Forest stratification of REDD pilot sites, using VHR data. Vincent Markiet, Johannes Reiche¹, Samuela Lagataki², Akosita Lewai², Wolf Forstreuter³ 1) Wageningen University, The Netherlands; 2) MSD, Forestry Department, Fiji; 3) SOPAC, South Pacific Counsel, Fiji

Content of presentation  Introduction  Goals  Study area  Data  Methodology  Preliminary results  Discussion

Introduction  M.Sc. RS/GIS, 2 year master WUR  M.Sc. internship exchange funded by GIZ.  4 month internship  Internship at forestry, supervised by Johannes Reiche (WUR)

Motivation  Forest classification is important: ● Forest management ● Monitoring of biodiversity  Objective: ● Investigate possibilities for classifying forest strata using object based classification.

Goals  Object-based forest strata classification scheme, using VHR data ● 3 forest classes (open forest, closed forest, scattered/degraded forest) more if time allows. ● Undisturbed, disturbed forest ● Integrate 1969 forest inventory classes

Study area  REDD+ test site ● Dogotuki, Vanua Levu ● District Makuata ● Mixture of plantation & native forest (lowland forest)

Data  VHR World View data ● Multispectral 0.5m spatial resolution ● 5 VHR images (acquired July & October 2013) ● 4 MSS bands (Red, Green, Blue, Near-infrared)  Digital Elevation Model (DEM) ● Resolution 25m  Reference data ● NFI plots (forest types: Open-, Closed-, MU forest) ● 1969 NFI topo sheets

Object based classification (1) ● Alternative classification technique ● Combines spectral & spatial information ● Object based classification enables detailed forest segmentation. ● Improved land cover & land use mapping ● Semi- or/and automized classification ● Erdas Imagine objective tool

Object based classification (2)  Use reference data to select training samples.  Object based segmentation ● Different input parameters (weighted) ● Size ● Shape ● Reflectance values ● Texture Source: Erikson, (2014)

Methodology (1) Image segmentation Training and basic classification Advanced classification Integrate auxiliary data (DEM) Validation and accuracy assessment

Methodology (2)  Validation & accuracy assessment ● Confusion Matrix ● Quantitative method of accuracy assessment ● Reference data vs classified object segments ● Classified area compared to test area Classified data Reference data Class OFCFSFRow total OF CF SF Column total OF = Open forest, CF = Closed forest, SF = scattered forest

Methodology (3)  Forest Inventory 1969 used as reference data

Object segmentation

Mixture of vegetation types -forest -grassland/shrubs Segmentation still not optimal. Fuzziness More filtering necessary Grasslands conflict with forest segmentation

Preliminary results  Forest / Non-forest  Segmentation should focus towards forest strata classes. Forest segmentation False colour 432 RGB image

Discussion points  Overall goal: Investigate possibilities for classifying forest strata using object based classification.  Challenges ● Spectral homogeneity among forest classes ● Forest border determination is challenging ● Lot of trial and error necessary with testing best segmentation parameters. Many possible combinations. ● Good reference data is essential for assessment. ● Ground spectral information

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