Figure 1

Remote Sensing Industry Expects Growth

A Summary of the 10-Year Industry Forecast

Kevin Birch
Resource Policy Analyst
Oregon Department of Forestry
Salem Ore.
Rick Jones
Director Northwest Operations
Space Imaging
Portland Ore.
Jim Muckenhoupt
Remote Sensing Analyst
Space Imaging
Portland Ore.


Riparian vegetation provides a number of important functions to the ecosystem. These include adding nutrients to streams from litter fall, bank stabilization by the root masses of trees within the area, shade which controls water temperature and large woody debris for stream channel development.

One of the goals of the Oregon Forest Practices Act is to maintain riparian areas in mature forest conditions that provide large conifers over time. Thus, riparian zones may provide much of the older forest on private forestland.

While society’s strategy for providing fish and wildlife values rests heavily on riparian vegetation, we do not have a good inventory of the existing conditions. Many ground-based samples have been designed to avoid riparian areas or have not specifically noted which inventory plots were taken in riparian areas. As local watershed councils and landowners plan to improve aquatic habitat conditions for various species such as coho salmon, they need to know about the condition of the riparian vegetation in the watershed. For example, decisions about how to improve water temperature, where to place woody debris to increase pool habitats, or where the most effective refuges might be located are decisions that would benefit from knowledge about riparian vegetation.

Most of the consistent information describing large areas of forest vegetation has been produced from satellite imagery because it is the most cost-effective way to inventory large areas. One of the key satellites used in mapping vegetation throughout the state of Oregon has been Landsat. The Landsat satellites collect spectral data for individual pixels that are approximately 30 meters in size, roughly equivalent to a baseball diamond. While Landsat is an excellent, cost-effective platform for regional mapping, the size of the pixel presents difficulty when attempting to map riparian vegetation. Riparian zones are typically diverse areas, and in many areas are no wider than 30 meters. Thus, detailed classifications of riparian vegetation types from Landsat are difficult.

With the growing constellation of high-resolution satellites, leveraging automated processes to map riparian vegetation types has become feasible. The Oregon Department of Forestry undertook a project to evaluate the effectiveness of using higher resolution satellite data to map riparian vegetation. The project, which is currently in process, was developed to evaluate three different image datasets for both their spatial/spectral capabilities to map riparian vegetation and their cost-effectiveness to do so. The datasets utilized include:

  • Landsat 25m Multispectral merged with IRS-C 5m Panchromatic
  • IKONOS 4m Multispectral
  • IKONOS 1m Color

Study Area

The study area for this project was the fifth-field watershed in the Yaquina River Basin predominantly covered by the Elk City and Harlan 7 1/2 minute U.S. Geological Survey quadrangles (Figure 1). It is approximately 57,000 acres in size and contains 235 miles of streams on forest and agricultural lands. The watershed is predominantly publicly owned with about 73 percent of the land owned by the federal government, 13 percent owned by the state and only 14 percent owned by private landowners. The majority of the watershed is zoned as forestland and only six percent of the land is zoned as agricultural. However, for riparian protection purposes, this small amount of agricultural land is very important because it is made up of long narrow pieces that follow the river bottoms. Although the agricultural land makes up only six percent of the land, it contains 27 percent of the streams that have been identified by the Oregon Department of Fish and Wildlife as important for coho salmon.

Classification System

Figure 5
For this project we used a classification system that consists of three distinct attributes: cover type, size and canopy closure (Figure 5). The purpose for generating three layers instead of a single layer was to facilitate differing combinations of these variables so that the data could be easily cross- walked and compared to different classification schemes developed by both the state and the federal governments.


Mapping riparian vegetation, rather than sampling, has the advantage of allowing us to ask specific questions about the geospatial location of features of interest. The amounts and locations of different riparian conditions are critical to answering basic questions about effects of different land-use practices on the landscape and how the landscape may change over time. For example, a recent clear-cut will over time grow back into forest. However, land managed for agriculture will likely remain agriculture without a change in management. Understanding the differences enables us to model the recruitment of large woody debris into the future by running different types of simulation models.

To create the map, Space Imaging classified each image source independently, differentiating between upland and riparian areas by means of a buffer. The buffer width was chosen to be 500 meters. This width corresponds to areas where there is generally riparian vegetation. While the Oregon Forest Practices Act was designed to provide increasing protection as the stream size increases, up to 100 feet from a stream, this larger buffer size was selected to include more area to maintain flexibility in further policy analyses.

Figure 2

Figure 3

Figure 4
Once the buffers were generated, Space Imaging derived polygons from the imagery based on a minimum mapping unit of 0.5 hectare because of the spectral and textural similarity of the features in the imagery. Next, a hybrid supervised/unsupervised classification method was utilized to classify the cover type, size and canopy closure layers. As with most natural features, a certain amount of heterogeneity is inherent on the landscape and thus needed to be accounted for in the classification. While our minds tend to clump similar features (i.e, develop polygons around features), image processing tends to split features. To account for these phenomena and to label areas where features are considered similar enough to be in the same class, decision rules were generated to group the cover type, size and canopy closure variables into the discrete variables of the classification scheme. This information was then used to generate the maps illustrated in Figures 2 through 4.


Maintaining riparian vegetation is an important part of the strategy to produce and maintain fish and wildlife habitat. Thus the development of policy options to manage these areas requires a thorough understanding of the condition of these areas over time. Unfortunately, the large landscape-scale data sets that are currently available do not seem to adequately describe riparian vegetation; thus the use of higher resolution data sources is required.

While this project is in process, preliminary results illustrate that higher resolution sensors are capable of identifying the vegetation types within diverse linear features such as riparian zones. Once the classification of the vegetation types is complete, an accuracy assessment will be conducted in conjunction with the Pacific Northwest Research Station of the USDA Forest Service. This assessment will provide valuable insight into how well the information derived from the imagery coincides with the vegetation on the ground. This information will provide managers and policy makers with key information which can be used as decision criteria on a platform that can be used under various policy considerations.

Sensors & Systems | Monitoring, Analyzing and Adapting to Global Change | Stay in tune with the transformation. Subscribe to the free weekly newsletter.