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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
●TextureSource: Erikson, (2014)
Methodology (1)
Image segmentatio
n
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 areaClassified data
Reference data
Class OF CF SF Row total
OF 50 5 10 65
CF 25 50 60 135
SF 25 45 30 100
Column total
100 100 100 300
OF = Open forest, CF = Closed forest, SF = scattered forest
Methodology (3)
Forest Inventory 1969 used as reference data
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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 segmentationFalse 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
