Light Detection and Ranging for Implementing Water-Oriented Forest Management in a Semiarid Sub-Catchment (Valencia, Spain)

Abstract

[EN] Diminishing rainfall interception in semiarid forests may offset reductions of available water either for ecosystem or human needs under the climate change predictions. Forest managers should be aware of this issue and develop specific hydrology-oriented silvicultural treatments. In this sense, relating forest-water processes to detailed forest structure information, as provided by light detection and ranging (LiDAR), is essential. In this study, low-density LiDAR data are used to characterize forest structure and throughfall water. First, LiDAR was validated as an estimator of the forest structure variables (canopy cover, canopy height, and leaf area index) in a semiarid forest, with acceptable root mean square errors (6.2% cover, 0.4m(2)m(-2) LAI and 0.7m height). Secondly, the LiDAR-derived variables were related to the measured throughfall by means of simple and multiple regression models. The best model included canopy cover and stand height, and improved upon previous results based on field measures from r(2) = 0.76-0.95. Finally, the model was used to estimate the throughfall in the whole sub-catchment, where very low or low throughfall (<74%) was identified in 47.5% of the area. Low-density LiDAR was proved to be useful in guiding hydrology-oriented silviculture to increase the amount of throughfall water.