Vegetation Index Normalized Difference

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Relationship between the normalized difference vegetation index and leaf area in vineyards

Relationship between the normalized difference vegetation index and leaf area in vineyards

Brazil is the fifth largest wine producer in the Southern Hemisphere and the Encosta Superior da Serra do Nordeste, known as Serra Gaúcha, is the most important producing region in the country and responsible for 85% of the national wine production (IBRAVIN 2017). The maximization of the relationship between quantity and quality is sought by balancing the leaf area (source) and fruit load (drainage) in vineyards intended for producing wine grapes (Mandelli et al. 2009). The leaf area index (LAI), defined as the total one-sided area of leaf tissue per unit ground surface area, is an important agronomical parameter related to photosynthetic capacity, water use, microclimate, canopy vigor, grape quality and enological potential (Drissi et al. 2009). The monitoring of leaf area and canopy development allows evaluating plant health conditions and guide management practices, including crop protection and canopy adjustments . In vineyards, summer pruning is a common leaf area management practice used to regulate canopy density and leaf distribution, thus promoting the aeration to reduce the likelihood of diseases (Mandelli et al. 2009), and increasing berry sun exposure to favor the concentration of phenolic substances (Bergqvist et al. 2001).
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Comparing the Normalized Difference Infrared Index (NDII) with root zone
storage in a lumped conceptual model

Comparing the Normalized Difference Infrared Index (NDII) with root zone storage in a lumped conceptual model

Estimates of vegetation water content (the amount of water in stems and leaves) are of interest to assess the vegetation wa- ter status in agriculture and forestry and have been used for drought assessment (Cheng et al., 2006; Gao, 1996; Gao and Goetz, 1995; Ustin et al., 2004; Peñuelas et al., 1993). Ev- idence from physically based radiative transfer models and laboratory studies suggests that changes in water content in plant tissues have a large effect on the leaf reflectance in sev- eral regions of the 0.7–2.5 µm spectrum (Fensholt and Sand- holt, 2003). Tucker (1980) suggested that the spectral interval between 1.55 and 1.75 µm (SWIR) is the most suitable region for remotely sensed leaf water content. It is well known that these wavelengths are negatively related to leaf water content due to a large absorption by leaf water (Tucker, 1980; Cec- cato et al., 2002). However, variations in leaf internal struc- ture and leaf dry matter content also influence the SWIR re- flectance. Therefore, SWIR reflectance values alone are not suitable for retrieving vegetation water content. To improve the accuracy of estimating the vegetation water content, a combination of SWIR and NIR (0.7–0.9 µm) reflectance in- formation was utilized because NIR is only affected by leaf internal structure and leaf dry matter content but not by water content. A combination of SWIR and NIR reflectance infor- mation can remove the effect of leaf internal structure and leaf dry matter content and can improve the accuracy of re- trieving the vegetation water content (Ceccato et al., 2001; Yilmaz et al., 2008; Fensholt and Sandholt, 2003).
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Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.

The dominant crops grown in the study areas include cotton, maize (spring maize and sum- mer maize), watermelon, grape, tomato, and wheat. The vegetation cover fraction for each crop type over the growing season is presented in Fig 2. Cotton, spring maize, watermelon, tomato, and grape are planted in early April and begin their growth mostly during the June–July period. For harvest, watermelon and tomato are harvested in August, spring maize is harvested in early September, and grapes and cotton are harvested during the August–September and Septem- ber–October periods. Winter wheat is planted in early November, begins its growth in the next April, and is reaped for harvest in late June. After that, some fields are in rotation, and others are planted to summer crops such as summer maize. Thus, we divided the winter wheat into two classes depending on whether summer crops are planted in the same field or not.
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Agronomic performance of white oats cultivated under fertigation with treated sewage effluent and definition of critical limits of Normalized Difference Vegetation Index

Agronomic performance of white oats cultivated under fertigation with treated sewage effluent and definition of critical limits of Normalized Difference Vegetation Index

ABSTRACT: One of the benefits of irrigation with wastewater in agriculture is the reduction in the use of mineral fertilizers and the increase in crop yield. For wastewater application, the use of remote sensing can help to define specific management areas. The aim of this study was to evaluate the yield and the growth of irrigated white oats (Avena sativa L.) with varying treated sewage effluent (TSE) levels. These attributes were then correlated with Normalized Difference Vegetation Index (NDVI) values obtained at four phenological stages of the crop and used to define critical limits of NDVI. The experiment consisted of five TSE treatments differing in irrigation depths (11, 31, 60, 87, and 100%). Mean NDVI values in the crop were determined with an active terrestrial sensor (GreenSeeker), which were then correlated with height,
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Temporal variation of normalized difference vegetation index (NDVI) and calculation of the crop coefficient (Kc ) from NDVI in areas cultivated with irrigated soybean

