1. Summary
Spectral libraries provide spectral properties of materials and components in a wide range of wavelengths. In urban areas, the exploitation of surface and building spectral properties is of increasing importance for a broad range of applications: urban climate-change modeling [1]; assessment of the heating/cooling effects of urban materials in urban heat-island mitigation strategies [2,3]; or identification of surface materials such as roof materials, for mapping and inventories [4], for weathering-condition status detection [5], detection of specific/dangerous materials such as asbestos [6,7], and detection of solar panels [8] or metallic roofs [9].
The CASMATTELE project targets several of these applications. Its purpose is the development of a tool for the automatic detection of roof materials, whatever their degree of weathering, using Earth observation (EO) data and artificial intelligence (AI) techniques [10]. The project is motivated by health, security, energy, and ecological issues. The project’s outputs are meant to be used among others for roof-material inventories. This includes detection of: roof-renovation, solar panels, and asbestos- or metallic-roofs. The area of application of CASMATTELE is Wallonia, southern Belgium (Figure 1). This region of 16,901 km² (55.1% of the Belgian territory) is characterized by a rather old building stock, since about 80% of the 1,364,244 buildings in the region was built before 1981 [11]. The buildings of Wallonia are characterized by the use of a large variety of roof materials due to the region’s large number of geographical sub-regions, its development since the industrial revolution, and its strong economic connections with surrounding European regions that ensure building-material supply and influence building practices. The roof materials of Wallonia are therefore representative of the materials used in Northwestern Europe. Moreover, the humid, temperate climate of Wallonia is characterized by mild winters and precipitation spread over all the months of the year, as is also typical of large parts of France, the Netherlands, the United Kingdom, Ireland, Luxembourg, Northwestern Germany, and Western Denmark [12]. Wallonia is therefore also representative of the climatic conditions encountered in Northwest Europe to which building materials are exposed.
With the prospect of identifying roof materials from EO imagery, as is the case in CASMATTELE, spectral libraries can be used as training data, typically when hyperspectral data are used as input for unsupervised classification [13], for the determination of spectral features for the discrimination of specific materials [14,15,16], or for the determination of relevant material classes as functions of the spectral richness of multispectral data used in supervised classification [17].
Several spectral libraries containing urban roof-material spectra have already been compiled in different countries across the world (Table 1). In the USA, the MODIS UCSB Emissivity Library was one of the first spectral libraries and accounts for 18 roof-material spectra in the spectral range 3300–14,500 nm [18,19]. Herold et al. [20] collected three roof-material spectra over Santa Barbara, California, in the spectral range 350–2400 nm. The USGS spectral library version 7.0, built from various localizations in the USA, accounts for 16 roof-material spectra in the spectral range 350–2500 nm [21]. The ASTER spectral library 2.0, also built from various US localizations, accounts for 18 roof-material spectra in the spectral range 400–15,400 nm [22] and was updated to the ECOSTRESS spectral library 1.0 [23]. Moving to Israël, Ben-Dor et al. [24] built one of the first urban spectral libraries from Tel Aviv material samples, which accounts for two roof-material spectra in the spectral range 430–940 nm. In Germany, on the one hand, Heiden et al. [13,25] compiled 13 roof-material spectra in the spectral range 400–2500 nm, using samples from Dresden and Potsdam. On the other hand, the Karlsruhe Library of Urban Materials (KLUM) compiled spectra using UAVs and contains 38 roof-material spectra acquired over Karlsruhe [26].
Over Madrid, Spain, the DESIREX spectral library contains one roof-material spectrum [27]. In Serdang, Malaysia, Nasarudin and Shafri [28] acquired seven roof-material spectra in the spectral range 350–2500 nm. The London Urban Micromet data-Archive Spectral Library of impervious Urban Materials (LUMA SLUM) contains 16 roof material spectra acquired over London, UK, in the spectral range 350–2500 nm [29]. Zambrano-Prado et al. [30] built a 39 roof-material spectral library, representative of the Mediterranean regions, in the spectral range 400–1000 nm. These spectral features were acquired either in situ, in a laboratory, or via a combination of airborne and in situ data. The authors highlight that building materials can vary regionally due to different available construction materials, and that their spectral libraries often represent a particular region [26].
