Use of remotely sensed auxiliary data for improving sample-based forest inventories

Abstract

Over the past decades it has been shown that remotely sensed auxiliary data have a potential to increase the precision of key estimators in sample-based forest surveys. This thesis was motivated by the increasing availability of remotely sensed data, and the objectives were to investigate how this type of auxiliary data can be used for improving both the design and the estimators in sample-based surveys. Two different modes of inference were studied: model-based inference and design-based inference. Empirical data for the studies were acquired from a boreal forest area in the Kuortane region of western Finland. The data comprised a combination of auxiliary information derived from airborne LiDAR and Landsat data, and field sample plot data collected using a modification of the 10 Finnish National Forest Inventory. The studied forest attribute was growing stock volume. In Paper I, remotely sensed data were applied at the design stage, using a newly developed design which spreads the sample efficiently in the space of auxiliary data. The analysis was carried out through Monte Carlo sampling simulation using a simulated population developed by way of a copula technique utilizing empirical data from Kuortane. The results of the study showed that the new design resulted in a higher precision when compared to a traditional design where the samples were spread only in the space of geographical data. In Paper II, remotely sensed auxiliary data were applied in connection with model-assisted estimation. The auxiliary data were used mainly in the estimation stage, but also in the design stage through probabilityproportional-to-size sampling utilizing Landsat data. The results showed that LiDAR auxiliary data considerably improved the precision compared to estimation based only on field samples. Additionally, in spite of their low correlation with growing stock volume, adding Landsat data as auxiliary data further improved the precision of the estimators. In Paper III, the focus was set on model-based inference and the influence of the use of different models on the precision of estimators. For this study, a second simulated population was developed utilizing the empirical data, including only non-zero growing stock volume observations. The results revealed that the choice of model form in model-based inference had minor to moderate effects on the precision of the estimators. Furthermore, as expected, it was found that model-based prediction and model-assisted estimation performed almost equally well. In Paper IV, the precision of model-based prediction and model-assisted estimation was compared in a case where field and remotely sensed data were geographically mismatched. The same simulated population as used in Paper III was employed in this study. The results showed that the precision in most cases decreased considerably, and more so when LiDAR auxiliary data were applied, compared to when Landsat auxiliary data were used. As for the choice of inferential framework, it was revealed that model-based inference in this case had some advantages compared to design-based inference through model-assisted estimators. The results of this thesis are important for the development of forest inventories to meet the requirements which stem from an increasing number of international commitments and agreements related to forests.