scholarly journals A MODIS-based scalable remote sensing method to estimate sowing and harvest dates of soybean crops in Mato Grosso, Brazil

Heliyon ◽  
2021 ◽  
pp. e07436
Author(s):  
Minghui Zhang ◽  
Gabriel Abrahao ◽  
Avery Cohn ◽  
Jake Campolo ◽  
Sally Thompson
Keyword(s):  
Remote Sensing ◽  
Mato Grosso ◽  
Harvest Dates

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

scholarly journals Uso de Imagens de Satélite Como Ferramenta para Análise da Vegetação do Monumento Natural Morro de Santo Antônio/MT

UNICIÊNCIAS ◽  
2018 ◽  
Vol 22 (1) ◽  
pp. 17
Author(s):  
Celso Arruda Souza ◽  
Victor Hugo Morais Danelichen
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Vegetation Dynamics ◽  
The State ◽  
Brazilian Cerrado ◽  
Conservation Unit ◽  
Conservation Units ◽  
Mato Grosso ◽  
Vegetation Indexes ◽  
Natural Monument

ecossistema em diversas escalas. Diante disso, o objetivo deste trabalho foi avaliar os efeitos sobre o uso e ocupação do solo, após a criação da unidade de conservação Monumento Natural Morro de Santo Antônio no Estado de Mato Grosso. O estudo foi realizado no Morro de Santo Antônio, distante 15 km da capital do Estado de Mato Grosso, localizado na divisa dos municípios de Cuiabá e Santo Antônio de Leverger no Cerrado Brasileiro. Foram adquiridas, junto ao USGS, imagens dos satélites Landsat 5 e 8, dos anos de 2005 a 2015, com resolução espacial de 30 m. Para o estudo da dinâmica da vegetação foram computados os índices de vegetação e de umidade da superfície NDMI. Os resultados apresentados neste trabalho demonstram que após a criação da MoNa, o índice foi menor no ano de 2006, enquanto que no ano de 2015 foi maior.Palavras-chave: Sensoriamento Remoto. Unidade Conservação. Índices de Vegetação.AbstractSatellite imagery is a great tool for monitoring ecosystem conservation units at different scales. Therefore, the objective of this work was to evaluate the effects on the soil use and occupation after the creation of the conservation unit Morro de Santo Antônio Natural Monument in the state of Mato Grosso. The study was carried out in Morro de Santo Antônio, distant 15 km from the capital of the State of Mato Grosso, located at the border of the municipalities of Cuiabá and Santo Antônio de Leverger in  Brazilian Cerrado. Images from the Landsat 5 and 8 satellites were acquired from the USGS from 2005 to 2015, with spatial resolution of 30 m. For the study of vegetation dynamics, vegetation and moisture indexes of the NDMI surface were computed. The results presented in this study demonstrate that after the MoNa creation, the index was lower in 2006, while in the year 2015,  was higher.Keywords: Remote Sensing. Conservation Unit. Vegetation Indexes.

scholarly journals ANÁLISE DA DINAMICA HIDROGEOMORFOLÓGICA NO RIO MIRANDA, ESTADO DE MATO GROSSO DO SUL POR MEIO DE IMAGENS LANDSAT SENSORES TM E OLI

2020 ◽  
Vol 17 (01) ◽  
pp. 53-69
Author(s):  
Ivo Augusto Lopes Magalhães ◽  
Everton de Carvalho ◽  
Aguinaldo Silva ◽  
Beatriz Lima de Paula
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Landsat Images ◽  
Mato Grosso ◽  
Meandering Channel ◽  
River Geomorphology ◽  
Del Rio ◽  
Mato Grosso Do Sul ◽  
Abandoned Meanders

