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    Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests
    (Taylor & Francis Ltd, 2024) Bulut, Sinan; Gunlu, Alkan; Aksoy, Hasan; Bolat, Ferhat; Soenmez, Muecahit Yilmaz
    Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of T & uuml;rkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (h(mean)), top height (h(top)), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson's correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the h(mean) - h(top) (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, h(mean), h(top), BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.
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    Öğe
    UAV and satellite-based prediction of aboveground biomass in scots pine stands: a comparative analysis of regression and neural network approaches
    (Springer Heidelberg, 2025) Aksoy, Hasan; Gunlu, Alkan
    Forest ecosystems play a vital role in balancing the global climate through functions such as regulating carbon emissions, carbon sequestration, and energy and water cycles. Aboveground biomass (AGB) is a critical component in forest management to understand better and predict the global carbon cycle. However, traditional methods used in AGB measurement involve time-consuming, costly, and labor-intensive processes. Sentinel-1 (active), Sentinel-2, and Landsat (passive) satellite imagery, which is freely accessible and offers global coverage with frequent updates, and recently developed remote sensing platforms such as Unmanned Aerial Vehicle (UAV) serve as a valuable data source for consistent and continuous monitoring of aboveground biomass. This research focuses on modeling the relationships between AGB and data obtained from various remote sensing sources, including Sentinel-1, Sentinel-2, Landsat 8, and UAV imagery, within pure Scots pine stands in northern T & uuml;rkiye. The study employs multiple linear regression (MLR) and artificial neural networks (ANNs) to establish these relationships. AGB values for each sample plot were calculated using an allometric equation. Backscatter coefficients and band brightness values were extracted from Sentinel-1 imagery, while reflectance values and vegetation indices were generated from Sentinel-2, Landsat 8 OLI, and UAV imagery. Additionally, texture features were computed for varying window sizes (3 x 3, 5 x 5, 7 x 7, 9 x 9, 11 x 11, 13 x 13, and 15 x 15) and orientations (0 degrees, 45 degrees, 90 degrees, and 135 degrees) based on data from Sentinel-2 and Landsat 8 OLI images for each sample plot. The relationships between remote sensing data and AGB were modeled using both MLR and ANN techniques. The findings revealed that the most accurate AGB estimation (R-2=0.82; RMSE = 0.35 ton ha(-)(1)) was achieved using the texture variables derived from the 9 x 9 window size of Sentinel-2 imagery via the ANNs modeling approach, outperforming other image sources and MLR analysis.