Spatio-temporal variation of aquatic macrophyte cover in a reservoir using Landsat images and Google Earth Engine

Luz, Aline Guidolin da; Bleninger, Tobias; Polli, Bruna Arcie; Lipski, Bernardo

doi:10.1590/2318-0331.272220220074

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Several human changes, including agriculture irrigation, construction of electric power plants, and urbanization, can lead to a reduction in water flow, expand lentic environments, and favor the propagation of aquatic macrophyte plants, proliferation and spread of Mansonia spp. [7,8]. Members of this genus were prevalent in inundated regions along the floodplain of the Solimões-Amazonas Rivers between Tabatinga (Amazonas State) and Ajurixi (Para ´state) municipalities, Amazonas state, Brazil, where the macrophyte aquatic plants were ample [9].…”

Section: Introductionmentioning

confidence: 99%

Bacterial diversity on larval and female Mansonia spp. from different localities of Porto Velho, Rondonia, Brazil

Oliveira,

Rojas,

Amorim

et al. 2023

PLoS ONE

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Studies based on the bacterial diversity present in Mansonia spp. are limited; therefore, the aim of this study was to investigate the bacterial diversity in females and larvae of Mansonia spp., describe the differences between the groups identified, and compare the microbiota of larvae from different collection sites. Sequences of the 16S rRNA region from the larvae and females of Mansonia spp. were analyzed. Diversity analyzes were performed to verify the possible bacterial differences between the groups and the collection sites. The results showed Pseudomonas was the most abundant genus in both females and larvae, followed by Wolbachia in females and Rikenellaceae and Desulfovibrio in larvae. Desulfovibrio and Sulfurospirillum, sulfate- and sulfur-reducing bacteria, respectively, were abundant on the larvae. Aminomonas, an amino acid-degrading bacterium, was found only in larvae, whereas Rickettsia was identified in females. Bacterial diversity was observed between females and larvae of Mansonia and between larvae from different collection sites. In addition, the results suggest that the environment influenced bacterial diversity.

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Section: Introductionmentioning

confidence: 99%

Bacterial diversity on larval and female Mansonia spp. from different localities of Porto Velho, Rondonia, Brazil

Oliveira,

Rojas,

Amorim

et al. 2023

PLoS ONE

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An integrated feature selection approach to high water stress yield prediction

Li,

Zhou,

Cheng

et al. 2023

Front. Plant Sci.

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The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

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Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Li,

Cheng,

Chen

et al. 2024

Remote Sensing

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Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R2 at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R2 = 0.725, RMSE = 0.814 t·ha−1, MSE = 0.663 t·ha−1). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R2 of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction.

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Spatio-temporal variation of aquatic macrophyte cover in a reservoir using Landsat images and Google Earth Engine

Cited by 3 publications

References 10 publications

Bacterial diversity on larval and female Mansonia spp. from different localities of Porto Velho, Rondonia, Brazil

Bacterial diversity on larval and female Mansonia spp. from different localities of Porto Velho, Rondonia, Brazil

An integrated feature selection approach to high water stress yield prediction

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

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