30 results on '"Teodoro, Paulo Eduardo"'
Search Results
2. Environmental dynamics of the Juruá watershed in the Amazon
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da Silva Abel, Elton Luis, Delgado, Rafael Coll, Vilanova, Regiane Souza, Teodoro, Paulo Eduardo, da Silva Junior, Carlos Antonio, Abreu, Marcel Carvalho, and Silva, Guilherme Fernando Capristo
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- 2021
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3. Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor.
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Santana, Dthenifer Cordeiro, de Oliveira, Izabela Cristina, Cavalheiro, Sâmela Beutinger, das Chagas, Paulo Henrique Menezes, Teixeira Filho, Marcelo Carvalho Minhoto, Della-Silva, João Lucas, Teodoro, Larissa Pereira Ribeiro, Campos, Cid Naudi Silva, Baio, Fábio Henrique Rojo, da Silva Junior, Carlos Antonio, and Teodoro, Paulo Eduardo
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MACHINE learning ,RANDOM forest algorithms ,GENOTYPES ,GROWING season ,PLANT breeding ,SOYBEAN ,SOYBEAN farming - Abstract
Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Hyperspectral Response of the Soybean Crop as a Function of Target Spot (Corynespora cassiicola) Using Machine Learning to Classify Severity Levels.
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de Queiroz Otone, José Donizete, Theodoro, Gustavo de Faria, Santana, Dthenifer Cordeiro, Teodoro, Larissa Pereira Ribeiro, de Oliveira, Job Teixeira, de Oliveira, Izabela Cristina, da Silva Junior, Carlos Antonio, Teodoro, Paulo Eduardo, and Baio, Fabio Henrique Rojo
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MACHINE learning ,NORMALIZED difference vegetation index ,SOYBEAN ,CORYNESPORA ,GROWING season ,REMOTE sensing - Abstract
Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Assessment of evapotranspiration estimates based on surface and satellite data and its relationship with El Niño–Southern Oscillation in the Rio de Janeiro State
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Tito, Tiago Marques, Delgado, Rafael Coll, de Carvalho, Daniel Costa, Teodoro, Paulo Eduardo, de Almeida, Catherine Torres, da Silva Junior, Carlos Antonio, dos Santos, Erleyvaldo Bispo, and da Silva Júnior, Luiz Augusto Siciliano
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- 2020
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6. Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil
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da Cunha, Elias Rodrigues, Santos, Celso Augusto Guimarães, da Silva, Richarde Marques, Bacani, Vitor Matheus, Teodoro, Paulo Eduardo, Panachuki, Elói, and de Souza Oliveira, Naelmo
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- 2020
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7. Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning.
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Pereira Ribeiro Teodoro, Larissa, Estevão, Rosilene, Santana, Dthenifer Cordeiro, Oliveira, Izabela Cristina de, Lopes, Maria Teresa Gomes, Azevedo, Gileno Brito de, Rojo Baio, Fábio Henrique, da Silva Junior, Carlos Antonio, and Teodoro, Paulo Eduardo
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EUCALYPTUS ,MACHINE learning ,ARTIFICIAL neural networks ,FOREST management ,FOREST surveys ,TREE farms - Abstract
The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest.
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Rodrigues, Dágila Melo, Coradi, Paulo Carteri, Timm, Newiton da Silva, Fornari, Michele, Grellmann, Paulo, Amado, Telmo Jorge Carneiro, Teodoro, Paulo Eduardo, Teodoro, Larissa Pereira Ribeiro, Baio, Fábio Henrique Rojo, and Chiomento, José Luís Trevizan
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AGRICULTURAL remote sensing ,REMOTE sensing ,SUSTAINABLE agriculture ,DETECTORS ,ELECTROMAGNETIC waves - Abstract
In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, we outline how remote sensing tools can support a range of agricultural processes from field to storage through crop yield estimation, grain quality monitoring, storage unit identification and characterization, and production process planning. The use of sensors in the field and post-harvest processes allows for accurate real-time monitoring of operations and grain quality, enabling decision-making supported by computer tools such as the Internet of Things (IoT) and artificial intelligence algorithms. This way, grain producers can get ahead, track and reduce losses, and maintain grain quality from field to consumer. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus.
