Using Satellite Images to Evaluate Land Cover Changes and Forest Canopy Density (Case study: Gyan region, Nahavand)

Using Satellite Images to Evaluate Land Cover Changes and Forest Canopy Density (Case study: Gyan region, Nahavand)


Using Satellite Images to Evaluate Land Cover Changes and Forest Canopy Density (Case study: Gyan region, Nahavand)

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Using Satellite Images to Evaluate Land Cover Changes and Forest Canopy Density (Case study: Gyan region, Nahavand)

ارائه دهنده: Provider: Mohammad Mahdi Karami

اساتید راهنما: Supervisors: Dr. Hossein Torabzadeh Khorasani

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Morteza Heydari Mozafar and Dr. Hassan Khotan Lo

زمان و تاریخ ارائه: Time and date of presentation: 2023

مکان ارائه: Place of presentation:

چکیده: Abstract: Considering the important role that forest ecosystems have on the quality of living organisms, including humans, continuous monitoring of their condition is very important. To overcome the disadvantages of terrestrial methods, satellite images are usually used to monitor forests. The purpose of this research is to investigate the changes in land cover and the density of forests in the Gyan region using Landsat satellite images from 1372 to 1399. These forests, with an area of approximately 25 hectares, are part of the Zagros marginal forests, which are located near Nahavand. Identification of changes was done using post classification method after classification. For this purpose, three supervised classification methods (maximum likelihood, support vector machine and artificial neural network) were used. The comparison of the results with ground data showed that the artificial neural network method on the TM sensor image, with an overall accuracy of 79.48% and a Kappa coefficient of 0.63, and on an OLI sensor image, with an overall accuracy of 83.36% and a Kappa coefficient of 0.76, it provides more accuracy than the maximum likelihood and support vector machine methods. After classification of TM and OLI maps, in order to identify changes, maps of different years were compared. Based on this, the average annual forest destruction rate in the study area was estimated to be 0.9 hectares per year, which is equal to 0.12%. That is, during the studied period (27 years), about 3.32% of the forest area of the region has decreased, and the major share of this decrease is related to the managed forest areas. In addition to forest level changes, there is a need to monitor the number of trees in order to evaluate forest quality changes. FCD model was used to estimate the canopy density of Gyan forest, which is calculated based on four layers, including Advanced Vegetation Index, Bare Soil Index, Shadow Index and Thermal Index. Thus, the Vegetation Density Index and the Advanced Shadow Index were calculated and finally, the forest density map was prepared according to the classes provided by the Forestry Organization (six classes). The accuracy of canopy density maps, made by visual interpretation of aerial photos and Google Earth images, was over 75%. The biggest mismatch was in low density areas, which shows the weakness of the FCD model in thinner areas