Abstract
In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.
Highlights
In forest management, information on forest productivity is essential for planning silvicultural operations [1]
We found that models constructed with normalized hyperspectral data produced lower root mean square error (RMSE) values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels
We considered it to be important that airborne laser scanner (ALS) variables representing both the status and the change were included in models of H40 site index, which is a proxy for change in dominant height over a given period of time
Summary
In forest management, information on forest productivity is essential for planning silvicultural operations [1]. Forecasts of forest development [2] under a certain management regime depend on the forest productivity. Analyses of future scenarios with regard to the effect of different management regimes on carbon sequestration, and the causal relationships between changing climate and carbon sequestration, are important. In this context, and with the changes in growth patterns that are expected by changing climate [10,11], the importance of information about forest productivity becomes even more vital
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