Assessing Post-Storm Forest Dynamics in the Pyrenees Using High-Resolution LIDAR Data and Aerial Photographs

We evaluated how historical storm events have shaped the current forest landscape in three Pyrenean subalpine forests(NE Spain).For this purpose we related forest damage estimations obtained from multi-temporal aerial photographic comparisons to the current forest typology generated from airborne Li DAR data, and we examined the role of past natural disturbance on the current spatial distribution of forest structural types.We found six forest structural types in the landscape: early regeneration(T1 and T2), young even-aged stands(T3), uneven-aged stands(T4) and adult stands(T5and T6).All of the types were related to the timing and severity of past storms, with early-regeneration structures being found in areas markedly affected in recent times, and adult stands predominating in those areas that had suffered lowest damage levels within the study period.In general, landscapes where high or medium levels of damage were recurrent also presented higher levels of spatial heterogeneity,whereas the opposite pattern was found in the less markedly affected landscape, characterized by thepresence of large regular patches.Our results show the critical role that storm regimes in terms of timing and severity of past storms can play in shaping current forest structure and future dynamics in subalpine forests.The knowledge gained could be used to help define alternative forest management strategies oriented toward the enhancement of landscape heterogeneity as a measure to face future environmental uncertainty.

Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging(Li DAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the Li DAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne Li DAR DEM derivatives. Quasi-3 D relief image maps are generated based on the sky-view factor(SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km^(2) study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of Li DAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m^(2). Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of Li DAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.