genigraphics research poster template 36x48...art in your poster file by selecting the insert,...
TRANSCRIPT
Poster Print Size: This poster template is 36” high by 48” wide. It can be used to print any poster with a 3:4 aspect ratio.
Placeholders: The various elements included in this poster are ones we often see in medical, research, and scientific posters. Feel free to edit, move, add, and delete items, or change the layout to suit your needs. Always check with your conference organizer for specific requirements.
Image Quality: You can place digital photos or logo art in your poster file by selecting the Insert, Picture command, or by using standard copy & paste. For best results, all graphic elements should be at least 150-200 pixels per inch in their final printed size. For instance, a 1600 x 1200 pixel photo will usually look fine up to 8“-10” wide on your printed poster.
To preview the print quality of images, select a magnification of 100% when previewing your poster. This will give you a good idea of what it will look like in print. If you are laying out a large poster and using half-scale dimensions, be sure to preview your graphics at 200% to see them at their final printed size.
Please note that graphics from websites (such as the logo on your hospital's or university's home page) will only be 72dpi and not suitable for printing.
[This sidebar area does not print.]
Change Color Theme: This template is designed to use the built-in color themes in the newer versions of PowerPoint.
To change the color theme, select the Design tab, then select the Colors drop-down list.
The default color theme for this template is “Office”, so you can always return to that after trying some of the alternatives.
Printing Your Poster: Once your poster file is ready, visit www.genigraphics.com to order a high-quality, affordable poster print. Every order receives a free design review and we can deliver as fast as next business day within the US and Canada.
Genigraphics® has been producing output from PowerPoint® longer than anyone in the industry; dating back to when we helped Microsoft® design the PowerPoint® software.
US and Canada: 1-800-790-4001
Email: [email protected]
[This sidebar area does not print.]
Mapping Global Water Extent: Validating the Accuracy of Landsat
30m Satellite Data Using RapidEye 5m Data Byron A. Marroquin; Amy Hudson; Matthew Hansen, PhD
University of Maryland, College Park, Department of Geographical Sciences
Byron A. Marroquin
University of Maryland
240.821.8238
Contact
1. Byron
2.
3.
4.
5.
6.
7.
8.
9.
10.
Over the years, remote sensing has become a powerful tool for
tracking changes on the Earth’s surface. An accurate depiction of
water allows researchers to track issues from warming in the Artic
(Carroll et al., 2011) to monitoring the dynamics of frozen ground
(Trofaier et al., 2013). Past studies have been limited by the resolution
and temporal frequency of their databases, using MODIS 250m and
Landsat 30m data provided by NASA. BlackBridge (RapidEye), an
independent German geospatial information provider, offers more
accurate data at 5m resolution.
The purpose of this quantitative study is three-fold: it identifies the
location and extent of water cover; validates Landsat water cover at 30
meters versus RapidEye 5 at meters; and estimates how much water is
being omitted between the two data sets–on a global scale. The study
divides the Earth into 4 strata and randomly samples 35 spectral
images from each stratum. RapidEye (2010-2013) images are used as
reference data to visually and statistically assess the accuracy of pre-
classified Landsat image composites used in the Hansen et al. (2013)
study. Utilizing a raster-based approach, water extent was identified
by employing a classification tree method (Potapov et al., 2012).
Changes in water availability due to climate change may adversely
affect human and physical environments. Higher resolution data
allows for more accurate research that can serve for multidimensional
applications including resource management and sustainability,
climate-risk mapping and disaster relief, to tracking impacts on
ecosystems. The results of this study will allow researchers to justify
the use of more accurate data to monitor the extent and change of
water bodies such as lakes, rivers, streams, etc. across the globe.
Future studies should focus on validating higher resolution data
through field work, and comparing predicative modeling results to
Landsat data.
