Abstract
Remote sensing has facilitated the techniques used for the mapping, modelling and understanding of forest parameters. Remote sensing applications usually use information from either passive optical systems or active radar sensors. These systems have shown satisfactory results for estimating, for example, aboveground biomass in some biomes. However, they presented significant limitations for ecological applications, as the sensitivity from these sensors has been shown to be limited in forests with medium levels of aboveground biomass. On the other hand, LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and above ground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have low acquisition density with global geographical cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data (geological, meteorological, slope, vegetation indices etc.) in order to propose a forest canopy height map with good precision and high spatial resolution. In addition, estimating forest canopy heights from large-footprint satellite LiDAR waveforms, is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. Therefore, the research carried out in this thesis aimed at: 1) estimate, and validate canopy heights using raw data from airborne LiDAR and then evaluate the potential of spaceborne LiDAR GLAS data at estimating forest canopy heights. 2) evaluate the fusion potential of LiDAR (using either sapceborne and airborne data) and ancillary data for forest canopy height estimation at very large scales. This research work was carried out over the French Guiana.The estimation of the canopy heights using the airborne showed an RMSE on the canopy height estimates of 1.6 m. Next, the potential of GLAS for the estimation of canopy heights was assessed using multiple linear (ML) and Random Forest (RF) regressions using waveform metrics and principal component analssis (PCA). Results showed canopy height estimations with similar precisions using either LiDAR metrics or the principal components (PCs) (RMSE ~ 3.6 m). However, a regression model (ML or RF) based on the PCA of waveform samples is an interesting alternative for canopy height estimation as it does not require the extraction of some metrics from LiDAR waveforms that are in general difficult to derive in dense forests, such as those in French Guiana. Next, canopy heights extracted from both airborne and spaceborne LiDAR were first used to map canopy heights from available mapped environmental data (geological, meteorological, slope, vegetation indices etc.). Results showed an RMSE on the canopy height estimates of 6.5 m from the GLAS dataset and of 5.8 m from the airborne LiDAR dataset. Then, in order to improve the precision of the canopy height estimates, regression-kriging (kriging of random forest regression residuals) was used. Results indicated a decrease in the RMSE from 6.5 to 4.2 m for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset. Finally, in order to study the impact of the spatial sampling of future LiDAR missions on the precision of canopy height estimates, six subsets were derived from the airborne LiDAR dataset with flight line spacing of 5, 10, 20, 30, 40 and 50 km (corresponding to 0.29, 0.11, 0.08, 0.05, 0.04, and 0.03 points/km², respectively). Results indicated that using the regression-kriging approach, the precision on the canopy height map was 1.8 m with flight line spacing of 5 km and decreased to an RMSE of 4.8 m for the configuration for the 50 km flight line spacing.