Land use model for carbon conservation across a midwestern USA landscape

Landscape and Urban Planning 69 (2004) 451–465

Land use model for carbon conservation across amidwestern USA landscape

David H. Wang1, Kimberly E. Medley∗Department of Geography, Miami University, Oxford, OH 45056, USA

Received 17 February 2003; received in revised form 8 August 2003; accepted 5 January 2004

Abstract

Land management to promote forest growth can conserve and sequester a significant amount of terrestrial carbon andthereby reduce the buildup of atmospheric carbon. This study uses geographic information systems (GIS) technology todevelop a multi-criteria land use model for Butler County in southwestern Ohio that ranks rural land areas for restorativeforest management based on their potential for carbon conservation. Our results identify 161 sites, or 18% of the county studyarea outside of incorporated urban areas, that have topographic and soil conditions productive for forest growth, and 21% of themapped area is on transitional (uncultivated and undeveloped) lands that are potentially available for restorative management.The study strongly supports the planned protection of lands along stream corridors; 43% of the transitional lands ranked highpotential are within a 100 m stream buffer and 80% are within a 500 m buffer. It also reveals “gaps” between the predictedcarbon conservation map and regional plans for conservation, particularly showing the need to consider incorporating woodlotsinto privately owned farms on flat uplands with deep soil profiles. The study provides a valuable ecological planning resourcethat prioritizes lands for rural conservation based on their functional contributions to the regional carbon balance.© 2004 Elsevier B.V. All rights reserved.

Keywords: Forest restoration; Landscape ecology; Temperate forests

1. Introduction

Land conversion, while operating at a local scalethrough resource management decisions, can haveglobal environmental effects (Boag et al., 1994; Dale,1994). Land clearing and/or degradation can be sig-nificant CO2 sources (output > input) and a positiveforce on global warming (Dale, 1994; IGBP TerrestrialWorking Group, 1998; Houghton and Hackler, 2000).

∗ Corresponding author. Tel.:+1-513-529-1558;fax: +1-513-529-1948.E-mail address: [email protected] (K.E. Medley).

1 Present address: Dell Computer Corporation Inc., 501 DellWay, Round Rock, TX 78682, USA.

In contrast, land can also be managed for high bioticproduction and create a potential CO2 sink (Krankinaand Dixon, 1994; Houghton, 1996; Martin et al.,2001). Land management, which attempts to slowdown deforestation and promotes reforestation, issupported as an important element in the control of fu-ture increases in atmospheric CO2 (Faeth et al., 1994;Moffat, 1997; Ingham, 2000; e.g. Brown, 1996forAsia).NRC (1998)estimates that USA forests accountfor approximately 90% of the continental storage ca-pacity of carbon and that 55% occurs in nonindustrialprivate forests. Gains in forest area, through expan-sion of forest stands (resurgent forests sensuMoffat,1997) or other agroforestry alternatives (Josiah andKemperman, 1998) and their management for carbon

0169-2046/$20.00 © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.landurbplan.2004.01.002

452 D.H. Wang, K.E. Medley / Landscape and Urban Planning 69 (2004) 451–465

absorption are considered as appropriate offsets underArticles 3.3 and 3.4 of the Kyoto Protocol (Murrayet al., 2000). Techniques are now being developedto standardize carbon accounts and establish plansfor carbon conservation in forestry management (e.g.Faeth et al., 1994; Hoover et al., 2000).

We present a case study that examines the potentialfor Butler County, Ohio to increase forest lands forcarbon conservation. Forests experiencing net growthwill sequester CO2 from the atmosphere as part of thegrowing process, and thus can offset the excess incre-ments of CO2 releases that are currently responsiblefor the atmospheric buildup. For example,Houghtonand Hackler (2000)indicate that changes in USA landuse resulted in a 25% carbon loss before 1940 but nowforest regrowth, particularly in the east and southeast,contributes to positive carbon accrual. Their regionalprojections on potential rates of carbon accrual in tem-perate forests justify the need to include restorativeland management practices in the development of acarbon account (cf.Moffat, 1997; Caspersen et al.,2000).

Our study builds a land use model to show landsin Butler County, Ohio with high restorative poten-tial for the accrual and storage of terrestrial carbonin forest vegetation. We focus on transitional landsthat are out of cultivation and undeveloped, such asabandoned fields, shrub lands, and forests. Theselands may conserve carbon by: (1) accumulatingabove-ground biomass in protected regrowth forests;(2) ensuring carbon accrual and efficient harvestingunder integrated management regimes such as agri-cultural woodlots and fencerows; and (3) promotingsoil carbon storage under undisturbed forest systems.The study shows how GIS may be used to incorpo-rate multiple criteria in decision making about landuse (Malczewski, 1996; Eastman et al., 2000), andparticularly the prioritization of lands for protection(seeJennings, 2000for GAP analysis). One importantplanning outcome from the study is a ranked classi-fication of the county, based on projected potentialsfor land settings to conserve and accumulate carbonin forest growth. The map may be directly applied tothe designation of lands for experimental studies ofcarbon sequestration or be used to set existing stud-ies within the study area in a more regional context(e.g.Hoover et al., 2000; Bateman and Lovett, 2001;Curtis et al., 2002). A second planning output is a crit-

ical examination of current plans for conservation inthe county and their relationship with the distributionof lands ranked high for carbon conservation. Transi-tional lands that are ranked high for their restorativepotential to sequester carbon should be reconsideredas land areas for protection in future plans for urbandevelopment and rural conservation in southwesternOhio.

2. Study area

2.1. Physical-environmental setting

Butler County is located in southwestern Ohioalong the Indiana border and 76 km northwest ofCincinnati along the Ohio River (Fig. 1). Located justnorth of the most recent (Wisconsin) glacial bound-ary, the topography is mostly depositional with gentlyrolling moraines and elevations ranging from 161to 355 m. The Great Miami River flows southwestthrough the eastern portion of the county and IndianCreek, Four Mile Creek, and Seven Mile Creek arethree major streams that flow from the northwest intothe Great Miami River Valley. Soils are composed ofglacial deposits, varying in size from gravelly glacialtill to silt and clay particles; they are deep, mediumto fine textured, and moderately well drained (Lerchet al., 1980). Natural vegetation consists mostly ofmixed hardwood forests, at the boundary between the

Fig. 1. Butler County in Southwest Ohio, USA, showing theexpansion of urban areas from the south in Hamilton County andpersistence of agriculture to the north in Preble County, Ohio.

