Forest management plans need up-to-date inventory data such as tree species, DBH, height, and the number of trees. The collection of these data is laborious, time-consuming, and costly when performed by traditional ground measurements. The aim of this study; (i) demonstrate how to use handheld laser scanning (LiDAR) technology in forest inventories, and (ii) compare stand parameters calculated with LiDAR and ground measurements. To this end, four sample plots were scanned by a LiDAR device in Artvin-Şavşat. Then, the accuracy of LiDAR data was examined by comparing it with ground truth. DBH, the number of trees, stand topheight, stand volume, and canopy closure parameters were captured by LiDAR with an accuracy rate of 97.8%, 98.0%, 96.6%, 75.4%, and 100%, respectively. The mean errors in these parameters were 0.68 cm, 14 trees/ha, 0.8 m, 155.7 m3/ha. Since the mean error was relatively high for the stand volume, six standing trees were scanned by LiDAR, and then, they were felled and volumized using the section method. Ground measurements showed that LiDAR could calculate stem volumes with a mean accuracy of 95.0%. Thus, the high error rate in stand volumes was attributed to the reference data derived by existing volume tables. On the other hand, tree species and stand types could not be identified with LiDAR point clouds. However, they were identified in the office through high-quality video records taken by a 4K camera mounted on the handheld LiDAR. No difference was observed between stand types determined by two methods. It was concluded that the handheld LiDAR technology could calculate many stand parameters with acceptable accuracy levels efficiently. Amenajman planı yenilemeleri kapsamında yapılan orman envanterlerinde örnek alanlar içerisinde bulunan ağaçların türü, çapı, boyu, sayısı gibi birçok meşcere parametresine ait güncel verilere ihtiyaç duyulmaktadır. Bu verilerin geleneksel yersel ölçüm metotlarıyla tespiti zahmetli, zaman alıcı ve masraflıdır. Bu çalışmanın amacı; (i) orman envanterlerinde el-tipi lazer tarama (LiDAR) teknolojisinden yararlanma olanaklarını göstermek ve (ii) örnek alanlarda LiDAR verisiyle hesaplanan meşcere parametrelerini yersel ölçümlerle karşılaştırmaktır. Bu doğrultuda, Artvin-Şavşat’ta 12. Amenajman Başmühendisliği tarafından arazi ölçümleri gerçekleştirilen dört örnek alan LiDAR cihazı ile taranmıştır. Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle karşılaştırılarak LiDAR verisinin doğruluğu sınanmıştır. Göğüs çapı, ağaç sayısı, meşcere üst boyu, meşcere hacmi ve kapalılık parametrelerinin LiDAR cihazıyla sırasıyla; ort. %97,8, %98,0, %96,6, %75,4 ve %100 doğrulukla tahmin edildiği görülmüştür. Bu parametrelerdeki ort. hata değerleri aynı sırayla; 0,68 cm, 14 ad/ha, 0,8 m, 155,7 m3/ha'dır. Hektardaki meşcere hacminde gözlenen yüksek hata üzerine, arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve daha sonra kesilerek, seksiyon yöntemiyle hacimlendirilmiştir. Yerde ölçülen gövde hacimlerinin LiDAR ile ort. %95,0 doğrulukla tespit edilebildiği görülmüştür. Dolayısıyla, meşcere hacimlerindeki nispeten yüksek hata oranlarının LiDAR yönteminden değil, envanterde kullanılan mevcut hacim tablolarından kaynaklandığı anlaşılmıştır. Buna karşılık, LiDAR ile üretilen nokta bulutları üzerinden ağaç türü ve meşcere tipleri belirlenememiştir. Ancak, cihazın video kayıtları izlenerek örnek alanların meşcere tiplerine büroda karar verilmiştir. Envanter karnesindeki meşcere tipleri ve büroda verilen tipler arasında uyumsuzluğa rastlanmamıştır. Çalışmanın sonunda, orman amenajman planlarında yer alan birçok meşcere parametresinin el-tipi mobil LiDAR teknolojisiyle arazide daha az vakit harcanarak kabul edilebilir doğruluk düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır.

