1. Introduction
Storm events with strong wind gusts can cause severe windthrows to large forest areas—their immediate assessment is crucial for several reasons. Although windthrow is a significant natural phenomenon in forests, it causes direct economic losses for forestry, which may be heightened by fungal infestation of the windthrown trunks [
1]. Bark beetle dispersal should be anticipated to protect the remaining standing trees and prevent subsequent indirect economic losses [
2,
3]. Thus, foresters aim to immediately clear up windthrown areas and plan an adapted reforestation [
4]. Since resources of manpower are generally limited, both the extent and degree of the affected forest area and its accessibility must be known shortly after the storm. In recent years, extreme storm events have regularly hit central Europe; their frequency is expected to increase in the future due to a changing climate [
5,
6]. Usually, a first rough overview of windthrown areas is obtained by field surveys or by the interpretation of aerial imagery (airplane and unmanned aerial vehicle (UAV)). As both methods are time consuming and therefore costly, and field surveys are often restricted due to security concerns, there is an increasing need for faster and more efficient methods.
Remote sensing is continually evolving to offer efficient tools to obtain the required information more quickly (compared to the methods mentioned above), objectively, and over large areas. Depending on the size of the area of interest, the detection of windthrows is mainly carried out using data either from optical sensors, airborne laser scanning (ALS), or synthetic aperture radar (SAR). Both spaceborne and airborne imagery with varying spatial resolutions have been used for windthrow detection. Landsat imagery with a spatial resolution of 30 m was mainly used for relatively generalised mapping of windthrows over large areas [
7,
8,
9]. More detailed detections of windthrows were obtained by Elatawneh et al. [
10] and Einzmann et al. [
11] based on RapidEye imagery with a spatial resolution of 5 m and change detection techniques. Several studies showed good single windthrown tree detection rates using high spatial resolution imagery from airplanes or UAVs [
12,
13,
14]. A common critical shortcoming of optical imagery is its dependence on daylight and cloud-free conditions, which usually is not to be expected immediately after a storm event. For example, Elatawneh et al. [
10] reported on latencies of six weeks to obtain cloud-free imagery. These uncertainties regarding data availability diminishes their applicability in the framework of fast response. Promising and even more advantageous results were reported using approaches based on ALS data [
15,
16]. A great benefit of ALS data is the feasibility to detect single windthrown trees that were covered by the canopies of intact foliage. The disadvantages are relatively high cost and its usually limited applicability over larger areas.
Great potential is given by the use of SAR data, since they are acquired virtually independently of daylight and cloud cover. Active remote sensing techniques, such as SAR, can provide reliable and consistent data at different scales with a high temporal resolution [
17]. Employing SAR data is therefore becoming indispensable in the framework of fast response. Generally, SAR backscatter from forested areas has been reported to be dependent on object and a few sensor properties, such as frequency (X-, C-, L-band) and polarisation, e.g., co-polarised horizontal-horizontal (HH) and vertical-vertical (VV), and the cross-polarised vertical-horizontal (VH) and horizontal-vertical (HV). If a higher frequency, e.g., C-band, is used, then the microwave energy is mainly scattered back to the sensor from the upper tree crown and less from beneath. In contrast, more backscatter information is obtained from beneath if lower frequencies are used (e.g., L-band), as stronger penetration into the canopy is observed [
18]. The effect of the polarisation is less distinct, but cross-polarised (VH or HV) backscatter generally shows higher correlation with biomass than co-polarised channels [
19]. Object properties that might affect backscatter from forested areas can be separated into two categories. The first consists of properties that directly affect the dielectric constant of the trees, specifically the wood’s temperature [
20], especially when below the freezing point [
21], and the internal and external moisture conditions [
22,
23]. Particularly, wet snow impacts backscatter to a great extent [
24]. The second comprises structural properties of a forest, such as the size, orientation, and spatial pattern of trees as a whole, but also of their branches and leaves [
25,
26,
27]. In addition, Ahern et al. [
28] and Rüetschi et al. [
29] reported on a distinct impact of phenological changes in foliage on backscatter in broadleaved forests. Thus, SAR backscatter can be used to detect major changes in forest structure, e.g., as caused by windthrows.
