Mapping Hemlocks to Estimate Potential Canopy Gaps Following Hemlock Woolly Adelgid Infestations in the Southern Appalachian Mountains

Transcription

1 Mapping Hemlocks to Estimate Potential Canopy Gaps Following Hemlock Woolly Adelgid Infestations in the Southern Appalachian Mountains Tuula Kantola, Maria Tchakerian, Päivi Lyytikäinen-Saarenmaa, Robert Coulson and Douglas Streett SFIWC 2013

2 Roger Barbour, 2003 Background Hemlock woolly adelgid (HWA) Causing widespread elimination of hemlocks in Eastern USA Forest canopy gaps are created by hemlock mortality More homogeneous landscapes are generated by broadleaved tree species Potential areas for invasive plant species such as Tree of heaven and Mimosa Eastern and Carolina hemlocks Foundation tree species in eastern North America Role in maintaining stream quality Critical habitat for animals and plants Species have shown no resistance to infestations by HWA Severe mortality and reduction of hemlocks seems to be unavoidable 7/16/2013 2

3 Research problem Despite the importance of the hemlocks, information about species distributions is limited Detailed field data about hemlocks is lacking in most areas Patch sizes Total area covered Spatial extent and pattern Extensive field surveys are very expensive and time consuming Remote, inaccessible areas Better mapping methods are critically needed to evaluate the impacts of HWA Remote Sensing may be the answer The objective of this study was to develop a method for mapping the areas covered by dead and living hemlocks using remote sensed data 7/16/ Mike Riley, hemlock-the-hemlock-woolly-adelgid-and-mycorrhizal- fungi-citizen-science/

4 Material Study area Grandfather Ranger district (35 55 N, W) Southern Appalachians, NC Total of 1278 km 2 Complex topography and humid temperate climate Mainly forested areas Wide diversity of different tree species (over 60) Subsample of 40 km 2 for this study Remote sensing data False color imagery (CIR) with 1 m spatial resolution Leaf-on, acquired in 2012 True color imagery (RGB) with 15 cm spatial resolution Leaf-off, acquired in 2010 Airborne scanning LiDAR data 7/16/ Acquired 2003

5 Methods Tree-wise classification approach 3 x 3 pixel window size To get intra-crown variation Two phase classification Increasing the sampling precision decreases the sampling accuracy Too many different land cover classes Dead trees are classified as urban areas or bare ground Phase one Creation of forest mask To rule out urban, bare ground, water and shadowed areas Decision tree classification Height from LiDAR derived CHM NDVI values Resulting classification was filtered and other than forest extracted 7/17/2013 Forest mask ~29 km 2 5

6 Classification /Phase two Support vector machine classification (SVM) Supervised classification method Training vectors are mapped to multidimensional space with kernel function SVM finds a linear hyperplane with maximal margin to separate classes NIR, R, G and NDVI were used Training vectors for three classes Hemlocks Dead Hemlocks Hardwood species No pines were found in the area Made comparing two imageries RGB 2010 CIR 2012 CIR 2012 RGB /18/2013 6

7 Results (Forest) Overall classification accuracy 92.2% Kappa-value of 0.87 Testing data of 530 pixels/trees Test data Classification Dead Hardwood Hemlock Dead 88.46% 0.00% 0.00% Hardwood 0.00% 99.55% 13.66% Hemlock 11.54% 0.45% 86.34% Hemlocks Classification accuracy over 86% 88% for dead Over 33% of the area 2.3% of those dead ~1% of total area of forest Could be found everywhere Mostly in drainages Usually as mixed species Clustered and stripped pattern can be observed 7/16/2013 7

8 Dead Hemlocks 7/16/2013 8

9 Results/ final classification 7/16/2013 Classification combined with digital elevation model (DEM) 9

10 Discussion Spectral signature of hemlocks differed from other tree species in the area It may be possible to map hemlocks at good detection rate Resulting maps can be used in future HWA studies Pines couldn t be found in the subarea Several pine species in GF area We assumed the dead trees being hemlocks Part of them may be some other tree species Problems with shaded areas Terrain very rugged in the area plenty of shadows in every image tile Proportion of hemlocks even bigger in the shadows? More recent LiDAR could improve the classification Field data from the study area is needed Current reference data created by visual assessment To evaluate the accuracy of the reference data Uncertainties with using other imagery 7/16/

11 Conclusions / Future steps We developed and evaluated a new method for hemlock mapping Two phases Two different classification methods Hemlocks were detected at ~86% rate Dead at ~88% rate Multi-temporal remote sensing data enable monitor changes in species distributions CIR images LiDAR Next step is to map the extent of living and dead hemlocks in the whole GF area Forest mask without LiDAR Rule based object segmentation Patch sizes 7/17/2013 Spatial pattern 11

12 Take home message No one knows the full extent of HWA damages. Remote Sensing applications can provide powerful tools for monitoring these impacts. Different scales Other invasive species than HWA 7/17/

13 Thank you! 7/16/