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FINAL TECHNICAL REPORT

NASA Grant NSG-7453

University of Vermont

School of Natural Resouces

Burlington, Vermont

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FINAL TECHNICAL REPORT

NATIONAL AERONAUTICS AND SPACE ADMINISTRATION

Grant NSG-7453

by

Remote Sensing Applications Program

School of Natural Resources

The University of Vermont

Burlington, Vermont 05405

Roy A. Whitmore, Jr. - Program Director and Principal Investigator

Gary S. Smith - Program Coordinator

Report Period

June I, 1978 - May 31, 1987

Period of Emphasis

June i, 1984 - May 31, 1987

Prepared for

National Aeronautics and Space Administration

Washington, D.C. 20546
TABLEOFCONTENTS

Preface ..............................

List of Figures ..........................

List of Tables ..........................

Introduction ...........................

Background and Objectives .....................

Primary Data Sources .......................

St. Albans Area Land Cover to Land Use Transformation .......

Modeling of Land Cover and Soil Attributes ............

Mapping Forest Productivity by Integrating Remotely Sensed

Data with Soils Data in a GIS ................

Enhancement of Land Cover/Use Classification ...........

Mapping Sewage Disposal Capability ................

Impact on Agriculture of a Proposed Highway ............

Landfill Siting Analysis .....................

Summary and Conclusions ......................

References ............................

Appendix .............................

Page

ii

iii

iv

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49
PREFACE

This report is an effort to bring together the major projects undertaken

during the last three years of our grant from University Applications of the

National Aeronautics and SpaceAdministration. It is not our intention to

cover everything that was done to demonstrate the application of remote

sensing technology or later to integrate this technology with other data

sources. Rather it touches only those activities that proved to have

practical application for us and our cooperators.

Wewould like to thank those organizations and agencies that worked with

us and provided muchdata, specifically, the Eastern RemoteSensing

Applications Center, GoddardSpace Flight Center; Soil Conservation Service,

USDA; Vermont Department of Forests, Parks and Recreation, Vermont Department

of Water Resources (now Vermont Department of Environmental Conservation) and

the Vermont Water Resources Research Center of the University of Vermont.

Many individuals played important roles in carrying out these

demonstrations and studies. We particularly wish to recognize Jack McCarthy,

Remote Sensing Technologist; D. Scott Denkers, Remote Sensing Technologist;

Lee J. Kauppila, Research Computer Specialist; David A. Sousa, Computer

Operator/Programmer and Eileen A. Powers, Natural Resource Data Specialist.

±i
LIST OFFIGURES

Figure

i.

.

.

.

Classified Thematic Mapper data image of St. Albans,

Vermont region that has been enhanced by the

incorporation of a digitized composite transportation

and urban land cover layer to

create an improved land use data layer .........

Site index of northern hardwood stands in the

Fairfield Pond Region of Vermont. The

transportation network

has been overlaid on this map ..............

Land use map of the St. Albans Bay Watershed

produced through manual photo interpretation and

on the ground

verification ......................

On-site sewage disposal capability of the Town of

Colchester, Vermont. This illustrates the type

of soil interpretations that can be achieved

using a comprehensive soils data base

in a geographic information system ...........

Page

ii

24

29

38

_ iii
Table

1.

2.

.

.

.

.

o

.

9.

10.

LIST OF TABLES

Land use in the Stevens Brook Watershed .........

Erosion potential of agricultural lands in the

Steven Brook Watershed ................

Calculation matrix for the derivation of Runoff

Curve Numbers for the Stevens Brook Watershed .....

Area by calculated Runoff Curve Number for the

Stevens Brook Watershed ................

Satellite derived land cover classifications for the

Fairfield Pond area of northwestern Vermont ......

Area of general soils class in the Fairfield Pond

study area ......................

Area of northern hardwood stands in the Fairfield

Pond study site by site index class .........

Land Use in the St. Albans Bay Watershed ........

Comparison of TM classification and photo interpreted

classification before data layer integration .....

Comparison of TH classifications and photo interpreted

classification after data layer integration ......

Page

13

15

17

18

21

22

25

28

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iv
]INTRODUCTION

During the last two decades remote sensing technology has advanced at a

rapid pace. Numerous demonstration projects have been completed and have

shown that these technologies are useful to resource managers. The LANDSAT

series of permanently orbiting satellites with multispectral sensors have

provided resource managers with sources of data never before available.

Remote sensing scientists working independently and in cooperation with

private, local, state and federal agencies have repeatedly demonstrated

specific applications.

Since its inception in 1978, the Remote Sensing Applications Program of

the School of Natural Resources at the University of Vermont has undertaken

some thirty-five demonstration projects where the data acquired by the Multi

Spectral Scanner and more recently the Thematic Mapper aboard the LANDSAT

series of satellites have been a major input. Success has been variable

depending upon the specific objective of a project. In some instances other

remote sensing devices were able to provide the data needed either more

efficiently, at lower cost, or at a resolution that was necessary to

accomplish the specific objective. A listing of the major projects carried

out is included in Appendix A.

We know that there is still a shortfall in the acceptance of these

technologies and their routine use. Part of this is due to insufficient

knowledge on the part of potential users but also because of the realization

that a single remote sensing data source does not provide all of the answers

needed.

While remote sensing can involve a number or variety of sensing stimuli,

the most common sensing device is the eye. When applied to image analysis,

1
the humaneye and brain apply the elements of image interpretation to solve

and identify particular features or phenomenon. Until the launch of the first

LANDSAT satellite in 1972, the aerial photograph was the principle source of

remotely sensed information. To glean information from an aerial photograph

requires intensive human interpretation of the imagery. Digital multispectral

data has altered our aerial information gathering process. Computer

processing of digital data can now be used reliably to map land cover type

information to at least a level I classification and often times to level II,

according to the USGS Land CoverUse Classification scheme (Anderson et al).

The LANDSAT sensor systems and those of other imaging satellites such as GOES,

AVHRR and SEASAT have made global imaging on a repetitive basis a reality. If

the flow of current information from imaging satellites remains uninterrupted

and affordable, large area assessment projects directed at monitoring global

change can move forward.

The speed with which a digital image can be processed into a usable

product far exceeds that of manual interpretation techniques. It is this

speed and the availability of regular repeat coverage that lures organizations

to try using digitally processed image data. More accurate Level I and II

classification results are being achieved with the finer spatial and spectral

resolutions available on today's imaging satellites Unfortunately

classification beyond Level II, that most regional and local officials are

familiar with, is not possible with standard image processing techniques.

Digital image processing within a computer makes extensive use of only one or

two of the elements of image interpretation. In most cases only tone,

represented by a range of digital values is analyzed in a classification

algorithm. Some use of a derived texture value is also possible. Other

elements are considered only superficially in the training site selection

2
process (shape, size and association) and in the naming convention for the

final product. Expansion beyond the Level I and II classification limit

requires the use of information sources that are not available in the image

data set. Information sources such as soils, topography and ownership are

needed to extend classification systems beyond Level II and to shift the

results from simply a land cover product to a more powerful land use output.

