Land Use and Population Changein the Coastal Ecosystem

Dave Clark

NOAA/National Geophysical Data Center

C-GTOS Working Group Meeting

March 3-6, 2003

Ispra, Italy

Land Use/Land Cover Variables/IndicatorsDirect• Forest Cover Change• Vegetation Cover and Height Class• Leaf Area Index• Surface Roughness• Topography• Morph-Tectonics• Basin ID• Eco-systems• Length of Growing Period• Cultivation Intensity• Acreage Converted to

pasture/agriculture.• Desertification rate• Dry Surface Area (% exposed land)

Indirect• Albedo• Carbon Dioxide Flux• Soil Carbon Content• Evapotranspiration• Water Storage Fluxes• Biogeochemical Trans. Land to

Ocean• Soil Annual Loss /Erosion• Soil Depth• Soil pH• Soil Infiltration Rate• Soil Type• Habitat

Conversion/Fragmentation

Human Population Variables/Indicators

Direct• Total Population• Urban Population• Population Density• Physiologic Density• # Resident Population• # Transient Population• Age Distribution• Population Growth Rate• Pop. Living Below Poverty Line• Pop. Engaged in Infrml. Econ. Act.

Indirect• Conventions Ratified• Pollution Abate/Contr. Expend.• Microbial water-born disease• Safe Water• Sanitation• Runoff• Municipal Wastes• Water Balance• Water Use Intensity• Winter Gales• Natural Disasters• Human Development Index• Gross Domestic Product• Per Capita Income• Environmental Sustainability Index• Energy Use• Motor Vehicle Network• # and Size Dam Projects• Land Area Protected• Forest Deforestation• Forest Total Area• Labor Force in Agriculture• Arable Land• Pesticide Use• Fertilizers

Other Relevant Activities Using Indicators

• Land Use/Land Cover Change– Core IGBP project, like LOICZ

• International Human Dimensions Program on Global Environmental Change

Population and Land use

• From US Pew Oceans Commission’s Report “Coastal Sprawl: The Effects of Urban Design on Aquatic Ecosystems in the US”

• Land use is growing faster than population

• Could be interesting to look at this relationship for the coastal environment

Results of Discussions

• Move land cover to Habitat• Impact priorities

– Economic effects – Urbanization

• Indicator priorities– Population/rate of change– Urbanization/rate of change– Cultivation intensity/rate of change

Distribution of ecoregions around the continental U.S super-imposed

on night lights data showing the level of urbanization.

Nighttime Imagery

• Use as an indicator of urbanization

• Use an an indicator of land use

• Use an indicator of population

• Research now into change studies

• Could be a significant contribution in looking at change in the coastal ecosystem

DMSP OLS Imagery

• Used for nighttime imaging of clouds by moonlight

• Digital database from 1992 to present

• Analog data from 1972; program in place to digitize analog data for change studies

• DOD system merged into NOAA weather satellites as NPOESS; 1st satellite ~2010

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) Italy, Croatia, Bosnia, Herzegovina, Tunisia

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) - Florida

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected

in both yearsBlack: Lights Saturated in

both periods

Change Detection Results (1993 versus 2000) Egypt, Israel, Lebanon, Jordan, Syria

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) - United Arab Emirates

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) - Korea

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) – Hong Kong

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) - Taiwan

Red: Lights brighter in 2000Cyan: Lights brighter in 1993White: Lights saturated in both periods

Red: Lights brighter in 2000Yellow: New lights in 2000

Blue: Lights brighter in 1993Blue/Grey: Dim lights detected in both years

Black: Lights Saturated in both periods

Change Detection Results (1993 versus 2000) - Japan

Nighttime Lights of Phoenix, Arizona Processed to Show Changes From 1992/93 to 2000Derived From Low Light Imaging Data From The U.S. Air Force

Defense Meteorological Satellite Program (DMSP) Operational Linescan System(OLS)

Extent of Lights in 2000Brightness Increase from 1992

Major highways

Saturated in both Dates

Secondary roads

Data for each year derived from cloud-freesections of a four month time series ofOLS observation.

Data processing by the NOAA NationalGeophysical Data Center, Boulder,Colorado.

1991

19911991

19912000

20002000

2000

Landsat TM images provided by I Cubed.http://www.i3.com

Landsat TM

Landsat TM

Landsat TM

Landsat TM

Future Activities to Note• "Studying Land Use Effects in Coastal Zones with

Remote Sensing and GIS” in Turkey, April 2003• Add Chris Elvidge, PI for DMSP at NGDC to

working group• NASA Interdisciplinary Science NRA

– “What are the consequences of climate and sea level changes and increased human activities on coastal regions?”

– Possible proposal: use nighttime lights time series (1992-2003) to model changes in population density, percent cover of impervious surfaces, and land cover in coastal areas worldwide

– NOI due March 14, 2003

We are using the BIOME-BGC and RHESSys ecosystem model, and AVHRR NDVI time series to analyze the carbon dynamics of the US with and without the current level of development.

While satellite derived measures such as NDVI cannot quantify the carbon fluxes, they are useful to study the changes in land surface dynamics as a result of development. To study such changes, we used AVHRR NDVI over the east coast of the U.S. with predominantly broadleaf forest canopies. For each 1x1 degree area, we calculated the onset of greenness for urban and rural areas. The onset of greenness is strongly related to carbon sequestration potential.

We ran the ecosystem model, BGC, using the eco-regions of the continental U.S as a template. The conterminous U.S is divided into 84 eco-regions. These eco-regions represent similar conditions of climate, vegetation, topography and soil conditions. For each ecoregion, we developed a set of daily climate data covering 18 years (1980-1997), dominant land cover from MRLC, dominant soil properties and topography. For each eco-region, we ran the BGC model to compute net primary production.