In the past, only few studies have addressed this topic. For example, Green [
30] used airborne AIRSAR data to show the impact of windthrown areas on L- and C-band backscatter. Fransson et al. [
31] found a significant difference in CARABAS-II VHF backscatter between intact and windthrown areas at a spatial resolution of 2.5 m. In addition to these studies using airborne sensors, spaceborne SARs have been successfully applied to detect windthrows. The AIRSAR-based findings of Green [
30] were supported and extended in the studies of Eriksson et al. [
32] and Thiele et al. [
33]—they reported on a sensitivity of HH backscatter to windthrown areas in ALOS PALSAR L-band, RADARSAT-2 C-band, and TerraSAR-X X-band data. The post-event increase in backscatter within windthrown areas in C-band was partly attributed to the increased surface roughness [
32]. Ulander et al. [
34] tested different spatial resolutions between 10 and 30 m from RADARSAT-1 and ENVISAT-ASAR C-band data to detect windthrows. They concluded that the spatial resolution of 30 m from ASAR data using Image Mode Precision (IMP) and Alternating Polarisation Precision (APP) products was not sufficient. In contrast, windthrown areas were partly detected using 10 m spatial resolution RADARSAT-1 HH data acquired in fine mode. Only recently, a threshold-based approach using ALOS PALSAR L-band data enabled the successful detection of windthrows [
35]. The potential for an immediate detection of windthrows has gained currency with Sentinel-1 (S-1) C-band data, which provide a spatial resolution similar to RADARSAT-1 but with improved radiometric stability and a user-friendly free and open data policy [
36].
In the present study, the potential of S-1 backscatter data to detect the locations of windthrows was evaluated in the framework of fast response. For this appraisal, we considered two study areas in Switzerland and northern Germany that were hit by two different storm events. The study areas differ regarding topography, climate, forest structure, tree species composition, and forest management. In a first step, temporal composites that include several S-1 acquisitions were generated for close time periods before and after the storm events. In a second step, for the study area in Switzerland, differences in backscatter before and after the storm event and between windthrown and intact forest areas were analysed. In a third step, in the same study area, a change detection method consisting of two parameters was developed and trained to detect windthrows with a minimum size of 0.5 ha. With our method, we were able to rapidly detect the locations of windthrown areas. Since it is not possible to determine their exact boundary, no quantitative use of area estimates is intended. The same method was applied to the German study area and then validated. Finally, the use of different numbers of S-1 acquisitions and their influence on the correct detection of windthrows was determined, and the required minimum latency for acquisitions after a storm event was assessed.
3. Results
3.1. Statistics from Image Differencing
Table 3 shows statistics of the
difference composites calculated within the windthrow reference and the whole forest mask of the study area CH. At VV-pol., the mean backscatter difference from mainly intact forest was negligible: 0.05 dB. The mean over windthrown areas was higher at 0.5 dB. The standard deviation was slightly higher within windthrown areas, with 1.78 vs. 1.58 dB. Similar backscatter behaviour was observed at VH polarisation, with different mean backscatter values of 0.31 and 0.97 dB from mainly intact vs. windthrown forest. The standard deviation changed from 1.6 to 1.81 dB for the two cases. Thus, a distinct change in backscatter was recorded at VV- and VH-pol., with higher mean values of 0.45 and 0.68 dB from the windthrown areas, respectively. This indicated higher backscatter in both polarisations from these areas after the storm. Mann-Whitney-U-tests for both polarisations indicated a highly significant (
p < 0.001) disparity in the distribution of the
difference between values from within the whole forest mask and the windthrow reference mask.
The scatterplots that are shown in
Figure 7 illustrate the same effect. The distribution of the point cloud in the scatterplots differs between the two rows. In
Figure 7a,b, showing backscatter from the
whole forest mask, the point cloud is approximately located on the equivalence line. In
Figure 7c,d however, the centre of the point cloud is observed
above the equivalence line, indicating higher backscatter after the storm event. When comparing the scatterplots in
Figure 7c,d, this observation is better articulated in VH-pol., consistent with the larger degree of positive change shown in
Table 3.