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Gregori, Gabriella Silva de, de Souza Loureiro, Elisângela, Amorim Pessoa, Luis Gustavo, Azevedo, Gileno Brito de, Azevedo, Glauce Taís de Oliveira Sousa, Santana, Dthenifer Cordeiro, Oliveira, Izabela Cristina de, Oliveira, João Lucas Gouveia de, Teodoro, Larissa Pereira Ribeiro, Baio, Fábio Henrique Rojo, Silva Junior, Carlos Antonio da, Teodoro, Paulo Eduardo, and Shiratsuchi, Luciano Shozo
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MACHINE learning ,ARTIFICIAL neural networks ,JUMPING plant-lice ,SUPPORT vector machines ,HEMIPTERA ,EUCALYPTUS - Abstract
Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) algorithms can help process the data faster and more accurately. The objectives were: (I) to evaluate the spectral behavior of the G. brimblecombei attack levels; (II) find the most accurate ML algorithm for classifying pest attack levels; (III) find the input configuration that improves performance of the algorithms. Data were collected from a clonal eucalyptus plantation (clone AEC 0144—Eucalyptus urophilla) aged 10.3 months old. Eighty sample evaluations were carried out considering the following severity levels: control (no shells), low infestation (N1), intermediate infestation (N2), and high infestation (N3), for which leaf spectral reflectances were obtained using a spectroradiometer. The spectral range acquired by the equipment was 350 to 2500 nm. After obtaining the wavelengths, they were grouped into representative interval means in 28 bands. Data were submitted to the following ML algorithms: artificial neural networks (ANN), REPTree (DT) and J48 decision trees, random forest (RF), support vector machine (SVM), and conventional logistic regression (LR) analysis. Two input configurations were tested: using only the wavelengths (ALL) and using the spectral bands (SB) to classify the attack levels. The output variable was the severity of G. brimblecombei attack. There were differences in the hyperspectral behavior of the leaves for the different attack levels. The highest attack level shows the greatest distinction and the highest reflectance values. LR and SVM show better accuracy in classifying the severity levels of G. brimblecombei attack. For the correct classification percentage, the RL and SVM algorithms performed better, both with accuracy above 90%. Both algorithms achieved F-score values close to 0.90 and above 0.8 for Kappa. The entire spectral range guaranteed the best accuracy for both algorithms. [ABSTRACT FROM AUTHOR]
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- 2023
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10. Spectral trend of vegetation with rainfall in events of El Niño-Southern Oscillation for Atlantic Forest biome, Brazil
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de Oliveira Souza, Thais Cristina, Delgado, Rafael Coll, Magistrali, Iris Cristiane, dos Santos, Gilsonley Lopes, de Carvalho, Daniel Costa, Teodoro, Paulo Eduardo, da Silva Júnior, Carlos Antônio, and Caúla, Rodrigo Hotzz
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- 2018
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11. Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management.
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Baio, Fábio Henrique Rojo, Santana, Dthenifer Cordeiro, Teodoro, Larissa Pereira Ribeiro, Oliveira, Izabela Cristina de, Gava, Ricardo, de Oliveira, João Lucas Gouveia, Silva Junior, Carlos Antonio da, Teodoro, Paulo Eduardo, and Shiratsuchi, Luciano Shozo
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IRRIGATION management ,MACHINE learning ,PEARSON correlation (Statistics) ,RANDOM forest algorithms ,SUPPORT vector machines - Abstract
Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models.
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Gava, Ricardo, Santana, Dthenifer Cordeiro, Cotrim, Mayara Favero, Rossi, Fernando Saragosa, Teodoro, Larissa Pereira Ribeiro, da Silva Junior, Carlos Antonio, and Teodoro, Paulo Eduardo
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Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to verify which ML technique is more accurate in the identification of soybean cultivars. Information was extracted from five central irrigation pivots in the same region and with the same sowing date in the 2015/2016 crop year, in which each pivot was cultivated with a different cultivar, in which the cultivars used were: CV1—P98y12 RR, CV2—Desafio RR, CV3—M6410 IPRO, CV4—M7110 IPRO, and CV5—NA5909 RR. A cloud-free orbital image of the site was acquired from the Google Earth Engine platform. In addition to the spectral bands alone, a total of 13 vegetation indices were calculated. The models tested were: artificial neural networks (ANN), radial basis function network (RBF), decision tree algorithms J48 (DT) and reduced error pruning tree (REP), random forest (RF), and support vector machine (SVM). The five soybean cultivars were classified by the six-machine learning (ML) models in stratified randomized cross-validation with k-fold = 10 and 10 repetitions (100 runs for each model). After obtaining the r and MAE statistics, analysis of variance was performed considering a 6 × 3 factorial scheme (models versus inputs) with 10 repetitions (folds). The means were grouped by the Scott–Knott test at 5% probability. The spectral bands were the most accurate among the tested inputs in the identification of soybean cultivars. ANN was the most accurate model in identifying soybean cultivars. [ABSTRACT FROM AUTHOR]
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- 2022
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13. Using Remote Sensing to Quantify the Joint Effects of Climate and Land Use/Land Cover Changes on the Caatinga Biome of Northeast Brazilian.
