Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery
Abstract
:1. Introduction
2. Materials and Data Sets
2.1. Study Area
2.2. Remotely Sensed Imagery
2.3. Thematic Layers
2.3.1. Plant Phenology (PP)
2.3.2. Normalized Digital Surface Model (nDSM)
2.3.3. Garden Parcels
- 1.
- does not overlap with an agricultural parcel or a railroad;
- 2.
- contains at least one main buildings;
- 3.
- or contains one or more side buildings, which share a border with an administrative parcel that contains one or more main buildings;
- 4.
- or does not contain a building but overlaps with a building block that contains more than 60% administrative parcels with one or more main buildings;
- 5.
- has been cut by roads, main buildings, and buildings > 20 m2;
- 6.
- has been cleaned from slivers.
- 7.
- The above procedures produced more than 15,000 garden parcels in study area.
2.3.4. Field Inventory
3. Greenspace Mapping in Gardens
3.1. Classification Designs
3.2. Image Segmentation
3.3. Classification Procedures
3.3.1. Classification Using Single Satellite Imagery (Schemes a to c)
3.3.2. Classification Integrating Multi-Temporal Stereo Satellite Imagery (Schemes d to f)
3.4. Accuracy Assessment
4. Results and Discussion
4.1. Validation of Thematic Layers
4.1.1. nDSM Layer
4.1.2. Garden Parcels
4.1.3. Image Objects
4.2. A Higher Spatial Resolution Improves Greenspace Mapping in Gardens
4.3. Time-Series and Stereo Imagery Improve Greenspace Mapping in Gardens
4.4. Greenspace Landscapes in Gardens Parcels
4.5. Applicability and Limitation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Satellite | Acquisition Date | Spatial Resolution | Multi-Spectral Band | Stereo Image |
---|---|---|---|---|
ALOS-2 | 16 May 2019 | 2.5 m | Blue: 420–500 nm; Green: 520–600 nm; Red: 610–690 nm; Near-Infrared: 760–890 nm | No |
SPOT-7 | 22 July 2019 | 1.5 m | Blue: 455–525 nm; Green: 530–590 nm; Red: 625–695 nm; Near-Infrared: 760–890 nm | No |
Pleiades-1A | 4 December 2019 | 0.5 m | Blue: 430–550 nm; Green: 490–610 nm; Red: 600–720 nm; Near Infrared: 750–950 nm | No |
7 March 2020 | 0.5 m | No | ||
31 July 2020 | 0.5 m | Yes |
Building Density | Building Height | Greenspace Coverage | LCZ Class |
---|---|---|---|
≥0.25 | ≥8 m | ≥67% | 111 * |
34−67% | 112 | ||
≤34% | 113 | ||
<8 m | ≥67% | 121 * | |
34−67% | 122 | ||
≤34% | 123 | ||
<0.25 | ≥8 m | ≥67% | 211 * |
34%−67% | 212 | ||
≤34% | 213 | ||
<8 m | ≥67% | 221 | |
34−67% | 222 | ||
≤34% | 213 |
Class Name (Object Level) | Feature (Threshold) | Class Name (Object Level) | Feature (Threshold) |
---|---|---|---|
Unshaded area (25) | Brightness (36); Mean Red (387) | Shaded area (25) | Brightness (36); Mean Red (387) |
Tree & Building shadow (55) | Area (100 m2); Brightness (24) Relative border to green (0.5) | ||
Unshaded green (25) | NDVI (0.24); Mean Blue (445) | Shaded green (55) | NDVI (0.13) in tree shadow NDVI (0.08) in building show |
