Espectros Vis-NIR do Solo e Fuzzy K-Médias Aplicados na Delimitação de Unidades de Mapeamento de Solos em Topossequências

Rizzo, Rodnei; Demattê, José Alexandre Melo; Lacerda, Marilusa Pinto Coelho

doi:10.1590/01000683rbcs20140694

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…It is as these in recent years, there have witnessed a significant increase in the use of geotechnological tools (remote sensing [RS] and GIS) to study soil erosive processes. The principle objectives of many of these contributions are: (a) the multi-temporal and spatial modeling of erosive processes (Zeng et al, 2013); (b) the analysis of the effects of the changes in vegetal cover and land use (Lech-hab et al, 2015); (c) the quantification of soil loss (Parveen and Kumar, 2012;Farhan et al, 2013;Amsalu and Mengaw, 2014;Lahlaoi et al, 2015;Ganasri and Ramesh, 2016;Tahiri et al, 2016); (d) the zoning of the hazard and risk control (Prasannakumar et al, 2012;Gaatib and Larabi, 2014;Uddin et al, 2014;Chirala et al, 2015); and (e) soil mapping and classification (Alves et al, 2015;Rizzo et al, 2015;Moura-Bueno et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

2019

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The activation of erosive processes on the slopes of the drainage catchments in the Ecuadorian coastal region represents a serious environmental problem that results in a loss of soil resources with the consequent reduction in agricultural productivity, an increasing lack of areas with vegetation, and decreased water quality. These issues increase, among other things, the probability of flooding. The purpose of this research is to zone soil erosion risk in the Manabí province, which is located on the Ecuadorian coast. The methodology is supported by the employment of Geographic Information Technologies (GIT) in order to access and process information of interest, such as: precipitation data with which to calculate the R Factor of the Universal Soil Loss Equation (USLE); topography, which was employed to create a slope map from the Digital Elevation Model (DEM) ASTER; vegetation cover, by applying the Standard Difference Vegetation Index (NDVI); land use, through the interpretation of orthophotos and a field survey; and infiltration capacity, obtained by considering the texture of the soil. The factors were combined by means a weighted relationship (map algebra) and exploiting the potential of software for the design of Geographic Information Systems (GIS). The results indicate the presence of soils with little or without vegetal protection, in areas with steep slopes, moderate rainfall and extensive farming activities. These characteristics determine conditions of high soil erodability, and propitiate the triggering of erosive processes. This scenario shows the need for autonomous governments to implement policies, strategies and actions so as to promote the strengthening of institutional and community capacities that will promote environmental sustainability.

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Section: Introductionmentioning

confidence: 99%

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

2019

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“…Automated tools, free and with a user-friendly interface have contributed to the training of qualified professionals and researches in digital soil mapping (DSM) approach. Rizzo et al (2015) observed the lack of qualified professionals who have knowledge about pedological mapping, computational tools, and remote sensing data. One program that has gained ground in the computational tools is the R programing language (R Development Core Team, 2018).…”

Section: Introductionmentioning

confidence: 99%

AlradSpectra: a Quantification Tool for Soil Properties Using Spectroscopic Data in R

Dotto

Dalmolin

Caten

et al. 2019

Rev. Bras. Ciênc. Solo

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Soil reflectance spectroscopy has become an innovative method for soil property quantification supplying data for studies in soil fertility, soil classification, digital soil mapping, while reducing laboratory time and applying a clean technology. This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra. It contains several tools to process spectroscopic data and generate models to predict soil properties. The GUI was developed to accomplish tasks such as perform a large range of spectral preprocessing techniques, implement several multivariate calibration methods, generate statistics assessment and graphical output, validate the models using independent dataset, and predict unknown variables using soil spectral data. AlradSpectra has four main modules: Import Data, Spectral Preprocessing, Modeling, and Prediction. The implementation of AlradSpectra is demonstrated by applying visible near-infrared reflectance spectroscopy for soil organic carbon (SOC) prediction. The data contains the value of SOC and Vis-NIR reflectance for 595 soil samples. The prediction statistic assessment of SOC was performed applying all spectral preprocessing and methods. The R 2 considering all models ranged from 0.54 to 0.80. In the partial least squares regression (PLSR) models, the performances were similar to multiple linear regression (MLR) and support vector machines (SVM). The lowest error in the SOC prediction was achieved by PLSR method with standard normal variate (SNV) preprocessing reaching an R 2 of 0.80, the smallest root mean square error (RMSE) of 0.47 %, and ratio of performance to inter-quartile distance (RPIQ) of 3.12. The capacity of performing multiple tasks, being free and open-source, easy to operate, and requiring no initial knowledge of R programming language are features that make AlradSpectra a useful tool to perform different modeling approaches and predict the desired soil variable.

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“…Demattê and Terra (2014) used Vis-NIR DRS to both characterize soils in a catena in Brazil using hierarchical clustering analysis and predict soil properties using multiple linear regression. Rizzo et al (2015) successfully individualized soils of five catenas in Brazil using Vis-NIR DRS data along with fuzzy k-means.…”

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Tropical Soil Toposequence Characterization via pXRF Spectrometry

Silva¹,

Weindorf

Silva³

et al. 2019

Soil Science Soc of Amer J

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Variability of elemental composition in a toposequence of quartzite‐derived soils in a tropical environment was evaluated via portable x‐ray fluorescence (pXRF) spectrometry. The toposequence featured different degrees of weathering and variable soil formation processes. Specifically, this study aimed to: (i) use elemental composition obtained through pXRF as a tool to investigate the degrees of weathering–leaching and pedogenic processes; and (ii) correlate elemental data with physicochemical properties of soils. Four soil profiles (Lithic Ustorthent, Typic Ustorthent, Typic Dystrudept, and Xanthic Hapludox) were described and sampled. Laboratory analysis for soil physicochemical characterization and pXRF were performed. Soils featured high contents of SiO2 due to the predominance of quartz in the rock. However, pXRF analysis revealed that the soils showed a decrease in such content with depth. Potassium contents tended to be greater in the C horizons since they are less weathered and retained greater amounts of K‐bearing minerals, such as muscovite. Al, Fe, Ca, Ti, Cu, and Sr increased their contents with increasing clay content in soils, while Zr generally remained stable. Ba was leached out of soils during weathering. Both CaO and Sr were positively correlated with exchangeable contents of Ca2+ and Mg2+, base saturation, pH, and cation exchange capacity, while inversely correlated to Al3+ saturation. Fe, Al, Ti, Ca, K, Cu, Rb, and Zr accumulated in soil in comparison with the parent material. Variability of elemental contents in soils with differential degrees of weathering–leaching can be easily accessed with pXRF and such data correlate well with several soil physicochemical properties. Core Ideas Toposequence of quartzite‐derived soils variability assessed by pXRF. Soils presented varying weathering degrees, from Entisols to Oxisols. pXRF elemental data correlated well with soil physical and chemical properties. Soil mineralogy and pXRF data elucidated soils variability with weathering. Al, Fe, Ca, Ti, Cu, and Sr increased with increasing clay content.

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Espectros Vis-NIR do Solo e Fuzzy K-Médias Aplicados na Delimitação de Unidades de Mapeamento de Solos em Topossequências

Cited by 4 publications

References 12 publications

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

AlradSpectra: a Quantification Tool for Soil Properties Using Spectroscopic Data in R

Tropical Soil Toposequence Characterization via pXRF Spectrometry

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