Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands

We aimed to map and analyze LAI by using Landsat 8 and Sentinel-2 time series and the corresponding ground measurements collected in pure Anatolian black pine [Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe] stands within seven-month (from June to December) period. A total of 30 sample plots were selected and seven-month changes of LAI values were determined through hemispherical photography for each sample plot. Remote sensing (reflectance values and vegetation indices obtained from Landsat-8 and Sentinel-2) and topographic (elevation, aspect, and slope) data were used to model the LAI for each month using multiple linear regression (MLR) method. Additionally, the data for all months were combined and modeled. In this case, autoregressive modeling techniques were used to solve the temporal autocorrelation problem. Our study indicated that the models developed from Sentinel-2 give more successful results than Landsat 8 on monthly LAI models. The most successful models were obtained in June by using the reflectance values ([Formula: see text] = 0.39, RMSE = 0.3138 m(2) m(−2)), reflectance values–topographic data ([Formula: see text] = 0.59, RMSE = 0.3174 m(2) m(−2)), vegetation indices–topographic data ([Formula: see text] = 0.82, RMSE = 0.2126 m(2) m(−2)), and reflectance values–vegetation indices–topographic data ([Formula: see text] = 0.93, RMSE = 0.1060 m(2) m(−2)). Among the autoregressive modeling techniques, the highest success was obtained with the Landsat 8 OLI using the moving average (2) procedure (R(2) = 0.56). This study is significant that it is the first to analyze the monthly effect on LAI modeling and mapping in pure Anatolian black pine stands using both reflectance values, vegetation indices, and topographic data.