High & low green (55) | −Log(canny_NIR) + Brightness (51.24) | High & low green (55) | −Log(canny_NIR)+Brightness (36.81) |
Deciduous & evergreen (55) | Mean NIR (236); Ratio G/R(1.34); Hue-R_G_B (0.21) | Deciduous & evergreen (55) | Mean NIR (197); Ratio G/R (1.15); Hue-R_G_B (0.16) |
High deciduous & evergreen (55) | Ratio G/R (1.52); GLCM-H_NIR (0.14); GLCM-H_G (0.19); Hue-R_G_B (0.31) | High deciduous & evergreen (55) | Ratio G/R (1.29); GLCM-H_NIR (0.06); GLCM-H_G (0.12); Hue-R_G_B (0.22) |
Low deciduous & evergreen (55) | Mean NIR (279); Ratio R/NIR (0.56); Hue-G_R_NIR (0.23); GLCM-H_NIR (0.1) | Low deciduous & evergreen (55) | Mean NIR (234); Ratio R/NIR (0.46); Hue-G_R_NIR (0.14); GLCM-H_NIR (0.08) |
Plant Phenology (55) | 0.17; 0.25 | Plant phenology (55) | 0.12; 0.21 |
nDSM (55) | 5 m | nDSM (55) | 5 m |
Scheme a- ALOS-2 Imagery | Scheme b- SPOT-7 Imagery | Scheme c- Pleiades-1A Imagery | |||||||||||||||||
OA | 71.13 | 75.38 | 79.25 | ||||||||||||||||
Kappa | 0.634 | 0.688 | 0.735 | ||||||||||||||||
Reference | |||||||||||||||||||
HD | HE | LD | LE | NV | UA | HD | HE | LD | LE | NV | UA | HD | HE | LD | LE | NV | UA | ||
Classified | HD | 152 | 11 | 19 | 16 | 7 | 74.15 | 163 | 11 | 15 | 11 | 8 | 78.37 | 171 | 8 | 13 | 12 | 7 | 81.04 |
HE | 17 | 60 | 13 | 10 | 3 | 58.25 | 13 | 69 | 14 | 9 | 4 | 63.3 | 11 | 73 | 11 | 7 | 2 | 70.19 | |
LD | 22 | 9 | 82 | 13 | 5 | 62.6 | 19 | 7 | 88 | 12 | 6 | 66.67 | 15 | 9 | 97 | 9 | 5 | 71.85 | |
LE | 16 | 12 | 12 | 131 | 5 | 74.29 | 15 | 8 | 10 | 138 | 1 | 80.23 | 13 | 7 | 7 | 145 | 3 | 82.86 | |
OT | 14 | 9 | 10 | 8 | 144 | 77.84 | 11 | 6 | 9 | 8 | 145 | 81.01 | 11 | 4 | 8 | 5 | 147 | 84.57 | |
PA | 68.78 | 59.41 | 60.29 | 73.6 | 87.8 | 73.76 | 68.32 | 64.71 | 77.53 | 88.41 | 77.38 | 72.28 | 71.32 | 81.46 | 90.24 | ||||
Scheme d- Multi-Temporal Pleiades Imagery | Scheme e- Stereo Pleiades Imagery | Scheme f- Multi-Temporal Stereo Pleiades Imagery | |||||||||||||||||
OA | 84.5 | 86.13 | 92.75 | ||||||||||||||||
Kappa | 0.803 | 0.822 | 0.908 | ||||||||||||||||
Reference | |||||||||||||||||||
HD | HE | LD | LE | NV | UA | HD | HE | LD | LE | NV | UA | HD | HE | LD | LE | NV | UA | ||
Classified | HD | 184 | 5 | 6 | 11 | 6 | 86.79 | 188 | 12 | 6 | 7 | 4 | 87.50 | 203 | 3 | 6 | 3 | 3 | 93.12 |
HE | 7 | 81 | 10 | 5 | 1 | 77.88 | 15 | 84 | 3 | 3 | 1 | 80.77 | 7 | 95 | 2 | 2 | 0 | 89.62 | |
LD | 15 | 6 | 107 | 4 | 3 | 79.26 | 5 | 2 | 111 | 13 | 4 | 83.46 | 5 | 1 | 121 | 4 | 2 | 90.98 | |
LE | 9 | 4 | 8 | 150 | 0 | 87.72 | 6 | 3 | 12 | 152 | 2 | 87.36 | 3 | 1 | 5 | 166 | 2 | 93.79 | |
OT | 6 | 5 | 5 | 8 | 154 | 86.52 | 8 | 3 | 6 | 3 | 153 | 88.44 | 3 | 1 | 2 | 3 | 157 | 94.58 | |
PA | 83.26 | 80.20 | 78.68 | 84.27 | 93.90 | 85.52 | 83.17 | 81.62 | 85.39 | 93.29 | 91.86 | 94.06 | 88.97 | 93.26 | 95.73 |
Greenspace Type | Scheme a | Scheme b | Scheme c | Scheme d | Scheme e | Scheme f |
---|---|---|---|---|---|---|
