1. Introduction
Every year, wildfires affect millions of hectares of forest woodlands and other vegetation, causing the loss of many human and animal lives along with immense economic damage, in terms of resources destroyed and the costs of suppression [
1]. Especially in the Mediterranean basin where the present study is mainly focused, the rate of wildfire incidents is increasing with an alarming rate [
2].
To overcome the negative impacts of wildfires and preserve sustainability in forest ecosystems, governments are compelled to undertake a variety of restoration and rehabilitation measures [
3]. To implement such actions, rapid, reliable, and detailed information regarding the state of the fire-affected areas is required [
4]. Furthermore, it has been shown that a successful implementation of the necessary protection measures against any illegal activity in the affected areas, such as uncontrolled expansion of agricultural activities and tourism, encroaching or illegal construction, would require explicit spatial information regarding the location and extent of the burned areas [
4,
5].
Since the early 1980s, satellite remote sensing has been extensively used for mapping and managing burned areas [
6,
7]. As a result, up until now a variety of satellite data with different spatial resolutions has been extensively used for mapping fire-affected areas at local, regional and global scale [
8]. Traditionally, medium and coarse resolution satellite data such as Landsat TM (30 m), Landsat MSS (80 m), MODIS (250 m), AVHRR (1km), and SPOT-VGT (1 km) have been used for extraction of fire-related information. In recent years, however, the availability of Very High Resolution (VHR) satellite imagery such as IKONOS, WorldView, and QuickBird has provided new possibilities in burned area mapping at local scales [
9]. Since fire plays a crucial role in many ecological processes at the local level (e.g., vegetation composition, biodiversity, soil erosion, and the hydrological cycle), the use of VHR data provide very detailed thematic products and consecutively valuable information. Examples of the successful use of VHR imagery in burned area mapping can be found in [
10,
11] and [
12].
Up until now, several classification techniques have been applied in burned area mapping, including maximum likelihood classification [
13,
14], logistic regression [
15], classification and regression trees[
14,
16] linear and/or nonlinear spectral mixture analysis [
17,
18], thresholding of Vegetation Indices (VIs) [
14,
19], Neural Networks [
20], Neuro-Fuzzy techniques [
21], Support Vector Machines (SVMs) [
22,
23,
24], and Object Based Image Analysis (OBIA) [
25,
26]. However, the selection of the optimal method each time depends on several factors, such as the scale and the goals of the current project.
ESA has recently developed, applied and validated several algorithms for the regular, consistent and accurate mapping of burned areas across the globe based on three different sensors (ERS-2 ATSR, ENVISAT AATST/MERIS and SPOT Vegetation) [
27]. The findings of this research (Fire CCI Project) will be made available to the whole scientific community. However, due to the course spatial resolution of the aforementioned sensors, the produced maps have limited use for local scale applications. The production of such maps at very high spatial resolution is an open research interest.
The goal of generating accurate burned area map products at a local level is usually accomplished using VHR imagery combined with one of the previously mentioned techniques. Specifically, the growing availability of VHR satellite imagery along with the development of advanced image analysis techniques (e.g., OBIA, SVM, Neuro-Fuzzy classification) resulted in the production of accurate burned area maps with limited human interaction [
28,
29]. In addition to the above, there are recent cases in which the reliability of the resulting thematic (land cover) maps was increased with the inclusion of additional features in the classification process, such as texture and spatial indicators [
28,
29].
When the use of higher-order features is combined with the application of advanced image analysis techniques, such as feature selection methods and advanced classification methodologies, an increase in accuracy and reliability in land cover mapping has been reported [
30,
31,
32]. Although Vegetation Indices (VIs) and textural features have been exploited as additional sources of information in burned area mapping by [
33] and [
34], the use of higher-order features has not been yet fully investigated and remains an active topic of research. In particular, none of the previously mentioned studies has evaluated multiple higher-order feature categories neither applied an advanced feature selection technique.
The aim of this work is to map recently burned areas using the Support Vector Machine (SVM) classifier [
35] and the FuzCoC feature selection (FS) method [
36] on VHR IKONOS imagery. The specific objectives are to:
to investigate whether the quality and accuracy of burned area maps produced by an SVM classifier increase with the addition of higher-order features to the original VHR IKONOS spectral bands, and
to compare two classification approaches, namely the object-oriented and pixel-based classification approaches, in order to identify which one is the most appropriate for operational burned area mapping.
