Assessing Railway Landscape by AHP Process with GIS: A Study of the Yunnan-Vietnam Railway
Abstract
:1. Introduction
2. Literature Review
2.1. Comparison of Landscape Assessment Methods
2.2. Spatial Technologies for Landscape Assessment
2.3. AHP Method and This Research
3. Materials and Methods
3.1. Study Area
3.2. Select Relative Indicators and Establish the Hierarchical Framework
3.3. Reclassify Each Index and Use the Delphi Method
3.4. Calculate the Landscape Value in GIS
3.5. Check the Correlation Coefficient of Landscape Value
3.6. Visualize the Landscape Value in Each County
4. Results
5. Discussions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Source | Content | Format | Spatial Resolution |
---|---|---|---|
DIVA-GIS | Administrative divisions; rivers; roads; railroads; ethnic groups | Vector | |
World Clim | Monthly temperature | Raster | 1000 m |
CIESIN 1 | Economic and population data | Excel table | |
Earth Explorer | Sentinel-2 satellite images; DEM | Raster | 500 m |
Global Forest Watch | Biodiversity significance | Raster | 100 m |
Earth data | MODIS Land Cover-MCD12Q1 (2001; 2018) | Raster | 500 m |
Online Map (Baidu map and open street map) | Scenic spots (parks, mountains, historical monuments, scenic resorts) | Vector | |
Social media (Flickr) | Popular photo-shooting sites | Vector | |
Archive of Mulhouse | Historical photo sites by YVR | Vector |
Indicator | Index | ID | References | Sources |
---|---|---|---|---|
Visual quality (B1) | Scenic spots | C1 | [46] | Baidu and open street map |
Visibility | C2 | Archive of Mulhouse | ||
Stream density | C3 | DIVA-GIS | ||
Ecology (B2) | NDVI 2 | C4 | [47] | Earth Explorer |
Naturalness | C5 | Earth Explorer | ||
Biodiversity | C6 | Global Forest Watch | ||
Technology (B3) | Historical richness | C7 | [48] | Archive |
Engineering difficulty | C8 | Earth Explorer | ||
Climate suitability | C9 | World Clim | ||
Social culture (B4) | Population density | C10 | [49] | CIESIN |
Cultural diversity | C11 | DIVA-GIS | ||
Economy growth | C12 | CIESIN | ||
Tourism (B5) | Touristic services | C13 | [50] | Baidu and open street map |
Accessibility | C14 | DIVA-GIS | ||
Popularity | C15 | Flickr |
Index | Class (Score) |
---|---|
C1 | 5, Distance < 50 4, 50 m < Distance < 150 3, 150 m < Distance < 1000 2, 1000 m < Distance < 5000 1, Distance > 5000 (m) [52] |
C2 | 5, Distance < 50 4, 50 m < Distance < 150 3, 150 m < Distance < 1000 2, 1000 m < Distance < 5000 1, Distance > 5000 (m) [53] |
C3 | 1, Density < 0.079 2, 0.079 < Density < 0.098 3, 0.098 < Density < 0.117 4, 0.117 < Density < 0.145 5, Density > 0.145 (/km2) |
C4 | 1, NDVI < 0.155 2, 0.155 < NDVI < 0.244 3, 0.244 < NDVI < 0.338 4, 0.338 < NDVI < 0.481 5, NDVI > 0.481 |
C5 | 1, Urban area; 2, Bare area 3, Grassland; 4, Shrubland; 5, Forest |
C6 | 1, value: 0–1; 2, value: 1–4 3, value: 4–5; 4, value: 5–7 5, value > 7 |
C7 | 1, Density < 0.307 2, 0.307 < Density < 1.539 3, 1.539 < Density < 5.233 4, 5.233 < Density < 19.395 5, Density > 19.395 (/km2) |
C8 | 1, slope < 4.696 2, 4.696 < slope < 10.531 3, 10.531 < slope < 17.442 4, 17.442 < slope < 25.855 5, slope > 25.855 (degree) |
C9 | 1, Temp < 20.091 2, 20.091 < Temp < 21.590 3, 21.590 < Temp < 25.782 4, 25.782 < Temp < 27.984 5, Temp > 27.984 (°C) |
C10 | 1, Density < 20 2, 20 < Density < 150 3, 150 < Density < 500 4, 500 < Density < 1000 5, Density > 1000 (person/km2) |
C11 | 1, Density < 0.082 2, 0.082 < Density < 0.306 3, 0.306 < Density < 0.621 4, 0.621 < Density < 1.068 5, Density > 1.068 (/km2) |