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Jardim, Alexandre Maniçoba da Rosa Ferraz, Araújo Júnior, George do Nascimento, Silva, Marcos Vinícius da, Santos, Anderson dos, Silva, Jhon Lennon Bezerra da, Pandorfi, Héliton, Oliveira-Júnior, José Francisco de, Teixeira, Antônio Heriberto de Castro, Teodoro, Paulo Eduardo, de Lima, João L. M. P., Silva Junior, Carlos Antonio da, Souza, Luciana Sandra Bastos de, Silva, Emanuel Araújo, and Silva, Thieres George Freire da
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LAND cover ,REMOTE sensing ,LAND use ,STANDARD deviations ,LEAF area index ,ARID regions - Abstract
Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (V
C ). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa ) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa . The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region's area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1 ). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin's concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions. [ABSTRACT FROM AUTHOR]- Published
- 2022
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14. Twenty-year impact of fire foci and its relationship with climate variables in Brazilian regions.
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Teodoro, Paulo Eduardo, da Silva Junior, Carlos Antonio, Delgado, Rafael Coll, Lima, Mendelson, Teodoro, Larissa Pereira Ribeiro, Baio, Fabio Henrique Rojo, de Azevedo, Gileno Brito, de Oliveira Sousa Azevedo, Glauce Táıs, de Andréa Pantaleão, Ariane, Capristo-Silva, Guilherme Fernando, and Facco, Cassiele Uliana
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EL Nino ,LAND surface temperature ,FIRE management ,SUGARCANE growing ,DROUGHT management ,FOREST fires ,REMOTE sensing ,TREND analysis - Abstract
In recent years, Brazil has become a major global contributor to the occurrence of national fires and greenhouse gas emissions. Therefore, this study aimed to evaluate the fire foci data of the past 20 years to determine their relationship with climatic variables in various Brazilian regions. The variables evaluated included fire foci, land surface temperature, rainfall, and standardized precipitation index, which were obtained via remote sensing from 2000 to 2019. The data were subjected to trend analyses (Mann–Kendall and Pettitt tests) and a multivariate analysis of canonical variables for evaluation. The results showed that the Midwest and North regions had the highest occurrence of fire foci throughout the study period, and that the North region had the highest accumulated annual rainfall. Thus, these regions require specific public policies to prevent future fires. Overall, the Midwest, Southeast, and South regions exhibit significant increasing fire foci tendencies. Our results reveal that this trend is related to the El Niño-Southern Oscillation (ENSO) phenomena, which alter climatic variables such as precipitation, land surface temperature, and the standardized precipitation index. Finally, the sugarcane growing area had a significant linear relationship with fire foci in the Southeast region, especially in the state of São Paulo, the major national sugarcane producer. [ABSTRACT FROM AUTHOR]
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- 2022
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15. Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach.
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Lourençoni, Thais, da Silva Junior, Carlos Antonio, Lima, Mendelson, Teodoro, Paulo Eduardo, Pelissari, Tatiane Deoti, dos Santos, Regimar Garcia, Teodoro, Larissa Pereira Ribeiro, Luz, Iago Manuelson, and Rossi, Fernando Saragosa
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DEFORESTATION ,SOYBEAN farming ,REMOTE sensing ,CLOUD computing ,SOYBEAN - Abstract
The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo. [ABSTRACT FROM AUTHOR]
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- 2021
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16. High‐throughput phenotyping of soybean genotypes under base saturation stress conditions.
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Andrade, Sinomar Moreira, Teodoro, Larissa Pereira Ribeiro, Baio, Fábio Henrique Rojo, Campos, Cid Naudi Silva, Roque, Cassiano Garcia, Silva Júnior, Carlos Antonio da, Coradi, Paulo Carteri, and Teodoro, Paulo Eduardo
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AGRICULTURAL remote sensing ,PLANT breeding ,AGRICULTURAL technology ,GENOTYPES ,PRECISION farming ,NUTRITIONAL status ,SOYBEAN - Abstract
The search for high‐yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and adequate base saturation levels in the soil and that vegetation indexes can be efficient auxiliary tools in plant phenotyping for this purpose. The objective of this study was to evaluate the nutritional status and agronomic performance of soya bean genotypes grown in low and recommended base saturation conditions using high‐throughput phenotyping. The research was carried out in 2017/2018 and 2018/2019 crop seasons, in which two field experiments in each season were installed. In experiment I, genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) were evaluated without soil correction (low saturation condition). In experiment II, liming was performed three months before sowing of the genotypes to raise the base saturation to 60% (recommended saturation). Canopy spectral behaviour at the following wavelengths was evaluated: green (550 nm), red (660 nm), red edge (735 nm) and near‐infrared (790 nm), and the vegetation indices (Vis) NDVI, SAVI, EVI and MSAVI were calculated. The variables evaluated were leaf calcium and magnesium contents and grain yield. The use of VI's was efficient in assessing the performance of genotypes soya bean at different base saturation levels. The EVI showed moderate correlation with the nutritional and agronomic variables measured in each base saturation level. The approach used enabled both to identify genotypes tolerant to low base saturation soils and the ones with better response to liming. [ABSTRACT FROM AUTHOR]
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- 2021
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17. Application of remote sensing in environmental impact assessment: a case study of dam rupture in Brumadinho, Minas Gerais, Brazil.