The rest of the paper is organized as follows.
Section 2 presents the datasets used in this study, whereas
Section 3 describes the proposed methodology. Experimental results along with the validation process are presented in
Section 4.
Section 5 compares the two approaches (pixel and object) with respect to their potential use on an operational basis. Finally,
Section 6 reports some final conclusions.
2. Study Area
The proposed methodology has been tested in two areas of Greece recently affected by severe forest fires. The first one is Mount Parnitha (
Figure 1a) which is located in Attica, central part of Greece. The area was affected by a large forest fire (4990.10 hectares) in the summer of 2007. Mount Parnitha is the highest (1413 m) and most extended mountain of Attica and has been declared a national park since 1961. In this region, Mediterranean-type climatic conditions with hot summers and mild winters are characteristically prevailing. The second study area is the Greek island of Rhodes (
Figure 1b), which is located in the south-eastern Aegean Sea. Rhodes is the largest of the Dodecanese islands in terms of both land area and population. The large fire on the island of Rhodes occurred in the summer of 2008 (22 July 2008), affecting an area of 11,863.69 hectares.
For the purposes of the analysis we used two pan-sharpened IKONOS images (1m). In both cases the satellite images were captured soon after the fire events. More precisely, the imagery for the case study of Parnitha’s fire was captured on 8 July 2007, ten days after the fire event. In the case of Rhodes’ fire the imagery was captured immediately after the fire event (1 August 2008).
The acquired satellite images were geometrically corrected. In order to assist the validation procedure, two reference maps were created, manually delineating the burned areas on the images. The remaining parts of the images were labeled as “Unburned”.
Figure 1.
Location of the study areas. (a) First study area Mount Parnitha, Attiki, Greece (b) Second study area Rhodes Island, Greece.
Figure 1.
Location of the study areas. (a) First study area Mount Parnitha, Attiki, Greece (b) Second study area Rhodes Island, Greece.
4. Experimental Results
This section presents the obtained results from the application of the proposed methodologies in the Parnitha and Rhodes datasets. Pixel-based and object-based classifications for the Parnitha datasets are presented first, followed by the results obtained by Rhodes classifications. The two approaches in both cases are subsequently compared in terms of their effectiveness in burned areas mapping.
To assess the classification models’ ability to map burned areas accurately, we estimated the agreement between the burned area maps resulting from the classification process and the reference map. For each individual classification, the confusion matrix has been used to provide a basic description of the thematic maps accuracy [
80]. The statistical measures derived from that matrices, namely, the Kappa index of agreement (KIA), overall accuracy (OA), producer’s accuracy (PA), and user’s accuracy (UA) [
81] were used to describe the accuracy of the derived maps. Additionally, the accuracy of the produced maps was also assessed using the probability of false alarm (
Pf) metric [
82]:
where
Mff is the number of correctly classified fire pixels,
Mnn is the number of correctly classified non-fire pixels,
Mnf is the number of pixels assigned as non-fire by the classification while assigned as fire in the reference map and
Mfn is the number of pixels assigned as fire by the classification while assigned as non-fire in the reference map. In our case, the
Pf metric describes the probability a pixel to be erroneously classified as “Burned”.
All maps were converted to raster images of 1m pixel size and an image-to-image comparison was performed using all pixels. To do so, the resulting maps were compared with a reference map featuring the two classes, namely, “Burned” and “Unburned”, same as the two classifications. All of the aforementioned statistical measures were calculated using all pixels of the image.
4.1. SVM Pixel-Based Classification Results for the Parnitha Dataset
In this section, the mapping products derived from the SVM pixel-based classifications are initially presented. The first objective of the present study is to evaluate whether the addition of extra features to the original IKONOS bands increases or not the accuracy and the quality of the burned area maps. To this end, we compared three thematic maps derived from three different feature sets, namely, the full space (172 features), the reduced space (the four FuzCoC selected features, see
Section 3.3), and original spectral space (the four bands of the IKONOS image).
