rural land use and demographic change in a rapidly urbanizing environment

Landscapeand Urban Planning, 16 (1988) 345-356 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands

345

RURAL LAND USE AND DEMOGRAPHIC CHANGE IN A RAPIDLY URBANIZING ENVIRONMENT

W.A. BEFORT, A.E. LULOFF and M. MORRONE

Department of Forest Resources and Department ofResource Economics and Community Development, University of New Hampshire, Durham, NH 03824 (U.S.A.)

(Accepted for publication 8 October 1987 )

ABSTRACT

Befort, W.A., Luloff A.E. and Morrone, M., 1988. Rural land use and demographic change in a rapidly urbanizing environment. Landscape Urban Plann., 16: 345-356.

Aerial photographs taken in 1953, I974 and 1982 of the two rapidly developing seacoast counties of New Hampshire were interpreted into six classes of land use, and the interpretations were overlaid in a grid-based geographic

information system to locate and quantify land- use change in 50 townships over three decades. Where developed land abutted on forest, a zone of development influence was delineated: this yielded results at variance with other recent land-use estimates. Regression analysis indicated that growth of developed land area within townships was strongly related to area of township, prior extent of development, metropolitan proximity, and presence of interstate highway interchanges.

INTRODUCTION

In coastal New Hampshire, the impact of rapid population growth upon the land base has been the subject of extensive speculation (in more than one sense), and various sets of data bearing on the question have been generated. Two successive University of New Hampshire studies, of which this is the second, represent rigorous attempts to track changes in land use in this area by comparing aerial photographs of urbanizing areas over time.

Rockingham and Strafford Counties share

New Hampshire’s 25km seacoast (Fig. 1). An interstate highway joins them to the Boston and Portland metropolitan areas, about 100 km south and north along the coast. Until the 1950’s, declining mill towns and abandoned farmland were the dominant features of the economic landscape. Since then, both counties have experienced sustained population growth at rates exceeding regional and national aver- ages (Table 1 ), with Rockingham County alone accounting for more than 10% of total New England growth between 1970 and 1980. Migration is the main growth contributor in

0169-2046/88/$03.50 0 1988 Elsevier Science Publishers B.V

346

Fig. I. General map of Rockingham-Strafford County region, showing nearby metropolitan areas and major highways.

HAMPSHIRE

ATLANTIC OCEAN

HIGHWAYS -

TABLE 1

Increase in population in absolute and percentage terms, for nation, region, state and county units, 1950- I980

United States and subregions

Population growth

1950-1960 1960-1970 1970-1980

N % N % N %

United States 27 997 000’ 18.5 23 979 000 13.4 23 244 000 11.4 New England 1 195000 12.8 1 338 000 12.7 501 000 4.2 New Hampshire 74 000 13.8 131000 21.5 I83 000 24.8 Rockingham County 28 970 41.4 39 922 40.3 51 394 37.0 Strafford County 8232 16.0 IO 632 17.8 14 977 21.3

‘For United States, New England and New Hampshire, population counts are rounded to nearest thousand.

347

4SOM

L

&,A WOODS SURROUNDED BY

7 DWELLINGS ,,DROPS Od

BECAUSE UNINFLUENCED CORE

,S UNDER P-HA MAPPING

M,NIMUM--

FOREST

I

SIXTY 30X60,4 HOUSELOTS PLUS 80~ 1NFLUENCE ZONES LEAVE HALF

OF 50-HA WOODLAND STILL IN FOREST CATEGORY (INFLUENCE zob)E

IN HATCHED PATTERN).

Fig. 2. Examples show results of extending boundaries of developed land 80 m into neighboring forest.

both counties, exceeding the rate of natural increase by factors of 2.8 in Rockingham and 2.2 in Strafford County (Luloff et al., 1985 ). Half the migrants to New Hampshire come from neighboring Massachusetts (Luloff and Tay- lor, 1978; Luloff et al., 1980), many con- sciously seeking a more rural environment (Ilvento and Luloff, 1982). Between 1970 and 1980 rural non-farm population in the 2 counties increased 53%, while farm population - an order of magnitude smaller - declined 55%.

