Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy
Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/21/4090 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846173209838223360 |
|---|---|
| author | Changda Zhu Fubin Zhu Cheng Li Wenhao Lu Zihan Fang Zhaofu Li Jianjun Pan |
| author_facet | Changda Zhu Fubin Zhu Cheng Li Wenhao Lu Zihan Fang Zhaofu Li Jianjun Pan |
| author_sort | Changda Zhu |
| collection | DOAJ |
| description | Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping. |
| format | Article |
| id | doaj-art-f75d0619e3ce46f19e1c166869a8b156 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f75d0619e3ce46f19e1c166869a8b1562024-11-08T14:40:47ZengMDPI AGRemote Sensing2072-42922024-11-011621409010.3390/rs16214090Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction AccuracyChangda Zhu0Fubin Zhu1Cheng Li2Wenhao Lu3Zihan Fang4Zhaofu Li5Jianjun Pan6College of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, No. 1 Weigang, Xuanwu District, Nanjing 210095, ChinaTopography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping.https://www.mdpi.com/2072-4292/16/21/4090soil–landscape unitssoil space predictionsoil type and attributelandform classificationrelative slope position |
| spellingShingle | Changda Zhu Fubin Zhu Cheng Li Wenhao Lu Zihan Fang Zhaofu Li Jianjun Pan Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy Remote Sensing soil–landscape units soil space prediction soil type and attribute landform classification relative slope position |
| title | Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy |
| title_full | Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy |
| title_fullStr | Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy |
| title_full_unstemmed | Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy |
| title_short | Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy |
| title_sort | constructing soil landscape units based on slope position and land use to improve soil prediction accuracy |
| topic | soil–landscape units soil space prediction soil type and attribute landform classification relative slope position |
| url | https://www.mdpi.com/2072-4292/16/21/4090 |
| work_keys_str_mv | AT changdazhu constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy AT fubinzhu constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy AT chengli constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy AT wenhaolu constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy AT zihanfang constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy AT zhaofuli constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy AT jianjunpan constructingsoillandscapeunitsbasedonslopepositionandlandusetoimprovesoilpredictionaccuracy |