Abstract
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way.
Highlights
Controlling and managing above-ground vegetation has crucial importance in studies related to reducing carbon emissions [1] or associated with deforestation and forest degradation [2]
This work aimed to evaluate the ability of four phytovolume estimation methods, all obtained using the difference between digital surface model (DSM) and digital terrain model (DTM), based on very high-resolution spatial imagery collected by a multispectral sensor onboard a Unmanned aerial vehicles (UAVs)
In the four methods compared in this paper, the DSM was derived from a UAV-photogrammetric project based on the structure from motion (SfM) algorithm, and the four DTMs were based on the unclassified dense point cloud produced by a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC) [34], a multispectral vegetation index (FMI), and a cloth simulation filter (FCS) [35]
Summary
Controlling and managing above-ground vegetation has crucial importance in studies related to reducing carbon emissions [1] or associated with deforestation and forest degradation [2]. Both effects are increased by wildland fires and progressive abandonment of farming lands [3,4]. Biomass is a key structural variable in all the studies about ecosystem dynamics, biodiversity, and sustainability [3,5,6]. Biomass estimation in a forest environment can be undertaken in a direct way by harvesting and weighing sampled plant material [14] or indirectly by measuring morphological variables and applying mathematical models. A mixed model consists of harvesting a sample of plant material and calibrating the mathematical model using the collected data [4,9]
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