3.2. Parameter Combination Evaluation—Training of Method in Development Area
Figure 8 exemplarily shows four windthrow maps that were based on different parameter combinations. Obviously, the parameter combination has a great influence on the produced map. Increasing the value of the parameter
a resulted in fewer windthrow objects in the map. Changing the parameter
a from 2.8 to 2.9, while keeping the parameter
n constant, resulted in fewer objects in the whole map, as seen in
Figure 8a,b. The same applied to an increase of the parameter
n. With an
n of 25 instead of 15 and a constant value of
a, fewer objects were classified as windthrown (
Figure 8a,c). Even fewer objects were classified as windthrown when higher
a (2.9) and
n (27) parameters were applied (see
Figure 8d).
Since it was assumed that the detection performance of windthrown areas strongly depends on the parameter combination, further investigations on this issue were carried out in the study area CH. Windthrow maps that were based on different parameter combinations were produced and compared with the reference windthrown areas.
The resulting PAs and UAs are listed in the performance measure matrices in
Figure 9. The highest PAs of 0.92 were obtained when low values for
n and
a were used (see
Figure 9a). In contrast to the PAs, high values for
n and
a produced higher UAs, with a maximum of 0.81 (
Figure 9b).
Taking both performance measures to be equally critical, the best parameter combination was obtained with n = 27 and a = 2.9, resulting in a correct detection of 22 (from a total of 26) reference windthrown areas (PA of 0.85). Twenty-four (from a total of 37) of the suggested windthrown areas were included in the reference, resulting in a UA of 0.65.
Visual inspection of the windthrow map with the best performance measures showed that most referenced windthrown areas were correctly detected. However, the retrieved areas of these objects were not entirely congruent, i.e., often underestimated in comparison to the area of the reference. Furthermore, the lower UA value was mainly caused by two issues. First, five of the suggested areas were included in the original reference but were then excluded due to the minimum windthrow extent limit of 0.5 ha. Second, a systematic error was observed at forest edges, which resulted in six wrongly suggested areas.
3.3. Method Evaluation in the German Validation Area Mecklenburg-Vorpommern
To test the applicability of the method, the best parameter combination for the study area CH was tested in the independent study area DE. As three different windthrow classes were available in the reference of this area, the performance measures PA and UA were calculated for each class.
Table 4 shows the contingency tables that were generated for each class. Using the parameter combination of
a = 2.9,
n = 27, the method suggested 33 windthrow objects in the area. Twenty-eight of the suggested areas were referenced as one of the three windthrow classes, so generally windthrow was detected with a UA of 0.85. Class-specific accuracies were highly diverse between the three classes.
Seven out of the eight referenced ‘areal windthrow’ areas were suggested by the method, resulting in a fairly high PA of 0.88 for the class. In contrast, the class-specific UA was low, at 0.21. The two classes with scattered windthrows had worse performance. For the class ‘single standing trees’, PA and UA were quite low at 0.29 and 0.15, respectively. The PA of the class ‘single fallen trees’ was even lower, at 0.07. The high UA value of 0.48 was mainly due to the much higher number of 225 referenced windthrown areas of the type ‘single fallen trees’.
Similar characteristics as in CH were observed when the windthrow map was visually inspected. The areas of the suggested objects also underestimated the true extent of the windthrows. Two referenced windthrown areas were detected but then excluded due to their extent. However, a systematic error at forest edges was not observed in DE.
3.4. Influence of the Number of S-1 Acquisitions
Figure 10 illustrates the influence of the number of S-1 acquisitions that were used in the LRW processing on the quality of the detection map. Generally, best performance was achieved using five or six post-storm S-1 acquisitions (map quality corresponds to the mean of PA and UA). Only in this case was a map quality of ≥ 0.75 observed for both study areas. A difference between the two study areas is seen in the shape of the lines. For the study area CH, the map quality gradually rose from a low 0.59 with one acquisition to a higher quality of 0.75 with five or six acquisitions. A gradual decrease to 0.65 was then observed with 10 acquisitions. The general shape of the line was similar for the study area DE: an increase from 0.55 with one acquisition to around 0.9 with five and six, followed by a decrease when using additional acquisitions. However, the map quality for the study areas DE did not rise as gradually. The ascending acquisitions appeared to contribute more to a higher map quality than the descending ones.
