Seasonal dynamics of vegetation indices as a criterion for grouping grassland typologies

Moreira, Andreise; Bremm, Carolina; Fontana, Denise Cybis; Kuplich, Tatiana Mora

doi:10.1590/1678-992x-2017-0173

Cited by 13 publications

(8 citation statements)

References 19 publications

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“…Thus, the vegetation indices (particularly, NDVI, OSAVI, EVI, and others) are widely used for plant remote sensing on the basis of manual measurements and different mobile platforms, including UAS and satellites [23,49,53]. Vis can be used for the remote sensing of spatial [59] and temporal [109] changes in the green vegetation, including the estimation of the morphological characteristics of plants (e.g., LAI or the total biomass [74,99,101]) or activity of important physiological processes (e.g., the light absorption by leaves or gross primary productivity (GPP) [110]). These changes can be observed for weeks, season(s), year(s), or tens of years [49,106,109].…”

Section: Reflectance Index Formula Referencesmentioning

confidence: 99%

“…Vis can be used for the remote sensing of spatial [59] and temporal [109] changes in the green vegetation, including the estimation of the morphological characteristics of plants (e.g., LAI or the total biomass [74,99,101]) or activity of important physiological processes (e.g., the light absorption by leaves or gross primary productivity (GPP) [110]). These changes can be observed for weeks, season(s), year(s), or tens of years [49,106,109]. Flying platforms, including UAS and satellites, are widely used for measurements of the vegetation indices; particularly, satellites can be used for measuring the GPP and other parameters on an ecosystem and landscape scale.…”

Section: Reflectance Index Formula Referencesmentioning

confidence: 99%

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Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

2021

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Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

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Section: Reflectance Index Formula Referencesmentioning

confidence: 99%

Section: Reflectance Index Formula Referencesmentioning

confidence: 99%

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

2021

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“…The rocky substrate consists of intermediate volcanic rocks (andesites and basalts) [92]. The area belongs to the Pampa Biome, where the Vachellia caven grasslands predominate [93]. However, there is a high grassland conversion to crop areas, mainly from rice and secondarily to soybeans, corn, and vegetables.…”

Section: Study Areamentioning

confidence: 99%

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

et al. 2020

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The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.

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“…Moreover, the specific features of each biome are essential to formulate proper conservation strategies, appropriate infrastructure, and to understand the impacts of land use changes in each environment (Oliveira et al 2017;Wagle et al 2017). Natural grasslands are fundamental to preserve water resources and carbon accumulation in the soil (Moreira et al 2019) and around the world they cover vast regions including the North American Great Plains, the Eurasian steppes of Russia, China and Mongolia, and the South American Pampas (Chaneton et al 2012). In Brazilian territory, the Pampa biome represents 2.07% and is recognized as containing a rich biodiversity characterized by a meadow mosaic, with small scrub vegetation areas and forests (Ruviaro et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessing uncertainties in estimating surface energy fluxes from remote sensing over natural grasslands in Brazil

Käfer

Rocha

Veeck

et al. 2022

Theor Appl Climatol

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Evapotranspiration (ET) is one of the main fluxes in the global water cycle. As the Pampa biome carries a rich biodiversity, accurate information on the ET dynamics is essential to support its proper monitoring and establish conservation strategies. In this context, we assessed an operational methodology based on the S-SEBI model to estimate energy fluxes over the natural grasslands of the Brazilian Pampa between 2014-2019. The S-SEBI is a remote sensing-based ET model that requires a minimum of meteorological inputs, being fully applicable for regions lacking in situ observations. Therefore, we investigated the model performance considering radiation data from both ERA5 reanalysis and Eddy Covariance measurements. Furthermore, comparisons from satellite-based estimates with in situ data were performed with and without energy balance closure (EBC).Results showed that the RMSE is impacted in 0.32 mm/day when reanalysis data are used, indicating that the meteorological inputs have low sensitivity on daily ET estimates from the S-SEBI model. In contrast, the instantaneous energy balance components are more affected. The strong climate seasonality of the Brazilian Pampa impacts the evaporative fraction, which is more evident in late summer and autumn and may compromise the performance of the model. The effects in the daily ET are lower when in situ data without EBC are considered as ground truth. However, they are less correlated with the remote sensing-based estimates. These insights are useful to monitor water and energy fluxes from local to regional scale and provide the opportunity to capture ET trends over the biome.

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Seasonal dynamics of vegetation indices as a criterion for grouping grassland typologies

Cited by 13 publications

References 19 publications

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

Assessing uncertainties in estimating surface energy fluxes from remote sensing over natural grasslands in Brazil

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