The enormous amount of rubble from partial or total collapse of buildings/structures caused by an earthquake hitting urbanized areas with vulnerable historical centres, like those of many Italian towns, must be rapidly mapped and characterized after catastrophic events. This activity is mandatory to support the emergency activities and ensure the accessibility, rescue and first assistance to population. During the post-emergency phase, reliable information about the distribution and characterization of seismic rubble in term of volume/weight and typology is fundamental for their proper management which involves handling, removal, pile up and transportation to recycling facilities or final disposal sites [1
]. Despite this information being particularly valuable for optimizing the post emergency responses, there are not many methods to provide extensive and reliable estimates of the amount of seismic urban rubble in terms of volume/weight and typology distribution, including the detection of possibly dangerous materials. With this aim, remote sensing techniques, coupled with GIS, can provide valuable opportunities [2
In addition to airborne surveys based on altimetric LiDAR (Laser Imaging Detection and Ranging) and photogrammetric data, High Resolution/Very High Resolution (HR/VHR) satellite imagery has been widely used for assessing damages and rubble detailed distributions linked to catastrophic events including earthquakes [7
], since it makes it possible to exploit the intrinsic capacity for repetitive, multispectral and possibly stereoscopic acquisition, capable of supporting the monitoring of evolution of post-emergency scenarios. Indeed, Sentinel-2 data were exploited by Copernicus Emergency Management Service (EMS) [13
] in order to derive the building damage maps, which we used for localizing and delimiting the rubble heaps’ boundaries [1
The Italian Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) has been involved in several activities for supporting the post event activities after the earthquake occurred on the August 24, 2016 in Central Italy. In a previous paper, we proposed an original application based on EO (Earth Observation) in order to characterize the urban rubble heaps’ volume originating from buildings affected by partial or total collapse. The study focused on the area of Amatrice town, one of the most affected urban areas of Central Apennine during the long-lasting seismic event of 2016-2017 [1
]. These activities aimed at the optimization of the management of relevant quantities of urban rubble following seismic event and the implementation of good practices and specific procedures to efficiently plan the subsequent removal and transport operations [15
]. Moreover, in order to share geospatial data and outputs with the Public Authorities involved (i.e., Civil Protection) a specific Spatial Data Infrastructure (SDI) was developed [16
] and implemented (it is hosted by ENEA and accessible by means of a WebGIS interface).
The main urban rubble features to be assessed are heaps’ location and delimitation, their volume and weight, and the typologies of the principal collapsed building materials, including dangerous constituents (i.e., asbestos), which may have a high impact on their handling.
Due to the high spectral and spatial heterogeneity of urbanized areas, which dramatically increases in the case of rubble, this information is often difficult to assess using only the usual methods, which are mainly based on in situ surveys [5
]. After a catastrophic event, local conditions often reduce the possibility of reaching many locations and carrying out field sampling procedures, thus affecting the timeliness and stability of results. Indeed, surveys including altimetric LiDAR and orthophotos from aerial platforms can provide detailed and synoptic information [8
], but the necessity of repetitive and extensive monitoring of the emergency areas in order to follow the phenomena and crisis scenario evolution make them extremely burdensome and expensive. Currently, these needs can be better met by integrating EO data, provided by the last-generation satellite missions equipped with active (SAR) and passive (multi/hyperspectral) HR/VHR (High-Resolution/Very High-Resolution) sensors, even with the possibility of being further complemented and calibrated/validated using data provided by in situ measurements and other aero-spatial proximal platforms (i.e., UAV, Unmanned Aerial Vehicle) in a suitable GIS environment [2
The information derived from the aerial surveys carried out in the aftermath of a seismic event could provide a robust calibration basis for all data subsequently detected by high resolution multispectral satellite sensors, which would thus allow adequate monitoring in all post-emergency phases [9
]. The Sentinel-2 and other Landsat-like HR satellite sensors have been extensively exploited for urban assessment in various EO-based applications [24
In the present paper, taking into account previous works [1