The obtained results (
Table 6) indicate that the use of the higher-order features increases the classification accuracies of the derived burned area maps. Additionally, no Hughes’ phenomenon is present in our case, since the classification accuracy is maximized when all the 172 features are considered (OA 97.47%, KIA = 0.934). The classification based on the dataset with the reduced space achieved marginally lower classification accuracy (OA 97.27%, KIA = 0.928) in comparison to the classification based on the IKONOS
FullSpace-PIXEL dataset. However, in both cases (full space and reduced space), the thematic maps attained higher overall classification accuracies in comparison to the thematic map derived from the IKONOS
RGBNIR-PIXEL dataset (OA of 95.95%, KIA = 0.894). The SVM
FuzCoC-PIXEL classification resulted in higher UA, particularly in the case of the “Burned” class, where the difference with respect to the other two classifications (SVM
RGBNIR-PIXEL and SVM
FullSpace-PIXEL) is substantial (approximately 4% in both cases). This implies that the SVM classifier in the reduced space is less prone to overestimate the “Burned” class (that is, it produces fewer misclassification in unburned areas) compared to the other two cases. Moreover, the probability of false alarm
Pf is more than 2.5 times lower in the case of SVM
FuzCoC-PIXEL, which means that this scheme is less prone to misclassify non-fire pixels than the classification scheme in the two other cases. These results reveal that SVM performed better in the datasets where additional information was included.
In addition to the quantitative measures presented so far, a visual assessment of the burned area maps was also carried out. In order to preserve space, this analysis considers only the two main cases of interest, that is, the SVM
RGBNIR-PIXEL and SVM
FuzCoC-PIXEL classifications, since the SVM
FullSpace-PIXEL case involves many practical inefficiencies.
Figure 3 presents the resulting burned area maps in both cases, along with the reference map. A careful examination of the maps reveals that SVM
RGBNIR-PIXEL produced a much higher number of misclassifications than the SVM
FuzCoC-PIXEL case, especially in unburned areas. This fact highlights the practical significance of the difference between the two classifications, regarding the UA and
Pf discussed above. Although the absolute value of
Pf in both cases is very small, the higher noise level in the SVM
RGBNIR-PIXEL map is quite distinguishable within the unburned areas.
Table 6.
Accuracy measures for SVM pixel-based classifications (Parnitha).
Table 6.
Accuracy measures for SVM pixel-based classifications (Parnitha).
Classification | Class | PA | UA | OA | KIA | Pf |
---|
SVMFullSpace-PIXEL | Burned | 95.99 | 94.42 | 97.47 | 0.934 | 0.015 |
Unburned | 98.00 | 98.58 |
SVMRGBNIR-PIXEL | Burned | 91.09 | 93.24 | 95.95 | 0.894 | 0.018 |
Unburned | 97.67 | 96.89 |
SVMFuzCoC-PIXEL | Burned | 92.37 | 97.08 | 97.27 | 0.928 | 0.007 |
Unburned | 99.02 | 97.35 |
A closer examination of the SVMRGBNIR-PIXEL map revealed that the misclassified pixels were mainly located in shadowed areas (mainly tree shadows). Furthermore, commission errors (areas erroneously classified as “Burned”) were observed on bare soil, roads, and recently ploughed fields, whereas omission errors (areas erroneously classified as “Unburned”) were observed on areas with surface fires, slightly burned vegetation, and burned rocky areas. Especially in the case of the rocky sites inside the burned forested areas, the algorithm failed to correctly classify them as burned. This was not unexpected due to the high spectral similarity of those pixels (burned rocky areas) with other unburned pixels. The classifier correctly mapped rocks as unburned areas (since rock does not get burned), although those areas would be mapped within the burned area perimeter in operational burned area mapping. Errors were also observed on the borders of the two classes.
Examination and visual interpretation of the SVM
FuzCoC-PIXEL map revealed that the classification quality was higher comparatively to the SVM
RGBNIR-PIXEL map, mainly due to the reduced noise effects.
Figure 4 depicts a detail of the two maps inside the burned are perimeter. It becomes apparent that the SVM
RGBNIR-PIXEL map is affected by a much higher degree of the salt-and-pepper effect than the SVM
FuzCoC-PIXEL map, which exhibits reduced noise effects. Moreover, the classes in the SVM
FuzCoC-PIXEL were characterized from greater homogeneity comparatively to the respective classes in the SVM
RGBNIR-PIXEL. Misclassification errors were also found in the same areas as in the previous classification (SVM
FuzCoC-PIXEL) examined. Nevertheless, it should be mentioned that in both cases it was difficult for the classifier to correctly delimit the boundaries of the unburned vegetation patches.
Figure 3.
SVM pixel-based burned area maps for the Parnitha dataset: (a) reference map, (b) SVMRGBNIR-PIXEL classification, and (c) SVMFuzCoC-PIXEL classification.
