1209

Landscape representations based on land cover databases diff er signifi cantly from the real landscape. Using a land cover database with high uncertainty as input for emission inventory analyses can cause propagation of systematic and random errors. Th e objective of this study was to analyze how diff erent land cover representations introduce systematic errors into the results of regional N

2O emission inventories. Surface areas of

grassland, ditches, and ditch banks were estimated for two polders in the Dutch fen meadow landscape using fi ve land cover representations: four commonly used databases and a detailed fi eld map, which most closely resembles the real landscape. Th ese estimated surface areas were scaled up to the Dutch western fen meadow landscape. Based on the estimated surface areas agricultural N

2O emissions were estimated using

diff erent inventory techniques. All four common databases overestimated the grassland area when compared to the fi eld map. Th is caused a considerable overestimation of agricultural N

2O emissions, ranging from 9% for more detailed databases

to 11% for the coarsest database. Th e eff ect of poor land cover representation was larger for an inventory method based on a process model than for inventory methods based on simple emission factors. Although the eff ect of errors in land cover representations may be small compared to the eff ect of uncertainties in emission factors, these eff ects are systematic (i.e., cause bias) and do not cancel out by spatial upscaling. Moreover, bias in land cover representations can be quantifi ed or reduced by careful selection of the land cover database.

Eff ect of Land Cover Data on Nitrous Oxide Inventory in Fen Meadows

Linda Nol,* Peter H. Verburg, Gerard B. M. Heuvelink, and Karin Molenaar Wageningen University

Every land cover map or database is a simplifi cation of the

complexity of a real landscape (Arbia et al., 1998; Regnauld,

2001; Schmit et al., 2006). However, the scale and mapping

technique are a source of variation when comparing diff erent land

cover maps (Ellis, 2004; Bach et al., 2006; Schmit et al., 2006;

Verburg et al., 2006). Diff erences between a land cover database and

a real landscape are a source of error when the database is utilized

(Foody, 2002; Fang et al., 2006; Fassnacht et al., 2006). Th e large

dependence of greenhouse gas emissions on land use makes land

cover data an essential input in greenhouse gas inventories (Kern et

al., 1997; Plant, 1999; Denier van der Gon et al., 2000; Matthews

et al., 2000). Recently Huff man et al. (2006) acknowledged

the need for highly accurate, high-resolution, and nationally

consistent land cover data, while others have argued for statistically

rigorous and accurate assessment of thematic maps (Stehman

and Czaplewski, 1998; Heuvelink and Burrough, 2002). A lot of

research has been performed to improve greenhouse gas inventories

(Li et al., 1992; Kroeze, 1994; Denier van der Gon and Bleeker,

2005; Stacey et al., 2006), but careful analysis of how systematic

errors in land cover data aff ect these inventories has received little

attention. Often considerable emphasis is given to the provision of

the most exact input data possible for soil and climate while little

thought is given to the quality and accuracy of land cover or land

use data (Jansen, 1998; Bach et al., 2006). Bareth et al. (2001)

noted that the accuracy of spatial data should be regarded with more

importance in the estimation of N2O emissions.

Signatories to the Kyoto Protocol (UNFCCC, 1997) must an-

nually report emissions of their greenhouse gases CO2, CH

4, and

N2O. Th e Intergovernmental Panel on Climate Change (IPCC)

has established Good Practice Guidelines for reporting and upscal-

ing national greenhouse gas emissions. Th e inventory methods are

divided into three levels of increasing complexity and classifi ed as:

Tier 1, Tier 2, and Tier 3 (IPCC, 1997; IPCC, 2000). Th e Tier

1 and Tier 2 methods are based on the assumption that the total

N2O emission is a summation of emissions from diff erent sources,

which can be estimated by activity data (data on production and

consumption of N) multiplied by an emission factor (the fraction

of soil N emitted as N2O). Th e Tier 1 method is the most basic

method and uses default emission factors and spatially coarse ac-

tivity data. Th e Tier 2 method consists of the same basic relation-

ships but is based on emission factors and activity data which are

country-specifi c for the most important land uses and activities.

Th e Tier 3 method on the other hand requires nonlinear process

modeling. Examples of frequently used process models, as part of

Land Dynamics, Environmental Science Group, Wageningen Univ., P.O. Box 47, 6700

AA Wageningen, The Netherlands.

