Towards spatial localisation of harmful algal blooms;statistics-based spatial anomaly detection

J. D. Shutlera and M. G. Grantb and P. I. Millera

aPlymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH U.K.;bDept. Electronics and Computer Science, University of Southampton, Southampton, SO17

1BJ U.K.

ABSTRACTHarmful algal blooms are believed to be increasing in occurrence and their toxins can be concentrated by filter-feeding shellfish and cause amnesia or paralysis when ingested. As a result fisheries and beaches in the vicinity ofblooms may need to be closed and the local population informed. For this avoidance planning timely informationon the existence of a bloom, its species and an accurate map of its extent would be prudent. Current researchto detect these blooms from space has mainly concentrated on spectral approaches towards determining species.We present a novel statistics-based background-subtraction technique that produces improved descriptions ofan anomaly’s extent from remotely-sensed ocean colour data. This is achieved by extracting bulk informationfrom a background model; this is complemented by a computer vision ramp filtering technique to specificallydetect the perimeter of the anomaly. The complete extraction technique uses temporal-variance estimates whichcontrol the subtraction of the scene of interest from the time-weighted background estimate, producing confidencemaps of anomaly extent. Through the variance estimates the method learns the associated noise present in thedata sequence, providing robustness, and allowing generic application. Further, the use of the median for thebackground model reduces the effects of anomalies that appear within the time sequence used to generate it,allowing seasonal variations in the background levels to be closely followed. To illustrate the detection algorithm’sapplication, it has been applied to two spectrally different oceanic regions.

Keywords: HAB, anomaly detection, remote sensing, ocean colour, ramps

1. INTRODUCTION1.1. Harmful Algal BloomsHarmful algal blooms (HABs) are believed to be increasing in occurrence around the world. Algal toxins canbe concentrated by filter-feeding shellfish and cause amnesia or paralysis when ingested (1). As a result thisis an area of growing international concern. Current collaborative programmes include the Global Ecologyand Oceanography of Harmful Algal Blooms (GEOHAB), a joint Scientific Committee on Oceanic Research -Intergovernmental Oceanographic Commission (SCOR-IOC) programme on HABs; the Harmful Algal BloomInitiation and Prediction in Large European Marine Ecosystems (HABILE), a European Commission projectand the Data Integration System for MARine pollution and water quality (DISMAR), an Information SocietyTechnologies (IST) project. The discrimination of these blooms from space benefits both the capability of earlywarning systems and the study of environmental factors affecting the initiation of blooms. Measurements ofocean colour (visible light measurements between 400 − 700 nm) are routinely sampled by various orbitingsensors including: the Orbital Science Corporation Sea Viewing Wide Field of View Sensor (SeaWiFS), NASA’sModerate Resolution Imaging Spectrometer (MODIS) and the European Space Agency’s Medium ResolutionImaging Spectrometer (MERIS). These data are processed to determine water-leaving radiance and estimates ofchlorophyll enabling monitoring of algal blooms. For purely scientific monitoring, information on the existenceof a bloom is required to initiate further investigation through in-situ sampling. However, for early warning andavoidance planning the existence of a bloom, its species, an accurate map of its spatial extent and predictedgrowth or movement would all be useful. Current modelling efforts are working towards predicting the growthof algal blooms e.g. (2). Due to the spatially large areas over which these blooms occur, Earth observation dataprovides input into these models. Earth observation research has tended to concentrate on determining species,e.g. (3; 4; 5; 6), whereas these data are also ideally suited to accurately map the spatial extent of a bloom.

Corresponding author information: J. D. Shutler - email: jams@pml.ac.uk

1.2. Determining spatial extent using computer vision techniques

The inherent problem of occlusion in ocean colour data (by sun glint and clouds) provides a difficult dataset toanalyse. The high occlusion supports the use of analyses which exploit any temporal correlation in the imagesequence, reducing the overall effect of the scene by scene occlusion. Temporal evidence gathering techniques,e.g. (7), are able to handle occlusion; however, as blooms are of variable shape (or spatially non-specific), theseapproaches will fail, as essentially they require some form of spatial model. Therefore, we concentrate on atemporal statistical approach, removing the need for a specific spatial model.

