4 research outputs found
Estimation of Aboveground Carbon Using Active and Passive Satellite Image in Pure Crimean Pine Stands
No full textBu çalışma, Sinop Orman Bölge Müdürlüğü, Boyabat Orman İşletme Müdürlüğü, Elekçamı Orman İşletme Şefliği sınırları içerisinde yayılış gösteren saf karaçam meşcerelerinde gerçekleştirilmiştir. Çalışma kapsamında toplam 247 adet örnek alan verisi kullanılmıştır. Envanter verilerinden yararlanılarak her bir örnek alan için topraküstü karbon (TÜK) değerleri hesaplanmıştır. Her bir örnek alan için Landsat 5 TM bant yansıma ve vejetasyon indis, ALOS-PALSAR uydu görüntüsüne ait HH ve HV polarizasyonlarından parlaklık ve geri yansıtım ile örnek alanlara ilişkin eğim, bakı ve yükselti değerleri hesaplanmıştır. TÜK değerleri ile yukarıda açıklanan değişkenler arasındaki ilişkiler çoğul regresyon analizi ile modellenmiştir. Toplam 14 adet model geliştirilmiştir. Geliştirilen model başarıları incelendiğinde, Landsat 5 TM vejetasyon indis, ALOS-PALSAR parlaklık ve geri yansıtım değişkenleri ile eğim, bakı ve yükselti değerlerinin bağımsız değişkenler olarak yer aldığı modelde belirtme katsayısı (R_düz^2=0.655; Sy.x=0.18147) elde edilmiştir.This study was conducted in pure Crimean stands located within the boundaries of Sinop Regional Directorate of Forestry, Boyabat Forest Enterprise, Elekçamı Forest Planning Unit. A total of 247 sample plot inventory data were utilized. Using inventory data, aboveground carbon values were calculated for each sample plot. Then, for each sample plot, band brightness and vegetation indice obtained from the Landsat 5 TM satellite image, polarization (HH and HV) brightness and backscattering obtained from the ALOS-PALSAR satellite image, as well as slope, aspect, and elevation values, were calculated. The relationships between aboveground carbon values and the variables mentioned above were modeled using multiple regression analysis. A total of 14 models were developed. When examining the success of the developed models, the highest model accuracy (R_adj^2=0.655;Sy.x=0.18147) was achieved in the model that included vegetation indices from the Landsat 5 TM satellite image, brightness and backscattering values from the ALOS-PALSAR satellite image, along with slope, aspect, and elevation as independent variables
Mixed effect models for predicting breast height diameter from stump diameter of Oriental beech in Göldağ
No full textDiameter at breast height (DBH) is the simplest, most common and most important tree dimension in forest inventory and is closely correlated with wood volume, height and biomass. In this study, a number of linear and nonlinear models predicting diameter at breast height from stump diameter were developed and evaluated for Oriental beech (Fagus orientalisLipsky) stands located in the forest region of Ayancık, in the northeast of Turkey. A set of 1,501 pairs of diameter at breast height-stump measurements, originating from 70 sample plots of even-aged Oriental beech stands, were used in this study. About 80 % of the otal data (1,160 trees in 55 sample plots) was used to fit a number of linear and nonlinear model parameters; the remaining 341 trees in 15 sample plots were randomly reserved for model validation and calibration response. The power model data set was found to produce the most satisfactory fits with the Adjusted Coefficient of Determination, R2adj (0.990), Root Mean Square Error, RMSE (1.25), Akaike’s Information Criterion, AIC (3820.5), Schwarz’s Bayesian Information Criterion, BIC (3837.2), and Absolute Bias (1.25). The nonlinear mixed-effect modeling approach for power model with R2adj(0.993), AIC (3598), BIC (3610.1), Absolute Bias (0.73) and RMSE (1.04) provided much better fitting and precise predictions for DBH from stump diameter than the conventional nonlinear fixed effect model structures for this model. The calibration response including tree DBH and stump diameter measurements of the four largest trees in a calibrated sample plot in calibration produced the highest Bias, -5.31 %, and RMSE, -6.30 %, the greatest reduction percentage
Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Turkiye
