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Growth and production models can help to simulate the growth of tree dimensions to predict forest productivity at different levels. In this context, the following questions arise: (i) is it possible to recognize the growth pattern of eucalyptus species based on spectral features using machine learning (ML) for data modeling? (ii) what spectral features provides better accuracy? and (iii) what ML algorithms are most accurate for performing this modeling? To answer these questions, the present study evaluated the use of ML techniques using breast height and total plant height to classify the growth of five species of eucalyptus and Corymbria citriodora in an unsupervised learning, and the obtained classes for induce ML algorithms to recognize the species with relation to their growth using vegetation indices (VIs) and spectral bands (SBs). It were evaluated five eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis e E. urograndis) and C. citriodora in experimental design of randomized blocks with four replicates, with 20 plants inside each experimental plot. The diameter at breast height and total plant height at stand level were obtained by measuring five trees in each experimental unit in seven measurements. During this same period, a flight was carried out using a remotely piloted aircraft for the acquisition of spectral variables (SBs and VIs). For recognition of eucalyptus species in relation to their growth two machine learning approaches were employed: supervised and unsupervised. The average accuracy obtained from 10-fold cross-validation, employing Random Forest algorithm and 24 features, was 0.76. This result shows that the proposed approach is appropriate to recognize different eucalyptus species based on their growth.
Bruno Rodrigues de Oliveira; Arlindo Ananias Pereira da Silva; Larissa Pereira Ribeiro Teodoro; Gileno Brito de Azevedo; Glauce Taís De Oliveira Sousa Azevedo; Fábio Henrique Rojo Baio; Renato Lustosa Sobrinho; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro. Eucalyptus growth recognition using machine learning methods and spectral variables. Forest Ecology and Management 2021, 497, 119496 .
AMA StyleBruno Rodrigues de Oliveira, Arlindo Ananias Pereira da Silva, Larissa Pereira Ribeiro Teodoro, Gileno Brito de Azevedo, Glauce Taís De Oliveira Sousa Azevedo, Fábio Henrique Rojo Baio, Renato Lustosa Sobrinho, Carlos Antonio Da Silva Junior, Paulo Eduardo Teodoro. Eucalyptus growth recognition using machine learning methods and spectral variables. Forest Ecology and Management. 2021; 497 ():119496.
Chicago/Turabian StyleBruno Rodrigues de Oliveira; Arlindo Ananias Pereira da Silva; Larissa Pereira Ribeiro Teodoro; Gileno Brito de Azevedo; Glauce Taís De Oliveira Sousa Azevedo; Fábio Henrique Rojo Baio; Renato Lustosa Sobrinho; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro. 2021. "Eucalyptus growth recognition using machine learning methods and spectral variables." Forest Ecology and Management 497, no. : 119496.
Boron (B) is an essential element whose deficiency results in rapid inhibition in the growth of plants, acting on their meristematic growth. Real-time monitoring of B fertilization in eucalyptus is helpful for guiding precision diagnosis and efficient management of plant boron nutrition. This research hypothesizes that different boron levels alter the reflectance of different wavelengths in eucalyptus. In this context, the objective of this study was to identify spectral ranges that can be used to monitor the boron status in eucalyptus plants. The experiment was carried out in a greenhouse, in which the treatments consisted of increasing boron levels in the form of boric acid (17% of B), whose levels varied from deficit to toxicity. Thus, five treatments were established: no boron, 1, 10, 20, and 40 mg/dm3 of boron. The remote sensing data used were bands, heights, and vegetation indices calculated after obtaining the spectral curves in each treatment. Our findings show that it is possible to accurately distinguish the boron levels in eucalyptus using hyper and multispectral bands. The 350–371 nm spectral range can be used for detecting boron-deficient plants. Plants with adequate boron levels can be identified by using the 426–444 nm, 1811–1910 nm, 1948–2115 nm, and 2124–2208 nm spectral ranges. Finally, the 425–475 nm spectral range can be used to find boron-toxicity plants.
Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Larissa Pereira Ribeiro Teodoro; João Lucas Della Silva; Luciano Shozo Shiratsuchi; Fábio Henrique Rojo Baio; Cácio Luiz Boechat; Guilherme Fernando Capristo-Silva. Is it possible to detect boron deficiency in eucalyptus using hyper and multispectral sensors? Infrared Physics & Technology 2021, 116, 103810 .
AMA StyleCarlos Antonio Da Silva Junior, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, João Lucas Della Silva, Luciano Shozo Shiratsuchi, Fábio Henrique Rojo Baio, Cácio Luiz Boechat, Guilherme Fernando Capristo-Silva. Is it possible to detect boron deficiency in eucalyptus using hyper and multispectral sensors? Infrared Physics & Technology. 2021; 116 ():103810.
Chicago/Turabian StyleCarlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Larissa Pereira Ribeiro Teodoro; João Lucas Della Silva; Luciano Shozo Shiratsuchi; Fábio Henrique Rojo Baio; Cácio Luiz Boechat; Guilherme Fernando Capristo-Silva. 2021. "Is it possible to detect boron deficiency in eucalyptus using hyper and multispectral sensors?" Infrared Physics & Technology 116, no. : 103810.
