Múltiples índices espectrales para predecir la variabilidad de atributos estructurales y funcionales en zonas áridas

Buzzi, Mariana Andrea; Rueter, Barbara Lisa; Ghermandi, Luciana

doi:10.25260/ea.17.27.1.0.315

Cited by 5 publications

(3 citation statements)

References 12 publications

(14 reference statements)

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“…Vegetation indices are a tool that provides rapid, relevant, and non-destructive information resulting from combinations of spectral bands recorded by remote sensing satellites. Their function is to measure various variables such as chlorophyll, biomass, leaf area index, among others [8,9].…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning

Martínez-Macias,

Martínez-Sifuentes,

Márquez-Guerrero

et al. 2024

Preprint

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Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.

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Section: Introductionmentioning

confidence: 99%

Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning

Martínez-Macias,

Martínez-Sifuentes,

Márquez-Guerrero

et al. 2024

Preprint

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“…Arid and semi-arid rangelands are highly variable in space and time. In particular, the relationship between structural and functional attributes of arid and semi-arid steppes is one of the core study focus in landscape ecology [1]. Remote sensing information such as the Normalized Difference Vegetation Index (NDVI) is widely used as a proxy for vegetation primary productivity [2,3], and as an integrative indicator of ecosystem structure and functioning [4].…”

Section: Introductionmentioning

confidence: 99%

Relationship between NDVI of Patches and Cover Area of Grasses, Shrubs and Bare Soil Components of a Semi-Arid Steppe from North-West Patagonia, Argentina

Fariña

Aramayo

Perri

et al. 2023

Grasses

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Distinguishing the contributions of different vegetation cover such as shrubs and grasses components into the primary production in arid and semi-arid rangelands is a key step to understanding changes at a landscape scale. The aim was to assess the contribution of shrubs, grasses and bare soil components into a total biophysical variable at a patch level, and the relationship between that biophysical variable and remote sensing vegetation index, in a grass–shrub steppe from North-West Patagonia, Argentina. We conducted a field survey in the period 2015–2017 to analyzing the relationship between monthly values of Normalized Difference Vegetation Index (NDVI) of two grasses, two shrub species and bare soil, weighted by their cover area at a patch level, and the concomitant patch NDVI records, respectively. The contribution of the patch components to the total NDVI value at a patch level was additive. The relationship between the weighted NDVI of patch components and the concomitant NDVI value at a patch level along time was linear for perennial grasses and deciduous shrub–grass patches, but linearity was not significant for most perennial shrub–grass patches. Differences among patch compositions and their surface reflectance suggest the need to move forward in a more precise distinction of the floristic composition of patches, to better understanding their contribution to NDVI temporal dynamics at a landscape scale.

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“…It continues today because spectra are disturbed by internal factors (plant physiological state) and/or external ones: the measurement setup, lighting, atmospheric disturbances, soil type and moisture, and the optical characteristics, spatial distribution and proportions of all the constituents of a scene (Chang et al 2016). Then, to classify land cover features, such as the biophysical characteristics of the vegetation, many algorithms have been developed in terms of combinations of spectral bands (Buzzi et al 2017), employing neural networks (Civco 1993), based on the inversion of radiative transfer models (Fang et al 2003) and by multispectral approaches (Salas et al 2016), among others. The most widely type of algorithm used is the mathematical combination of bands of visible and near-infrared reflectances, in the form of spectral indices (Xue and Su 2017).…”

Section: Introductionmentioning

confidence: 99%

Maximum discrimination index: a tool for land cover identification

Lencina

Weber

2019

Int. J. Environ. Sci. Technol.

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This work presents an adaptable index that is applied to a pair of covers to be discriminated. Its adaptability relies on the procedure to determine the numerical value of the wavelengths or bands involved: the maximization of an operator based on the geometric mean of squared differences. This index is applied to the particular case of discrimination of wheat from ryegrass in different phenological stages. The maximum discrimination index outperforms other indices such as the normalized difference vegetation index, advanced normalized vegetation index and normalized difference greenness index. Its efficacy of discrimination is characterized and compared with the normalized difference greenness index (the second with better performance). It is observed that the proposed index has a more predictable behavior and reaches a discrimination accuracy as high as 95.5%. The maximum discrimination index could be adjusted to different covers and employed as a tool for discrimination. Spectral signatures coming from any platform: field, aerial or satellite, can be handled.

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Múltiples índices espectrales para predecir la variabilidad de atributos estructurales y funcionales en zonas áridas

Cited by 5 publications

References 12 publications

Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning

Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning

Relationship between NDVI of Patches and Cover Area of Grasses, Shrubs and Bare Soil Components of a Semi-Arid Steppe from North-West Patagonia, Argentina

Maximum discrimination index: a tool for land cover identification

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