The present study was conceived as a follow- on to an earlier New Hampshire project (Cop- pelman et al., 1978) in which standard U.S. Department of Agriculture aerial photos taken in 1953 and 1974 were interpreted in five land- use classes: agricultural, developed, forest, idle and “other” lands. This study added 1982 photography to the series. Like the earlier project, it was based on complete mapping of land uses rather than on sampling of photographed points, and its results were compiled at township level. A sixth class, open water, was added;

more importantly, the land-use maps were dig- itized and entered in a computer geographic information system for analysis, whereas out- put from the earlier project had been in tabular form only. Our objectives were twofold: to produce new maps and tables of land-use change over three decades for use by local planners and to relate the observed changes to a selected set of geographic and demographic variables.

MAPPING METHODS

Apart from the Coppelman study, there are two other airphoto-derived sources of land-use information for Rockingham and Strafford Counties: U.S. Geological Survey Land Use/ Land Cover maps and U.S. Forest Service For- est Survey data. The USGS maps were made by interpretation of high-altitude, small-scale ( 1: 60 000 and smaller) photography taken during the mid-1970’s, and depict land use at a single point in time. They were compiled at the 1:250 000 scale, with land uses mapped into 9 broad categories and 37 potential sub- categories (Anderson et al., 1976), to a minimum area of 4 ha ( 10 acres) in the most closely mapped classes (Loelkes, 1982 ). Forest Serv- ice land-use data are generated in the course of decennial Forest Surveys, and are based on point-sampling of 1: 40 000 and larger-scale photography (Ferguson and Jensen, 1963; Kingsley, 1976; Frieswyk and Malley, 1985). The 1973 and 1983 Forest Surveys of New Hampshire each employed about 16 000 sample points ( 1 point 145 ha- ’ ), independently chosen for each successive survey. Survey methods are heavily weighted towards forestry, 7 of the 12 photointerpreted classes being subdivisions of the forest land category.

By comparison with these surveys, the present project combined a rather coarse classification system with fairly high spatial resolution. All interpretation was carried out on 1: 40 000 and 1: 20 000 scale photography, one interpreter performing all delineations.

Fig. 3. Vertical aerial photo illustrates a typical land-use pattern in coastal New Hampshire: thickening residential development along a rural road bordered by old fields and woodland.

The following type definitions were used: “agricultural”: cropland, pasture, orchards,

nurseries, greenhouses, farmsteads; “forest”: land supporting tree growth with

30% crown closure, and with no other ap- parent use;

“developed”: residential, commercial, industrial, transportation, extractive and recreational areas; -

“idle”: abandoned farmland in transition to forest; cutover forest land;

“open water”: rivers and streams, lakes and - ponds, estuaries and embayments;

“other”: swamps and marshes, beaches,

plex patterns, and lets the user know the size of the largest inclusions that may exist in an ap- parently homogeneous type. In this study, all types were mapped to a 2-ha minimum; that is, nothing smaller than a circle approximately 160 m in diameter on the ground received sep- arate delineation as a type. The following interpretation rules were imposed:

linear features narrower than 80 m were not mapped, with the exception of limited-ac-

cess highways; the boundary of developed land was ex- tended 80 m into neighboring woodland. The intent of the first rule is obvious. Pro-

open sand, bare rock. ducers and users of thematic maps dislike ex- In any type-mapping project, it is necessary tremely long and narrow type polygons;

to fix the size of the smallest type area to be moreover, grid-oriented geographic informa- delineated. This minimum determines how far tion systems cannot deal well with linear fea- the interpreter must go in disentangling com- tures. The effects, however, are substantial.

