Pauline Stenberg, Miina Rautiainen, Terhikki Manninen, Pekka Voipio, Heikki Smolander. (2004). Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica vol. 38 no. 1 article id 431. https://doi.org/10.14214/sf.431

Keywords: Landsat ETM ; Leaf Area Index; spectral vegetation indices; boreal coniferous forests

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Estimation of leaf area index (LAI) using spectral vegetation indices (SVIs) was studied based on data from 683 plots on two Scots pine and Norway spruce dominated sites in Finland. The SVIs studied included the normalised difference vegetation index (NDVI), the simple ratio (SR), and the reduced simple ratio (RSR), and were calculated from Landsat ETM images of the two sites. Regular grids of size 1 km2 with gridpoints placed at 50 m intervals were established at the sites and measurements of LAI using the LAI-2000 instrument were taken at the gridpoints. SVI-LAI relationships were examined at plot scale, where the plots were defined as circular areas of radius 70 m around each gridpoint. Plotwise mean LAI was computed as a weighted average of LAI readings taken around the gridpoints belonging to the plot. Mean LAI for the plots ranged from 0.36 to 3.72 (hemisurface area). All of the studied SVIs showed fair positive correlation with LAI but RSR responded more dynamically to LAI than did SR or NDVI. Especially NDVI showed poor sensitivity to changes in LAI. RSR explained 63% of the variation in LAI when all plots were included (n = 683) and the coefficient of determination rose to 75% when data was restricted to homogeneous plots (n = 381). Maps of estimated LAI using RSR showed good agreement with maps of measured LAI for the two sites.

Stenberg, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: pauline.stenberg@helsinki.fi
Rautiainen, Department of Forest Ecology, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: mr@nn.fi
Manninen, Finnish Meteorological Institute, Meteorological research, Ozone and UV radiation research, P.O. Box 503, FIN-00101 Helsinki, Finland E-mail: tm@nn.fi
Voipio, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: pv@nn.fi
Smolander, Finnish Forest Research Institute, Suonenjoki Research Station, FIN-77600 Suonenjoki, Finland E-mail: hs@nn.fi

Category : Research note

article id 324, category Research note

Matti Katila. (2006). Empirical errors of small area estimates from the multisource National Forest Inventory in Eastern Finland. Silva Fennica vol. 40 no. 4 article id 324. https://doi.org/10.14214/sf.324

Keywords: k-nearest neighbours estimation; Landsat ETM ; multisource forest inventory; synthetic estimation

Abstract | View details | Full text in PDF | Author Info

The precision of multisource national forest inventory (MS-NFI) estimators and simple synthetic estimators based on NFI field data only was assessed employing an independent inventory data set of several small areas in Eastern Finland. There were seven test units of size 100 km² and three test units of size 1 km² for which a systematic field sampling was carried out. The ‘improved’ MS-NFI method yielded the most precise estimates for mean volume and mean volume of pine and spruce: relative root mean square errors (RMSE^*) were 5%, 12% and 15% for 100 km² test units and 13%, 27% and 40% for 1 km² test units respectively. The stratified MS-NFI method was best for broad-leaved volume estimation. Synthetic estimation based on the NFI9 field plots post-stratified with coarse scale forest variable maps from NFI8 resulted in RMSE^*s comparable to those of the ordinary MS-NFI in areas of 100 km² for mean volume and mean volume of pine and spruce. The amount of variation between the field inventory estimates for the test units explained by the MS-NFI estimators remained the same or increased when the size of the area increased from of 1 km² to 100 km² and up to 2000 km². The validation of the largest areas was made against the NFI9 field inventory estimates for groups of municipalities in the study area.

Katila, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mk@nn.fi

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