4. Discussion
The results clearly show that windthrows in forests have significant influence on C-band backscatter. Higher backscatter from windthrown areas was observed in both polarisations shortly after the storm event. Given that backscatter changes in intact forest areas were less substantial, it was possible to use this diverging behaviour to detect the location of windthrown areas. The detection rate for areal windthrows was very promising for two independent study areas. The detection of scattered windthrown trees was not feasible with the presented approach.
4.1. Diverging Backscatter Behaviour between Windthrown and Intact Forest Areas
Positive changes in backscatter from windthrown areas were observed after the storm event in both polarisations (VV, VH). Backscatter from forested areas is strongly influenced by the structural properties and the arrangement of the trees [
25,
27]. As a windthrow usually causes changes to the forest structure of an area, a change in backscatter from this area can be expected as well. In both study areas, changes in backscatter were observed within a short period of time before and after the storm event. Other factors that cause considerable changes to the forest structure in such a short period of time could be neglected in both cases. Hence, a substantial change in backscatter at a location was expected to be a change in forest structure indicating storm damage at this location.
Other influences on backscatter that cause potential errors were mitigated by applying multitemporal compositing of acquisitions from ascending and descending tracks, reducing the influence of both signal noise and speckle. A similar mitigating effect was expected on the potential influence of precipitation, as the impact of single precipitation events was reduced. A strong influence from changing temperatures appeared to be negligible in both our datasets, as the temperatures stayed consistently above the freezing point in both areas throughout the acquisition periods.
Whereas almost no change in backscatter from the mainly intact forest areas could be observed at VV-pol., the change was substantially higher in windthrown areas (after the storm), clearly indicating a change in forest structure. The increased backscatter could be attributed to two causes: (1) the more ‘chaotic’ arrangement of the microwave energy scattering trunks and branches in windthrown areas leading to an increased surface roughness (cf. [
32]) and (2) a reduced attenuation of the microwave energy within the forest canopy leading to a greater contribution from ground scattering (cf. [
22]). A higher standard deviation was noted within the damaged areas. This can also be explained by the more ‘chaotic’ arrangement with more diverse scatterers than before the storm event. The same change in standard deviation was also observed at VH-pol., coupled with an even higher positive change in backscatter in windthrown areas. A plausible explanation for this behaviour could be that the scattering mechanisms change as the wooden material is oriented more randomly post-windthrow. Cross-polarised backscatter is more sensitive to this random arrangement of the scatterers than co-polarised channels [
19]. Thus, a higher sensitivity of VH-pol. to structural changes in windthrows could be expected. Unexpectedly, we also measured a slightly positive change in backscatter in supposedly intact forest areas. It was assumed that this was caused by smaller windthrown areas or even single windthrown trees that were not visible (due to closed canopy, topography, and shadows) in the Planet imagery for the reference production.
Comparisons of our measured SAR backscatter from windthrown areas with other studies using spaceborne data revealed similar findings. Similar to our study, Eriksson et al. [
32] also obtained higher C-band backscatter (HH-pol.) from windthrown areas after a storm event in coniferous forest stands in southern Sweden. In addition to Radarsat-2 C-band, they also inspected ALOS PALSAR L- and TerraSAR-X X-band data. Analysis of HH-pol. backscatter for both revealed lower and higher backscatter after the storm event for L- and X-band, respectively. Lower backscatter from windthrown areas was also observed in a study that was carried out in mixed temperate forest in southern Germany using ALOS PALSAR L-band data [
35]. Thus, it appears that the change in backscatter from windthrown areas tends to be positive at shorter radar wavelengths and negative at longer ones.
4.2. Windthrow Maps
The two windthrow maps based on the proposed method strongly depended on the chosen parameter combination (
a and
n). The map that was based on the best combination for the study area CH showed promising performance measures. Especially the high PA should meet the demands of potential map operators. The UA was not as high as the PA, but this could be explained to a large degree. First, almost half of the apparent false positives were windthrown areas that were excluded due to their extent. If they were also excluded from the UA calculation, then a value of 0.75 would have been achieved. As map operators are mainly interested in the location and distribution of windthrow, we assume that they would not be disturbed in their work by suggested windthrown areas smaller than 0.5 ha. Second, errors in the forest mask caused many false positives at forest edges. The forest mask used for the study area CH was generated based on an aerial image point cloud. Points that are located at the forest edges are prone to misplacement, as viewing geometry and horizontal branches have a considerable influence on the point cloud generation [
49].