D.H. Wang, K.E. Medley / Landscape and Urban Planning 69 (2004) 451–465 453

beech-maple association on glacial till to the northand the western mesophytic association located southof the glacial boundary (Braun, 1950). Some canopytrees that are common in these forests includeFagusgrandifolia, Acer saccharum, Liriodendron tulipifera,Fraxinus americana, andQuercus alba.

2.2. Population and land use

Settlement and historic land use practices acrossMidwestern USA converted previously forested landinto agricultural and urban land uses (seeRickettset al., 1999for the Southern Great Lakes;Houghtonand Hackler, 2000for East Central United States;Medley et al., 2003for Southwest Ohio). ButlerCounty covers a land area of 1218 km2 that is bor-dered to the north by intensive and mostly stablecorn-belt agriculture in Preble County (Medley et al.,1995) and to the south by a dense and expanding ur-ban center, Cincinnati, in Hamilton County (Fig. 1).The county is undergoing a rapid increase in popu-lation (Fig. 2) and an increase in urban/residentiallands. According to the US Census of Population,population growth was 11% between 1980 and 1990,and 14.2% in the 1990s. By 2000, about 59% of thepopulation was in incorporated urban areas that grewby about 18% during the last decade. Early populationincreases were in major urban centers located alongthe Great Miami River valley industrial region, whichstretches from Cincinnati to Montgomery County ineastern Butler County, but now the primary changeis toward dispersed single-family dwellings as more

0

200000

400000

600000

800000

1000000

1940 1950 1960 1970 1980 1990 2000 Year

Population

Hamilton

Butler

Preble

Fig. 2. Population change in Butler, Hamilton, and Preble Countiesin southwestern Ohio. Data available from the US Census ofPopulation 2000.

people move to rural settings. While the two manu-facturing cities of Hamilton and Middletown alongthe Great Miami River account for over half of thecounty’s population, townships are areas of higherhousehold growth rates. Energy demands are concur-rently rising to serve this dispersed population acrossthe region. Located in Cincinnati, Cinergy Corpora-tion is one of the largest energy producing companiesin the USA, managing ten coal-fired plants and onecoal gasification plant (Stowell, 2001).

Much land in Butler County is either in agriculturalproduction or undergoing urban-residential develop-ment. As a case study, we examine the distribution ofopen lands, transitional between these two land uses,as lands potentially available for carbon conservationthrough forest production. We focus on regional plan-ning decisions for these lands outside of incorporatedurban areas, thereby examining a county study areathat is 79% of the total county area or 959 km2.

3. Data and methods

This study adopts GIS technology to construct amulti-criteria carbon conservation model, compare themodel with the distribution of current land cover andprojected land-use plans, and prepare a landscape con-servation plan for Butler County, Ohio (Fig. 3). First,the carbon-conservation map was constructed by rank-ing a set of environmental attributes considered mostimportant for forest production, and by identifyingthe spatial distribution of highly ranked sites. The se-questration of carbon in woody biomass is one of themost important mechanisms for removing carbon fromthe atmosphere, and a land-use change from agricul-ture to forest is associated with significant gains insoil carbon over losses attributable to decompositionand/or mineralization (Lal, 2002; Lal et al., 2003).Second, a map of current (1997) land cover was dig-itized from large-scale maps (1:12,000) compiled bythe Butler County Office of Planning and Develop-ment. The study considered all lands that are not inagriculture or urban-residential development and oc-cur outside of incorporated urban areas. These transi-tional lands are potentially available for protection andrestorative management. Third, the study examined thedegree to which transitional lands on highly-rankedsites for carbon conservation are considered as areas


I. Carbon Conservation Model

Texture

LoamSiltloam

...Gravel

Aspect

NortheastSouth

...Southwest

Slopeform

LowlandConcaveUplandConvex

Fertility

Very lowLow

MediumHigh

Erosion(K-factor)

Very lowlow

MediumHigh

Digital ElevationModel

Soils MappingUnits

Depth

>1210-127-9<7

Slope

0-2%3-6%7-12%>12%

Climatic Data(precipitation,

temperature, etc.)

Ranked Overlay Analysis

CarbonConservation Map

II. Landscape Analysis

LandscapeAnalysis Land Cover

III. Restorative Planning

PlannedLand Use

LandscapeConservation

Plan

GapAnalysis

Fig. 3. Flow diagram showing the GIS data layers and modeling methods that were used to create the landscape conservation map,determine its relation with current land cover, and compile priorities for a landscape conservation plan.

for protection in the long-range Butler County regionalplan and/or concur with the goals for conservationpresented by the local nonprofit Three Valley Conser-vation Trust (cf.Fairfax and Guenzler, 2001). Similarto the gap analysis methodology in practice for the

conservation of biodiversity (e.g.Scott et al., 1993;Jennings, 2000), the study looked for gaps in the ex-isting plans between what they designate for protec-tion and those identified as potentially important forcarbon conservation. Because GIS technology allows


the incorporation of many spatial attributes and theirheterogeneity into the modeling process (Malczewski,1996), we focus on its use as a tool for estimatingland potential for carbon accrual in forest growth andplanning local management for conservation in ButlerCounty.

3.1. The carbon conservation model

Research on forest productivity supports the impor-tant influence of climatic, topographic and soil condi-tions on tree growth (Table 1). Our study did not in-clude climate measurements because the four meteo-rological stations in the county did not portray enoughdata to distinguish variation at a suitable resolution.Site classifications based upon topography and soilscarried with it an implied classification with respectto moisture availability.

Topographic attributes considered in the model in-clude elevation, slope, slope aspect, and slope form.These data were compiled from a digital elevationmodel (DEM) for Butler County. The DEM, in rasterformat at a 50 m×50 m resolution, was originally pre-pared from 1:250,000 topographic quadrangle maps inthe Geographic Information Systems Laboratory of theDepartment of Geography, Miami University. Thesedata were analyzed using GRASS 4.2 under SUN So-laris 2.1 UNIX environment, on the UTM-16 coordi-nate system. Topographic relief, at less than 200 m,was considered too small to have a significant influ-ence on forest type and productivity (e.g.Iverson et al.,1997). Elevation data assisted indirectly in the desig-nation of upland and lowland sites, and for the com-putation of slope and slope aspect.