Abstract
Pre-classified permanent water mask composite data (Landsat 30m) from Hansen et al. (2013) study
RapidEye 5m image blocks ▫ Utilizes bands 1 – 5
Data
Climate and land use change are global phenomena. Surface water is a first-order indicator of a region’s water resources and tracking changes in surface water over time can illuminate the effects of climate and/or land use change on hydrological systems. Satellite data, including Landsat and RapidEye imagery, are well-suited to this task. Results from this study are promising. The rate of water agreement between RapidEye and Landsat increases as the stratum number increases. Results suggest that differences in agreement are due to the lower resolution of Landsat; smaller ponds and narrow rivers are not detected by Landsat, as in the examples from China and Senegal. Due to their location, these regions may also experience higher presence of ephemeral water; without adequate time series data for RapidEye, comparing data becomes increasingly difficult. However, strata 3 and 4 contain larger bodies of water, and result in higher levels of agreement between the two data sets. Although our approach to classifying water accurately characterized larger water bodies, it was limited by water bodies such as lakes and rivers that have widths smaller than the sensor’s spatial resolution (Carroll et. al., 2009). Additionally, rivers seldom remain static due to seasonal and yearly variations in precipitation, complicating comparisons. Ephemeral water bodies, such as floodplains, ponds, and flooded crop fields, are also a confounding factor. Using Google Earth for surface cover verification limited the study due to the temporal frequency, resolution, and availability of the images provided. Overall RapidEye proved to be more sensitive in detecting surface water. Our next step in the study will be to quantify how much water Landsat omits compared to RapidEye and to identify the area of water body below which Landsat has no capability to detect. Results will confirm the degree to which 5m RapidEye data are required to monitor this key resource.
Discussion/Conclusion Results
BSOS SRI
(2014)
Quantitative remote sensing ▫ Satellites scan the earth’s surface from a distance in order to
collect information about it. ▫ Sensors optically (passively) records radiated sunlight or
emitted thermal energy from the earth’s surface in different bands of the electromagnetic spectrum
Raster-based classification approach is utilized to classify digital images through the use of a supervised classification tree
Classifying water cover (Hansen, 2014): 1. Water cover classification scheme 2. Input multispectral data 3. Algorithm for relating above variables 4. Validation (accuracy assessment) of final map product
Algorithms: ▫ Supervised classification technique: user-defined training
pixels are used to create characteristic class signatures which are used to assign labels to all pixels in the image (Hansen, 2012) (water vs. non-water)
▫ Decision trees – hierarchical splitting of data into more homogenous subsets (water vs. non-water)
Validation: Landsat vs. RapidEye data Statistics:
▫ Sample Mean Per Stratum, Weighted by Block Area (WBA)
= %𝑤𝑎𝑡𝑒𝑟 𝑏𝑙𝑜𝑐𝑘 𝑎𝑟𝑒𝑎𝑛1
𝑡𝑜𝑡𝑎𝑙 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑡𝑟𝑎𝑡𝑢𝑚
▫ Water Area Estimate (within each stratum)= 𝑆𝑎𝑚𝑝𝑙𝑒 𝑚𝑒𝑎𝑛 𝑝𝑒𝑟 𝑠𝑡𝑟𝑎𝑡𝑢𝑚 𝑊𝐵𝐴 ×(𝑡𝑜𝑡𝑎𝑙 # 𝑏𝑙𝑜𝑐𝑘𝑠 𝑤𝑖𝑡ℎ𝑖𝑛 𝑠𝑡𝑟𝑎𝑡𝑢𝑚) × (20km)2
▫ [Global % Agreement Estimate]
= Sample Mean Per Stratum WBA 𝑖 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑆𝑡𝑟𝑎𝑡𝑢𝑚𝑖4
1=1
𝑇𝑜𝑡𝑎𝑙 𝐺𝑙𝑜𝑏𝑎𝑙 𝐴𝑟𝑒𝑎 →
𝐿𝑎𝑛𝑑𝑠𝑎𝑡
𝑅𝑎𝑝𝑖𝑑𝐸𝑦𝑒=
% 𝐿𝑎𝑛𝑑𝑠𝑎𝑡 𝑢𝑛𝑑𝑒𝑟𝑒𝑠𝑡.
Strata distribution – approx. 35 random samples from each stratum Programs: PCI Geomatics, Focus; ESRI, ArcGIS 10.2; Google Earth
Methods
Carroll, M.L., Townshend, J.G.R., DiMiceli, C.M., Loboda, T., & Sohlberg, R.A.
(2011). Shrinking lakes of the artic: Spatial relationship and trajectory of
change. Geophysical Research Letters, 38, L20406.
Carroll, M.L., Townshend, J.R., DiMiceli, C.M., Noojipady, P., & Sohlberg, R.A.
(2009). A new global raster water mask at 250m resolution.
International Journal of Digital Earth, 2, 291-308.
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A.,
Tyukavina, A.,…Townshend, J.R.G. (2013). High-resolution global maps of
21st-century forest cover change. Science, 342, 850-853.
