Hemispherical photography has been used for many years to measure the physical characteristics of forests, but most related image processing work has focused on leafy canopies or conifers. The boreal forest contains l...Hemispherical photography has been used for many years to measure the physical characteristics of forests, but most related image processing work has focused on leafy canopies or conifers. The boreal forest contains large areas of deciduous trees that remain leafless for over half the year, influencing surface albedo and snow dynamics. Hemispherical photographs of these sparse, twiggy canopies are difficult to acquire and analyze due to bright bark and reflections from snow. This Note presents new methods for producing binary images from hemispherical photographs of a leafless boreal birch forest. Firstly, a thresholding method based on differences between colour panes provides a quick way to remove bright sunlit patches on vegetation. Secondly, an algorithm for joining up fragmented pieces of tree after thresholding ensures a continuous canopy. These methods reduce the estimated hemispherical sky view fraction by up to 6% and 3%, respectively. Although the processing remains subjective to some degree, these tools help to standardize analysis and allow the use of some photographs that might have previously been considered unsuitable for scientific purposes.展开更多
Identification of type of leafless trees using both fall imagery and field-based surveys is a global concern in the forest science community. Few studies were devoted to separate leafless trees from others in the grow...Identification of type of leafless trees using both fall imagery and field-based surveys is a global concern in the forest science community. Few studies were devoted to separate leafless trees from others in the growth season using remote sensing imagery. But this study was the first attempt to identify the type of leafless tree in the fall imagery. We investigated the potential of the Simple Linear Iterative Clustering (SLIC) and k-mean segmentation techniques, and texture and color image analyses to identify leafless poplar trees using imagery collected in a leaf-off season. For the first time in this study, the star shaped feature identifier was found through a binary image that was successful in identifying leaf-off poplar plantations. Optimal threshold values of Normalized Difference Vegetation Index (NDVI) and Normalized Green Index (NGI) indices were able to differentiate highly vegetated land, green farms, and gardens from the grasses that sometimes grow between poplar plantation lines. A Coefficient of Variation (CV) of red color intensity and histogram of value were also successful in separating bare soil and other land cover types. Imagery was processed and analyzed in a Matlab software. In this study, leafless poplar plantation was identified with a user accuracy of 84% and the overall accuracy was obtained 81.3%. This method provides a framework for identification of leafless poplar trees that may be beneficial for distinguishing other types of leafless trees.展开更多
文摘Hemispherical photography has been used for many years to measure the physical characteristics of forests, but most related image processing work has focused on leafy canopies or conifers. The boreal forest contains large areas of deciduous trees that remain leafless for over half the year, influencing surface albedo and snow dynamics. Hemispherical photographs of these sparse, twiggy canopies are difficult to acquire and analyze due to bright bark and reflections from snow. This Note presents new methods for producing binary images from hemispherical photographs of a leafless boreal birch forest. Firstly, a thresholding method based on differences between colour panes provides a quick way to remove bright sunlit patches on vegetation. Secondly, an algorithm for joining up fragmented pieces of tree after thresholding ensures a continuous canopy. These methods reduce the estimated hemispherical sky view fraction by up to 6% and 3%, respectively. Although the processing remains subjective to some degree, these tools help to standardize analysis and allow the use of some photographs that might have previously been considered unsuitable for scientific purposes.
文摘Identification of type of leafless trees using both fall imagery and field-based surveys is a global concern in the forest science community. Few studies were devoted to separate leafless trees from others in the growth season using remote sensing imagery. But this study was the first attempt to identify the type of leafless tree in the fall imagery. We investigated the potential of the Simple Linear Iterative Clustering (SLIC) and k-mean segmentation techniques, and texture and color image analyses to identify leafless poplar trees using imagery collected in a leaf-off season. For the first time in this study, the star shaped feature identifier was found through a binary image that was successful in identifying leaf-off poplar plantations. Optimal threshold values of Normalized Difference Vegetation Index (NDVI) and Normalized Green Index (NGI) indices were able to differentiate highly vegetated land, green farms, and gardens from the grasses that sometimes grow between poplar plantation lines. A Coefficient of Variation (CV) of red color intensity and histogram of value were also successful in separating bare soil and other land cover types. Imagery was processed and analyzed in a Matlab software. In this study, leafless poplar plantation was identified with a user accuracy of 84% and the overall accuracy was obtained 81.3%. This method provides a framework for identification of leafless poplar trees that may be beneficial for distinguishing other types of leafless trees.