Copyright © 2008 by the American Society of Agronomy, Crop Science

Society of America, and Soil Science Society of America. All rights

reserved. No part of this periodical may be reproduced or transmitted

in any form or by any means, electronic or mechanical, including pho-

tocopying, recording, or any information storage and retrieval system,

without permission in writing from the publisher.

Published in J. Environ. Qual. 37:1209–1219 (2008).

doi:10.2134/jeq2007.0438

Received 21 Aug. 2007.

*Corresponding author (linda.nol@wur.nl).

© ASA, CSSA, SSSA

677 S. Segoe Rd., Madison, WI 53711 USA

TECHNICAL REPORTS: LANDSCAPE AND WATERSHED PROCESSES

1210 Journal of Environmental Quality • Volume 37 • May–June 2008

the Tier 3 method, are DNDC (Li et al., 1992; Li, 2000) and

CENTURY (Parton, 1996).

Many countries are still striving to fulfi ll the Kyoto report-

ing requirements (Brown et al., 2002; Bolan et al., 2004; Saggar

et al., 2004). Especially problematic are inventory methods for

N2O emissions from agricultural soils (Lokupitiya and Paustian,

2006). For the Netherlands Kuikman et al. (2004) stated that cur-

rent reporting to the Kyoto protocol is incomplete or inaccurate:

several sources may not have been identifi ed and others may well

be reported incompletely. Accordingly, it is important to focus on

decreasing the uncertainty and improving data quality of N2O

emissions from agricultural soils. An important source of N2O

emissions from agricultural soils is the emission from ‘cultivation

of histosols’, which diff ers from estimation of other agricultural

N2O sources because it requires spatial input data. Cultivation of

histosols leads to oxidation of organic matter from peat soils due to

the lowering of ground water tables in cultivated areas. Emission

of N2O from cultivated histosols in the Netherlands has been esti-

mated to contribute 10% of the direct N2O emissions from soils

and 5% of the total N2O emissions from agriculture (Klein Gold-

ewijk et al., 2005). Histosols cover a signifi cant area (approximately

9% of the land surface) in the Netherlands (Kuikman et al., 2005;

CBS, 2007) and are predominantly situated in the fen meadow

landscape in the western part of the Netherlands. Th e main land-

scape elements of Dutch fen meadow systems are grassland parcels,

ditches, and ditch banks, each with specifi c emission characteristics

(Best and Jacobs, 1997; Van Beek et al., 2004).

Th e estimation of land surface area occupied by histosols and

the main landscape elements depend on the available spatial input

information and associated resolution. Th e scale of analysis or kind

of information an investigator desires also infl uences the outcomes

of the inventory. For example, if an investigator can choose be-

tween diff erent land cover databases, each with a diff erent resolu-

tion, then the choice for a certain database depends on the element

of interest (Woodcock and Strahler, 1987). Th e optimum scale

of analysis is usually the scale at which processes, in this case N2O

emission, occur (Allen et al., 1984). Denitrifi cation and nitrifi ca-

tion are the most important processes in converting N into N2O in

soils (Firestone and Davidson, 1989). Th ese processes take place at

the microbial scale, whereas national inventories require emissions

to be reported on a national scale. Th ese inventories are often based

on emission factors derived from small-scale chamber measure-

ments (0.03–6 m2). Th e chamber measurements in fen meadow

landscapes have mainly taken place on grassland parcels, preferably

not too close to the ditch (Ambus and Christensen, 1994). Since

diff erent landscape elements have diff erent emission characteris-

tics, it is worthwhile estimating the surface area of the diff erent

landscape elements using land cover databases and investigating

the eff ect of using these land cover databases on the N2O emission

inventory. Th e objective of this paper was to analyze how diff erent

land cover representations potentially introduce systematic errors

into the results of regional N2O emission inventories. To this end

fi ve diff erent land cover databases with diff erences in spatial resolu-

tion and accuracy were used in combination with four emission

inventory methods. Understanding the infl uence of land cover

databases on regional emission may help in the further refi nement

of reporting protocols.

Materials and MethodsNitrous oxide emissions were calculated using diff erent

upscaling methods based on alternative land cover databases

for two representative landscapes in the Dutch fen meadow

system. Regional implications of using alternative land cover

databases were analyzed by scaling the results up to the fen

meadow landscape of the western Netherlands.

