autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil
The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innova...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/3/604 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850280517712216064 |
|---|---|
| author | Hugo Rodrigues Marcos Bacis Ceddia Gustavo Mattos Vasques Sabine Grunwald Ebrahim Babaeian André Luis Oliveira Villela |
| author_facet | Hugo Rodrigues Marcos Bacis Ceddia Gustavo Mattos Vasques Sabine Grunwald Ebrahim Babaeian André Luis Oliveira Villela |
| author_sort | Hugo Rodrigues |
| collection | DOAJ |
| description | The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innovative algorithm that delineates RA (autoRA—automatic reference areas) is presented, and its efficiency is evaluated in Sátiro Dias, Bahia, Brazil. autoRA integrates multiple environmental covariates (e.g., geomorphology, geology, digital elevation models, temperature, precipitation, etc.) using the Gower’s Dissimilarity Index to capture landscape variability more comprehensively. One hundred and two soil profiles were collected under a specialist’s manual delineation to establish baseline mapping soil taxonomy. We tested autoRA coverages ranging from 10% to 50%, comparing them to RA manual delineation and a conventional “Total Area” (TA) approach. Environmental heterogeneity was insufficiently sampled at lower coverages (autoRA at 10–20%), resulting in poor classification accuracy (0.11–0.14). In contrast, larger coverages significantly improved performance: 30% yielded an accuracy of 0.85, while 40% and 50% reached 0.96. Notably, 40% struck the best balance between high accuracy (kappa = 0.65) and minimal redundancy, outperforming RA manual delineation (accuracy = 0.75) and closely matching the best TA outcomes. These findings underscore the advantage of applying an automated, diversity-driven strategy like autoRA before field campaigns, ensuring the representative sampling of critical environmental gradients to improve DSM workflows. |
| format | Article |
| id | doaj-art-eaf4a86078eb4f2aac147e0faaa292f0 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-eaf4a86078eb4f2aac147e0faaa292f02025-08-20T01:48:41ZengMDPI AGLand2073-445X2025-03-0114360410.3390/land14030604autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, BrazilHugo Rodrigues0Marcos Bacis Ceddia1Gustavo Mattos Vasques2Sabine Grunwald3Ebrahim Babaeian4André Luis Oliveira Villela5Laboratory of Soil and Water in Agroecosystems (LASA), Postgraduation in Soil Science, Agronomy Institute, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, BrazilLaboratory of Soil and Water in Agroecosystems (LASA), Postgraduation in Soil Science, Agronomy Institute, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, BrazilBrazilian Research Brazilian Agricultural Research Corporation—Soils, Rio de Janeiro 22460-000, BrazilPedometrics, Landscape Analysis & GIS Laboratory, Soil, Water, and Ecosystem Sciences Department, University of Florida, Gainesville, FL 32611, USAEnvironmental Soil Physics Lab, Soil, Water, and Ecosystem Sciences Department, University of Florida, Gainesville, FL 32611, USATechnical College of the Federal Rural University of Rio de Janeiro, Rio de Janeiro 23897-000, BrazilThe reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innovative algorithm that delineates RA (autoRA—automatic reference areas) is presented, and its efficiency is evaluated in Sátiro Dias, Bahia, Brazil. autoRA integrates multiple environmental covariates (e.g., geomorphology, geology, digital elevation models, temperature, precipitation, etc.) using the Gower’s Dissimilarity Index to capture landscape variability more comprehensively. One hundred and two soil profiles were collected under a specialist’s manual delineation to establish baseline mapping soil taxonomy. We tested autoRA coverages ranging from 10% to 50%, comparing them to RA manual delineation and a conventional “Total Area” (TA) approach. Environmental heterogeneity was insufficiently sampled at lower coverages (autoRA at 10–20%), resulting in poor classification accuracy (0.11–0.14). In contrast, larger coverages significantly improved performance: 30% yielded an accuracy of 0.85, while 40% and 50% reached 0.96. Notably, 40% struck the best balance between high accuracy (kappa = 0.65) and minimal redundancy, outperforming RA manual delineation (accuracy = 0.75) and closely matching the best TA outcomes. These findings underscore the advantage of applying an automated, diversity-driven strategy like autoRA before field campaigns, ensuring the representative sampling of critical environmental gradients to improve DSM workflows.https://www.mdpi.com/2073-445X/14/3/604soil class mappingdigital soil mappingpreviously mapped areas |
| spellingShingle | Hugo Rodrigues Marcos Bacis Ceddia Gustavo Mattos Vasques Sabine Grunwald Ebrahim Babaeian André Luis Oliveira Villela autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil Land soil class mapping digital soil mapping previously mapped areas |
| title | autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil |
| title_full | autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil |
| title_fullStr | autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil |
| title_full_unstemmed | autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil |
| title_short | autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil |
| title_sort | autora an algorithm to automatically delineate reference areas a case study to map soil classes in bahia brazil |
| topic | soil class mapping digital soil mapping previously mapped areas |
| url | https://www.mdpi.com/2073-445X/14/3/604 |
| work_keys_str_mv | AT hugorodrigues autoraanalgorithmtoautomaticallydelineatereferenceareasacasestudytomapsoilclassesinbahiabrazil AT marcosbacisceddia autoraanalgorithmtoautomaticallydelineatereferenceareasacasestudytomapsoilclassesinbahiabrazil AT gustavomattosvasques autoraanalgorithmtoautomaticallydelineatereferenceareasacasestudytomapsoilclassesinbahiabrazil AT sabinegrunwald autoraanalgorithmtoautomaticallydelineatereferenceareasacasestudytomapsoilclassesinbahiabrazil AT ebrahimbabaeian autoraanalgorithmtoautomaticallydelineatereferenceareasacasestudytomapsoilclassesinbahiabrazil AT andreluisoliveiravillela autoraanalgorithmtoautomaticallydelineatereferenceareasacasestudytomapsoilclassesinbahiabrazil |