Integration of LiDAR and QuickBird imagery for mapping riparian zones in Australian tropical savannas.

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for managing their functions associated with water quality, biodiversity, and wildlife habitats. The objective of this research was to integrate Light Detection and Ranging (LiDAR) and high spatial resolution QuickBird-2 imagery to estimate riparian zone attributes. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first obtained from the LiDAR data. The LiDAR-derived products and the QuickBird bands were then combined in an object-oriented approach to map riparian vegetation, streambed, vegetation overhang, bare ground, woodlands and rangelands. These products were also used to assess the riparian zone width. The overall result was a combined method, taking advantage of both optical and airborne laser systems, for mapping riparian forest structural parameters and riparian zone dimensions. This work shows the accuracy able to be obtained by integrating LiDAR data with high spatial resolution optical imagery to provide more detailed information for riparian zone management

Mean height and variability of height derived from Lidar data and Landsat images relationship

The mean height and standard deviat ion of the height of the forest canopy, derived from lidar data show to be important variables to summarize forest st ructure. However lidar data has a limited spat ial extent and very high economic cost . Landsat data provide useful st ructural informat ion in the horizontal plane and have easy access. The integrat ion of both data sources is an interest ing goal for sustainable forest management. Different spect ral indices (NDVI and Tasseled Cap) were obtained from 3 Landsat scenes (March 2000, June 2001 and September 2001). In addit ion, mean and standard deviat ion of lidar height werecalculated in 30x30m blocks. Correlat ion and forward stepwise regression analysis was applied between these two variables sets. Best correlat ion coefficients are achieved among mean lidar height versus NDVI and wetness for the three dates (range between 0.65 to -0.73). Others authors indicate that wetness is one of the best spectral indices to characterize forest st ructure. Best regression models include NDVI and wetness of June and September as dependent variables (adjusted r2: 0.55 – 0.62). These results show that lidar data can be useful for training Landsat to map forest st ructure but it should be interest ing to opt imize this approach