Figure 3.
SVM pixel-based burned area maps for the Parnitha dataset: (a) reference map, (b) SVMRGBNIR-PIXEL classification, and (c) SVMFuzCoC-PIXEL classification.
Figure 4.
Delineation of the unburned patches in the pixel-based classifications and the reduced noise effects after the FuzCoC feature selection: (a) SVMRGBNIR-PIXEL classification and (b) SVMFuzCoC-PIXEL classification. The blue areas depict the burned areas after classifications. The background of the figures is a false composite (NIR-Red-Green) of the IKONOS image.
Figure 4.
Delineation of the unburned patches in the pixel-based classifications and the reduced noise effects after the FuzCoC feature selection: (a) SVMRGBNIR-PIXEL classification and (b) SVMFuzCoC-PIXEL classification. The blue areas depict the burned areas after classifications. The background of the figures is a false composite (NIR-Red-Green) of the IKONOS image.
In conclusion, the numerical and visual comparison of the burned area maps indicated that the use of selected FuzCoC features along with the SVM classifier resulted in a map product with higher accuracy and reliability.
4.2. SVM Object-Based Classification Results for the Parnitha Dataset
This section presents the results obtained from the implementation of the SVM object-based classification models.
Table 7 reports the results of the accuracy assessment procedure for both cases (with and without FS). The obtained results suggest that both methods achieve highly accurate classifications. In particular, the SVM
OBJECT classification attained an OA of 97.17% (KIA = 0.926), whereas the SVM
FuzCoC-OBJECT an OA of 97.85% (KIA = 0.943). Similarly to the pixel-based classifications, the application of the SVM in the reduced feature space (the three features selected by the FuzCoC FS methodology) resulted in increased UA for the “Burned” class compared to the full space classification, although the difference is somewhat smaller in this case (2.49%). Moreover, the value of
Pf for the SVM
FuzCoC-OBJECT classification is more than two times smaller than that for the SVM
OBJECT classification.
Table 7.
Accuracy measures for SVM object-based classifications (Parnitha).
Table 7.
Accuracy measures for SVM object-based classifications (Parnitha).
Classification | Class | PA | UA | OA | KIA | Pf |
---|
SVMOBJECT | Burned | 94.64 | 94.56 | 97.17 | 0.926 | 0.015 |
Unburned | 98.08 | 98.11 |
SVMFuzCoC-OBJECT | Burned | 94.64 | 97.05 | 97.85 | 0.943 | 0.007 |
Unburned | 98.99 | 98.13 |
Figure 5.
SVM object based burned area maps for the Parnitha dataset: (a) SVMOBJECT classification and (b) SVMFuzCoC-OBJECT classification.
Figure 5.
SVM object based burned area maps for the Parnitha dataset: (a) SVMOBJECT classification and (b) SVMFuzCoC-OBJECT classification.
In order to explain the misclassification errors in the burned area maps, a visual examination of the classification maps was also conducted, which is depicted in
Figure 5. In general, both burned area maps show a reasonably accurate visual depiction of the classes of interest in this area. The main misclassifications were observed in objects with shadows, bare soil, roads, surface fire, slightly burned vegetation, objects with old dry vegetation (especially coniferous) and recently ploughed fields. Moreover, misclassifications were observed in mixed objects, that is, objects containing both classes, namely, “Burned” and “Unburned”. In these cases, the segmentation process failed to partition the image in homogeneous regions, leading to the creation of objects with two classes. This problem exists mainly in dense forested areas which suffered from surface fires and in bare lands with sparsely distributed shrubs. Considering this fact, it seems that the segmentation process is of great importance for the accuracy of the classification and should be further investigated.
The SVM classifier applied on the IKONOS
FuzCoC-OBJECT dataset performed slightly better inside the burned area, as compared to the SVM applied on the IKONOS
OBJECT dataset. Finally, a visual examination of the classifications revealed that in both cases the SVM classifier correctly classifies the unburned islands of vegetation. An example of unburned patches inside the fire perimeter is depicted in
Figure 6 for both cases.
Figure 6.
An example of unburned patches in the object-based classifications: (a) SVMOBJECT classification and (b) SVMFuzCoC-OBJECT classification. The blue areas depict the burned areas after classifications.
Figure 6.
An example of unburned patches in the object-based classifications: (a) SVMOBJECT classification and (b) SVMFuzCoC-OBJECT classification. The blue areas depict the burned areas after classifications.