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Souza, Ana Paula Damasceno, Teodoro, Paulo Eduardo, Teodoro, Larissa Pereira Ribeiro, Taveira, Aline Cordeiro, de Oliveira-Júnior, José Francisco, Della-Silva, João Lucas, Baio, Fabio Henrique Rojo, Lima, Mendelson, and da Silva Junior, Carlos Antonio
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DAMS ,REMOTE sensing ,ENVIRONMENTAL impact analysis ,TOTAL suspended solids ,TAILINGS dams ,NATURAL resources ,EMERGENCY management ,MINE accidents - Abstract
The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environmental impact assessment. In order to find answers to that problem, the aim of this study was to identify and analyze the spatiality of the impacted area by rupture of the tailing dam of the Córrego do Feijão mine in Brumadinho, Minas Gerais, by using orbital remote sensing. Land use and land occupation, phytoplankton chlorophyll-a, water turbidity, total suspended solids on water, and carbon sequestration efficiency by vegetation (CO
2 Flux) were estimated by orbital imagery from the Landsat-8/OLI and MSI/Sentinel-2 sensors in order to assess the environmental impacts generated by the disaster. Data were extracted from spectral models in which the variables that best demonstrated the land use variation over the years were sought. Mean comparison by t-test was performed to compare the time series analyzed, that is, before and after the disaster. Through the analysis of water quality, it was observed that the environmental impact was calamitous to natural resources, especially water from Córrego do Feijão. [ABSTRACT FROM AUTHOR]- Published
- 2021
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18. 19-year remotely sensed data in the forecast of spectral models of the environment.
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Rossi, Fernando Saragosa, Silva Junior, Carlos Antonio da, Oliveira-Júnior, José Francisco de, Teodoro, Paulo Eduardo, Shiratsuchi, Luciano Shozo, Lima, Mendelson, Teodoro, Larissa Pereira Ribeiro, Tiago, Auana Vicente, and Capristo-Silva, Guilherme Fernando
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BOX-Jenkins forecasting ,REMOTE sensing ,SOIL management ,FORECASTING ,ALBEDO ,TIME series analysis - Abstract
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 CO
2 Flux 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 CO2 Flux tends to decrease in its seasonality, indicating a higher CO2 absorption. [ABSTRACT FROM AUTHOR]- Published
- 2021
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19. RELATIONSHIP BETWEEN VEGETATION INDICES AND AGRONOMIC PERFORMANCE OF MAIZE VARIETIES UNDER DIFFERENT NITROGEN RATES.
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da Silva FLORES, Marcela, PASCHOALETE, Willian Menitti, Rojo BAIO, Fabio Henrique, Silva CAMPOS, Cid Naudi, de Andréa PANTALEÃO, Ariane, Ribeiro TEODORO, Larissa Pereira, da SILVA JÚNIOR, Carlos Antônio, and TEODORO, Paulo Eduardo
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AGRONOMY ,CORN varieties ,EFFECT of nitrogen on plants ,PRECISION farming ,NORMALIZED difference vegetation index - Abstract
Copyright of Bioscience Journal is the property of Bioscience Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2020
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20. Remote sensing for updating the boundaries between the brazilian Cerrado-Amazonia biomes.
