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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

bmarroqu@terpmail.umd.edu

240.821.8238

Contact

1. Byron

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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 bmarroqu@terpmail.umd.edu 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