GEOSTATISTICAL TECHNIQUE FOR GROWING STOCK ESTIMATES ON SMALL FOREST STANDS USING INVENTORY, ENVIROMENTAL AND LANDSAT 8 DATA

Azra Čabaravdić,Sanela Mulić,Merisa Osmanović,Galib Mahmutović

doi:10.54652/rsf.2016.v46.i1.75

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.

Highlights

Regular forest inventory on state owned forest delivers plenty of data and information enabling detailed insight in forest structure and quantities
Diameter at the breast height and height are recorded as geo-position of circle centre and local estimates of forest attributes are produced following standard inventory procedure
Following figures present observed versus predicted growing stock quantities of two regression types for broadleaves and conifers that are similar as the main species: beech and fir too

Summary

MATERIAL AND METHODS

Study area includes part of the state owned forest stands in Management unit Oskova. Study area, as a part of the Forest Management area "Sprečko" is located in the north east part of Bosnia situated between Longitudes 18° 29 - 18° 37' and Latitudes 44° 23 - 44° 17' (Figure 1). Geostatistical Technique for growing stock estimates on small forest stands using Inventory, Enviromental and Landsat 8 data where Yi , xk,i , and i are, respectively, dependent variable, kth independent variable, and the Gaussian error at location i , (ui , vi ) is the x,y coordinate of the kth location, and coefficients i (ui , vi ) are varying conditions on the location (NAKAYA 2014). Growing stock estimates on known sample-based geolocation in small forest stand (less 10 ha) are determined as prediction on nonsampled points. Growing stock distribution for broadleaves and conifers are compared in order to analyze differences in ranges of OLS and GWR estimates. In order to perform OLS and GWR regressions modelling, correlation between target variables (growing stock for the main species, groups: conifers and broadleaves and total) and spectral and environmental predictors are calculated (Table 5).

Hill shade

GWR totali

Findings

OLS GWR OLS GWR OLS GWR OLS GWR OLS GWR