U.S. Department of the Interior

U.S. Geological Survey

Vegetation Monitoring: Remote Sensing of Vegetation

B. Ralston, GCMRC, October 15, 2008
Vegetation of Colorado River E

Mix of commuRelated to mu

-Wildlife-Recreatio-Cultural/a

Separate manobjectives

-exotic spe-SSQ – shhabitat/bac
Objective of Presentation

 Review results of 2002 Vegetation 

Effort

 Explain capabilities and utility of 

area change detection

 Provide examples application of v

map for other resources.

 Anticipated Activities/Results in F
Background

 Protocol Evaluation Panel Conve

2000.

 Recommended river-wide GIS cov

vegetated area for habitat analysi

 May 2002 digital CIR flown for rive Vegetation Mapping Project Initia

2003. 

 Final Open File Report completed 

-http://pubs.usgs.gov/of/2008
Why Map Vegetation?

Vegetation is dynamic

-Operations-Local climate 

Primary component of animal 

habitat

- arthropods – food base- Nesting habitat/ground 

cover riparian birds/small mammals

Knowing types and amount of 

vegetative cover can help explain other observed biological interactions

Vegetated area is a component 

of AMP Information needs
Vegetation Mapping Approach 

Field and Lab Ba-Vegetation Class 

- Ground-based - TWINSPAN

- Selection of tra

- Identification o

signature of cl

- Classifying ima

- Automated cla

to assign clas

- Accuracy Asse
Vegetation Mapping of CRE
Classification Categories

-Wetland - Phragmites/Scirpus, 

combined with Typha domingensis/Carex aquatilis (common reed/cattails/sedges)-Baccharis emoryi/Salix exigua - (seepwillow/coyote willow)-Tamarix ramosissima/Aster spinosa (saltcedar)-Pluchea sericea (arrowweed)-Prosopis glandulosa/Acacia greggii/Baccharis sarothroides (mesquite/catclaw acacia/desert broom)-Sparse Shrubs (desert shrubs, bunch grasses)-Non-vegetated (rocks, sand)
Classification Accuracy

- Initial accuracies varied with each 

49-100%. 

- Accuracies affected by similar refl

values of classes, density of vegesparse, small foliage of dry adapt

- Accuracies improved to >80% amo

classes with application of fuzzy 

*(alternative assessment approach that allows membership to particular classes. Accuracies categories of agreement such that a class mighcorrect instead of simply correct or incorrect.)
Vegetation Mapping of CRE
Vegetation Mapping of CRE

0

100200300400500600700

Arrowweed

Baccharis/Salix

Mesquite/Acacia

Ve

Area Cover (hectares)

05

101520253035

Arrowweed

Baccharis/Salix

Mesquite/Acac

% Area by Class
Area by Geomorphic R

0

100200300400500600700800

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Ca

Lower G

Geomorphic reach

Area (ha)
Application: Comparing 1998 an

Imagery – CIR Allows Area ChaClasses
Application: Change Dete

1992-2002 of Vegetated A
Application: Community 

Change/Biotic Interactions

Used locof saltcewillow frmap and from NPcorrelate vegetatispecies beaver d(Mortens2008)
Limitations of Remote Sens

 Only provides area cover of general ca

 Does not provide information about commu

herbaceous exotics) – need ground based sremote sensing

 Change detection at class level is depe

image quality

 Reflectance value overlap reduces ability to 

vegetation classes

 Object-oriented and additional environment

be used to increase accuracies

http://pubs.usgs.gov/of/2008/1216/
FY09 Vegetation Mapping A

 Complete mapping of 

2005 vegetation classes with accuracy assessment in May 2009 (coinciding with 2009 over flight).

 Compute change 

detection of 2002 – 2005 vegetation classes.

 Develop draft report by 

summer/fall 2009