Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Chagas, C. da S.; Vieira, Carlos Antônio Oliveira; Filho, Elpı́dio Inácio Fernandes

doi:10.1590/s0100-06832013000200005

Cited by 22 publications

(8 citation statements)

References 34 publications

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“…Modified catchment area and altitude above the channel were used in one model each, showing their restricted influence on prediction of soil properties. Six multispectral bands from Landsat 5 TM; derived indexes: NDVI (band 4 -band 3)/(band 4 + band 3); clay minerals (band 5/band 7); Iron Oxide (band 3/band 1) Yang et al (1997), Sabins (1997Sabins ( , 1999, Chagas et al (2013), Pinheiro et al (2013) (1,2,3) (1,2,3) (1,2,3) (1,2,3) (1,2,3) (1,2,3)…”

Section: Resultsmentioning

confidence: 99%

“…The indexes were the Normalized Difference Vegetation Index (NDVI), the iron oxide index (ratio between band 3/band 1), and the clay mineral index (ratio between band 5/band 7). The iron oxide index highlights the presence of iron oxides and sulfates, and the clay mineral index highlights the presence of clay minerals, such as alunite, illite, kaolinite, and montmorillonite (Sabins, 1997;Chagas et al, 2013). These last two indexes are commonly used in remote sensing applied to geology studies to recognize hydrothermal alteration and unaltered rocks (Sabins, 1999).…”

Section: Input Covariatesmentioning

confidence: 99%

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Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Pinheiro

Carvalho

Chagas

et al. 2018

Rev. Bras. Ciênc. Solo

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Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees-RT and Multiple Linear Regressions-MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km²) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil.Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R² values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R 2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R 2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions.

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Section: Resultsmentioning

confidence: 99%

Section: Input Covariatesmentioning

confidence: 99%

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Pinheiro

Carvalho

Chagas

et al. 2018

Rev. Bras. Ciênc. Solo

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“…In this sense, Figure 6a presents the relative importance of the remaining covariates to each phytophysiognomies at the detailed level, that considers soil depth (fifth taxonomic level). Directly or indirectly, soil properties and spectral responses of vegetation detected by remote sensors have been related in several studies [25,55,56]. The correlation can be explained by high clay content in subsurface horizons, natural fertility presented by some soil types, or even due to the organic matter levels in the topsoil [57].…”

Section: Phytophysiognomies and Landscape Relationshipsmentioning

confidence: 99%

Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships

et al. 2019

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There is a relation of vegetation physiognomies with soil and geological conditions that can be represented spatially with the support of remote sensing data. The goal of this research was to map vegetation physiognomies in a mountainous area by using Sentinel-2 Multispectral Instrument (MSI) data and morphometrical covariates through data mining techniques. The research was based on red-edge (RE) bands, and indices, to classify phytophysiognomies at two taxonomic levels. The input data was pixel sampled based on field sample sites. Data mining procedures comprised covariate selection and supervised classification through the Random Forest model. Results showed the potential of bands 3, 5, and 6 to map phytophysiognomies for both seasons, as well as Green Chlorophyll (CLg) and SAVI indices. NDVI indices were important, particularly those calculated with bands 6, 7, 8, and 8A, which were placed at the RE position. The model performance showed reasonable success to Kappa index 0.72 and 0.56 for the first and fifth taxonomic level, respectively. The model presented confusion between Broadleaved dwarf-forest, Parkland Savanna, and Bushy grassland. Savanna formations occurred variably in the area while Bushy grasslands strictly occur in certain landscape positions. Broadleaved forests presented the best performance (first taxonomic level), and among its variation (fifth level) the model could precisely capture the pattern for those on deep soils from gneiss parent material. The approach was thus useful to capture intrinsic soil-plant relationships and its relation with remote sensing data, showing potential to map phytophysiognomies in two distinct taxonomic levels in poorly accessible areas.

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“…A lower number of internal neurons yielded better results. Using several sets and from three to twenty neurons in the internal layer, Chagas et al (2013) found using five to eight neurons in their study of two areas yielded the best results. Foody and Arora (1997) pointed out that greater and more complex networks are more efficient in characterizing a training set.…”

Section: Evaluation Of Artificial Neural Networkmentioning

confidence: 99%

Digital soil mapping using reference area and artificial neural networks

Arruda

Demattê

Chagas

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area.

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Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Cited by 22 publications

References 34 publications

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships

Digital soil mapping using reference area and artificial neural networks

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