Spatial assessment of sediment risk with integrated entropy-based WASPAS and fuzzy clustering methods in Turkey: impact of forestry activities and meteorological factors.

The sediment formed during erosion processes is acknowledged as one of the important contributors to environmental damage. In forest areas, sediment production occurs as a result of meteorological factors and wood harvesting, as well as forest road construction activities carried out for the continuity of forests. In this context, the aim of the study is to reveal the sediment risk in forest areas at the provincial level in Turkey by using entropy-based weighted aggregated sum product assessment (WASPAS) and fuzzy clustering methods. The results establish that the weights of the factors affecting the sediment risk are the forest road construction rate (0.3721), the wood harvesting rate (0.3463), the average precipitation (0.2227), and the average temperature (0.0587). Seven provinces were found in the highest risk class of the cluster in terms of sediment risk. Two of those (Yalova and Kocaeli) are located in the Marmara Region, while the others (Bartin, Düzce, Giresun, Ordu, and Zonguldak) are located in the Black Sea Region. Four of the provinces in the lowest risk class of the cluster (Bingöl, Bitlis, Erzincan, and Malatya) are located in the Eastern Anatolia Region, while a fifth (Siirt) is located in the Southeastern Anatolia Region. As is seen in the study, multi-criteria decision-making methods make it possible to evaluate different scenarios for sedimentation estimation and control and to select the most effective solutions. In conclusion, the use of WASPAS, entropy, and fuzzy clustering methods can provide a comprehensive and effective approach for assessing the sediment risk.

Measuring tree diameter using a LiDAR-equipped smartphone: a comparison of smartphone- and caliper-based DBH.

Tree diameter measurement is one of the most important stages of forest inventories to assess growing stock, aboveground biomass, and landscape restoration options, among others. This study investigates the accuracy of measuring tree diameters using a Light Detection and Ranging (LiDAR)-equipped smartphone vs. a normal caliper (reference data) and the opportunity to use low-cost smartphone-based applications in forest inventories. To estimate the diameter at breast height (DBH) of single trees, we used a smartphone with a third-party app that automatically analyzed three-dimensional (3D) point clouds. For two different measurement techniques, we compared the two measurement techniques based on DBH data from 55 Calabrian pine (Pinus brutia Ten.) and 50 oriental plane (Platanus orientalis L.) trees using the paired-sample t-test and Wilcoxon signed-rank test. Mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), percent bias (PBIAS), and coefficient of determination (R2) were used as precision and error statistics. Statistical differences were observed between the reference and smartphone-based DBH according to the paired-sample t-test and Wilcoxon signed-rank test. The R2 values obtained were determined as 0.91, 0.88, and 0.88 for Calabrian pine, oriental plane, and all tree species (105 trees), respectively. In addition to the overall accuracy performance of the comparison between reference and estimations, MAE, MSE, RMSE, and PBIAS values for the DBH of 105 tree stems were calculated as 1.56 cm, 5.42 cm, 2.33 cm, and - 5.10%, respectively. The estimation accuracies increased in regular stem forms compared with forked stems particularly observed on plane trees. Further experiments are needed to investigate the uncertainties associated with trees of different stem forms, species (coniferous or deciduous), different work environments, and different types of LiDAR and LiDAR-based app scanners.

High accuracy monitoring system to estimate forest road surface degradation on horizontal curves.

Well-maintained pavements reduce occurring severe accidents on horizontal curves. For this reason, the monitoring and evaluation of pavement conditions are important. This study evaluates pavement conditions considering volumetric degradation or displacement on 11 horizontal curves in forest roads, depending on meteorological conditions, traffic effects, and curve parameters. Within this context, pavement displacement (degradation) was investigated and measured with terrestrial laser scanning (TLS) for a year on a monthly basis. In this study, two multiple regression models were developed to estimate the degradation values of a forest road. According to model 1, which was developed to estimate the loss volume values, the adjusted R2 was 0.658. For model 2, which was developed to estimate the gain volume values, the adjusted R2 was 0.490. Validations of models were evaluated with different statistical tests. In conclusion, volumetric degradation can be calculated with TLS-based data. Forest road designers should determine horizontal curve characteristics, taking into consideration the pavement degradation and traffic safety.

Assessment of risk factors in forest road design and construction activities with fuzzy analytic hierarchy process approach in Turkey.

Forest road design and construction are time-consuming and complicated because various risk factors can be encountered during the process. The aim of this study is to comprehensively assess the risk factors in forest road design and construction using the fuzzy analytic hierarchy process (AHP) method in Turkey, thus contributing to the proper performance of these activities. Within the scope of the study, six main risk factors and 22 sub-risk factors were identified based on literature review. In order to determine the weights of the relevant risk factors, the opinions of three different groups [(group 1: academicians), (group 2: forest engineers (private sector employees + public sector employees)), (group 3: group 1 + group 2)] about the risk factors were obtained. Relevant risk factor weights were determined using the fuzzy AHP method. According to group 3, the most important main risk factors are technical risks and environmental risks. In addition, the most important sub-risk factors for each relevant main risk factor were incorrect road alignment, inadequate work safety in the field, insufficient capital, legal problems on the road alignment, landslide risk during road construction, and illegal logging. Differences were observed between groups 1 and 2 in the weight rankings of relevant risk factor. The results demonstrate that the fuzzy AHP method can be used effectively to assess the risks of forest road design and construction.