Saf Kızılçam (Pinus brutia Ten.) Meşcerelerinde Topraküstü Biyokütle ile Yaprak Alan İndeksi Arasındaki İlişkilerin Modellenmesi: Sarıyayla Orman İşletme Şefliği Örneği

Öz

Bu çalışma, Mersin Orman Bölge Müdürlüğü’ne bağlı Anamur Orman İşletme Müdürlüğü, Sarıyayla Orman İşletme Şefliği sınırları içerisinde yayılış gösteren saf kızılçam (Pinus brutia Ten.) meşcerelerinde gerçekleştirilmiştir. Çalışma kapsamında toplam 257 adet örnek alandan elde edilen envanter verileri kullanılmıştır. Her bir örnek alanın topraküstü biyokütle (TÜB) değeri, Şahin (2015) tarafından saf kızılçam meşcereleri için geliştirilmiş olan tek girişli biyokütle denklemi kullanılarak hesaplanmıştır. Envanter verilerine ek olarak, Ocak ayı hariç (yeterli kalitede uydu görüntüsü bulunmadığı için) olmak üzere her ay için Sentinel-2 uydu görüntülerinden yaprak alan indeksi (YAİ) raster verileri türetilmiştir. Her bir örnek alan, aylık YAİ görüntüleri üzerine coğrafi olarak aktarılmış ve buna bağlı olarak her bir örnek alan için aylık bazda YAİ değişkenlerinin maksimum, minimum, ortalama, toplam, değişim aralığı ve standart sapma değerleri hesaplanmıştır. TÜB ile her bir ay ve tüm aylara ilişkin YAİ değişkenleri arasındaki ilişkiler çoğul regresyon analizi ile modellenmiştir. Bu kapsamda toplam 12 adet regresyon modeli geliştirilmiştir. Aylara göre geliştirilen modellerde modellerin belirleme katsayısı (R²) 0,10 ile 0,178 arasında değişirken, tüm aylara ait verilerin birlikte değerlendirildiği modelde ise R² değeri 0,444 olarak bulunmuştur. Elde edilen bulgular, geliştirilen regresyon modellerinin saf kızılçam meşcerelerinde topraküstü biyokütlenin tahmininde potansiyel sunduğunu, ancak model performansının artırılması için daha kapsamlı ve çok yönlü bilimsel araştırmalara ihtiyaç duyulduğunu göstermektedir.

Anahtar Kelimeler

• Sentinel-2
• Regresyon analizi
• Orman kaynakları

Modeling the Relationship Between Aboveground Biomass and Leaf Area Index in Pure Turkish Red Pine (Pinus brutia Ten.) Stands: A Case Study from Sarıyayla Forest Planning Unit

Öz

This study was conducted in pure Turkish red pine (Pinus brutia Ten.) stands within the boundaries of Mersin Regional Directorate of Forestry, Anamur Forest Enterprise, Sarıyayla Forest Planning Unit. Data from a total of 257 sample plots were utilized in the analysis in the study. The aboveground biomass (AGB) of each sample plot was estimated using a single-entry biomass equation developed specifically for pure Calabrian pine stands by Şahin (2015). In addition to inventory data, monthly raster datasets of Leaf Area Index (LAI) were produced using Sentinel-2 satellite imagery, excluding the month of January (due to the lack of sufficiently high-quality imagery). Each sample plot was geographically overlaid onto the monthly LAI images, and for each month, statistical variables including maximum, minimum, mean, total, range, and standard deviation of LAI were calculated for every sample plot. The relationships between AGB values and the LAI-derived variables (both every month and across all months) were modeled using multiple regression analysis. In total, 12 regression models were developed. The coefficient of determination (R²) values of the monthly models ranged from 0.10 to 0.178, while the model incorporating data from all months yielded a substantially higher R² value of 0.444. The findings suggest that the developed regression models hold potential for estimating aboveground biomass in pure Pinus brutia stands; however, further comprehensive and multidisciplinary research is required to enhance model performance and applicability.

Anahtar Kelimeler

• Sentinel-2
• Regression analysis
• Forest ecosystems

Kaynakça

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