], a methodology for assessing the typologies of rubble heaps in a real seismic emergency/post emergency scenario was reported. To this end, the available EO data (altimetric LiDAR, multi/hyperspectral satellite and airborne VHR images) were conveniently coupled with GIS techniques. On the other hand, the methodology implemented in the present paper can be usefully exploited not only for supporting the post-emergency interventions and assessments, but also for providing a valuable tool for the building and infrastructures seismic vulnerability/risk analysis in pre-emergency and preparedness phases [7
Considering the relevant impact of significant concentrations of dangerous asbestos materials within the rubble piles upon their handling, the detection and quantification of this cover component through the acquired VHR multispectral data was also tested [30
]. To better discriminate the high heterogeneity of seismic rubble, Spectral Mixture Analysis (SMA) was preferred to commonly used hard classification schemes, which are more effective in cases of the prevalence of pixels with homogeneous covers, which can be univocally labelled according to their spectral signature. The SMA is based on the so-called soft classification approach, where each pixel is characterized by the presence of different pure materials (endmembers), each of which has its own spectral signature and contribute to the pixel reflectance spectrum proportionally to its relative abundance. SMA has been widely applied to overcome the mixed pixel problem, a typical issue associated with medium and coarse-resolution remote sensing imagery also in urban application [32
]. The spectral endmember signatures can be obtained from the imagery or laboratory/field measurements. Generally, however, due to different spectral configuration, atmospheric noises and difference in acquisition conditions, these latter are not in agreement with the spectral response of airborne or satellite digital imagery. Therefore, it can be advantageous to extract endmembers directly from imagery. Usually, the derivation of spectral endmembers from multi/hyperspectral imagery is typically achieved through the implicit Pixel Purity Index (PPI) or explicit use of convex geometry in multidimensional spectral space where pixels reflectance spread out. In general, PPI is used with hyperspectral imagery after their Minimum Noise Fraction (MNF) transform to minimize noise in selected components, whereas convex geometry methods, like Sequential Maximum Angular Convex Cone (SMACC), focus on extreme pixels in spectral channel multidimensional distributions as good candidates for endmembers [32
]. In addition to endmembers, the various SMA approaches also make it possible to obtain their abundances within the pixel, using its mixed reflectance responses through different algorithms, including SMACC [19
Even if urban and suburban environments, characterized by complex landscapes and elements with different spectral signatures, may reduce the performance of traditional SMA approaches with a fixed set of endmembers, the improvement of radiometry, spatial and spectral resolution of the new generation of VHR sensors make it possible to obtain sub-meter performances [37
]. In fact, although the SMA approach has been widely used for urban EO applications using the hyperspectral reflectance data, it also provides useful results by exploiting multispectral VHR satellite information coupled with advanced machine learning algorithms within classification and regression schema [24
]. In this study, the SMA approach was applied to WorldView-3 (WV3) pansharpened data, after their geometric and atmospheric pre-processing, to provide the related spectral endmember sets and abundances through the SMACC algorithm [40
The dramatically augmented availability of the VHR multi/hyperspectral and Synthetic Aperture Radar (SAR) data provided by increasing number of operative satellite remote sensing missions must be suitably coupled with advanced approaches based on data mining, machine learning and clustering scheme for properly monitoring and characterizing increasingly heterogeneous, complex and wide urban areas. In this context, the most recent machine learning algorithms based on Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Random Forest (RF) play a relevant role [41
]. In our implemented methodology, once the in situ acquired spectral signatures had been resampled to the same sensor configuration, these algorithms were tested within a supervised learning scheme, for mapping (classifying) the endmembers provided by SMA into the rubble material of interest detected on site.
2. Study Area
Central Italy is a rural and mountain landscape with several small ancient villages characterized by poorly engineered buildings (older residential units) and modern structures (newer buildings and life-lines made with anti-seismic criteria). The Apennines are a NW-SE oriented mountain belt affected by a multiphased contractioned and extensional tectonics [1
]. It is one of the most seismically active areas in Italy and Europe, as African European continental margin pile-up during the Neogene formed a fault and thrust system belt. Earthquakes and aftershocks periodically hit Central Italy (where Lazio, Umbria, Marche and Abruzzo Regions are located).
