Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Data
2.2.1. Sentinel-2A Remote-Sensing Data
2.2.2. Night-Time Lighting Data and POI Data
2.3. Research Methods
2.3.1. PLES Spatial Classification System
2.3.2. Object-Oriented Multi-Scale Segmentation Algorithm
2.3.3. Random Forest Classification Algorithm
3. Results
3.1. Object-Oriented Segmentation and Accuracy Evaluation of the Random Forest Algorithm
3.2. Analysis of the Spatial Evolution Pattern of PLES in the BTH Region
4. Discussion
5. Conclusions
- Firstly, multiple-scale segmentation of the study area was performed based on remote-sensing images (Sentinel); then, the spectral, textural, shape, and socio-economic characteristics of the objects were extracted from the remote-sensing image data, night-time light data, and POI data using the segmented objects. Representative samples were selected and labeled based on the nine secondary classifications under PLES categories. These samples were used to train a random forest algorithm, which was then used to classify all the objects in the entire study area. The results showed that features extracted based on object-oriented and multi-source data can better reflect the characteristics of objects from multiple dimensions, which is effective for identifying the functional types of PLES.
- In this study, the segmentation parameters were iteratively optimized using a multi-scale segmentation algorithm, resulting in a good segmentation effect, with the size of the segmented objects depending on the image features, and clear boundaries between different land covers. Nearly 2000 samples covering 9 second-level classifications were selected, and 11 features were extracted from multiple sources of data and used to train the random forest algorithm. The results showed that the random forest model had a strong anti-overfitting ability, could monitor the mutual influence between features, and identified small differences between different land covers. Finally, the model achieved an accuracy of 84.17% on the validation set and can thus meet the demands for large-scale land-use classification.
- The trained model was used to identify the spatial functional PLES types of land in the BTH region in 2016 and 2021, generating a 10 m resolution spatial distribution dataset. Based on this dataset, the spatial distribution patterns and development evolution of the PLES in the BTH region were statistically analyzed. It was found that the PLES in the study area exhibited a zonal distribution. The distribution between Bei**g and Tian** and across Hebei is extremely uneven, with Bei**g and Tian** occupying a substantial proportion of the urban living space, while other cities in Hebei, such as Shijiazhuang, have more clear land-use conflicts in terms of PLES. Furthermore, this imbalance has widened in the past 5 years. Overall, the production space in the BTH has decreased, while the living space and ecological space have increased. As the urban living space expands, urban green space also increases, and the overall layout of the PLES tends to be more reasonable.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Feature Type | Bands | Data Source |
---|---|---|
Spectral features | B8 | Sentinel-2A |
B4 | Sentinel-2A | |
B3 | Sentinel-2A | |
B2 | Sentinel-2A | |
NDVI | Sentinel-2A | |
Shape features | Area | Object segmentation |
Perimeter | Object segmentation | |
Texture features | Structure ratio | Object segmentation |
Second-order moment | GEE | |
Relevance | GEE | |
Entropy | GEE | |
Socioeconomic features | Night light value | NPP/VIIRS |
POIs density | Nucleation density of POIs |
Level 1 Classification | Level 2 Classification | Code |
---|---|---|
Production area | Agricultural space | 11 |
Industrial space | 12 | |
Transportation space | 13 | |
Living area | Rural living space | 21 |
Urban living space | 22 | |
Ecological area | Waters | 31 |
Urban ecological space | 32 | |
Non-urban ecological space | 33 | |
Unused space | 34 |
Plan A | Plan B | Plan C | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PA/% | UA/% | OA/% | Kappa | PA/% | UA/% | OA/% | Kappa | PA/% | UA/% | OA/% | Kappa | |
Agricultural space | 0.87 | 0.89 | 85.06 | 82.66 | 0.82 | 0.98 | 87.64% | 85.74% | 0.98 | 0.96 | 93.97 | 92.98 |
Industrial space | 0.89 | 0.96 | 0.93 | 0.96 | 0.93 | 1.00 | ||||||
Transportation space | 0.76 | 0.76 | 0.94 | 0.61 | 0.89 | 0.89 | ||||||
Rural living space | 0.89 | 0.91 | 0.90 | 0.93 | 0.97 | 0.97 | ||||||
Urban living space | 0.90 | 0.86 | 0.89 | 0.96 | 1.00 | 0.96 | ||||||
Waters | 0.95 | 0.93 | 1.00 | 0.91 | 0.84 | 0.84 | ||||||
Urban ecological Space | 0.68 | 0.73 | 0.75 | 0.77 | 0.94 | 1.00 | ||||||
Non-urban ecological space | 0.53 | 0.47 | 0.77 | 0.67 | 0.79 | 0.81 | ||||||
Unused space | 0.86 | 0.60 | 0.83 | 0.62 | 1.00 | 0.57 |
PLES Type | Area in 2016 (km2) | Area in 2021 (km2) |
---|---|---|
Agricultural space | 807.86 | 786.45 |
Industrial space | 26.05 | 27.14 |
Transport space | 61.42 | 44.46 |
Rural living space | 280.15 | 267.63 |
Urban living space | 35.13 | 40.02 |
Waters | 100.99 | 107.37 |
Urban ecological space | 22.17 | 28.89 |
Non-urban ecological space | 1500.08 | 1527.95 |
Unused space | 6.74 | 10.68 |
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Bu, Z.; Fu, J.; Jiang, D.; Lin, G. Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning. Land 2023, 12, 2029. https://doi.org/10.3390/land12112029
Bu Z, Fu J, Jiang D, Lin G. Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning. Land. 2023; 12(11):2029. https://doi.org/10.3390/land12112029
Chicago/Turabian StyleBu, Ziqiang, **gying Fu, Dong Jiang, and Gang Lin. 2023. "Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning" Land 12, no. 11: 2029. https://doi.org/10.3390/land12112029