Temporal variation of normalized difference vegetation index (NDVI) and calculation of the crop coefficient (Kc ) from NDVI in areas cultivated with irrigated soybean

For agricultural applications, the high periodicity provided by these vegetation indices products is of fundamental importance for analysis and monitoring of the phenological cycle of crops such as soybean over large areas (ESQUERDO & ZULLO, 2007; RUDORFF et al., 2007; WARDLOW et al., 2007; EPIPHANIO et al., 2010; PENG et al., 2013), which could vary from 75 to 210 days depending on the cultivar (GARCIA et al., 2007). Nevertheless, because the images are produced with different spatial and temporal resolutions, further studies are needed to assess the influence of these resolutions in the behavior of indexes from planting to harvest.
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Using MODIS Normalized Difference Vegetation Index to monitor seasonal and inter-annual dynamics of wetland vegetation in the Great Artesian Basin: a baseline for assessment of future changes in a unique ecosystem

Using MODIS Normalized Difference Vegetation Index to monitor seasonal and inter-annual dynamics of wetland vegetation in the Great Artesian Basin: a baseline for assessment of future changes in a unique ecosystem

The Great Artesian Basin mound springs (Australia) are unique wetland ecosystems of great significance. However, these unique ecosystems are endangered by anthropogenic water extraction. Relationships have been established between the vegetated wetland area and the discharge associated with individual springs, providing a potential means of monitoring groundwater flow using measurements of wetland area. Previous studies using this relationship to monitor Great Artesian Basin springs have used aerial photography or high resolution satellite images, giving sporadic temporal information. These “snapshot” studies need to be placed within a longer and more regular context to better assess changes in response to aquifer draw-downs. In this study, the potential of medium resolution MODIS Normalized Difference Vegetation Index data for studying the long-term and high frequency temporal dynamics of wetland vegetation at the Dalhousie Spring Complex of the GAB is tested. Photosynthetic activity within Dalhousie wetlands could be differentiated from surrounding land responses. The study showed good correlation between wetland vegetated area and groundwater flow, but also the important influence of natural species phenologies, rainfall, and human activity on the observed seasonal and inter-annual vegetation dynamic. Declining trends in the extent of wetland areas were observed over the 2000- 2009 period followed by a return of wetland vegetation since 2010. This study underlined the need to continue long-term medium resolution satellite studies of the Great Artesian Basin as these data provide a good understanding of variability within the wetlands, give temporal context for less frequent studies and a strong baseline for assessment of future changes.
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Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites

Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites

Abstract —PhenoSat is an experimental software tool that pro- duces phenological information from satellite vegetation index time series. The main characteristics and functionalities of the PhenoSat tool are presented, and its performance is compared against observed measures and other available software applica- tions. A multiyear experiment was carried out for different vege- tation types: vineyard, low shrublands, and seminatural meadows. Temporal satellite normalized difference vegetation index (NDVI) data provided by MODerate resolution Imaging Spectroradiome- ter and Satellite Pour l’Observation de la Terre VEGETATION were used to test the ability of the software in extracting vegetation dynamics information. Three important PhenoSat features were analyzed: extraction of the main growing season information, estimation of double growth season parameters, and the advan- tage of selecting a temporal region of interest. Seven noise re- duction filters were applied: cubic smoothing splines, polynomial curve fitting, Fourier series, Gaussian models, piecewise logistic, Savitzky–Golay (SG), and a combination of the last two. The results showed that PhenoSat is a useful tool to extract NDVI metrics related to vegetation dynamics, obtaining high significant correlations between observed and estimated parameters for most of the phenological stages and vegetation types studied. Using the combination of SG and piecewise logistic to fit the NDVI time series, PhenoSat obtained correlations higher than 0.71, except for the seminatural meadow start of season. The selection of a tem- poral region of interest improved the fitting process, consequently providing more reliable phenological information.
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Changes in vegetation cover of the Pantanal wetland detected by Vegetation Index: a strategy for conservation