The study area was struck by a Mw 6.0 mainshock on 24 August 2016. About 300 people died and 20,000 homeless were left on a wide area. The seismic sequence was characterized by a subsequent Mw 5.9 mainshock on 26 October, followed by a Mw 6.5 near the town of Norcia on the 30 October. The last mainshock, with a maximum Mw of 5.5, occurred in the southern part on 18 January 2017, but it triggered an avalanche that wiped out the Rigopiano Hotel, causing another 29 victims.
The central Apennine is periodically affected by normal-faulting sequences. The area was already severely damaged by the great earthquakes of Naples and Aquila of January-February 1703 and previously the source of a strong earthquake in October 1639, parameterized in CPTI15 (Parametric Catalogue of Italian Earthquakes) with a Mw 6.2, and with a less extensive severe damage distribution and lesser intensity achieved compared to this of 2016. Struck at the time were some fractions of Amatrice located to the west of it (Figure 1
b), while the earthquake was felt in L’Aquila, Ascoli Piceno and Rieti, but not in Rome. Overall, the earthquake of 2016 could be a twin event to that of 1639, generated—with the due differences in rupture length and directivity—from the same seismogenic source related to extensive tectonics. Therefore, such events are frequently followed by a long-lasting aftershock with many low-energy tremors. Between 2016 and 2017, more than 70,000 earthquakes were recorded in the study area [1
The consequences of such a long series of seismic events is that building collapse can occur several days after being seriously damaged by the most intense earthquakes.
Eight principal constituents (macro-classes) of urban rubble were identified in the field during the two-day survey, during which 150 hyperspectral signatures (about 2300 narrow bands for each spectral signature) were acquired. In Table 1
, the nine related macro-classes, including the asbestos macro-class, whose spectral signatures were derived from the literature, are reported.
The majority of these field data refer to the more abundant rubble materials, which correspond to the first five macro-classes, whose spectral signatures differed significantly among the various piles and urban places. Therefore, after a preliminary analysis and in order to preserve the spectral variability of these five materials, four different averaged signatures were recorded for each of them.
In Figure 3
, the four graphs show: the in situ hyperspectral signatures acquired on concrete (top left graph) and brick (top right graph) after the first pre-processing phase but before smoothing by filtering; the corresponding filtered spectral signatures of the concrete (lower left graph) and brick lower right graph) categories. The bottom image of Figure 3
represents some samples of the eight types of rubble materials.
The graph in the upper part of Figure 4
shows the multispectral signatures resampled using the WV3 sensor filter function (the asbestos signatures were omitted for clearness). The multispectral signatures of the 10 endmembers, extracted via SMACC from WV3 data of rubble heaps, are reported in the lower graph of Figure 4
From the WV3 pansharpened pre-processed imagery a small number (10) of pure material signatures, i.e., endmembers, were derived through the SMACC algorithm to be used as reference for the main material classes of urban rubble, by matching them with the resampled multispectral signatures of the in situ materials using machine learning classifiers. The SMACC algorithm thus provided a multilayer distribution of ten endmembers, plus shadow, whose basic statistics are shown in Table 2
The first five endmembers except end3
, plus shadow, are the most abundant, on average, as reported in Table 2
. In particular, the shadow shows high abundance values in a relevant number of pixels, as shown in Figure 5
, where the distributions of the abundance of the shadow and first five endmembers are depicted.
After the spectral resampling, each of the 25 resampled signatures, corresponding to one of the nine main macro-classes of urban rubble materials identified on field, was matched (classified) with those of EMs derived from the WV3 pansharpened data using several machine learning algorithms. The related results are reported in Table 3
. Please note that the x suffix, x = 1 to 4, in the first five class labels and x = 1 to 2 in the asbestos labels, refers to the four averaged signatures stored in the library for each of the five main rubble materials and to the two signatures available for asbestos, respectively. In the first 25 rows of Table 3
, the train classes corresponding to the 25 spectral signatures stored in the library (“Training” column) and the related class labels assigned by the exploited classifiers (3rd to 7th column) are reported, respectively.