349

TABLE 2

Map accuracy report, Strafford/Rockingham 1982 interpretations

Mapped types

Verified types

AGR DEV FOR IDL OTH WAT Total

Classification information

Correct Commission

(%) error (O/o)

AGR DEV FOR IDL OTH WAT Total Omission

errors

33 2 35 94 6 3 48 1 53 91 9

42 3 45 93 7 I 1 2 11 15 73 21

33 1 34 97 3 24 24 100 0

38 51 45 11 36 25 206 1 3% 6% 7% 0% 8% 4%

Summary information: points sampled 206; allowable misclassifications 22; observed misclassifications 15; minimum map accuracy 85%; risk of lesser accuracy 1%.

Most streams do not appear as open water, and both the direct and neighborhood effects of roads in extending the developed class are eliminated except in the case of superhighways.

trates 2 extreme, if improbable, consequences of this rule.

The second rule applied at the boundary between developed and forest land. These two

types interpenetrated to such an extent that special provision had to be made for separat- ing them. Moreover, because gradual inliltra- tion of woodland by housing is a common

mode of development in New Hampshire (as reflected and promoted in 2-acre and 5-acre zoning ordinances), a rule was needed that

would roughly model the process and allow the interpreter to determine, without the exercise of too much subjective judgment, whether a given woodland was still forest in the sense of being manageable timberland, or whether it had become “wooded exurb”, “urban forest” or some other subcategory belonging in the de-

veloped land class. The rule adopted - extend-

ing an 80 m influence zone into the woodland - meant that a single dwelling in the middle of a woods would “develop” a circular plot 160 m in diameter, barely enough to meet the 2- hectare delineation minimum. Figure 2 illus-

Figure 3 depicts some of the interpretation difficulties encountered in land-use mapping in this region.

There was no objective standard against which to check 1953 and 1974 interpretations, but delineations made on the 1982 photography were field-checked following the methods of Ginevan (1979) and Aronoff ( 1982) and found to be at least 85% accurate at the 99% confidence level. The accuracy report appears as Table 2. Idle land exhibited the lowest percentage of correct identifications, probably reflecting the ambiguity inherent in a transitional class.

Delineations were transferred to 7.5-min orthophoto quadrangles. Two-hectare sampling overlays were fitted to the Universal Trans- verse Mercator net; the interpreted type at the center of each grid cell was recorded on the MAP geographic information system (Berry, 1984), an inexpensive grid-based GIS available in microcomputer-compatible form. Quadrangle files were separated along town boundaries, and 1953, 1974 and 1982 files were created for each of the 50 townships in the 2

350

CIAPIFIFICIOC\ACI xl AGRICULTURE 183 CELLS 3.1%

DDDDDDDDDD 25 DEVELIIFED 1564 CELLS 26. 2%

FFFFFFFFFF 25 FOREST 1396 CELLS 2;. 4%

IIIIIIIIII 28 1 DLE 72 CELLS 1 . 2%

oooooouooo ;4 UIHER 21 CELLS 0.5%

wwwwwwwwww 42 CIFEN WATER 155 CELLS 2.6%

Fig. 4. 1974 land-use map of Salem. Rockingham County. Each character represents 2 ha.

counties, at 142 5 16 data points. In this study, digitizing was manual; however, moderate-cost equipment now available can save much labor in this phase. Once created, the maps were overlaid in the GIS, and all land-use changes were recorded and tabulated.

MAPPING RESULTS

The products of the mapping phase were land-use maps of the 50 towns at the three im- agery dates, and land-use change matrices showing movement of lands among categories over the intervals 1953-74, 1974-82 and

351

TABLE 3

Transition matrix for town of Salem’

1953 uses

1982 uses

AGR DEV FOR IDL OTH WAT TOT

AGR 160 894 57 8 0 0 1119 DEV 6 1337 24 0 0 0 1367 FOR 40 2295 1514 89 22 2 3962 IDL 2 67 4 0 0 0 73 OTH 0 30 2 0 0 0 32 WAT 0 2 8 0 0 312 322 TOT 208 4625 1609 97 22 314 6875

‘Column totals show 1982 land uses, row totals show 1953 uses. Individual entries show movement from 1953 class to 1982 class. Values are in hectares.

