presentation about the research project ``detecting forest ......presentation about the research...
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Presentation about the research project “Detecting forestdegradation with multi-spectral, seasonal and
multi-temporal classification trees in North CentralVietnam”
Belinda Freiheit
Student Research ColloquiumUniversity of Eberswalde
Research project conducted at the Geomatics Lab of the Geographical Institute of the HumboldtUniversity in Berlin, Supervisor: Dr. Dirk Pflugmacher
25. April 2013
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Outline
1 I-REDD+ Project
2 Project area
3 Research topic
4 Methodology
5 Results
6 Discussion
7 Outlook
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I-REDD+ Project
Impacts of Reducing Emissions fromDeforestation and Forest Degradation andEnhancing Carbon Stocks (I-REDD+)
I-REDD+ addresses issues at national tolocal level in four countries: Laos, Vietnam,China (Yunnan) and Indonesia
An interdisciplinary team with researchersand professionals from 9 countries worktogether
WP3 - Remote sensing based monitoring ofchange in land use and biomass
Figure: Paddy rice fields (a typical land use in the uplands ofVietnam; Source: HU Berlin 2013
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Project area
Con Cuong is a district ofthe Nghe An Provincelocated in the NorthCentral Vietnam
Characteristics of the area:Small-scale agricultural andagro-forestry land usepractice, mountainous
Figure: Map of the Con Cuong district with the GPS reference plots
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Research Topic
Detect forest cover changesin the project area withmeans of temporal Landsatscenes (1989 - 2012)
Data sources: 133multi-temporal Landsat images(TM 4/5 and ETM+), GPSpoints from field trip in2011/2012Challenge: frequent cloud cover,inaccurate reference data, spatialresolution, complex land cover!
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9220 Nghe An1:350.000
0 6 123Kilometer
ConCuong NgheAnStudy sites
127047 2009-02-14 Landsat TMRGB
Red: ETM_Meta_Band_5Green: ETM_Meta_Band_4Blue: ETM_Meta_Band_3
Diem
Moi
Figure: Landsat scene of the Con Cuong district in 2009 (dry season); Source: HU Berlin 2013
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Methodology
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Results I
Visual interpretation of the 9 x 9 pixel image extracts of the multi-temporalTC Angle Landsat images
Explore which reflectance values represent cleared land (comparing threshold)
Model-based cluster analysis to discriminate between non-forested andforested areas (threshold: 0.278 degree)
Forest resources account for 419 plots out of 748 plots with vegetation cover
Figure: Extract of four image chips out of 133 in total for plot no. 8 for the years 1989, 1996, 2008 and 2009 (from left to right)
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Results II
The four classes comprising natural vegetation (according to the LULCclassification):Closed canopy forests TEBC ( more than 60% canopy cover),Open forests TEBO (less or equal 60% canopy cover),Bamboo trees OWB (naturally regenerated),Bushes SEBO (mixed with small to medium trees)
Figure: Box and whisker plot of the TC Angle value of the four vegetation classes
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Results III
RF 1: with multi-spectral data from the dry season of 2012RF 2: with multi-spectral data from the rainy season of 2011RF 3: with multi-spectral data of both seasonsRF 4: with multi-temporal, multi-spectral data for the rainy seasonRF 5: with multi-temporal, multi-spectral data with temporal information (slope,intercept) for the rainy season
Figure: Overview of the results from the RF-CART with the vegetation classes as dependent variable
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Discussion
1 Limitations when working with optical sensors in tropical climate regions
2 Differing illumination and view angles (topographic effects)
3 Difficulty of distinguishing marginal variances of reflectance values within therespective vegetation classes
4 Field data exhibit inadequate land cover classes
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Outlook
Enlarge the study area to the Nghe An Province (eventually includingbordering areas in Laos)
Literature review about accomplished research and projects in the region
Focus on a thematic question: land cover changes and its impact on naturalforests in the context of REDD+
Study on shifting cultivation cycles to assess proper classification
Review appropriate methods in RS: vector change analysis, post-classificationetc.
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