Greenspace | −12.06 ± 5.25 | −9.72 ± 4.47 | −7.63 ± 4.36 | −5.44 ± 2.58 | −5.29 ± 2.45 | −3.53 ± 2.12 |
High Deciduous | −18.55 ± 7.31 | −16.87 ± 5.52 | −14.18 ± 5.89 | −11.32 ± 4.57 | −10.15 ± 3.96 | −7.06 ± 2.85 |
High Evergreen | 23.36 ± 8.39 | 20.08 ± 7.12 | 17.57 ± 7.24 | 14.34 ± 5.63 | 13.48 ± 5.02 | 10.63 ± 3.79 |
Low Deciduous | −25.19 ± 9.75 | −22.43 ± 7.65 | −19.83 ± 8.09 | −15.92 ± 5.18 | −15.36 ± 4.97 | −11.19 ± 4.01 |
Low Evergreen | 15.12 ± 6.36 | 12.79 ± 5.21 | 11.37 ± 4.74 | 8.16 ± 3.86 | 7.53 ± 3.03 | 5.22 ± 2.17 |
TA (ha) | PC (%) | PD (/ha) | ED (/ha) | LPI (%) | MPS (m2) | ||
---|---|---|---|---|---|---|---|
Garden | Green space | 1881.74 | 70.98 | 227.86 | 1402.33 | 78.45 | 92.93 |
High evergreen | 174.31 | 6.93 | 102.91 | 441.43 | 7.27 | 12.24 | |
High deciduous | 694.12 | 23.46 | 637.28 | 1860.27 | 26.42 | 29.81 | |
Low evergreen | 859.97 | 35.38 | 298.03 | 1103.72 | 40.13 | 55.58 | |
Low deciduous | 153.34 | 5.21 | 79.45 | 386.09 | 4.63 | 17.11 | |
Urban garden | Green space | 122.03 | 56.46 | 425.32 | 2049.57 | 60.69 | 21.35 |
High evergreen | 19.14 | 8.56 | 152.28 | 461.68 | 7.55 | 8.72 | |
High deciduous | 47.65 | 20.43 | 855.94 | 2387.14 | 23.61 | 15.19 | |
Low evergreen | 42.84 | 22.29 | 573.6 | 1778.39 | 25.83 | 23.08 | |
Low deciduous | 12.40 | 5.18 | 130.46 | 424.91 | 3.74 | 7.43 | |
Suburban garden | Green space | 926.18 | 70.85 | 146.83 | 1236.06 | 75.41 | 86.16 |
High evergreen | 89.46 | 7.75 | 108.43 | 463.04 | 6.48 | 11.36 | |
High deciduous | 318.59 | 25.79 | 542.74 | 1296.98 | 25.35 | 25.42 | |
Low evergreen | 441.11 | 31.51 | 314.56 | 982.95 | 38.52 | 44.91 | |
Low deciduous | 77.02 | 5.8 | 89.77 | 372.63 | 5.06 | 14.28 | |
Exurban garden | Green space | 833.54 | 82.43 | 65.09 | 591.15 | 94.37 | 153.48 |
High evergreen | 67.93 | 6.67 | 59.58 | 437.28 | 7.96 | 16.7 | |
High deciduous | 295.54 | 29.41 | 251.34 | 883.51 | 29.13 | 41.05 | |
Low evergreen | 406.62 | 40.12 | 115.67 | 601.73 | 51.47 | 73.19 | |
Low deciduous | 63.45 | 6.23 | 50.02 | 359.42 | 5.85 | 23.96 |
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Yan, J.; Van der Linden, S.; Tian, Y.; Van Valckenborgh, J.; Strosse, V.; Somers, B. Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery. Remote Sens. 2022, 14, 1169. https://doi.org/10.3390/rs14051169
Yan J, Van der Linden S, Tian Y, Van Valckenborgh J, Strosse V, Somers B. Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery. Remote Sensing. 2022; 14(5):1169. https://doi.org/10.3390/rs14051169
Chicago/Turabian StyleYan, Jingli, Stijn Van der Linden, Yunyu Tian, Jo Van Valckenborgh, Veerle Strosse, and Ben Somers. 2022. "Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery" Remote Sensing 14, no. 5: 1169. https://doi.org/10.3390/rs14051169
APA StyleYan, J., Van der Linden, S., Tian, Y., Van Valckenborgh, J., Strosse, V., & Somers, B. (2022). Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery. Remote Sensing, 14(5), 1169. https://doi.org/10.3390/rs14051169