Successful integration of these multiple data sets must consider and

accommodate differences in map scale, projection, resolution limit or minimum

mapping unit and data format. To glean the greatest benefit from different

data sources, it is necessary to use a total information system that is

equally capable in accommodating both map and map feature attributes. Such

capabilities exist in the form of geographic information systems. It is

through the use of geographic information systems (GIS) that local, regional,

national and global information needs can be met and managed. Within a GIS,

remote sensing, either digital image processing or traditional manual photo

interpretation, serves as an important data input source.

Successful implementation of such a total information system must first

start at the local level where sufficiently accurate ground reference

information is available to fully test methodologies. Soils, geologic,

topographic and ownership information are just a few of the ancillary data

sets needed for integration with the output of digital image processing to

provide the desired detailed classifications. As procedures are developed and

verified, projects can be scaled up to cover larger geographic regions.

Coverage of larger geographic regions may require some data aggregation or

generalization to be efficiently accommodated within current machine

processing capabilities. The University of Vermont School of Natural

Resources' Remote Sensing Applications Program has worked to enhance the

_ 3
merger of remote sensing with the manipulative powers of a geographic

information system.

-- 4
BACKGROUND ANDOBJECTIVES

The Remote Sensing Applications Program (RSAP) was established at the

University of Vermont in 1978 following our initial grant from NASA. All of

our efforts over the next six years were directed toward the development and

testing of applications of remotely sensed data and in particular, digital

processed data from the LANDSAT series of satellites. During this period, we

acquired a VAX 750 computer through outside funding sources. This computer

and associated hardware and software were key factors in the success of the

Remote Sensing Applications Program and the subsequent development of our

Geographic Information System (GIS) laboratory. These resources have been

dedicated almost exclusively to spatial mapping and the analysis of land

resource variables.

This computer has served as host for the ARC/INFO state-of-the-art

geographic information system and the ELAS image processing system. The

ARC/INFO system is a product of the Environmental Systems Research Institute

(ESRI) of Redlands, California. The ARC portion of the system handles the

graphic or map portion of the GIS and INFO, an incorporated relational data

base manager developed by HENCO Software, Inc. stores and analyzes all of the

map feature attributes. This combination has made the ARC/INFO system one of

the most powerful and versatile geographic information systems available. In

addition to the polygon formatted ARC/INFO system, the School of Natural

Resources maintains a grid cell based GIS called GRID that is also a product

of ESRI.

Image processing was accomplished with the ELAS software package,

displayed on an AED 767 color workstation. ELAS is a product of NASA's Earth

Resources Laboratory located at the National Space Technology Laboratory in

Slidell, Mississippi. Image processing output can be passed to the ARC/INFO

-- 5
via the GRIDsoftware and likewise ARC/INFOpolygon files can be passed to the

ELASimage processing system through a GRIDinterface.

The major thrust of efforts during the last three years has been to

develop techniques for the merger of satellite data with other geographically

related data sets to illustrate and define land conditions or capabilities.

Manydata layers such as soils and topography are very tedious and laborious

to enter into the geographic information system. However, once they are

within the computer's manipulative grasp, they require little if any

maintenance. Land cover/use is a very dynamic layer which changes constantly

because of and in spite of man. It is totally unrealistic to expect to

manually interpret this information from photography for a large area and

digitize results into a GIS on a regular basis. Such a task is ideally suited

for digital processing of satellite data. Land cover information so derived

can be converted to land use data by the merger of other data sets contained

in the GIS.

Modelling studies that use enhanceddata layers can depict such factors

as erosion potential, forest growth, water run off potential, and crop yields.

This type of information has widespread applications and affords the

opportunity to approach situations from a planning phase rather than from a

reactive phase to catastrophic events. By interfacing GIS technology with

that of remote sensing, the managermay play out a series of scenarios and

select the one that most closely depicts the desired objective. Policy makers

certainly would benefit from review of these types of analyses before making

manyall importantand sometimes irreversible decisions.

The material on the following pages describes a series of specific

investigations undertaken to demonstrate the integration of remote sensing and

GIS and development of usable techniques.

_ 6
PRIMARYDATASOURCES

To accurately test and evaluate data merging techniques, it was necessary

to have a large data base available from which to draw information and to test

results. The Vermont geographically referenced data sets provide such a data

base. During the course of this study information on almost one million acres

was entered in the GIS, providing an ideal test site. Creation of this data

base during this period was a cooperative agreement between the University and

the U.S. Soil Conservation Service. Since the termination of these studies,

authorization for a complete statewide data base was enacted by the Vermont

Legislature. The various data layers contain detailed map and text data on

soils, topography, transportation networks, stream and river courses,

political and watershed boundaries.

Classified land cover information was obtained from a variety of remotely

sensed data sources including color infrared photography which was manually

interpreted to polygon maps and LANDSAT Multispectral Scanner and Thematic

Mapper data. A major source of land cover data for much of this area was

acquired from Thematic Mapper scene E-40162-15062 acquired over northwestern

Vermont on October 26, 1982.

Selection of Thematic Mapper data to provide most of the land cover

information for this data base is the result of a series of conscience

decisions and compromises. While all other data layers in the data base were

in a visually pleasing and spatially accurate polygon format, the inclusion of

gridded data in a fixed grid format at first seemed to be a step down in

quality. It was necessary to recognize that land cover is constantly changing

and maintenance of this dynamic information layer by traditional photo

interpretation and subsequent digitizing is both costly and very time

consuming. Digital processed satellite data provided the only reasonable

-- 7
alternative available to regularly mapland cover over large areas. The

decision was made to use these data sets to test the utility of merging

satellite data with other data sets within a GIS to both improve land cover

classification results and to model change events.

Initial processing of this scene was performed at the Goddard Space

Flight Center on the IDIMS system with the assistance of NASA personnel. For

data reduction purposes, all 7 Thematic Mapper bands were entered into a

canonical analysis program. From the data transformation prescribed by the

canonical analysis, three new axes of information were found to describe 99%

of the variability in the scene. Initial classification of the area was

accomplished with the IDIMS' unsupervised classifier ISOCLAS. To supplement

the ISOCLAS results, supervised classification techniques were also used. A

total of 63 separate signatures were developed to identify ii separate land

cover classes. These classes were identified as water, hardwood forest,

softwood forest, bare soil, urban, hay/alfalfa, wetlands/brush and

grass/abandoned fields. At the completion of the classification process, the

data set was geometrically corrected to UTM coordinates with pixel size set at

one-quarter acre.

-- 8
ST. ALBANS AREA LAND COVER TO LAND USE TRANSFORMATION

BACKGROUND

Output from classified digital data acquired by satellite scanners

depicts land cover information as opposed to land use information.

Limitations in spatial resolution and spectral similarities between features

contribute to this shortcoming. Transportation networks are an excellent

example of a land use category that is generally not resolvable from satellite

data. One method for developing the detail of a transportation network is to

digitize a data overlay of this information which works fairly well for

orientation purposes. While this may be adequate for some purposes, it does

not produce an enhanced layer of information that can be used to develop

specific land use information. Many features such as soils, topography and

transportation networks are very stable and change little over time.

Land cover is a dynamic information layer that changes at varying rates

depending on the cover and the location. Maintenance of this data layer by

traditional photo interpretation and transfer and subsequent digitizing is

time consuming and difficult. The use of digital image processing techniques

can expedite this task.

METHODS

An area near St. Albans in northwestern Vermont was selected for study.