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A.,
Tyukavina, A.,…Townshend, J.R.G. (2013). High-resolution global maps of
21st-century forest cover change. Science, 342, 850-853. Data available
on-line from: http://earthenginepartners.appspot.com/science-2013-
global-forest.
Hansen, M.C. (2014, June 2). Supervised decision tree algorithms. Lecture
conducted from University of Maryland, College Park, MD.
Potapov, P.V., Turubanova, S.A., Hansen, M.C., Adusei, B., Broich, M., Altstatt,
A.,…Justice, C.O. (2012). Quantifying forest cover loss in Democratic
Republic of the Congo, 2000-2010, with Landsat ETM + data. Remote
Sensing Environment, 122, 106-116
Trofaier, A.M., Bartsch, A., Rees, W.G., & Leibman, M.O. (2013). Assessment of
spring floods and surface water extent over the Yamalo-Nenets
Autonomous District. Environmental Research Letters, 045026, 1-9
References
1. How valid is Landsat 30m in comparison to RapidEye 5m data? 2. How much water cover is being emitted or omitted by the higher
resolution data?
Research Questions
Data Sources MODIS Landsat 7 RapidEye
Resolution 250m x 250m 30m x 30m 5m x 5m
Global revisit time Varies 16 days 1 day
Availability Public (USGS) Public (USGS) Private (BlackBridge)
Advantages Less time & labor
intensive to classify
Less time & labor
intensive to classify;
medium resolution
High resolution
Disadvantages Low resolution Medium resolution Cost, availability, time &
labor intensive Byron A. Marroquin University of Maryland [email protected] 240.821.8238
Contact info
RapidEye Sensor Performance Specifications
Band Name Wavelength
1 Blue 440 – 510 nm
2 Green 520 – 590 nm
3 Red 630 – 685 nm
4 Red
Edge 690 – 730 nm
5 Near
IR 760 – 850 nm
Landsat 7 Sensor Performance Specifications
Band Name Wavelength
1 Blue 450 – 515 nm
2 Green 525 – 605 nm
3 Red 630 – 690 nm
4 NIR 750 – 900 nm
5 SWIR -1 1550 – 1750 nm
6 Thermal
IR 10400 – 12500 nm
7 SWIR -2 20900 – 23500 nm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009
LS
% W
ate
r
RE % Water
RE % water vs Landsat % water (stratum 1)
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016
Lan
dsa
t %
Wate
r
RE % Water
RE % water vs Landsat % water (stratum 2)
Strata Distribution
Stratum Water Threshold Number of Blocks
1 0% 78201
2 >0 and <0.08% 76292
3 0.08 and <2% 76265
4 >2% - 76437
0
0.005
0.01
0.015
0.02
0.025
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035
LS
% W
ate
r
RE % Water
RE % water vs LS % water (stratum 3)
0
0.02
0.04
0.06
0.08
0.1
0.12
0 0.01 0.02 0.03 0.04 0.05 0.06
LS
% W
ate
r
RE % Water
RE vs Landsat, % Water, Per Strata
Strata1
strata2
strata3
strata4
China, stratum 1
Canada (Quebec), stratum 4 Kazakhstan, stratum 3
Senegal, stratum 2
Statistics
Stratum # Blocks
Landsat %
water RE %
Water Landsat/RE % Water Landsat RE
Sample Mean (WBA) Water Area
Estimate (km)
1 78,201 0 0.0011805 0 0 36,926.5
2 76,292 0.000551284 0.0027228 0.202466709 16,823.4 83,091.1
3 76,265 0.006567762 0.0114984 0.571190327 200,356.1 350,770.2
4 76,437 0.312500098 0.3154697 1.0095028 n/a n/a
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
La
nd
sat
% W
ate
r
RE % Water
RE % water vs Landsat % water
(stratum 4)
Statistics
Stratum # Blocks
Landsat %
water RE %
Water Landsat/RE % Water Landsat RE
Sample Mean (WBA) Water Area
Estimate (km)
1 78,201 0 0.11805 0 0 36,926.5
2 76,292 0.0551284 0.27228 20.2466709 16,823.4 83,091.1
3 76,265 0.6567762 1.14984 57.1190327 200,356.1 350,770.2
4 76,437 31.2500098 31.54697 100.95028 n/a n/a
Landsat RapidEye
10.58 10.96
Global % Agreement
Estimate
% RE that Landsat Identified
96.59
% Landsat Underestimated
3.40