Remote Sensing, Climate and GIS Data Analysis · Remote Sensing, Climate and GIS Data Analysis Jay Angerer ... • Global Telecommunications System ... –Thematic Mapper ...

Remote Sensing, Climate and

GIS Data Analysis

Jay Angerer MOR2 Annual Meeting

June 2013

Remote Sensing • The term "remote sensing," first used in the United

States in the 1950s by Ms. Evelyn Pruitt of the U.S. Office of Naval Research

• Defined as the science—and art—of identifying, observing, and measuring an object without coming into direct contact with it.

• Involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials, by which they may be identified and categorized by class/type, substance, and spatial distribution.

From: http://earthobservatory.nasa.gov/Features/RemoteSensing/

Radiation • Unless it has a temperature of absolute zero (-

273°C) an object reflects, absorbs, and emits energy in a unique way, and at all times.

• This energy, called electromagnetic radiation, is emitted in waves that are able to transmit energy from one place to another.

• Soil, trees, air, the Sun, the Earth, and all the stars and planets are reflecting and emitting a wide range of electromagnetic waves.

From: http://earthobservatory.nasa.gov/Features/RemoteSensing/

Remote Sensing Process Example

1. Energy Source or Illumination (A)

2. Radiation and the Atmosphere (B)

3. Interaction with the Target (C)

From: http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/1924/

Remote Sensing Process Example

4. Recording of Energy by the Sensor (D)

5. Transmission, Reception, and Processing (E)

6. Interpretation and Analysis (F)

7. Application (G) From: http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/1924/

Electromagnetic Spectrum

• Electromagnetic radiation is emitted at different wavelengths and frequencies

• Remote sensing generally involves use of the ultraviolet to microwave portions of the spectrum

From: http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/1924/

Spectral Signatures

• For any given material, the amount of solar radiation that reflects, absorbs, or transmits varies with wavelength.

• This important property of matter makes it possible to identify different substances or classes and separate them by their spectral signatures (spectral curves)

From: http://www.fas.org/irp/imint/docs/rst/Intro/Part2_5.html

Spectral Signatures for Identifying Water Ponds

)2(

)3(

bRED

bNIRBandRatio

Band Ratio < 1.0 is identified as “clear water”

Cloud

Cloud Shadow

Vegetation Indices

• Uses differential between red and near infrared reflectance as measured by the satellite

• Actively growing plants show a contrast between strong absorption in the red and high reflectance in the near-infrared regions of the spectrum.

• The amount of absorption in the red and reflectance in the near-infrared varies with both the type of vegetation and the vigor of the plants.

Spectral Differences of Leaves

Source : http://rangeview.arizona.edu/Tutorials/intro.asp

NDVI Calculation

Calculated as NDVI = (NIR - VIS)/(NIR + VIS)

Source : http://earthobservatory.nasa.gov/Library/MeasuringVegetation/measuring_vegetation_2.html

!

!

!!

!

!

!

Altai

Sainshand

Mandalgobi

Arvaikheer

Dalanzadgad

Bayankhongor

NDVI – Vegetation Greenness

• Normalized Difference Vegetation Index (NDVI) is a satellite derived measurement of vegetation greenness

• NDVI is generally correlated to vegetation biomass in most regions

• Useful for many different applications

NDVI Data Sources • Advanced Very High Resolution Radiometer (AVHRR) –

Normalized Difference Vegetation Index (NDVI) data (GIMMs data) – 1981 to 2010

– 8 km resolution

– Widely used

– Available at http://www.glcf.umd.edu/data/gimms/

– New version should be available soon

Data Sources • Moderate Resolution Imaging Spectroradiometer (MODIS)

NDVI and Enhanced Vegetation Index (EVI) – 1 km, 500m, and 250 m resolution

– Available from 2000 to present

– Enhanced Vegetation Index (EVI) product builds in algorithms to adjust for soil distortions and canopy saturation

– Available from https://lpdaac.usgs.gov/get_data/data_pool

– Requires resampling and processing for use in GIS

Data Sources

• Expedited MODIS (eMODIS) – New product available from USGS

– 2000 to present

– Expedited means data are available within one day of last image acquisition in the composite window

– Resolution of 250m

– Geographic Projection

– Available for Asia region

– Download from:

– http://dds.cr.usgs.gov/emodis/CentralAsia/

Vegetation Condition Index

• Vegetation Condition Index is calculated by scaling NDVI for period of interest to the historical minimum and maximum

– (Current NDVI – minNDVI) / (maxNDVI – minNDVI) * 100

– Values less than 30 are considered drought

– Examine spatial extent and occurrence/intensity of drought for the time series

Vegetation Condition Index

Historical Time Series Analysis

• Time series analysis to examine trends in satellite greenness (NDVI) for historical record (nationwide) –Patterns of green-up and senescence

–Patterns in integrated NDVI (proxy for

biomass accumulation)

– Trends in vegetation condition index

Time Series Analysis

• TIMESAT software will be used for the developing the time series data – Calculates yearly beginning of season, end of season, amplitude,

integrated NDVI values

– Available from: http://www.nateko.lu.se/TIMESAT/timesat.asp?cat=0

Green-up End of Season

Integrated NDVI

Time Series Variables

a. Start of Season - time of year for the start of vegetation green-up

b. End of Season - time for which the vegetation greenness and biomass accumulation is declining

Time Series Variables

f. Seasonal amplitude - difference between the maximum greenness value and the base level.

g. Length of the season - time from

the start to the end of the season.

h. Small Seasonal Integral - integral of the difference between the function describing the season and the base level from season start to season end.

Time Series Variables

i. Large Seasonal Integral - integral of the function describing the season from the season start to the season end.

j. Base Value - the average of the left and right minimum values – represents average of the lowest levels of NDVI for a year

Mapping Time Series Output Start of Season

Mapping Time Series Output End of Season

Mapping Time Series Output Season Large Integral

Climate Data Source and Analysis

Data Sources • Unified Precipitation Dataset

– Product available from NOAA that uses optimal interpolation

– 0.50 degree resolution (~55 km) – Daily Product, 1979 to present – Available from:

ftp://ftp.cpc.ncep.noaa.gov/precip/CPC_UNI_PRCP/GAUGE_GLB/

• Global Telecommunications System (GTS) Station Data

– Daily climate data for reporting World Meteorological Organization (WMO) stations

– Includes about 26 stations in Mongolia – Archived by Texas A&M (2003 to present) – Station History (ftp://ftp.ncdc.noaa.gov/pub/data/gsod/ish-

history.txt)

APHRODITE: Asian Precipitation - Highly-Resolved

Observational Data Integration Towards Evaluation of Water Resources

• Daily, gridded rainfall and temperature data set for Asia – Resolution: 0.25° and 0.50°

– Interpolated surfaces from a variety of climate data sources

– Time period is 1951-2007

– Monsoon Asia product

– Data Available at:

http://www.chikyu.ac.jp/precip/index.html

APHRODITE Coverage

From: http://www.chikyu.ac.jp/precip/index.html

APHRODITE Data Sources • Individual collection - Negotiation with local

meteorological/hydrological organizations and/or local researchers

• Pre-compiled datasets Global Historical Climatology Network (GHCN) Carbon Dioxide Information Analysis Center (CDIAC) National Center for Atmospheric Research, Data Archive (NCAR-DS) National Climatic Data Center (NCDC) Food and Agriculture Organization of the United Nations (FAO) GEWEX Asian Monsoon Experiment-Tropics (GAME-T) data center The Mekong River Commission (MRC) European Climate Assessment & Dataset (ECAD)

• Global Telecommunication System

World Clim Data

• Monthly interpolated surfaces of climate data

• Resolution – 1 km

• Several Variables Available:

– Min. Temperature

– Max. Temperature

– Mean Temperature

– Precipitation

– Derived Bioclimatic Variables

World Clim Data

• Monthly interpolated surfaces of climate data

• Resolution – 1 km

• Several Variables Available:

– Min. Temperature

– Max. Temperature

– Mean Temperature

– Precipitation

– Derived Bioclimatic Variables

World Clim Data

• Data Sources – – Global Historical Climatology Network (GHCN), – FAO – WMO – International Center for Tropical Agriculture (CIAT), R-Hydronet – Climate databases for Australia, New Zealand, the Nordic European

Countries, Ecuador, Peru, Bolivia, among others.

