Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Preprocessing
2.3. Mapping Impervious Surface Area (ISA) Distribution
2.3.1. Water Mask
2.3.2. Minimum Noise Fraction (MNF)
2.3.3. Pixel Purity Index (PPI)
2.3.4. Extract Endmembers
2.3.5. Linear Spectral Mixture Analysis (LSMA)
3. Results
3.1. Impervious Surface Mapping
3.2. Precision Evaluation
- From 1995 to 2018, there was a dense ISA distribution area in the northeast of Figure 9, while the ISA distribution in the same position in Figure 6 was relatively thin. By checking the image of Google Earth in the corresponding year, it can be seen that this area includes an oil and gas injection plant, a sea cucumber seedling breeding base, and a salinization farm. Most of these are made up of vegetation and bare soil. The ISA is relatively small and not as dense as shown in Figure 9.
- From 2000 to 2015, there was a dense ISA distribution area in the northwest of Figure 9, while the ISA distribution in the same location in Figure 6 was relatively thin. By checking the images of Google Earth in the corresponding years, it can be seen that most of the area is composed of vegetation, cultivated land, and water. The ISA is relatively small and not as dense as shown in Figure 9.
- From 2005 to 2018, the ISA in the south of Figure 6 has an obvious expansion trend, forming a relatively dense ISA area, while the ISA in the corresponding position of Figure 9 is relatively thin. Viewing the image of Google Earth corresponding to the year, it can be seen that this area is composed of a large number of typical ISAs such as residential areas, factories, hospitals, and schools.
4. Discussion
4.1. Spatio-Temporal Analysis of Impervious Surface
4.1.1. Natural Factors on Urban Expansion
4.1.2. Socioeconomic Factors on Urban Expansion
4.1.3. Cultural Factors on Urban Expansion
4.2. Limitations of the Work
5. Conclusions
- (1)
- A pixel–based linear spectral mixture decomposition model was adopted for ISA extraction. The retrieved ISA thematic map fit the limited requirement of root mean square error (RMSE). Accuracy of ISA correct classification is greater than 83.08%. Further, the cross–comparison exhibits the general consistent with the ISA distribution of the land use classification map published by the National Basic Geographic Information Center.
- (2)
- Research shows that the gradual increasing trend can be captured on the expansion of ISA from 1995 to 2018. The scope of ISA was the smallest in 1995, but it was large in 2015 and 2018. Despite of the central region always shown as the high ISA density, it still keeps increasing annually and radiating the surrounding region, especially in the southward which has formed into a new large–scale and high intensity of ISA in 2015– 2018. Though the ISA patches scattered in the west region or along the northern and eastern part of the ocean coastline are still small, the expansion trend of ISA can be detected. The EII of ISA measuring the situation of its expansion changes from the lowest value 0.12% between 1995 and 2000 up to the highest 0.73% between 2000 and 2005. This shows that urban development was relatively slow from 1995 to 2000 and relatively fast from 2000 to 2005.
- (3)
- Our investigation shows that expansion of ISA over Dongying is related to three driving forces (physical geography, socioeconomic factors, and urban culture). Richly endowed by nature, the city’s natural geographical environment provides an elevated chance of further urbanization. The rapid increase of regional economy provides a fundamental driving force for expanding ISAs. The development of urban culture promotes the sustainable development of ISAs. These three driving forces interact with the ISA and provide a good foundation and conditions for ISA expansion. Our findings provide a scientific basis for future urban land use, construction planning, and environmental protection in the Dongying area.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Symbol Table | |
measured spectral radiance | |
DN | recorded electrical signal value |
G | sensor gain |
B | sensor bias |
L | total radiance received by the pixel at the sensor |
pixel surface reflectivity | |
average surface reflectance around the pixel | |
S | atmospheric spherical albedo |
La | atmospheric backscattering emissivity (atmospheric radiation) |
A | coefficient depending on atmospheric conditions |
B | coefficient depending on geometric conditions |
MIR | mid-infrared band |
Green | green light band |
b | number of spectral bands, b = 1,2…6 |
xb | reflectivity of band b |