Moreover, in the first row of Table 3
, beneath each classifier label, the related error rates (number of wrong classified samples/total number of samples) achieved are reported, indicating C-SVC and RF as the best performing algorithms (minimum error rate; note that RF’s 100% suitability against the training set is a priori guaranteed by definition). In the remaining row, the classification of the ten endmembers (end1÷end10), by means of each classifier, are shown.
Since the C-SVC algorithm is recognized to be robust against a poor training set and it was able to discriminate one main class more (i.e., the natural stone) than the RF, these classification results were selected to assess the distribution of the main materials. Instead, the results of RF were exploited to detect the presence of asbestos. Starting from the multilayer of endmembers abundance, the predominant endmember distribution (PED), for the three main grouped categories defined for the accuracy assessment, was derived through that of pixel maximum abundance (PMA) using suitable thresholds.
As shown in Figure 6
, the abundance of the first macro-class (concrete-rubble debris) is prevalent in a large number of pixels (see Figure 6
b—PED) and shows the highest values (as shown in Figure 6
a—PMA), while those of the other two macro-classes are lower in value and prevalent in a minor number of pixels.
Taking into account the abovementioned results, the PED categorical distributions corresponding to different minimum PMA thresholds (0.25, 0.35 and 0.45), selected on the basis of the PMA’s histogram (Figure 7
), were calculated to be used for the accuracy assessment. In this way the three rightmost fractions of the histogram distribution (Figure 7
) were selected to generate the maps whose accuracy was assessed.
In Figure 8
the recoded PED distribution obtained using a 0.25 PMA threshold is displayed.
The accuracy assessment was then performed by comparing the PED classes at randomly selected points with the classes independently derived (for the same points) by photointerpretation. The accuracy assessment results obtained using the three thresholds for PMA, are reported in the following confusion matrices with the related accuracy parameters (Table 4
, Table 5
and Table 6
The random sample was about 1–2% of the entire population, being at its maximum when the 0.25 threshold was used and decreasing sensibly with the threshold rise (Figure 6
). However, it must be underlined that the samples number effectively exploited for 0.25 PMA threshold is lower than that related to the 0.35 one, due to the exclusion of samples found contaminated by vegetation.
According to the RF classification results, endmembers n. 5 (proxy of the asbestos’ distribution) was worked out and reported in Figure 9
. The different colours represent the asbestos relative abundance on rubble heaps.
It must be highlighted that, probably due to misclassification of aged dark-red tiles erroneously assigned to worn asbestos, which is also caused by diffuse colonization of local roofs by micro flora, the asbestos distribution initially showed unlikely highest concentrations (abundance > 0.3) corresponding to intact tile roofs (rubble heaps n. 23–26, 27–29). Therefore, after a visual analysis, a maximum accepted value of 0.25 was applied to avoid this overestimate, providing a more realistic distribution of asbestos within rubble. Then, as shown in Figure 10
, the n. 2, 4, 8, 9, 10, 42 heaps are those characterized by areas with the highest asbestos’ concentrations.
Finally, the zonal processing of the raster map derived from the classification implementing the per pixel distribution of endmembers percentages, provided us with the relative percentages of the three main categories used for the accuracy assessment within the rubble heaps. The estimated distributions of the main materials’ typologies are shown in Figure 9
. The most abundant rubble heap typology (macro class) is represented by concrete/debris (range: 40–50%), followed by brick/tiles (range: 25–30%) and natural stones (range: 7–17%), with other materials (metal, plastic, bitumen sheath, etc.) deriving from the remaining endmembers completing the list (range: 3–25%).
In Table 7
an example of the quantitative results provided by the whole procedure is reported for several rubble heaps. Indeed, for each heap the procedure makes it possible to determine the volume and the main constituents’ percentages, including the information regarding the presence of asbestos.
The selection and exploitation of machine learning algorithms for matching (classifying) the SMACC endmembers’ spectral signatures to those derived from the field hyperspectral signatures was an important and critical step of the work.