Regarding Wallonia, two existing spectral libraries have been built in cities belonging to neighboring regions, namely London (LUMA SLUM) [29] and Karlsruhe (KLUM) [26], containing 30 and 38 roof material spectra, respectively, with one sample per spectrum. Looking a little bit closer to LUMA SLUM, its 30 roof-material spectral signatures belong to five roof-material classes: slates, tiles, metals, PVC, and membranes. Regarding KLUM, its 38 spectral signatures belong to two roof-material classes: tiles and metals. In the end, both these libraries only partially reflect the roof materials employed in Wallonia.
The Walloon Roof Material (WaRM) spectral library presented in this study is a first effort to reflect the diversity of the roof materials employed in Wallonia, and allows the prospect of roof-material detection using EO data. The library contains 26 roof material spectral signatures, including materials containing asbestos, belonging to seven roof-material classes: metals, tiles, slates, membranes, PVC, solar panels, and corrugated (asbestos-)cement sheets. Spectral signatures account for 2151 spectral bands between 350 and 2500 nm. Spectra were acquired under laboratory conditions, using an ASD FieldSpec3 Hi-Res spectrometer by direct contact between the sensor and the physical sample using a contact reflectance probe equipped with an internal light source. For calibration, we used a spectralon. ASD spectrometers were also employed by Ben-Dor et al. [24], Heiden et al. [13], Herold et al. [20], Ilehag et al. [26], Kokaly et al. [21], Nasarudin and Sharfri [28], and Sobrino [27]; we followed similar protocols, ensuring inter-comparability between the WaRM spectral library and existing spectral libraries. Given that the roof materials of Wallonia are representative of the materials used in Northwest Europe, and that its climatic conditions are also similar to those encountered in Northwest Europe and to which building materials are exposed, the WaRM spectral library can certainly be used for wider applications.
The following sections provide more details about the methodology (Section 2) and about the spectral library content (Section 3), while conclusions and prospects can be found in Section 4.
2. Methods
2.1. Sampling
An inventory of the main roof materials used in Wallonia has been made on the basis of the roof materials currently for sale, and on the basis of expert opinion, namely roofers and heritage experts. Samples were then gathered. A total of 62 roof samples were collected for 26 roof materials belonging to 7 classes (Table 2). These consist of physical samples obtained either thanks to a roofer or spotted in dedicated roof-material stores. An evaluation of their degree of weathering was made by visual appreciation and is also provided in Table 2; for the great majority of samples, the degree of weathering ranges from low (new material) to medium. Figure 2 provides illustrations of the aspect of the 7 classes of roof materials.
2.2. Measurement Protocol
Spectral-reflectance signatures of the samples were acquired under laboratory conditions using an ASD FieldSpec3 Hi-Res spectrometer and following established protocols [31,32,33].
The ASD spectrometer collects information in the spectral range 350–2500 nm, with a spectral sampling interval of 1.4 nm in the VNIR (400–1000 nm) and 2.0 nm in the SWIR (1000–2500 nm). There are three different spectral resolutions: 3.0 at 700 nm, 8.5 at 1400 nm, and 6.5 at 2100 nm.
The following protocol was observed for measurements in the laboratory. Before using the ASD spectrometer to measure the surface reflectance of a given sample, a 13 × 13 cm spectralon was used as the white reference, to calibrate the spectrometer. The spectrum was then measured 10 times for each sample, via direct contact between the sensor and the sample. During the measurements, a contact-reflectance probe equipped with an internal light source was used; it was always put in direct contact with the surface of the measured sample and held vertically, perpendicular to the surface. The protocol is therefore independent of external illumination conditions. The reflectance was recorded, and an average of the 10 spectra was taken as the reflectance spectrum of the given sample. The ASDToolkit was used for processing of the data [33]. In the end, spectra containing reflectance for 2151 spectral bands between 350 and 2500 nm were generated using the ASDToolkit.