Since better independent reference data was available for the study area DE, it was possible to conduct a precise performance evaluation by defining three different classes of windthrow severity. Comparison of the PA values of the different classes indicated that the method produced satisfactory results for the class ‘areal windthrow’. However, the performance measures were not satisfactory for the other two classes consisting of scattered windthrown trees. This could mainly be due to the spatial resolution of 20 m of Sentinel-1 [
36]. With 20 m spatial resolution, a single windthrown tree, even a tall tree, is not expected to be detected, since a minimal area of windthrow is required to obtain sufficient signal in the measured backscatter.
The training of the method in the study area CH might be influenced by the coarser spatial resolution of the Planet imagery, which only allowed for a collection of areal windthrows as reference data. As the selected parameters for the windthrow map for the study area DE were developed based on the reference data of the study area CH, the difference in performance was not surprising. Similar to the results that were obtained from the study area CH, a few false positives could be attributed to cases of windthrown areas smaller than 0.5 ha (UA of 0.9 if excluded from UA calculation). The issue with forest edges was not observed in DE. There were fewer misplaced forest edges because the forest mask for this area was based on high-resolution digital aerial images. This underscores the importance of a recent and accurate forest mask for a successful application of the detection method.
A striking difference between the two study areas was the number of suggested windthrown objects in the two maps. Fewer objects were suggested for the study area DE than for the study area CH, although the extent of this area was about 15 times larger. This could be due to three different reasons. First, the wind speeds of the storm event in the study area CH were considerably higher. The logical consequence would be more windthrow in this area. Second, even if both study areas consisted of mixed temperate forest, their species compositions were different. Third, the management regimes also differed between the two study areas. More strictly arranged plantations per area were counted in the study area DE. Analysis of the species’ or the management regime’s influence on the result was beyond the scope of this study.
4.3. Influence of the Number of S-1 Acquisitions
Both similarities and a difference were observed in the analysis of the required number of S-1 acquisitions for a satisfactory detection performance. In general, the map quality was best for both study areas, when five or six acquisitions were used in the LRW processing. This rise in quality can be explained by the supplementary information from the additional acquisitions. In general, the more acquisitions that were integrated in the LRW processing, the more the high internal variation in the SAR data (signal noise and speckle) was reduced. The subsequent decrease when using six to ten acquisitions can be explained by the temporal variation of the scatterers in the region: the longer the LRW processing time period, the greater the expected temporal variation, caused e.g., by clean-up activities. The difference in the shape of the line may be explained by the sequence of the acquisitions’ pass directions. Whereas ascending and descending acquisitions alternated for the study area CH, the sequence was different for the study area DE. The first ascending acquisition was only included in the
composite with three acquisitions. Hence, it would be expected to gain a higher increase in map quality as the ascending acquisition contains less redundant information than the two of the same pass direction. In addition, the second substantial increase between four and five acquisitions could be attributed to different meteorological conditions in the area. Meteorological records of the area indicated drier conditions for the last acquisition of the 16 October 2017 as compared to the others, which resulted in different backscatter that included even more supplementary information. The strong increase in quality from nine to ten acquisitions could be explained by the fact that only very few windthrow objects were suggested in the map based on ten acquisitions. This resulted in a perfect UA of 1 that lead to a high map quality. Surprisingly, slight declines were observed for the study area DE when the second and the fourth acquisitions were included. This might be attributed to the fact that the maps of DE were generated using the best parameter combination of CH (see
Section 3.2).
