Canopy Height Estimation of Three Sugarcane Varieties Using an Unmanned Aerial Vehicle (UAV)

Authors

DOI:

https://doi.org/10.5902/2236499465070

Keywords:

UAV, Remote Sensing, Structure from Motion, Canopy Height

Abstract

The objective of this study is to estimate the canopy height of three sugarcane varieties at different growth stages, with UAV data and to evaluate its relationship with two vegetation indices (VIs) (NDVI and EVI) at different spatial resolutions (3m, 10m and 30m). The indices were calculated using images from the PlanetScope, Sentinel-2, and Landsat 8 satellites, acquired as close as possible to the UAV imaging date. The estimated canopy height for each field was obtained by subtracting the Digital Surface Model (DSM) from the Digital Terrain Model (DTM), built by the Structure from Motion (SfM) technique with UAV RGB images as input. The average from each estimated height was compared with the average measured in the field, to verify the accuracy of the model. Both Pearson’s correlation and the Determination Coefficient (R²) were calculated between the estimated heights and the VIs. The average estimated canopy height and measurements in the field were different (p<0.05), with the model generally underestimating the height. However, the plantation’s surface models portrayed the spatial variability within the field. The use of GCPs is mandatory to reduce errors in estimation. Regarding the indices, the spatial resolution did not influence the correlation analysis, with NDVI showing higher values than EVI, except for area A. However, all values, for both coefficients, were below 0.5 for all areas. Despite that, a temporal analysis is necessary to improve the relationship between the canopy height and VIs. The potential of UAV data as a proxy to zonal management should be addressed in future studies.

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Author Biographies

Gabriela Zoli Simões, Instituto Nacional de Pesquisas Espaciais

Graduated in Environmental Engineering from the Federal Technological University of Paraná (2018). Currently a master's student in Remote Sensing at the National Institute for Space Research (INPE). He has experience in the area of ​​Environmental Sciences, with an emphasis on Environmental Engineering and Remote Sensing.

Hermann Johann Heinrich Kux, Instituto Nacional de Pesquisas Espaciais

He holds a degree in Geography from the University of São Paulo (1970) and a PhD in Geology from Universität Freiburg (1976). He is currently a senior researcher at the National Institute for Space Research. He has experience in the area of ​​Geosciences, with an emphasis on Remote Sensing, working mainly on the following topics: remote sensing, object-oriented classification, land use, synthetic aperture radar (sar) and urban planning. From 2016 onwards, evaluation of cameras operating in thermal infrared embedded in drones, for environmental studies.

Fábio Marcelo Breunig, Federal University of Santa Maria

He holds a degree in Geography from the Federal University of Santa Maria (2006), a Master's and PhD in Remote Sensing from the National Institute for Space Research (2008 and 2011, respectively). He has a post-doctorate and has held a research productivity grant (PQ) from CNPq since 2015. He is currently head of the Forestry Engineering department, coordinator of research and extension projects. His research and teaching activities are related to Remote Sensing of the Environment (agriculture, forest, water), GIS, Error Analysis and Environmental Modeling.

Luiz Henrique Pereira, IDGeo - Geointeligência Agrícola

Geographer graduating from UNESP (2007), with a Master's degree (2010) and PhD (2016) in Environmental Analysis and Geoprocessing from the Postgraduate Program in Geography/Institute of Geosciences and Exact Sciences/UNESP, Rio Claro. Develops research applied to agricultural territorial planning and management, focusing on dynamic geospatial modeling (Precision Agriculture, plant development of agricultural crops, productivity assessment in agricultural systems, and soil and water losses in river basins); and harvest monitoring in sugarcane plants. Works in the areas of Geoprocessing, Remote Sensing and Geospatial Data Governance. Experience as coordinator of geomatics and agricultural technology projects in the Bioenergy sector. He is a FAO/UN Consultant (Brasília-DF) in geoprocessing and remote sensing for agriculture and water resources, and is currently Research, Development and Innovation Manager at IDGeo? Intelligence in Geographic Data (Piracicaba-SP), conducting projects on the topics of Modeling agricultural and environmental systems, Remote Monitoring of sugarcane fields.

Geographer graduating from UNESP (2007), with a Master's degree (2010) and PhD (2016) in Environmental Analysis and Geoprocessing from the Postgraduate Program in Geography/Institute of Geosciences and Exact Sciences/UNESP, Rio Claro. Develops research applied to agricultural territorial planning and management, focusing on dynamic geospatial modeling (Precision Agriculture, plant development of agricultural crops, productivity assessment in agricultural systems, and soil and water losses in river basins); and harvest monitoring in sugarcane plants. Works in the areas of Geoprocessing, Remote Sensing and Geospatial Data Governance. Experience as coordinator of geomatics and agricultural technology projects in the Bioenergy sector. He is a FAO/UN Consultant (Brasília-DF) in geoprocessing and remote sensing for agriculture and water resources, and is currently Research, Development and Innovation Manager at IDGeo? Intelligence in Geographic Data (Piracicaba-SP), conducting projects on the topics of Modeling agricultural and environmental systems, Remote Monitoring of sugarcane fields.

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Published

2023-11-17

How to Cite

Simões, G. Z., Kux, H. J. H., Breunig, F. M., & Pereira, L. H. (2023). Canopy Height Estimation of Three Sugarcane Varieties Using an Unmanned Aerial Vehicle (UAV). Geografia Ensino & Pesquisa, 27, e65070. https://doi.org/10.5902/2236499465070

Issue

Vol. 27 (2023): Geografia Ensino & Pesquisa. Publicação Contínua

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Geoinformação e Sensoriamento Remoto em Geografia

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