The two data layers of slope and slope aspect weregenerated using the program r.slope.aspect in GRASS4.2. Slope is presented as a percentage and aspect indegrees counter-clockwise from the east. Slope formswere distinguished as erosional (upland and convex)and depositional (concave and lowland;Table 1). Theslope form map was generated in several steps. First,Wang (2001)ran a program (r.to.sites) to generate asites file from the DEM, which containsx, y coor-dinates and the elevation of sites at 50 m horizontalintervals (along bothx andy axes) across the county.The second step involved another program s.surf.tpsthat interpolates the sites to grid cells, computes pro-file curvatures (50 m2 resolution), and saves them as a

raster file. The profile curvature has negative values forconcave positions, positive for convex positions, andzero for all straight slopes, flat uplands and lowlands.Upland and lowland areas were distinguished usingrelative elevation and proximity to streams in a neigh-borhood analysis on the profile curvature map. Theremaining zero values (straight slope positions)were classified as erosional in the convex category(Table 1).

Soils data were originally compiled by the Nat-ural Resource Conservation Service (NRCS) at afield accuracy of about 0.25 ha, and registered to1:24,000 topographic sheets. These data were laterconverted to a digital raster file at a grid-cell resolu-tion of 30 m× 30 m (Roush, 1996), and imported intoGRASS 4.2. The data provided information on soildepth, texture (coarse sands, medium-textured loams,fine clays, etc.), organic matter content, and erodabil-ity (K-factor) for all soil mapping units (Lerch et al.,1980; Roush, 1996; Table 1). Soil texture was classedin nominal categories used by the US Departmentof Agriculture, according to percentages of sand,silt, and clay in the fraction of the soil that is lessthan 2 mm in diameter (Lerch et al., 1980). Gravel(>2 mm) was assigned to the sand texture class (e.g.sandy or gravelly loam). Soil depth at the surface(A1 or Ap horizon) ranges from about 10–50 cmin Butler County (Lerch et al., 1980). Soil erosionK-factor indicates the susceptibility of a soil to sheetand rill erosion by water; it is used as one of the sixfactors in the Universal Soil Loss Equation to predictthe average annual rate of soil loss. Possible valuesof K range from a minimum of 0.05 to maximumof 0.69 (Lerch et al., 1980). The higher theK value,the more susceptible a soil is to sheet and rill ero-sion by water. Soil organic matter was given in fourordinal categories as high, moderate, low, and verylow.

Because the environmental attributes were not ofthe same data type, they were ranked onto an ordinalscale. Statistically, more ranks will yield a more ac-curate modeling output, but are also subject to greatererrors due to improper ranking. We ranked all datalayers to four ranks in order to correspond with theranked data layer on soil organic matter. The scalingof the data was based on their hypothesized relation-ships with forest growth and carbon storage, and addi-tionally for soil depth and erosionK-factor, their data


Table 1Physical-environmental attributes considered in the construction of the carbon conservation model

Attributes Relationship with forest productivity Classification (ranking)

ClimatePrecipitation temperature

moisture solar radiationClimatic site factors are generally useful in providing arough index of productivity among adjacent forest regionsor among altitudinal zones within a geographic region.Micro-climatic differences are reflected in topographicalattributes

Assumed uniform for the region

TopographicElevation Hilly and mountainous topography can obviously affect the

potential forest productivity, primarily operating throughclimatic influences. Minor variations in topography wereused to distinguish upland from lowland positions

Not ranked

Slope Slope is negatively related with conditions for forestproductivity. Steep slopes show greater erosional lossesand lower moisture. Slope categories correspond withthose used by Natural Resource Conservation Service(Lerch et al., 1980)

<2%: 4

2–6%: 36–12%: 2>12%: 1

Aspect Both physical and chemical characteristics of soils onnortheast aspects are more favorable for tree growth thansoils found on southwest aspects. North-facing slopesreceive less sunlight, have smaller contrasts intemperature, and are invariably cooler and more moist,thus promoting better soil conditions (deeper litter, a mullhumus, deeper A horizons, higher base saturation) thando south aspects. In the mid-latitudes, east-facingpositions are more protected from westerly winds andstorms than west facing positions

E

N

S

W

SW

NE

SE

NW

1

4

3

3

3

3

2

2

Slope form Slope form influences surface temperature, soil moisture,erosive power of surface water flow, and the path of watermovement. Concave and lowland surfaces tend to besheltered from high winds, and accumulate more nutrientsand moisture than upland and especially convex surfaces

1

3

2

4

Upland

Convex

Concave

Lowland

SoilsTexture Soil texture is considered to be a major soil factor

influencing forest productivity because it is highly relatedwith soil moisture conditions as well as to the nutrientstorage capacity and the soil organic matter content.Coarse-textured soils (sand and gravel) cause excessivedrainage and high leaching. Fine textured soils (clays)cause poor drainage and water-logging, which in turnrestricts soil aeration, root respiration, and root growth.Medium-textured soils (loam and clay loam) are best fortree growth

Loam/clay loam: 4Sandy clay loam: 3Silt/silt loam: 3Fine sandy loam: 2Silt clay loam: 2Sandy loam: 2Gravely loam: 2Clay/silt clay: 1Sandy clay: 1Sand/gravel: 1

Soil depth The deeper the surface soil (O and A horizon), thegreater the volume of soil favorable for roots, and thegreater amount of tree growth these soils can sustain.Ranking based on conditions recorded for the county

>12: 410–12: 37–9: 2<7: 1


Table 1 (Continued )

Attributes Relationship with forest productivity Classification (ranking)

ErosionK-factor Erosion factorK indicates the susceptibility of a soil tosheet and rill erosion by water. The higher the K value,the more susceptible the soil is to sheet and rill erosion,resulting in a consequent loss of nutrients and productivecapacity in soils. Ranking based on conditions recordedfor the county

0.20, 0.24: 40.28, 0.32: 30.37: 20.43: 1

Organic matter Soil organic matter, though occupying a small fraction ofthe soil, has profound effects on soil properties. It isdirectly related with the cation-exchange capacity of soilsand also contributes to the amount of nutrients nutrientcontent of soils for tree growth

High: 4Moderate: 3Low: 2Very low: 1

The final selection, characterization, and ranking of these attributes were derived from research literature on forest ecology and modeling(Carmean, 1965; Spurr and Barnes, 1980; Shugart, 1984; Kimmerer, 1990; Bockheim, 1990; Iverson et al., 1994; Bailey, 1996; Iversonet al., 1997; Landsberg and Gower, 1997), and for soil texture, depth, erosionK-factor, and organic matter, their condition in the county(Lerch et al., 1980).

frequency distributions across the county (Table 1).Thus, each grid cell in the respective data layers hada value ranging from 0 (where there are no data) to 4(high).