Background subtraction is a simple but effective way of determining areas within a scene that have changed.For example, subtracting a daily ocean chlorophyll image from a background-level chlorophyll description pro-duces a chlorophyll anomaly map (8). Thresholding these differences can locate areas of further interest forguiding in-situ sampling. This method can be applied to locate algal blooms, as in the simplest case bloomswill exhibit increased levels of chlorophyll in comparison to the background levels. However, a mean backgroundimage may be biased by bloom events that occur within the image sequence used to calculate it, skewing thebackground chlorophyll levels. To overcome this, a two week gap between the end of the time series used tocalculate the mean image and the image to be subtracted can be introduced (8). This assumes that anomalieslast no longer than 2 weeks, and thus do not affect the background model. However, seasonal changes in this twoweek gap may be lost and blooms may last longer than 2 weeks. The use of a median relaxes the need for such agap as it will be less affected by blooms (anomalous values) within the time series used to generate it. This allowsseasonal changes to be more closely followed. High concentrations of chlorophyll are not necessarily conclusiveevidence of a HAB, supporting a synergy approach. (6) used a simple multi-band median subtraction method tohelp reduce false positives for a multi-spectral species-classifier trained to detect HAB events. The false positiveswere caused by high suspended sediment and/or harmless coccolithophore blooms causing the breakdown of thechlorophyll algorithm. Rolling estimates of the median background chlorophyll-a and water-leaving radiancelevels were determined and assumed to be bloom free. Global thresholds were then applied to the subtracteddata, the values of which were region specific. These foreground estimates were then used to restrict the areaanalysed by the classifier. However, spatial variations within a region will also exist; this combined with noisedue to the sensor, atmospheric correction, signal processing methods and the pixel’s position within a swath,further complicate the detection process. These characteristics of the data can cause the breakdown of the simplebackground subtraction and thresholding methods, suggesting the need for a more robust and generic solution.

In this paper we develop a statistically-based spatial anomaly detection technique for algal bloom detection.We present a subtraction method inspired by (9) (referred to as McKenna-Jabri hereafter), who demonstratedimprovements over simple background subtraction by using both intensity and edge information in a mutuallyreinforcing fashion to extract the silhouette of a walking person. Additionally, the temporal-variance of eachpixel within the time series governs the subtraction from the background model. Thus, the per-pixel varianceprovides a combined description of the noise and local variation within the data set and removes the need forglobal thresholds. For anomaly detection, we make some modifications to the McKenna-Jabri method. First, weuse a temporal-median in place of the temporal-mean, improving robustness to algal blooms appearing withinthe time series analysed. Second, discrete boundaries between algae and non-algae tend not to exist in oceancolour data, instead manifesting as a gradual transition or ramp response. Therefore, we further modify theapproach to use ramp filtering techniques (10) in place of edge data. Together, this combines bulk and perimeterinformation and provides a more accurate spatial description of bloom extent.To demonstrate the algorithm’sapplication, results from two different oceanic regions are presented and discussed, then conclusions are drawn.

2. ALGORITHM

Firstly, ramp detection is needed for an accurate description of the anomaly’s perimeter. Two background modelsare then needed, one intensity model and one ramp model. These models contain temporal median and varianceestimates used in the subtraction stage to create confidence maps of foreground data (anomalies) and backgrounddata. These intensity and ramp confidence maps are then combined to produce a binary mask describing theanomaly’s extent.

2.1. Optimal ramp filtering

McKenna-Jabri used the Sobel edge detector to improve the extractions of human silhouettes. Figure 1(b) showsthe result of applying a 3 × 3 Sobel edge detector to ocean colour data. The features of interest are the algalblooms present in the English channel and south of the Irish coast. Due to the structure of the data, many moreapparent edges have been labelled producing a cluttered and confusing result. This type of result has drivenresearch into specific edge detectors for marine remotely sensed data. e.g. (11). However, the features of interestare the edges of algal anomalies, where the concentration of the algal species gradually dissipates, appearing asa gradual variation in the data. This gradual variation can be increasing or decreasing dependent on the dataset(water leaving radiance, absorption, chlorophyll-a etc) suggesting a ramp filter would be more suitable, in placeof an edge detector. (10) (referred to as Petrou-Kittler hereafter) derived optimal smoothing filters for rampedges by noting that a step edge corrupted by Gaussian noise produces a ramp. Figure 1(e) and Figure 1(f) showtwo example transects through water leaving radiance data at 443 nm (Lw443) crossing the boundary of the algalbloom shown in Figure 1(d). These transects have then been overlain with the ramp model of Petrou-Kittler,illustrating the gradual change in response across the bloom boundary. The model is defined as:

c(x) ={

1− exp(−sx/2) for x ≥ 0exp(sx/2) for x ≤ 0 (1)

