Low-cost system for radiometric calibration of UAV-based multispectral imagery

Rosas, Jorge Tadeu Fim; Pinto, Francisco de Assis de Carvalho; Queiroz, Daniel Marçal de; Villar, Flora Maria de Melo; Martins, Rodrigo Nogueira; Silva, Samuel de Assis

doi:10.1080/14498596.2020.1860146

Cited by 10 publications

(3 citation statements)

References 41 publications

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“…In precision agriculture, the selection of RCMs and the evaluation of reflectance accuracy are often ignored ( Jiang et al, 2019 ; Rosas et al, 2020 ; Wan et al, 2020 ). The results presented in Figure 1 and Tables 2 , 3 show that the EL method is suitable for the conversion of high reflectance (e.g., the slabstone and the rice at the tillering stage), while the SEL method can obtain more accurate low reflectance (e.g., the grassland and the rice at jointing, booting, and heading stages).…”

Section: Discussionmentioning

confidence: 99%

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes

et al. 2022

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As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.

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Section: Discussionmentioning

confidence: 99%

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes

et al. 2022

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“…Care must be taken with homemade panels because, even though they may appear a particular shade to the human eye (visible spectrum), they may not be a similar reflectance across all wavelengths observed by a multispectral or hyperspectral sensor. Many studies have identified promising materials for homemade panels: plywood covered with matte paint (Rosas et al, 2020), gray linoleum, and black fine-weave cotton fabric (Cao et al, 2019).…”

Section: Radiometric Calibrationmentioning

confidence: 99%

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

et al. 2022

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Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process.Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the groundwith the Terrestrial Observation System-and remotely-from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain-the broad-extent, fine-grain domain-more accessible via NEON/UAS integration.

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“…Various studies have employed remote sensing to assess coffee plants, monitor diseases and water stress, and establish correlations among other biotic and abiotic factors using vegetation indices (VIs) measured by orbital sensors [12][13][14]. Others have utilized multispectral cameras coupled to UAVs to evaluate fruit ripening, seedling mortality, and correlations between field measurements and aerial images [15][16][17][18][19][20][21][22][23]. However, no studies have utilized remote sensing and UAVs to monitor farming practices, specifically the response of coffee seedlings transplanted in different containers.…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Barata,

Ferraz,

Bento

et al. 2024

Agriculture

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Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach.

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Low-cost system for radiometric calibration of UAV-based multispectral imagery

Cited by 10 publications

References 41 publications

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

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