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Silva Junior, Carlos Antonio da, Costa, Gerlane de Medeiros, Rossi, Fernando Saragosa, Vale, Jôine Cariele Evangelista do, Lima, Rogério Brito de, Lima, Mendelson, Oliveira-Junior, José Francisco de, Teodoro, Paulo Eduardo, and Santos, Reginaldo Carvalho
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REMOTE sensing ,NORMALIZED difference vegetation index ,BIOMES ,LAND cover - Abstract
• Remotely sensed data in establishment of new boundary between biomes. • Land Use and Land Cover Change and the advance of the Cerrado biome on the edge of the Brazilian Amazonia. • Biophysical-atmospheric aspects altered with the anthropic advance and its influence on public environmental policies. The Amazonia is the largest tropical forest in the world, with approximately 5 million km
2 occupying about 49.3% of Brazil in the North and Central-West regions and bordering the Cerrado sensu lato (Brazilian savannah). The border area between these two biomes is called the Cerrado-Amazonia transition that extends approximately 6240 km. Currently, these limits, as well as other areas of ecotonal stress, are not clearly defined mainly due to intense Land Use and Land Cover Change (LULCC). Remote sensing is a tool that tries to define and update these limits. In this context, the objective of this study was to use remote sensing tools to determine the transition between the two large Brazilian biomes. The study area comprised the transition between the Cerrado-Amazonia biomes in Brazil and an area composed of a buffer of 300 km was created around the official line of the abovementioned biomes. We used data from some remote sensors to calculate the composition of the previous year's pixel means for the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) (-1˜1), CO 2 Flux (μmol m−2 s-1 ), evaporative efficiency (mm yr-1 ), and Gross Primary Production (GPP) (g C m−2 d−1 ) variables. The statistical analyses were performed by softening the pixels of all the variables by means of the Kernel density (KD). Later, the analysis of the main components and the cluster between the biomes and the remote sensing variables were applied. Therefore, we sought to standardize the results and to find an overall mean between the standards to delimit a new area of the Amazonia and Cerrado biomes, called the Transition Area between Biomes (TAB). The variables analyzed by remote sensing were effective for identifying the Amazonia and Cerrado biomes characteristics that highlighted the evaporative efficiency variable in relation to other variables in both biomes. Thus, from the results achieved, a new boundary was determined that delimits the transition of the biomes that is different than the official government boundary. The analyses used in this study constitute important tools to foment the creation of public policies able to curb the anthropic actions on these biomes. [ABSTRACT FROM AUTHOR]- Published
- 2019
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21. Analysis of the impact on vegetation caused by abrupt deforestation via orbital sensor in the environmental disaster of Mariana, Brazil.
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da Silva Junior, Carlos Antonio, Coutinho, Andressa Dias, de Oliveira-Júnior, José Francisco, Teodoro, Paulo Eduardo, Lima, Mendelson, Shakir, Muhammad, de Gois, Givanildo, and Johann, Jerry Adriani
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ENVIRONMENTAL disasters ,VEGETATION & climate ,REMOTE sensing ,DECISION making ,LINEAR statistical models ,ARTIFICIAL neural networks - Abstract
The failure of the Fundão Dam in Mariana, more precisely in the subdistrict of Bento Rodrigues, state of Minas Gerais (Brazil) on November 5th, 2015, is considered to be "the biggest environmental tragedy in the country's history." About thirty-four million cubic meters of tailings were dumped into the river where, another 16 million continued to reach the Atlantic ocean. This disaster seriously affected the flora, fauna, economic activities and people's lives, including the loss of human lives. Remote sensing allows mapping the variability of terrain properties, such as vegetation, water and geology, both in space and time, offering a synoptic view and useful environmental information in future decision making. In this way, this research aims to analyze the impacts of the failure of the Fundão Dam in the municipality of Mariana-MG on the vegetation cover, by means of remote sensing techniques and analysis of digital processing of orbital optical images. In order to analyze the soil cover, Unmixing Espectral Linear Model (UELM) was used in order to separate soil, shade and vegetation classes. Subsequently, Artificial Neural Network (ANN) classification method was applied, followed by Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). The results showed a loss of 13.02% of the vegetation, about 1289 ha, and a reduction of 68.57% of shade (water), approximately 1347 ha. The UELM showed to be effective in the separation of each image-fraction, being an important stage for the success of the classification. The EVI was the index that best described the vegetation deficit in the affected areas spilling the sludge from waste. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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22. Nonparametric Statistics Applied to Fire Foci Obtained by Meteorological Satellites and Their Relationship to the MCD12Q1 Product in the State of Rio de Janeiro, Southeast Brazil.