It should be noted that special provisions were required for the measurements performed on samples containing asbestos, in agreement with the HSG248 guide [34]. Given the potential danger of free asbestos fibers for human health, these samples were placed under a hood prior to measurement, while the staff wore gloves and FFP3 facial masks during the entire process. At the end of the measurements, the equipment was meticulously cleaned, and all subsequent waste (masks, gloves, wipes) was eliminated according to the rules applicable to asbestos waste.
2.3. Final Spectral Signature Processing
Once all 62 sample spectra were obtained, spectra belonging to the same roof material were averaged.
3. Data Description
Spectral signatures are stored in 26 csv files, one per roof material listed in Table 2, containing two columns: one column for wavelengths in the spectral range 350–2500 nm, with a spectral interval of 1 nm, and the second column contains the reflectance value of the given material for each wavelength (2151 bands).
The analysis shows that spectral signatures are strongly influenced by the color of the roof materials, at least in the VIS (350–740 nm), in cases where they are colored or painted. It illustrates the importance of including the surface color building materials in their description [26].
The SWIR spectral range (1000–2500 nm) is in general more relevant to distinguishing the different types of material.
Exceptions are the similar properties and very close spectra of several black materials. Indeed, black concrete tiles, black anthracite natural and artificial slates, black EPDM membranes, and bitumen membranes, exhibit low and constant reflectance values in the entire investigated spectral range. This means that their spectral information is not sufficient to distinguish them from each other.
Section 3.1, Section 3.2, Section 3.3 and Section 3.4 provide an analysis of the spectra by roof-material class.
3.1. Tiles
Figure 3 shows the spectra of ceramic and concrete tiles of various colors.
A first observation is the difference between materials made of concrete on the one hand and of ceramic on the other hand. Concrete tiles show low and almost-constant reflectance throughout the investigated spectra, particularly in the NIR (740–1000 nm) and SWIR spectral ranges. Ceramic tiles exhibit reflectances which gradually increase with wavelength and are spectrally similar beyond 2000 nm. Unlike concrete tiles which do not show any specific absorption feature, all three ceramic tile spectra show an absorption around 2200 nm which is due to clay minerals.
A second observation is the influence of color on the spectra. Materials of similar color have close spectra in the VIS spectral range (see black and brown materials in Figure 3). For the ceramic tiles, their color also has a strong influence on their spectra in the NIR and SWIR spectral ranges (see ceramic tiles in Figure 3). The absorption band around 900 nm, visible in the spectrum of orange ceramic tiles, corresponds to iron oxides because it is the iron oxides that give the orange color. Brown and orange ceramic tiles exhibit a common absorption band around 1900 nm due to H2O.
3.2. Metals
Figure 4 gathers the spectra of metallic materials, either natural or painted. Spectra appear to be dominated by their coating and not by their chemical composition, at least in the VIS.
Regarding materials made of aluminum, spectra also differ in the NIR and SWIR depending on their coating. Regarding materials made of steel, all three spectra show similarities in the NIR and especially in the SWIR beyond 2100 nm. A common absorption band is observed around 2250 nm related to Fe-OH. The absorption band around 900 nm (iron oxides) in the orange-painted steel profile, like in the orange-ceramic tile spectra, should be noted.
Regarding natural materials (without painting), aluminum and zinc show similar behavior with a reflectance peak around 500 nm (cyan), an absorption around 1000 nm (edge of NIR), and the gradual increase in the SWIR with two absorption bands around 1950 and 2250 nm. Lead has a low reflectance in the VIS and does not show either the reflectance peak around 500 nm or the absorption around 1000 nm, but its reflectance increases similarly to aluminum and zinc in the SWIR.
3.3. Slates and Corrugated Sheets
Figure 5 shows the reflectance spectra of natural and artificial slates of various colors as well as corrugated asbestos-cement sheets. Observations are similar to those formulated for tiles (Section 3.1) and to a lesser extent for metals (Section 3.2).
A first observation is that spectral signatures are strongly influenced by the color of the materials, at least in the VIS spectral range. Regarding anthracite natural and artificial slates, they show particularly close spectra in the SWIR as well, unlike purple natural and artificial slates. Note the presence of the characteristic band of iron oxides around 900 nm, like in the spectrum of brown artificial slates without asbestos.