This paper evaluates compliance with environmental laws in the municipality of Sorriso and the impact of changing legislation on vegetation. To verify the size of the properties, the areas designated as legal reserves (LRs), permanent protection areas (APPs), and springs were studied. Details of compliance with the New Forest Code (NFC) were drawn from the Rural Environmental Register (CAR) database. The database provided by PRODES/CERRADO was used to gather data to monitor deforested areas. SojaMaps data were used to verify the areas used for soybean cultivation followed by the Perpendicular Crop Enhancement Index. The data were plotted and superimposed on the deforestation data provided by PRODES/CERRADO. The areas were calculated using QGiS software version 2.18.22. The results showed that only 20.04% of the original Cerrado vegetation cover remains in the municipality. The results also revealed environmental LR, APP, and spring deficits of 92,772.32, 1,656.28, and 322.86 ha, respectively. Measures such as the CAR in the New Forest Code are ineffective for inhibiting illegal deforestation, and new legislation authorized the loss of 75.22% of the APP areas due for recovery. The proposed changes to eliminate LRs will allow the suppression of 91,000 ha of vegetation in Sorriso. Expanding the Amazon Soy Moratorium to the Cerrado could bring immediate benefits to the maintenance of the last continuous forested areas in this biome.
Reginaldo Carvalho dos Santos; Carlos Antonio Da Silva Junior; Leandro Denis Battirola; Mendelson Lima. Importance of legislation for maintaining forests on private properties in the Brazilian Cerrado. Environment, Development and Sustainability 2021, 1 -15.
AMA StyleReginaldo Carvalho dos Santos, Carlos Antonio Da Silva Junior, Leandro Denis Battirola, Mendelson Lima. Importance of legislation for maintaining forests on private properties in the Brazilian Cerrado. Environment, Development and Sustainability. 2021; ():1-15.
Chicago/Turabian StyleReginaldo Carvalho dos Santos; Carlos Antonio Da Silva Junior; Leandro Denis Battirola; Mendelson Lima. 2021. "Importance of legislation for maintaining forests on private properties in the Brazilian Cerrado." Environment, Development and Sustainability , no. : 1-15.
The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios (ARIMA time-series model number). The study was carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS, both in Brazil. A time series of 19 years was performed with data acquired by remote sensing from the following satellites: i) Landsat-8 (OLI and TIRS), and ii) TERRA/AQUA (MODIS). The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time, with a satisfactory correlation in future predictions of the variables for the next ten years, in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality, and CO2Flux tends to decrease in its seasonality, indicating a higher CO2 absorption.
Fernando Saragosa Rossi; Carlos Antonio Da Silva Junior; José Francisco de Oliveira-Júnior; Paulo Eduardo Teodoro; Luciano Shozo Shiratsuchi; Mendelson Lima; Larissa Pereira Ribeiro Teodoro; Auana Vicente Tiago; Guilherme Fernando Capristo-Silva. 19-year remotely sensed data in the forecast of spectral models of the environment. International Journal of Digital Earth 2021, 1 -27.
AMA StyleFernando Saragosa Rossi, Carlos Antonio Da Silva Junior, José Francisco de Oliveira-Júnior, Paulo Eduardo Teodoro, Luciano Shozo Shiratsuchi, Mendelson Lima, Larissa Pereira Ribeiro Teodoro, Auana Vicente Tiago, Guilherme Fernando Capristo-Silva. 19-year remotely sensed data in the forecast of spectral models of the environment. International Journal of Digital Earth. 2021; ():1-27.
Chicago/Turabian StyleFernando Saragosa Rossi; Carlos Antonio Da Silva Junior; José Francisco de Oliveira-Júnior; Paulo Eduardo Teodoro; Luciano Shozo Shiratsuchi; Mendelson Lima; Larissa Pereira Ribeiro Teodoro; Auana Vicente Tiago; Guilherme Fernando Capristo-Silva. 2021. "19-year remotely sensed data in the forecast of spectral models of the environment." International Journal of Digital Earth , no. : 1-27.
Carrying out monitoring during the crop cycle through vegetation indices (VIs) with obtained unmanned aerial vehicle allows agility in decisions about management practices, as well as concerning nutritional deficiencies in crops, as nitrogen (N). This nutrient absorbed in greater quantity, and that most influences the grain yield in corn. This research hypothesized that different N topdressing levels can affect the agronomic performance of corn varieties and that those effects can be expressed by VIs. The objective was to evaluate the use of VIs in the monitoring of corn varieties submitted to different N levels. Two experiments were carried out in a randomized block design with three replicates in a factorial scheme, replicated for two crop seasons (2017/2018 and 2018/2019). The first factor was composed of 11 cultivars of corn. The second factor was composed of two contrasting N levels (60 kg ha-1 - low and 180 kg ha-1 - high). Vegetation indices (NDVI and NDRE) and agronomic traits (leaf N content, plant height, ear insertion height, stem diameter, ear length, number of rows per ear, number of grains per row, and grain yield) were evaluated. Our findings allow us to understand how top dressing can influence the agronomic performance of corn genotypes and their relationship with UAV-vegetation indices in two crop seasons using Sensefly Sequoia multispectral sensor. High N topdressing levels provides better agronomic and spectral response in corn, regardless of the variety used. This behavior can be confirmed through the NDVI and NDRE. High N topdressing levels provides a positive correlation between the VIs evaluated (NDVI and NDRE) with the grain yield in corn.
Dthenifer Cordeiro Santana; Mayara Favero Cotrim; Marcela Silva Flores; Fabio Henrique Rojo Baio; Luciano Shozo Shiratsuchi; Carlos Antonio Da Silva Junior; Larissa Pereira Ribeiro Teodoro; Paulo Eduardo Teodoro. UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing. Remote Sensing Applications: Society and Environment 2021, 23, 100534 .
AMA StyleDthenifer Cordeiro Santana, Mayara Favero Cotrim, Marcela Silva Flores, Fabio Henrique Rojo Baio, Luciano Shozo Shiratsuchi, Carlos Antonio Da Silva Junior, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro. UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing. Remote Sensing Applications: Society and Environment. 2021; 23 ():100534.