Changes in vegetation cover of the Pantanal wetland detected by Vegetation Index: a strategy for conservation

The analysis and the monitoring of alterations in vegetation cover are tools that can help to understand the spatial dynamics of the Brazilian Pantanal wetland and offer relevant information to public and private decision makers, who could adopt efficient measures through public policies, programs, projects and others, aiming preservation of the region’s landscape and biodiversity. However, monitoring those areas is a difficult task, because of the large size and its difficult access (Pott & Pott 2004). Thus, the use of remote sensing is essential for monitoring and, consequently, for preservation of the Brazilian Pantanal wetland. Due to the spectral response characteristics of the vegetation it is possible to utilize geoprocessing techniques for its identification and evaluation. An example of such techniques is the vegetation index. The Normalized Difference Vegetation Index (NDVI) is widely utilized in the assessment of several biophysical parameters, such as vegetation coverage, biomass, fraction of the photosynthetically active radiation and phenological variations (Huete et al. 2002; Prabakaran et al. 2013). Furthermore, it is a classic example of vegetation mapping utilizing remote sensing, as it deals with the information from the reflectance in the spectrum range of red and near-infrared wavelengths (Xie et al. 2008).
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Homogeneous zones of vegetation index for characterizing variability and site-specific management in vineyards

Homogeneous zones of vegetation index for characterizing variability and site-specific management in vineyards

help improve the performance of site-specific management practices, or the management of vineyards with different rates. Characterization using canopy proximal sensing has been a widely disseminated technique; however, vineyards in southeastern Brazil, where the utilization of annual double pruning results in a winter harvest, knowledge of the role of variability in improving vineyard management has not yet been applied. This study aimed to determine if post-veraison mapping of a normalized difference vegetation index could be used to assess the variability in grapevine vigor, water status, physiology, yield and berry quality attributes at harvest in an irrigated vineyard in southeastern Brazil. This normalized difference vegetation index was measured with an active canopy sensor, and spatial distribution maps over two growing seasons of a vineyard, managed on an annual double pruning basis, were generated. Attributes of physiological and technological berry maturation, leaf water potential, gas exchange, production, and fresh pruning weight were calculated. These normalized difference vegetation index maps allowed for the determination of variability in vegetative vigor and the productive potential of the vineyard; however, high levels of rainfall during the maturation period may reduce the potential of using these maps for determining berry parameters.
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Analysis of vegetation dynamics using the normalized difference vegetation index (NDVI) at the archipelago of Fernando de Noronha, Pernambuco, Brazil

Analysis of vegetation dynamics using the normalized difference vegetation index (NDVI) at the archipelago of Fernando de Noronha, Pernambuco, Brazil

After 19 years of intensive soil use and negative environmental impacts (MONTEIRO; DO SUL; COSTA, 2018) and positive, there was an expansion of areas with mixed vegetation, reaching 1325.07 ha, which represents almost 70% of the total area archipelago. This shows an increase of approximately 46% between 1999 and 2018. However, the current vegetation cover of the island can transmit to visitors and residents a preserved natural beauty, but the environmental impacts caused by invasive species in the area are diverse and negative for biodiversity of the region, giving the false impression of rich and diverse vegetation. According to Batistella (1996), Cleto (2013) and Mello (2014), Noronha presents exotic species that potentiate the elimination of most other species, altering biodiversity and being one of the main threats to the conservation of this insular environment. These were introduced from the 18th century, with the occupation of the Captaincy of Pernambuco. This promoted the destruction of the vegetation cover of all the large trees of the island, and was a preventive measures to avoid leaks through the construction of rafts, besides of the cut of the small trees had the objective of avoiding hiding places (CHAVES JÚNIOR, 2017).
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Nitrogen management in wheat based on the normalized difference vegetation index (NDVI)

Nitrogen management in wheat based on the normalized difference vegetation index (NDVI)