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Caúla, Rodrigo Hottz, Oliveira‐Júnior, José Francisco, Gois, Givanildo, Delgado, Rafael Coll, Pimentel, Luiz Cláudio Gomes, and Teodoro, Paulo Eduardo
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STATISTICAL accuracy ,ECONOMETRICS ,MATHEMATICAL statistics ,NONPARAMETRIC statistics - Abstract
This study shows the relationship between the fire foci and the MCD12Q1 product (MODIS) for the land use and occupation in the state of Rio de Janeiro (SRJ) and evaluates the growth and decay of trends fire foci through nonparametric tests, applied to the number of fire foci in the 2000-2012 period. The study period was divided into (1) 2001 to 2013 for assessment of the product MCD12Q1 back for the land use and occupation and application of nonparametric tests and (2) 2000 to 2012 for the descriptive statistics (total and percentage-%) of fire foci geared for regional assessment. To evaluate growth trends and decay of fire foci, the nonparametric tests (Mann-Kendall and Pettitt) were utilized. Based on data obtained by Instituto Brasileiro de Geografia e Estatística (IBGE), the land use and occupation in the SRJ has been modified from the last two census years (1995 and 2006). The Mann-Kendall test was significant for the year 2001, with trends of 0 · 19 and 0 · 08 (Mann-Kendall). Fire foci for the years 2001 and 2013 grew with an increase of 0 · 05 and 0 · 01 per year, respectively. The Pettitt statistic test applied for the year 2001 showed that almost 70% of the studied municipalities were above the critical threshold, which has not occurred for the year 2013. The North Fluminense Region with 596 fire foci (30 · 35%) was the one with the largest number of fire foci, and the municipality of Campos dos Goytacazes was the largest contributor, with 344 fire foci (57 · 72%). The Pasturage class (MCD12Q1 product) had the biggest number of fire foci, with 354 foci (56 · 64%) in 2001 and 564 foci (42 · 28%) in 2013 and the largest amount in the north of the SRJ. The study indicated that there was a significant increase in average fire foci. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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23. Fires Drive Long-Term Environmental Degradation in the Amazon Basin.
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Silva Junior, Carlos Antonio da, Lima, Mendelson, Teodoro, Paulo Eduardo, Oliveira-Júnior, José Francisco de, Rossi, Fernando Saragosa, Funatsu, Beatriz Miky, Butturi, Weslei, Lourençoni, Thaís, Kraeski, Aline, Pelissari, Tatiane Deoti, Moratelli, Francielli Aloisio, Arvor, Damien, Luz, Iago Manuelson dos Santos, Teodoro, Larissa Pereira Ribeiro, Dubreuil, Vincent, and Teixeira, Vinicius Modolo
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ENVIRONMENTAL degradation ,DROUGHT management ,BIODIVERSITY conservation ,FIRE management ,CARBON emissions ,REMOTE sensing ,PROTECTED areas ,TIME series analysis - Abstract
The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km
2 burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km2 burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin. [ABSTRACT FROM AUTHOR]- Published
- 2022
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24. Is it possible to detect boron deficiency in eucalyptus using hyper and multispectral sensors?
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Silva Junior, Carlos Antonio da, Teodoro, Paulo Eduardo, Teodoro, Larissa Pereira Ribeiro, Della-Silva, João Lucas, Shiratsuchi, Luciano Shozo, Baio, Fábio Henrique Rojo, Boechat, Cácio Luiz, and Capristo-Silva, Guilherme Fernando
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BORON , *EUCALYPTUS , *PLANT nutrition , *REMOTE sensing , *PLANT growth , *BORIC acid - Abstract
• Real-time monitoring of boron fertilization in eucalyptus is helpful for guiding precision diagnosis; • The 350–371 nm spectral range can be used for detecting boron-deficient plants; • Adequate boron levels can be identified by using the 426–444, 1811–1910, 1948–2115, and 2124–2208 nm; • The 425–475 nm spectral range can be used to find boron-toxicity plants. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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25. Estimating spray application rates in cotton using multispectral vegetation indices obtained using an unmanned aerial vehicle.