Atualmente no Brasil, os estudos sobre as dinâmicas hidrogeomorfológicas por meio de dados de sensoriamento remoto ainda são escassos. Em rios com extensos percursos e áreas inóspitas faz jus o uso de técnicas de sensoriamento remoto para análise e monitoramento ambiental. Diante do exposto, o objetivo deste estudo foi analisar as mudanças na geomorfologia fluvial do rio Miranda no estado de Mato Grosso do Sul, MS por meio de séries de imagens multitemporais dos Sensores Tematic Mapper do satélite Landsat – 5 e OLI do Satélite Landsat- 8. A planície de inundação do rio Miranda apresenta aproximadamente 600 m de largura e padrão de canal meandrante com índice de sinuosidade de 2.13. Identificou-se áreas em processo erosivo nas margens côncavas, deposição de sedimentos nas margens convexas e presença de meandros abandonados. O paleocinturão de meandros, abandonos de canais e meandros abandonados foram os fenômenos naturais que ocorreram com maior frequência e mais distinguíveis nas imagens Landsat para o período analisado. Palavras-chave: Geomorfologia fluvial. Geoprocessamento. Recursos hídricos. Imagens de satélite.   ANALYSIS OF HYDROGEOMORPHOLOGICAL DYNAMICS IN RIVER MIRANDA, MATO GROSSO DO SUL STATE BY IMAGE LANDSAT SENSORS TM AND OLI ABSTRACT Currently in Brazil, studies on the hydrogeomorphological dynamics through remote sensing data are still scarce. In rivers with extensive pathways and inhospitable areas the use of remote sensing techniques for analysis and monitoring environmental is justified. The objective of this study was to analyze the changes in the river geomorphology of the Miranda River in the state of Mato Grosso of Sul, MS, using multitemporal images series of the Landsat - 5 satellite and Landsat - 5 satellite OLS sensors The floodplain of the Miranda river is approximately 600 m wide and has a meandering channel pattern with a sinuosity index of 2.13. It was identified areas in erosive process in the concave margins, deposition of sediments in the convex margins and presence of abandoned meanders. The paleoculture of meanders, abandonments of channels and abandoned meanders were the natural phenomena that occurred more frequently and more distinguishable in Landsat images for the analyzed period. Keywords: Fluvial geomorphology. Geoprocessing. Water resources. Satellite images.   ANÁLISIS DE DINÁMICA HIDROGEOMORFOLÓGICA EN EL RÍO MIRANDA, ESTADO DE MATO GROSSO DEL SUR POR MEDIO DE IMÁGENES LANDSAT SENSORES TM Y OLI RESUMEN Actualmente en Brasil, los estudios sobre las dinámicas hidrogeomorfológicas por medio de datos de sensoriamiento remoto todavía son escasos. En ríos con extensos recorridos y áreas inhóspitas, el uso de técnicas de detección remota para análisis y monitoreo ambiental. El objetivo de este estudio fue analizar los cambios en la geomorfología fluvial del río Miranda en el estado del Mato Grosso do Sul, MS por medio de series de imágenes multitemporales de los Sensores Tematic Maper del satélite Landsat - 5 y OLI del Satélite Landsat- 8 La planicie de inundación del río Miranda presenta aproximadamente 600 m de ancho y patrón de canal meandrante con índice de sinuosidad de 2.13. Se identificaron áreas en proceso erosivo en los márgenes cóncavos, deposición de sedimentos en las márgenes convexas y presencia de meandros abandonados. La paleocrelación de meandros, abandonos de canales y meandros abandonados fueron los fenómenos naturales que ocurrieron con mayor frecuencia y más distinguibles en las imágenes Landsat para el período analizado. Palabras-clave: Geomorfología fluvial. Geoprocessamento. Recursos hídricos. Imágenes de satélite.

scholarly journals INFLUÊNCIA DO DESMATAMENTO NO ÍNDICE DE ÁREA FOLIAR E TEMPERATURA DA SUPERFÍCIE NO CERRADO MATO-GROSSENSE

Nativa ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 364 ◽  
Author(s):  
Israel Oliveira Ivo ◽  
Marcelo Sacardi Biudes ◽  
Nadja Gomes Machado ◽  
Vagner Marques Pavão
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Anthropogenic Activities ◽  
Inverse Correlation ◽  
Soil Surface ◽  
Thematic Mapper ◽  
Area Index ◽  
Mato Grosso