Besides the fact that both SVM models yielded high classification performances, the classifier demonstrated very high ability in discriminating the different classes inside and outside the fire perimeter. Particularly inside the fire perimeter, the classifier exhibited high ability in discriminating the unburned vegetation patches. As it was expected, the application of the object-based approach significantly reduced the salt-and-paper effect, compared to the pixel-based approaches. Moreover, in both approaches (pixel and object) the reduced space classifications resulted in the lowest Pf values. Finally, it should be emphasized that the use of the FuzCoC FS methodology significantly reduced the classification time and maintained high accuracy in the burned area map product.
4.3. SVM Pixel-Based Classification Results for the Rhodes Dataset
Here we present the application of the SVM pixel-based classification procedure on the second study (island of Rhodes). The obtained results (
Table 8) indicate that the highest OA was attained using all the available features (SVM
FullSpace-PIXEL), which is approximately 2.5% higher than the respective accuracy obtained using the reduced space (SVM
FuzCoC-PIXEL). The accuracy considering only the original space (SVM
RGBNIR-PIXEL) is substantially lower than either one of the two other cases. It is also evident that the Rhodes burned area mapping task constitutes a harder classification problem than the Parnitha one and, therefore, the differences between the three approaches (original, full, and reduced space, respectively) are larger. Consequently, the gains from the use of extra features along with an FS process become more obvious. In all cases, the SVM exhibits the tendency to overestimate the “Burned” class, a fact that can be easily perceived from the much lower PAs for the “Unburned” class. The SVM
FuzCoC-PIXEL exhibits a rather more balanced behavior in that respect, which is reflected in the smallest
Pf value observed for this case, although its OA is lower than the SVM
FullSpace-PIXEL classification. A lower
Pf value means that the SVM classifier is less prone to misclassify non-fire pixels.
Table 8.
Accuracy measures for SVM pixel-based classifications (Rhodes).
Table 8.
Accuracy measures for SVM pixel-based classifications (Rhodes).
Classification | Class | PA | UA | OA | KIA | Pf |
---|
SVMFullSpace-PIXEL | Burned | 95.20 | 90.36 | 89.97 | 0.766 | 0.075 |
Unburned | 79.32 | 89.02 |
SVMRGBNIR-PIXEL | Burned | 95.29 | 83.12 | 83.86 | 0.604 | 0.154 |
Unburned | 60.56 | 86.33 |
SVMFuzCoC-PIXEL | Burned | 89.47 | 91.82 | 87.59 | 0.722 | 0.060 |
Unburned | 83.77 | 79.62 |
In order to examine and compare the quality of the classification, we also conducted a visual inspection of the derived maps. Similarly to the analysis of
Section 4.1, we concentrate on the two main cases of interest, that is, the SVM
RGBNIR-PIXEL and the SVM
FuzCoC-PIXEL (
Figure 7). A close inspection of the classifications reveals that the SVM
FuzCoC-PIXEL map is of rather higher quality compared to the SVM
RGBNIR-PIXEL one, mainly due to the reduced noise effects. The SVM
RGBNIR-PIXEL map exhibits severe overestimation of the “Burned” class, with a large number of pixels outside the fire perimeter being misclassified. Conversely, the SVM
FuzCoC-PIXEL underestimates the “Burned” class inside the fire perimeter to some extent, but this effect is much milder than the overestimation one of the former classification.
Figure 7.
SVM pixel-based burned area maps for the Rhodes dataset: (a) reference map, (b) SVMRGBNIR-PIXEL classication, and (c) SVMFuzCoC-PIXEL classification.
Figure 7.
SVM pixel-based burned area maps for the Rhodes dataset: (a) reference map, (b) SVMRGBNIR-PIXEL classication, and (c) SVMFuzCoC-PIXEL classification.
4.4. SVM Object-Based Classifications for the Rhodes Dataset
This final subsection presents the application of the object-based approach to the Rhodes dataset. The quantitative results are reported in
Table 9. It is evident that the application of the FuzCoC FS resulted in substantially increased classification accuracy compared to the initial feature space, especially inside the burned area. More specifically, an OA of 79.26% (KIA = 0.477) was achieved by the SVM classification in the full space and an OA of 92.39% (KIA = 0.830) for the classification in the reduced space. All class-specific accuracies are substantially increased in the latter case, with the difference being greater for the “Unburned” class. This is attributed to the fact that a higher number of unburned areas inside the fire perimeter have been erroneously characterized as burned ones. The latter is also verified by the respective
Pf values, which in the case of the SVM
FuzCoC-OBJECT is approximately two times smaller than the SVM
OBJECT one, indicating that the former classification model is less prone in misclassifying unburned areas than the respective SVM
OBJECT model.
