Düzlerçamı Kızılçam Ormanında (Antalya) Toprak Üstü Orman Biyokütlesinin Modellenmesi

Abstract

Estimation of forest biomass is needed for monitoring the changes in carbon stocks as well as other purposes. This study reports on a test of the ability to estimate above ground biomass of Calabrian pine forests of Düzlerçamı, Antalya, Turkey using Landsat and ICESat/GLAS data. The field data has been collected in 2017 and plot-level estimates were calculated using the allometric equations. GLAS parameters and various Landsat vegetation indices were modeled using multiple regression analysis to estimate above ground biomass. In the first model (ModelA) height of median energy (HOME) and the ratio of HOME to maximum vegetation height (%HOME) parameter of GLAS showed relation with field based estimates of above ground biomass with a coefficient of determination (R2) of 0.87. Above ground biomass derived from ModelA and the variables obtained from Landsat indices has been used at the second model (ModelB) had a R2 of 0.52 meaning the GLAS data is poorly correlated with Landsat at the study area. A better statistical relationship has been found with Landsat data and AGB with a R2 of 0.91 in ModelC that uses Landsat pixel values of each bands and pixel values of the indices are used as independent variable to explain above ground biomass. The results demonstrate a current potential for above ground biomass estimation of forests using optical sensor data and satellite lidar where airborne lidar data is not widely available.

Keywords

• Above ground biomass,forest,ICESat/GLAS,Landsat

MODELING ABOVE GROUND BIOMASS IN CALABRIAN PINE FORESTS OF DÜZLERÇAMI (ANTALYA)

Abstract

Karbon stoklarındaki değişimlerin izlenmesi ile birlikte çeşitli diğer amaçlar için orman biyokütlesinin belirlenmesine ihtiyaç duyulmaktadır. Bu çalışma Düzlerçamı kızılçam ormanında (Antalya) toprak üstü orman biyokütlesinin Landsat ve ICESat/GLAS verileri kullanarak belirlenebilmesini test etmektedir. 2017 yılında arazi çalışmaları ile örneklem alanlarından toplanan veriler ile allometrik eşitlikler kullanılarak gerçek biyokütle verileri hesaplanmıştır. Toprak üstü orman biyokütlesinin modellenmesinde çok değişkenli regresyon analizi ile GLAS parametreleri ve çeşitli Landsat vejetasyon indekslerinden yararlanılmıştır. Birinci modelde (ModelA) GLAS verisinden medyan enerjinin yüksekliği (HOME) ve HOME’un maksimum yüksekliğe oranı parametrelerinin araziden toplanan biyokütle verileri ile olan ilişkisinde determinasyon katsayısı (R2) 0.87 olarak tespit edilmiştir. ModelA’dan elde edilen toprak üstü orman biyokütlesi ile çeşitli Landsat indekslerinin kullanıldığı ikinci modelde (ModelB) 0.52 bulunan R2 değeri GLAS verisinin çalışma alanında Landsat veriler ile zayıf bir korelasyonu bulunduğunu göstermiştir. Toprak üstü orman biyokütlesini açıklamak için Landsat indeks değerlerinin bağımsız değişken olarak kullanıldığı ModelC’de ise 0.91 R2 ile istatistiksel olarak daha anlamlı bir istatistiksel ilişki belirlenmiştir. Sonuçlar toprak üstü orman biyokütlesinin belirlenmesinde hava lidar verilerinin bulunmadığı durumlarda optik sensörlerin ve uydu tabanlı lidar verilerin güncel potansiyelini göstermektedir.

Keywords

• Toprak üstü biyokütle,orman,ICESat/GLAS,Landsat

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