A second observation is that materials containing asbestos can be distinguished from natural slates and artificial slates without asbestos. Indeed, the spectra of the artificial slates with asbestos and corrugated asbestos-cement sheets have a similar appearance, with absorptions around 1400, 1900, and 2320 nm due to the presence of chrysotile, namely asbestos fibers [21]. Higher reflectance can also be noted in the VIS compared to natural slates.
A third observation is the particular behavior of natural slates in the SWIR spectral range, namely their constant and low reflectance values. Only artificial black slates show a similar behavior in this spectral range.
Finally, a common absorption band around 2250 nm is observed in the spectra of all artificial slates without asbestos.
3.4. Membranes, PVC and Solar Panels
Figure 6 shows the spectra of the remaining roof materials, namely photovoltaic panels, black bituminous and EPDM membranes, and polycarbonate and fiberglass panels.
The comparison between black bituminous and EPDM membranes does not show significant differences in the investigated spectral range. Both materials are characterized by low and constant reflectance values like that of black concrete tiles (Figure 3) and anthracite natural and artificial slates (Figure 5).
The spectrum of photovoltaic panels is strongly influenced by the signature of the silicon composing the photovoltaic cells: strong absorption of all wavelengths between 450 and 1000 nm followed by a decrease in absorption (increase in reflectance).
The polycarbonate twin wall is characterized by quite low reflectance across the entire investigated spectral range. Reflectance values are slightly larger in the SWIR.
The fiberglass panel spectrum is flat in the VIS, with low reflectance values. Reflectance gradually increases in the NIR and SWIR, with moderate absorptions at 1670, 1940, 2260 nm. The absorptions around 1670 and 2260 nm are reported to be C–H related [21].
4. Conclusions and Prospects
The WaRM spectral library is a first effort to reflect the diversity of roof materials employed in Wallonia, with the prospect of being used for automatic roof-material identification using EO data and the IA techniques of the CASMATTELE project. The library accounts for 26 roof-material spectral signatures belonging to 7 roof-material classes (metals, tiles, slates, membranes, PVC, solar panels, and corrugated [asbestos-] cement sheet), which is larger than the existing spectral libraries created for cities in neighboring regions. Spectral signatures account for 2151 spectral bands between 350 and 2500 nm, and were acquired using well-known instrumentation and established protocols, ensuring inter-comparison with existing spectral libraries. Analysis of the spectra highlights the influence of color, at least in the VIS spectral range. The SWIR spectral range is in general more relevant to distinguish the different types of material. An exception is the similar properties and very close spectra of several black materials, meaning that their spectral signatures are not sufficiently different to distinguish them from each other. Additional information, such as roof texture and slope, is therefore required. Although building materials can vary regionally due to different available construction materials, the WaRM spectral library can certainly be used for wider applications, as Wallonia is representative of the building-material diversity and of the climatic conditions to which materials are exposed in large parts of Northwest Europe. In a next step, given its usefulness for various applications, the influence of the degree of weathering of materials on their spectral signatures could be investigated via an extension of the sampling, by taking samples of several degrees of weathering for each material.
Conceptualization, C.W., R.M. and E.H.; methodology, C.W., R.M. and E.H.; software, R.M.; validation, C.W., R.M. and E.H.; formal analysis, C.W. and R.M.; investigation, C.W. and R.M.; resources, E.H. and R.M.; data curation, C.W.; writing—original draft preparation, C.W.; writing—review and editing, C.W., R.M. and E.H.; visualization, C.W.; supervision, R.M. and E.H.; project administration, E.H.; funding acquisition, E.H. and C.W. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are openly available in Zenodo at
The authors thank the CEA for the loan of equipment and coaching. They also thank Dominique Bossiroy, heritage expert of the ISSeP, for his precious help, especially during the inventory of roofing materials and the management of samples containing asbestos. Finally, the authors thank the Service Public de Wallonie—Environnement for the funding of this research.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. Example of roof materials for the 7 material classes of the WaRM spectral library.