Chicago/Turabian StyleDthenifer Cordeiro Santana; Mayara Favero Cotrim; Marcela Silva Flores; Fabio Henrique Rojo Baio; Luciano Shozo Shiratsuchi; Carlos Antonio Da Silva Junior; Larissa Pereira Ribeiro Teodoro; Paulo Eduardo Teodoro. 2021. "UAV-based multispectral sensor to measure variations in corn as a function of nitrogen topdressing." Remote Sensing Applications: Society and Environment 23, no. : 100534.
Machine learning techniques (ML) have gained attention in precision agriculture practices since they efficiently address multiple applications, like estimating the growth and yield of trees in forest plantations. The combination between ML algorithms and spectral vegetation indices (VIs) from high-spatial-resolution line measurement, segment: 0.079024 m multispectral imagery, could optimize the prediction of these biometric variables. In this paper, we investigate the performance of ML techniques and VIs acquired with an unnamed aerial vehicle (UAV) to predict the diameter at breast height (DBH) and total height (Ht) of eucalyptus trees. An experimental site with six eucalyptus species was selected, and the Parrot Sequoia sensor was used. Several ML techniques were evaluated, like random forest (RF), REPTree (DT), alternating model tree (AT,) k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), linear regression (LR), and radial basis function (RBF). Each algorithm performance was verified using the correlation coefficient (r) and the mean absolute error (MAE). We used, as input, 34 VIs as numeric variables to predict DHB and Ht. We also added to the model a categorical variable as input identifying the different eucalyptus trees species. The RF technique obtained an overall superior estimation for all the tested configurations. Still, the RBF also showed a higher performance for predicting DHB, numerically surpassing the RF both in r and MAE, in some cases. For Ht variable, the technique that obtained the smallest MAE was SVM, though in a particular test. In this regard, we conclude that a combination of ML and VIs extracted from UAV-based imagery is suitable to estimate DBH and Ht in eucalyptus species. The approach presented constitutes an interesting contribution to the inventory and management of planted forests.
Ana da Silva; Marcus Borges; Tays Batista; Carlos Da Silva Junior; Danielle Furuya; Lucas Prado Osco; Larissa Teodoro; Fábio Baio; Ana Ramos; Wesley Gonçalves; José Marcato Junior; Paulo Teodoro; Hemerson Pistori. Predicting Eucalyptus Diameter at Breast Height and Total Height with UAV-Based Spectral Indices and Machine Learning. Forests 2021, 12, 582 .
AMA StyleAna da Silva, Marcus Borges, Tays Batista, Carlos Da Silva Junior, Danielle Furuya, Lucas Prado Osco, Larissa Teodoro, Fábio Baio, Ana Ramos, Wesley Gonçalves, José Marcato Junior, Paulo Teodoro, Hemerson Pistori. Predicting Eucalyptus Diameter at Breast Height and Total Height with UAV-Based Spectral Indices and Machine Learning. Forests. 2021; 12 (5):582.
Chicago/Turabian StyleAna da Silva; Marcus Borges; Tays Batista; Carlos Da Silva Junior; Danielle Furuya; Lucas Prado Osco; Larissa Teodoro; Fábio Baio; Ana Ramos; Wesley Gonçalves; José Marcato Junior; Paulo Teodoro; Hemerson Pistori. 2021. "Predicting Eucalyptus Diameter at Breast Height and Total Height with UAV-Based Spectral Indices and Machine Learning." Forests 12, no. 5: 582.
In recent years, the degradation of the Amazon forest has been the target of studies at the global level to identify elements and factors influencing this process. Thus, this study had as objective to study the vegetation degradation during the years of El Niño Southern Oscillation (ENSO) phenomenon in the State of Amazonas. We used remote sensing data, forest typologies, and weather elements from 14 conventional stations located in Amazonas. Data from the weather stations were used to estimate reference evapotranspiration by 7 empirical evapotranspiration methods. The standard FAO-56 method was used to compare the empirical methods and the method derived from remote sensing. The Standardised Precipitation-Evapotranspiration Index was used to verify the intensity of drought in the observed period. The results show differences in the correlations between the evapotranspiration methods used. The highest evapotranspiration was found in the Open Ombrophilous Forest. Fire foci were concentrated in Dense Ombrophilous Forest areas. The Standardised Precipitation-Evapotranspiration Index (SPEI) showed to be an efficient index to characterize the drought in El Niño events, besides showing the agricultural expansion in the region. The largest trends of the weather elements and the SPEI were during the El Niño phenomenon. A better understanding of the ENSO effects on vegetation could prevent uncontrolled forest loss. Governance interventions involving national and financial actors, as well as local populations, also play a role in this.
Regiane Souza Vilanova; Rafael Coll Delgado; Caio Frossard de Andrade; Gilsonley Lopes dos Santos; Iris Cristiane Magistrali; Carlos Magno Moreira de Oliveira; Paulo Eduardo Teodoro; Guilherme Fernando Capristo Silva; Carlos Antonio Da Silva Junior; Rafael De Ávila Rodrigues. Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon. Remote Sensing Applications: Society and Environment 2021, 23, 100531 .
AMA StyleRegiane Souza Vilanova, Rafael Coll Delgado, Caio Frossard de Andrade, Gilsonley Lopes dos Santos, Iris Cristiane Magistrali, Carlos Magno Moreira de Oliveira, Paulo Eduardo Teodoro, Guilherme Fernando Capristo Silva, Carlos Antonio Da Silva Junior, Rafael De Ávila Rodrigues. Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon. Remote Sensing Applications: Society and Environment. 2021; 23 ():100531.