Study AreaTh e Dutch fen meadow landscape (Fig. 1) was formed in the

Holocene, when the sea level was rising and large areas of the

Netherlands were covered by swamps. In medieval times, this land

was reclaimed for agricultural use. A popular way of reclaiming the

land was by ‘cope agreements’. In these agreements, the length of

the parcel was usually prescribed to be about ten times the width

of the parcel. Th is pattern of parcels with the same shape is still

recognizable in the fen meadows. However, a common strategy

was not applied everywhere resulting in areas with more irregular

reclamations. Between the sixteenth and the nineteenth century

oligotrophic peat was excavated and used as fuel. Today, lakes and

grassland intersected by wide ditches are located in these areas. Th e

western fen meadow landscape exists primarily of grassland on

peat soils and is intensively managed and owned by dairy farmers;

however, more and more grassland is being extensively managed

by nature organizations. We assumed the fen meadow landscape to

be located on peat soils according to the Dutch soil map 1:50,000

(De Vries et al., 2003a). Th e borders of the western fen meadow

landscape, located on peat soils, were assumed to be the ‘IJsselmeer’

at the north eastern boundary and the line dividing land below and

above sea level as the south eastern boundary of the landscape.

Th e Zegveld and Oukoop polders (Fig. 1) were chosen

as case studies of the two most dominant reclamation types

within the Dutch fen meadow landscape: cope (regular) and

irregular reclamation.

Th e Zegveld polder (52°08′ N 4°48′ E) has a surface area

of 670 ha (Fig. 1) and is representative of the ‘cope’ reclama-

tion type. Th e area was reclaimed in the eleventh century. Th e

village of Zegveld (Fig. 1; south corner) was the reclamation

Fig. 1. Location of the research polders in the Netherlands.

Nol et al.: Eff ect of Land Cover Data on Nitrous Oxide Inventory in Fen Meadows 1211

base from where the reclamation of the area started. Th e par-

cels stretch from Zegveld to the peat river Meije bordering the

north of the polder. Many farms have settled in the center of

the parcels. Th e polder was one of the latest reclamations in the

area, which gave the polder its peculiar shape. At the reclama-

tion base in the south the parcels are narrow becoming wider

toward the north. Th e polder is predominantly drained 60-cm

depth but an area of natural vegetation (25 ha) in the northwest

is drained at approximately 30 cm below surface level.

Th e Oukoop polder (52°03′ N 4°43′ E) is smaller than the

Zegveld polder and has a surface area of 168 ha (Fig. 1). Th e

area was reclaimed in the eleventh or twelfth century and is

representative of the reclamation type with wide ditches. Th e

polder was enclosed by reclamations from the Hollandse IJssel

river in the south, the Oude Rijn river in the north, and an old

stream (the ‘Oude Wetering’) in the east. Both peat soils are

classifi ed as Terric Histosol and originate from wood and reeds.

Land Cover DatabasesSurface areas occupied by grassland parcels, ditches, and

ditch banks were estimated for the two research polders and

for the entire western fen meadow landscape based on fi ve

land cover databases. Th e emissions of nitrous oxide from the

Dutch fen meadows diff er with elements in the landscape.

Th erefore, these landscape elements were separately accounted

for in the analysis. Th e fi ve land cover databases used, more

or less ranked in order of decreasing resolution and accuracy,

were: a detailed fi eld map unit of the distinguished landscape

elements, Top10Vector, LGN4, CBS soil use, and CLC2000.

Fang et al. (2006) pinpointed the importance of taking the

uncertainty of land cover databases into consideration when using

these for landscape studies. Th e fi ve databases used in this study

diff er in uncertainty. Uncertainties in vector databases can be

subdivided into geometric uncertainty and thematic uncertainty

(Heuvelink et al., 2007). Geometric or positional uncertainty is

uncertainty about the shape and the location of an object. Th emat-

ic uncertainty is uncertainty about the attribute values of an object

and occurs in both vector and raster data. It is mainly caused by

interpolation errors and wrong classifi cation of pixels or mapping

units (Bolstad and Smith, 1992; Foody, 2002; Steele et al., 1998;

Van Oort, 2005). Th e resolution or minimum mapping unit of the

land cover database is a source of geometric uncertainty (Hengl,

2006). Th is problem, the modifi able areal unit problem, is especial-

ly problematic when there are discrete changes within landscapes.

