Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil

Machado, Israel Rosa; Giasson, Élvio; Campos, Alcinei Ribeiro; Costa, José Janderson Ferreira; Silva, Elisângela Benedet da; Bonfatti, Benito Roberto

doi:10.1590/18069657rbcs20170193

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…substantially larger than the scale used in the present study, overall accuracy ranged from 39% to 80% (Machado et al, 2018). This indicates the pertinence of the map elaborated here.…”

Section: Resultsmentioning

confidence: 47%

“…In the future, elaboration of more detailed pedological surveys encompassing the state will allow classification of land use capability to be obtained with greater accuracy. In this task, new approaches tend to speed up and reduce the costs of the surveys (Fernandes et al, 2008;Machado et al, 2018;Menezes et al, 2014;Silva et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…The more detailed the pedological information is, the more detailed any thematic map derived from it will be (Silva et al, 2016). Considering current limitations in investment for producing detailed pedological maps, two research lines may contribute more effectively to this effort: the techniques of digital soil mapping (Arrouays et al, 2017;Hengl et al, 2014;Machado et al, 2018;Menezes et al, 2014;Silva et al, 2016;Souza et al, 2016;) and the incorporation of the empirical knowledge of the farmer, speeding up the soil surveys and making them less expensive (Fernandes et al, 2008;Schenatto et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Mapping land use capability in tropical conditions adapting criteria to different levels of agricultural management

Taveira¹,

Menezes

Teixeira

et al. 2018

Ciênc. agrotec.

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Land use capability is one of the most widespread technical-interpretative classification systems, however, regional adaptations may be necessary because different attributes may affect it. For these adaptations, the Minas Gerais soil map was used as the starting point for this study. The criteria to define the land use capability were adapted to management levels with small (level A) and medium (level B) application of capital and modern technology (level C). The aim of the present study was to map land use capability for Minas Gerais state, Brazil, following the criteria adapted to different levels of management and measure the accuracy of the resulting maps. The system of land use capability is widely used by INCRA in evaluations of rural properties. Erosion criterion was replaced by erodibility. The information was handled in a geographic information system. For validation, soil profiles from regional pedological surveys were sampled, classified, and its land use capability was compared to the land use capability shown on the map according to the different management levels. In spite of the small scale of the soil map, the maps of land use capability exhibited adequate accuracy: 73% (management level A), 71% (B), and 50% (C). Therefore, it can be applied in initial phases of regional planning studies, in which the level of details required is reduced (for example, in ecological-economic zoning). More detailed analyses still depend on detailed field surveys, as advocated by the system of land use capability.

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“…substantially larger than the scale used in the present study, overall accuracy ranged from 39% to 80% (Machado et al, 2018). This indicates the pertinence of the map elaborated here.…”

Section: Resultsmentioning

confidence: 47%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping land use capability in tropical conditions adapting criteria to different levels of agricultural management

Taveira¹,

Menezes

Teixeira

et al. 2018

Ciênc. agrotec.

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“…In the DSM field, machine-learning techniques are increasingly used to elucidate the spatial distribution of both soil type and soil properties across a large range of scales (Bui and Moran., 2001;Scull et al, 2005;Malone et al, 2009;Nelson and Odeh, 2009;Abdel-Kader, 2011;Lacoste et al, 2011;Lemercier et al, 2012;Kempen et al, 2012;Jafari et al, 2013;Nauman and Thompson, 2014;Brungard et al, 2015;Mosleh et al, 2016;Viloria et al, 2016;Nussbaum et al, 2018;Vaysse and Lagacherie, 2015;Ellili et al, 2019;Padarian et al, 2019;Arrouays et al, 2020).They were also applied to disaggregate superficial geology maps available at a 1 : 250 000 scale in Australia (Bui and Moran, 2001). The main advantage of these approaches is they allow the handling of both quantitative and categorical (ordinal or nominal) soil and environmental variables as explanatory covariates (Bui and Moran, 2001).…”

Section: Introductionmentioning

confidence: 99%

Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm

Ellili‐Bargaoui

Malone

Michot

et al. 2020

SOIL

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Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

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“…Despite this, PMs may still be important starting points for producing more detailed soil maps. A possible approach is the spatial disaggregation of the polygons of the combined PUs to better represent the spatial distribution of soils by individualizing and locating soil types in the landscape (Häring et al, 2012;Li et al, 2012;Odgers et al, 2014;Sarmento et al, 2017;Machado et al, 2018;Vincent et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Individualization of soil classes by disaggregation of physiographic map polygons

Costa

Giasson

Silva

et al. 2019

Pesq. agropec. bras.

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The objective of this work was to disaggregate the polygons of physiographic map units in order to individualize the soil classes in each one, representing them as simple soil map units and generating a more detailed soil map than the original one, making these data more useful for future reference. A physiographic map, on a 1:25,000 scale, of the Tarumãzinho watershed, located in the municipality of Águas Frias, in the state of Santa Catarina, Brazil, was used. For disaggregation, three geomorphometric parameters were applied: slope and landforms, both derived from the digital terrain model; and an elevation map. The boundaries of the physiographic units and the elevation, slope, and landform maps were subjected to cross tabulation to identify the existing combinations between the soil classes of each physiographic unit. Based on these combinations, rules were established to select typical areas of occurrence of each soil type in order to train a decision tree model to predict the occurrence of soil classes. The model was trained using the Weka software and was validated with a set of georeferenced soil profiles. Disaggregation enables the individualization and spatialization of soil classes and is useful in producing detailed soil maps.

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Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil

Cited by 11 publications

References 28 publications

Mapping land use capability in tropical conditions adapting criteria to different levels of agricultural management

Mapping land use capability in tropical conditions adapting criteria to different levels of agricultural management

Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm

Individualization of soil classes by disaggregation of physiographic map polygons

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