Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil

Veloso, Gabriel Alves; Ferreira, Manuel Eduardo; Júnior, Laerte Guimarães Ferreira; Silva, Bernardo Barbosa da

doi:10.1016/j.rsase.2020.100288

Cited by 19 publications

(19 citation statements)

References 19 publications

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“…Previous studies have reported good agreement between field measurements of pasture biomass and information extracted from satellite data [13][14][15]. However, the temporal frequency of satellite data and the spatial resolution needed to capture the forage production variation between or within paddocks have been an obstacle to achieve effective pasture monitoring using the commonly used satellite data, such as Terra/Aqua/MODIS (Moderate Resolution Imaging Spectroradiometer) (250 m near daily since 2000) [16,17] and Landsat 5/TM, 7/ETM+, 8/OLI (30 m every 16 days from 1984) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

et al. 2020

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Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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Section: Introductionmentioning

confidence: 99%

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

et al. 2020

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“…Diferentes índices de vegetação podem ser utilizados para o estudo e a avaliação da vegetação. Tais índices são calculados com o objetivo de ressaltar e distinguir o comportamento espectral da vegetação em relação ao solo e os demais alvos da superfície terrestre (Moreira, 2011) e têm sido utilizados em pesquisas que visam caracterizar certos parâmetros biofísicos da vegetação, tais como fitomassa (Filho et al, 2020), produtividade (Rodigheri et al, 2020), radiação fotossinteticamente ativa absorvida (Veloso et al, 2020) e índice de área foliar verde (Tang et al, 2020).…”

Section: Conversão Dos Números Digitais Em Valores Reais De Reflectânciaunclassified

Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

Krizek

Santos

2021

Rev. Bras. Geog. Fis.

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A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa. Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação. Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING

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“…A pecuária brasileira representa 31% do Produto Interno Bruto do agronegócio do país, e compreende uma atividade de mitigação à vulnerabilidade socioeconômica da região, sendo mais estável ao longo do ano que a agricultura. (Moraes et al, 2019;Nunes et al, 2016;Veloso et al, 2020). Entretanto, a sazonalidade anual na produção de forragem na região semiárida apresenta-se como grande adversidade para a produção de forragem sob sistema de sequeiro, podendo ocasionar desequilíbrio entre a sua oferta e a demanda de ração animal (Bell et al, 2018).…”

Section: Introductionunclassified

Lacunas de produtividades e estratégias de cultivo na melhoria da produção de forragem para a região semiárida brasileira - Revisão

Pinheiro¹,

Souza²,

Jardim³

et al. 2021

Rev. Bras. Geog. Fis.

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Livestock farming is a fundamental economic activity for the semi-arid region, but it is directly influenced by factors such as soil characteristics, forage quality and rainfall seasonality. The irregularity of these factors reduces productivity levels, making them dependent on climatic conditions. In this sense, the knowledge of the main causes that reduce productivity and the adoption of resilient agricultural practices are essential in reducing the seasonal effects of the climate, increasing the supply of forage in the period of water scarcity. The use of adapted forage crops and with productive potential constitutes an efficient agricultural resilience practice, among which stands out the forage cactus, sorghum, millet, pigeon pea and sunflower. These are constituted with a high capacity for water conversion into dry matter, nutritional complementation and increased production. In addition, the implementation of practices (e.g. intercropping, irrigation, densification, mulch, etc.) contribute to the optimization of natural resources and provide better results in the continuous subsidy of forage production. With this, the present study aimed to present the environmental characteristics of the Brazilian semiarid region, to expose concepts about the levels of productivity and the main factors of the production gaps, in addition to disseminating mitigation strategies of forage production. After compiling this information, it highlights the importance of adapted forage crops and the adoption of agricultural management practices as strategies to mitigate the seasonal effects of the climate, contributing to the continuous subsidy of forage production. At the same time, the Yield Gap analysis allows for a broader knowledge of the limiting factors of production, directing agricultural planning more efficiently.

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Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil

Cited by 19 publications

References 19 publications

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

Lacunas de produtividades e estratégias de cultivo na melhoria da produção de forragem para a região semiárida brasileira - Revisão

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