Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City
With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advanta...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9895 |
| _version_ | 1797455316883865600 |
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| author | Zihan Fang Wenhao Lu Fubin Zhu Changda Zhu Zhaofu Li Jianjun Pan |
| author_facet | Zihan Fang Wenhao Lu Fubin Zhu Changda Zhu Zhaofu Li Jianjun Pan |
| author_sort | Zihan Fang |
| collection | DOAJ |
| description | With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries. |
| first_indexed | 2024-03-09T15:52:41Z |
| format | Article |
| id | doaj.art-87873d5ce8264403a435e9aab880d313 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:41Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-87873d5ce8264403a435e9aab880d3132023-11-24T17:57:07ZengMDPI AGSensors1424-82202022-12-012224989510.3390/s22249895Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong CityZihan Fang0Wenhao Lu1Fubin Zhu2Changda Zhu3Zhaofu Li4Jianjun Pan5College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaWith the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.https://www.mdpi.com/1424-8220/22/24/9895landscape classificationland usemicrotopographysoil surveyUAVclustering |
| spellingShingle | Zihan Fang Wenhao Lu Fubin Zhu Changda Zhu Zhaofu Li Jianjun Pan Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City Sensors landscape classification land use microtopography soil survey UAV clustering |
| title | Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City |
| title_full | Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City |
| title_fullStr | Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City |
| title_full_unstemmed | Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City |
| title_short | Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City |
| title_sort | landscape classification system based on rkm clustering for soil survey uav images case study of the small hilly areas in jurong city |
| topic | landscape classification land use microtopography soil survey UAV clustering |
| url | https://www.mdpi.com/1424-8220/22/24/9895 |
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