Chicago/Turabian StyleRegiane Souza Vilanova; Rafael Coll Delgado; Caio Frossard de Andrade; Gilsonley Lopes dos Santos; Iris Cristiane Magistrali; Carlos Magno Moreira de Oliveira; Paulo Eduardo Teodoro; Guilherme Fernando Capristo Silva; Carlos Antonio Da Silva Junior; Rafael De Ávila Rodrigues. 2021. "Vegetation degradation in ENSO events: Drought assessment, soil use and vegetation evapotranspiration in the Western Brazilian Amazon." Remote Sensing Applications: Society and Environment 23, no. : 100531.
Rainfall is a climatic variable that dictates the daily rhythm of urban areas in Northeastern Brazil (NEB) and, therefore, understanding its dynamics is fundamental. The objectives of the study were (i) to validate the CHELSA product with data in situ, (ii) assess the spatial-temporality of the rains, and (iii) assess the trends and socio-environmental implications in the Metropolitan Region of Maceió (MRM). The monthly rainfall data observed between 1960 and 2016 were flawed and were filled with the imputation of data. These series were subjected to descriptive and exploratory statistics, statistical indicators, and the Mann–Kendall (MK) and Pettitt tests. CHELSA product was validated for MRM, and all stations obtained satisfactory determination coefficients (R2) and Pearson correlation (r). The standard error of the estimate (SEE), root mean square error (RMSE), and mean absolute error (MAE) were satisfactory. The highest annual rainfall accumulated occurred near the Mundaú and Manguaba lagoons. The Pettitt test identified that abrupt changes occur in El Niño and La Niña years (strong and weak). The monthly rain boxplots showed high variability in the rainy season (April–July). Outliers have been associated with extreme rainfall at MRM. The drought period was 5 months in all MRM seasons, except in Satuba and Pilar. The Mann–Kendall test and the Sen method showed a tendency for a significant increase in rainfall in Satuba and not significant in the Pilar, while in the others, there was a tendency for a decrease in rainfall. The MRM rainfall depends on physiographic factors, multiscale meteorological systems, and the coastal environment. These results will assist in planning conservationist practices, especially in areas of socio-environmental vulnerability.
José Francisco de Oliveira-Júnior; Washington Luiz Félix Correia Filho; Dimas De Barros Santiago; Givanildo de Gois; Micejane Da Silva Costa; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Felipe Machado Freire. Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications. Environmental Monitoring and Assessment 2021, 193, 1 -19.
AMA StyleJosé Francisco de Oliveira-Júnior, Washington Luiz Félix Correia Filho, Dimas De Barros Santiago, Givanildo de Gois, Micejane Da Silva Costa, Carlos Antonio Da Silva Junior, Paulo Eduardo Teodoro, Felipe Machado Freire. Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications. Environmental Monitoring and Assessment. 2021; 193 (5):1-19.
Chicago/Turabian StyleJosé Francisco de Oliveira-Júnior; Washington Luiz Félix Correia Filho; Dimas De Barros Santiago; Givanildo de Gois; Micejane Da Silva Costa; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Felipe Machado Freire. 2021. "Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications." Environmental Monitoring and Assessment 193, no. 5: 1-19.
José Francisco de Oliveira-Júnior; Washington Luiz Félix Correia Filho; Dimas De Barros Santiago; Givanildo de Gois; Micejane Da Silva Costa; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Felipe Machado Freire. Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications. 2021, 193, 263 .
AMA StyleJosé Francisco de Oliveira-Júnior, Washington Luiz Félix Correia Filho, Dimas De Barros Santiago, Givanildo de Gois, Micejane Da Silva Costa, Carlos Antonio Da Silva Junior, Paulo Eduardo Teodoro, Felipe Machado Freire. Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications. . 2021; 193 (5):263.
Chicago/Turabian StyleJosé Francisco de Oliveira-Júnior; Washington Luiz Félix Correia Filho; Dimas De Barros Santiago; Givanildo de Gois; Micejane Da Silva Costa; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Felipe Machado Freire. 2021. "Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications." 193, no. 5: 263.
The states of Mato Grosso (MT) and Mato Grosso do Sul (MS) are located in Midwest Brazil and are dependent on agribusiness, which makes their water regimes of fundamental importance. However, the existing weather stations in these states are limited, and rainfall products are therefore a valuable alternative. The objectives of this study are: (a) to validate the CHIRPS datasets for the states of MT and MS in the Brazilian Midwest region; (b) to evaluate the intraseasonal variability of regional rainfall via CHIRPS; and (c) to define the drought and wet periods on the decennial scale, based on the annual SPI. Three seasons were defined (rainy, drought, and transition), and the differences showed that the MS rainy season (56.65%) was reduced due to the veranicos that occur in February, with an increase in the percentages of the drought (13.83%) and transition (29.52%) seasons. The highest monthly rainfall occurs in the north of MS (328–390 mm), while intermediate rainfall (109–273 mm) occurs in the south of MS. This satisfactorily represents the formation of a rainfall gradient in a N–S direction during the rainy season, similar to other characterizations around the world. Based on the annual SPI assessed per decade, there were moderate droughts annually in the 1990s, 2000s, and 2010s, but not in the 1980s. There were significant differences between the decades due to the intensity and categorization of the ENSO phases (strong and moderate), and drier decades with moderate rainfall reduction in both states can be defined in the last 40 years. The data obtained via CHIRPS are satisfactory for the spatio‐temporal evaluation of regional rainfall, and for the use of SPI to detect drought and wet periods in the states of MT and MS.