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Martins, Pedro Henrique Alves, Baio, Fabio Henrique Rojo, Martins, Túlio Henrique Dresch, Fontoura, João Vitor Pereira Ferreira, Teodoro, Larissa Pereira Ribeiro, Silva Junior, Carlos Antonio da, and Teodoro, Paulo Eduardo
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SPRAYING & dusting in agriculture ,COTTON growing ,COTTON ,PLANT products ,PEST control ,PLANT protection - Abstract
Cotton has high production costs compared to other annual crops because large numbers of plant protection product (PPP) applications can be needed to control insect pests, diseases, and growth. The hypothesis underlying this study was that vegetation indices (VIs) could be used to estimate application rates for cotton. Our objectives were to (i) evaluate the relationship between different VIs and the application rates for cotton; (ii) propose a modification to the canopy chlorophyll content index (CCCI); and (iii) to develop a VI based equation that will indicate the ideal application rate needed to maximize deposition in the middle layer of a cotton crop. The experiments were carried out during the crop seasons 2017/18, and 2018/19 in the State of Mato Grosso do Sul, Brazil. A multispectral sensor installed in an unmanned aerial vehicle (UAV) was used to obtain the VIs, and the application rates evaluated were 40, 70, 100, and 130 L ha
−1 . The spray deposits on cotton leaves were measured using the mass balance analysis method. Our findings revealed that an increase in the VIs led to a rise in the application rate needed to maintain spray deposition on the middle layer of cotton plants. The CCCI is related to the rate variation in the cotton crop. However, our results showed that the proposed modified equation (the simplified modified canopy chlorophyll content index), which is based on the relative deposition, improves the estimation of the application rate that will optimize spray deposition in the middle layer of cotton plants. [ABSTRACT FROM AUTHOR]- Published
- 2021
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26. Environmental and climatic Interconnections: Impacts of forest fires in the Mato Grosso region of the Amazon.
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dos Santos, Daniel Henrique, Rossi, Fernando Saragosa, Della Silva, João Lucas, Pelissari, Tatiane Deoti, Lima, Mendelson, Teodoro, Larissa Pereira Ribeiro, Teodoro, Paulo Eduardo, and Silva Junior, Carlos Antonio da
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GREENHOUSE gases , *LAND surface temperature , *NORMALIZED difference vegetation index , *FOREST fires , *TREND analysis - Abstract
Wildfires in the Amazon biome of Mato Grosso cause extensive environmental, economic, and health damages, including biodiversity loss and high greenhouse gas emissions. This study used remote sensing to examine the relationship between fire severity and climatic factors, focusing on dNBR (Differenced Normalized Burn Ratio), precipitation, LST (Land Surface Temperature), SPI (Standardized Precipitation Index), NDVI (Normalized Difference Vegetation Index), and VCI (Vegetation Condition Index), analyzing data from 2001 to 2022. Statistical tests included Shapiro-Wilk, Tukey, regression kriging, Mann-Kendall for trend analysis, Pettitt for change points, and canonical variable tests. Regarding trends, only LST showed a significant trend starting in 2009, with the Northeast mesoregion showing the highest impact on temperature. dNBR correlated positively with NDVI and VCI, and negatively with precipitation and SPI. The northern mesoregion had a positive influence on dNBR and NDVI but negative for precipitation, SPI, and VCI. The southwestern mesoregion associated positively with dNBR and LST but negatively with the other variables. The Northeast and South-Central mesoregions showed positive correlations with most variables except dNBR and NDVI. These findings highlight the northern mesoregion's vulnerability due to its proximity to the central Amazon Forest and agri-cultural activity, indicating increased fire susceptibility with reduced humidity. • The northeast mesoregion of the Mato Grosso Amazon had the greatest impact on temperature. • There is vulnerability in the northern mesoregion due to its proximity to the central rainforest and agricultural activity. • The main positive correlations for dNBR were with the variables NDVI and VCI. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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27. Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor.
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Santana, Dthenifer Cordeiro, Otone, José Donizete de Queiroz, Baio, Fábio Henrique Rojo, Teodoro, Larissa Pereira Ribeiro, Alves, Marcos Eduardo Miranda, Junior, Carlos Antonio da Silva, and Teodoro, Paulo Eduardo
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ARTIFICIAL neural networks , *MACHINE learning , *SOYBEAN , *CLASSIFICATION algorithms , *SUPPORT vector machines , *PLANT diseases - Abstract
[Display omitted] • Hyperspectral reflectance for the levels of Asian soybean rust severity showed differences across the entire electromagnetic spectrum. • Plants with 50% disease severity have a curve close to healthy plants with lower reflectance. • More severely attacked plants have higher reflectance. Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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28. Improving the validation of ecological niche models with remote sensing analysis.