Dendroarchaeological samples archived in historical wooden artifacts can be used to reconstruct forest conditions during European settlement of North America. In this study, we dated and examined tree-ring records of white oak (Quercus alba L.) logs sampled from a kitchen and slave quarters from a historical plantation in western Virginia. The kitchen was constructed from logs harvested after the 1845 growing season. The slave quarters was constructed from logs harvested after the 1864 growing season. White oak establishment in the study area parallels a general pattern of forest expansion into clearings following the depopulation of Native Americans from the eastern United States between 1720 and 1789. Synchronized canopy openings in the 1770s and 1780s could be due to increased tree cutting by European settlers to erect wooded palisades for protection against Native Americans.

This study presents a comprehensive analysis of land use and land cover change within the Istanbul Regional Directorate of Forestry (RDF) utilizing semantic segmentation referred to as pixel-based classification. Focusing particularly on forest land dynamics, Sentinel-2 satellite imagery spanning five years from 2019 to 2023 was processed using a U-Net architecture. The study area encompasses diverse forest ecosystems, urban/built-up areas, water bodies, rangelands, wetlands, and agricultural lands. Through the application of advanced remote sensing techniques, significant changes in forest and rangeland were identified and quantified, 15.250 and 13.226 hectares of area decreased in five years, shedding light on the drivers and implications of land use transformations in this critical region. Controversially, built area and agricultural lands were increased by 13.878 and 15.953 hectares over 5 years. The findings contribute to a deeper understanding of forest dynamics and inform sustainable management strategies for preserving the ecological integrity and socio-economic value of forested landscapes within the Istanbul RDF. Additionally, the results reveal the average F-1 Score for each land cover class is approximately 90% for each year, with forested areas achieving an average F-1 score of about 92%, demonstrating the robustness and accuracy of the classification approach.

Background: Accurate prediction of diameter at breast height (DBH) from stump diameter measurements is essential for sustainable forest management, particularly in post-harvest assessments, illegal logging investigations, and carbon accounting protocols in Quercus cerris stands. Traditional allometric models often fail to capture the complex nonlinear relationships inherent in tree growth patterns, necessitating advanced computational approaches. This study aims to develop and validate artificial neural network (ANN) models for predicting DBH from stump diameter in Q. cerris stands across diverse ecological conditions in Turkish forests. Results: We conducted an extensive field survey across 103 systematically distributed sample plots encompassing 1,077 individual Q. cerris trees within the Bursa Regional Directorate of Forestry, Türkiye. The study area represented diverse site conditions including five site quality classes, stand age, and varying stand densities. Fourteen traditional regression models were compared against five distinct ANN architectures with varying activation functions. The ANN model employing hyperbolic tangent sigmoid activation function between input and hidden layers coupled with pure-linear function between hidden and output layers (ANN-2) with 8 neurons demonstrated superior predictive performance (SSE = 567.07, RMSE = 2.290, MSE = 4.797, R²adj = 0.9484). Among conventional models, quadratic regression (Model 4) exhibited competitive performance but showed evidence of heteroscedasticity and model assumption violations. Conclusion: This research demonstrates the superior capability of artificial neural networks in modeling complex DBH-stump diameter relationships in Q. cerris compared to traditional statistical approaches. The developed ANN models provide forest managers with robust, accurate tools for retrospective forest assessments, illegal logging quantification, and sustainable management planning.

Local calibration methods were evaluated for diameter at breast height (dbh) growth predictions in 11 tree species in Virginia, USA, using a model form based on the Forest Vegetation Simulator Southern Variant (FVS-Sn) large tree dbh regression model. Data from 1090 remeasured forest inventory plots from the USDA Forest Service’s Forest Inventory and Analysis (FIA) database were used to calibrate FVS dbh growth predictions to local conditions and evaluate four calibration methods based on the following information: 1) median prediction errors calculated from locally observed dbh pairs before and after a five-year remeasurement period; 2) a random intercept estimated from locally observed dbh using mixed-effects regression; 3) a simple linear regression (SLR) model fitted to observed and predicted dbh at the local scale; and 4) an SLR model with regression through the origin. Calibration methods were assessed using leave-one-out cross-validation, comparing model predictions to observed dbh growth from withheld trees. Equivalence testing indicated median or regression-based local calibration methods achieved prediction-error tolerances over 5–7 year growth intervals as small as 0.11 cm (0.03 cm for two regression-based methods) for all species, whereas the random-intercept approach only achieved a minimum tolerance of 0.2 cm. Compared to uncalibrated models, local calibration substantially reduced prediction errors, demonstrating efficacy in increasing prediction accuracy, even with sparse FIA dbh growth data used for local-calibration.