科研之友
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摘要
Previous studies have used remote-sensing images to map tree mortality caused by mountain pine beetle (Dendroctonus ponderosae; MPB) in relatively homogeneous lodgepole pine (Pinus contorta) forests; however, classification methods have not been tested for the patchy landscape of ponderosa pine-dominated (Pinus ponderosae) montane forests characterized by highly variable tree density. This study explores two supervised classification methods to identify MPB-caused mortality (red attack) in heterogeneous montane forests of the Colorado Front Range using 1m-resolution 2011 imagery of the National Agriculture Imagery Program (NAIP): maximum likelihood using the red, green, and blue bands, and the red-green index (RGI), and a thresholding technique using the RGI. Two variations of the RGI threshold method were also explored: the addition of a green-band threshold and the incorporation of a focal analysis. Evaluation pixels were used to assess the accuracy of the classification methods. The maximum likelihood (97 Percentage Correctly Classified (PCC); 11% error of commission for red attack) and RGI threshold (85 PCC; 46% error of commission for red attack) classification methods overestimated the red attack. The RGI and green band threshold classification reduced the error of commission (5%) and had high overall accuracy (97 PCC). In a comparison of classification methods across tree-density sites, we found the maximum likelihood classification had a very high accuracy in the high-density site (95 PCC), but substantially lower accuracy in the low-density site (85 PCC) due to the presence of more visible cover types. The RGI threshold classification with the green band constraint produced more consistent PCCs across tree densities: high (93.7 PCC), moderate (95.2 PCC), and low (92.0 PCC). Our results indicate forest structure may affect the classification accuracy and should be considered when selecting a classification method for a landscape.
- 出版日期2015-11-10