A substituição do Cerrado mato-grossense para práticas agrícolas e a dinâmica fenológica alteram os índices biofísicos da superfície como a temperatura da superfície (Tsup) e o índice de área foliar (IAF). Assim, o objetivo desse estudo foi avaliar a variação do IAF e da Tsup por sensoriamento remoto em uma área de Cerrado no interior do estado de Mato Grosso. Imagens do sensor Thematic Mapper (TM) Landsat 5 foram utilizadas para estimar o IAF e a Tsup de uma área de Cerrado (CE), cana-de-açúcar (CA), pastagem (PA) e soja (SJ) em 2011. O IAF e a Tsup apresentaram correlação inversa. O IAF diminuiu e a Tsup aumentou ao longo da estação seca. Os maiores IAF e menores Tsup foram observados no CE, enquanto que os menores IAF e maiores Tsup foram observados na SJ. Os padrões temporais e espaciais do IAF e da Tsup na área de estudo ocorreram dirigidos pela precipitação, atividades antropogênicas e pelo próprio ciclo fenológico da vegetação.Palavras-chave: superfície do solo, antropização, aquecimento da superfície, sensoriamento remoto. INFLUENCE OF DEFORESTATION ON LEAF AREA INDEX AND SURFACE TEMPERATURE IN THE CERRADO OF MATO GROSSO ABSTRACT:The substitution of the Cerrado of Mato Grosso for agricultural practices and phenological dynamics alter the biophysical indexes of the surface such as surface temperature (Tsup) and leaf area index (LAI). Thus, the objective of this study was to evaluate the variation of LAI and Tsup by remote sensing in a Cerrado area in the state of Mato Grosso. The images of the Thematic Mapper (TM) Landsat 5 sensor were used to estimate the LAI and Tsup of an area of Cerrado (CE), sugarcane (CA), pasture (PA) and soybean (SJ) in 2011. The LAI and Tsup presented an inverse correlation. LAI declined and Tsup increased throughout the dry season. The higher LAI and lower Tsup were observed in the CE, while the lower LAI and higher Tsup were observed in SJ. The temporal and spatial patterns of LAI and Tsup in the study area were driven by precipitation, anthropogenic activities and by the phenological cycle of vegetation itself.Keywords: soil surface, anthropization, surface heating, remote sensing.

scholarly journals Comparison of Machine Learning Techniques in Cotton Yield Prediction Using Satellite Remote Sensing

2021 ◽  
Author(s):  
Francielle Morelli-Ferreira ◽  
Nayane Jaqueline Costa Maia ◽  
Danilo Tedesco ◽  
Elizabeth Haruna Kazama ◽  
Franciele Morlin Carneiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Vegetation Indices ◽  
Growing Season ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Cotton Yield ◽  
K Nearest Neighbor ◽  
Mato Grosso ◽  
Learning Techniques

The use of machine learning techniques to predict yield based on remote sensing is a no-return path and studies conducted on farm aim to help rural producers in decision-making. Thus, commercial fields equipped with technologies in Mato Grosso, Brazil, were monitored by satellite images to predict cotton yield using supervised learning techniques. The objective of this research was to identify how early in the growing season, which vegetation indices and which machine learning algorithms are best to predict cotton yield at the farm level. For that, we went through the following steps: 1) We observed the yield in 398 ha (3 fields) and eight vegetation indices (VI) were calculated on five dates during the growing season. 2) Scenarios were created to facilitate the analysis and interpretation of results: Scenario 1: All Data (8 indices on 5 dates = 40 inputs) and Scenario 2: best variable selected by Stepwise regression (1 input). 3) In the search for the best algorithm, hyperparameter adjustments, calibrations and tests using machine learning were performed to predict yield and performances were evaluated. Scenario 1 had the best metrics in all fields of study, and the Multilayer Perceptron (MLP) and Random Forest (RF) algorithms showed the best performances with adjusted R2 of 47% and RMSE of only 0.24 t ha-1, however, in this scenario all predictive inputs that were generated throughout the growing season (approx. 180 days) are needed, so we optimized the prediction and tested only the best VI in each field, and found that among the eight VIs, the Simple Ratio (SR), driven by the K-Nearest Neighbor (KNN) algorithm predicts with 0.26 and 0.28 t ha-1 of RMSE and 5.20% MAPE, anticipating the cotton yield with low error by ±143 days, and with important aspect of requiring less computational demand in the generation of the prediction when compared to MLP and RF, for example, enabling its use as a technique that helps predict cotton yield, resulting in time savings for planning, whether in marketing or in crop management strategies.

scholarly journals Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thais Lourençoni ◽  
Carlos Antonio da Silva Junior ◽  
Mendelson Lima ◽  
Paulo Eduardo Teodoro ◽  
Tatiane Deoti Pelissari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Kernel Density ◽  
Cloud Platform ◽  
Mato Grosso ◽  
Monitoring Programs ◽  
Data Variability ◽  
Southern Amazonia ◽  
Remote Sensing Techniques ◽  
Crop Enhancement

AbstractThe 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.

scholarly journals Model for soybean production forecast based on prevailing physical conditions

2017 ◽  
Vol 52 (2) ◽  
pp. 95-103 ◽  
Author(s):  
Anibal Gusso ◽  
Damien Arvor ◽  
Jorge Ricardo Ducati
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Local Level ◽  
The State ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Production Forecast ◽  
Mato Grosso ◽  
Soybean Production

Abstract: The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R2=0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R2=0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.

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