Enlarge this image.Figure 3. Reflectance spectra of ceramic and concrete tiles for 2151 bands between 350 and 2500 nm.
Enlarge this image.Figure 4. Reflectance spectra of metallic roof materials for 2151 bands between 350 and 2500 nm.
Enlarge this image.Figure 5. Reflectance spectra of natural and artificial slates, and asbestos roof materials, for 2151 bands between 350 and 2500 nm.
Enlarge this image.Figure 6. Reflectance spectra of five roof materials for 2151 bands between 350 and 2500 nm.
Existing spectral libraries of roof materials presented chronologically and by study area.
| Reference | Study Area | Spectral Range [nm] | Instrument | Data Acquisition | Number of Roof Material Spectra |
|---|---|---|---|---|---|
| Wan et al. [ |
Various localizations, USA | 3300–14,500 | MIDAC M2510-C FTIR | Laboratory | 18 |
| Herold et al. [ |
Santa Barbara, CA, USA | 350–2500 | ASD FieldSpec3 | In situ | 3 |
| Kokaly et al. [ |
Various localizations, USA | 350–2500 | ASD FieldSpec3 | Laboratory | 16 |
| Baldridge et al. [ |
Various localizations, USA | 400–15,400 | Perkin–Elmer Lambda 900UV/VIS/NIR, Perknic-Nicolet 520 FTIR | Laboratory | 18 |
| Ben-Dor et al. [ |
Tel-Aviv, Israël | 430–940 | CASI, ASD FieldSpec3 | Airborne |
2 |
| Heiden et al. [ |
Dresden and Potsdam, Germany | 350–2500 | HyMap and ASD FieldSpec3 | Airborne |
13 |
| Ilehag et al. [ |
Karlsruhe, Germany | 350–2500 | ASD FieldSpec-4 |
In situ | 38 |
| Sobrino [ |
Madrid, Spain | 350–2500 | Airborne Hyperspectral Scanner (AHS), ASD FieldSpec3, GER | Airborne |
1 |
| Nasarudin and Shafri [ |
Campus of university, Malaysia | 350–2500 | ASD FieldSpec3 | In situ | 15 |
| Kotthaus et al. [ |
London, UK | 350–15,400 | HR-1024 field spectro-radiometer, M200 FTIR | Laboratory | 30 |
| Zambrano-Prado et al. [ |
Mediterranean regions | 400–1000 | AISA Eagle 2 sensor | Laboratory | 39 |
| WaRM | Various localizations, Wallonia, Belgium | 350–2500 | ASD FieldSpec3 Hi-Res | Laboratory |
26 |
Main roof materials used in Wallonia and their sampling.
| Roof Material | Class | Number of Samples | Degree of Weathering |
|---|---|---|---|
| Aluminium—Black painting | Metal | 1 | Low |
| Aluminum—Grey painting | Metal | 1 | Low to medium |
| Aluminum—Natural | Metal | 1 | Low |
| Artificial slate with asbestos—Brown color | Slate | 6 | Medium |
| Artificial slate without asbestos—Anthracite color | Slate | 4 | Low |
| Artificial slate without asbestos—Brown color | Slate | 1 | Low |
| Artificial slate without asbestos—Light gray color | Slate | 2 | Low |
| Artificial slate without asbestos—Purple color | Slate | 1 | Low |
| Bitumen membrane—Black color | Membrane | 3 | Low to medium |
| Ceramic tile—Black color | Tile | 8 | Low to high |
| Ceramic tile—Brown color | Tile | 4 | Low |
| Ceramic tile—Orange color | Tile | 6 | Low to high |
| Fiberglass roofing panel—Translucent and corrugated | PVC | 1 | High |
| Concrete tile—Black color | Tile | 3 | Low to medium |
| Concrete tile—Brown color | Tile | 1 | Low |
| Corrugated asbestos cement sheet | Corrugated cement sheet | 2 | Medium |
| EPDM membrane—Black color | Membrane | 1 | Low |
| Lead—Natural | Metal | 2 | Medium |
| Natural slate—Anthracite color | Slate | 2 | Low to medium |
