgTools to the entire data set of available Landsat ETM+ images; and 3) Validate the classification results. ImgTools will be available to the projetc participants at no cost. 5

Objectif 2 Le développement des méthodes d évaluation de perte et de changements de la couverture forestière par L UMD 6

Forest loss 2000-2010 per départments

Objectif 3 Former un groupe d expert Congolais sur les techniques de quantification de carbone forestier Elaboration d une équation allométrique Environ 15 jeunes congolais ont participé a la formation de haut de niveau organisé par Winrock a Brazzaville et en foret dans un site du Nord Congo 8

Formation sur le terrain 10

Formation sur le terrain et au bureau OSFAC Brazzaville 11

Table Excel calcul stock C 13

Forest carbon stocks of the Republic of Congo and estimate of emissions from deforestation Atelier scientifique régional sur les équations allométriques 2-5 avril 2013 à Yaoundé, Cameroun

Carte du couvert forestier et du carbone forestier

Carbon stock data The above ground biomass (AGB) in live vegetation was modeled using a robust method of combining a data fusion model with 4,079 in situ inventory plots, 150,449 samples of forest structure from Lidar sensor, and 13 optical and microwave satellite imagery representing metrics of canopy structure, greenness, moisture and topography. The below ground biomass (BGB) in live vegetation was estimated using Mokany et al. 2006 equation. The carbon stocks of live biomass were estimated as 50% of the total live biomass.

The area weighted average of forest carbon stocks for forest area defined at 25% canopy cover from MODIS VCF data was estimated at 160 t C ha -1 across the whole country. Table 1 reports the hectares of forest defined as greater than 25% canopy cover and associated area weighted carbon stock (t C ha -1 ) per department. The departments with the highest area weighted average of forest carbon stocks are Lekoumou (182 t C ha -1 ), Sangha (181 t C ha -1 ), with lowest forest carbon stocks is Bouenza (67 t C ha -1 ).

Emissions from deforestation

Uncertainty Uncertainty around final emission estimate was quantified using a randomized Monte Carlo-style approach. The uncertainties from the following components were incorporated in the final emission estimates: 1. Estimates of forest loss (Hansen et al. 2010) 2. Estimates of aboveground biomass (Saatchi et al. 2011) 3. Relationship between aboveground and belowground biomass (Mokany et al. 2006)

Uncertainty

Carte de Carbone de la Lékoumou 21

Protocole d installation des parcelles 25

Conclusion Ce projet est d un grand bénéfice pour la République du Congo car permet la formation des futurs experts Les autres pays devraient bénéficier de cet appui scientifique a cause de l étendue important du bassin forestier du Congo. Il serait mieux de créer dans chaque pays des unités de monitoring des forets. 26

Perspectives Finaliser les études sur l élaboration d une ou des équation(s) allométrique(s) pour les forets du Congo afin d améliorer l estimation des stocks et donc des émissions de CO2; Multiplier des vérités terrain afin de calibrer les résultats obtenus. 27

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