No full textRemote sensing technologies have been extensively used in forest management in predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 satellite images to estimate stand volume, basal area, number of trees, mean diameter, and top height. 180 temporary sample plots were taken in pure Crimean pine stands with varied structure. Reflectance, vegetation indices, and eight texture values were generated from Landsat 8 and Sentinel-2 satellite images. The stand parameters were modelled with the remotely sensed data using multiple linear regression, support vector machine, and deep learning techniques. The results showed that the support vector machine technique provided the highest level of model performance with 45 degrees orientation for number of trees (R-2 = 0.98, RMSE%=5.97) and 90 degrees orientation for basal area (R-2=0.91, RMSE%=15.22). The results indicated that the texture values presented better results than the reflectance and the vegetation indices in estimating the stand parameters
COVİD-19 PANDEMİSİ DÖNEMİNDE DIŞA DÖNÜK VE İÇE DÖNÜK KİŞİLİK ÖZELLİKLERİNİN KARANTİNA DENEYİMİ ÜZERİNDEKİ ETKİSİ
No full textThe purpose of this research is to understand more closely the effect of an individual's extroversion and introversion personality traits on their experiences during the quarantine and social isolation period experienced during the Covid-19 period. The study, which plans to measure the experience of the individual in this process through factors such as difficulty in adapting to the quarantine period and compliance with the rules during the quarantine period, was developed by Benet-Martinez and John (1998) and adapted in Turkish by Sümer and Sümer (2005), in order to determine the personality traits of the individual. Inventory (BFI)” was used. A single-item scale was developed to measure adaptation difficulty, and for compliance with the rules, Van Rooij, B., de Bruijn, A. L., Reinders Folmer, C., Kooistra, E., Kuiper, M. E., Brownlee, M., & Fine, A. ( 2020) and the reliability and validity study of which was conducted by Oral, T. & Gunlu, A. (2021), the Social
Distance Scale (Compliance with Covid-19 mitigation measures in the United States) scale was used. coefficient was found as α = 0.70 (Age range is 18-70 years; mean age is 31.95). In addition, confirmatory factor analysis results for validity studies showed that the single-factor structure fit well (X2/sd=0.65, p>.001, RMSEA=0.00, SRMR=0.00, GFI=0.99, AGFI=0.99, CFI=1.00). No significant relationship was found between adaptation difficulty and compliance factors and personality traits of the individual, but these results are potentially of great significance for future research.Bu araştırmanın amacı, Covid-19 dönemi süresince yaşanan karantina ve sosyal izolasyon döneminde bireyin dışadönüklük ve içedönüklük kişilik özelliklerine sahip olmasının deneyimleri üzerindeki etkisini daha yakından anlamaktır. Karantina dönemine adapte olmada zorluk ve karantina döneminde kurallara uyum gibi faktörler üzerinden bireyin bu süreçteki deneyimini ölçmeyi planlayan çalışma, bireyin kişilik özelliklerini belirlemek amacıyla Benet-Martinez ve John (1998) tarafından geliştirilen ve Türkçe uyarlaması Sümer ve Sümer (2005) tarafından yapılan “Beş
Faktör Envanteri (BFI)” kullanılmıştır. Adaptasyon zorluğu ölçümü için tek maddeli ölçek geliştirilmiş, kurallara uyum için ise Van Rooij, B., de Bruijn, A.L., Reinders Folmer, C., Kooistra, E., Kuiper, M. E., Brownlee, M., & Fine, A. (2020) tarafından geliştirilen ve güvenilirlik ve geçerlilik çalışması Oral, T. & Gunlu, A. (2021) tarafından yapılan Covid-19 Döneminde Sosyal Mesafe Ölçeği ( Compliance with Covid-19 mitigation measures in the United States) ölçeği kullanılmıştır.Ölçeğin Cronbach Alfa güvenirlik katsayısı α = 0.70 olarak bulunmuştur (Yaş aralığı 18-70 yaş; yaş ortalaması ise 31.95). Ayrıca geçerlik çalışmaları için yapılan doğrulayıcı faktör analizi sonuçları tek faktörlü yapının iyi uyum verdiğini göstermiştir (X2/sd=0.65, p>.001, RMSEA=0.00, SRMR=0.00, GFI=0.99, AGFI=0.99, CFI=1.00) Araştırmanın sonucunda adaptasyon zorluğu ve kurallara uyum faktörleri ile bireyin kişilik özellikleri arasında anlamlı bir ilişki bulunamamıştır, fakat bu sonuçlar gelecek araştırmalar için potansiyel olarak büyük anlam taşımaktadır.M.S. - Master of Scienc