Spearman’s rank correlations were calculatedamong the ranked physical-environmental attributesto check the degree of correlation and possible re-dundancies in their respective contribution to themodel (Table 2). Wang (2001)took 1000 randomsample points on the maps, obtained the correspond-ing attribute values, and calculated their correlations.In environmental modeling, it is often the case thatmodel attributes are inter-correlated to some extent,but unless the correlations were extremely high (e.g.rs > 0.7), the attributes were assumed to provide apotential contribution to the final model (cf.Carmean,1996; Iverson et al., 1997).

Table 2Spearman’s rank correlations, measuring relationships between the ranked topographic and soil attributes used in the model, based on arandom sample of 1000 sites taken from the study area

SL AS SF TX DP KF OM

Slope (SL) 1 −0.06 0.05 0.09 0.17 0.17 0.18Aspect (AS) 1 −0.03 −0.05 −0.09 −0.06 −0.07Slope form (SF) 1 −0.01 0.08 0.03 0.04Texture (TX) 1 0.06 −0.06 0.11Depth (DP) 1 0.47 0.47K-factor (KF) 1 0.47Organic matter (OM) 1

We prepared the carbon conservation map byadding the respective environmental attributes por-trayed as GIS layers. The procedure did not weightthe attributes, because their relative importances forforest productivity are not clearly documented inthe literature, or may be only locally defined (e.g.Spurr and Barnes, 1980; Barnes et al., 1982; Iversonet al., 1997). Based on the frequency distributionof the summed ranks, the composite map was re-classified to show those grid cells with high ranksor potential for forest growth and carbon sequestra-tion. For planning purposes, the study consideredhighly-ranked areas that captured at least 10% ofthe county land area. A smoothing filter operationwas applied to the high-ranked grid cells, usingr.neighbors in GRASS 4.2. This neighborhood oper-ation looks at each grid cell in the composite layer


and calculates the most frequent category value as-signed to cells in a defined neighborhood (3× 3cells) around the cell. The program output was anew raster map layer in which each cell was as-signed the most frequent rank within its 3× 3 cellneighborhood. After the smoothing procedure,Wang(2001) filtered the highly-ranked areas on the mapby a threshold of 1 ha in order to focus landscapeanalyses on sites at 1 ha or greater for restorativemanagement.

3.2. Landscape analyses of sites for carbonconservation

Butler County Office of Planning and Developmentcompleted a 1996–1997 survey of land use/cover inthe county, using air photos and field checks. Theseland use/cover data were registered onto 16 large-scalemaps (1:12,000) in the South Ohio State Plane coor-dinate system.Wang (2001)manually digitized themaps, converted the vector files to raster format, andthen imported them into GRASS 4.2 under the UTMsystem. The land use/cover data were classified intofour classes: urban/rural residential land, agriculturalland (row crops and pasture), transitional land (oldfield, shrub, and forest), and other land uses (indus-trial, business, transportation, gravel pit, cemetery,etc.). Landscape analyses focused on transitionallands and their relationship with sites for carbonconservation.

We compared landscape structural attributes for themodeled potential sites, current distribution of transi-tional lands, and for the potential sites on transitionallands. Land-cover proportions were calculated in re-lation to the county study area and as a proportionof the land area modeled high for carbon conserva-tion. Patch fragmentation was measured as the num-ber of patches per unit area and their size-class dis-tribution, and patch configuration was documented bythe spatial distribution of the patches and their rel-ative compactness. The compactness ratio (CR) wascalculated by dividing the area of a patch by the areaof a circle having the same perimeter as the patchand then taking the square root of the ratio (Forman,1995). All analyses were carried out using the r.le pro-grams in GRASS: a set of programs for the quanti-tative analysis of landscape structure (Baker and Cai,1992).

3.3. Restorative management planning

Restorative management suggestions were identi-fied by coordinating the landscape model for carbonconservation with existing plans for land managementin the region. For this study, we combined the 1998Butler County Long Range Development Plan andgoals of the Three Valley Conservation Trust withthe carbon conservation map to identify the degreeto which a single integrated plan can meet multipleobjectives that includes carbon conservation. A geo-graphic overlay identified gaps between the countyplan for open space and predicted sites of high po-tential for carbon conservation, as they may enterfurther planning discussions for protection in thecounty.

4. Results

As part of the Wisconsin till plain, Butler Countytopography reflects glacial deposition followed bystream erosion. Slopes across the county study area(i.e. outside of incorporated urban areas) range from 0to 22%, with an average slope of about 2.4%. Most ofthe county study area (57%) occurs on highly rankedlow-slope (<2%) settings. These areas predominateon level uplands and in the broad Great Miami RiverValley. Areas of severe (>12%) and high (7–12%)slopes, ranked 1 and 2, respectively, only occupy 5%of the county study area. They are most often asso-ciated with areas of shallow glacial till and exposedshale along the west bank of the Great Miami Rivervalley. Slope aspect also reflects the stream networkin the county. The Great Miami River is exceptionallybroad and level-bottomed, and lower-order streamsflow northwest-southeast into the Miami River andnortheast–southwest into these tributaries. About 16%of the county study area is of no aspect (level), rankedlow; of which the Miami River Valley accounts for80% and a northwest plateau accounts for 10%. South-west aspects (ranked very low) occupy 14% of thecounty study area along the lower-order tributaries.Northeast aspects (high conditions) occupy 13% ofthe county study area along the main tributaries of theGreat Miami River. Despite the generally low slopeconditions documented for the county, nearly all ofthe study area is classed either on convex (ranked very


low, 46.6%) or concave (ranked high, 42.8%) posi-tions. These slope forms correspond with the respec-tive stream network (lowlands bordered by concaveand convex slopes), and show distinctively small ar-eas of upland plateaus at about 3.7% of county studyarea.

The spatial distribution of soil attributes is relatedwith the textural composition of the glacial depositsand erosional or depositional processes along streams.Most of the county study area is ranked moderate(74.2%) on the basis of soil texture, which are mostlyfine to medium-textured silt loams. Highly-rankedloams and clay loams are found mainly on con-cave slope positions near the Great Miami Riverand its tributaries (12.5%). Low-order tributaries anduplands are ranked mostly low or very low condi-tion (13.3%). Soil depth at the surface, A1 or Aphorizon, ranges from 10 to 50 cm with distinctivedifferences across the county study area. The ma-jority of the study area (70%) is classed as low ormoderate with surface soils at a depth of 3–5 cm.Shallow soils (<3 cm; ranked very low) occupy 13%of the county on steep slopes near the Great Mi-ami River. Deep surface soils (>5 cm; ranked high;17%) occur on concave slope positions near streamsand on upland till plains. Similarly, soil erodability(K-factor) is generally ranked low for the countystudy area (74%), reflecting the dissected landscape,predominance of slopes, and erodible soil textures.Highly erodible soils (K = 0.47; ranked very low)characterize 7% of the study area, again correspond-ing with the steep, exposed slopes along the GreatMiami River. The least erodible soils (K ≤ 0.24;ranked high) occur on lowlands along the Great Mi-ami River and its tributaries and some isolated flatuplands, together occupying 5.5% of the county.Most soils in the county study area (80%) containlow organic matter. Soils with high organic matterare distributed along streams and at upland locationswith deep surface soils (11%). Less than 2% of thecounty study area has very low organic matter, onsites mainly located in the northeast along low-ordertributaries.

