RESULTS DISCUSSION

REFERENCES

ACKNOWLEDGEMENTS

ABSTRACT

METHODS

A threshold method was employed to map inundated regions based on the low backscatter coefficient of the SAR data. Using the method of Cao et al. (2019), SAR data with the appropriate power transform will follow a bimodal Gaussian distribution. We exploit this characteristic to automatically deter-mine a threshold for the image to classify water and non-water regions. The image is split into tiles (Figure 4) due to different sections of the SAR scene behaving differently because of a wide swath (Cao et al. 2019). Each tile is analysed separately to determine its unique threshold. Each tile is further split into an array of s x s pixels (yellow squares in Figure 4) to determine which sets of pixels within this tile behaves as a bimodal Gaussian distribution using the maximum normalized between-class variance (BCV) (Cao et al. 2019, Demirkaya et al. 2004). Based on simulations, it was determined that a maximum value of BCV greater than 0.65 can be assumed to be bimodal. Within each tile, we determine which set of s x s pixels (red boxes in Figure 4) are bimodal and determine the threshold for that tile using these boxes. The value of s is varied to maximize the number of bimodal sets of pixels within each tile to determine an optimal threshold for that tile. An automatic threshold is selected using either the mode of the distribution or the local minimum separating the peaks in the bimodal distribution. The mean of the thresholds for each set of s x s pixels is used as the threshold for the entire tile. This pro-cess is repeated for each tile in the image to generate a binary output displaying the classified water regions (Figure 2 and 3). Because this method also classifies water which is more permanent rather than a result of flooding events, a set of images can be used to remove the common classifications among all images, assuming sufficient temporal coverage. This method can be applied to both VV (vertical-vertical) and VH (vertical-horizontal) polarizations to improve accuracy as well as to for coherence images to further refine the method. This method of tiling also has the potential to be used in machine learning algorithms to detect changes among tiles for flood detection.

Cao, H.; Zhang, H.; Wang, C.; Zhang, B. Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas. Water 2019, 11, 786.

Copernicus Sentinel Data, 2015. Retrieved from ASF DAAC on April, 2020, processed by ESA. https://search.asf.alaska.edu/

Demirkaya, O.; Asyali, M.H. Determination of image bimodality thresholds for di�erent intensity distributions.

Figure 4: Schematic of an image split into tiles (blue squares), boxes (yellow squares) with possible varying size among tiles and red higlighted boxes indicating a box which has been identified to have a bimodal distribution.

The results show a fixed s of 500 which can be varied to improve the re-sults by increasing the number of bimodal boxes within each tile which would allow for a better refinement of the automatic threshold. Each tile was threshold using the mean of the modes of the boxes which were identi-fied to have a bimodal distribution using the BCV threshold. Clearly, arti-facts exist in Figures 2 and 3 from the tiling as we can see the tiles within the binary classified images. These arrtifacts are likely due to a fixed s as well as using the mode for automatic thresholding. The mode is easilly shifted when the bimodal distribution contains more pixels in the non-water class than the water class thus classifying more pixels as water than should actually be classified. It is a first approach to the automation and has clearly been able to identify water bodies in Figures 2 and 3 but with a need for re-finement in the approach. An alternate approach to indentifying an automat-ic threshold is to use a local minium algorithm and then using the mean local minimum as a threshold to classify each of the tiles. This method is more computationaly unstable as the histograms are not smooth and thus have many minimum as well as maximums and thus determining the de-sired local minimum is challenging. Cao et al. 2019 have applied a convo-lution filter to the histogram to smooth it to aid in fixing this problem and stabalize the determination of the local minimum. Applying a local mini-mum algorthim and then taking the mean of the computed local minimum of the bimodal distributions for each tile to automatically threshold each tile would likely should dramatically improve upon using the mode, as the mode is easily shifted whereas the local minimum should be a more stable value. This would be a next step to improving upon the automation of the flood mapping presented here. Potential flooding is seen in these classified binary images in Figure 2 from 2020220 and 20200303 in the middle north-ern section of the images. The current binary classified images also include permanent standing water, which we are not necessarily interested in thus we might try and remove it. Because we have several classified images through time we could simply remove the common classifcations from each image as these images span a month which is likely longer than flooding water would remain in a particular location and thus we could assume it was permanent water or other identified objects. Refining both the box size and the automatic threshold should significantly improve the quality of the classified images and remove tiling artifacts, further refinements to the clas-sified images could then be explored. This tiling method also gives a frame-work to apply a machine learning change detection based algorithm to detect changes between tiles as well as boxes and then attribute these changes to flooding. This could then be compared with the automated threshold method to compare and verify results.

Flooding is one of the most prevalent natural disasters in the world, causing many fatalities and high economic loss. Thus, there is a need to quantify flooding both rapidly and accurately in ways which can aid in disaster man-agement and decision making. This includes determining the spatial extent of the flooding through time, as well as the depth of the water. The method used here implements SAR data provided by ESA Sentinel-1A/B which pro-vides a large spatial extent ~100s of kms and 6 to 12 day repeat period which is ideal for map flooding. The method implemented uses both SAR amplitude and coherence products to derive binary flooding maps. The method used splits each image into n tiles which will then each be analyzed separately. Each tile is split into a grid of s by s pixels (box) which are then each analyzed to check for a bimodal distribution. The s is determined by it-erating through several choices of s and determining which value of s yields the maximum number bimodal distributed boxes. Once s is determined, it is used in the specific tile which it is contained and then each box is analyzed to determine an optimal threshold. The threshold determined automatically by either using the mode of the distributions, LM, or KI algorithm. Combin-ing all of the bimodal threshold boxes generates the final binary flood map which can then be further refined if needed.

Automatic Flood mapping using Sentinel-1A/B SAR dataClay Woods1,*, Kristy Tiampo1,*, Margaret Glasscoe2

1University of Colorado at Boulder Department of Geological Sciences, *University of Colorado at Boulder Cooperative Institute for Research in Environmental Sciences, 2NASA Jet Propulsion Laboratory California Institute of Technology

Figure 2:

Figure 3:

Figure 1a: Location 1, which is north of Loukolela in the Congo. This particular location was chosen because of heavy rains in the Demo-cratic Republic of Congo during the early part of 2020. Overlain is the SAR frame which was used in the processing.

Figure 1b: Location 2, which includes Vadodara. This location was chosen due to heavy rain fall in the Gujarat area. This area is also of interest be-cause Vadodara is a city and we are in-terested if we can detect flooding in cities at the scale of Vadodara. Overlain is the SAR frame which was used in the processing.

BACKGROUND

20200208 VH Binary classifcation image from Location 1 20200220 VH Binary classifcation image from Location 1 20200303 VH Binary classifcation image from Location 1

20200614 VH Binary classifcation image from Location 2 20200626 VH Binary classifcation image from Location 2 20200708 VH Binary classifcation image from Location 2