In this study, the potential of remote sensing in tropical forests is examined in relation to the diversification of sensors. We report here on the comparison of alternative methods that use multisource data from Airborne Laser Scanning (ALS), Airborne CIR and ALOS AVNIR-2 to estimate stem volume and basal area, in Laos. Multivariate linear regression analyses with stepwise selection of predictors were implemented for modelling. The predictors of ALS metrics were calculated by means of the canopy height distribution approach, while predictors from both spectral and textual features were respectively generated for Airborne CIR and ALOS AVNIR-2 data. With respect to the estimation capacity from individual data sources after leave-one-out cross-validation, the ALS data proved superior, with the lowest RMSE of 36.92% for stem volume and 47.35% for basal area, whereas Airborne CIR and ALOS AVNIR-2 remained at similar accuracy levels, but fell well behind the ALS data. By integrating ALS metrics with other predictors from Airborne CIR or ALOS AVNIR-2, hybrid modelling was further tested respectively. The results showed that only the hybrid model for stem volume involving ALS and Airborne CIR improved the accuracy of 1.9% in terms of relative RMSE than that of using ALS alone.
|Original language||English (US)|
|Number of pages||11|
|Journal||ISPRS Journal of Photogrammetry and Remote Sensing|
|State||Published - Nov 2011|
Bibliographical noteFunding Information:
The authors thank the SUFORD project and the Finnish Ministry of Foreign Affairs for their support for the data acquisition. We are also very grateful to the staff of the University of Eastern Finland and the European Forest Institute (EFI), especially Matti Maltamo and Tuula Nuutinen, for their invaluable suggestions and constructive comments. Funding for this research was received from a Ponsse Grant 2010 managed by the Foundation for European Forest Research (FEFR) . Last but not least, the comments provided by the two anonymous reviewers are gratefully acknowledged.
- ALOS AVNIR-2
- Airborne CIR
- Forest monitoring
- Tropical forest