Table 1
Normalised difference vegetation index threshold values used in urban studies in Poland.
| NDVI Threshold for vegetation | Image data used | Research area | References |
|---|---|---|---|
| 0.3 | Landsat TM, GSD 30 m, 3 Jul. 2006 | Warsaw | Tomaszewska et al. (2011) |
| 0.1 | MODIS, GSD 250 m, 3 Jul. 2006 | Warsaw | Tomaszewska et al. (2011) |
| 0.1 | Digital orthophoto, GSD 0.1 m, May 2014 | Wroclaw | Kubalska and Preuss (2014) |
| 0.2 | IKONOS-2, GSD 1(4) m, 18 Aug. 2005 | Lublin | Krukowski et al. (2016) |
| 0.2 | Landsat 8, GSD 30 m, 3 Jul. 2015 | Łódź | Będkowski and Bielecki (2017) |
| 0.1 | Pléiades 1A, GSD 0.5 m, May 2012 | Warsaw | Pyra and Adamczyk (2018) |
| 0.1 | CIR-orthophoto, GSD 0.25 m, 2015 | Łódź | Pluto-Kossakowska et al. (2018) |
| 0.2 | IKONOS-2, GSD 1(4) m, 18 Aug. 2011 | Lublin | Krukowski (2018) |
| 0.1 | CIR aerial orthophoto, GSD 0.25 m, 2015 | Łódź | Worm et al. (2019) |
| 0.6 | Sentinel 2, GSD 10 m, summer 2018, 2019 | Poland | Łachowski and Łęczek (2020) |
| 0.2 | IKONOS-2, June 2005, GSD = 0.8 m PAN (3.2 m MS) QuickBird-2, September 2006, GSD = 0.6 m PAN (2.4 m MS) WorldView-2, October 2014, GSD = 0.5 m PAN (2.0 m MS) Aerial orthophotomap (CIR), May 2017, GSD = 0.25 m | Poland | Zięba-Kulawik and Wężyk (2022) |
Fig. 1
Research area, based on the data from the Head Office of Geodesy and Cartography (Land and Building Records).
Table 2
Structure of land use in Łódź [km2].
| Total | Agricultural land | Forest, woody, and bushy land | Residential areas | Industrial areas | Transport areas | Groundwater | Other |
|---|---|---|---|---|---|---|---|
| 293.25 | 113.76 | 24.67 | 47.13 | 13.91 | 42.37 | 1.33 | 1.15 |
[i] Source: Statistics Poland (2020).
Fig. 2
The main steps of the investigation.
Fig. 3
Visualization Visualisation of a dataset item, based on Geoportal (2021). From left to right: RGB composition, R, G and B bands that form the input tensor and normalised difference vegetation index, which serves as a target tensor.
Fig. 4
Modified Pix2Pix discriminator model visualisation.
Fig. 5
Modified Pix2Pix generator sub-model visualisation.
Fig. 6
Colour palette used during visual inspection of Figs 7–15.
Fig. 7
Inference result – abandoned land; based on Geoportal (2021). From left to right: PAN, NDVItrue, NDVIartificial and NDVIdiff.
Fig. 8
Inference result – wooded area: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Fig. 9
Inference result – small parking lot: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Fig. 10
Inference result – buildings: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Fig. 11
Inference result – residential area: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Fig. 12
Inference result – commercial facility: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Fig. 13
Inference result – commercial facility parking lot: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Fig. 14
Inference result – farmland: PAN, NDVItrue, NDVIartificial and NDVIdiff; based on Geoportal (2021).
Table 3
Test set evaluation metrics.
| SSIM | PSNR | RSME | |
|---|---|---|---|
| AVG | 0.7569 | 26.6459 | 0.0504 |
| STD | 0.1083 | 3.6577 | 0.0193 |
| MIN | 0.3589 | 16.3343 | 0.0026 |
| MAX | 0.9987 | 51.7674 | 0.1525 |
Fig. 15
Sliding window inference (NDVIartificial) of an orthophoto used to compute the test dataset); based on Geoportal (2021). The scene (51.76174 E, 19.42149 N) presents Łódź, Poland. Red values indicate high NDVI values (closer to 1). Blue ones represent small values (closer to −1).
Fig. 16
Sliding window inference (NDVIartificial) of an archival 1966 greyscale aerial image; based on GUGiK (Head Office of Geodesy and Cartography b.d.). The scene presents Łódź, Poland. Red overlay indicates values where 0.5 < NDVI < 1.