TABLE 4

Land-use distribution in Rockingham and Strafford Counties, 1953-82, by various estimates (values in ha X IO’)

County/land uses

Time period

1953

Present Coppelman’

c. 1975

Present Coppelman USGS

1982

Present

Rockingham AGR IDL FOR DEV OTH WAT TOT

22.5 30.0 11.4 17.5 16.6 9.7 4.4 7.7 2.3 8.1 N/A 1.3

133.0 131.8 123.1 129.0 133.9 96.6 18.0 4.2 39.5 18.3 23.2 67.3 4.2 4.1 5.7 4.9 7.4 7.0 7.4 Omitted 7.5 Omitted 7.5 7.6

189.5 177.8 189.5 177.8 188.6 189.5

Strafford AGR 9.4 13.8 6.9 9.9 9.1 6.1 IDL 1.6 2.3 1.0 2.9 N/A 1.0 FOR 71.1 74.7 68.7 72.9 74.6 60.2 DEV 12.1 2.3 16.9 6.9 9.9 25.7 OTH 0.7 0.9 1.3 1.4 2.2 1.8 WAT 3.7 Omitted 3.8 Omitted 3.8 3.8 TOT 98.6 94.0 98.6 94.0 99.6 98.6

‘The Coppelman study excluded water areas. ‘USGS Land Use/Land Cover data, available only in percentage form, were applied to 1980 U.S. Census areas of the counties. The USGS has no category for “idle” land; the “other” category is the sum of USGS “wetland” and “barren land” classes.

1953-82. A sample map of the town of Salem remained in that use by 1982, with 894 mov- appears as Fig. 4, and a sample matrix cover- ing to developed. Transitions from forest to ing the same town appears as Table 3. It is ev- developed were 2.5 times as great. ident from the matrix that developed land Table 4 gives the distribution of land uses for experienced the greatest growth. Of the 1119 each county for each survey date, and com- ha in agriculture in Salem in 1953, only 160 pares data from this study against its predeces-

352

T.ABLE 5

Percentage of forested land in Rockingham and Strafford Counties, 1950’s to 1980’s

County/study Time period

c. 1955 c. 1975 c. 1985

Rockingham Present Forest Survey’

70 65 51 74 75 73

Strafford Present Forest Survey

72 70 61 75 77 79

‘L1.S. Forest Survey photo-sampling estimates

sor, the Coppelman et al. ( 1978) study, and against USGS Land Use/Land Cover map data for the mid-1970’s (OSP, 1986). The general trends in land use shown by this study’s data and that of its predecessor are the same, as might be expected from two applications of similar techniques to the same information

base. But the numerical results are quite differ- ent. The present study shows substantially greater developed area at all dates than the Coppelman study, and smaller agricultural and forest areas.

Table 5 compares percentages of land placed in the forest class by this study against the For- est Survey’s photo-sampling estimates for approximately the same dates. The difference is slight at the outset, but increases rapidly with the passage of time: in the most recent period, Forest Service estimates of forested land ex- ceed ours by 43% in Rockingham County and 29% in Strafford.

Turning the land-use maps into gridded form is a sampling process, and the result is subject to sampling error apart from any errors in photointerpretation. For each date, 93 631 sam- ples were taken across Rockingham County and 48 885 across Strafford County. This sampling rate gave 95% assurance that any Rock- ingham class-area total was within 610 ha of its orthophoto-delineated size, and that any

TABLE 6

Regression of 1982 developed land on selected variables, Strafford and Rockingham County mumcipalities

Simple I

Model 1 Intercept Dev53 0.79 Area 0.44 Pop50 0.62 kmCity -0.25 Hwy 0.26 Int 0.41 County -0.1 I

r’10.79: Adj. R’~0.76

Standardized Unstandardized regression regression coefficient coefficient

0 2133.77 0.55 1.15 0.32 0. IO 0.06 0.02

-0.25 -24.78 0.07 2 17.80 0.21 563.12

- 0.02 - 37.53

Standard error

62 I .2? 0.32 0.03 0.04 8.65

243.86 220.60 180.05

f. value

12.85 13.39 0.19 8.21 0.80 6.52 0.04

Significance

0.001 0.00 I NS 0.01 NS 0.05 NS

Model 2 Intercept Dev53 0.79 Area 0.44 kmCity -0.25 Int 0.41

R’=0.79; Adj. R’=0.7?

