Research Information System
Birdal A. C., Avdan U.(Executive)
Project Supported by Higher Education Institutions, BAP Research Project, 2020 - 2022
Biomass is an expression of the amount of living organic matter above the surface of trees per unit area. Biomass is a volume multiplied by density. Biomass material resources are classified as terrestrial forests, grasses, energy crops and other plant residues, aquatic strains and algae as aquatic inhabitants, industrial wastes as waste material, urban wastes and animal wastes. For biomass studies, trees are divided into 5 homogeneously trunk structure, bark structure, live and dead branches, leaves, large, medium and small size roots. Forests are used as fuel, timber and feed from past to present. Biomass estimation helps to accurately predict some of these factors, which are an impact on the life of forests, and determine their potential. When expressed as dry weight per unit area, evaluating the total surface biomass of forests defined as biomass density is a useful way of determining the amount of resources available for all conventional uses. The amount of biomass in a forest can be defined as a result of the difference between production by photosynthesis and consumption by respiration and harvesting processes. As can be understood from this definition, the amount of biomass in forest structures, illegal tree cuttings and so on. plays an important role in the follow-up of such changes. Changes in forest biomass density naturally occur in succession. These changes are known as natural effects of forest fire and climate change besides silviculture, harvest and degradation which are considered human activities. Biomass of forests is also important for global change issues. Biomass of forests provides estimates of carbon pools in forest vegetation (50% of vegetation consists of carbon). Biomass density estimation also assists in how much carbon dioxide can be removed from the atmosphere with the reconstruction of forests or green areas (replacing destroyed, destroyed ones). This is of particular interest as countries look at forests as a means of reducing greenhouse gas emissions, particularly carbon dioxide. Applications such as sustainable forest management, slowing deforestation, and collecting firewood that cause less damage to trees reduce emissions. In addition, biomass density estimates of forests are important for many other investigations. Medium Resolution Satellite Based Optical Remote Sensing (Landsat, MODIS), High Resolution Satellite Based Optical Remote Sensing (Ikonos, Quickbird), Satellite Source Radar or Microwave Sensor (ERS, JERS, Sentinel, EnviSAT, PALSAR), Terrestrial Laser Scanning and Lidar biomass estimations are performed with leading remote sensing methods. These studies are generally limited with the presence of an average volume value on large-scale forest areas. The aim of this study is to perform a higher accuracy biomass calculation with the above mentioned methods and high resolution aerial photographs to be taken with the help of Unmanned Aerial Vehicles (UAVs), to distribute them on a single tree basis and to perform accuracy analysis with terrestrial measurements. Airborne Laser Scanning or UAV applications may not be suitable for all studies. These reasons can create problems not only in terms of the size of the work area, but also in terms of cost. In such cases, considering the cost, it is possible to turn to satellite images which will provide a lower spatial resolution than UAV systems with a certain loss of accuracy. It is considered that ergonomic results can be achieved in larger working areas by accepting the results to be reached via UAV as a sample area as a result of a correlation that will be formed along the satellite image frame compared to the previously described terrestrial biomass measurements. It is also intended to develop a tool that can be used in open source Geographic Information Systems. In the literature, biomass estimations performed with many classical remote sensing methods are available. However, these studies have generally been carried out in forest areas covering large areas (tropical forests etc.) and have not achieved satisfactory results in terms of accuracy. With the method proposed in this thesis, it is planned to obtain more accurate results in the medium-sized study areas. If necessary, with the help of dense point cloud data created with the help of UAV, the opportunity to work on trees on a singular basis will be created. Furthermore, since the study will or will not address more than one tree species, it will be an important step for other species studies. The potential of remote sensing studies is growing rapidly with the developments in UAV systems together with satellite technologies. With the high accuracy results obtained, a big contribution will be made to the creation of litter data that can be used in forest based studies.