José Francisco de Oliveira‐Júnior; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Fernando Saragosa Rossi; Claudio José Cavalcante Blanco; Mendelson Lima; Givanildo de Gois; Washington Luiz Félix Correia Filho; Dimas De Barros Santiago; Mário Henrique Guilherme dos Santos Vanderley. Confronting CHIRPS dataset and in situ stations in the detection of wet and drought conditions in the Brazilian Midwest. International Journal of Climatology 2021, 1 .
AMA StyleJosé Francisco de Oliveira‐Júnior, Carlos Antonio Da Silva Junior, Paulo Eduardo Teodoro, Fernando Saragosa Rossi, Claudio José Cavalcante Blanco, Mendelson Lima, Givanildo de Gois, Washington Luiz Félix Correia Filho, Dimas De Barros Santiago, Mário Henrique Guilherme dos Santos Vanderley. Confronting CHIRPS dataset and in situ stations in the detection of wet and drought conditions in the Brazilian Midwest. International Journal of Climatology. 2021; ():1.
Chicago/Turabian StyleJosé Francisco de Oliveira‐Júnior; Carlos Antonio Da Silva Junior; Paulo Eduardo Teodoro; Fernando Saragosa Rossi; Claudio José Cavalcante Blanco; Mendelson Lima; Givanildo de Gois; Washington Luiz Félix Correia Filho; Dimas De Barros Santiago; Mário Henrique Guilherme dos Santos Vanderley. 2021. "Confronting CHIRPS dataset and in situ stations in the detection of wet and drought conditions in the Brazilian Midwest." International Journal of Climatology , no. : 1.
Forest canopies have an important influence on the global climate balance. Through the analysis of the temperature of the canopy, it is possible to infer about the physiological aspects of the plants, helping to understand the behavior of the vegetation and, consequently, in the environmental monitoring and management of green areas. This study aims to validate the MOD11A2 V006 product from canopy surface temperature data obtained by an infrared radiation sensor. For the validation of the MOD11A2 product, a comparative analysis was performed between the land surface temperature (LST) data, obtained by the MODIS sensor, and the canopy temperature data, obtained by the SI-111 infrared radiation sensor coupled to the Itatiaia National Park (PNI) micrometeorological tower. Meteorological variables and land surface temperature collected from January to December 2018 in the PNI were also analyzed. The results reveal that the MOD11A2 product overestimates the canopy temperature in the daytime (MB ranging from 1.56 to 3.57 °C) and underestimates in the night time (MB ranging from - 0.18 to - 4.22 °C). During daytime, the months corresponding to the dry season presented a very high correlation (r = 0.74 and 0.86) and the highest values for the Willmott index (d = 0.70 and 0.64). At nighttime, the MOD11A2 product did not present a good performance for the LST estimation, especially in the rainy season. Therefore, we observed that the MOD11A2 product has limitations to estimate the land surface temperature and that possible changes in the algorithm of this product can be performed for high atmospheric humidity conditions.
Melina Daniel De Andrade; Rafael Coll Delgado; Sady Júnior Martins Da Costa De Menezes; Rafael De Ávila Rodrigues; Paulo Eduardo Teodoro; Carlos Antonio Da Silva Junior; Marcos Gervasio Pereira. Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest. Environmental Monitoring and Assessment 2021, 193, 1 -20.
AMA StyleMelina Daniel De Andrade, Rafael Coll Delgado, Sady Júnior Martins Da Costa De Menezes, Rafael De Ávila Rodrigues, Paulo Eduardo Teodoro, Carlos Antonio Da Silva Junior, Marcos Gervasio Pereira. Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest. Environmental Monitoring and Assessment. 2021; 193 (1):1-20.
Chicago/Turabian StyleMelina Daniel De Andrade; Rafael Coll Delgado; Sady Júnior Martins Da Costa De Menezes; Rafael De Ávila Rodrigues; Paulo Eduardo Teodoro; Carlos Antonio Da Silva Junior; Marcos Gervasio Pereira. 2021. "Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest." Environmental Monitoring and Assessment 193, no. 1: 1-20.
The performance of the CCCma (Canadian Centre for Climate Modelling and Analysis) and GFDL (Geophysical Fluid Dynamic Laboratory) models in the baseline period (1961–2000) and for the future IPCC scenario Representative Concentration Pathways (RCP) 6.0 (2046–2065) were evaluated through the descriptive statistics using spatial interpolation methods (Kriging and Ordinary Co-Kriging) using the exponential, gaussian and spherical spatial models. The daily temperature and rain data from the CCCma and GFDL models were used for the current baseline climate (1961–2000) and the future scenario (2046–2065), data from the National Oceanic and Atmospheric Administration (NOAA) were used for comparisons with the two models and the product MCD12Q1 derived from Moderate resolution spectroradiometer sensor was used to check which areas had higher and lower values of temperature and rain in the State of Rio de Janeiro. For validation, data were compared and geostatistical analysis was performed using Kriging techniques. The averages of air temperature and precipitation showed similar patterns in both models. The CCCma model presented the data closest to the NOAA reanalysis data, with the GFDL model underestimating most precipitation and air temperature data. Through the product MCD12Q1 of the MODIS sensor, it is possible to register marked differences in relation to rain and temperature and their respective land use for the State. For the geostatistics data, it was possible to verify that, for the past and future scenarios, the exponential transitive model presented in the majority of the cases the least degree of spatial dependence (GDE), being therefore considered the best. When comparing the GDE of the two climatic models, it is verified that the CCCma presented the best geostatistical performance and can be used in works to simulate scenarios of future climate changes.
