Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach
Abstract
:1. Introduction
2. Study Area, Data Source, and Methods
2.1. Study Area
2.2. Data Source
2.2.1. Remote Sensing Data
2.2.2. Social Data
2.2.3. Land Surface Data
2.2.4. Support Data
2.3. Methods
2.3.1. Parcel Generation
2.3.2. Data Set Combining and Feature Extraction Methods
2.3.3. Function of LCZ Data
2.3.4. Sampling
2.3.5. Supervised Classification and Mapping
2.3.6. Post-Classification Process
3. Results
3.1. Classification Performance with Difference Data Combinations
3.2. Data Set Importance and Correlation Results
3.3. Urban Land Use Dynamics in Dalian for 2000–2020
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Type | Data Name | Spatial Resolution (m) | Description | Data Source |
---|---|---|---|---|
Remote Sensing Data | Sentinel-2 | 10–60 | Level-2A Sentinel-2 remote sensing images | Google Earth Engine (GEE) (https://earthengine.google.com, accessed on 23 June 2024) |
Landsat-7 | 30 | Landsat-7 Level 2 Collection 2 Tier 1 images | GEE | |
Landsat-8 | 30 | Landsat-8 Level 2 Collection 2 Tier 1 images | GEE | |
Normalized remote sensing Indexes | 10–30 | Normalized Difference Vegetation Index (NDVI) | GEE | |
Normalized Difference Build-up Index (NDBI) | ||||
Normalized Difference Water Index (NDWI) | ||||
Luojia-1 | 130 | Nighttime light data | Luojia-1 official site (http://59.175.109.173:8888/app/login.html, accessed on 23 June 2024) [27] | |
Social Data | Gaode Point of Interest (POI) data | / | Includes the point’s name, latitude, longitude, and its social function | Accessed via Gaode Map API (https://lbs.amap.com/api/webservice/guide/api/search, accessed on 23 June 2024) |
Land Surface Data | LCZ | 100 | Describes the building patterns in different urban areas based on buildings, land use, and vegetation | Zhao et al. [87] |
ALOS DSM | 30 | Global Digital Surface Model (DSM) data for elevation | GEE | |
NASA SRTM Digital Elevation 30 m | 30 | Near-global digital elevation model | GEE | |
Support data | FROM-GLC10 | 10 | 10 m resolution Global Land Cover (GLC) data | Gong et al. [88] (available at https://data-starcloud.pcl.ac.cn/, accessed on 23 June 2024) |
GAIA data | 30 | Global Annual Impervious Area (GAIA) data | GEE | |
OpenStreetMap (OSM) road data | / | Used for road information extraction | Accessed via OSM (https://planet.openstreetmap.org/, accessed on 23 June 2024) | |
Global Urban Boundaries | 30 | City boundaries reference generated using GAIA and nighttime data | Li et al. [89] (available at https://data-starcloud.pcl.ac.cn/, accessed on 23 June 2024) | |
EULUC-China 2018 | / | Essential Urban Land Use Categories Map for China in 2018 [42] | Gong et al. [42] (available at https://data-starcloud.pcl.ac.cn/, accessed on 23 June 2024) |
LCZ Class Number | LCZ Class Name |
---|---|
1 | Compact high-rise |
2 | Compact mid-rise |
3 | Compact low-rise |
4 | Open high-rise |
5 | Open mid-rise |
6 | Open low-rise |
7 | Lightweight low-rise |
8 | Large low-rise |
9 | Sparsely built |
10 | Heavy industry |
11 | Dense trees |
12 | Scattered trees |
13 | Bush, scrub |
14 | Low plants |
15 | Bare rock or paved |
16 | Bare soil or sand |
17 | Water |
Data Set Name | Remote Sensing (RS) Data | Gaode POI Data | ALOS DSM Data | LCZ Data |
---|---|---|---|---|
RS | √ | |||
RS + DSM | √ | √ | ||
RS + POI | √ | √ | ||
RS + LCZ | √ | √ | ||
RS + LCZ + DSM | √ | √ | √ |
Level 1 | Level 2 |
---|---|
01 Residential | 0101 Residential |
02 Commercial | 0201 Business office |
0202 Commercial service | |
03 Industrial | 0301 Industrial |
04 Transportation | 0401 Road |
0402 Transportation stations | |
0403 Airport facilities | |
05 Public management and service | 0501 Administrative |
0502 Educational | |
0503 Medical | |
0504 Sport and cultural | |
0505 Park and greenspace | |
06 Bare land or construction | 0601 Bare land or construction |
07 Water | 0701 Water |
Type | Total Object Category Count | Max. Object Category Count | Max. Category Percentage | Classification Result | Chaos Index |
---|---|---|---|---|---|
No mapped object in the land parcel | 0 | 0 | - | 0 | −1 |
Single-type parcel | ≥1 | 1 | 1 | MAX category | 0 |
Dominant-type parcel | ≥2 | 1 | >0.5 | MAX category | |
Mixed-type parcel | ≥2 | 1 | <0.5 | MIX category | |
Special (MAX and the second biggest category are green space or residential) | ≥2 | 1 | - | MIXB category |
Level | Mean Accuracy (%) | Standard Deviation | |
---|---|---|---|
RS | LV1 | 69.91 | 0.0518 |
LV2 | 61.96 | 0.0447 | |
RS + DSM | LV1 | 76.21 | 0.0417 |
LV2 | 69.24 | 0.0424 | |
RS + POI | LV1 | 74.59 | 0.0468 |
LV2 | 68.23 | 0.0567 | |
RS + LCZ | LV1 | 74.24 | 0.0460 |
LV2 | 66.30 | 0.0559 | |
RS + LCZ + DSM | LV1 | 78.80 | 0.0402 |
LV2 | 70.79 | 0.0554 |
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Share and Cite
Tian, T.; Yu, L.; Tu, Y.; Chen, B.; Gong, P. Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach. Remote Sens. 2024, 16, 3125. https://doi.org/10.3390/rs16173125
Tian T, Yu L, Tu Y, Chen B, Gong P. Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach. Remote Sensing. 2024; 16(17):3125. https://doi.org/10.3390/rs16173125
Chicago/Turabian StyleTian, Tian, Le Yu, Ying Tu, Bin Chen, and Peng Gong. 2024. "Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach" Remote Sensing 16, no. 17: 3125. https://doi.org/10.3390/rs16173125
APA StyleTian, T., Yu, L., Tu, Y., Chen, B., & Gong, P. (2024). Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach. Remote Sensing, 16(17), 3125. https://doi.org/10.3390/rs16173125