0 2033.45 577.07 0.63 1.30 0.16 68.39 0.001 0.30 0.09 0.02 14.42 0.001

-0.22 -21.73 7.67 8.03 0.01 0.24 636.49 199.28 10.20 0.01

353

Strafford total was within 40 ha of delineated area.

Land-use tabulations and selected maps were published in 2 bulletins for use by local and state planners (Befort et al., 1987a, b).

DATA ANALYSIS

To identify the impact of community growth and sociodemographic variables on developed land-use change at the urban fringe, a least- squares estimate of the parameters in the following equation was made:

Dev82=a,+B, Dev53+/3* Area+p3 Pop50

+ p4 kmCity + p5 Hwy + /$ Int + p, County + E

where for each of the 50 municipalities in Strafford and Rockingham Counties: Dev82 = hectares of developed land in

1982; Dev53 = hectares of developed land in

1953; Area = total hectares; Pop50 = population at 1950 Census; kmCity = kilometers to nearest major ur-

ban center;

Hwy = dummy: presence/absence of major state highway;

Int = dummy: presence/absence of interstate highway;

County = dummy: county to which munici- pality belongs;

with o! representing the intercept and E a nor- mally-distributed error term.

Specification of this model follows from recent work in rural development and growth (Brown and Wardwell, 1980; Sofranko and Williams, 1980; Hawley and Mazie, 1982; Steahr and Luloff, 1985) and from land-use studies which have focused on land demands in the rural-urban fringe (Corsi, 1974; Hu- shak, 1975). Proximity to either of 2 dominant markets for northern New England (Boston, MA and Portland, Maine), both

roughly equidistant from the seacoast counties, was included on the basis of research by Dorf and Emerson ( 1978 ), Williams et al. ( 1978 ) and Findeis ( 1986) on industrial and commercial sitings. Further, the residential preference literature (Brown and Wardwell, 1980; Hawley and Mazie, 1982) has indicated that close proximity to metropolitan centers weighs heavily in the migration decision making process. Both individuals and firms want the quality of life characteristic of rural set- tings and the cultural, financial and commercial facilities found in large urban areas. Highway access has been found to play a significant role in development processes in New Hampshire (Chittenden et al., 1982) and else- where (Corsi, 1974; Lichter and Fuguitt, 1980). Land-use and area data for the model were drawn directly from the geographic information system.

Outcomes (Table 6) indicated that 76% of the variation in 1982 developed area within towns could be accounted for by 4 of the 7 variables incorporated in the model (Model 1) : area of 1953 developed land, total land area, distance to nearest city and presence of an interstate highway interchange. A new estimate was made using only the 4 significant variables, to purge the parameter estimates of po- tentially suppressing factors. According to this revised model (Model 2)) the amount of developed land in 1953 was the most significant predictor by a factor of at least 2. Adjusted coefficient of determination for the revised model was 0.77.

DISCUSSION

At the one point in time at which an inde- pendent check against another mapping study is available, the USGS results tally more closely with Coppelman’s estimates than with ours. Nor are our results congruent with estimates of forested land published by the U.S. Forest Service; the Forest Survey data show an essen- tially constant percentage of forest cover over

the past 30 years in both counties, whereas our interpretations show a rapid loss of forest type, much of it due (as Table 3 indicates for the town of Salem) to expansion of developed land.

