Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

Pereira, R. M. Faisca Rodrigues; Casaroli, Derblai; Vellame, Lucas Melo; Júnior, José Alves; Evangelista, Adão Wagner Pêgo

doi:10.1590/1983-40632016v4639303

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…After rainy days with low levels of solar radiation, NDVI values decreased in all land uses compared to dry days with high levels of solar radiation, as was also reported in Lucas and Schuler [58] and Pereira et al [56]. Thus, by analyzing the surface albedo, NDVI, and surface temperature simultaneously, spatial variations Other authors obtained similar NDVI values in southeast Brazil, such as Fernandes et al [43], who found average values of NDVI for sugarcane between 0.4 and 0.7; Pereira et al [56], who found average values of NDVI for sugarcane between 0.4 and 0.75; and Castanheira et al [57], who found an average value of 0.67 for silviculture. Data normalization has made vegetation features and characteristics more discernible in comparison to other input parameters.…”

Section: Input Parameters Of Safer Algorithmsupporting

confidence: 83%

“…Data normalization has made vegetation features and characteristics more discernible in comparison to other input parameters. After rainy days with low levels of solar radiation, NDVI values decreased in all land uses compared to dry days with high levels of solar radiation, as was also reported in Lucas and Schuler [58] and Pereira et al [56]. Thus, by analyzing the surface albedo, NDVI, and surface temperature simultaneously, spatial variations can be mainly attributed to variations in R G and soil moisture [6].…”

Section: Input Parameters Of Safer Algorithmsupporting

confidence: 74%

“…Figure 8 shows the spatial distribution of the NDVI for each DOY and Table 4 presents the mean values and standard deviation for each DOY and relevant land use. Other authors obtained similar NDVI values in southeast Brazil, such as Fernandes et al [43], who found average values of NDVI for sugarcane between 0.4 and 0.7; Pereira et al [56], who found average values of NDVI for sugarcane between 0.4 and 0.75; and Castanheira et al [57], who found an average value of 0.67 for silviculture. Data normalization has made vegetation features and characteristics more discernible in comparison to other input parameters.…”

Section: Input Parameters Of Safer Algorithmmentioning

confidence: 62%

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Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil

2018

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Sentinel-2 images at 10-m resolution were used for modeling crop coefficients and biomass production with the application of the so-called SAFER (Simple Algorithm for Evapotranspiration Retrieving) and Monteith model for biomass production in an area nearby the city of Águas de Santa Bárbara, in the central-western part of São Paulo State, Brazil, which presents a vast agricultural landscape mosaic, to analyze the effects of the end of the recent ENSO's (El Niño-Southern Oscillation) most active period (2016/2017) and its posteriori effects on vegetation (until early 2018). Surface albedo, temperature, net radiation, and NDVI (Normalized Difference Vegetation Index) from the main land uses were extracted to process microclimatic comparisons. Crop coefficient (dimensionless) and biomass production (kg.ha −1 .day −1 ) ranges for the period studied were 0.92-1.35 and 22-104 kg·ha −1 ·day −1 (in the area occupied by sugarcane crop), 0.56-0.94 and 15-73 kg·ha −1 ·day −1 (pasture), 1.17-1.56 and 25-210 kg·ha −1 ·day −1 (silviculture), and 1.05-1.36 and 30-134 kg·ha −1 ·day −1 (forest). According to the spatial and temporal consistencies, and after comparison with previous point and large-scale studies with similar climatic and thermal conditions, the SAFER and Monteith modelsshowed the ability to quantify and differentiate the large-scale crop coefficients and biomass production of different land uses in the southeast Brazil region. The SAFER algorithm with Sentinel-2 images obtained crop coefficients that indicated plant growth stages and local thermohydrological conditions at a 10-m resolution. The results are important for land use, crop yield and reforestation planning, and for water management plans for actual and future water demand scenarios, and this methodology is useful for monitoring rural and water parameters, and for precision agriculture applications.

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Section: Input Parameters Of Safer Algorithmsupporting

confidence: 83%

Section: Input Parameters Of Safer Algorithmsupporting

confidence: 74%

Section: Input Parameters Of Safer Algorithmmentioning

confidence: 62%

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Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil

2018

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“…This process repeats for targets on other levels. Different targets on the second level caused differences in the first-level DN values and hence a greater standard deviation for the Bermudagrass and sugarcane classes when compared to classes that had no canopy, such as bare soil and straw [33][34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations

Girolamo-Neto

Sanches

Neves

et al. 2019

Drones

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Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudagrass in sugarcane crops using remote sensing data, however, is a challenge considering their spectral similarity. To overcome this limitation, this paper aims to explore the potential of texture features derived from images acquired by an optical sensor onboard anunmanned aerial vehicle (UAV) to detect Bermudagrass in sugarcane. Aerial images with a spatial resolution of 2 cm were acquired from a sugarcane field in Brazil. The Green-Red Vegetation Index and several texture metrics derived from the gray-level co-occurrence matrix were calculated to perform an automatic classification using arandom forest algorithm. Adding texture metrics to the classification process improved the overall accuracy from 83.00% to 92.54%, and this improvement was greater considering larger window sizes, since they representeda texture transition between two targets. Production losses induced by Bermudagrass presence reached 12.1 tons × ha−1 in the study site. This study not only demonstrated the capacity of UAV images to overcome the well-known limitation of detecting Bermudagrass in sugarcane crops, but also highlighted the importance of texture for high-accuracy quantification of weed invasion in sugarcane crops.

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“…The radiometric correction is a multi-stage process from radiometric calibration to atmospheric correction. It is important to increase the quality of correlations between spectral data and surface, especially to estimate the vegetation index (Pereira and Casaroli 2016). The geometric correction is a process transforming any pixel of the Landsat 8 OLI/TIRS images into a new coordinate system in a specified map project.…”

Section: Data Processingmentioning

confidence: 99%

Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

et al. 2019

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This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

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Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

Cited by 9 publications

References 23 publications

Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil

Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil

Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations

Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

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