The distribution patterns described for the indi-vidual physical-environmental attributes and theirrespective correlations (Table 2) identify some spatialinterrelationships across the county study area. Onflood plains and terraces, where soils were formed

in recent alluvium, soils are deep and contain highorganic matter on least erodible sites, but are typi-cally coarse-textured. On the upland till plains (e.g.northwest plateau), where soils were formed in deepglacial deposits and a surface layer of loess, soilsare medium-textured, deep, least erodible, and withhigh organic matter. Both upland and lowland set-tings show conditions suitable for productive forestgrowth. These same sites are also subject to highsoil carbon losses under agriculture due to highrates of decomposition and mineralization in ex-posed soils (Lal, 2002). In the southwest part ofthe county, however, site conditions established bysoil texture, soil depth, susceptibility to erosion,and organic matter are low or very low. The steepslopes, more shallow glacial till, and exposed shalein this area are ranked low for forest growth andsoil–carbon storage. Despite general spatial corre-spondences, the calculated correlation coefficientsare low between the topographic and soil attributes(rs < 0.20), showing the importance of parent ma-terial on drainage conditions at the local scale as itcompares with regional topographic conditions. Cor-relations are significant among soil depth, organicmatter, and erosionK-factor (rs = 0.47; probabil-ity < 0.01), but below a level that would suggestredundancy. All seven attributes were included inthe additive overlay for the carbon conservationmodel.

The summed ranks of the grid cells on the com-posite map ranged from 2 to 28 (Fig. 4). Based ontheir frequency distribution, the map was reclassifiedsuch that grid cells with ranks equal to or greaterthan 20 were considered as high potential for car-bon sequestration in forest growth. The map wasfurther generalized, using neighborhood and filter-ing operations, to focus attention on suitable areas(polygons) at a minimum size equal to 1 ha. Thecarbon conservation map (Fig. 5) shows land areaswith high potential for forest growth and carbonstorage. The spatial distribution of these areas, ac-cordingly, shows relationships with the stream net-works and topographic conditions. Upland till plainsnorth and west of Oxford, northwest of Jacksonburg,and south of Monroe along the east border of thecounty show high potential for carbon accrual. Floodplains are another major part of the high potentialareas.


Fig. 4. Frequency distribution of ranks on the composite carbon conservation map. Rank 20 was taken as the break point for thereclassification of high potential land areas for carbon conservation.

4.1. Landscape analysis of sites for carbonconservation

A total 161 potential sites were mapped in thecounty study area that occupy 17,202 ha or 18% ofthe county study area (Table 3, Fig. 5). The aver-

Fig. 5. Carbon conservation map showing areas greater than 1 ha that ranked high for forest production and carbon accrual in ButlerCounty, Ohio USA.

age size of the sites is 107 ha (standard deviation= 186 ha), but 69% of the land occurs in sites be-tween 20 and 30 ha. The average compactness ratioof the sites is 0.69, with patches varying from smalland more round shapes on the uplands to very linearand narrow patches along the stream corridors. In


Table 3Landscape attributes of the carbon conservation sites, transitional lands, and carbon conservation sites on transitional lands outside ofincorporated urban areas in Butler County, Ohio, USA

Carbon conservation sites Transitional lands Carbon conservation siteson transitional lands

Land area (ha) 17202 27677 3627Study area percentage (%) 15.2 24.4 3.2Patches (#) 161 674 988Mean patch size (ha) 107 43 4Compactness ratio 0.69 0.37 0.54

relation with the county, high potential sites are gen-erally more flat (1.9% versus 2.4% slope) and facemore northward. The soils on these sites are deeper(4.6 cm versus 3.3 cm), less erodible (0.32K-factorversus 0.36K-factor), and have higher organic matter(3 versus 2.2). About 47% of the potential sites areon concave and 51% on convex slope positions. Theconvex sites are associated with upland plateaus thatexhibit productive soil conditions.

Current land use/cover of Butler County was clas-sified into five categories: cultivated farmlands, tran-sitional lands (old field, shrub, pasture and forest),urban-residential, and other land uses (industry, ceme-tery, gravel pit, etc.). Butler County is predominantlyin agricultural uses, which occupy the largest part ofthe county (54,015 ha or 44% of the county area).Rural residences and urban land take up 31% of thecounty, including 22% in incorporated urban areas.The county also shows a large land percentage as tran-sitional, occupying 23% of the county or 27,677 ha.

Transitional lands in the county occur in 674 patches(Table 3). The average patch size is 43 ha but 56% ofthe patches are smaller than 5 ha. Compared with thesites modeled for carbon conservation, these transi-tional lands are more fragmented, smaller in size, andwith less compact shapes.

By masking the map of potential sites for carbonconservation with the map of transitional land, a mapshowing transitional lands with high potential for car-bon conservation was generated. In total, 988 patchesoccupy 3627 ha in the county study area (Table 3),which means that 21% of the potential sites are locatedon transitional lands, thus available for restorativemanagement. Of these patches, the largest is 115 ha,the mean size is 5.7 ha, and most of the patches (90%)are smaller than 5 ha. The average compactness ratiois 0.54. Both transitional lands and potential sites

show a spatial relationship with streams. Fifty-onepercent of the transitional land occurs within a 100 mbuffer from streams; a percentage that reduces to 40and 31% for 200 and 500 m buffers, respectively. Forpotential sites, 22% are located within a 100 m streambuffer, 37% in a 200 m buffer, and 63% in a 500 mbuffer. For transitional lands with high potential forcarbon conservation, 43% are in a 100 m buffer, 61%in a 200 m buffer, and 80% in a 500 m buffer, re-spectively. Carbon conservation sites on transitionallands also occur on the upland plateaus but they aregenerally smaller and more isolated.