| Natural slate—Purple color | Slate | 2 | Low |
| Photovoltaic solar panel | Solar panel | 1 | Medium |
| Polycarbonate panel—Twin wall | PVC | 1 | Low |
| Steel—Black painting | Metal | 2 | Low to medium |
| Steel—Grey painting | Metal | 1 | Low to medium |
| Steel—Orange painting | Metal | 1 | Low |
| Zinc—Natural | Metal | 4 | Low to high |
References
1. Hamdi, R.; Kusaka, H.; Van Doan, Q.; Cai, P.; He, H.; Luo, G.; Kuang, W.; Caluwaerts, S.; Duchêne, F.; Van Schaeybroek, B. et al. The State-of-the-Art of Urban Climate Change Modeling and Observations. Earth Syst. Environ.; 2020; 4, pp. 631-646. [DOI: https://dx.doi.org/10.1007/s41748-020-00193-3]
2. Al Qattan, A.A.M. Using Cool Coating for Pavements, Asphalt, Façades and Building Roofs in the Urban Environment to Reduce the Summer Urban Heat Effect in Giza Square, Egypt. Innovating Strategies and Solutions for Urban Performance and Regeneration; Piselli, C.; Altan, H.; Balaban, O.; Kremer, P. Springer International Publishing: Cham, Switzerland, 2022; pp. 117-126. ISBN 978-3-030-98187-7
3. Santamouris, M.; Yun, G.Y. Recent Development and Research Priorities on Cool and Super Cool Materials to Mitigate Urban Heat Island. Renew. Energy; 2020; 161, pp. 792-807. [DOI: https://dx.doi.org/10.1016/j.renene.2020.07.109]
4. Abriha, D.; Kovács, Z.; Ninsawat, S.; Bertalan, L.; Balázs, B.; Szabó, S. Identification of Roofing Materials with Discriminant Function Analysis and Random Forest Classifiers on Pan-Sharpened WorldView-2 Imagery—A Comparison. Hung. Geogr. Bull.; 2018; 67, pp. 375-392. [DOI: https://dx.doi.org/10.15201/hungeobull.67.4.6]
5. Samsudin, S.H.; Shafri, H.Z.M.; Hamedianfar, A. Development of Spectral Indices for Roofing Material Condition Status Detection Using Field Spectroscopy and WorldView-3 Data. J. Appl. Remote Sens.; 2016; 10, 025021. [DOI: https://dx.doi.org/10.1117/1.JRS.10.025021]
6. Abbasi, M.; Mostafa, S.; Vieira, A.S.; Patorniti, N.; Stewart, R.A. Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review. Sustainability; 2022; 14, 8068. [DOI: https://dx.doi.org/10.3390/su14138068]
7. Wu, P.-Y.; Mjörnell, K.; Sandels, C.; Mangold, M. Machine Learning in Hazardous Building Material Management: Research Status and Applications. Recent Prog. Mater.; 2021; 3, 17. [DOI: https://dx.doi.org/10.21926/rpm.2102017]
8. Souffer, I.; Sghiouar, M.; Sebari, I.; Zefri, Y.; Hajji, H.; Aniba, G. Automatic Extraction of Photovoltaic Panels from UAV Imagery with Object-Based Image Analysis and Machine Learning. Lecture Notes in Electrical Engineering Volume 745; Springer: Singapore, 2022; pp. 699-709. ISBN 9789813368927
9. Banolia, C.; Deshpande, S.; Balamuralidhar, P. Industrial/Metal Roof Detection from Hyperspectral Image in an Urban Scene. Proceedings of the Remote Sensing Technologies and Applications in Urban Environments VII; Berlin, Germany, 26 October 2022; Chrysoulakis, N.; Erbertseder, T.; Zhang, Y. 21.
10. Wyard, C.; Beaumont, B.; Grippa, T.; Nys, G.-A.; Fauvel, H.; Hallot, É. Mapping Roof Materials Using WV3 Imagery and a State-of-the-Art OBIA Processing Chain: Application over Liège, Belgium. Proceedings of the ESA Living Planet Symposium 2022; Bonn, Germany, 23–27 May 2022.
