WaMaPro – a user friendly tool for water surface
derivation from SAR data – and further products derived
from optical data
J. Huth a, M. Ahrens a, I. Klein a, U. Gessner a, J. Hoffmann b, C. Kuenzer a
a German
Aerospace Center (DLR), Earth Observation Center (EOC), German Remote Sensing Data
Center (DFD), Land Surface Department, Oberpfaffenhofen, 82234 Wessling, Germany.
b German Aerospace Center (DLR), Space Agency, 53227 Bonn, Germany.
Overview
WaMaPro introduction
Implementation as a tool
WaMaPro application in environmental research projects
Further products related to water body mapping activities
Introduction
-Slide 3
WaMaPro – Aims and Goals
WaMaPro = Water Mask Processor
Original Ideas:
Data analyses over time – with a focus on thematic analyses
Create a simple image processing tool possible to hand over to project
partners and people working in developing countries (access to software)
Target user group: environmental scientists, remote sensing beginners, etc.
Data we process with WaMaPro:
TerraSAR-X
Envisat-ASAR
Sentinel-1A
other SAR data possible
WaMaPro – Principles and Techniques
Input data
(DN)
Watermask
Medianfilter
(3x3)
Remove ‚islands’
and ‚lakes’
(island size, lake
size)
Threshold
Definition
(water and land
threshold)
B5
Convert to binary
images
B1
(water)
B2
(land)
Image Dilation
(sesize)
Comparison
(B3 && B2) || B1
B3
Morphological
Closing
(fix value)
B4
DN values
3x3 Median Filter (speckle effect)
Threshold definition (water and land)
B1 confident water: image dilation
(growing with sesize) = B3
B2 confident land
Comparison of B2 and B3 (buffer
area of unconfident land/water e.g.
more soils) = B4
Morphological Closing (grow+shrink)
for edge smoothing with fixed value
(as if sesize is1)
Removing of lakes and islands with
user given size
WaMaPro – Principles and Techniques
B1 - confident water
B2 - confident land
B3 - dilate B1
B4 – water after buffer comparison
WaMaPro test cases – Mekong Delta, Vietnam
TSX
WaMaPro - watermask
Overlay
WaMaPro test cases – Yellow River Delta, China
TSX
WaMaPro - watermask
Overlay
WaMaPro test cases – Mali, Africa
TSX
WaMaPro - watermask
Overlay
WaMaPro test cases – North sea coast - Watt, Netherlands
Challenges: Wind artefacts on open ocean water; inland water shows good result
TSX
WaMaPro - watermask
Overlay
MARTINIS, S., KUENZER, C., WENDLEDER, A., HUTH, J., TWELE, A., ROTH, A., DECH, S.: Comparing four different
approaches for operational SAR-based water and flood detection. Submitted to International Journal of Remote Sensing
Accuracy Assessment (e.g. Vietnam – flood vs. dry season)
Flood season
Dry season
Validation data of Jan. 2009
Blue – water
20 km
Accuracy Assessment
Watermask Validation for 2009-01-13
percentage of val.points [%]
40
35
30
25
20
15
10
5
0
0m
1-2m
2-5m
5-8m
>8m
distance between measured and calculated land-water
boundary [m]
source: WISDOM project
GPS points at the land-water boundary
with simultaneuosly acquired TerraSAR-X data
25% of points located at the land-water boundary
55% within 1-8m distance of watermask to land-water boundary
GPS accuracy 2-10m (DGPS not possible)
Geometric accuracy of TSX data approx. 1 pixels
20% „outliers“ (> 8m)
Huth et al. (2009): Automated inundation monitoring using TerraSAR-X multi-temporal imagery. European Geosciences Union,
EGU, General Assembly 2009, 19.-24. Apr. 2009, Vienna, Austria.
