A partial least squares – Path modeling analysis for the understanding of biodiversity loss in rural and urban watersheds in Portugal

Fernandes, Luís Filipe Sanches; Fernandes, António Carlos Pinheiro; Ferreira, Ana Rita; Cortes, Rui; Pacheco, Fernando António Leal

doi:10.1016/j.scitotenv.2018.01.127

Cited by 65 publications

(35 citation statements)

References 46 publications

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“…For comparison-and to assess the differences between linear and non-linear models-a prominent linear modeling technique was used: Partial least square regression. PLS is widely used by the remote sensing community for vegetation analysis [78][79][80], soil related studies [81][82][83], and climate and ecological studies [84][85][86][87] amongst others.…”

Section: Pls Regressionmentioning

confidence: 99%

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

et al. 2019

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The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R2) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R2 in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 106 km2). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

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Section: Pls Regressionmentioning

confidence: 99%

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

et al. 2019

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“…The thematic maps (e.g., Figures 1-3) were prepared in ArcMap software of ESRI [47], a common tool in spatial analysis of hydrologic and environmental data widely used in many studies [42,44,[48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66]. The base information was compiled from various spatial databases, namely the maps published by the Brazilian Institute for Geography and Statistics (https://ww2.ibge.gov.br) on the 1: 100,000 scale, and the digital terrain model obtained from the ASTER GDEN V2 satellite image with a spatial resolution of 30 m.…”

Section: Thematic Mapsmentioning

confidence: 99%

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

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Valera

Costa

et al. 2019

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Riparian vegetation represents a protective barrier between human activities installed in catchments and capable of generating and exporting large amounts of contaminants, and stream water that is expected to keep quality overtime. This study explored the combined effect of landscape composition and buffer strip width (L) on stream water quality. The landscape composition was assessed by the forest (F) to agriculture (A) ratio (F/A), and the water quality by an index (IWQ) expressed as a function of physico-chemical parameters. The combined effect (F/A × L) was quantified by a multiple regression model with an interaction term. The study was carried out in eight catchments of Uberaba River Basin Environmental Protection Area, located in the state of Minas Gerais, Brazil, and characterized by very different F/A and L values. The results related to improved water quality (larger IWQ values) with increasing values of F/A and L, which were not surprising given the abundant similar reports widespread in the scientific literature. But the effect of F/A × L on IWQ was enlightening. The interaction between F/A and L reduced the range of L values required to sustain IWQ at a fair level by some 40%, which is remarkable. The interaction was related to the spatial distribution of infiltration capacity within the studied catchments. The high F/A catchments should comprise a larger number of infiltration patches, allowing a dominance of subsurface flow widespread within the soil layer, a condition that improves the probability of soil water to cross and interact with a buffer strip before reaching the stream. Conversely, the low F/A catchments are prone to the generation of an overland flow network, because the absence of permanent vegetation substantially reduces the number of infiltration patches. The overland flow network channelizes runoff and conveys the surface water into specific confluence points within the stream, reducing or even hampering an interaction with a buffer strip. Notwithstanding the interaction, the calculated L ranges (45–175 m) are much larger than the maximum width imposed by the Brazilian Forest Code (30 m), a result that deserves reflection.

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“…The SEM constructed for effluent T-N is different from the modified path model, as it was possible to extensively model the variable effects that were difficult to be utilized in the path model. Sanches Fernandes et al (2018) [29] described the type of connection between a latent variable and the measured variables to which it is linked as two types of model, the reflective model and formative model. In this research, the formative model was implemented.…”

Section: Sem For Effluent T-nmentioning

confidence: 99%

“…Although they have mainly been applied to verify a hypothesis in the field of social science, recently applications to the natural sciences, including biology, have also been frequently reported [20][21][22][23][24][25]. For the biology, it is easy to find out its recent applications which include SEM for biological communities change in river ecosystem [26][27][28][29]. The first SEM trial on the water-quality field was by Zou et al (1994) [30], who pointed out the drawback of the general regression model is that it cannot reflect measurement errors, and emphasized the usefulness of SEM considering both the composition of latent variables and the causal relation between them in one model.…”

mentioning

confidence: 99%

Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

et al. 2019

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The removal process of activated sludge in sewage treatment plants is very nonlinear, and removal performance has a complex causal relationship depending on environmental factors, influent load, and operating factors. In this study, how causal relationships are expressed in collected data was identified by structural equation modeling. First, path modeling was attempted as a preliminary step in structural equation model (SEM) construction and, as a result, the nutrient-removal mechanism could not be sufficiently represented as a direct causal relationship between measured variables. However, as a result of the deduced SEMs for effluent total nitrogen (T-N) and total phosphorus (T-P) concentrations, accompanied by exploratory factor analysis to extract latent variables, a causal network was formed that describes the direct or indirect effect of the latent factors and measured variables. Hereby, this study suggests that it is possible to construct an SEM explaining the nutrient-removal mechanism of the activated-sludge process with latent variables. Moreover, nonlinear features embedded in the mechanism could be represented by SEM, which is a model based on linearity, by including causal relations and variables that were not derived by path analysis. This attempt to model the direct and indirect causalities of the process could enhance the understanding of the process, and help decision making such as changing the driving conditions that would be required.The efficacy of the second approach, the attempt to understand via statistical analysis of the obtained data from the activated-sludge process, mainly depends on the quality and quantity of the obtained data. Unlike the mathematical model-based approach, this method makes it possible to use various measurement variables that cannot be utilized for mathematical models such as pH and oxidation reduction potential (ORP). Besides the sensor variables, operational factors such as the F/M ratio and airflow rate can be analyzed with influent/effluent water-quality variables within a dataset with the same utility. The most popular methodology applied to data analysis is principal component analysis (PCA), which has been adapted for sewage-treatment-process monitoring and identification of the operational state [8][9][10]. For the other approaches to enhance the understanding of the processing mechanism, there have been attempts to use signal processing and pattern recognition to detect operational abnormality or sensor faults. Wimberger et al. (2008) [11] applied a signal filter to detect the faults of the sewage-treatment process of sequencing batch reactor types. Baklouti et al. (2018) [12] tried to detect sensor faults using an improved particle filter. Chow et al. (2018) [13] developed a fault-detection and alarm algorithm based on the correlation analysis of signals of ultraviolet (UV) spectroscopy.A causal model can be considered to produce a more intuitive result for the purpose of supporting the judgment of the process operator and improving understanding the pr...

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A partial least squares – Path modeling analysis for the understanding of biodiversity loss in rural and urban watersheds in Portugal

Cited by 65 publications

References 46 publications

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

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