Depending on the resolution and shape of data elements, almost

any result may be obtained (Openshaw, 1983). In this paper the ef-

fect of the diff erences in geometry and resolution on the estimation

of the prevalence of the diff erent landscape elements, important to

nitrous oxide emission, was evaluated. Details about the used land

cover databases are given in Table 1.

Th e goal of the fi eld map was to accurately delineate ditches

and ditch banks (positional uncertainty < 0.2 m in width), thus

yielding the surface area of these landscape elements with negligible

bias (i.e., much smaller than bias associated with the four com-

monly used databases). Th e aim was to measure all ditch widths

in the polder, but due to inaccessibility a number of ditch widths

had to be estimated. In polder Zegveld 91% of all ditches were

measured, 8% were estimated in the fi eld, and 1% was estimated

using the Top10Vector and aerial photographs. In polder Oukoop

68% of all ditches were measured, 12% were estimated in the

fi eld, and 20% were estimated using the Top10Vector and aerial

photographs. Th e boundary between ditch bank and grassland was

defi ned as the line that separates areas with a clear slope gradient

from those without a slope or with minimal relief (slope < 1°). Th e

surface area of ditches and ditch banks were then calculated using

the widths from the fi eld map and the lengths from the Top10Vec-

tor topographic database. Th is was acceptable because the bias of

the Top10Vector in ditch lengths was small compared to the bias

in ditch widths. Th e Top10Vector was used as the basis for the fi eld

map, the ditches were adjusted to the measured ditch widths and

ditch banks were added. In the database resulting from the fi eld

map, a distinction was made between intensively and extensively

managed grassland. Th e extensively managed grassland was man-

aged by a governmental organization, and was unfertilized and

grazed by sheep and beef cattle. Th e grazing pressure is lower than

on the intensively managed grassland, used for dairy cattle.

Th e Top10Vector database is a detailed topographic database

of the Netherlands made by the Dutch National Mapping Agen-

cy (TDK). Th e Top10Vector is a vector fi le with a closed fi eld

structure; built up from coded lines enabling the user to select

fi elds with certain characteristics. Th e Top10Vector is based on

aerial photograph interpretation in combination with fi eld inves-

tigation. It consists of several point, line, and polygon layers. Th e

database is partly updated every year and the entire Netherlands

is updated each 4 yr. Th e geometric uncertainty of the Top10Vec-

tor database is estimated at 2 m (Van Buren et al., 2003).

Th e CBS soil use database consists of soil use areas and

boundaries. For agricultural land cover the only distinction made

is between horticulture under glass and other agricultural use.

Th e Top10Vector was used for the basic geometry (water, rail-

roads, and roads). Th e geometric uncertainty of the topography

is also 2 m (CBS, 2002). Th e main diff erence is the larger mini-

mum mapping unit of the CBS soil use database (Table 1), which

leads to an additional source of geometric uncertainty. In the

analysis of the results, the linkage between the two databases was

taken into account. Th e CBS soil use database provides insight

into the distribution of diff erent soil use types in the Netherlands

and is used by the Statistics Netherlands (CBS) for deriving sur-

face area and density statistics for regional classifi cations.

Th e LGN4 is a land use database for the Netherlands and is

based on satellite images from 1999 and 2000 (De Wit, 2001).

Th e LGN4 exists of grid data and vector data of crops. Th e grid

data contain the dominant land cover type per 25 by 25 m grid

cell. In total 39 land cover types are distinguished. In this research

only grid data were used, because only surface areas water and

grassland are of interest. Th e main diff erence between LGN4 and

the CBS soil use database is that LGN4 focuses on agricultural land

cover whereas CBS soil use is more focused on urban land cover.

Th e category agriculture is split into ten classes and the category

nature has seventeen classes where a distinction is made between

intensively and extensively managed grassland. Validation of the

LGN4 was estimated by checking 4000 points using aerial photos

1212 Journal of Environmental Quality • Volume 37 • May–June 2008

and the Top10Vector. Th e overall thematic accuracy of the LGN4

was estimated to be 92.2% (GeoDesk, 2006). However, large dif-

ferences exist between classes. Classes with large abundances are

generally more accurate than less abundant classes.