From this model, an optimal ramp smoothing filter defined in polar coordinates (r,θ), symmetric about r = 0 isdefined as:

h(r) =

eAr [L1 sin(Ar) + L2 cos(Ar)]+e−Ar [L3 sin(Ar) + L4 cos(Ar)] −w ≤ r ≤ 0+L5r + L6e

sr + L7

−h(−r) 0 < r ≤ w

(2)

w is the half width of the filter and h(−w) = 0, h(w) = 0. The parameters L1 −L7 and A are dependent on thehalf width of the filter w and are provided in table II of (10). To guarantee adequate sampling w is chosen suchthat w ≤ 3τ , where τ is the period in pixels of the features to be detected. As can be seen from equation 1, thevalue of s is defined by the gradient of the ramp you desire to detect and w is dependent on s and the desiredaccuracy of the ramp filter. Therefore, s and w were set by by examining transects of SeaWiFS data throughverified blooms (at Lw443, Lw490 and Lw555). The values used here are w = 7, s = 0.3, as illustrated in Figure1. Figure 1(c) shows the result of applying this ramp filter to the data of Figure 1(a).

2.2. Background model

The background model is generated using the time series data prior to and including the scene of interest.McKenna-Jabri defined the background model to consist of a temporal background image and associated variance,one for the perimeter descriptor and one for the intensity. The background images are produced using a weightedsum technique or exponential forgetting. This allows more recent events to have more of an effect on thebackground model, the strength of which can be adjusted. The background image effectively becomes a longterm median average of the scene, similar to a long exposure time on photographic film. The time weightedmedian pixel MIxy generated from I images is:

MIxy=

I∑i=0

T Medixy+ (1− T ) Mi−1xy

(3)

where Medixy is the new median value computed up to and including image i, T is the time constant andMi−1xy

is the previous time weighted median value estimate, up to and including image i − 1. The associatedtime weighted variance pixel σ2

Ixyis defined as:

σ2Ixy

=I∑

i=0

T (Pixy −MIxy )2 + (1− T ) σ2i−1xy

(4)

(a) Chlorophyll-a response. (b) Sobel operator result.

(c) Ramp filter result.

(e)

S.W. England

(f)

(d) Example transects through an algalbloom (manually labelled boundary).

0

0.2

0.4

-30 -20 -10 0 10 20 30

c(x)

x

0

0.2

0.4

-30 -20 -10 0 10 20 30

c(x)

x

0.6

0.8

1

0.6

0.8

1

s=0.3Transect

(e) Transect through Lw443 data from 1(d).

0

0.2

0.4

-30 -20 -10 0 10 20 30

c(x)

x

0

0.2

0.4

-30 -20 -10 0 10 20 30

c(x)

x

0.6

0.8

1

0.6

0.8

1

s=0.3Transect

(f) Transect through Lw443 data from 1(d).

Figure 1. 1(a) SeaWiFS data at 1225 UTC 20 July 2000; 1(b) result of edge detection and 1(c) the equivalent rampfilter response. 1(e) and 1(f) show example transects over the Karenia mikimotoi bloom boundary in 1(d) (off the UK’sSouth West coast at 1302 UTC 20 July 2002), overlaid with the Petrou-Kittler ramp model c(x) with s = 0.3

where σ2i−1xy

is the pixel variance up to and including image i − 1. We note the use of the median in thevariance calculation in place of the mean. Due to the nature of the application dataset the mean will be a biasedestimate of the average, whereas, the median will be less effected by anomalous values making it a more suitablereference for the variance calculation. The median is calculated by a iterative method to reduce memory andcomputational costs incurred when processing large sequences of high bit-depth images. The background modelnow consists of four different images. Two time-weighted median images of intensity and ramp data ( MiIxy

,MrIxy ), and their corresponding variance estimates (σ2iIxy , σ2rIxy ).