11. StatBel. Statistique Cadastrale du Parc de Bâtiments, Belgique et Régions, 2022. Available online: https://bestat.statbel.fgov.be/bestat/crosstable.xhtml?view=43d7cdce-3647-4f5c-86f1-a4e0c864f692 (accessed on 9 February 2023).
12. Beck, H.E.; Zimmermann, N.E.; McVicar, T.R.; Vergopolan, N.; Berg, A.; Wood, E.F. Present and Future Köppen-Geiger Climate Classification Maps at 1-Km Resolution. Sci. Data; 2018; 5, 180214. [DOI: https://dx.doi.org/10.1038/sdata.2018.214]
13. Heiden, U.; Segl, K.; Roessner, S.; Kaufmann, H. Determination of Robust Spectral Features for Identification of Urban Surface Materials in Hyperspectral Remote Sensing Data. Remote Sens. Environ.; 2007; 111, pp. 537-552. [DOI: https://dx.doi.org/10.1016/j.rse.2007.04.008]
14. Krówczyńska, M.; Wilk, E.; Pabjanek, P.; Kycko, M. Hyperspectral Discrimination of Asbestos-cement Roofing. Geomat. Environ. Eng.; 2017; 11, 47. [DOI: https://dx.doi.org/10.7494/geom.2017.11.1.47]
15. Le Bris, A.; Chehata, N.; Briottet, X.; Paparoditis, N. Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries. Proceedings of the 23. ISPRS Congress, International Society for Photogrammetry and Remote Sensing (ISPRS). INT.; Prague, Czech Republic, 12–19 July 2016; Volume 3, [DOI: https://dx.doi.org/10.5194/isprs-annals-III-7-33-2016]
16. Long, Y.; Rivard, B.; Rogge, D.; Tian, M. Hyperspectral Band Selection Using the N-Dimensional Spectral Solid Angle Method for the Improved Discrimination of Spectrally Similar Targets. Int. J. Appl. Earth Obs. Geoinf.; 2019; 79, pp. 35-47. [DOI: https://dx.doi.org/10.1016/j.jag.2019.03.002]
17. Braun, A.; Warth, G.; Bachofer, F.; Hochschild, V. Identification of Roof Materials in High-Resolution Multispectral Images for Urban Planning and Monitoring. Proceedings of the 2019 Joint Urban Remote Sensing Event (JURSE); Vannes, France, 22–24 May 2019; pp. 1-4.
18. Wan, Z.; Ng, D.; Dozier, J. Spectral Emissivity Measurements of Land-Surface Materials and Related Radiative Transfer Simulations. Adv. Sp. Res.; 1994; 14, pp. 91-94. [DOI: https://dx.doi.org/10.1016/0273-1177(94)90197-X]
19. Snyder, W.C.; Wan, Z. BRDF Models to Predict Spectral Reflectance and Emissivity in the Thermal Infrared. IEEE Trans. Geosci. Remote Sens.; 1998; 36, pp. 214-225. [DOI: https://dx.doi.org/10.1109/36.655331]
20. Herold, M.; Roberts, D.A.; Gardner, M.E.; Dennison, P.E. Spectrometry for Urban Area Remote Sensing—Development and Analysis of a Spectral Library from 350 to 2400 Nm. Remote Sens. Environ.; 2004; 91, pp. 304-319. [DOI: https://dx.doi.org/10.1016/j.rse.2004.02.013]
21. Kokaly, R.F.; Clark, R.N.; Swayze, G.A.; Livo, K.E.; Hoefen, T.M.; Pearson, N.C.; Wise, R.A.; Benzel, W.M.; Lowers, H.A.; Driscoll, R.L. et al. USGS Spectral Library Version 7; U.S. Geological Survey: Reston, VA, USA, 2017.