The global assessment of the algorithms’ performance was accomplished taking into account both the right classification of training materials, summarized by the related error rate (Table 3
), and the number/typology of main materials (including asbestos) detected through the endmembers.
The endmembers labelled by BVM classifier accounted for the four main materials (cem, matc, lat, matn
) plus gua
(see Table 3
), but failed to discriminate the training samples. In addition, the results showed an unreliable assignment of the asbestos class. Conversely, the C-SVC performed the best with regard to training sample classification, and was also able to associate the four endmembers with the four main materials. Although the KNN performance in classifying the training set was slightly better than that of BVM, it wasn’t able to avoid unrealistic asbestos labelling for the three training samples with respect to other rubble materials. However, this algorithm was able to account for four main rubble materials through the endmembers so as the RF. The latter was, in particular, the only one able to detect the likely presence of asbestos (end5
). The related endmember (n. 5) showed an average abundance of few percent but with the highest standard deviation meaning a significant concentration of this dangerous material in specific heaps (Table 5
). Finally, the C4.5 was the worst performing, both in classifying the training set and in the number of main materials associated with endmembers.
Following these criteria, the C-SVC and RF were the best performing. Moreover, these two algorithms agreed with the labelling of the five endmembers. They were able to discriminate four main materials with differences in terms of natural stones (matn
), which was detected by C-SVC but not by RF, which instead was able to classify rubble debris (calc
), which was missed by the other classifier. Therefore, the ten endmembers provided by the SMA from pansharpened WV3 data were satisfyingly mapped to the in situ spectral signatures of the rubble materials using C-SVC machine learning algorithm, which made it possible to obtain the most effective discrimination of the main building materials (first 5 endmembers of Table 1
In agreement with the general schema of the mapping model, which includes validation and accuracy assessment based on independent measurements, an original method for accomplishing this necessary test on the obtained continuous distribution of rubble typologies was implemented. It is based on the pixel maximum abundance (PMA) and predominant endmember (PED) distributions, derived from the SMA outputs. The PMA histogram distribution (Figure 6
) confirms once again the extreme spectral heterogeneity of rubble, with a maximum of less than 0.3. Such an approach allowed us to assess various PED distributions (obtained using different PMA thresholds) using photointerpretation method, i.e., by comparing corresponding locations on the RGB high-resolution aerophotos. According to the typical thematic accuracy assessment it was possible to evaluate overall accuracy (O.A.) and K parameters through related confusion matrices. As expected, the overall accuracy (O.A.) assessed for the three PMA rising thresholds (Table 4
, Table 5
and Table 6
) increases with the threshold increase due to endmember predominance increasing at pixel level, which in turn makes correct photointerpretation easier. Indeed, both O.A. and K accuracy global parameters are maximum (i.e., 0.888 and 0.754, respectively) at the 0.45 threshold, which is in substantial agreement with the photointerpretation results. Although this relative abundance value is low, it seems sufficient to allow the pixel main material to be recognized by the independent expert interpreter.
Additionally, the PED maps obtained with the two lowest thresholds (0.35 and 0.25) correspond to a poorer/moderate agreement with the photointerpretation according to the decrease in endmember predominance [55
The classification of materials present on the surface of rubble heaps is one of the most important results achieved in the present study. Among these, asbestos is of great interest due to its implications for human health (for both earthquake survivor population and rescue teams). The present work showed that concrete debris and brick tiles are more abundant (Table 7
and Figure 11
a) within the study area and about 50% of the rubble heaps may contain asbestos (in different quantities; Table 7
and Figure 11
It does not mean that 50% of rubbles contains asbestos in concentrations that could be harmful to human health. However, the present research demonstrate that it is possible to identify the potential presence of this hazardous substance and support management of waste and rubbles to be removed. Sustainable land reclamation is one of the main goals of ENEA as it has a great socioeconomic importance with a high impact on economic development [56
]. Results of the present study are of extreme practical and logistical relevance, and it could be replicated to improve the effectiveness of management in post-earthquake emergencies around the world.