Iris C. Magistrali; Rafael C. Delgado; Gilsonley L. dos Santos; Marcos G. Pereira; Evandro C. de Oliveira; Leonardo De O. Neves; Leonardo P. de Souza; Paulo E. Teodoro; Carlos A. Silva Junior. Performance of CCCma and GFDL climate models using remote sensing and surface data for the state of Rio de Janeiro-Brazil. Remote Sensing Applications: Society and Environment 2020, 21, 100446 .
AMA StyleIris C. Magistrali, Rafael C. Delgado, Gilsonley L. dos Santos, Marcos G. Pereira, Evandro C. de Oliveira, Leonardo De O. Neves, Leonardo P. de Souza, Paulo E. Teodoro, Carlos A. Silva Junior. Performance of CCCma and GFDL climate models using remote sensing and surface data for the state of Rio de Janeiro-Brazil. Remote Sensing Applications: Society and Environment. 2020; 21 ():100446.
Chicago/Turabian StyleIris C. Magistrali; Rafael C. Delgado; Gilsonley L. dos Santos; Marcos G. Pereira; Evandro C. de Oliveira; Leonardo De O. Neves; Leonardo P. de Souza; Paulo E. Teodoro; Carlos A. Silva Junior. 2020. "Performance of CCCma and GFDL climate models using remote sensing and surface data for the state of Rio de Janeiro-Brazil." Remote Sensing Applications: Society and Environment 21, no. : 100446.
Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.
Lucas Osco; José Junior; Ana Ramos; Danielle Furuya; Dthenifer Santana; Larissa Teodoro; Wesley Gonçalves; Fábio Baio; Hemerson Pistori; Carlos Junior; Paulo Teodoro. Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques. Remote Sensing 2020, 12, 3237 .
AMA StyleLucas Osco, José Junior, Ana Ramos, Danielle Furuya, Dthenifer Santana, Larissa Teodoro, Wesley Gonçalves, Fábio Baio, Hemerson Pistori, Carlos Junior, Paulo Teodoro. Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques. Remote Sensing. 2020; 12 (19):3237.
Chicago/Turabian StyleLucas Osco; José Junior; Ana Ramos; Danielle Furuya; Dthenifer Santana; Larissa Teodoro; Wesley Gonçalves; Fábio Baio; Hemerson Pistori; Carlos Junior; Paulo Teodoro. 2020. "Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques." Remote Sensing 12, no. 19: 3237.
Carlos Antonio Da Silva Junior. Review of Modelling the Brumadinho tailings dam failure... 2020, 1 .
AMA StyleCarlos Antonio Da Silva Junior. Review of Modelling the Brumadinho tailings dam failure... . 2020; ():1.
Chicago/Turabian StyleCarlos Antonio Da Silva Junior. 2020. "Review of Modelling the Brumadinho tailings dam failure..." , no. : 1.
The objective is to evaluate the fire foci dynamics via environmental satellites and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Brazil. Data considered the period between 2000 and 2017 and was obtained from CPTEC/INPE. Annual and monthly analyzes were performed based on descriptive, exploratory (boxplot) and multivariate statistics analyzes (cluster analysis (CA), principal component analysis (PCA)) and Poisson regression models (based on 2000 and 2010 census data). CA based on the Ward method identified five fire foci homogeneous groups (G1 to G5), while Coruripe did not classify within any group (NA); therefore, the CA technique was consistent (CCC = 0.772). Group G1 is found in all regions of Alagoas, while G2, G5, and NA groups are found in Baixo São Francisco, Litoral, and Zona da Mata regions. Most fire foci were observed in the Litoral region. Seasonally, the largest records were from October to December months for all groups, influenced by the sugarcane harvesting period. The G4 group and Coruripe accounted for 60,767 foci (32.1%). The highest number of fire foci occurred in 2012 and 2015 (between 8000 and 9000 foci), caused by the action of the El Niño–Southern Oscillation. The Poisson regression showed that the dynamics of fire foci are directly associated with the Gini index and Human Development Index (models 1 and 3). Based on the PCA, the three components captured 78.8% of the total variance explained, and they were strongly influenced by the variables: population, GDP, and demographic density. The municipality of Maceió has the largest contribution from the fire foci, with values higher than 40%, and in PC1 and PC2 are related to urban densification and population growth.
José Francisco De Oliveira-Júnior; Washington Luiz Félix Correia Filho; Laurízio Emanuel Ribeiro Alves; Gustavo Bastos Lyra; Givanildo De Gois; Carlos Antonio Da Silva Junior; Paulo José Dos Santos; Bruno Serafini Sobral. Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil. Environmental Monitoring and Assessment 2020, 192, 1 -26.
AMA StyleJosé Francisco De Oliveira-Júnior, Washington Luiz Félix Correia Filho, Laurízio Emanuel Ribeiro Alves, Gustavo Bastos Lyra, Givanildo De Gois, Carlos Antonio Da Silva Junior, Paulo José Dos Santos, Bruno Serafini Sobral. Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil. Environmental Monitoring and Assessment. 2020; 192 (10):1-26.
Chicago/Turabian StyleJosé Francisco De Oliveira-Júnior; Washington Luiz Félix Correia Filho; Laurízio Emanuel Ribeiro Alves; Gustavo Bastos Lyra; Givanildo De Gois; Carlos Antonio Da Silva Junior; Paulo José Dos Santos; Bruno Serafini Sobral. 2020. "Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil." Environmental Monitoring and Assessment 192, no. 10: 1-26.
