Estimating forest parameters using point cloud data

Abstract

The spatial distributions and statistical properties of stand attributes must be understood in order to characterize the dynamic forest ecosystem. In this context dendrometry is an invaluable tool in forestry when quantitative characterisation of forests or individual trees are required. Diameter at Breast Height (DBH) and Tree Height (TH) are two significant parameters in dendrometry and heavily correlated with Leaf Area Index. Leaf Area Index (LAI) is described as a dimensionless parameter that has a significant impact in forestry applications and characterising the canopy's structural vegetation in general. With conventional methods, LAI can be calculated with destructive sample collection or with a relatively new non-destructive method called hemispherical photography. Conventional measurements of DBH and TH, although not destructive, are also very time and manpower consuming. With the engagement of modern surveying instruments in forestry, obtaining forest stand parameters for large areas in short time has recently become more prominent and possible with the use of LiDAR technology. Although promising, LiDAR data evaluation techniques for forest stand parameters calculation are still subject to development. This thesis work aims to make a comparative evaluation of existing novel techniques with newly proposed methods for estimating forest stand parameters, namely DBH, TH and LAI. For this purpose Point Cloud Data (PCD) from different sources such as Airborne LiDAR Systems (ALS), Terrestrial Laser Scan (TLS), and Unmanned Aerial Vehicle (UAV) have been evaluated. These data sources have been chosen since they are greatly preferred for forestry operations, and their results can be quantitatively compared against the conventional method results. In-situ data was collected to assess LAI, DBH and TH estimations from PCD through varying sample locations including deciduous, coniferous, mixed forest type. Sampling zone spans from northern parts of Istanbul Urban forest area to a research forest under the supervision of Istanbul University-Cerrahpasa, in Istanbul, Turkey. In-situ measurements were accepted as ground truth, and the results obtained from PCD evaluation were compared against them in terms of their overall error statistics, as well as their performances due to the computational cost and challenges in data acquisitions. The results obtained from the study show that segmentation and removal of wood materials from TLS based PCD by using neural network algorithms and connected component analysis methods, albeit, complex and computer resource demanding, have a promising future on the calculation of effective LAI values of large areas in a very short time span. Similarly, the forestry PCD obtained by TLS has the best performance among other PCD at both DBH and TH estimation