It is twenty-five miles north of Burlington and a major transportation hub on

the route to Montreal. The area is agricultural with the exception of St.

Albans city. For many years the shoreline of St. Albans Bay has been lined

with camps and cottages, and in recent years some suburban development has

occurred, especially in areas convenient to Interstate 89.

_ 9
Examination of the land cover classification for this area prepared from

Thematic Mapper data revealed two major shortcomings. First, the

classification was unable to resolve the road network in the area, and

secondly, it exhibited the usual confusion associated with the mapping of a

heterogeneous urban area. For the most part the classification of the

agricultural and forest lands was quite acceptable and a good indicator of

land use.

The first step in overcoming these shortcomings was to convert the

gridded or raster data format of the classified image to polygon format within

the ARC/INFOgeographic information system. Following this, the road network

was digitized and a newdata layer created. At the sametime the areas of

confusion in the urban portions of St. Albans were corrected through manual

photo interpretation of 1982 1:24,000 black-and-white panchromatic aerial

photography. This was then mergedwith the digitized road network to create a

correcting mask and mergedwith the polygon format classified image data to

create the land use classification shownin Figure I.

RESULTS

The primary purpose for carrying out the procedures described here was to

determine the feasibility of improving the utility of remotely sensed data, in

this case TMdata, through the addition of other information by harnessing the

capabilities of a GIS. The interfacing of remote sensing and GIS technologies

allowed us to achieve results that neither could have done alone. In effect,

some of the misclassification in the classified TM data can be corrected

through the use of a GIS.

10
8C._LIE t_ - , "

Figure i. Classified Thematic Mapper data image of St. Albans, Vermont region

that has been enhanced by the incorporation of a digitized

composite transportation and urban land cover layer to create a

land use data layer.

II
MODELINGOFLANDCOVER AND SOIL ATTRIBUTES

BACKGROUND

Existing land cover/use plays a significant role in soil erosion and

runoff and can have a great impact on the water quality of an area. Nonpoint

pollution has been identified as a major water quality problem and many

nonpoint problems are related to soil erosion. Any comprehensive analysis of

water quality problems must integrate the land cover/use and the changes that

occur with the more permanent or non-changing attributes of the soils. When

this is done, it is then possible to model erosion and or runoff potential by

merging remotely sensed data with soils and other data layers within the

capability of a GIS.

METHODS

The Stevens Brook watershed, an 11,037 acre subwatershed of the St.

Albans Bay watershed was selected as a study area. The area is located in

northwestern Vermont twenty-five miles north of Burlington. The entire

watershed is being monitored as part of a long-term cooperative study of the

Vermont Water Resources Research Center and the Soil Conservation Service of

the U.S. Department of Agriculture. The area is primarily agricultural but

since the City of St. Albans drains into Stevens Brook, urban land represents

a third of the watershed (Table i).

Land cover was determined from classified data acquired from Thematic

Mapper scene E-40162-15062 over northwestern Vermont. Only agricultural land

cover consisting of lands in hay/alfalfa, bare soil and corn were considered

in the analysis.

Erosion factors are used to predict the erodibility of a soil and its

tolerance to erosion in relation to specific kinds of land use and treatment.

12
Table I. Land Use in the Stevens Brook Subwatershed.

Land Use Acres Percent

Urban 3,686 33.4

Agriculture 6,854 62.1

Forest 232 2.1

Wetland 265 2.4

Total 11,037 i00.0

_ 13
The soil erodibility factor (K) is a measure of the susceptibility of the soil

to erosion by water. Soils having the highest K values are the most erodible.

K values range from .01 to 0.64 for the soils in the area. To estimate

erosion potential (PE) the erosion factor is modified by a slope length factor

(LS).

The attributes needed to develop soil erodibility factors existed in the

data base associated with the soils mapdata layer. Slope length factors were

derived from the topographic data layer and when combined with the erodibility

factor, produced the erosion potential for each of the three major

agricultural land use types. The results of the data layer integration are

shown in Table 2.

Another example of where the merger of remotely sensed data with other

data layers can produce valuable map and statistical information is in the

creation of Runoff Curve Numbers. This calculation incorporates land use,

soil hydrologic characteristics and the slope of the land. Hydrologic soil

groups are used to estimate runoff from precipitation. Soils are placed in

one of four groups on the basis of the intake of water after the soils have

been wetted and have received precipitation from long-duration storms. Group

A soils have a high infiltration (low runoff potential) when thoroughly wet

and have a high rate of water transmission. Group B soils have a moderate

infiltration rate when thoroughly wet and a moderate rate of water

transmission. Group C soils have a slow infiltration rate when thoroughly wet

and a slow rate of water transmission. Group D soils have a very slow

infiltration rate (high runoff potential when thoroughly wet and a very slow

rate of water transmission.

Data on the hydrologic grouping was available in the data base associated

with the soils data layer for this area. A calculation matrix for the

-- 14
Table 2. Erosion potential of agricultural lands in the Stevens Brook

Watershed.

Land Cover Type

Potential Erosion Ranges

Slight Moderate Severe

PE<.I PE.I-.26 PE>.26 Total

Hay/alfalfa

Bare soil and cornfields

Grassland

Percent Percent Percent

13 7 2 22

17 12 7 36

20 14 8 42

Total 50 33 17 I00

-- 15
derivation of Runoff Curve Numbers is shown in Table 3. A summary of the

Runoff Curve Number calculations for the Stevens Brook Watershed is shown in

Table 4.

RESULTS

The purpose of this study is not to indicate how accurately the mergers

developed can predict erosion potential and runoff. We have developed models

which produce estimates based on remotely sensed data and a soils data base

both of which are subject to error. However, a merger of the type presented,

though subject to error, does enable us to model these factors by simulating

changes in land use. For example, we can simulate the conversion of an

existing pasture to a cultivated crop and predict the change in erosion

potential and runoff. A model of the form presented here has other desirable

characteristics. It requires only limited ground truth or remotely sensed

data to periodically update the land use on a regional basis in order to keep

the model functional.

-- 16
Table 3. Calculation matrix for the derivation of Runoff Curve Numbers for

the Stevens Brook Watershed.

Cover Description

Soil Hydrologic Group

A B C D

Cultivated & Bare Soil

Pasture or Range

Meadow (Hay, Alfalfa)

Forest

Urban

67 76 83 86

52 69 79 84

30 58 72 78

25 55 70 77

74 84 90 92

-- 17
Table 4. Area by calculated Runoff Curve Number for the Stevens Brook

Watershed.

Runoff Curve Number Acres

25 342

55 2,943

70 4,105

74 41

77 1,739

84 509

90 952

92 406

TOTAL 11,037

The average Runoff Curve Number for the Stevens Brook Watershed is 68.9.

-- 18
MAPPINGFOREST PRODUCTIVITY BY INTEGRATING

REMOTELY SENSED DATA WITH SOILS DATA IN A GIS

BACKGROUND

Worldwide, the land base devoted to the production of renewable natural

resources continues to shrink. As this trend continues, land productivity

becomes an increasingly important issue. In recent years agriculturists have

devoted considerable effort to the determination of those agricultural lands

having the highest productivity. Many studies have been conducted to

determine the extent of land use change on prime and good agricultural soils

both here in the United States and throughout the world. Ascertaining and

mapping the productivity of forest lands is equally important.

