Presented at the Central Valley Region Academic Conference, November 11-12, 1999, Modesto, CA.

Nina Maggi Kelly
Environmental Monitoring Extension Specialist
Ecosystem Sciences Division
Department of Environmental Sciences, Policy and Management
University of California - Berkeley
151 Hilgard Hall #3110
Berkeley, CA 94720-3110
510.642.7272
510.642.1477 fax
mkelly@nature.berkeley.edu

This is the outline for a talk I gave at the Central Valley Region Academic Conference, on November 11-12, 1999, in Modesto, CA. The outline does not include the graphics shown in the presentation. I have included the websites from which I downloaded images for the presentation, or found helpful in the creation of the presentation.

GIS and remote sensing are often mentioned as useful aids in the monitoring and management of natural resources. In this presentation I want to:

1. Review the basics of Geographical Information Systems for use in natural resource monitoring and management, including data considerations, analyses, and data products.
2. Review principles and data considerations of remote sensing of natural resources for monitoring and management;
3. Show an example of a GIS/RS integration in an environmental management application (NOAA's Coastal Change Analysis Project); and
4. Briefly review the situation in California with respect to data sources and costs.

GIS are an assortment of tools for collecting, managing, analyzing, and producing digital spatial data. The strengths of GIS are in their ability to integrate digital spatial data from many sources, and to analyze those spatial data.

Common GIS software systems include ESRI products such as ArcView and Arc/Info, but there are others such as MapInfo, or Open source products, including GMT, and MicroDEM.

GIS data varies from the physical to the cultural, and includes spatial overlays of topography (or Digital Elevation Models), soils, landcover, transportation, vegetation, and local monitoring data can be included in a GIS.

There are four important considerations regarding GIS data: (1) the format of the data - raster or vector, (2) the scale of source data, (3) the projection system used, and the accuracy of the data.

1. Spatial data format is either raster or vector. (http://www.utexas.edu/depts/grg/gcraft/notes/notes.html)
2. Scale. The ability to show detail in a map is determined by its scale, and one must match the appropriate scale to the level of detail required in the project. Enlarging a small-scale map does not increase its level of accuracy or detail.
3. Geographic projection systems. (http://www.utexas.edu/depts/grg/gcraft/notes/mapproj/mapproj_f.html) All maps have a projection system, which translates a globe onto a surface. The projection system chosen will influence measurements made from the map. Distances and areal measures are affected by the projection system of the map. Usually, state GIS data are projected in UTM, or the State Plane coordinate system.

• Global coverage, in California, we are in zone 10 of the UTM grid. The UTM projection requires that error must not exceed 1/1000 (0.1%), and at the center of the zone, the error is 1/2,500 (0.04%).

• State by state system, in California, we use the Lambert Conformal Conic projection. The State plane projection system requires that error must not exceed 1/10,000 (0.01%) anywhere on the map.

1. Data accuracy. (http://www.utexas.edu/depts/grg/gcraft/notes/error/error_f.html) Accuracy is the degree to which information on a map matches true or accepted values. A map has several types of accuracy: Horizontal accuracy, vertical accuracy, attribute accuracy, conceptual accuracy, and logical accuracy. Precision refers to the level of measurement and exactness of description in a GIS database.

The analysis capabilities of GIS are very powerful, and include such methods as: mapping (http://edcwww.cr.usgs.gov/earthshots/slow/Imperial/Imperial), proximity routines (http://www.ccg.leeds.ac.uk/mce/mce-disp.htm), surface analyses, and linking GIS data to environmental models like sediment delivery models.

Remote sensing data considerations:

1. Spatial resolution, which usually refers to the pixel size (grain), and sometimes implies the scene size (domain);
2. Spectral resolution, which is the portion of the electromagnetic spectrum in which the sensor scans;
3. Temporal resolution, which indicates how frequently the sensor is overhead; and
4. Products. Many common and uncommon uses are made of remotely sensed data.

Remote sensor - examples:

• Satellites (there are many more, these are used in the talk to demonstrate changes in spatial and spectral resolution:

0. GOES imagery - NOAA weather satellites (http://rsd.gsfc.nasa.gov/goesb/chesters/web/goesproject.html)
1. AVHRR - NOAA global monitoring satellite: SST (http://sgiot2.wwb.noaa.gov/COASTWATCH/), and crop green-up (http://www.kars.ukans.edu/greenlab/greenreport/green2.html)
2. Landsat TM and ETM - USA satellite (http://landsat7.usgs.gov/)
3. IKONOS - a new, private industry satellite (http://www.spaceimage.com/)

• Aerial photography: archived sources, or custom flights

0. NAPP, NHAP and DOQQs (http://edcwww.cr.usgs.gov/Webglis/glisbin/glismain.pl)

• Airborne digital imaging

1. ADAR imagery (http://www.possys.com/)

• California sources

Remotely sensed data in a GIS analysis is useful because is provides near real time data, in a spatially continuous coverage, the user can define the map classification system, and change information is easily derived.

• Example from Coastal Change Analysis Project (C-CAP). (http://www.csc.noaa.gov/ccap/text/california.html)

1. CCAP - San Francisco Bay: 1986
2. CCAP - San Francisco Bay: 1993
3. CCAP - Change Image
4. Elkhorn Slough example

• GIS software - ArcView

Spatial data - sources.

Spatial data - costs.

I am the chair of a new DANR workgroup: Monitoring Landscape Change, and this information is available on the workgroup's website: http://nature.berkeley.edu/~mkelly/monitoring.html.