22. Baldridge, A.M.; Hook, S.J.; Grove, C.I.; Rivera, G. The ASTER Spectral Library Version 2.0. Remote Sens. Environ.; 2009; 113, pp. 711-715. [DOI: https://dx.doi.org/10.1016/j.rse.2008.11.007]
23. Meerdink, S.K.; Hook, S.J.; Roberts, D.A.; Abbott, E.A. The Ecostress Spectral Library Version 1.0. Remote Sens. Environ.; 2019; 230, 111196. [DOI: https://dx.doi.org/10.1016/j.rse.2019.05.015]
24. Ben-Dor, E.; Levin, N.; Saaroni, H. A Spectral Based Recognition of the Urban Environment Using the Visible and Near-Infrared Spectral Region (0.4-1.1 Μm). A Case Study over Tel-Aviv, Israel. Int. J. Remote Sens.; 2001; 22, pp. 2193-2218. [DOI: https://dx.doi.org/10.1080/014311601300190677]
25. Heiden, U.; Roessner, S.; Segl, K.; Kaufmann, H. Analysis of Spectral Signatures of Urban Surfaces for Their Identification Using Hyperspectral HyMap Data. Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482); Rome, Italy, 8–9 November 2001; pp. 173-177.
26. Ilehag, R.; Schenk, A.; Huang, Y.; Hinz, S. KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials. Remote Sens.; 2019; 11, 2149. [DOI: https://dx.doi.org/10.3390/rs11182149]
27. Sobrino, J.A.; Oltra-Carrió, R.; Sòria, G.; Bianchi, R.; Paganini, M. Impact of Spatial Resolution and Satellite Overpass Time on Evaluation of the Surface Urban Heat Island Effects. Remote Sens. Environ.; 2012; 117, pp. 50-56. [DOI: https://dx.doi.org/10.1016/j.rse.2011.04.042]
28. Nasarudin, N.E.M.; Shafri, H.Z.M. Development and Utilization of Urban Spectral Library for Remote Sensing of Urban Environment. J. Urban Environ. Eng.; 2011; 5, pp. 44-56. [DOI: https://dx.doi.org/10.4090/juee.2011.v5n1.044056]
29. Kotthaus, S.; Smith, T.E.L.; Wooster, M.J.; Grimmond, C.S.B. Derivation of an Urban Materials Spectral Library through Emittance and Reflectance Spectroscopy. ISPRS J. Photogramm. Remote Sens.; 2014; 94, pp. 194-212. [DOI: https://dx.doi.org/10.1016/j.isprsjprs.2014.05.005]
30. Zambrano-Prado, P.; Josa, A.; Rieradevall, J.; Pérez-Aragüés, F.; Marchan, J.F.; Gassó-Domingo, S.; Gabarrell, X. Laboratory-Based Spectral Data Acquisition of Roof Materials. Int. J. Remote Sens.; 2020; 41, pp. 9180-9205. [DOI: https://dx.doi.org/10.1080/01431161.2020.1798548]
31. Kalacska, M.; Arroyo-Mora, J.P.; Soffer, R.J.; Elmer, K. ASD FieldSpec3 Field Measurement Protocols. Protocols.io; 2019; Available online: https://dx.doi.org/10.17504/protocols.io.qu7dwzn (accessed on 20 December 2022).
32. Soffer, R.J.; Ifimov, G.; Arroyo-Mora, J.P.; Kalacska, M. Validation of Airborne Hyperspectral Imagery from Laboratory Panel Characterization to Image Quality Assessment: Implications for an Arctic Peatland Surrogate Simulation Site. Can. J. Remote Sens.; 2019; 45, pp. 476-508. [DOI: https://dx.doi.org/10.1080/07038992.2019.1650334]
33. Elmer, K.; Soffer, R.J.; Arroyo-Mora, J.P.; Kalacska, M. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data; 2020; 5, 96. [DOI: https://dx.doi.org/10.3390/data5040096]
34. HSE (Health and Safety Executive). Asbestos: The Analysts’ Guide; 2nd ed. TSO (The Stationary Office): Norwich, UK, 2021; 240p. ISBN 9780616667079
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Marion, Rodolphe 2
; Hallot, Eric 1 1 Remote Sensing and Geodata Unit, Institut Scientifique de Service Public (ISSeP), 4000 Liège, Belgium
2 Commissariat à l’Energie Atomique et aux Énergies Alternatives, CEA/DAM/DIF, F-91297 Arpajon, France