2.3. Background subtraction

The subtraction is independently performed on the intensity and ramp data producing two confidence maps whichare then combined. The confidence maps label regions within the image sequence as foreground or background.The higher the confidence C, the more likely the pixel PIxy

is part of the foreground. The confidence levelis set using two thresholding levels, m and n. These define the number of standard deviations between theforeground and background objects. Subtraction of image I (the last image in the time series used to calculatethe background models) begins with:

4DIxy = | MIxy − PIxy | (5)

which is the absolute difference between the background and the current pixel PIxy. We are interested in the

percentage difference between the background pixel value and the current pixel value, instead of the absolutedifference. Therefore we define the reliability R of the pixel being part of the foreground as:

RIxy=4DIxy

GIxy

(6)

and to ensure that RIxyis bounded by 1:

GIxy= max{PIxy

,MIxy} (7)

The weighted measure RIxy4D

Ixycan be used to determine the confidence. If RIxy 4D

Ixy< nσIxy then

the pixel has a 0% confidence, whereas if RIxy 4DIxy

> mσIxy then it has a 100% confidence level. All othercases are scaled linearly between 0 and 100% using:

CIxy=

(RIxy4D

Ixy− n σIxy )

(m σIxy − n σIxy )(8)

where σIxyis the temporal standard deviation. This process is independently repeated for both the ramp and

intensity data, producing two sets of confidence maps (CiIxy , CrIxy ) which are then combined to produce thefinal confidence map:

CcIxy= max{CrIxy

, CiIxy} (9)

2.4. Handling zero values in the ramp filter

Ocean colour data will inherently contain missing values due to occlusion by clouds, sun glint and other atmo-spheric effects. Areas of coastline will have a similar effect as once inland the ocean colour response will be zero.Dependent on the data being analysed, these areas of zero values can produce false ramp responses. These effectscan be reduced by excluding any areas containing zero values (e.g. areas of clouds) when generating the rampbackground model. However, areas along the coastline must be included otherwise the ramp background modelwill be unable to extract data along the coast, an area of particular importance for HAB monitoring. In contrast,the ramp filter is applied to all values in the scene of interest (the scene to be extracted, image I) and maskingthe result using the cloud mask from the original input data removes the majority of any false responses.

3. RESULTS

To illustrate the algorithm’s application, it was applied to SeaWiFS time series data of the Goban Spur (northeast Atlantic) and the Baltic Sea. These two oceanic regions are spectrally very different and both have annualoccurrences of algal blooms (12; 13). The Goban Spur lies on the continental margin between the north eastAtlantic and the Celtic Sea. Blooms of the harmless coccolithophorid Emiliana huxleyi regularly form along thethe western European continental shelf break between Ushant and western Ireland between early April and earlyJuly. These blooms are readily detectable from remotely-sensed data due to their ability to backscatter lightstrongly in the visible (14). However, variations in concentrations at depth can complicate the determination oftheir extent. Figure 3(a) shows the presence of a coccolithophore bloom along the shelf edge at 1348 UTC 15June 2004. This particular bloom remained visible in these SeaWiFS data from 15 May 2004 - 30 July 2004.The Baltic Sea is an important source of fishing and a centre for tourism, while also being a busy transportroute between its five bordering nations. Due to its enclosed nature, most input is from freshwater rivers andhence the water contains high levels of sediment and colour dissolved organic matter complicating its spectralresponse. In the Baltic Sea the presence of a Cyanobacteria bloom was visible between 06 - 22 July 2002 andthe species was verified by in-situ sampling.

Data were analysed for both regions and suitable values of m = 7.0, n = 0.5 (Goban Spur) and m = 5.0,n = 1.5 (Baltic Sea) for the ramp subtraction were set. m = 1.2, n = 0.0 (Goban Spur), m = 1.2, n = 1.0(Baltic Sea) were used for the intensity subtraction (equation 8). All parameters were determined by iterativelyexamining multiple data sets. T = 0.9 (intensity) and T = 0.7 (ramp) (equations 3 and 4) were set in a similarmanner. These values remained fixed for the following analyses.