Table 9.
Accuracy measures for Rhodes SVM object-based classifications
Table 9.
Accuracy measures for Rhodes SVM object-based classifications
Classification | Class | PA | UA | OA | KIA | Pf |
---|
SVMOBJECT | Burned | 87.24 | 84.30 | 79.26 | 0.477 | 0.114 |
Unburned | 59.30 | 64.90 |
SVMFuzCoC-OBJECT | Burned | 92.88 | 95.67 | 92.39 | 0.830 | 0.051 |
Unburned | 91.43 | 86.30 |
In addition to the numerical results presented above, a visual inspection of the classification maps has also been conducted (
Figure 8). The two maps differ in terms of the location and the pattern of their respective omission and commission errors. Specifically, the map produced based on the dataset with the FuzCoC selected features (
Figure 8b) misclassified relatively small areas across the whole scene, especially outside the fire perimeter. Inside the fire perimeter the classifier succeed in accurately differentiating the burned from the unburned areas. On the other hand, a visual inspection of the map derived from the SVM
OBJECT model (
Figure 8a) revealed that the SVM classifier exhibited the tendency to overestimate the class “Burned” against “Unburned” class. In the SVM
OBJECT case the salt-and-pepper effect was minimized compared to the SVM
FuzCoC-OBJECT one, but at the expense of misclassifying a substantially higher number of unburned objects within the fire perimeter (patches of healthy vegetation, bare soil, low vegetation areas and roads) as burned ones (
Figure 8a).
According to the above, the classification quality of the map derived from the SVMFuzCoC-OBJECT is higher compared to the respective one derived from the SVMOBJECT model. The land cover types which are typically incorrectly classified in burned area mapping (e.g., shadows, bare soils, etc.) were still misclassified in both cases.
Figure 8.
SVM object based burned area maps for the Rhodes dataset (a) SVMOBJECT classification (b) SVMFuzCoC-OBJECT classification.
Figure 8.
SVM object based burned area maps for the Rhodes dataset (a) SVMOBJECT classification (b) SVMFuzCoC-OBJECT classification.
5. Discussion
A collateral aim of this work is to examine and compare the developed methodologies with respect to their potential use on an operational basis. In general, any developed burned area mapping methodology should meet the following criteria in order to be applicable on operational basis: it should be rapid, reliable and automated [
9]. Hence, the evaluation of the developed burned area mapping methods of the current study should also take into account the aforementioned criteria.
Beginning with the pixel-based classification schemes, it is important to note that the whole pre-processing procedure until the stage of the classification was very time-consuming. During the procedure, the generation of a large volume of data required considerable storage capacity. The manipulation of such high-volume data was very time-consuming and the computational demands were extremely high. Specifically in the case of the full space pixel-based classifications, the aforementioned difficulties were immense. The process of the production of the thematic maps from the datasets with all the available features (172) required special treatment, using advanced programming tools. For this case, the images were split into multiple parts (over 250 pieces in each different case) and each different part was classified separately. To formulate the final thematic maps, the various parts of the initial datasets were merged back together. These processes are not easily applicable by typical remote sensing users and it is practically impossible to be conducted using common remote sensing software. To this end, the use of a FS method is considered of great importance when additional information is added on the original dataset and the whole classification process must be carried out using the simplest, easiest and fastest way. Even for the original or the reduced spaces, though, the computational and storage requirements of the pixel-based approach was still very high, due the large size of the VHR image. Overall, despite the rapid advent of computation systems technology, the high computational demand of these approaches will remain a deterrent factor on a long term basis, especially if we consider the need for regional-wide or nation-wide burned area mapping at multiple time points.
Considering the object-based classification schemes, the whole process of object extraction, FS and subsequent classification was less complicated, labor-intensive, and time-consuming than the pixel-based pre-processing counterparts. It should be noted that in any case examined (pixel and object), the FS process greatly decreased the volume of data that had to be processed, both in terms of storage requirements and computational demands with respect to the full area classification. Although these requirements are much less for the object-based approach than the pixel-based one, the relative gains from the application of the FS procedure are still substantial.
