Using GEDI lidar data and airborne laser scanning to assess height growth dynamics in fast-growing species:a showcase in Spain

Background:The NASA’s Global Ecosystem Dynamics Investigation(GEDI)satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis.The GEDI data can validate and update statistics from nationwide airborne laser scanning(ALS).We present a case in the Northwest of Spain using GEDI statistics and nationwide ALS surveys to estimate forest dynamics in three fast-growing forest ecosystems comprising 211,346 ha.The objectives were:i)to analyze the potential of GEDI to detect disturbances,ii)to investigate uncertainty source regarding non-positive height increments from the 2015–2017 ALS data to the 2019 GEDI laser shots and iii)to estimate height growth using polygons from the Forest Map of Spain(FMS).A set of 258 National Forest Inventory plots were used to validate the observed height dynamics.Results:The spatio-temporal assessment from ALS surveying to GEDI scanning allowed the large-scale detection of harvests.The mean annual height growths were 0.79(SD=0.63),0.60(SD=0.42)and 0.94(SD=0.75)m for Pinus pinaster,Pinus radiata and Eucalyptus spp.,respectively.The median annual values from the ALS-GEDI positive increments were close to NFI-based growth values computed for Pinus pinaster and Pinus radiata,respectively.The effect of edge border,spatial co-registration of GEDI shots and the influence of forest cover in the observed dynamics were important factors to considering when processing ALS data and GEDI shots.Discussion:The use of GEDI laser data provides valuable insights for forest industry operations especially when accounting for fast changes.However,errors derived from positioning,ground finder and canopy structure can introduce uncertainty to understand the detected growth patterns as documented in this study.The analysis of forest growth using ALS and GEDI would benefit from the generalization of common rules and data processing schemes as the GEDI mission is increasingly being utilized in the forest remote sensing community.

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

Background:Black alder(Alnus glutinosa)forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex(class Oomycetes),“alder Phytopththora”.Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity.Data obtained by unmanned aerial vehicles(UAVs)may be particularly useful for such tasks due to the high resolution,flexibility of acquisition and cost efficiency of this type of data.In this study,A.glutinosa decline was assessed by considering four categories of tree health status in the field:asymptomatic,dead and defoliation above and below a 50% threshold.A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles-red green blue(RGB)data were analysed using classical random forest(RF)and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony.A total of 34 remote sensing variables were considered,including a set of vegetation indices,texture features from the normalized difference vegetation index(NDVI)and a digital surface model(DSM),topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level.Results:The four categories identified by the RF yielded an overall accuracy of 67%,while aggregation of the legend to three classes(asymptomatic,defoliated,dead)and to two classes(alive,dead)improved the overall accuracy to 72% and 91% respectively.On the other hand,the confusion matrix,computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%,80% and 94% for four-,three-and two-level classifications,respectively.Discussion:The study findings provide forest managers with an alternative robust classification method for the rapid,effective assessment of areas affected and non-affected by the disease,thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forests.