Random Forest (RF) is a machine learning technique that has been proved to be highly accurate in several agricultural applications. However, to yield prediction, how much this technique may be improved with the adoption of a ranking-based strategy is still an unknown issue. Here we propose a ranking-based approach to potentialize the RF method for maize yield prediction. This approach is based on the correlation parameter of individual vegetation indices (VIs). The VIs were individually ranked based on a merit metric that measures the improvement on the Pearson’s correlation coefficient by using RF against a baseline method. As a result, only the most relevant VIs were considered as input features to the RF model. We used 33 VIs extracted from multispectral UAV-based (unmanned aerial vehicle) imagery. The multispectral data were generated with two different sensors: Sequoia and MicaSense; during the 2017/2018 and 2018/2019 crop seasons, respectively. Amongst all the evaluated indices, NDVI, NDRE, and GNDVI were the top three in the ranking-based analysis, and their combination with RF increased the maize yield prediction. Our approach also outperformed other known machine learning methods, like support vector machine and artificial neural network. Additive regression, using the RF as the base weak learner, provided a higher accuracy with a correlation coefficient and MAE (Mean Absolute Error) of 0.78 and 853.11 kg ha−1, respectively. We conclude that the ranking-based strategy of VIs is appropriate to predict maize yield using machine learning methods and data derived from multispectral images. We demonstrated that our approach reduces the number of VIs needed to determine a high accuracy and relative low MAE, and the approach may contribute to decision-making actions, resulting in accurate management of maize fields.
Ana Paula Marques Ramos; Lucas Prado Osco; Danielle Elis Garcia Furuya; Wesley Nunes Gonçalves; Dthenifer Cordeiro Santana; Larissa Pereira Ribeiro Teodoro; Carlos Antonio Da Silva Junior; Guilherme Fernando Capristo-Silva; Jonathan Li; Fábio Henrique Rojo Baio; José Marcato Junior; Paulo Eduardo Teodoro; Hemerson Pistori. A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Computers and Electronics in Agriculture 2020, 178, 105791 .
AMA StyleAna Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Carlos Antonio Da Silva Junior, Guilherme Fernando Capristo-Silva, Jonathan Li, Fábio Henrique Rojo Baio, José Marcato Junior, Paulo Eduardo Teodoro, Hemerson Pistori. A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Computers and Electronics in Agriculture. 2020; 178 ():105791.
Chicago/Turabian StyleAna Paula Marques Ramos; Lucas Prado Osco; Danielle Elis Garcia Furuya; Wesley Nunes Gonçalves; Dthenifer Cordeiro Santana; Larissa Pereira Ribeiro Teodoro; Carlos Antonio Da Silva Junior; Guilherme Fernando Capristo-Silva; Jonathan Li; Fábio Henrique Rojo Baio; José Marcato Junior; Paulo Eduardo Teodoro; Hemerson Pistori. 2020. "A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices." Computers and Electronics in Agriculture 178, no. : 105791.
The objective of this study was to evaluate the annual SPI (Standardized Precipitation Index) obtained from the DrinC software based on multivariate analysis in the identification of rainfall and drought extremes in the State of Alagoas and its relationship with El Niño‐Southern Oscillation. Monthly rainfall data from 1960 to 2016 from ANA (National Water Agency). Annual SPI (SPI‐12) has been designed for comparison with ENSO phases via Oceanic Niño Index (ONI) for 3.4 region and in identifying climate extremes in the State of Alagoas. The Principal Component Analysis and Cluster Analysis techniques were applied to the rainfall series of SPI‐12. Extreme events were identified in both rainy and drought periods according to SPI‐12, and were associated with the ENSO phases (El Niño, La Niña, and Neutral). The first four PC's explained 46.68% of the variance. Our findings are crucial for agriculture and civil defense since northeastern Brazil has several areas of risk and social vulnerability. This article is protected by copyright. All rights reserved.
Micejane Da Silva Costa; José Francisco De Oliveira‐Júnior; Paulo José Dos Santos; Washington Luiz Félix Correia Filho; Givanildo De Gois; Cláudio José Cavalcante Blanco; Paulo Eduardo Teodoro; Carlos Antonio Da Silva Junior; Dimas De Barros Santiago; Edson De Oliveira Souza; Alexandre Maniçoba Da Rosa Ferraz Jardim. Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation. International Journal of Climatology 2020, 41, 1 .
AMA StyleMicejane Da Silva Costa, José Francisco De Oliveira‐Júnior, Paulo José Dos Santos, Washington Luiz Félix Correia Filho, Givanildo De Gois, Cláudio José Cavalcante Blanco, Paulo Eduardo Teodoro, Carlos Antonio Da Silva Junior, Dimas De Barros Santiago, Edson De Oliveira Souza, Alexandre Maniçoba Da Rosa Ferraz Jardim. Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation. International Journal of Climatology. 2020; 41 (S1):1.
Chicago/Turabian StyleMicejane Da Silva Costa; José Francisco De Oliveira‐Júnior; Paulo José Dos Santos; Washington Luiz Félix Correia Filho; Givanildo De Gois; Cláudio José Cavalcante Blanco; Paulo Eduardo Teodoro; Carlos Antonio Da Silva Junior; Dimas De Barros Santiago; Edson De Oliveira Souza; Alexandre Maniçoba Da Rosa Ferraz Jardim. 2020. "Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation." International Journal of Climatology 41, no. S1: 1.
Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied crop years, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.
Willyan Ronaldo Becker; Jonathan Richetti; Erivelto Mercante; Júlio César Dalla Mora Esquerdo; Carlos Antonio Da Silva Junior; Alex Paludo; Jerry Adriani Johann. Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil. Semina: Ciências Agrárias 2020, 41, 2419 -2428.
AMA StyleWillyan Ronaldo Becker, Jonathan Richetti, Erivelto Mercante, Júlio César Dalla Mora Esquerdo, Carlos Antonio Da Silva Junior, Alex Paludo, Jerry Adriani Johann. Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil. Semina: Ciências Agrárias. 2020; 41 (5supl1):2419-2428.
