Abstract
In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. Images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs.
Highlights
Aerial photography technology has undergone sophisticated development in recent years
The use of aerial photography to classify forest types, perform land cover change studies, and monitor disaster areas for the forestry industry subsequently becomes a crucial source of forest inventory-related information.[1,2,3,4,5,6]
Images that underwent image segmentation using the segmentation scale 200 generated a higher number of image blocks, whereas those that underwent image segmentation using the segmentation scale 400 produced a lower number of image blocks because its parameter values were more tolerant of image pixel value changes (Table 4)
Summary
Aerial photography technology has undergone sophisticated development in recent years. The use of aerial photography to classify forest types, perform land cover change studies, and monitor disaster areas for the forestry industry subsequently becomes a crucial source of forest inventory-related information.[1,2,3,4,5,6] The forest land cover situation in Taiwan is remarkably complex, and mixed forest types are markedly prevalent. The artificial stereoscopic interpretation method is generally adopted in the use of aerial photographs to determine forest types in Taiwan; this method creates interpretation results that are often inconsistent. Image classification is another method that can be employed to determine forest types.
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