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Classifying Oriental Beech (Fagus orientalis Lipsky.) Forest Sites Using Direct, Indirect and Remote Sensing Methods:
Alkan Günlü1, Emin Zeki Baskent2, Ali İhsan Kadiogullari3
1Karadeniz Technical University, Faculty of Forestry 61080, Trabzon-Turkey. alkan61@ktu.edu.tr.
Sensors (Basel, Switzerland)
|November 24, 2016
Summary
Forest site productivity was assessed using direct, site index (SI), and remote sensing methods. Quickbird satellite imagery showed significant agreement with direct forest site classification, aiding forest management decisions.
Area of Science:
- Forestry and Environmental Science
- Remote Sensing and GIS Applications
Background:
- Accurate forest site classification is crucial for effective forest management.
- Traditional methods include direct assessment of edaphic, climate, and topographic factors, and indirect site index (SI) calculations.
- Remote sensing offers a complementary approach for large-scale forest assessment.
Purpose of the Study:
- To compare the effectiveness of direct, site index (SI), and remote sensing methods in classifying forest sites.
- To evaluate the consistency and accuracy of different classification techniques using Geographic Information Systems (GIS).
- To determine the best approach for informing forest management decisions.
Main Methods:
- Forest sites were classified using direct methods (soil, climate, topography), indirect methods (site index equations), and remote sensing (Landsat 7 ETM, Quickbird satellite images).
- Supervised classification was employed for satellite imagery, with accuracy assessed using kappa statistics.
- GIS was used to overlay polygon themes from the three methods and compute areas for each class.
Main Results:
- Direct and SI methods showed consistency, accurately identifying dry sites (Class IV).
- Remote sensing methods, particularly Quickbird, demonstrated high accuracy (90.7%) and significant agreement with direct classification (p=0.002).
- Landsat 7 ETM showed less significant correlation with direct methods (p=0.134), while SI had a moderate association with direct methods (p=0.033).
Conclusions:
- Quickbird satellite imagery provides a reliable and accurate method for forest site classification, comparable to direct and SI methods.
- Remote sensing, especially high-resolution imagery, can significantly enhance forest management decision-making.
- The integration of GIS with remote sensing data offers a powerful tool for detailed forest resource assessment.
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