LASmoons: Alejandro Hinojosa

Alejandro Hinojosa (recipient of three LASmoons)
Earth Sciences Division
CICESE, MEXICO

Background:
The Baja California peninsula in Mexico is a land feature drifting away from the continent due to tectonic plate movement leaving in its path scars of well-defined and studied faults system. An aerial LiDAR survey of the Agua Blanca fault corridor was collected by NCALM to primarily delineate its trace and locate offset features along its path to eventually estimate fault slip rates. Faults may act as barriers or conduits of water that may enable the development of vegetation patches. It is known the presence of water springs and native long-lived high vegetation patches along the Agua Blanca fault. As a secondary use of the aerial LiDAR survey, we intend to demonstrate that the spatial distribution of native long-lived high trees (like oaks) in the region is influenced by the Agua Blanca fault, indirectly by the persistent water resource from its springs.

lasmoons_Alejandro_Hinojosa_1
Goal:

The aim of this research is to assess through remote sensing the relation of the spatial distribution of native vegetation patches and the Agua Blanca Fault in Ensenada, Baja California, Mexico. We plan to use spatial analysis tools on passive (optical) and active sensors data to achieve our goal. A Canopy Height Model (CHM) will be calculated from the LiDAR data using the “pit-free” algorithm of (Khosravipour et.al., 2014) that can be implemented with LAStools. We will then investigae spatial correlation of the fault traces delineated from a Digital Terrain Model (DTM) and the vegetation patches obtained from the CHM. Hydrology models will be applied to the DTM in order to differentiate vegetation patches occurring in accumulation zones (like canyons) from those occurring along fault traces.

Data:
+ 75 square km of aerial LiDAR along Agua Blanca Fault corridor collected by NCALM on July 2014.
+ average point density: 5 pts/m2

LAStools processing:
1)
quality control of LiDAR [lasoverlap, lascontrol, lasinfo, lasgrid]
2) create a tiling with buffers [lastile]
3) classify points and create a DTM and DSM [lasgroundlas2dem, blast2dem]
4).normalized the LiDAR tiles [lasheight]
5) generate a Canopy Height Model (CHM) using the pit-free method of Khosravipour et al. (2014) with the workflow described here [lasthin, las2dem, lasgrid]

Reference:
Hooper, E. C. D. (1991). Fluid migration along growth faults in compacting sediments. Journal of Petroleum Geology, 14(2), 161-180.
Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T.J., Hussin, Y.A., 2014. Generating pit-free Canopy Height Models from Airborne LiDAR. PE&RS = Photogrammetric Engineering and Remote Sensing 80, 863-872.
Carter, R. E., y Klinka, K. (1990). Relationships between growing-season soil water-deficit, mineralizable soil nitrogen and site index of coastal Douglas fir. Forest Ecology and Management, 30(1), 301-311.

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