• Interpolated using the the ANUSPLIN software using latitude, longitude, and elevation as independent variables

• Time period is 1950 to 2000

• Data available online at: http://www.worldclim.org/download

Rainfall Coefficient of Variability (CV) Mapping

• The CV of annual rainfall has been suggested as a threshold indicator for equilibrium vs. non-equilibrium conditions on rangelands

• Areas having CVs of greater than 33% may be indicative of non-equilibrium conditions

• Interpolated rainfall time series provide a means to examine the spatial extent of areas having high rainfall variability

• The data can be analyze to map the CV to identify potential equilibrium/non-equilibrium zones

Rainfall CV Mapping

von Wehrden, H., J. Hanspach, P. Kaczensky, J. Fischer and K. Wesche. 2012. A global assessment of the non-equilibrium concept in rangelands. Ecological Applications, 22(2):393-399

Texas A&M splining of WMO Dataset

Unified Precipitation Data 1979 to 2012 CV

Coefficient of Variation

0 to 33%

33 to 50%

50.00000001 - 75

75.00000001 - 100

100.0000001 - 150

Comparison of 10-Year CVs

1980-1989

1985-1994 Coefficient of Variation

0 to 33%

33 to 50%

50 - 75

75 - 100

100 - 150

Comparison of 10-Year CVs

1990-1999

1995-2004

Coefficient of Variation

0 to 33%

33 to 50%

50 - 75

75 - 100

100 - 150

Comparison of 10-Year CVs 2000-2009

2003-2012

Coefficient of Variation

0 to 33%

33 to 50%

50 - 75

75 - 100

100 - 150

Next Steps

• Examine other climate data sets (WorldClim and APHRODITE) to see if similar patterns emerge.

• Compare interpolation methods

Integration with GIS and other Remote Sensing Data

• GIS provides a means of examining information in relation to boundaries, locations, and other remote sensing data

• Integration of imagery with other Remote sensing products like digital elevation models (DEM) can allow examination of data by slope, aspect, etc.

Data Data can be created in-house, downloaded, or

purchased.

Two types:

Spatial data: map, photos, graphics

Attribute data: descriptions, database

Spatial vs. Attribute

Spatial Data

(Map, Photo)

“Where”

Attribute Data

(Characteristics, Tables)

“What”

Vector vs. Raster

Spatial Data

Represented by:

Points

Lines

Polygons

Geographic representation of data

vector

Spatial Attribute

id_no name length (f)

1 Deer Trail 500

2 Spring Lane 750

3 Woods Road 1000

id_no acres infestation spread_rate

1 4 SPB 4/m

2 3.5 SPB 12/m

points

lines

polygons

points, lines, polygons

id_no nest_type eggs condition

1 rcw 4 good

2 cardinal 0 fair

3 bluebird 3 fair

4 rcw 2 good

5 cardinal 6 good

GIS

Vector Data

• Points, Lines, Polygons

• Can be used to describe real world features such as roads, property boundaries, pipelines, cities, deer blinds, transect locations, etc.

• Attributes can give details about a feature such as name, length, transect production, deer counts etc.

Example of Vector Data

Attribute Data

Raster Data

• Comprised of Pixels

• Pixel size determines resolution

• Examples:

– Satellite imagery

– Aerial Imagery

– Radar

– LIDAR

Raster Data: Resolution

35 cm 2 m 10 m 20 m

Normalized Difference Vegetation Index (NDVI) Product – 250 m resolution

Integrating Data with Boundary and Other Useful Information

Enhanced Vegetation Index (EVI) Product – 250 m resolution

Imagery and Terrain

• Offers opportunity

to examine data in relation to elevation, slope and hillshade

• This example uses integration of aerial photography, digital elevation data, and hill shading

Integrating With a GIS

Integrating With a GIS !

!

!

!

!

!

!

!O8a

O4a

O2a

O6a

O7a

O5a

O1a

Other Products Useful For Rangelands

• Google Earth • NASA, USGS, and NOAA's Landsat satellite program

with the following sensors: – Multispectral Scanner (MSS) – Thematic Mapper (TM) and Enhanced Thematic Mapper

(ETM+) – http://landsat.usgs.gov/products_data_at_no_charge.php

• Ikonos • Quickbird • Digital Elevation Data

– Shuttle Radar Topography Mission (http://srtm.usgs.gov/index.php)

– ASTER Global Digital Elevation Map (http://asterweb.jpl.nasa.gov/gdem.asp)

Questions or Comments?