Utilização de redes neurais artificiais na classificação de níveis de degradação em pastagens

Chagas, C. S.; Vieira, Carlos Antônio Oliveira; Filho, Elpídio Inácio Fernandes; Júnior, Waldir de Carvalho

doi:10.1590/s1415-43662009000300014

Cited by 23 publications

(20 citation statements)

References 16 publications

(16 reference statements)

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“…The area occupied by grasslands with balanced growth increased at the end of spring, whereas the areas with low or very low growth decreased (Table 3). However, the area of grasslands with low growth remained high throughout the study period, indicating low photosynthetic activity when compared with historical series, which is a clear sign of degradation from overgrazing, erosion, and poor soil fertility, as well as of soil worsening and climate conditions (Nascimento et al, 2006;Chagas et al, 2009). The predominant vegetation development class was low or very low, for which the intensity pattern of the photosynthetically active vegetation denotes unstable growth conditions, which accounted for 72% of the area in late September.…”

Section: Resultsmentioning

confidence: 93%

“…Fonseca et al (2007), based on soil effects, attempted to use Landsat 7 images and an agrometeorological model to predict the availability of forage in municipalities in southern Brazil, but obtained unsatisfactory results. Nascimento et al (2006) and Chagas et al (2009) identified grassland degradation levels through high resolution images, whose detailed soil exposition hindered the classification of the areas with higher degradation levels.…”

Section: Introductionmentioning

confidence: 99%

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Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Hott

Carvalho

Antunes

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.Index terms: growth index, pastures, remote sensing, time series, Zona da Mata. Termos para indexação: índice de crescimento, pastagens, sensoriamento remoto, séries temporais, Zona da Mata. Crescimento vegetativo de pastagens baseado em dados hipertemporais de NDVI do sensor Modis

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Section: Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Hott

Carvalho

Antunes

et al. 2016

Pesq. agropec. bras.

View full text Add to dashboard Cite

show abstract

“…Chagas et al (2009) unem as classes referentes a áreas urbanas e solo exposto, para evitar esse tipo de confusão, no trabalho destes autores as maiores confusões não foram referentes a essa classe, apesar de mesmo assim apresentar uma certa confusão com outras classes.…”

Section: Análise Das Matrizes De Confusõesunclassified

Dinâmica Do Uso E Cobertura Da Terra Do Entorno Do Reservatório Do Passo Real – Rs

Rex¹,

Kazama²,

Côrte³

et al. 2018

Enci Bio

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“…ANNs can be applied in many areas, like soil digital mapping based on soil-landscape relationships (Arruda et al, 2013), classification of degradation levels of pastures (Chagas et al, 2009), rainfall erosivity (Moreira et al, 2006, estimation of reference evapotranspiration through air temperature data (Alves Sobrinho et al, 2011), modeling of soil penetration resistance (Santos et al, 2012) or even identification and classification of soybean cultivars by planting region (Galão et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network model for simulation of water distribution in sprinkle irrigation

Menezes

Azevedo

Eyng

et al. 2015

Rev. bras. eng. agríc. ambient.

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A B S T R A C TDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN) to simulate sprinkler precipitation, using the values of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Modelo em redes neurais artificiais para a simulação da precipitação na irrigação por aspersão R E S U M O A determinação dos coeficientes de uniformidade de distribuição da água dos sistemas de irrigação por aspersão depende, em geral, de ensaios em campo que envolvem tempo e recursos financeiros. Uma alternativa para reduzir custo e tempo é a utilização de simulações. Objetivou-se, com este trabalho, o desenvolvimento de um modelo em redes neurais artificiais (RNA) para simular a precipitação de um aspersor cujos parâmetros de entrada são os valores de pressão de operação, velocidade do vento, direção do vento e diâmetro do bocal do aspersor. Ensaios foram feitos em campo com um aspersor operando em uma malha de 16 x 16 coletores com espaçamento de 1,5 m e diferentes combinações de bocais, pressões e condições de vento. O modelo RNA apresentou bons resultados na simulação da precipitação com correlação de Spearman (r s ) entre os dados obtidos em ensaio de campo e os dados simulados, apresentando valores entre 0,92 e 0,97 e índice de concordância de Willmott (d) entre 0,950 e 0,991 para dez ensaios analisados. O modelo RNA mostrou-se promissor na simulação da precipitação em sistemas de irrigação por aspersão. Key words:sprinkler water distribution uniformity artificial intelligence computational model Palavras-chave: aspersor uniformidade de distribuição de água inteligência artificial modelo computacional

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Utilização de redes neurais artificiais na classificação de níveis de degradação em pastagens

Cited by 23 publications

References 16 publications

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Dinâmica Do Uso E Cobertura Da Terra Do Entorno Do Reservatório Do Passo Real – Rs

Artificial neural network model for simulation of water distribution in sprinkle irrigation

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