The most widely accepted measure of forest productivity is site index.

This measure which uses the average height of dominant and codominant trees of

a given species at a specified age has long been considered as a useful

measure of forest land productivity. Because site index is costly to measure

over large areas, numerous investigators have attempted to develop methods

whereby it can be measured indirectly through association with other

variables. Some of the variables used include topographic features,

vegetation and soils. Post and Curtis (1970) developed a set of site index

equations based upon elevation, latitude and soil series group for northern

hardwood stands growing on glacial acid till soils in the Green Mountains of

Vermont.

METHODS

A 1600-hectare area (Vermont orthophoto map sheet #112260) encompassing

part of the town of Fairfield in northwestern Vermont was selected for forest

land productivity mapping in this study. The land cover is a mixture of

forest and agricultural areas almost totally in private ownership. The

-- 19
topography is gently rolling and ranges from Fairfield Pond at an elevation of

550 feet to hills in the northwest that rise to just under 1,000 feet. Most

of the forested area is comprised of even aged stands that were established on

abandoned agricultural lands.

Land cover for the study area was derived from a Landsat 4 thematic

mapper scene (E-40162-15062) acquired on October 26, 1982. All seven thematic

mapper bands were processed by canonical analysis and three new axes of

information were found to describe ninety-nine percent of the variability in

the scene. Sixty-three separate signatures were developed to identify eleven

land cover classes. These include water, hardwoods, softwoods, bare soil,

urban, hay/alfalfa, wetlands/brush, and grass/abandoned fields (Table 5). The

classified data set was geometrically corrected to UTM coordinates using a

pixel size of one-quarter acre and subsequently converted from raster (grid)

to polygon format. Thematic mapper data alone were unable to resolve the road

network and were upgraded by merging it with a correcting mask composed of a

digitized road right-of-way data layer.

Soils data for the study area were derived from the Franklin County Soil

Survey as published by the Soil Conservation Service (Table 6). Soil series

polygons had been previously digitized and labeled. Text data for the various

labels were contained in the S01-5 data base. The soils polygon map for the

study area was subset from the larger geographically registered Franklin

County soils data base which had been checked and edited by SCS soil

scientists.

A topographic-data layer was created by digitizing one hundred foot

contour intervals from the 1:62500 USGS quad sheet covering the study area.

An overlay of land cover and soils was created showing hardwood forest areas

by soil series. This in turn was overlaid with the topography layer. The

-- 20
Table 5. Satellite derived land cover classification for the Fairfield Pond

area of northwestern Vermont. 1982.

Category Acres Hectares Percent

Transportation and Urban

Hay and Alfalfa

Bare soils, Cornfields

Grasslands, Abandoned fields

Brushlands, Wetlands

Hardwoods

Softwoods

Water

Total

145 59 4

500 202 12

161 65 4

226 92 6

205 83 5

1565 633 40

685 277 17

467 189 12

3954 1600 I00

-- 21
Table 6. Area of general soils class in the Fairfield Pond study area.

General soils class

Area

Acres Hectares Percent

Limerick-Rumney

Georgia-St. Albans

Au Gres-Wareham

Munson-Buxton-Belgrade

Scantic-Raynham-Binghamville

Windsor-Missisquoi

Carlisle-Terric Medisaprists

Woodstock-Tunbridge

Peru-Stowe

Cabot Westbury

Water

Total

*less tha 0.5 percent

81 33 2

13 5 *

113 46 3

207 46 5

317 129 8

230 93 6

25 I0 1

1251 506 32

923 373 23

327 132 8

467 189 12

3954 1600 i00

-- 22
data values associated with each layer were processed using the equations

developed by Post and Curtis. The resulting values were assigned to the

corresponding hardwoods-soil series-elevation polygon.

RESULTS

The final output site index classification map provides a visual

representation of the geographic distribution of each site index class (Figure

2). Most site index equations in the northeast are expressed in predicted

height at age fifty as opposed to age seventy-five of the Post and Curtis

equations. In conformance with common practice, the data were converted to

the age fifty base. The study area is fifty-seven percent forested and twothirds of this is northern hardwoods. Twenty percent of the hardwood forest

area is site index seventy or higher (Table 7). The remaining eighty percent

of the hardwood stands are of site index sixty-nine or lower.

No attempt was made to assess the accuracy of the site index

classification. To do so would not have been a test of the technique or

methodology employed, but a test of the site index equations developed by Post

and Curtis which was not the purpose of this study. The techniques developed

and used in this study were designed to provide an efficient and economical

method for mapping forest land productivity as expressed by site index.

In instances where site index can be estimated by soil attributes,

elevation, aspect, latitude or any other parameter that can be geographically

referenced and input to a data base, then this tool will work. When combined

with land cover, i.e. cover type or species derived from remotely sensed data,

the geographic information system is capable of providing locational and

statistical data on site productivity for the use of the forest land manager.

-- 23
Figure 2.

I'_A I R F I E L[) I) 0 N D , \; T ,

NORTHERN HARDWOODS S I TE INDEX

POST ANO CURTIS SOIL-SITE EQUATIONS

j ,_ . .

f r--.-'--

g

ORIG!,_AL PAGE IS

OF POOR QUALITY

Site index of northern hardwood stands in the Fairfield Pond Region

of Vermont. The transportation network has been overlaid on this

map.

24
Table 7. Area of northern hardwood stands in the Fairfield Pond study site by

site index class.

Site Index Class

(Hr. at age 50)

Area

Acres Hectares Percent

55-59

60-64

65-69

70-74

Total

613 248 40

315 128 20

306 124 20

312 126 20

1546 626 I00

-- 25
ENHANCEMENT OF LAND COVER/USE CLASSIFICATION

BACKGROUND

Knowledge of the capabilities of the earth's soils and the effective

application of this knowledge is important to projects analyzing global

change. By combining interpreted soils information with satellite data,

additional information can be developed that is not possible through the use

of either data set alone. In Vermont and other areas where detailed soils

mapping has been completed, soils can be grouped into categories of high

potential, good potential and low potential for agricultural production. This

information can be applied to a classified satellite data set to further

refine land cover categories that were not separable by spectral response

alone. This is particularly true for grass cover crops and areas in perpetual

grass cover that are not reliably distinguishable through normal

classification procedures. For example, in dairy farming regions like

Vermont, it is difficult to accurately distinguish hay crops like alfalfa and

timothy which are considered cultivated crops by the Soil Conservation Service

from those fields used as pasture. Farmers usually use fields with better

soils to grow cultivated crops and use those with poorer soils for pasture.

METHODS

The testing of classification accuracies and evaluation of the effects of

merging other data sets with the satellite land cover data to improve mapping

results requires considerable ground reference information. We were fortunate

to have available a large watershed of approximately 33,000 acres for which

very accurate land use information is maintained. This watershed is monitored

by the Vermont Water Resources Research Center, which is a part of the School

of Natural Resources. All of this information is maintained within our GIS.

-- 26
The St. Albans Bay Watershed is located in northwestern Vermont, twentyfive miles north of Burlington, the state's largest urban area. This

watershed drains 52.1 square miles (33,344 acres) of farm, forest, and urban

land into St. Albans Bay on Lake Champlain (Table 8).

