Use of ALS, Airborne CIR and ALOS AVNIR-2 data for estimating tropical forest attributes in Lao PDR |
| |
| Authors: | Zhengyang Hou Qing Xu Timo Tokola |
| |
| Institution: | aEuropean Forest Institute (EFI), Torikatu 34, FI-80100 Joensuu, Finland;bUniversity of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland |
| |
| Abstract: | 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. |
| |
| Keywords: | ALS Airborne CIR ALOS AVNIR-2 Tropical forest Forest monitoring |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