Both relationships were close to the 1:1 line, indicating the suitability of the proposed models. For shoot biomass (Figure 2A) and amount of accumulated N (Figure 2B), the values of the coefficient of residual mass (CRM=−0.16 and −0.07, close to zero, indicating optimal adjustment), coefficient of correlation (r=0.79 and 0.86, regarded as “high”), Willmott’s index of agreement (d=0.82 and 0.92, close to 1, indicating close correspondence between predicted and actual values), and coefficient “c” (c=0.66 and 0.79, regarded as “good” and “very good”, respectively) indicate the adequacy and accuracy of the proposed models for predicting the amounts of shoot biomass and accumulated N as a function of NDVI. In relation to shoot biomass, there was a slightly overestimation of the proposed model when applied to the cultivars used in 2015 (Figure 2A). This can be related to the fact that two different cultivars were used for model validation in 2015, namely TBIO Sinuelo and TBIO Toruk. These cultivars present more erect leaves than the cultivars used in 2014. Despite these differences, statistical performance indices were satisfactory and indicated good reliability of the model. Furthermore, considering the amount of N accumulated in shoots, the proposed model presented better performance, which is indicated by the statistical indices (Figure 2B). Considering the use of these models for variable rate N fertilization, the amount of accumulated N in shoots is the most interesting variable, since it indicates the actual amount of this nutrient that was absorbed, which is, in turn, directly related to the true N demand at the stage of six fully expanded leaves. Considering the model shown in figure 1B, it can be affirmed that this can be used with high confidence and accuracy for predicting accumulated N in shoots for the set of cultivars used in the present study, even though these have morphological differences that can slightly affect NDVI readings. GROHS et al. (2009) concluded that the Greenseeker ® sensor can be used
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Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil

Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil

variable vegetation development due to meteorological conditions. In this sense, a higher accumulated rainfall, which resulted in lower solar radiation, associated with higher air temperatures (Figure 7) may have favored vegetative growth and plant biomass accumulation in the 2015/2016 season. Generally, the NDVI values and the area below the curve of the temporal NDVI profiles were greater in 2015/2016 compared to 2014/2015 (Figure 6). The higher average temperatures recorded in 2015, in September (+1.4 ºC), December (+1.5 ºC), January (2.2 ºC), February (2.7 ºC) and April (+2.7 ºC) may have favored the vegetative plant development and green biomass accumulation while leaf area was preserved for a longer period at the end of the cycle. Autumn temperatures affect the length of the vegetative cycle because lower air temperatures or the occurrence of frosts accelerates leaf fall. Also, rainfall was higher than the historical average in the 2015/2016 season, during almost the entire vegetative cycle, with emphasis in September (+173 mm), October (+246 mm), December (+143 mm) and March (+306 mm) (Figure 7).
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Normalized difference vegetation index of the conservation unit Gama – Cabeça-de-Veado, Brasilia

Normalized difference vegetation index of the conservation unit Gama – Cabeça-de-Veado, Brasilia

As transformações espectrais consistem na utilização de operadores matemáticos e estatísticos para a produção dos índices de vegetação, dentre eles o Índice de Vegetação da Diferença Normalizada (Normalized Difference Vegetation In- dex - NDVI), que explora a diferença existente entre as res- postas da vegetação nas bandas relativas ao vermelho e ao infravermelho próximo, otimizando e ampliando as informações delas advindas. O NDVI é calculado pela razão entre a dife- rença das reflectâncias nas regiões do infravermelho próximo (IVP) e do vermelho (V) e a soma dessas duas reflectâncias. No caso do sensor ETM+ (LANDSAT), o índice é calculado por meio das bandas espectrais 3 e 4, que representam o V e o IVP, respectivamente (Crósta, 1992). O resultado do NDVI é, portanto, um índice que identifica a densidade da vegeta- ção (Marceau et al.,1989; Ulaby et al., 1993).
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Sugarcane leaf area estimate obtained from the corrected Normalized Difference Vegetation Index (NDVI)

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

interaction between solar radiation and vegetation canopies have increased the use of data from orbital remote sensors in sugarcane monitoring. However, the constituents of the atmosphere affect the reflectance values obtained by imaging sensors. This study aimed at improving a sugarcane Leaf Area Index (LAI) estimation model, concerning the Normalized Difference Vegetation Index (NDVI) subjected to atmospheric correction. The model generated by the NDVI with atmospheric correction showed the best results (R² = 0.84; d = 0.95; MAE = 0.44; RMSE = 0.55), in relation to the other models compared. LAI estimation with this model, during the sugarcane plant cycle, reached a maximum of 4.8 at the vegetative growth phase and 2.3 at the end of the maturation phase. Thus, the use of atmospheric correction to estimate the sugarcane LAI is recommended, since this procedure increases the correlations between the LAI estimated by image and by plant parameters.
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Normalized Difference Vegetation Index (NDVI) analysis for evaluation of forest fires in the summer of 2016 in Portugal