Agriculture is the most common use of land in the St. Albans Bay

Watershed. About 60% of the watershed or 20,000 acres is in cropland,

hayland, or pasture. Hay and corn are the principal crops. Trends in

agricultural land use include converting the better quality pastures to crop

or hayland and the poorer quality pastures to woodland, recreational, or urban

use. Residential development of former farmland is ongoing, especially on the

higher land overlooking Lake Champlain.

Woodland covers about 25% of the watershed or I0,000 acres. Since the

average farmer maintains 10-20% of his land as forest, probably 5000 acres or

more of this woodland is associated with farms. More than 80% of the forested

land is hardwood, mainly beech, birch, and maple. The balance is softwood,

primarily red and white pine.

Urban and industrial uses, including 3000 acres for Interstate 89,

account for about 12% of the watershed, or 4,000 acres. These uses are

concentrated around St. Albans City, but occur also around Georgia Center, in

suburban developments off the interstate, and around the perimeter of the Bay,

in the form of camps and cottages.

A detailed land use map of the St. Albans Bay Watershed that was used for

ground reference information is shown in Figure 3. It was prepared through

manual photo interpretation and on the ground surveys. The opportunity to

draw upon existing resources provided us with much of the data required for

this work. If we had been faced with the extensive data entry and data base

building tasks, most of this work would not have been possible.

-- 27
Table 8. Land Use in the St. Albans Bay Watershed.

Land Use Acres Hectares Percent

Urban 4,001 1,619 12.0

Agriculture 19,673 7,961 59.0

Forest 8,669 3,508 26.0

Wetland 1,001 405 3.0

Total 33,344 13,493 i00.0

-- 28
-. _ ."

Land Use in Lhe St.

Albans Bay Watershed

• Agriculture

• Woodland

[] Residenfial

• Commer cIai

• Commercial - Industrial

• Commercial - Residentio

• Open-idle

• Wetland ar Waler

- • Ins|ilu|ionol

• Urban

.. • TronsporJal_on

_'_ School ol NatL,roI ker_,:_r._-

OF POOR QUALITY

Figure 3. Land use map of the St. Albans Bay Watershed produced through

manual photo interpretation and on the ground verilJcatlon.

29

l
Our experience has shown that the polygon format is more desirable when

raster or grid satellite data is being merged with other data sets that are in

a polygon format. This allows the addition or merger of data sets with finer

resolution (ie. road networks) than that of the grid satellite image.

Resultant maps plotted at relatively large scales (eg. 1:24,000) are also

superior in appearance to traditional grid cell maps. This greatly improves

their acceptance by the user community. However, there are many instances

where the only product desired is a set of statistics and a map is not

required or desired. This is frequently true for analyses of large areas.

In order to retain the benefits of the polygon system's ability to

accommodate data sets of varying resolution, the Remote Sensing Applications

Program developed a method to efficiently merge large areas of satellite data

with other data sets. This was accomplished through the conversion of the

satellite's grid cell data to a point coverage data set within the ARC/INFO

geographic information system. Since the ARC/INFO system treats point

coverages as polygon coverages in a degraded form, they can readily be merged

with other data sets within the GIS. This conversion to point data uses the

coordinates of the cell's center to define the point. The resultant points no

longer are restricted to a simple XYZ address and description. They now have

a polygon attribute table attached that will maintain the results of any

action taken on a point.

Specific benefits of this approach include: i) reduced processing time

over normal grid cell to polygon conversion, 2) retained ability to merge data

sets of varying resolutions, and 3) greater attribute or data base management

capabilities.

_ 3O
The classified TMdata set was converted to point format and then

overlaid on the previously prepared polygon format land use map. A comparison

of the classified TM data and the manually photo interpreted data is shown in

Table 9. At this point no attempts had been made to improve the TM data

classification by adding masks or integrating other data sources. Errors in

classification are apparent and are greatest in the urban areas and in the

agricultural areas between those areas that are tilled and those that are

untilled.

The next step in classification improvement was to create a new data

layer by digitizing the road network within the watershed. Next the areas of

confusion in the urban portions of the watershed were corrected through manual

photo interpretation. This was also digitized and merged with digitized road

network to create a correcting mask. The correcting mask was merged with the

point (polygon) classified TM data.

Next all of the soils occurring on agricultural lands were grouped

according to productivity potential. Higher productivity soils are devoted to

alfalfa production while those of lower productivity are used for hay. From

the analysis shown in Table 9 there is confusion and lack of clear

separability when based on spectral attributes alone. The results of

modifying the classification with soil productivity data and the urban-highway

mask are shown in Table I0.

RESULTS

Improvement in-the classification results can be seen by comparing the

various classes in Table 9 with those in Table i0. This study shows the

potential for integrating satellite data with other data layers to improve the

accuracy of the classification. Data layers such as soils and road networks

change little over time and require little effort in updating. Limited ground

-- 31
Table9. Comparison of TMclassificationsandphotointerpretedclassificationbeforedatalayerinterpretation.

BareSoil Brush

P.I. Class. Corn Wetlands Grassland Hardwoods Hayland Softwoods Urban Water Total

Ag. Land 136 II 49 543 191 1 26 957

Barn 253 17 47 79 175 3 49 623

Commercial 244 24 49 55 4 146 522

Comm/Indus 267 8 30 87 91 169 483

Comm/Res 509 3 217 319 277 48 951 2,324

Corn 7,258 282 741 1,415 3,658 75 352 31 13,812

Farmstead 36 7 12 14 5 12 1 87

Hay 4,737 307 2,059 2,432 18,495 216 412 2 28,660

Hay/Pasture 1,601 286 1,468 2,147 7,S06 134 279 ! 13,722

Institution 128 9 16 25 140 43 361

Open-Idle 393 55 205 655 406 60 214 1,998

Open-Rec. 40 7 47 74 258 4 37 467

Pasture 1,953 562 2,022 3,790 7,291 483 413 14 16,528

Residential 1,421 137 571 1,317 1,623 186 722 60 6,037

Roads 324 19 88 262 232 29 150 4 1,108

Sew. Treat. 8 4 1 13 6 32

Urban 291 25 104 86 262 1,008 1,776

Water 50 34 5 128 3 34 152 5,987 6,393

Wetland 138 297 46 592 38 61 78 13 1,263

Woodland 1,679 2,174 1,962 13,909 2,206 4,020 1,451 46 27,447

Woodland/Past. 468 449 1,152 4,223 1,948 1,219 398 7 9,415

TOTAL 21,934 4,686 10,861 32,081 44,993 6,844 7,068 6,166 134,633

-- 32
Tablei0. Comparison of TMclassificationsandphotointerpretedclassificationafterdatalayerinterpretation.

