Seasonal behavior of vegetation determined by sensor on an unmanned aerial vehicle

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...

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Bibliographic Details
Published in:Anais da Academia Brasileira de Ciências 2021, Vol.93 (1), p.e20200712
Main Authors: Felix, Filipe C, Avalos, Fabio A P, Lima, Wellington DE, Cândido, Bernardo M, Silva, Marx L N, Mincato, Ronaldo L
Format: Article
Language:English
Subjects:
Coffea spp
Environmental Monitoring
Forests
MULTIDISCIPLINARY SCIENCES
Remote Sensing Technology
Seasons
Soil
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Summary: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.
ISSN:0001-3765
1678-2690
1678-2690
DOI:10.1590/0001-3765202120200712