3.1. Goban Spur

Due to the highly reflective nature of these blooms, water leaving radiance data at 490 nm (Lw490) have beenanalysed here. An example result from applying the algorithm to the Goban Spur time series data can be seenin figures 2 and 3. Figure 2 shows the background model for the Goban Spur generated using the two monthsof data up to and including 15 June 2004. A high per-pixel variance can be seen across the entire scene (Figure2(b)), whereas in comparison the ramp variance model exhibits lower values (Figure 2(d)). Even though thebackground model is generated using medians, the effects of previous anomalies during May and early June arevisible along the shelf edge in Figure 2(a). These background models were then used to extract the data of 1348UTC 15 June 2004, producing the results in Figure 3. The anomaly of interest is the highly reflective area (inwhite) along the shelf edge in Figure 3(a). The intensity confidence (Figure 3(b)) correctly identifies the anomaly,the results of which are complemented by those of the ramp confidence (Figure 3(c)), producing the combinedconfidence map in Figure 3(d).The high scene variance in Figure 2(b) produces an intensity subtraction resultwhich corresponds well with areas of very high values in the original scene (Figure 3(a)). In comparison thelow ramp variance of Figure 2(d) allows a ramp subtraction result which captures the boundary of the anomalywhere the values are increasing, but have yet to peak. Figures 3(e) show the final perimeter overlaid on theoriginal scene. Figure 3(f) shows the result of repeating the whole process for three different wavelengths (Lw490,Lw510 and Lw555) and combining the results. This perimeter has been overlain on a simulated true colour imageof the coccolithophore bloom.

3.2. Baltic Sea

Due to the high sediment and colour dissolved organic matter in the Baltic Sea, standard chlorophyll algorithmstend to break down. Therefore, we concentrate on analysing the normalised water leaving radiance data at 555nm, (Lw555). An example result of applying the algorithm to the Baltic sea time series water-leaving radiancedata for 1057 UTC 09 June 2002 can be seen in Figure 4. The areas of black in Figure 4(a) within the Balticsea are zero values due to occlusion by clouds. Figure 4(b) shows the results of the per-pixel subtraction on theLw555 data, resulting in a sparse collection of pixels. The ramp subtraction in Figure 4(c) clearly allows detectionof the anomaly and combining the two approaches improves this further, Figure 4(d). The background modelsfor the Baltic Sea (not shown) exhibited similar characteristics to the Goban Spur models of Figure 2. However,the ramp variance response was considerably lower, as reflected in the strong ramp subtraction result. Thepoor per-pixel result illustrates the complex optical nature of the Baltic Sea. The low ramp temporal-variance

(a) (b)

(c) (d)

Figure 2. An example Goban Spur background model: The per-pixel temporal median 2(a) and variance estimates 2(b)and the ramp temporal median 2(c) and its associated variance estimates 2(d).

supports the use of the ramp filter as it illustrates that there is very little variation in ramp response, exceptwhen anomalies occur. In part this is due to the ability to optimise the filter to detect the ramps of a certaingradient. The result also emphasises the advantage of feature detection as these methods will tend to be morerobust to noise as they search for spatial patterns, rather than analysing individual pixels.

4. DISCUSSION

The simple application to the detection of a coccolithophore bloom helps to illustrate the advantage of usingramp detection over a pixel based thresholding approach. Using these filters it is possible to detect variations inconcentrations (e.g. as the concentrations change from low to high producing a ramp function). Even thoughthese blooms can be simply identified in ocean colour data, thresholding techniques on these data to producesimple maps of extent would tend to be incomplete. This would cause inaccuracies when analysing inter-annualcoccolithophore bloom extent, an area of current interest as the annual growth patterns of these blooms arethought to be linked to climate change, e.g. (14). Thresholding techniques would also contain false positivesfor areas of consistently high reflectance (e.g. high sediment in river estuaries). Subtraction from a backgroundmodel (as used here) can remove these false positives. The second application to data of the Baltic Sea illustratesthe algorithms generic nature including its ability to handle occlusion due to cloud.

The low absolute differences between the intensity subtraction parameters, m and n, illustrate that thedifferences between current and background pixel intensities tend to be low (in the order of 1 standard deviation).The high absolute difference between the ramp m and n parameters reinforces the advantage of including a spatialdescription in the subtraction, rather than concentrating on just the individual pixels. Further, through applyingthis novel algorithm to two spectrally different oceanic regions it has been demonstrated how the two halves ofthe method can complement each other well.

