LEE- Geomatik Mühendisliği-Doktora
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ÖgeEstimating forest parameters using point cloud data(Graduate School, 2022-08-05) Arslan, Adil Enis ; Erten, Esra ; 501112601 ; Geomatics EngineeringThe 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
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ÖgeImproving the performance of remote sensing-based water budget components across mid- and small- scale basins(Graduate School, 2022-07-19) Kayan, Gökhan ; Erten, Esra ; Türker, Umut ; 501152601 ; Geomatics EngineeringIn the last few decades, many global basins have been threatened by rapid urban growth and global warming, resulting in changes in their climate regime. Climate change has increased the incidence of extreme weather events, uncertain water availability, water scarcity, and water pollution. Remote sensing (RS) has emerged as a powerful technique that provides estimations with high spatiotemporal resolution and broad spatial coverage. In recent years, the efficacy of RS products for water budget (WB) analysis has been widely tested and implemented in global and regional basins. Although RS products provide high temporal and spatial resolution images with a near-global coverage, uncertainty is still a significant problem. The main goal of this study is to utilize two different approaches to minimize the uncertainty of the products and to improve RS-based WB estimations in mid- and small- scale basins. The first approach aims to improve the efficacy of water WB estimations from various hydrological data products in the Sakarya basin by; (1) Evaluating the uncertainties of hydrological data products, (2) Merging four precipitation (P) and six evapotranspiration (ET) products using the error variances, and (3) Employing the Constrained Kalman Filter (CKF) method to distribute residual errors (r) among WB components based on their relative uncertainties. The results showed that applying bias correction before the merging process improved estimations of P products with decreasing root mean square error (RMSE), except PERSIANN. VIC and bias-corrected CMORPH products outperformed other ET and bias-corrected P products, respectively, in terms of mean merging weights. The terrestrial water storage change (ΔS) is the primary reason for non-closure errors. This is mainly caused by the two facts. First, the Sakarya basin is a relatively small basin that GRACE can not simply resolve. Second, while P, ET, and Q mostly describe the surface water dynamics, ΔS includes both the surface water and ground water. It is well known that surface water and ground water have completely different dynamic behaviors. The change in surface water is much faster than the change in groundwater. The CKF results were insensitive to variations in uncertainties of runoff (Q). P derived from the CKF was the best output, with the highest correlation coefficient (CC) and the smallest root mean square deviation (RMSD). In the second approach, the annual r in the WB equation arising from the uncertainties of the RS products was minimized by applying fuzzy correction coefficients to each WB component. For analysis, three different fuzzy linear regression (FLR) models with fourteen different sub-models were used in the two basins having different spatial characteristics, namely Sakarya and Cyprus basins. The performance of sub-models is better in the Sakarya basin than that in the Cyprus basin, which has a higher leakage error due to across ocean/land boundary. Moreover, the Cyprus basin is too small for some low-resolution RS-based products to resolve. The Zeng and Hojati sub-models outperformed Tanaka sub-models in the Sakarya basin, whereas Zeng Case-I, Zeng Case-II, and Hojati (degree of fitting index (h) =0.9) sub-models showed the best performance in the Cyprus basin. The best fuzzy sub-models reduced the error up to 68% and 52% in terms of mean absolute error compared to non-fuzzy model in the Sakarya and Cyprus basins, respectively. Further evaluations showed that the best sub-model P well captured the temporal patterns of gauge observations in both basins. Moreover, they have the best consistency with gauge observations in terms of RMSE, Kling-Gupta efficiency (KGE), and percent bias (PBIAS) in the both basins. The results proved that the second approach will provide valuable insights into WB analysis in ungauged basins by incorporating the fuzzy logic approach into hydrological RS products. In general, the FLR and CKF derived P, ET, and Q showed similar seasonal variation with peak and bottom values appeared in nearly the same years. In terms of CC, RMSE, and bias, fuzzy outputs show closest agreement with CKF outputs for Q, with slightly less agreement for P and ET, and much less agreement for ΔS. It can be concluded that the majority of the errors in the second approach are caused by fuzzy ΔS.