Implementation as a tool
-Slide 13
WaMaPro – open-source based tool implementation
WaMaPro – open-source based tool implementation – GUI
WaMaPro – open-source based tool implementation – result
Application in environmental
research projects
-Slide 17
Slide 18
-Slide 19
KUENZER, C., GUO, H., LEINENKUGEL, L, HUTH, J., LI, X., and S. DECH,
2013: Flood mapping and flood dynamics of the Mekong Delta: An
ENVISAT-ASAR-WSM based Time Series Analyses, Remote Sensing 5
(doi:10.3390/rs5020687), 687-715
Slide 20
KUENZER, C., GUO, H., LEINENKUGEL, L, HUTH, J., LI, X., and S. DECH,
2013: Flood mapping and flood dynamics of the Mekong Delta: An
ENVISAT-ASAR-WSM based Time Series Analyses, Remote Sensing 5
(doi:10.3390/rs5020687), 687-715
2007-2011: 51 observations
10 per year in rainy season,
ASAR, 150m
Slide 21
Mekong Delta - Rainy Season 2007
b
a
c
b
Gulf of
c
Thailand
South
China Sea
N
45 km
0
15
Largest common coverage
Coastline
Inundation mapping with TerraSAR-X vs. ASAR
Kuenzer et al., (2013): Varying Scale and Capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap
Data to Assess Urban Flood Situations: A Case Study of the Mekong Delta in Can Tho Province. In: Remote Sensing
Zeitreihenuntersuchung Überflutung 2005-2011
Coastal analyses – Inundation frequency 2005 – 2011
-2005 -2006 -2007
-2008
-2009
ASAR WSM data – 150 m resolution-Slide 24
2011
canal
oil field
Yellow River
aquaculture
wetland (reed)
brine ponds
TSX stripmap data – 3 m resolution
KUENZER, C., HUTH, J., MARTINIS, S., LU, L., DECH, S., 2015: SAR Time Series for the Analysis of Inundation Patterns in the
Yellow River Delta, China. In: Kuenzer, C., Dech, S., Wagner, W. (eds.), 2015: Remote Sensing Time Series Analyses revealing
Land Surface Dynamics. In print, will be published in April, Springer, The Netherlands
Inundation Frequency at East Dongting Lake
2012
2013
Datasets: TSX stripmap 3m resolution
2012 – 14 scenes (Jan–Dec) – 2013 – 8 scenes (Jan–Mar)
- Yellow to orange – flood prone
- Blue – permanent water bodies
Flood Mapping of Northern Namibia with Sentinel-1A
Publications related to WaMaPro
- GSTAIGER, V., GEBHARDT, S., HUTH, J., WEHRMANN; T. and C. KUENZER, 2012: Multi-sensoral
derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data. International Journal of
Remote Sensing, Vol. 33, 22, 7291-7304, DOI:10.1080/01431161.2012.700421
- KUENZER, C., GUO, H., LEINENKUGEL, L, HUTH, J., LI, X., and S. DECH, 2013: Flood mapping and
flood dynamics of the Mekong Delta: An ENVISAT-ASAR-WSM based Time Series Analyses, Remote
Sensing 5 (doi:10.3390/rs5020687), 687-715
- KUENZER, C., GUO, H., SCHLEGEL, I., VO, Q.T., LI, X., DECH, S., 2013: Scale and the Capability of
Envisat ASAR-WSM, TerraSAR-X Scansar, and TerraSAR-X Stripmap Data to assess urban Flood
Situations: A Case Study in Can Tho Province of the Mekong Delta, Remote Sensing, 5, 5122-5142;
doi:10.3390/rs5105122
- KUENZER, C., HUTH, J., MARTINIS, S., LU, L., DECH, S., 2015: SAR Time Series for the Analysis of
Inundation Patterns in the Yellow River Delta, China. In: Kuenzer, C., Dech, S., Wagner, W. (eds.),
2015: Remote Sensing Time Series Analyses revealing Land Surface Dynamics. In print, will be
published in April, Springer, The Netherlands
- MARTINIS, S., KUENZER, C., WENDLEDER, A., HUTH, J., TWELE, A., ROTH, A., DECH, S., 2015:
Comparing four different approaches for operational SAR-based water and flood detection. Submitted to
International Journal of Remote Sensing
- HUTH, et al., 2015: Deriving Water Surfaces with WaMaPro – Observations of Water Surface Dynamics
of the Yellow River Delta, China. Accepted oral presentation at ISRSE36, Berlin
Ausbreitung von Aquakulturflächen 1995-2010
1995
2004
2010
Global WaterPack
-Slide 30
Processed Data for 2013
Used Data for Classification
Input
Workflow: water detection
1. Preprocessing
MODIS daily data
(09GQ) Terra + Aqua
NIR
MODIS daily data
(10A1) Terra + Aqua
Thematic info: cloud,
lake ice, ocean
Ancillary Data Filter to remove misclassification
MODIS Water
(MOD44W)
DTM
Static Water Mask
(training areas)
Calculates the NIR mean for individual tile within training areas (excluding
NIR > 20%, cloud covered or no data pixel)
2. Water
detection
Dynamic upper threshold for individual tile: mean + 2std
Slope to remove
misclassification due
to relief
DT classification
Intermediate
result
Thematic product for each tile of Terra and Aqua: water, land, cloud*,
lake ice* -----> combination of both for each day (*from 10A1 product)
3. Temporal
scan and
remove of
clouds and
no data
1. Temporal filter to remove
cloud shadows (logical scan of
pixel within the temporal stack)
End result
Mosaiking,
resampling and
tiling to MODIS
extent and 250-m
resolution -> Slope
calculation
2. Temporal filter to remove
clouds/no data and replace by
values before/after the cloud
Cloud free product (water, land) for each day
Water Cover Duration
Min & Max extent
Confidence layer with amount
of cloud per pixel
Global WaterPack 2013
High intra-annual variability
Lake Poyang (China)
Lake Dongting (China)
Hydropower dams and water reservoirs
Koksaray Reservoir
American Falls Reservoir
Shardara Reservoir
DOY
Publications related to Global WaterPack
- Klein, I., Dietz, A.J., Gessner, U., Galayeva, A., Myrzakhmetov, A., Kuenzer, C., 2014. Evaluation
of seasonal water body extents in Central Asia over the past 27 years derived from mediumresolution remote sensing data. Int. J. Appl. Earth Obs. Geoinf. 26, 335–349.
- Klein, I., Dietz, A., Gessner, U., Dech, S., Kuenzer, C. 2015. Results of the Global WaterPack: a
novel product to assess inland water body dynamics on a daily basis. In: Remote Sensing Letters
(6), 78-87
Software to share: WaMaPro Tool
contact: [email protected]
Thank you very much for your attention!
[email protected]