Currently, neither of the classification approaches are fully automated and rapid. To this end, none of the developed methodologies meet the operational criteria described above. However, the comparison between the pixel and the object-based approaches indicate that the latter fulfills these criteria to a higher degree. Thus, if it becomes possible to incorporate all the processes required for the implementation of the proposed object-based classification scheme into a single software in the near future, then it will be possible to conduct the classifications in a semi-automated way. The procedure cannot be fully automated since several parameters need to be adjusted.
The discussion presented so far indicates that the object-based classification scheme is more appropriate than the pixel-based one, in mapping recently burned areas using VHR imagery. The previous findings are further reinforced by the results of the numerical and visual comparison of the derived thematic maps. Generally, the maps produced from both approaches were more or less very accurate. In particular, all the classifications in the case of Parnitha exceeded 95% OA, whereas the accuracy of the produced thematic maps in the case Rhodes exceeded 87% percent when considering the additional features. The experimental analysis presented above reveals that the use of advanced features along with the FuzCoC FS had a positive effect with respect to classifications accuracy in both pixel-based and object-based classifications.
The experimental results from the pixel-based classifications shows that in both cases examined (Parnitha and Rhodes), the SVM classifier performed better in the datasets were additional features were included (with and without FS). The implementation of the SVM classifier in the full space (172 features) resulted in all cases in slightly higher classification accuracy compared to the reduced space (FS selected features) classification. Despite that, the gains from the implementation of the FS method in terms of computational demands and data manipulation equalizes the slight loss in the classification accuracy. Moreover, the visual inspection of the pixel-based derived maps reveals two more gains from the addition of higher-order features. The first one is the reduction of the salt-and-pepper effect and the second one is the increase of homogeneity inside the classes of interest. The depiction of the reality in the produced maps based on the reduced space was always higher in comparison to the maps based on the original datasets.
Focusing on the SVM object-based classification schemes, it becomes apparent that the application of the FuzCoC FS resulted in increased accuracies compared to the full feature space, for both the Parnitha and the Rhodes object-based classifications. The difference in the former case is practically negligent (0.68% difference in OA), whereas in the latter case a substantial difference of more than 13% in OA is observed. The magnitude of the increase in classification accuracy is problem-dependent. The Parnitha dataset defines a much simpler classification task, since both object-based classification exhibit OA higher than 97%. However, Rhodes’ study area is characterized by a far more heterogeneous terrain and the gains from the FS procedure are much higher.
A comparison of the numerical results between the pixel-based and the object-based approaches reveals that the latter compares favorably to the former, exhibiting higher overall accuracy. The gains in accuracy after the implementation of the SVM object-based classification appear to be marginal for the Parnitha case. However, in the case of Rhodes the gains in accuracy are quite substantial (4.8% difference in OA). Regarding the visual inspection of the pixel and object classifications, the classes in the object-based classifications were more homogeneous and the classes’ depiction was more realistic. An example is given in
Figure 9, which depicts a detail of the thematic maps obtained from the SVM
FuzCoC-OBJECT and the SVM
FuzCoC-PIXEL classifications in Parnitha. It can be easily observed that the object-based classification resulted in a much more homogeneous “Burned” area characterization. Taking also into consideration the practical inefficiencies of the pixel-based approach, the object-based approach is deemed more appropriate for the production of accurate and realistic burned area maps.
The land cover types which are usually incorrectly classified in burned area mapping (e.g., shadows, bare soils,
etc.) were still misclassified in both SVM approaches for all cases (full, reduced, and original space). However, the number of the erroneously classified areas was significantly diminished using the object-based approach. Particularly for the case of shadowed areas, the application of SVM on objects exhibits higher discriminating ability comparatively to the SVM on pixels.
Figure 10 presents an illustrative example of such a case.
Due to the high costs involved in the acquisition of VHR images, the analysis of the current study was unfortunately confined in two study areas only. The experiments in these two study areas were conducted independently from each another. To this end, we cannot infer any further about the transferability of the proposed methodology. Taking a closer look at the features selected by FuzCoC for the two test areas (
Table 3 and
Table 4), we can observe that certain features are selected in both cases (apart perhaps from the size of the window). Nevertheless, an analysis of this sort would require a much larger number of test cases, so that the intersection of the most frequently selected features could be identified in a statistically robust manner. In any case, the investigation of the herein proposed methodology’s transferability properties—or the discussion of whether this is possible altogether—is outside the scope of the present study and constitutes the subject of a future work.
