GIS and Remote Sensing

Biography

Biography: Yousef Erfanifard

Abstract

Statement of the Problem: Estimating quantitative and qualitative characteristics of large forest and woodland sites on remotely sensed datasets has been a focal point in vegetation management. This is of increasing interest in arid and semi-arid regions since it is possible to observe individuals on the datasets. Despite previous studies, it still is vague for some scientists how to measure the characteristics of plants in open woodlands on UltraCam-D (UCD) imagery with spatial resolution of 6 cm. The objective of this study was to develop a methodology for measurement of canopy density of trees on very high spatial resolution (VHSR) airborne UCD imagery in Zagros woodlands, Iran. Methodology & Theoretical Orientation: A 30 ha plot (500 × 600 m²) fully covered with Persian oak (Quercus persica) was selected for this research. The UCD imagery was classified by kNN classification algorithm using different nearest neighbours including k=1 to 10 and the results were tested comparing to the true canopy density of the study area to find out the most suitable one. Findings: Comparing the results of different k nearest neighbours (1 to 10), it was concluded that k=4 is the most suitable one with no significant difference between the observed and estimated canopy density (t=0.04 at 0.05 level). The results also showed that the canopy density map with 30 Ar cell size (80% overall accuracy and 0.61 KHAT coefficient) was the most suitable map. Conclusion & Significance: It was also concluded that the UCD VHSR imagery could produce a precise map of the canopy density in the study area due to its very high spatial resolution. One of the advantages of the proposed method was that users can choose different maps according to their needs. It helps users make management decisions and regularly monitor changes in woodlands more efficiently.