Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation
Different land parcels possess unique microclimates, soils, and biological conditions, which in turn significantly influence the land parcels themselves, impacting biodiversity, hydrological relationships, land degradation, geological disasters, and other ecological environments. Therefore, research...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/13/2/158 |
| _version_ | 1797297748818526208 |
|---|---|
| author | Fei Liu Huizhong Lu Lilei Wu Rui Li Xinjun Wang Longxi Cao |
| author_facet | Fei Liu Huizhong Lu Lilei Wu Rui Li Xinjun Wang Longxi Cao |
| author_sort | Fei Liu |
| collection | DOAJ |
| description | Different land parcels possess unique microclimates, soils, and biological conditions, which in turn significantly influence the land parcels themselves, impacting biodiversity, hydrological relationships, land degradation, geological disasters, and other ecological environments. Therefore, researching an efficient and accurate method capable of extracting land parcels with the least internal heterogeneity at the macro, meso, and micro scales is extremely important. Multi-scale segmentation, based on scale and resolution analysis techniques, is a bottom-up merging technology that minimizes internal heterogeneity within regions and maximizes heterogeneity between different units. This approach is extensively applied in multi-scale spectral feature extraction and classification and is further combined with deep learning techniques to enhance the accuracy of image classification. This study, using Xinghai County in Qinghai Province as an example, employs multi-scale segmentation and hydrological analysis methods to extract land parcels at different spatial scales. The results show (1) that the land parcels extracted using the hydrological analysis method are catchment units centered around rivers, including slopes on both sides of the river. In contrast, multi-scale segmentation extracts regions comprising land parcels with similar properties, enabling the segregation of slopes and channels into independent units. (2) At a classification threshold of 19, multi-scale segmentation divides the study area into five different types of land parcels, reflecting the heterogeneity of terrain undulations and their hydrological connections. When the classification threshold is set to 31, the study area is divided into 15 types of land parcels, primarily highlighting micro-topographic features. (3) Multi-scale segmentation can merge and categorize areas with the least heterogeneity in land parcels, facilitating subsequent statistical analysis. Therefore, mesoscale land parcels extracted through multi-scale segmentation are invaluable for analyzing regional Earth surface processes such as soil erosion, sediment distribution and transportation. Microscale land parcels are significantly important for identifying high-risk areas in relation to geological disasters like landslides and collapses. |
| first_indexed | 2024-03-07T22:24:53Z |
| format | Article |
| id | doaj.art-88f3866e8b214e0d9bf5df1a28a2e25b |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-07T22:24:53Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-88f3866e8b214e0d9bf5df1a28a2e25b2024-02-23T15:24:03ZengMDPI AGLand2073-445X2024-01-0113215810.3390/land13020158Automatic Extraction for Land Parcels Based on Multi-Scale SegmentationFei Liu0Huizhong Lu1Lilei Wu2Rui Li3Xinjun Wang4Longxi Cao5College of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaNational Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaCollege of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, ChinaChina Academy of Transportation Sciences, Beijing 100029, ChinaCollege of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, ChinaDifferent land parcels possess unique microclimates, soils, and biological conditions, which in turn significantly influence the land parcels themselves, impacting biodiversity, hydrological relationships, land degradation, geological disasters, and other ecological environments. Therefore, researching an efficient and accurate method capable of extracting land parcels with the least internal heterogeneity at the macro, meso, and micro scales is extremely important. Multi-scale segmentation, based on scale and resolution analysis techniques, is a bottom-up merging technology that minimizes internal heterogeneity within regions and maximizes heterogeneity between different units. This approach is extensively applied in multi-scale spectral feature extraction and classification and is further combined with deep learning techniques to enhance the accuracy of image classification. This study, using Xinghai County in Qinghai Province as an example, employs multi-scale segmentation and hydrological analysis methods to extract land parcels at different spatial scales. The results show (1) that the land parcels extracted using the hydrological analysis method are catchment units centered around rivers, including slopes on both sides of the river. In contrast, multi-scale segmentation extracts regions comprising land parcels with similar properties, enabling the segregation of slopes and channels into independent units. (2) At a classification threshold of 19, multi-scale segmentation divides the study area into five different types of land parcels, reflecting the heterogeneity of terrain undulations and their hydrological connections. When the classification threshold is set to 31, the study area is divided into 15 types of land parcels, primarily highlighting micro-topographic features. (3) Multi-scale segmentation can merge and categorize areas with the least heterogeneity in land parcels, facilitating subsequent statistical analysis. Therefore, mesoscale land parcels extracted through multi-scale segmentation are invaluable for analyzing regional Earth surface processes such as soil erosion, sediment distribution and transportation. Microscale land parcels are significantly important for identifying high-risk areas in relation to geological disasters like landslides and collapses.https://www.mdpi.com/2073-445X/13/2/158multi-scale segmentationland parcelshydrological analysis methodprincipal component analysisRstoolbox |
| spellingShingle | Fei Liu Huizhong Lu Lilei Wu Rui Li Xinjun Wang Longxi Cao Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation Land multi-scale segmentation land parcels hydrological analysis method principal component analysis Rstoolbox |
| title | Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation |
| title_full | Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation |
| title_fullStr | Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation |
| title_full_unstemmed | Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation |
| title_short | Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation |
| title_sort | automatic extraction for land parcels based on multi scale segmentation |
| topic | multi-scale segmentation land parcels hydrological analysis method principal component analysis Rstoolbox |
| url | https://www.mdpi.com/2073-445X/13/2/158 |
| work_keys_str_mv | AT feiliu automaticextractionforlandparcelsbasedonmultiscalesegmentation AT huizhonglu automaticextractionforlandparcelsbasedonmultiscalesegmentation AT lileiwu automaticextractionforlandparcelsbasedonmultiscalesegmentation AT ruili automaticextractionforlandparcelsbasedonmultiscalesegmentation AT xinjunwang automaticextractionforlandparcelsbasedonmultiscalesegmentation AT longxicao automaticextractionforlandparcelsbasedonmultiscalesegmentation |