Abstract
Geographic information systems make it possible to obtain fine scale maps for environmental monitoring from airborne sensors on aerial platforms, such as unmanned aerial vehicles (UAVs), which offer products with low costs and high space-time resolution. The present study assessed the performance of an UAV in the evaluation of the seasonal behavior of five vegetation coverages: Coffea spp., Eucalyptus spp., Pinus spp. and two forest remnants. For this, vegetation indices (Excess Green and Excess Red minus Green), meteorological data and moisture of surface soils were used. In addition, Sentinel-2 satellite images were used to validate these results. The highest correlations with soil moisture were found in coffee and Forest Remnant 1. The Coffea spp. had the indices with the highest correlation to the studied soil properties. However, the UAV images also provided relevant results for understanding the dynamics of forest remnants. The Excess Green index (p = 0.96) had the highest correlation coefficients for Coffea spp., while the Excess Red minus Green index was the best index for forest remnants (p = 0.75). The results confirmed that low-cost UAVs have the potential to be used as a support tool for phenological studies and can also validate satellite-derived data.
Highlights
Unmanned aerial vehicles (UAVs) have dynamized Earth’s surface studies by collecting data at low altitudes (Getzin et al 2012)
Pinus spp. and Eucalyptus spp. areas have not been evaluated by UAV due to the difficulty of these platforms in mapping homogeneous coverings (Matese et al 2015, Pádua et al 2017) formed by vegetal clones
The results obtained in this study are noteworthy because the vegetation indices indicated the correlation between vegetation dynamics and soil moisture, i.e., that the availability of moisture is related to evapotranspiration
Summary
Unmanned aerial vehicles (UAVs) have dynamized Earth’s surface studies by collecting data at low altitudes (Getzin et al 2012) The combination of these platforms with computer vision algorithms ensures the generation of highquality products, such as orthophotomosaics and three-dimensional (3D) models, which have the potential to detect and identify wildlife and flora classes. UAVs represent an important tool in agro-environmental studies, considering the imminent needs for conserving natural resources and promoting food security (Anderson & Gaston 2013, Koh & Wich 2012) In this scenario, these platforms appear to be a potential new option for monitoring vegetation phenology (Zeng et al 2020, Klosterman et al 2018, Dandois & Ellis 2013) since they offer new opportunities for the scale-appropriate measurement of ecological phenomena, delivering fine spatial resolution data at user-controlled revisiting periods with relatively low cost (Hardin et al 2019, Anderson & Gaston 2013). UAVs allow the assessment of vegetation structure, such as the detection and monitoring of natural gaps and species identification for forest inventory and agricultural mapping by vegetation indices
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