Chicago/Turabian StyleWillyan Ronaldo Becker; Jonathan Richetti; Erivelto Mercante; Júlio César Dalla Mora Esquerdo; Carlos Antonio Da Silva Junior; Alex Paludo; Jerry Adriani Johann. 2020. "Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil." Semina: Ciências Agrárias 41, no. 5supl1: 2419-2428.
The Brazilian government once again threatens its natural heritage by issuing a decree that liberates the sugarcane plantations in the Pantanal and the Amazon regions. The production of a non-sanctioned crop is likely to become the newest driver of deforestation in these biomes. Direct conversion of forests, migration of livestock to new forested areas, rising land values, the danger of forest fires spreading and of carbon emissions from burning sugarcane during harvesting can all create a carbon balance debt and impact on water balance that could take centuries to offset and will compromise the sustainability of the Brazilian ethanol sector.
Mendelson Lima; Carlos Antonio Da Silva Junior; Tatiane Deotti Pelissari; Thaís Lourençoni; Iago Manuelson Santos Luz; Francis Junior Araujo Lopes. Sugarcane: Brazilian public policies threaten the Amazon and Pantanal biomes. Perspectives in Ecology and Conservation 2020, 18, 210 -212.
AMA StyleMendelson Lima, Carlos Antonio Da Silva Junior, Tatiane Deotti Pelissari, Thaís Lourençoni, Iago Manuelson Santos Luz, Francis Junior Araujo Lopes. Sugarcane: Brazilian public policies threaten the Amazon and Pantanal biomes. Perspectives in Ecology and Conservation. 2020; 18 (3):210-212.
Chicago/Turabian StyleMendelson Lima; Carlos Antonio Da Silva Junior; Tatiane Deotti Pelissari; Thaís Lourençoni; Iago Manuelson Santos Luz; Francis Junior Araujo Lopes. 2020. "Sugarcane: Brazilian public policies threaten the Amazon and Pantanal biomes." Perspectives in Ecology and Conservation 18, no. 3: 210-212.
Monitoring soybean areas by remote sensing is extremely useful, especially in Brazil, which has a large territorial extension and where soybean cultivation has spread to all regions of the country. In this sense, the development of remote sensing techniques that enable the quantification and discrimination of soybean areas and now in cultivated cultivar level is of crucial importance for the soybean production chain in Brazil. This study aimed to discriminate soybean cultivars as a function of different hyperspectral bands using the sensor-system MSI-Sentinel-2 (Vis-NIR-SWIR) as a simulation and sample sizes using multivariate statistics to determine if the specific bands of this sensor are capable of performing such discrimination. Four soybean cultivation areas in the Midwest region cultivated with four cultivars (BMX Potência, NA5909, Don Mario, and FT Campo Mourão) were assessed. Spectral readings from each sample soybean leaf were performed, and a total of 2400 vegetation spectral readings were obtained. Data were composed of 28 bands and 22 reflectance factor height (RID) values for each soybean cultivar. Multivariate statistical analysis was performed to verify the association between soybean cultivars and their relationship with hyperspectral bands, as well as to verify the possibility of cultivar differentiation based on hyperspectral bands. The results obtained demonstrated to be possible discriminate soybean cultivars by using multivariate techniques applied to multi and hyperspectral data. The bands that contributed significantly (>5%) to cultivar differentiation in order of importance were: B26, B27, A17, A21, A20 and A14. Discriminant analysis was efficient in the cultivar classification, and canonical variable analysis revealed bands associated with specific discrimination of each cultivar. Bands that most contributed to cultivar discrimination were also identified for MSI orbital sensor.
Carlos Antonio Da Silva Junior; Larissa Pereira Ribeiro Teodoro; Paulo Eduardo Teodoro; Fábio Henrique Rojo Baio; Ariane De Andrea Pantaleão; Guilherme Fernando Capristo-Silva; Cassiele Uliana Facco; José Francisco de Oliveira-Júnior; Luciano Shozo Shiratsuchi; Vladimir Skripachev; Mendelson Lima; Marcos Rafael Nanni. Simulating multispectral MSI bandsets (Sentinel-2) from hyperspectral observations via spectroradiometer for identifying soybean cultivars. Remote Sensing Applications: Society and Environment 2020, 19, 100328 .
AMA StyleCarlos Antonio Da Silva Junior, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Fábio Henrique Rojo Baio, Ariane De Andrea Pantaleão, Guilherme Fernando Capristo-Silva, Cassiele Uliana Facco, José Francisco de Oliveira-Júnior, Luciano Shozo Shiratsuchi, Vladimir Skripachev, Mendelson Lima, Marcos Rafael Nanni. Simulating multispectral MSI bandsets (Sentinel-2) from hyperspectral observations via spectroradiometer for identifying soybean cultivars. Remote Sensing Applications: Society and Environment. 2020; 19 ():100328.
Chicago/Turabian StyleCarlos Antonio Da Silva Junior; Larissa Pereira Ribeiro Teodoro; Paulo Eduardo Teodoro; Fábio Henrique Rojo Baio; Ariane De Andrea Pantaleão; Guilherme Fernando Capristo-Silva; Cassiele Uliana Facco; José Francisco de Oliveira-Júnior; Luciano Shozo Shiratsuchi; Vladimir Skripachev; Mendelson Lima; Marcos Rafael Nanni. 2020. "Simulating multispectral MSI bandsets (Sentinel-2) from hyperspectral observations via spectroradiometer for identifying soybean cultivars." Remote Sensing Applications: Society and Environment 19, no. : 100328.