Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelling the surface circulation and thermal structure of a tropical reservoir using three-dimensional hydrodynamic lake model and remote-sensing data

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determining large scale actual evapotranspiration using agro-meteorological and remote sensing data in the northwest of Sao Paulo State, Brazil

The best irrigation management depends on accurate estimation of reference evapotranspiration (ET0) and then selection of the appropriate crop coefficient for each phenological stage. However, the evaluation of water productivity on a large scale can be done by using actual evapotranspiration (ETa), determined by coupling agrometeorological and remote sensing data. This paper describes methodologies used for estimating ETa for 20 centerpivots using three different approaches: the traditional FAO crop coefficient (K-c) method and two remote sensing algorithms, one called SEBAL and other named TEIXEIRA. The methods were applied to one Landsat 5 Thematic Mapper image acquired in July 2010 over the Northwest portion of the Sao Paulo State, Brazil. The corn, bean and sugar cane crops are grown under center pivot sprinkler irrigation. ET0 was calculated by the Penman-Monteith method with data from one automated weather station close to the study site. The results showed that for the crops at effective full cover, SEBAL and TEIXEIRA's methods agreed well comparing with the traditional method. However, both remote sensing methods overestimated ETa according to the degree of exposed soil, with the TEIXEIRA method presenting closer ETa values with those resulted from the traditional FAO K-c method. This study showed that remote sensing algorithms can be useful tools for monitoring and establishing realistic K-c values to further determine ETa on a large scale. However, several images during the growing seasons must be used to establish the necessary adjustments to the traditional FAO crop coefficient method.

Confiabilidade do teste de identificação de transtornos devido ao uso de álcool (AUDIT) em adolescentes

The objective of this study was to estimate the reliability of the alcohol use disorders identification test (AUDIT) applied to adolescents. Evaluations were performed with 141 students of ages ranging from 14 to 17 years (14.93±0.62). The internal consistency of the instrument was estimated by Cronbach’s alpha coefficient and its reproducibility by weighted Kappa statistics by score and by a reliability interval of 95%. The AUDIT presented excellent internal consistency (α=0.80, rinter-item=0.29). Most questions present reproducibility ranked as “good”. It is concluded that the AUDIT is a reliable instrument to survey information about alcohol use in adolescents.

Coupling remote sensing bio-optical and three-dimensional hydrodynamic modeling to study the phytoplankton dynamics in a tropical hydroelectric reservoir

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Impairment of neutrophil migration to remote inflammatory site during lung histoplasmosis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Tokamak TCABR: Acquisition system, data analysis, and remote participation using MDSplus

Each plasma physics laboratory has a proprietary scheme to control and data acquisition system. Usually, it is different from one laboratory to another. It means that each laboratory has its own way to control the experiment and retrieving data from the database. Fusion research relies to a great extent on international collaboration and this private system makes it difficult to follow the work remotely. The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The choice of MDSplus (Model Driven System plus) is proved by the fact that it is widely utilized, and the scientists from different institutions may use the same system in different experiments in different tokamaks without the need to know how each system treats its acquisition system and data analysis. Another important point is the fact that the MDSplus has a library system that allows communication between different types of language (JAVA, Fortran, C, C++, Python) and programs such as MATLAB, IDL, OCTAVE. In the case of tokamak TCABR interfaces (object of this paper) between the system already in use and MDSplus were developed, instead of using the MDSplus at all stages, from the control, and data acquisition to the data analysis. This was done in the way to preserve a complex system already in operation and otherwise it would take a long time to migrate. This implementation also allows add new components using the MDSplus fully at all stages. (c) 2012 Elsevier B.V. All rights reserved.

Unmanned platform for long-range remote analysis of volatile compounds in air samples

This paper describes a long-range remotely controlled CE system built on an all-terrain vehicle. A four-stroke engine and a set of 12-V batteries were used to provide power to a series of subsystems that include drivers, communication, computers, and a capillary electrophoresis module. This dedicated instrument allows air sampling using a polypropylene porous tube, coupled to a flow system that transports the sample to the inlet of a fused-silica capillary. A hybrid approach was used for the construction of the analytical subsystem combining a conventional fused-silica capillary (used for separation) and a laser machined microfluidic block, made of PMMA. A solid-state cooling approach was also integrated in the CE module to enable controlling the temperature and therefore increasing the useful range of the robot. Although ultimately intended for detection of chemical warfare agents, the proposed system was used to analyze a series of volatile organic acids. As such, the system allowed the separation and detection of formic, acetic, and propionic acids with signal-to-noise ratios of 414, 150, and 115, respectively, after sampling by only 30 s and performing an electrokinetic injection during 2.0 s at 1.0 kV.

Accessibility in Digital Television: Designing Remote Controls

Free to air television is still an important tool to provide information and communication in many countries. Therefore, the universal access to the television system is very important. This paper presents a set of Digital Television accessible remote control devices designed for the Brazilian Digital Television. A research was conducted, interviewing people with disabilities in Brazil. Three remote control models were proposed, consolidating the main identified requirements, being accessible for a diverse group of impairments.