The radiometric and geometric resolution parameters of the remote sensors play an important role in allowing suitable mapping of the spectral-spatial heterogeneity typical of urban and industrial/infrastructure areas. In particular, they are fundamental for the discrimination of the seismic rubble at the required scale. To achieve this goal, the panchromatic and multispectral information provided by WV3 sensors were fused using the Gram-Schmidt pansharpening method. Subsequently, they were atmospherically corrected in order to obtain a suitable input at 31 cm of a.g.r. for the SMA procedure. In general, even though widely exploited for their improved geometric resolution, the quantitative use of synthetic spectral reflectance of pansharpened products is not widespread, since the degradation of the quality during the fusing processes does not make it possible to fully preserve the original spectral properties of the acquisition channels. In this case, rather effective results were obtained by performing the pansharpening process using the spectrally conservative Gram-Schmidt method prior to atmospheric correction. The inverse procedure (atmospheric correction carried out before pansharpening process) did not produce the quality level in the results.
The implemented methodology provided the relative abundance distribution of the considered rubble materials. The results are in agreement with the information that has been made available until now with respect to transportation and final disposal. Indeed, concrete and rubble debris were the most abundant seismic rubble typologies. These findings are consistent with the typical proportion of building materials within the centre of Amatrice and the propensity of debris to spread over heaps surface in case of building collapses (Figure 7
, Figure 9
). The clean-up and removal of rubble has not yet been completed in the central Apennines, and relevant information obtained in the future will be useful for refining the implemented methodology. For instance, one of the crucial feedbacks will be the correspondence between the material distribution retrieved at the surface and the inner part of the rubble heap.
Although detailed information about the asbestos distribution over rubble heaps was not available, the global presence of this dangerous material, mainly as a constituent of roof covers, has been reported by various independent observations made in the field and by transportation operators in charge of clearing the rubble from the centre of Amatrice. In this context, it was not possible to test the accuracy of the asbestos distribution retrieved through RF machine learning algorithm.
In the context of post-event activities related to seismic events, the effective management of the huge amount of resulting rubble and debris in urban areas represents one of the major issues and can be usefully supported by EO-based monitoring and mapping applications, also exploiting the last generation of multispectral satellite VHR sensors. The surveys from the aerial platform can provide detailed up-to-date information, but are extremely expensive for the continuous monitoring of emergency areas required for controlling the evolution of the earthquake urban scenario, which instead can be repetitively mapped by available sensors on board orbiting satellite platforms, which can provide an increasing amount of territorial data with continuously improving spatial and spectral resolution. Results obtained at the present time through the innovative methodology developed in this work show the applicability of the WV3 satellite VHR data for providing effective surface estimates of the distribution of seismic rubble types in heaps distributed within urban areas. This information can be advantageously integrated with that from related volumes derived from aerial surveys carried out in the aftermath of the earthquake (Table 2
and Table 7
; Figure 8
and Figure 10
), providing suitably support to the management activities related to the urban rubble. The stereoscopic acquisition capabilities of VHR satellite sensors could also support the estimation of volumes in monitoring the evolution of the emergency scenario, possibly with a “one-off” calibration based on the LiDAR information deriving from an initial aerial survey. The preliminary analysis of rubble heaps with EO tools can also have a relevant impact on cost of post emergency action like rubble management and the definition of priority in order to guarantee human health protection.
Thus, this original EO-based methodology allowed us to produce realistic estimates of the types and volumes of seismic rubble in the historical centre of Amatrice. It can be generalized with a high degree of automation and can be easily applied to similar situations, even with possible improvements deriving from the integration of the SAR (Synthetic Aperture Radar) and hyperspectral data provided by ongoing EO missions (Copernicus Sentinel-1, Cosmo SkyMed, PRISMA, EnMap). Future activities will include a greater exploitation of high-resolution and hyperspectral satellite data, for assessing both volumes and typologies of earthquake rubble heaps, also investigating the possibility to detect hazardous rubble materials (i.e., asbestos).