C12 | 1, Rate < 12.96% 2, 12.96% < Rate < 13.11% 3, 13.11% < Rate < 13.46% 4, 13.46% < Rate < 13.89% 5, Rate > 13.89% |
C13 | 1, Density < 0.290 2, 0.290 < Density < 0.871 3, 0.871 < Density < 3.192 4, 3.192 < Density < 16.831 5, Density > 0.621 (/km2) |
C14 | 1, Density < 0.048 2, 0.048 < Density < 0.071 3, 0.071 < Density < 0.092 4, 0.092 < Density < 0.118 5, Density > 0.118 (/km2) |
C15 | 1, Density < 2.361 2, 2.361 < Density < 2.721 3, 2.721 < Density < 5.443 4, 5.443 < Density < 21.775 5, Density > 21.775 (/km2) |
Indicator | Comparison of Two Indexes | Result (Experts) | Weight (The First Index) | Consistency 3 |
---|---|---|---|---|
B1 | Scenic spots VS Visibility | 1/1 | 42.86% | 0.0000 |
Visibility: VS Stream density | 3/1 | 42.86% | ||
Stream density VS Scenic spots | 1/3 | 14.29% | ||
B2 | NDVI VS Naturalness | 1/3 | 10.0% | 0.0000 |
Naturalness VS Biodiversity | 1/2 | 30.0% | ||
Biodiversity VS NDVI | 6/1 | 60.0% | ||
B3 | Historical richness VS Engineering difficulty | 1/3 | 26.50% | 0.0372 |
Engineering difficulty VS Climate suitability | 5/1 | 63.33% | ||
Climate suitability VS Historical richness | 1/3 | 10.62% | ||
B4 | Population density VS Cultural diversity | 1/4 | 12.26% | 0.0176 |
Cultural diversity VS Economy growth | 2/1 | 55.71% | ||
Economy growth VS Population density | 3/1 | 32.02% | ||
B5 | Touristic services VS Accessibility | 1/3 | 27.21% | 0.0713 |
Accessibility VS Popularity | 4/1 | 11.99% | ||
Popularity VS Touristic services | 1/3 | 60.80% |
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C2 | −0.19 | |||||||||||||
C3 | −0.03 | −0.14 | ||||||||||||
C4 | −0.04 | 0.07 | 0.04 | |||||||||||
C5 | 0.02 | 0.19 | 0.10 | 0.12 | ||||||||||
C6 | −0.13 | −0.10 | 0.31 | 0.22 | 0.15 | |||||||||
C7 | 0.12 | 0.07 | −0.24 | −0.26 | 0.13 | 0.20 | ||||||||
C8 | −0.02 | 0.16 | −0.13 | 0.18 | 0.15 | 0.42 | 0.52 | |||||||
C9 | −0.05 | −0.12 | 0.52 | 0.22 | −0.12 | −0.05 | −0.73 | −0.38 | ||||||
C10 | 0.22 | −0.16 | 0.24 | −0.32 | −0.17 | −0.44 | −0.37 | −0.68 | 0.37 | |||||
C11 | 0.17 | −0.15 | 0.46 | −0.19 | −0.06 | −0.22 | −0.18 | −0.44 | 0.40 | 0.72 | ||||
C12 | 0.06 | 0.08 | −0.51 | −0.36 | 0.05 | −0.29 | 0.65 | 0.21 | −0.83 | −0.12 | −0.14 | |||
C13 | 0.34 | −0.14 | 0.01 | −0.04 | 0.05 | 0.31 | −0.04 | −0.32 | 0.02 | 0.48 | 0.50 | 0.18 | ||
C14 | 0.17 | −0.10 | 0.31 | −0.03 | −0.12 | 0.03 | −0.25 | −0.38 | 0.36 | 0.55 | 0.48 | −0.35 | 0.31 | |
C15 | 0.42 | −0.02 | 0.21 | −0.25 | 0.02 | 0.01 | 0.10 | −0.18 | 0.05 | 0.47 | 0.61 | 0.08 | 0.62 | −0.11 |
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Sang, K.; Fontana, G.L.; Piovan, S.E. Assessing Railway Landscape by AHP Process with GIS: A Study of the Yunnan-Vietnam Railway. Remote Sens. 2022, 14, 603. https://doi.org/10.3390/rs14030603
Sang K, Fontana GL, Piovan SE. Assessing Railway Landscape by AHP Process with GIS: A Study of the Yunnan-Vietnam Railway. Remote Sensing. 2022; 14(3):603. https://doi.org/10.3390/rs14030603
Chicago/Turabian StyleSang, Kun, Giovanni Luigi Fontana, and Silvia Elena Piovan. 2022. "Assessing Railway Landscape by AHP Process with GIS: A Study of the Yunnan-Vietnam Railway" Remote Sensing 14, no. 3: 603. https://doi.org/10.3390/rs14030603
APA StyleSang, K., Fontana, G. L., & Piovan, S. E. (2022). Assessing Railway Landscape by AHP Process with GIS: A Study of the Yunnan-Vietnam Railway. Remote Sensing, 14(3), 603. https://doi.org/10.3390/rs14030603