Normalized Difference Vegetation Index (NDVI) analysis for evaluation of forest fires in the summer of 2016 in Portugal

and over 130 people injured (Figure 1.1). The Portuguese government has declared three days of national mourning following what Prime Minister has called ”the greatest tragedy of human lives” witnessed in the country in years. The high number of forest fires in Portugal is mainly due to its climate, characterized by high precipitation in the winter, which allows the growth of biomass fuel and by a very long and dry summer. The fact that the hot season coincides with the driest period of the year makes it easier to the occurrence of a fire given the state of dryness of the vegetation. Contrary of what was supposed to be expected, or what is normal in other parts of the globe, most fires are not instigated by natural origins but by arson.
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Normalized entropy aggregation for inhomogeneous large-scale data

Normalized entropy aggregation for inhomogeneous large-scale data

vectors results in a less informative probability distribution for the parameters, which should lead to a smaller departure from total uncertainty as compared to the situation where the support vectors are less wide. It is expected, then, that the normalized entropy methodologies provide better results when the am- plitude of the support vectors are smaller. The results of the simulation study are in agreement with this interpretation. Nevertheless, when the same analysis is done considering a matrix of explanatory variables X, with higher condition number, as presented in Table 5, even though the normalized entropy methodolo- gies provide worse results, as already discussed, the Bagging procedure provides even worse results: while k b β −βk 2 changes from 4.25 to 15.59 for the information
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Species representativeness of Fabaceae in restrictive soils explains the difference in structure of two types of Chaco vegetation

Species representativeness of Fabaceae in restrictive soils explains the difference in structure of two types of Chaco vegetation

might be true for other Neotropical communities. The main evidences that support this generalization are (i) the differentiation of assemblages according to the soil type; (ii) the greater explanation of the spatially structured environmental fraction; and (iii) the greater association of species of Fabaceae with restrictive environments. Such aspects suggest that the typical Fabaceae assemblages from Chaco can play a major role in structuring the vegetation, especially in the Porto Murtinho region, where the natural areas are strongly affected by agriculture and cattle raising. Therefore, our study provides indications of an important contribution of Fabaceae to the structuring of the ecological community. Nevertheless, it shows its close relationship with seasonal environments, especially in areas where soil is a restrictive factor.
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Desenvolvimento de um sistema sensor para índices de vegetação e posicionamento globalDevelopment of a sensor system for vegetation index and global positioning

Desenvolvimento de um sistema sensor para índices de vegetação e posicionamento globalDevelopment of a sensor system for vegetation index and global positioning

Computadores de placa única, como a BeagleBone Black (BBB), são alternativas de baixo custo que permitem integrar várias funções em um mesmo sistema sensor como posicioname[r]

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Influence of Urbanization on the Dynamics of the Urban Vegetation Coverage Index (VCI) in Erechim (RS)

Influence of Urbanization on the Dynamics of the Urban Vegetation Coverage Index (VCI) in Erechim (RS)

The greater occurrence of vegetation in these places occurs due to the presence of slopes as a consequence of the irregular relief, which makes the urbanization process difficult. This factor creates obstacles, mainly for implantation in areas with high urban density and other infrastructures, resulting in the occupation of other areas, where there are urban voids (Erechim, 2011). These vegetation areas are protected by Federal Law (Law no. 12.651, of May 25, 2012) (Brasil, 2012), according to the Master Plan for Sustainable Urban and Environmental Development of Erechim. Thus, for the vegetation to be properly analyzed in the urban environment, its distribution and spatial dimension must be taken into consideration (Bargos & Matias, 2012). Based on the mapping of vegetation fragments, native vegetation in year 2010 was quantified as 1,922.67 ha (hectares) and 328.84 ha of forestry areas, totaling 2,251.51 ha of vegetation coverage in the urban perimeter of Erechim. In 2015, native vegetation areas totaled 1,687.70 ha and 333.83 ha of forestry areas, corresponding to 2,021.53 ha of total vegetation coverage of the urban perimeter (Table 1).
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Estimate soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index

Estimate soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index

surface temperature measurements to allow application of the CWSI theory to partially vegetated fields without a priori knowledge of the percent vegetation cover. Based on the trapezoid assumption and the CWSI theory, Moran et al. (1994) introduced the Water Deficit Index (WDI) for evaluating field evapotranspiration rates and relative field water deficit for both full-cover and partially vegetated sites. For a given pixel with

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