Geostatistical Technique for Growing Stock Estimates on Small Forest Stands Using Inventory, Enviromental and Landsat 8 Data

Abstract

UDK: 630*52:311
 Regular forest inventory on state owned forest delivers plenty of data and information enabling detailed insight in forest structure and quantities. Current methodology for forest assessment on private properties considers time-consuming, low-intensive terrestrial measurement and observation on scattered small forest stands distributed on hilly and plane position around complex of state owned forests.
 Here are evaluated two modeling techniques: ordinary least square (OLS) regression and geographically weighted regression (GWR) estimating growing stock quantities of point sample inside the smallest state owned forest stands (area less then 10 ha). Used material contained forest attributes local estimates from regular inventory distributed in unique management class: beech and fir mixed forest on deep silicate soil, environmental and transformed spectral Landsat 8 data.
 Obtained results pointed out statistical significance of normalized standardized spectral radiance of NIR and SWIR Landsat bands in regression models. The GWR estimates achieve up to almost 30% higher variability explanation then OLS models. Also, GWR showed wider range then OLS estimates with smaller prediction errors. Evaluation on sample stand level resulted in reliable estimates of particular species or groups and total mean growing stock for all small stands. Further research about potential of GWR and other geo-statistical techniques for forest attribute estimates on more intensive point sample inside small spatial unit and/or whole spatial unit is recommended.