Presently, acquiring a large number of VHR imagery seems very difficult, either due to their high purchase cost or due to their limited availability within a very short time framework. Satellite imaging start-ups such as Skybox and Planet Labs are expected to considerably alleviate these difficulties, by providing low cost high resolution imagery in a timely manner. The aim of these two start-up companies—and similar ones—is to set to launch a large number of small imaging satellites which will be able to revisit and photograph huge areas of the planet several times each day. The provision of high spatial- and temporal-resolution images in low prices is expected to pave the way for new innovations in many scientific fields, including the field of burned area mapping.
Figure 9.
The enhanced quality of the burned area maps (Parnitha) after the implementation of the object-based approach. (a) The homogeneous “Burned” class (yellow color) in the SVMFuzCoC-OBJECT classification. (b) The overestimated “Burned” class in the SVMFuzCoC-PIXEL classification.
Figure 9.
The enhanced quality of the burned area maps (Parnitha) after the implementation of the object-based approach. (a) The homogeneous “Burned” class (yellow color) in the SVMFuzCoC-OBJECT classification. (b) The overestimated “Burned” class in the SVMFuzCoC-PIXEL classification.
Figure 10.
The enhanced quality of the burned area maps (Parnitha) in the shadowed areas after the implementation of the object-based approach: (a) Unburned areas in the SVMFuzCoC-OBJECT classification. (b) SVMFuzCoC-PIXEL classification. (Red color: shadowed areas wrongly classified as burned. Yellow color: Unburned areas).
Figure 10.
The enhanced quality of the burned area maps (Parnitha) in the shadowed areas after the implementation of the object-based approach: (a) Unburned areas in the SVMFuzCoC-OBJECT classification. (b) SVMFuzCoC-PIXEL classification. (Red color: shadowed areas wrongly classified as burned. Yellow color: Unburned areas).
6. Conclusions
In this paper, we investigated the influence of the higher order spectral and spatial features for accurately mapping recently burned areas, using IKONOS imagery. Our analysis considers both pixel-based and object-based approaches, using two advanced image analysis techniques: an efficient filtering method based on FuzCoC and the SVM classifier. In both cases the implementation of SVM on VHR imagery resulted in the production of burned area maps of very high classification accuracies. However, a closer examination of the results revealed that the quality of the burned area maps derived from the object-based image analysis is higher compared to the respective maps from the pixel-based image analysis.
The results from the SVM pixel-based classifications indicate that the use of the additional features (with and without feature selection) instead of the original spectral bands improves the accuracy and reliability of the produced burned area map. However, SVM’s performance inside the class “Burned” was higher in the case of the FuzCoC selected feature space in both areas examined (Parnitha and Rhodes). Moreover, the application of the FuzCoC FS methodology substantially reduced the salt-and-pepper effect and improved class homogeneity inside the main class of interest, that is, the “Burned area” class. Nevertheless, the classification using the dataset with all the available features is practically almost impossible to be conducted using the common remote sensing software. Therefore, the use of an efficient dimensionality reduction method should be regarded as a perquisite step when additional information is added in the classification process.
The experimental results from the SVM object-based classifications in the full space (119 extracted features) and in the reduced space (FuzCoC selected features), showed that the latter results in increased classification accuracies. The absolute gains in accuracy were marginal for the easier classification task of the Parnitha dataset, but the difference was substantial for the more challenging Rhodes dataset. These findings support the argument that an efficient feature selection pre-filtering procedure is always beneficial in conjunction with the object-based image analysis.
The examination and comparison of the two developed classification schemes regarding their use on an operational basis shows that the proposed methodologies present some implementation challenges. Nevertheless, the object-based classification schemes meet the requirements for operational burned area mapping to a higher degree compared to the pixel-based approaches. More specifically, the object-based approach is less labor-intensive and time-consuming than the pixel-based one. Additionally, the burned area maps derived from the SVM object-based classification scheme are more accurate and reliable than the pixel-based burned area maps. Presently, the main drawback of the object-oriented SVM methods is the fact that they are not implemented in a single software interface. Overall, our paper makes a strong case over the use of advanced image analysis techniques in burned area mapping. Future incorporation of those techniques in a commercial software will open perspectives in operational burned area mapping.