4.2. Restorative management plans

Butler County presented a long-range plan in 1998that proposed to put more land under permanent pro-tection as county parks or expanded greenways (cf.Hawkins and Selman, 2002) along streams. Accordingto the Department of Development of Butler County,protected lands (parks and reserves) would be ex-panded from 556 to 1093 ha or to just under 1% of thecounty land area. As of 2002, lands in county parksand reserves equaled 931 ha (0.8%). All of the reservesand 14 of the 15 parks occur along the Great MiamiRiver and Indian Creek. About 42% of the proposedland areas for protection are on transitional lands and27% or 3727 ha are located on the predicted high po-tential sites for carbon conservation identified by thisstudy. Therefore, these protected lands will not onlyprotect watersheds, but also promote the overall con-servation of carbon in forest growth and soil profiles.

The Three Valley Conservation Trust is a non-profitorganization actively working to permanently protectagricultural, natural, and open space lands along FourMile Creek, Seven Mile Creek, and Indian Creek val-leys in Butler County. The majority of the lands along


these three stream corridors are in transition and avail-able for possible conservation easements. The carbonconservation map shows that most of the land in thesecorridors is also ranked high for carbon conservation,which supports and thereby strengthens the Trust’s ob-jectives for preservation.

Yet, it is also obvious that there exist “gaps” be-tween the plan and the predicted carbon conservationmap. First, protected lands in the county plan coverless than one third of the predicted high potential siteson transitional lands; most potential sites are not underprotection. Second, planned activities by the countyand the Three Valley Conservation Trust do not ade-quately capture the more isolated plateau sites. Theseupland till plains diversify the settings for productiveforest growth in Butler County. Much of these landsare now in agriculture, supporting a diversification ofland activities on working farms and greater supportfor farmland protection from urban-residential devel-opment.

5. Discussion

This study identifies land areas for temperate forestrestoration and management based on their rankedpotential for carbon conservation in Butler County,Ohio, USA. We emphasize our use of a simple GISmodel that integrates environmental data for thecounty and relates them with existing data on landcover and planned land use. Environmental and landcover data are now readily available for regionalplanning, facilitating the development and applica-tion of ecological landscape plans (e.g.Lathrop andBognar, 1998; Hawkins and Selman, 2002; Leitãoand Ahern, 2002). Carbon conservation providesone functional attribute that can be used to priori-tize and guide ecological planning decisions aboutthe expansion of forestlands across highly-modifiedlandscapes. Policies to control climate change willnecessarily have a strong local dimension in how theyregulate carbon emissions and promote sequestra-tion through land-management practices. High levelsof coal combustion in southwestern Ohio certainlytarget it as a region that needs to consider diverseapproaches to conservation and support sequestrationon forestlands in future carbon accounts (Stowell,2001).

At the eastern edge of the cornbelt in the Midwest-ern USA, Butler County remains important for agri-culture because of the distribution of productive soilsand suitable topographic conditions (Hudson, 1994).The historical clearing of the forests, particularly fromproductive sites, was complete and persistent (Ebinger,1997). Our study takes a conservative approach to for-est restorative planning in that it only selects land areasthat ranked in the top 12% for carbon conservation,and then focuses attention on the distribution of thesehighly-ranked sites on lands that are out of agricul-ture and not currently developed as urban-residentiallands. The carbon conservation model supports exist-ing plans for protection, especially along riparian cor-ridors in the county, and advocates an expansion tojust above 3% of the land area. Now recognized asa “hotspot of change” toward urban-residential devel-opment (sensuGobster et al., 2000, p. 11), land man-agement decisions become increasingly more perma-nent and must consider the ecological role as well asthe economic value of forest lands across changinglandscapes (Sampson and DeCoster, 2000; Thorsnes,2002). For example, concurrent with the expansion ofCincinnati, Hamilton County increased its park areato nearly 4% of the county, or a land area compa-rable to the carbon conservation sites found on tran-sitional lands in this study (Table 3). Although cur-rently recognized as “one of the most heavily impacted[ecoregions] on the continent” (Ricketts et al., 1999,p. 165), our plan for carbon conservation shows poten-tial to contribute positively to forest restoration acrossa highly modified landscape (Schwartz, 1997). Carbonaccrual through net production in midwestern USAforests should be among the highest for all temper-ate forest regions (e.g.Curtis et al., 2002for CentralIndiana, USA). Soil carbon gains in afforested landsare predicted to be very high and CO2 losses due todecomposition and/or mineralization are not predictedto increase at a rate comparable to its influx into soils(Jackson, 2000; Tan, 2000; Lal et al., 2003).

Similar to the gap-analysis procedure currently usedin state-level conservation plans (Jennings, 2000), ourstudy also looked for gaps between the highly-rankedsites for carbon conservation and two current plansfor land protection in the region. While there is strongsupport for protecting riparian corridors, less atten-tion is directed toward the protection of upland siteswith deep and productive soils. These areas are mostly


in agriculture but under increasing threat for develop-ment toward urban-residential land uses (cf.Gobsteret al., 2000). Initiatives for carbon conservation mightalso support existing farmland preservation programsand consider diverse agroforestry practices on workingfarms (Josiah and Kemperman, 1998). Non-industrialprivate forests, distributed as farm woodlots across ru-ral landscapes, make up a significant portion of theregional forest cover (cf.NRC, 1998for the easternUSA) and can be managed privately in a way thatwill ensure sequestration and provide positive contri-butions to regional carbon accounts. Collaborative ar-rangements for this goal need to consider the socialand economic values placed on woodlots by farm op-erators (e.g.Maguire, 1999; Erickson et al., 2002) andbetter ensure their economic contributions to workingfarms (Sampson and DeCoster, 2000). In the USA,for example, timber certification under the AmericanTree Farm System and the Stewardship Incentive Pro-gram can guide approved forestry practices (Garrettand Buck, 1997; Sedjo et al., 1998), and conserva-tion easements provide a mechanism for protection(Gustanski, 2000) in association with sponsored sub-sidy programs. Ecological landscape planning for car-bon conservation, when it is linked to economic bene-fits, can promote a diversity of gains that should arisefrom more multifunctional landscapes (sensuNaveh,2001).

6. Conclusion

Despite the ongoing debates on carbon policy, util-ity companies are already exploring a number of waysto adjust their carbon accounts. We provide a case ex-ample on how to prioritize lands for forest restorationbased on their ecological potential for carbon seques-tration across an urbanizing county in the Midwest-ern USA. The study clearly supports continued effortsby the county and local land trusts to protect ripar-ian corridors. These sites show potential to sequestercarbon in productive forest growth and in soils thatare protected from human disturbances. It also empha-sizes the potential contributions of sustaining privatefarm woodlots on upland productive soils. Our plan forforest restoration, though conservative in area, showspotential to contribute to the ecologic and economicvalue of a rapidly changing landscape.

