Forests host diverse ecosystems that involve various habitats. There are many complex interactions between living and non-living things in most forests. It is important to conduct observations and assessments in large forestlands where short-term and long-term direct or indirect negative impacts may occur so that they are known and measured. Scientific studies have been carried out by utilizing the various data offered by today's advanced technology with satellite imagery becoming more readily available. In this study, differenced Normalized Burn Ratio (dNBR=∆NBR) and satellite images with two different resolutions were used to generate pre-and post-wildfire spatial data. An area affected by wildfire in the Mediterranean Region of Turkey was selected as the study area. Google Earth Engine (GEE) and Geographic Information System (GIS) were used to delineate areas affected by wildfire using Sentinel-2 and Landsat 8 multispectral imagery. In order to compare the differences between the two sets of imagery, burn severity levels (low, medium-low, medium-high, and highest) and the effect of water surface were considered. For the most impacted burnt lands, areas detected with Sentinel 2 and Landsat 8 are 31.90% and 32.59%, respectively. However, burn severity classes were also observed in water surface areas likely due to interactions between land cover and water reflectance. The overall results support the use of both satellite platforms and the dNBR for burn severity mapping in mediumand large-scale post-wildfire studies.
In fully mechanized forest harvesting systems, tree felling activities are mostly performed by using harvesters or feller-bunchers. In some regions of Turkey, where terrain conditions and stand characteristics are suitable, fully mechanized harvesting systems have been recently practiced by some of the logging contractors as private forest industry demands for large amounts of forest products throughout the year. Thus, performances of these newly practiced harvesting systems should be carefully analyzed in order to implement productive and cost-effective mechanized harvesting systems. In this study, productivity of whole-tree harvesting using a feller-buncher was investigated based on stand parameters including tree height, DBH, and volume. The DBH of the felled trees were divided into four classes (i.e. very small: 16-19 cm, small: 20-23 cm, medium: 24-27 cm and large: 28-31 cm) to investigate the effects of various DBH class on the time consumption of cutting stage and productivity of the feller-buncher. To estimate productivity of feller-buncher in harvesting operation, multiple linear and polynomial regressions were also developed and discussed after the interpretation of diagnostic plots. The results indicated that the average productivity of the feller-buncher was 74.96 m3 /h which was closely related with tree height (r = 0.63), DBH (r = 0.67), and volume (r = 0.67). The average moving time was the most time-consuming stage (60%), followed by cutting (29%) and bunching stages (11%). It was found that DBH classes caused statistically significant (p < 0.05) effects on the time spent on cutting stage and productivity of the feller-buncher. The cutting time and productivity increased from very small to large diameter classes, while bunching time increased from very small to small diameter and then medium diameter to large diameter classes. Polynomial regression had a positive impact on the performance of the estimation model of manually field-measured data based on the error parameters.
Sulak alan ekosistem planlamalarının izlenmesinde ve değerlendirilmesinde uydu görüntülerinden elde edilen vejetasyon indeksleri yaygın olarak kullanılmaktadır. Yüksek çözünürlüklü birçok uydu görüntüsü olmasına rağmen Landsat uydu verileri uzun dönemli arazi değişimlerinin izlenmesi ve değerlendirilmesinde önemli avantajlara sahiptir. Bu çalışma, Aslantaş baraj gölünün uydu görüntüsü yardımıyla haritalanması ve çalışma alanında meydana gelen arazi örtüsü zamansal değişiminin değerlendirilmesini kapsamaktadır.
Abstracts:Land surveying has importance in monitoring, planning and detailed mapping for natural resources in lands. Consider the success in management plans
Son yıllarda küresel ısınmanın etkisi ile orman yangınları giderek yıkıcı tahribatlara neden olmaktadır. Orman yangınlarının, orman alanlarında meydana getirdiği tahribatın belirlenmesi zaman alıcı ve maliyetli bir iştir. Orman yangınları sonucunda yanan orman alanlarının ve yanma şiddeti açısından alanın haritalanması, rehabilitasyon çalışmaları açısından büyük önem taşımaktadır. Orman alanlarına ait haritalama ve izleme çalışmalarında uzaktan algılama ve CBS teknikleri yaygın bir şekilde kullanılmaktadır. Uzaktan algılama, pratik, uygun maliyetli ve hassas sonuçlar vermesi açısından orman yangınları sonrasında yanan alan büyüklüğü ve yanma şiddeti açısından alanın haritalanmasında önemli avantajlar sunmaktadır. Bu çalışmada, 2022 yılı eylül ayında Mersin ili Gülnar ilçesinde meydana gelen orman yangınına ait yanan alan miktarının belirlenmesi ve farklı uzaktan algılama indislerinin yanan alan büyüklüğünün belirlenmesindeki performansları karşılaştırılmıştır. Çalışma alanına ait haritalama kapsamında Sentinel-2 uydu görüntüsü kullanılmıştır. Yanan alan miktarı, dNDVI (Differenced normalized difference vegetation index), dSAVI (Differenced soil adjusted vegetation index) ve dNBR (Differenced normalized burn ratio) indislerine göre tahmin edilmiştir. Çalışma kapsamında kullanılan üç farklı indise ait performans değerleri doğruluk analizi ile ortaya konmuştur. Yanan alan büyüklüğünün tespit edilmesinde, dNDVI, dSAVI ve dNBR indislerine ait genel doğruluk değerleri sırasıyla % 75.56, % 84.44 ve % 88.89 olarak bulunmuştur. dNDVI indisine ait doğruluk oranı kabul edilebilir düzeydeyken, dSAVI ve dNBR indisleri yanan alan büyüklüğünün tespit edilmesinde oldukça iyi performans göstermiştir. Orman yangınları sonucu zarar gören alanlar, uydu görüntüleri ve uzaktan algılama teknikleri ile hızlı ve hassas bir şekilde tespit edilebilmektedir.
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