Litter mass (LM) on the forest floor is an important component of the carbon (C) cycle in forest ecosystems. A considerably amount of LM is accumulated in the conifer forests in the Mediterranean Region of Turkey. Therefore, LM should be considered to estimate above ground biomass (AGB) more accurately in these forests. Rapid and affordable methods are necessary for LM inventory. Satellite remote sensing data and GIS-based environmental variables, which cover broad geographic regions, are alternative data sources in this scope. In this work, the LM was modeled by means of generalized additive model (GAM) using the predictor variables including the Normalized Difference Vegetation Index (NDVI) images (derived from RapidEye, SPOT-5, Aster) and environmental data (heat index-HI and radiation index-RI). According to the cross validation test, the best model using NDVI_ASTER and HI as predictor variables explained a total variation of 49% of the LM as response variable. We may conclude that the LM on the floor of brutian pine stands can be moderately predicted using satellite data and environmental variables. We consider that the results of this study may contribute to estimation of AGB and C storages more accurately in pure brutian pine stands.

森林地面的凋落物质量（LM）是森林生态系统中碳（C）循环的重要组成部分。土耳其地中海地区的针叶林中积累了大量的LM。因此，应该考虑使用LM来更准确地估算这些森林中的地上生物量（AGB）。LM库存必须采用快速且负担得起的方法。覆盖广泛地理区域的卫星遥感数据和基于GIS的环境变量是该范围内的替代数据源。在这项工作中，通过广义加性模型（GAM）使用预测变量对LM进行建模，这些变量包括标准差植被指数（NDVI）图像（源自RapidEye，SPOT-5，Aster）和环境数据（热指数HI和辐射指数-RI）。根据交叉验证测试，作为预测变量的_ASTER和HI解释了LM作为响应变量的总变化为49％。我们可以得出的结论是，可以使用卫星数据和环境变量来适当预测残酷松林地上的LM。我们认为，这项研究的结果可能有助于更准确地估算纯野红松林中的AGB和C储量。