Acknowledgements

The Cinergy Corporation provided partial fund-ing for the project. We are grateful for the facilitiesprovided by the Geographic Information SystemsLaboratory in the Department of Geography at MiamiUniversity, and thank Dave Fehr at the Butler CountyDepartment of Development for the land-cover andplanning maps, board members of the Three ValleyConservation Trust for their conversations on its mis-sion, Bill Renwick and Avram Renwick for commentson the MA thesis completed by D. (Houxiang) Wang,and two anonymous reviewers for comments on anearlier draft of the manuscript.

References

Bailey, R.G., 1996. Ecosystem Geography. Springer-Verlag, NewYork.

Baker, W.L., Cai, Y., 1992. The r.le programs for multiscaleanalysis of landscape structure using the GRASS GIS. Landsc.Ecol. 7, 291–302.

Barnes, B.V., Pregitzer, K.S., Spoes, T.A., Spooner, V.H., 1982.Ecological forest site classification. J. Forest. 8098, 493–498.

Bateman, I.J., Lovett, A.A., 2001. Estimating and valuing thecarbon sequestered in softwood and hardwood trees, timberproducts and forest soils in Wales. J. Environ. Manage. 60,301–323.

Boag, S., White, D.H., Howden, D.M., 1994. Monitoring andreducing greenhouse gas emissions from agricultural, forestryand other human activities. Clim. Change 27, 5–11.

Bockheim, J.G., 1990. Forest soils. In: Young, R.A., Giese,R.L. (Eds.), Introduction to Forest Science. Wiley, New York,pp. 86–97.

Braun, E.L., 1950. Deciduous Forests of Eastern North America.Hafner, New York.

Brown, S., 1996. Mitigation potential of carbon dioxide emissionsby management of forests in Asia. Ambio 25, 273–278.

Carmean, W.H., 1996. Forest site-quality estimation using forestecosystem classification in northwest Ontario. Environ. Monit.Assess. 39, 493–508.

Carmean, W.H., 1965. Black oak site quality in relation to soiland topography in southeastern Ohio. Soil Sci. Soc. Proc. 1965,308–312.

Caspersen, J.P., Pacala, S.W., Jenkins, J.C., Hurtt, G.C., Moorcroft,P.R., Birdsey, R.A., 2000. Contributions of land-use history tocarbon accumulation in U.S. forests. Science 290, 1148–1151.

Curtis, P.S., Hanson, P.J., Bolstad, P., Barford, C., Randolph,J.C., Schmid, H.P., Wilson, K.B., 2002. Biometric andeddy-covariance based estimates of annual carbon storage infive eastern North American deciduous forests. Agric. For.Meteorol. 113, 3–19.

Dale, V.H. (Ed.), 1994. Effects of Land-Use Change onAtmospheric CO2 Concentrations. Springer-Verlag, New York.


Eastman, J.R., Weigen, J., Kyem, P.A.K., Toledono, J., 2000.Raster procedures for multi-criteria/multi-objective decisions.Photogram. Eng. Remote Sensing 61 (5), 539–547.

Ebinger, J.E., 1997. Forest communities of the Midwestern UnitedStates. In: Schwartz, M.W. (Ed.), Conservation in HighlyFragmented Landscapes. Chapman & Hall, New York, pp. 3–23.

Erickson, D.L., Ryan, R.L., De Young, R., 2002. Woodlots inthe rural landscape: landowner motivations and managementattitudes in a Michigan (USA) case study. Landsc. Urban Plan.58, 101–112.

Faeth, P., Cort, C., Livernash, R., 1994. Evaluating the CarbonSequestration Benefits of Forestry Projects in DevelopingCountries. World Resources Institute, Washington, DC.

Fairfax, S.K., Guenzler, D., 2001. Conservation Trusts. UniversityPress of Kansas, Lawrence, Kansas.

Forman, R.T.T., 1995. Land Mosaics: the Ecology of Landscapesand Regions. Cambridge University Press, New York.

Garrett, H.E.G., Buck, L., 1997. Agroforestry practice and policyin the United States of America. For. Ecol. Manage 91, 5–15.

Gobster, P.H., Haight, R.G., Shriner, D., 2000. Landscape changein the Midwest. J. Forest. 98 (3), 9–14.

Gustanski, J.A., 2000. Protecting the land: conservation easements,voluntary actions, and private lands. In: Gustanski, J.A., Squires,R.H. (Eds.), Protecting the Land. Conservation Easements Past,Present, and Future. Island Press, Washington, DC, pp. 9–25.

Hawkins, V., Selman, P., 2002. Landscape scale planning: exploringalternative land use scenarios. Landsc. Urban Plan. 60, 211–224.

Hoover, C.M., Birdsey, R.A., Heath, L.S., Stout, S.L., 2000. Howto estimate the carbon sequestration on small forest tracks. J.Forest. 98 (9), 13–19.

Houghton, R.A., 1996. Converting terrestrial ecosystems fromsources to sinks of carbon. Ambio 25, 267–272.

Houghton, R.A., Hackler, J.L., 2000. Changes in terrestrial carbonstorage in the United States. I: the roles of agriculture andforestry. Global Ecol. Biogeog. 9, 125–144.

Hudson, J.C., 1994. The Making of the Corn Belt. IndianaUniversity Press, Bloomington, Indiana.

IGBP (International Geosphere-Biosphere Program) TerrestrialCarbon Working Group, 1998. The terrestrial carbon cycle:implications for the Kyoto Protocol. Science 280:1393–1394.

Ingham, C.D., 2000. Carbon forestry: encouraging the positives.J. Forest. 98 (9), 3.

Iverson, L.R., Brown, S., Prasad, A., Mitasova, H., Gillespie, A.,Lugo, A., 1994. Use of GIS for estimating potential and actualforest biomass for continental South and Southeast Asia. In:Dale, V.H. (Ed.), Effects of Land-Use Change on AtmosphericCO2 Concentrations. Springer-Verlag, New York, pp. 67–116.

Iverson, L.R., Dale, M.E., Scott, C.T., Prasad, A., 1997.A GIS-derived integrated moisture index to predict forestcomposition and productivity of Ohio forests (U.S.A.). Landsc.Ecol. 12, 331–348.

Jackson, R.B., 2000. Belowground processes and global change.Ecol. Appl. 10 (2), 399–411.

Jennings, M.D., 2000. Gap analysis: concepts, methods, and recentresults. Landsc. Ecol. 15, 5–20.

