Fumiaki Kitahara

Accurate digital canopy height models (DCHMs) derived from historical aerial photographs are essential for reconstructing long-term forest structural dynamics; however, the influence of photogrammetric software on DCHM quality and reliability remains insufficiently evaluated. This study compared the performance of two structure-from-motion (SfM) photogrammetric platforms, Metashape and Pix4Dmatic, for processing old aerial photographs and generating DCHMs in Ishikawa prefecture. Software performance was assessed using image processing efficiency, geometric accuracy based on root mean square error (RMSE), and correlation between derived DCHMs and National Forest Inventory (NFI) measurements. The results revealed that Metashape required shorter image processing times for the digital surface model generation and produced denser point clouds with broader spatial coverage. By contrast, Pix4Dmatic achieved higher geometric accuracy, with RMSE values of 0.571 m, 0.870 m, and 2.120 m in the X, Y, and Z directions, respectively. The Metashape-derived DCHM showed a higher mean value (15.267 ± 5.882 m) than Pix4Dmatic (14.749 ± 5.834 m), but Pix4Dmatic-generated DCHMs showed a closer relationship (r = 0.880) with NFI data (15.322 ± 5.451 m). These findings demonstrate that photogrammetric software selection substantially influences three-dimensional reconstruction from old aerial imagery and affects the reliability of DCHM generation. This study provides practical guidance for selecting SfM software for forest structural analysis and long-term forest monitoring.

Context: Understanding tree mortality is critical for sustainable forest management. Long-term tree mortality may differ depending on the stand development process and can be influenced by forest management regimes. Logistic regression is widely used to explain tree mortality based on site productivity, age, size, and competition. However, the explanatory variables are interrelated. Thus, we attempted to explain long-term Japanese cedar tree mortality by considering interrelated variables. Aims: The aim of this study was to elucidate the direct and indirect effects of site productivity, age, individual size, and competition on the long-term mortality of Japanese cedars. Methods: Data were collected from 5130 even-aged Japanese cedar trees over approximately 50 years. We compared each variable between dead and living trees. We then constructed a mortality model using a conventional logistic approach and selected the best model for the stepwise methods. Finally, we applied a piecewise structural equation model (SEM) to identify these variables’ direct and indirect effects. We compared the conventional logistic model and piecewise SEM models and discussed the advantage of applying the SEM models. Results: Annual mortality was approximately 4% in the most fertile stands, increasing gradually with decreasing site fertility. Dead tree size and competition status differed according to age and site productivity. Competition, individual size, and stand density were selected for the best logistic model (area under the curve (AUC) = 0.74, Brier score = 0.042), whereas age and site productivity were not (p > 0.05). The piecewise SEM results showed that age and site productivity indirectly affected tree mortality through individual size and stand density (Fisher’s C = 4.569, p = 0.102). Conclusion: Long-term Japanese cedar tree mortality can be explained by individual size and competition as direct influencing factors and age and site productivity as indirect influencing factors. This indicated that hidden factors cannot be explained using the conventional logistic approach. Further studies are required to explore the potential factors contributing to tree mortality thoroughly.

Understanding the tree growth process is essential for sustainable forest management. Future yields are affected by various forest management regimes such as thinning; therefore, accurate predictions of tree growth are needed under various thinning intensities. This study compared the accuracy of individual-level distance-independent diameter growth models constructed for different thinning intensities (thinning intensity-dependent multiple models: TDM model) against the model designed to include all thinning intensities (thinning intensity-independent single model: TIS model) to understand how model accuracy is affected by thinning intensity. We used long-term permanent plot data of Japanese cedar (Cryptomeria japonica) stands in Japan, which was gathered from four plots where thinning was conducted at different thinning intensities: (1) intensive (41% and 38% of trees removed at 25 and 37 years old, respectively), (2) moderate (38% and 34%), (3) light (32% and 34%), and (4) no thinning. First, we specified high interpretability distance-independent competition indices, and we compared the model accuracy both in TDM and TIS models. The results show that the relative spacing index was the best competition index both in TDM and TIS models across all thinning intensities, and the differences in the RMSE (Root mean square error) and rRMSE (relative RMSE) in both TDM and TIS models were 0.001–0.01 cm and 0.2–2%, respectively. In the TIS model, rRMSE varied with thinning intensity; the rRMSE was the lowest for moderate thinning intensity (45.8%) and the highest for no thinning (59.4%). In addition, bias values were negative for the TIS model for all thinning intensities. These results suggest that the TIS model could express diameter growth regardless of thinning intensities. However, the rRMSE had varied with thinning intensity and bias had negative values in the TIS model. Therefore, more model improvements are required for accurate predictions of long-term growth of actual Japanese cedar stands.

The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey—a national plot sampling survey in Japan—was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.

Due to the large number of sample plots and variables to be measured, inexperienced surveyors are expected to take field measurements in National Forest Inventories (NFIs). However, very little information exists on the data quality that can be expected from inexperienced surveyors given different levels of training. We evaluated the quality of data produced by inexperienced undergraduate students when measuring the most fundamental variables: tree diameter using a diameter tape and height using an ultrasonic Vertex III hypsometer. We found that a single training session on how to use the instruments and how to reduce measurement errors was insufficient for inexperienced surveyors to achieve measurement quality objectives (MQOs). Providing a single feedback of control team measurements significantly improved data quality, except in the measurements of tree height of broad-leaved trees, but additional feedback did not contribute to further improvement. We propose that field training courses for inexperienced surveyors incorporate a one-day exercise with feedback instruction.

We propose the hypothesis that the enlargement of the common bile duct (CBD) or gallbladder (GB) that is occasionally demonstrated on magnetic resonance cholangiopancreatography (MRCP) after secretin stimulation is caused by pancreatobiliary reflux. Recently, occult pancreatobiliary reflux (OPR) has been demonstrated in patients without morphological pancreatobiliary maljunction (MPBM). The aim of this study was to evaluate the efficacy of secretin-stimulating MRCP (SMRCP) in the diagnosis of pancreatobiliary reflux. The study included 14 patients with MPBM and 32 patients with a normal pancreatobiliary junction. OPR was evaluated by bile collection and diagnosed in seven of the 32 patients. All the patients underwent SMRCP; the related findings were considered positive when enlargement of the CBD or GB was observed. Positive findings on SMRCP were observed in all MPBM patients. In the patients with normal pancreatobiliary junction, there was significant difference between the mean amylase levels in the patients with positive and negative SMRCP findings (mean, 4,755.7 and 29.7 IU/l). The sensitivity and specificity of SMRCP for diagnosing OPR was 85.7% and 68.0%, respectively. SMRCP provides a non invasive method for excluding PBR and can identify patients who could benefit from bile duct sampling to diagnose OPR.