(a) The scene to be analysed. (b) Per-pixel confidence.

(c) Ramp confidence. (d) Combined confidences.

(e) Figure 3(a) overlaid with the final perimeter(in white).

(f) Perimeter (in white) from combining Lw490,Lw510 and Lw555 results overlaid on the bloom.

Figure 3. Example results of Lw490 data over the Goban Spur at 1348 UTC 15 June 2004.

(a) (b)

(c) (d)

Figure 4. Example results of Lw555 nm data over the Baltic sea at 1057 UTC 09 June 2002. 4(a) The single pass to beanalysed (note the large amount of missing data due to cloud); 4(b) the per-pixel confidence ; 4(c) the ramp confidenceand 4(d) the combined confidences.

The ‘blocks’ of data visible in Figure 2(c) and 2(d) are due to the broad extent and low peak of the rampfilter. This flattening of the filter response results from the low gradient of the ramps it was derived to detect.The performance of the ramp filter is known to degrade as the half width w decreases. In theory increasingthe half width would improve the response, as the filter would be better described, although this will have adetrimental effect on detail, as the smaller features will lost. Therefore, a balance between the performance ofthe ramp filter and the size of the features we would like to detect has been presented.

The collection of in-situ bloom-extent ground truth data is hampered by the very nature of these blooms.The large extent, unpredictability, complexity and location of these blooms can make the collection of these databoth difficult and costly. Manually labelled data, go some way to solving this. However, this is open to humaninterpretation allowing unquantifiable errors to be present. With the advent of continuous studies of marinehabitats like the Baltic Sea and harmful algal bloom projects such as HABILE and GEOHAB, the collection ofthis much needed ground truth data will hopefully be forthcoming.

5. CONCLUSIONS

The algorithms presented here build upon previous work (6; 8; 9) and are based around temporal-variancecontrolled background subtraction from a temporal-median background model. These novel techniques havebeen used to successfully detect spatial anomalies in time series ocean colour data. The temporal-variance basedbackground-subtraction allows subtraction using the temporal statistics of the scene, essentially providing abackground model that incorporates a description of the system dynamics, including any noise. This removesthe need for fixed threshold levels of previous methods and provides a measure of confidence as to whether apixel is within an anomaly or not, resulting in a more generic and robust technique. Furthermore, variationsin remotely sensed data of the natural environment tend to include gradual variations rather than discreteboundaries, therefore the inclusion of a ramp model has allowed improved performance over using the intensityvalues alone. The generic nature has been demonstrated by application to two spectrally different oceanic regions,producing improved results in comparison to previous approaches. The application of this algorithm is not limitedto ocean colour data. It is also applicable to terrestrial and atmospheric remotely-sensed data. Aerosol anomalydetection could provide a method of detecting sudden atmospheric pollution events (e.g. volcanic eruptions orexplosions). For example, large aerosol plumes generated by volcanic eruptions need to be avoided by aircraftas they can severely damage their engines. Further, these techniques could be applied to burn scar and forestclearing detection and monitoring.

These algorithms are to be run operationally by the UK National Environment Research Council RemoteSensing Data Analysis Service (NERC RSDAS) for near-real time MODIS data (15) of the Celtic Sea and SouthWestern approaches. This will allow future work to concentrate on using the final masks to spatially constrainthe inputs of a spectral classifier, towards automatically determining HABs from remotely-sensed data.

ACKNOWLEDGMENTS

J. Shutler and P. Miller would both like to gratefully acknowledge partial funding by the Natural EnvironmentResearch Council (NERC) through the Remote Sensing Data Analysis Service (RSDAS), by the EuropeanCommission Framework 5 project Harmful Algal Bloom Initiation and Prediction in Large European MarineEcosystems (HABILE), contract number EVK3-CT2001-00063 and through the Information Society Technologies(IST) project Data Integration System for MARine pollution and water quality (DISMAR). M. Grant wouldlike to gratefully acknowledge support by the US Army’s European Research Office, contract number N68171-

01-C-9002. All SeaWiFS data were kindly processed and provided by the NERC RSDAS through the SeaWiFSproject (code 970.2).

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