Josiah, S.J., Kemperman, J., 1998. Emerging agroforestryopportunities in the upper Midwest. J. Forest. 96 (11), 4–35.

Kimmerer, T.W., 1990. Structure and function of forest trees. In:Young, R.A., Giese, R.L. (Eds.). Introduction to Forest Science.Wiley, New York, pp. 67–85.

Krankina, O.N., Dixon, R.K., 1994. Forest management optionsto conserve and sequester terrestrial carbon in the Russianfederation. World Resour. Rev. 6, 88–101.

Lal, R., 2002. Why carbon sequestration in agricultural soils. In:Kimble, J.M., Lal, R., Follett, R.F. (Eds.), Agricultural Practicesand Policies for Carbon Sequestration in Soil. Lewis Publishers,Boca Raton, pp. 21–30.

Lal, R., Kimble, J.M., Birdsey, R.A., Heath, L.S., 2003. Researchand development priorities for carbon sequestration in forestsoils. In: Kimble, J.M., Heath, L.S., Birdsey, R.A., Lal, R.(Eds.), The Potential of U.S. Forest Soils to Sequester Carbonand Mitigate the Greenhouse Effect. CRC Press, Boca Raton,pp. 409–420.

Landsberg, J.J., Gower, S.T., 1997. Applications of PhysiologicalEcology to Forest Management. Academic Press, New York.

Lathrop Jr., R.G., Bognar, J.A., 1998. Applying GIS and landscapeecological principles to evaluate land conservation alternatives.Landsc. Urban Plan. 41, 27–41.

Leitão, A.B., Ahern, J., 2002. Applying landscape ecologicalconcepts and metrics in sustainable landscape planning. Landsc.Urban Plan. 59, 65–93.

Lerch, N.K., Hale, W.F., Lemaster, D.D., 1980. Soil Survey ofButler County, Ohio. Soil Conservation Service, US Departmentof Agriculture, Washington, DC.

Maguire, L.A., 1999. Social perspectives. In: Hunter, M.L.,Jr. (Ed.), Maintaining Biodiversity in Forest Ecosystems.Cambridge University Press, Cambridge, pp. 639–665.

Malczewski, J., 1996. A GIS-based approach to multiple criteriagroup decision-making. Int. J. G.I.S. 10 (8), 955–971.

Martin, P.H., Nabuurs, G.-J., Aubinet, M., Karjalainen, T., Vine,E.L., Kinsman, J., Heath, L.S., 2001. Carbon sinks in temperateforests. Annu. Rev. Energy Environ. 26, 435–465.

Medley, K.E., Okey, B.W., Barrett, G.W., Lucas, M.F., Renwick,W.H., 1995. Landscape change with agricultural intensificationin a rural watershed, Southwestern Ohio, USA. Landsc. Ecol.10, 161–176.

Medley, K.E., Pobocik, C.M., Okey, B.W., 2003. Historical changesin forest cover and land ownership. Ann. Assoc. Am. Geog.93 (1), 104–120.

Moffat, A.S., 1997. Resurgent forests can be greenhouse gassponges. Science 277, 315–316.

Murray, B.C., Prisley, S.P., Birdsey, R.A., Sampson, R.N., 2000.Carbon sinks in the Kyoto Protocol. J. Forest. 98 (9), 6–11.

NRC (National Research Council), 1998. Forested Landscapes inPerspective. Washington, DC: National Academy Press.

Naveh, Z., 2001. Ten major premises for a holistic conceptionof multifunctional landscapes. Landsc. Urban Plan. 57, 269–284.

Ricketts, T.H., Dinerstein, E., Olson, D.M., Loucks, C.J.,Eichbaum, W., DellaSala, D., Kavanagh, K., Hedao, P., Hurley,P.T., Carney, K.M., Abel, R., Walters, S., 1999. TerrestrialEcoregions of North America. Washington, DC: Island Press.

Roush, R.L., 1996. Digital Soil Survey Project for Butler County,Ohio. MA Internship Report. Miami University, Oxford, Ohio.


Sampson, N., DeCoster, L., 2000. Forest fragmentation:implications for sustainable private forests. J. Forest. 98, 4–8.

Scott, J.M., Davis, F., Blair, C., Noss, R., 1993. Gap analysis: ageographic approach to protection of biological diversity. Wildl.Monogr. 123, 1–41.

Schwartz, M.W., 1997. Introduction. In: Schwartz, M.W. (Ed.),Conservation in Highly Fragmented Landscapes, Chapman &Hall, New York, pp. xiii–xvi.

Sedjo, R.A., Goetzl, A., Moffat, S.O., 1998. Sustainability ofTemperate Forests. Resources for the Future, Washington, DC.

Shugart, H.H., 1984. A Theory of Forest Dynamics.Springer-Verlag, New York.

Spurr, S.H., Barnes V.B., 1980. Forest Ecology. Wiley, New York.Stowell, J., 2001. A Decade of Progress, 1990–2000,

Cinergy’s Environmental Story. Environmental Technologiesand Communications, Inc. (accessed athttp://www.cinergy.com/environmenton 31 July 2003).

Tan, K.H., 2000. Environmental Soil Science, second edition.(Revised and Expanded). Marcel Dekker, New York.

Thorsnes, P., 2002. The value of a suburban forest preserve:estimates from sales of vacant residential building lots. LandEcon. 78, 426–441.

Wang, H., 2001. Landscape Model for Carbon Conservation.Unpublished MA Thesis. Miami University, Oxford, OH.

David H. Wang is a database analyst at Dell Corporations, In-corporated. He completed undergraduate training in cartographyat the Zengzhou Institute of Surveying and Mapping in China,a 1-year program in GIS and Land Planning at Naples Univer-sity in Italy, and a masters in geography and masters in systemsanalysis at Miami University, Ohio in the USA. He now applieshis training in landscape planning and analyses to the computermanagement of large and complex data sets.

Kimberly E. Medley is an associate professor of Geography atMiami University. She completed an undergraduate degree in con-servation from Kent State University, Ohio, a master of geographyand a PhD in plant ecology from Michigan State University. She isinterested in why forests vary geographically in their compositionand structure, and how an understanding of that variation can bebest applied to resource conservation across highly-modified land-scapes in Ohio and East Africa. Her teaching contributes to thephysical-environmental core area of geography and comparativestudies of human-environment relationships.

http://www.cinergy.com/environment

http://www.cinergy.com/environment

Land use model for carbon conservation across a midwestern USA landscape

Documents

Transcript of Land use model for carbon conservation across a midwestern USA landscape