Inventory of Single Oak Trees Using Object- Based Method on WorldView-2 Satellite Images and on Earth
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیایی
yousef taghi mollaei
1
(PhD student of forestry in Ilam university)
Abdolali Karamshahi
2
(Associate Professor and Faculty Member of Forest Sciences Department in University of Ilam)
Seyyed Yousef Erfanifard
3
(Associate Professor, Department of Natural Resources and Environment, College of Agriculture, Shiraz University, Shiraz, Iran)
کلید واژه: Classification, Remote Sensing, Canopy, Zagros forests, Separation of single trees, Haft-Barm of Shiraz,
چکیده مقاله :
Remote sensing provides data types and useful resources for forest mapping. Today,one of the most commonly used application in forestry is the identification of singletree and tree species compassion using object-based analysis and classification ofsatellite or aerial images. Forest data, which is derived from remote sensing methods,mainly focuses on the mass i.e. parts of the forest that are largely homogeneous, inparticular, interconnected) and plot-level data. Haft-Barm Lake is the case study whichis located in Fars province, representing closed forest in which oak is the valuablespecies. High Resolution Satellite Imagery of WV-2 has been used in this study. Inthis study, A UAV equipped with a compact digital camera has been used calibratedand modified to record not only the visual but also the near infrared reflection (NIR) ofpossibly infested oaks. The present study evaluated the estimation of forest parametersby focusing on single tree extraction using Object-Based method of classification witha complex matrix evaluation and AUC method with the help of the 4th UAV phantombird image in two distinct regions. The object-based classification has the highest andbest accuracy in estimating single-tree parameters. Object-Based classification methodis a useful method to identify Oak tree Zagros Mountains forest. This study confirmsthat using WV-2 data one can extract the parameters of single trees in the forest. An overall Kappa Index of Agreement (KIA) of 0.97 and 0.96 for each study site has been achieved. It is also concluded that while UAV has the potential to provide flexible and feasible solutions for forest mapping, some issues related to image quality still need to be addressed in order to improve the classification performance.
Remote sensing provides data types and useful resources for forest mapping. Today,one of the most commonly used application in forestry is the identification of singletree and tree species compassion using object-based analysis and classification ofsatellite or aerial images. Forest data, which is derived from remote sensing methods,mainly focuses on the mass i.e. parts of the forest that are largely homogeneous, inparticular, interconnected) and plot-level data. Haft-Barm Lake is the case study whichis located in Fars province, representing closed forest in which oak is the valuablespecies. High Resolution Satellite Imagery of WV-2 has been used in this study. Inthis study, A UAV equipped with a compact digital camera has been used calibratedand modified to record not only the visual but also the near infrared reflection (NIR) ofpossibly infested oaks. The present study evaluated the estimation of forest parametersby focusing on single tree extraction using Object-Based method of classification witha complex matrix evaluation and AUC method with the help of the 4th UAV phantombird image in two distinct regions. The object-based classification has the highest andbest accuracy in estimating single-tree parameters. Object-Based classification methodis a useful method to identify Oak tree Zagros Mountains forest. This study confirmsthat using WV-2 data one can extract the parameters of single trees in the forest. An overall Kappa Index of Agreement (KIA) of 0.97 and 0.96 for each study site has been achieved. It is also concluded that while UAV has the potential to provide flexible and feasible solutions for forest mapping, some issues related to image quality still need to be addressed in order to improve the classification performance.