BareSoil Brush

P.I. Class. Corn WetlandsGrasslandHardwoodsHaylandSoftwoodsUrbanWater Total

Ag.Land 136 11 57 69 183 i 26 483

Barn 623 623

Commercial 522 522

Comm/Indus 652 652

Comm/Res 2,324 2,324

Corn 7,258 282 1,044 1,415 3,355 75 352 31 13,812

Farmstead 87 87

Hay 4,737 307 4,432 2,432 16,122 216 412 2 28,660

Hay/Pasture 1,601 286 1,929 2,147 7,345 134 279 1 13,722

Institution 361 361

Open-Idle 393 55 124 665 487 60 214 1,998

Open-Rec. 40 7 78 74 227 4 37 467

Pasture 1,953 562 3,576 3,790 5,737 483 413 14 16,528

Residential 6,037 6,037

Roads 1,158 1,158

Sew. Treat. 32 32

Urban 2,250 2,250

Water 50 34 4 128 4 34 152 5,987 6,393

Wetland 138 297 57 592 27 61 78 13 1,202

Woodland 1,679 2,174 2,838 13,909 1,330 4,020 1,451 46 27,447

Woodland/Past. 468 449 1,795 4,223 1,305 1,219 398 7 9,864

TOTAL 18,450 4,457 15,934 29,444 36,122 6,307 17,858 6,101 134,683

_ 33
truthing on an as needed basis would keep these data layers current.

Agricultural land cover includes those crops such as pasture which changes

little with time while crops such as hay, alfalfa and corn are rotated on a

regular basis. The dynamic portion of this cover must be regularly updated

through on-the-ground examination or small format aerial photography.

Regardless of the method it would only have to be used on the areas of highest

productivity. Since the areas of lower productivity are more constant in

their cover, less field or other updating would be necessary. The integration

of digital multispectral data with soils and other prepared data layers within

the database and mapping capabilities of a GIS proved to be a successful

method of improving the land cover/use classification.

34
MAPPINGSEWAGEDISPOSALCAPABILITY

BACKGROUND

One of the greatest obstacles to comprehensive monitoring of global

change is the assembly of all necessary information. Such a task requires the

knowledge of many professions and the subsequent integration of this

information into a plan or model. Within current computer capabilities there

exists the capability to capture and utilize the information from many

experts, without the individuals being present. These computer based systems

are known as "Expert Systems."

We have worked to establish such a system for soils information. The

heart of the system is the SOI-5 soil interpretation records developed by the

Soil Conservation Service of the U.S. Department of Agriculture. These

interpretation records are available for every mapped soil identified by the

Soil Conservation Service. Information within the data base are divided into

8 separate categories which include: i) Identification, 2) Estimated soil

properties, 3) Use interpretations, 4) Capability and predicted yields, 5)

Woodland suitability, 6) Wind breaks, 7) Wildlife habitat suitability, and 8)

Potential native plant suitability. All of these data were derived from soil

surveys.

The soil survey is a detailed inventory and evaluation of the most basic

resource of an area -- the soil. It is useful in adjusting land use,

including urbanization, to the limitations and potentials of natural resources

and the environment. It also can help avoid soil-related failures in uses of

land.

During the conduct of a soil survey extensive notes are made about the

nature and unique aspects of behavior of the soils. These notes include data

on erosion, yield estimates, flooding, the functioning of septic tank disposal

_ 35
systems and other factors affecting the soils under various uses and

management. This field experience and the measureddata are used as a basis

for predicting soil behavior.

This information is useful in the planning and management of soils for

crops and pasture, building sites, transportation systems and sanitary

facilities. From these data, the potential of a particular soil for a

specified land use can be determined. Soil limitations to these land uses can

be identified and failures caused by unfavorable soil properties can be

avoided. A site where soil properties are favorable can be selected, or

practices that will overcome the soil limitation can be initiated.

Planners and others using the soil survey can evaluate the impact of

specific land uses on the overall productivity of an area and on the

environment. Productivity and the environment are closely related to the

nature of the soil. Planning should maintain or create a land-use pattern in

harmony with the natural soil. Health officials, engineers and other

specialists can find information that is useful in locating septic fields,

sources of sand and gravel and sites that cause difficulty in excavation. The

safe disposal of wastes, for example, is related to the properties of the

soil.

METHODS

By incorporating the SOI-5 records into the INFO relational data base

manager of the ARC/INFO GIS it is possible to perform many mapping assignments

which in the past required the direct involvement of a trained soil scientist.

During the last year of our grant, we acquired the latest edition of the soil

interpretation records for all counties currently in our data base and

restructured the data base to facilitate queries. The resultant interpreted

-- 36
soil maps, when used as either a stand alone product or in conjunction with

remotely sensed land use information can be a powerful planning tool.

In order to demonstrate the type of analyses that are possible using this

data base, a study depicting the capability of the soil to support on-site

sewage disposal was developed.

The town of Colchester, Vermont just north of Burlington was chosen as a

study site. Colchester was formerly a farming community but is rapidly

becoming a bedroom community for Burlington. Few farms remain and

development, both residential and commercial, is proceeding at a rapid rate.

The first step was to group the Soil Potential Index (SPI) values

developed by the Soil Conservation Service. The SPI is the numerical rating

of the soil's relative potential for septic tank absorption fields. It is

used to rank soils from very high to very low potential and is derived from

indices of soil performance, cost of corrective measures, and costs

established for continuing limitations, if any. The SPI equation is:

SPI = P - CM - CL, where:

P = performance index (P is set at I00 as a locally established

standard)

CM = index of costs of corrective measures needed to overcome or

minimize the effects of soil limitations.

CL = index of costs resulting from continuing limitations. CL is

included in the costs of Corrective Measures (CM).

Thus for this study, SPI = P - CM

The resulting values between ) and i00 for the soils are broken into groups

for specific septic disposal application or method. Three groups were used

for this study.

RESULTS

The output of the calculations is shown in Figure 4. This map shows the

capability of soils in Colchester for on-site sewage disposal. More than one37
irJi__iFf(fl,Ifl/-,_'7'HL IF/_.I/II\7'

FJ gure 4. On-site sewage disposal capability for the Town of Colchester,

Vermor_t. Thls illustrates oiLe type oi soil iz, terp_etation that can

be accomplished using the SOI-5 soils data base.

38

GR,G,['_L ?AGE IS

OF POOR QUALITY
half of the area of the town is not suitable for on-site disposal. Before

development can proceed in these areas, a comprehensive sewage system and

treatment facility would have to be constructed. The method is efficient and

revealing because it shows problem and non-problem areas. These products can

be used to help the Town of Colchester better plan and manage its future.

39
IMPACT ON AGRICULTURE OF A PROPOSED HIGHWAY

BACKGROUND

As with many other locations in the world, the metropolitan area of

Burlington, Vermont is growing quite rapidly. With this growth has come

conflict between existing land use activities and pressures for expansion of

new facilities to meet population needs. Within the next few years, the

communities of Williston, Essex and Colchester will probably be bisected by a

circumferential highway that will pass to the northwest of Burlington. The

initial environmental impact study, or EIS was found to be deficient in its

analysis of the effect this highway would have on the remaining farms in the

three town area. The Vermont Agency of Transportation retained a private

consulting firm to evaluate this issue. The School of Natural Resources was

retained by this consultant to perform the mapping and photo interpretation

work for this project.

The objective of this study was to evaluate the proposed highway corridor

location, in relation to existing agricultural land and activities. From this

evaluation, recommendations could he made that might help mitigate negative

impacts from the location of the route.

METHODS

Our activities consisted of two separate but related tasks. The first

involved the use of the School's GIS to display and analyze existing and

proposed activities in the three towns that were related to the impact of the

highway on agriculture. The second task involved the evaluation of other

highways that had been built in the state and the associated impacts on the

agricultural activities in the surrounding areas. This evaluation involved

the interpretation of aerial photography acquired before and after

construction.