4.4. Comparison with Existing Windthrow Detection Methods
When compared with recent windthrow detection methods that used spaceborne remote sensing data, our approach was competitive. Thiele et al. [
33] generated change maps using TerraSAR-X Spotlight data with a spatial resolution of about 1 m. Their approach achieved a UA of about 0.7, however, no information was provided on the PA. The recent study of Tanase et al. [
35] that used L-band data with a pixel spacing of 30 m achieved a PA of 0.67 and a UA of 0.54 shortly after the storm event. Comparable methods that used optical data attained higher accuracies than the aforementioned SAR-based studies. Einzmann et al. [
11] achieved PA and UA of about 0.95 using high resolution RapidEye data to detect windthrown areas in mixed temperate forests in southern Germany. Another study using medium resolution LANDSAT data detected windthrow in mixed temperate forests of European Russia and boreal forests of Minnesota with producer’s and user’s accuracies of approximately 0.68 and 0.95, respectively [
9]. However, the inevitable disadvantage of methods using optical data is their dependence on daylight and weather, impeding their potential for rapid application after a storm event.
Table 5 schematically illustrates the advantages of our multi-track LRW SAR approach in a fast response framework by comparing the latencies to secure the required data, cost, and areal coverage between different remote sensing systems. Airborne sensors, compared to spaceborne, are often expensive (ordered flights), typically restricted to smaller regions, and short latency is not guaranteed due to their weather dependence. In contrast, spaceborne sensors cover larger areas and can be—depending on the sensor—relatively cheap. Latency is the main difference between spaceborne optical and SAR. The use of optical data is mainly restricted due to cloud cover. The advantage of multi- as compared to exact repeat track SAR is the lower latency of the first. The approach using multiple tracks enables a quicker collection of the required data, as it is able to handle SAR acquisitions made with differing viewing angles.
4.5. Practical Use
In addition to the presented and discussed performance measures, a few points should be mentioned for practical map operators. Our approach underestimated the extent of the windthrown area for all windthrow objects. Thus, one should be aware that the produced windthrow map represents the core region and that it is not a true representation of the actual area extent of windthrow within the affected region. Therefore, quantitative applications, e.g., estimating windthrown timber volume or number of stems are not feasible with our approach. Nevertheless, the map accurately indicated the correct locations of many windthrows in both study areas. By getting an overview of the potential locations, one gathers a perception of the windthrow distribution within a region.
Investigation of the influence of the number of S-1 acquisitions used in the LRW processing indicated that around five acquisitions were required for a satisfactory detection rate in both study areas. Thus, the generation of a windthrow map could be conducted within approximately two weeks after a storm event in Europe. The actual time period is mainly dependent on the latitude of the affected region. Generally, the higher the latitude, the higher the S-1 revisit rate, and consequently the shorter the required time period [
50].
For the present study, the parameter combination was chosen that resulted in the best performance with respect to UA and PA in the study area CH. A different parameter combination might result in a better performance for other areas. The two parameters can be changed and set in a flexible way, allowing for a modification of the method’s sensitivity according to its scope. Decreasing a (magnitude of backscatter change) and/or decreasing n (minimal count of associated candidate pixels) reduced the sensitivity, resulting in more suggested windthrow objects and a representation that is closer to the true windthrown area. Beyond providing a better representation of the area, it usually enhanced PA while simultaneously degrading UA. The opposite behaviour was observed when a and n were increased. Hence, the map operator can help to decide whether a high PA or UA, or a trade-off in between the two, is preferred and best meets the user’s specific requirements.
The analysis of this study and the performance measures mentioned in this manuscript are valid for windthrown areas of a minimum size of 0.5 ha. Lower performance measures should be expected when applying the method for areas that are smaller than 0.5 ha. Nevertheless, it was observed that a few smaller areas were detected, as discussed in
Section 4.2.
4.6. Outlook
With the launch of the RADARSAT Constellation Mission (RCM) planned in early 2019, an increased number of SAR C-band acquisitions will be potentially available soon [
51]. When combined with S-1 data, the time period could be substantially shorter to receive the number of required acquisitions for a windthrow detection of similar quality. With three further sensors available soon, the time period could be significantly reduced, allowing a rapid detection of windthrow within less than one week.
We are currently working on applying the proposed method to storm events in winter. Winter conditions are expected to be subject to more obstacles to a successful application of the method. Because temperatures that are below the freezing point would have to be reckoned with, the influence of freeze/thaw on the backscatter is likely to be confounded with the windthrow signal. In addition, snow cover could have an impact too, as wet snow has been reported to lower C-band sparse forest backscatter by up to 2 dB [
24].