We interpret these differences as arising mainly from our method of delineating developed land uses, i.e. from our assumption that the process of development in a wooded landscape is best modeled by subtracting a fringe of woodland from the forest category and including it as developed land. We consider that this subtraction is justified by the effective withdrawal of such woodland from forest management as ownerships become fraction- ated. a trend documented in Vermont and New Hampshire by the U.S. Forest Service (King- sley and Birch, 1977). Subdivision restric- tions. including ‘-acre and 5-acre ( l-2 ha) minimum lot size requirements in numerous towns. have created a class of wooded land that can neither be further subdivided into an une- quivocally “developed” landscape nor man- aged economically as forestland. The difficulty of dealing with this class of land is indicated by the fact that no “urban forest” data appear in the 1983 Forest Survey of New Hampshire (Frieswyk and Malley, 1985), although that category was part of the survey data gathering scheme. To leave it all in the forest category is to foster the impression that increased population has little or no impact on forestlands. We chose to err on the side of a more conservative estimate of the forest class. There are certainly tenable alternatives to our procedure: it has at least the merits of: ( 1 ) being easy to apply in photointerpretation: (2 ) reflecting local planning and zoning realities; (3) making some distinction between forest and trees.

Our interpretation rules excluded narrow linear features. and thus underestimated the obvious “developing” effect of road networks on forested land. This omission also occurred in the Coppelman mapping study, but there it was uncompensated by any extension of developed influence into forest: this probably ac-

counts for much of the discrepancy between the present study and its predecessor.

Type definitions and interpretation conven- tions often exert a significant influence on the outcome of remote sensing investigations. Written to help interpreters distinguish differ- ent information classes consistently, they inev- itably contain elements of arbitrariness and artificiality which are more keenly felt in type- mapping work, where the interpreter must continually trace boundaries, than in poinr- sampling, where borderline cases are the exception and not the rule. This subjective fact tends to promote confidence among point- samplers and diffidence among mappers. On the other hand, sampling without mapping is open to the objections that it imposes no pen- alty on inconsistency and that it can easily fail to allow for neighborhood effects if the vari-

able of interest - in this case land use. as op- posed to cover - is not directly observable and

must be inferred. Aside from the question of whether our maps

accurately reflect the shifting balance of land uses across the 3 decades, regression analysis of factors contributing to the undisputed growth in developed land between 1953 and 1982 yielded a result in which the variables excluded were at least as interesting as those found significant. Despite the fact that Rock- ingham County began its development earlier and thus had a substantially larger population base, neither population size in 1950 nor the county dummy variable achieved significance: nor did the presence of several well-traveled state roads threading both counties appear to influence the extent of developed land in 1982. Of the variables found significant. by far the most important were the size of a township and its extent of development in 1953 - which says no more than that large towns with substantial development at the outset were likely to be in the same case 30 years later! Proximity to a metropolitan area and presence of a super- highway were the only exogenous variables sig-

355

nificantly related to development, and the latter might as easily be an effect as a cause.

CONCLUSIONS

Our estimates of the extent of forestland in coastal New Hampshire were comparable to those found by other workers at the outset of the period under study. During 30 years of ac- celerating population growth, we found substantial encroachment on forestland by development, where others found little or no change in forested area. The disagreement is complicated by methodological factors, as dis- cussed above. Without passing judgment as to what estimates are “correct”, we observe that:

- it may be important for local planners to generate their own spatial data using proce- dures and definitions based on local condi- tions and perceptions, and to test the consequences of alternative methodologies;

- the mapping-and-GIS approach yields map products. These can be formally or informally checked for accuracy by users, whereas sampling data are opaque unless the entire process of generating them is replicated;

- the tools are well within reach of local planners. Aerial photo coverage dating as far back as the 1940s is available for a few dollars per print. Most of the United States is covered in the 7.5min orthophotoquad series. The straightforward, grid-based GIS we used is on the market in a version compatible with the most common microcomputers.

The regression model exemplifies the em- ployment of GIS-derived land-use areas to- gether with readily accessible geographic and demographic data in exploring determinants of land-use change. Across 50 diverse towns in two counties, only the obvious factors - prior extent of developed land and total land area - turn out to be highly significant predictors of future development among the seven variables tested. The pattern adumbrated seems to be one of inexorable expansion from metropolitan centers along pathways marked out by in-

terstate highways. For communities in the region attempting (in the words of so many local master plans) “to maintain rural character”, this may in turn suggest that purely local planning and zoning efforts are unlikely to buy more than small amounts of time.

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