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José-Silva, Leandro, dos Santos, Reginaldo Carvalho, de Lima, Bruna Martins, Lima, Mendelson, de Oliveira-Júnior, José Francisco, Teodoro, Paulo Eduardo, Eisenlohr, Pedro V., and da Silva Junior, Carlos Antonio
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REMOTE sensing , *IMAGE analysis , *ECOLOGICAL niche , *BERTHOLLETIA , *VEGETATION & climate - Abstract
Ecological Niche models (ENMs) are tools that allow us to approximate the area of suitability for a species, thereby allowing elaboration of conservation strategies. The validation of these models in situ is not always possible due to costly access remote areas where conserved species are often found. The goal of our study was to provide a new validation concept for ENMs by applying remote sensing (SR) techniques, such as Geographic Object-Based Image Analysis (GEOBIA), which enables mapping of large areas and provides detailed information on land use. To assess the GEOBIA validation technique, we selected the species Bertholletia excelsa (Brazil nut), a tree that has great importance as a non-timber forest product and is considered vulnerable by the International Union for Conservation of Nature (IUCN). Models were built on the ‘biomod2’ package, and evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and True Skill Statistics (TSS) metrics. Images were obtained from the orbital Operational Land Imager (OLI) on board the Landsat-8 satellite and the thematic maps were evaluated using Kappa and Overall Accuracy Statistics. We calculated vegetation indices (EVI, SAVI, LAI, and NDVI) and applied them to the GEOBIA technique. A total of 693 possible sites of B. excelsa were detected. Of these, 25 accessible sites were used for validation, and 45 new records of B. excelsa were added in the study area. GEOBIA was demonstrated to have high potential for validating ENMs, as well as in the extraction of arboreal species from medium-resolution spatial images. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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29. Carbon dioxide spatial variability and dynamics for contrasting land uses in central Brazil agricultural frontier from remote sensing data.
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Rossi, Fernando Saragosa, de Araújo Santos, Gustavo André, de Souza Maria, Luciano, Lourençoni, Thaís, Pelissari, Tatiane Deoti, Della-Silva, João Lucas, Oliveira Júnior, José Wagner, Silva, Adriana de Avila e, Lima, Mendelson, Teodoro, Paulo Eduardo, Teodoro, Larissa Pereira Ribeiro, de Oliveira-Júnior, José Francisco, La Scala Jr, Newton, and Silva Junior, Carlos Antonio da
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REMOTE sensing , *CARBON dioxide , *MODIS (Spectroradiometer) , *LAND use , *CARBON dioxide sinks , *DEFORESTATION - Abstract
Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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30. Temporal record and spatial distribution of fire foci in State of Minas Gerais, Brazil.
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Marinho, Ana Aguiar Real, Gois, Givanildo de, Oliveira-Júnior, José Francisco de, Correia Filho, Washington Luiz Félix, Santiago, Dimas de Barros, Silva Junior, Carlos Antonio da, Teodoro, Paulo Eduardo, de Souza, Amaury, Capristo-Silva, Guilherme Fernando, Freitas, Welington Kiffer de, and Rogério, Josicléa Pereira
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FIRE management , *FIRE , *ENVIRONMENTAL risk , *SOCIOECONOMIC factors , *REMOTE sensing , *ENVIRONMENTAL management , *TIME series analysis ,LA Nina - Abstract
The objectives of this study are: (i) to evaluate the space-temporal variability of fire foci by environmental satellites, CHIRPS and remote sensing products based on applied statistics, and (ii) to identify the relational pattern between the distribution of fire foci and the environmental, meteorological, and socioeconomic variables in the mesoregions of Minas Gerais (MG) - Brazil. This study used a time series of fire foci from 1998 to 2015 via BDQueimadas. The temporal record of fire foci was evaluated by Mann-Kendall (MK), Pettitt (P), Shapiro-Wilk (SW), and Bartlett (B) tests. The spatial distribution by burned area (MCD64A1-MODIS) and the Kernel density - (radius 20 km) were estimated. The environmental variables analyzed were: rainfall (mm) and maximum temperature (°C), besides proxies to vegetation canopy: NDVI, SAVI, and EVI. PCA was applied to explain the interaction between fire foci and demographic, environmental, and geographical variables for MG. The MK test indicated a significant increasing trend in fire foci in MG. The SW and B tests were significant for non-normality and homogeneity of data. The P test pointed to abrupt changes in the 2001 and 2002 cycles (El Niño and La Niña moderated), which contributes to the annual increase and in winter and spring, which is identified by the Kernel density maps. Burned areas highlighted the northern and northwestern regions of MG, Triângulo Mineiro, Jequitinhonha, and South/Southwest MG, in the 3rd quarter (increased 17%) and the 4th quarter (increased 88%). The PCA resulted in three PCs that explained 71.49% of the total variation. The SAVI was the variable that stood out, with 11.12% of the total variation, followed by Belo Horizonte, the most representative in MG. We emphasize that the applied conceptual theoretical model defined here can act in the environmental management of fire risk. However, public policies should follow the technical-scientific guidelines in the mitigation of the resulting socioeconomic - environmental damages. Image 1 • There was a significant increasing trend in fire foci in Minas Gerais. • The North and Northwest regions of MG, Triângulo Mineiro, and Jequitinhonha have more burned areas. • The proposed theoretical-conceptual model can act in environmental fire risk management. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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