_ 4O
I. GIS Mapping and Evaluation

The GIS analysis involved both the use of existing digital data and the

generation and incorporation of new information into the computer system. All

information was compiled on and incorporated into the GIS using overlays of

the Vermont orthophoto base maps. The proposed highway location was

transferred from very large scale aerial photography on to the orthophotos

using a Map-O-Graph and Stereo Zoom Transfer Scope. Data layers used in this

analysis included: I) soils, 2) existing transportation network, 3) surface

waters, 4) farm boundaries, 5) zoning boundaries, 6) sewer service areas, and

7) town boundaries.

Using the data base management capabilities for attribute handling,

numerous maps of individual "layers" and composite overlays were generated.

The interpretations included agricultural soil potential and a ranking of the

various farms in the three towns by both viability and suitability. Farm

ranking was provided by a panel of agricultural experts from Chittenden

County. Farms that were ranked as "suitable" had the physical resources (eg.

good soil and facilities) to be a successful farm. The "viability" ranking

considered other factors and included the owner's desire to remain in

agriculture and monetary resources. All of these factors had to be considered

to provide an unbiased analysis.

Besides the agricultural resources, each of the town's zoning plans and

areas that had been enhanced by the availability of a sewer system were

included in the analysis. If a town had not acted to protect its agricultural

interests, and had in fact acted to enhance an area for commercial/residential

development, then citing the negative impact of the proposed highway may seem

inappropriate.

In all, a series of approximately a dozen maps were created and used at

the public meetings held in each town. Particular attention was paid to the

-- 41
points where this proposed highway crossed existing roads. In some locations

an intersection was proposed and in other cases, an overpass was planned.

Clearly an intersection would create greater development pressure than would

an overpass.

II. Photo Interpretation of Similar Sites.

Three separate sites within Vermont were identified for temporal

evaluation. Sites were selected in Brattleboro, White River and St.

Johnsbury, Vermont. All of these locations had a limited access highway built

within the twenty years. In all three cases, it was clear that the road had

adversely affected agriculture in the area, either through the loss of acreage

from the road itself, or by limiting the accessibility of portions of a farm

to the owner. It was clear that the strength of the individual's desire to

remain in agriculture was one of the principal determining factors in either

the demise or continuation of a farm, following the road's construction.

RESULTS

This project clearly demonstrated the effectiveness of remote sensing and

GIS technologies to evaluate impacts such as new highway construction. From

this analysis, it was clear that agriculture in the area would he adversely

impacted, largely through expanded development pressures. With only one

exception, this study found that the proposed road location would not consume

large quantities of existing farm land. A recommendation was made to move one

section of the proposed road location in Colchester to reduce the loss of

agricultural land in an intensive "truck farm" area. In addition, two

proposed intersections, one in Williston and the other in Colchester were

recommended for conversion to overpasses to reduce development pressures on

neighboring farm land.

42
LANDFILL SITING ANALYSIS

While efforts to recycle more of our waste products are intensified, a

need still exists to identify suitable sites for safe disposal of that which

cannot be recycled. Through funding from the Chittenden Regional Planning

Commission, we were able to use the geographic information system to conduct

an initial landfill site screening analysis. Physical limitations to the site

were used to reduce the area of consideration. These exclusion factors

included high elevation regions, very permeable soils, and areas with high

water tables. The resultant reduced area of potential sites was output in map

form.

Areas for potential sites were photo interpreted to determine current

land use. Areas under development and prime agricultural lands were

eliminated from the analysis at this point. This further reduced the areas of

interest and facilitated the identification of sites that would be ground

checked and subjected to more intensive engineering studies.

-- 43
SUMMARY AND CONCLUSIONS

Land managers increasingly are becoming dependent upon remote sensing and

automated analysis techniques for information gathering and synthesis. There

is good reason for this. Remote sensing and geographic information system

(GIS) techniques provide quick and economical information gathering for large

areas.

Remote sensing has grown dramatically during the late 1970s and the

1980s. New satellites, carrying new sensors with increased spatial and

spectral resolution, have become operational and continue to provide users

with timely synoptic data. Remote sensing, however, cannot be considered in a

vacuum. The outputs of remote sensing classification and analysis are most

effective when combined with a total natural resources data base within the

capabilities of a computerized GIS. The utility of remote sensing products

increases when they can be compared and merged with data from other sources

such as transportation networks, soils surveys, meteorological data, geologic

maps and census data. There is an increase in the flexibility of these

procedures when the computer and a geographic information system are used in

combination as a spatial analysis tool. This combination of various data

sources frequently produces synergistic results that are of greater use than

any or all of the individual sources taken separately. We see GIS and remote

sensing as one entity concerned with capturing, storing, processing, analyzing

and outputting geographic data.

We have presented some examples of the successes, as well as the

problems, in integrating remote sensing and geographic information systems.

Most have been attempts to demonstrate and solve the problems of cooperators

in the wide user community. At this point, three years after the termination

of the project, many of these techniques have become routine operations for

44
municipal and state government planners and managers in Vermont and our

neighboring states.

The need to exploit remotely sensed data and the potential that

geographic information systems offer for managing and analyzing such data

continues to grow. As the potential for increased use has grown, so has the

capability to utilize this potential. Since the termination of this project,

new and greatly improved computer hardware and software have become available.

New microcomputers with vastly enlarged memory, multi-fold increases in

operating speed and storage capacity that was previously available only on

mainframe computers are a reality. Improved raster GIS software systems have

been developed for these high performance microcomputers. Vector GIS systems

previously reserved for mini and mainframe systems are available to operate on

these enhanced microcomputers. Image processing that were developed on mini

computer systems have been revised to run on this new generation of

microcomputers. At the same time vendors of software that ran on the previous

generation of microcomputers, have been able to expand and upgrade their

offerings. Although this trend toward more powerful microcomputer systems

will not eliminate the need for GIS systems configured for larger computers,

true GIS capability is now available, at reasonable cost, to many users who

cannot afford the larger systems.

One of the more exciting areas that is beginning to emerge is the

integration of both raster and vector formats on a single computer screen.

This technology will allow satellite imagery or digital aerial photography to

be presented as a background to a vector display. Through direct on-line

photo interpretation it will be possible to interactively update land cover

maps, assess agricultural areas by soil characteristics and many other tasks.

45
In addition to greatly improved microcomputers and software, the

development of peripheral hardware has accelerated at a parallel pace.

Digitizers and plotters of greatly improved capability are available at

reasonable cost. A most useful and exciting technological upgrading has been

the development of low cost partial- and full-page scanners. Just a few years

ago, polygon maps such as soils maps had to be digitized or sent out to be

scanned on very expensive equipment. This was also true for aerial

photographs. Both options were time consuming and expensive. Now for less

than two thousand dollars, a full-page high resolution scanner can create

raster or vector files from line drawings or continuous tone black-and-white

or color images. None of these technological developments was available for

use during the period of our investigations. Now we have them and their use

by planning and managerial practitioners has become routine.

As a result of this technology, we have the capability to query the data

bases that we create for both spatial and non-spatial information. This was a

key factor in the conduct of these studies. Equally important is the

capability to join both types of information through relationships developed

within the structure of the GIS. Functions of the GIS software such as

overlay, merge, union, map join, intersect and dissolve enabled us to

integrate data from multiple sources into a single data layer. We can create

data that did not previously exist.

