FANC Finnish Association for Nature Conservation Mapping forest degradation in Madagascar

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1 FANC Finnish Association for Nature Conservation Mapping forest degradation in Madagascar Igor Glushkov, Dmitry Aksenov, Olli Turunen, Titta Lassila, Angela Tarimy, Daulphin Razafipahatelo

2 Team The Finnish Association for Nature Conservation (FANC) Finland Transparent World Russia National experts (Conservation international, University of Antananarivo) Local specialists and stakeholders (Association Mitsinjo, ValBio, Durrell, Manombo Special Reserve)

3 Collaborators, sponsors Ministry of Foreign Affairs of Finland Ministry of Environment of Madagascar Madagascar National Parks Helsinki University Finnair airline company SEAS OI, Scanex WCS, WWF, Duke Lemur Center, Missuri Botanical Garden Institute for the Conservation of Tropical Enviroment

4 Past and current forests mapping National level Global level 2007 Madagascar Vegetation mapping Project, MODIS and Landsat based 2007 KEW Forest Change , Landsat based 2008 (updated 2013) Intact Forests Landscape, Landsat based UMD/GFW Global Forests Cover and Change maps, Landsat based

5 General goals of mapping project Create network of forest mappers from local to international level Create mapping and communication capacity in local level Support Malagasy scientific terminology Create tools for people to communicate offline/online with thematic maps Create clearing house mechanism for forest monitoring (transparent, low cost, easy available) Create series of high scale forests intactness / degradation maps

6 Why to measure forest intactness / degradation? The forest biodiversity is sensitive to human influence and depends on the level of the forest transformation The intactness of forest is important for decisionmaking on conservation priorities Forest transformation/degradation map is required for better understanding the transformation reasons and making decisions for more sustainable landuse from local to international level

7 Signs of intactness Deadwood BIG trees Never loged Natural disturbances

8 Slash n burn (TAVY) Alien species/plantation Fires Selective logging Agriculture/Rice cultivation Grazing Mining Old tavi Local gold mining

9 How to measure the level of forest intactness / degradation? from Ground Evaluating classification criteria and adapte to local level. Developing of 3 days seminars for local groups, field trips, data collecting Simple GIS training (QGIS,GPS using)

10 How to measure the level of forest intactness / degradation? Classification scheme for Manombo region 7 forest classes from intact to disturbed translated to local names of forest stands types About 20 years cycle of forest regenerations may be reduced to 1 2 years due to disturbance circumstances

11 Data to be collected in the field GPS coordinates Subjective proposal for the forest class Dominant tree species Diameter of the biggest trees Age and height of the upper layer trees Amount of dead wood (range) Altitude (GPS) Exposition of slope (west / east) Young / old forest (rough devision) Special (tree) species Photos

12 From field trips, 8 regions, more then 3000 GPS points, about 2000 from forest stands Field surveys of various forests in eastern Madagascar by Malagasy colleagues from local associations and by joint Malagasy FANC TW teams

13 How to measure the level of forest intactness / degradation? from Space Direct measurment: Hight, Biomass. Global ICESat/GLAS, RadarSAT etc. Simard,Pinto et al Regional airborne LiDAR data Asner, Maskaro et al Indirect method: Fragmentation analysis Global Intact Forest Landscapes maps Potapov, Yaroshenko et al Or combining both above with classification of space images time series Regional Mapping deforestation and forest degradation Margono, Turubanova et al. 2014

14 How to measure the level of forest intactness / degradation? Canopy structure approach Intact / old growth humid forests usually have more than one layer of closed canopy (Multi layer ML) Degraded / secondary forests humid have a single layer of closed canopy (Single layer SL) Intact forests may have a simple canopy structure (single layer) only in specific habitats or landscape positions (SL "on top of ridges")

15 Simple forest canopy structure classification A. Multi layer (ML) B. Single layer with big tress (SL BT) C. Single layer (SL) A B C

16 How to measure the level of forest intactness / degradation? From ground to space Multi layer (ML) Single layer with big tress (SLBT) No canopy structure, have spectral characteristics visible on images or on time series set of images Single layer (SL)

17 How to measure the level of forest intactness / degradation? Canopy structure approach Using SPOT 5 (also SPOT 6/7 in the future) panchromatic channel (detailed (2.5 m ground resolution) enough to detect single big trees crowns and canopy irregularities) Mapping tree crown shadows marking irregularities in the canopy (a kind of reversed single trees crown mapping method) Calculating density of shadows and classifying forest stands polygons on this base

18 How to measure the level of forest intactness / degradation? Calculating local minimum reflections pixels density on SPOT panchromatic channel

19 Employing the Random Forests algorithm for classifying forest stands by all their parameters measured by DEM and SPOT images STEP 1 STEP 2 STEP 3 Segmentation of SPOT images by spectral channels, 10m resolution (GRASS GIS module i.segment with the following parameters: min area 10 pixels, similarity threshold = 0.03 for spectral channels). Adding main statistical parameters from spectral image channels and DEM data to each polygon: mean, standard deviation, minimum, maximum, range to each polygon; as well as canopy complexity data. Creating training dataset, using high resolution image and field observation data as reference. Creating model using the advanced decision trees algorithm R:randomForest. model of classification, data statistics for each class, estimate model quality and uncertainties. Applying model to whole set of data and export result map (QGIS). SRTM2 SPOT 5 2.5м SPOT 5 10м

20 Data sources Space images Base images more then 70 scenes SPOT 5 from (SEAS OI) More then 900 sub scenes WV1,2 from (SCANEX) For the map based portal: Landsat 8

21 Canopy coverage High Medium Low Very low The classification scheme 4.1a. Savoka & Savoka with single trees 4.1b. Pure bamboo thickets?? 4.1c. Pure ravenala thickets?? 4.1d. Filippia thickets?? 4.2. Mosaic of ramarasana, crops, savoka & woodlands 4.3a. Bare ground 4.3b. Ramarasana 4.3c. Roranga 3.1a. SL closed mountain forests 3.1b. SL closed lowland forests 3.1c. SL closed littoral forests 3.1d. Eucalyptus plantations 3.1e. Pine plantations 3.2a. SL sparse high altitude forests 3.2b. SL sparse slopes & valleys (not found yet) 2.1a. SL with big trees top of ridges forests 2.1b. SL with big trees lowland forests 2.1c. SL with big trees littoral forests 1.1. ML closed 1.2. ML with gaps 1.3. ML with large gaps Non forest Single layer SL with big trees Multilayer Canopy structure complexity

22 Map based portal dark green most intact ML; bright green mixed intact ML and SL; yellow degraded SL

23 Igor Glushkov Dmitry Aksenov Thank you! Misaotra! Spasibo! Angela Tarimy Olli Turunen Daulphin Razafipahatelo