k | kth end element, k= 1,2,3,4 |
sk | proportion of the area occupied by k |
akb | reflectivity of k in band b |
eb | residual |
n | total number of endmembers in the image |
Sa | ISA at the beginning of the study |
Sb | ISA at the end of the study |
T | time interval |
TLA | total area of the study area |
Name Abbreviation Table | |
ISA | Impervious Surface Area |
MNF | Minimum Noise Fraction |
PPI | Pixel Purity Index |
LSMA | Linear Spectral Mixture Analysis |
USGS | the United States Geological Survey |
NLCD | National Land Cover Database |
VIS | Vegetable–Impervious surface–Soil |
TM | Thematic Mapper |
OLI | Operational Land Imager |
DN | Digital Number |
BIL | Band Interleaved by Line |
FLAASH | Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes |
MNDWI | Modified Normalized Difference Water Index |
RMSE | Root Mean Square Error |
IIFA | Investment in Fixed Assets |
IRED | Investment in Real Estate Development |
UPDI | Urban Per capita Disposable Income |
UPCC | Urban Per Capita Consumption |
PGDP | Primary industry Gross Domestic Product |
SGDP | Secondary industry Gross Domestic Product |
TGDP | Tertiary industry Gross Domestic Product |
YEP | Year-End Population |
CE | Cultural Expenses |
CROE | Cultural Relic Operation Expenses |
IT | Inbound Tourists |
ALLSSA | the Anti-Leakage Least-Squares Spectral Analysis |
JUST | Jumps Upon Spectrum and Trend |
EII | Expansion Intensity Index |
SMA | Spectral Mixture Analysis |
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Image Sequence Number | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
Central Longitude | 118°54′51.75″ | |||||
Central Latitude | 37°28′16.25″ | |||||
Sensor Type | Landsat TM5 | Landsat-8 OLI | ||||
Sensor Altitude | 705 km | |||||
Ground Elevation | 8.8 m | |||||
Pixel Size | 30 m | |||||
Flight Date | 22 February 1995 | 4 February 2000 | 1 February 2005 | 14 January 2010 | 13 February 2015 | 20 January 2018 |
Atmospheric Models | Mid-Latitude Winter | |||||
Aerosol Model | Rural |
MNF | Eigenvalue | Cumulative Percentage |
---|---|---|
1 | 209.1497 | 67.00% |
2 | 57.9419 | 85.56% |
3 | 17.5451 | 91.19% |
4 | 14.0268 | 95.68% |
5 | 8.1962 | 98.30% |
6 | 5.2913 | 100.00% |
Year | 1995 | 2000 | 2005 | 2010 | 2015 | 2018 |
---|---|---|---|---|---|---|
ISA ratio (%) | 9.42 | 10.04 | 13.71 | 15.36 | 16.74 | 17.47 |
Period | 1995–2000 | 2000–2005 | 2005–2010 | 2010–2015 | 2015–2018 |
---|---|---|---|---|---|
ISA EII (%) | 0.12 | 0.73 | 0.33 | 0.41 | 0.24 |
Year | Overall Accuracy | Kappa Coefficient | Selected ISA Pixels | Correctly Classified ISA Pixels | ISA Accuracy |
---|---|---|---|---|---|
1995 | 92.32% | 0.88 | 2651 | 2354 | 88.80% |
2000 | 90.81% | 0.87 | 3390 | 3225 | 95.13% |
2005 | 91.00% | 0.88 | 3074 | 2554 | 83.08% |
2010 | 89.56% | 0.86 | 3726 | 3519 | 94.44% |
2015 | 88.90% | 0.85 | 2592 | 2534 | 97.76% |
2018 | 87.61% | 0.83 | 2371 | 2311 | 97.47% |
Indicators | IIFA (×1012 CNY) | IRED (×1012 CNY) | UPDI (×105 CNY/year) | UPCC (×103 CNY/year) | PGDP (×109 CNY) | SGDP (×109 CNY) | TGDP (×109 CNY) |
---|---|---|---|---|---|---|---|
1995 | 11.37 | — | 6.17 | 4.17 | 2.83 | 16.25 | 2.12 |
2000 | 19.75 | 4.02 | 8.60 | 7.00 | 3.01 | 31.24 | 5.62 |
2005 | 60.57 | 4.70 | 14.94 | 9.63 | 4.60 | 66.36 | 16.37 |
2010 | 134.90 | 10.03 | 23.80 | 14.74 | 8.23 | 113.68 | 41.96 |
2015 | 308.47 | 19.39 | 38.74 | 23.14 | 13.31 | 149.34 | 77.03 |
2018 | 255.75 | 16.72 | 47.91 | 28.90 | 14.65 | 162.71 | 101.12 |
Indicators | YEP (×105) | CE (×105 CNY) | CROE (×105 CNY) | IT (×103 Person Times) |
---|---|---|---|---|
1995 | 16.411 | — | — | — |
2000 | 17.213 | 87.1 | 87.1 | 1.028 |
2005 | 18.05 | 216.46 | 216.46 | 1.3 |
2010 | 18.487 | 654.55 | 654.55 | 33 |
2015 | 19.062 | 1005.4 | 1239.4 | 58 |
2018 | 19.668 | 1648.6 | 1209.3 | 64 |
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Shen, J.; Shuai, Y.; Li, P.; Cao, Y.; Ma, X. Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data. Remote Sens. 2021, 13, 3666. https://doi.org/10.3390/rs13183666
Shen J, Shuai Y, Li P, Cao Y, Ma X. Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data. Remote Sensing. 2021; 13(18):3666. https://doi.org/10.3390/rs13183666
Chicago/Turabian StyleShen, Jiaqi, Yanmin Shuai, Peixian Li, Yuxi Cao, and Xianwei Ma. 2021. "Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data" Remote Sensing 13, no. 18: 3666. https://doi.org/10.3390/rs13183666
APA StyleShen, J., Shuai, Y., Li, P., Cao, Y., & Ma, X. (2021). Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data. Remote Sensing, 13(18), 3666. https://doi.org/10.3390/rs13183666