Once data sets have been entered, the flexibility of the GIS allows

additional data sets to be added for the same or other geographic locations.

These may include data sets from past and future points in time thus creating

a data base that permits study of change.

The landscape of the earth will continue to change as demands for

resources accelerate. We must continue to develop methods that will enable us

46
to analyze and predict the changes that will result from these demands.

merging of remote sensing and GIS technologies provides a method for

monitoring and analyzing these changes.

The

47
REFERENCES

Anderson, J.R., Hardy, E.E. and Roach, J.T. 1972. A land use classification

system for use with remote sensor data. Washington, D.C. U.S.

Geological Survey Circular 671. 16 pp.

Post, B.W. and R.O. Curtis. 1970. Estimation of Northern Hardwood Site Index

from Soils and Topography in the Green Mountains of Vermont. Bulletin

664 Agricultural Experiment Station, University of Vermont, Burlington,

Vermont.

_ 48
UVM/SCHOOL OF NATURAL RESOURCES

REMOTE SENSING APPLICATIONS PROJECTS

YEAR TITLE/DESCRIPTION

COOPERATING

AGENCIES

1978-

1987

General Education and

Technology Transfer

NASA/ERRSAC

VT DEPT. OF FORESTS AND PARKS

VT DEPT. OF FISH AND WILDLIFE

STATE OFFICE/SCS

UVM/COLLEGE OF AGRICULTURE

VT STATE PLANNING OFFICE

NH STATE PLANNING OFFICE

NH FISH AND GAME DEPT.

NH WATER RESOURCES DEPT.

CENTRAL VERMONT PUBLIC SERVICE CORP.

1978-

1981

1978-

1980

1979

1979-

1981

1979-

1980

1979-

1986

Systems Development for

Data Handling and Analysis

Vermont Land Cover

Classification

Land Use Change

Demonstration

Land Cover/Use

Classification for

Planning

Vermont Remote Sensing Data

Inventory

Land Cover/Use Change for

Watershed Monitoring

Forest Insect Defoliation

Detection

UVM/COMPUTING CENTER

PENN STATE UNIVERSITY

UVM SCHOOL OF NATURAL RESOURCES

VT AGENCY OF ENVIRONMENTAL

CONSERVATION

ERRSAC

VT DEPT. FORESTS AND PARKS

VT DEPT. WATER RESOURCES

VT FISH AND WILDLIFE DEPT.

ADIRONDACK PARK COMMISSION

CHARLOTTE PLANNING COMMISSION

CHITTENDEN COUNTY REGIONAL PLANNING

COMMISSION

CENTRAL VERMONT REGIONAL PLANNING

COMMISSION

TOWN MANAGERS OF FAYSTON,

WAITSFIELD, AND WARREN

VT TAX DEPARTMENT

UVM/DEPT. OF GEOGRAPHY

U.S. FOREST SERVICE

VT HIGHWAY DEPT.

UVM/WATER RESOURCES RESEARCH CENTER

SOIL CONSERVATION SERVICE

CHITTENDEN COUNTY SCS

CRREL-CORPS OF ENGINEERS

VT DEPT. FORESTS AND PARKS

U.S. FOREST SERVICE

_ 49
1979-

1980

1980

1980

1980-

1982

1980

1980

1981-

1982

1981-

1983

1981

1981-

1982

1981-

CONT.

1981-

1982

1981

1981-

1982

Lake and Pond Watershed

Cover Classification

River Watershed Cover

Inventory

Coniferous Species

Differentiation

Water Quality Monitoring

Water Use Conflict Study

Water Body Inventory

Brighton Land Cover/Use

Mapping for Planning

Morristown Farmland

Inventory and Agricultural

Land Study

Site Analysis for Planning

New Hampshire LANDSAT

Demonstration

Small Format Aerial

Photography

LANDSAT Demonstration to

Essex County Soil

Forest Insect Defoliation

Mapping

Northeast Kingdom LANDSAT

Land/Cover Classification

VT DEPT. WATER RESOURCES

STATE OFFICE/SCS

UVM/FORESTRY DEPT.

VT DEPT. OF FORESTS AND PARKS

VT DEPT. WATER RESOURCES

ERRSAC

ENVIRONMENTAL PROTECTION AGENCY

UVM/RECREATION MANAGEMENT PROGRAM

UVM/WILDLIFE BIOLOGY PROGRAM

VT FISH AND WILDLIFE DEPT.

SOIL CONSERVATION SERVICE

BRIGHTON PLANNING COMMISSION

ST. REGIS PAPER COMPANY

CENTRAL VERMONT REGIONAL PLANNING

COMMISSION

MORRISTOWN PLANNING COMMISSION

VERMONT EXTENSION SERVICE

LAMOILLE COUNTY DEVELOPMENT COUNCIL

CITY OF WINOOSKI

NEW HAMPSHIRE STATE AGENCIES

INCLUDING STATE PLANNING, FISH

AND GAME, FORESTRY COMMISSION

ERRSAC

UNIVERSITY OF NEW HAMPSHIRE

VT DEPT. FORESTS AND PARKS

UVM SCHOOL OF NATURAL RESOURCES

CENTRAL VERMONT PUBLIC SERVICE CORP.

UVM COLLEGE OF AGRICULTURE

ESSEX COUNTY SOIL CONSERVATION

VT DEPT. FORESTS AND PARKS

ST. REGIS PAPER COMPANY

50
1982

1982

1981-

1982

1982-

1983

1982-

1983

1982

1982-

1983

1982

1982-

1983

1983-

1985

1983-

1987

1983-

1984

Tinmouth Channel Cover Type

Mapping

New Hampshire Deer Yard

Mapping

Southern Lake Champlain

Water Study

Spring Phosphorous

Concentrations of VT's

Lakes & Ponds

Aquatic Vegetation Mapping

Power Line Right of Way

Spruce Budworm Stand

Condition Rating

Watershed Cover Type

Mapping

Vermont Cover Type and

Soils Boundary Digitizing,

Integration and Map

Production

Thematic Mapper

Classification

Creation of the Vermont

State Data Base

TMIS/GIS Demonstration

CENTRAL VERMONT PUBLIC SERVICE CORP.

VT DEPT. OF FISH AND WILDLIFE

NEW HAMPSHIRE FISH AND GAME DEPT.

VT DEPT. OF WATER RESOURCES

ENVIRONMENTAL PROTECTION AGENCY

VT DEPT. OF WATER RESOURCES

VT DEPT. OF WATER RESOURCES

VERMONT ELECTRIC POWER CO., INC.

INTERNATIONAL PAPER CO.

CENTRAL VERMONT PUBLIC SERVICE CORP.

VERMONT DEPT. OF AGRICULTURE

NASA, GODDARD

SOIL CONSERVATION SERVICE

U.S. FOREST SERVICE

VT AGENCY OF ENVIRONMENTAL

CONSERVATION

SOIL CONSERVATION SERVICE

VT AGENCY OF ENVIRONMENTAL

CONSERVATION

VT STATE PLANNING

VT DEPT. OF TRANSPORTATION

VT. DEPT. OF AGRICULTURE

GREEN MOUNTAIN NATIONAL FOREST

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