Decomposition of standing dead trees in the southern Appalachian Mountains

Oecologia ◽  
1982 ◽  
Vol 52 (2) ◽  
pp. 214-215 ◽  
Author(s):  
Mark E. Harmon
Keyword(s):  
Appalachian Mountains ◽  
Southern Appalachian Mountains ◽  
Southern Appalachian ◽  
Dead Trees ◽  
Standing Dead ◽  
Standing Dead Trees

Phenotypic Variation in Climate-Associated Traits of Red Spruce (Picea rubens Sarg.) along Elevation Gradients in the Southern Appalachian Mountains

Castanea ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. 128
Author(s):  
John R. Butnor ◽  
Brittany M. Verrico ◽  
Kurt H. Johnsen ◽  
Christopher A. Maier ◽  
Victor Vankus ◽  
...  
Keyword(s):  
Phenotypic Variation ◽  
Appalachian Mountains ◽  
Picea Rubens ◽  
Red Spruce ◽  
Elevation Gradients ◽  
Southern Appalachian Mountains ◽  
Southern Appalachian

scholarly journals Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

2019 ◽  
Vol 11 (22) ◽  
pp. 2614 ◽  
Author(s):  
Nina Amiri ◽  
Peter Krzystek ◽  
Marco Heurich ◽  
Andrew Skidmore
Keyword(s):  
Tree Species ◽  
Feature Selection Method ◽  
Silver Fir ◽  
Maximum Point ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Wavelength ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

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