TITLE:
A New Multistep Approach to Identify Leaf-Off Poplar Plantations Using Airborne Imagery
AUTHORS:
Ramzan Ali Khorrami, Zahra Naeimi, Michael Wing, Hossein Ahani, Sayed M. Bateni
KEYWORDS:
Leafless Poplar Plantations, Fall Ultracam Imagery, Star Shaped-Feature, K-Mean Segmentation, Texture and Color Image Analysis
JOURNAL NAME:
Journal of Geographic Information System,
Vol.14 No.6,
December
30,
2022
ABSTRACT: 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.