Th e CLC2000 database is produced by the European En-

vironment Agency (EEA). Th e database was made as part of

the project COoRdinate INformation on the Environment

(CORINE). CLC2000 is a raster image, which has a resolution

of 100 m. Th e CLC2000 is based on satellite images, which were

interpreted by national teams. In the Netherlands vector data-

bases of land cover (Hazeu, 2003) were developed for 1986 and

2000 where changes in land cover between these years were also

mapped. Th e minimum mapping unit for these vector databases

is 25 ha and for changes in land cover between 1986 and 2000

the minimum mapping unit is 5 ha. Th ese national databases

were joined together and converted into the raster database

CLC2000 using the majority rule (Büttner et al., 2002). Th is da-

tabase distinguishes 44 land cover classes. Th e thematic accuracy

of the CLC2000 was estimated to be 87.0 ± 0.5% (EEA, 2006).

Nitrous Oxide Emission EstimationFor the Zegveld and Oukoop polders N

2O emissions were

estimated using four methods: IPCC Tier 1, Tier 2a, Tier 2b,

and Tier 3. Th e IPCC Tier 1 method estimates emissions by

multiplying global activity data by default emission factors (Ta-

ble 2). Th e emission factor is the fraction of N emitted as N2O.

Emission factors and activity data from the Good Practice

Guidance (IPCC, 2000) and the IPCC Guidelines (IPCC,

1997) were used. When activity data were not indicated in the

Good Practice Guidance, estimates from CBS (2007) were

used. In the Tier 1 method land cover data are used for the esti-

mation of the emission due to the cultivation of histosols. Th e

estimated surface area of grassland on peat soil from each land

cover database and the default emission factor were used. In the

polder Oukoop, only negligible N2O emissions from ditches

and ditch banks were measured (based on weekly closed cham-

ber measurements by Schrier (personal communication, 2005,

2006, and 2007) see also Table 3). Th e emissions from ditches

and ditch banks in polder Zegveld were also assumed negli-

gible, because the soil, land use, and hydrological conditions in

this polder are very similar to those of polder Oukoop.

Two alternative specifi cations of the IPCC Tier 2 method are

considered in this study hereafter referred to as Tier 2a and Tier

2b. Th e Tier 2a method uses activity data and emission factors as

reported in the most recent Dutch inventory report (Klein Gold-

ewijk et al., 2005) while the Tier 2b method uses polder-specifi c

activity data (i.e., number of animals, amount of fertilizer used)

gathered from door-by-door interviews with farmers. Five of the

twenty farmers in Zegveld were interviewed; together they own

31% of the area in the polder. In Oukoop all eleven farmers were

interviewed. All farmers could give animal numbers, separated into

mature dairy cattle, yearlings, calves, sheep, lambs, goats, and pigs.

Based on these interviews, an estimation of the total amount of

cattle, manure amendment, and fertilizer application was made.

For the Tier 2a method activity data from the municipality or

agricultural region (CBS, 2007) were used. Th is information was

scaled down to the scale of the research polders as follows. Agricul-

tural activity data in the Netherlands, such as the number of cows

and the amount of chemical fertilizers used in an area, are cor-

related to the amount of grassland in that area. Th erefore, activity

data for the polder were estimated by multiplying the activity data

from municipality/agricultural region by the ratio of grassland in

the polder to grassland in the municipality/agricultural region.

For the Tier 3 level inventory the process model INITIA-

TOR (De Vries et al., 2003b) was used. Th e model was devel-

oped to represent the crucial processes in the N chain by simple

process descriptions, calculated in yearly time steps. Input data

were taken from the CBS (2007) concerning animal numbers,

manure management systems, and fertilizer use. Inputs from

the land cover databases were also used. Soil characteristics

from the Dutch Soil map (Stiboka, 1969) and hydrological

characteristics (Wolf et al., 2003) were added. INITIATOR

uses a process model in which N2O emission is a function

of denitrifi cation and nitrifi cation in the soil (De Vries et al.,

2003b). Unlike the IPCC methods, the emission factors and

denitrifi cation and nitrifi cation fractions vary as a function of

soil type and groundwater level in INITIATOR (Table 2).

Analysis of variance (ANOVA) was used to analyze wheth-

er diff erences between land cover databases for the polders are

signifi cant and whether diff erences between inventory meth-

ods are signifi cant.

Regional UpscalingIn addition to comparing the calculated emissions for the two

research polders an assessment of the regional implications of the

use of diff erent databases was made for the entire Dutch western

fen meadow landscape. Th e fi eld map of the research polders

was scaled up to estimate the surface areas of diff erent landscape

Table 1. Characteristics of land cover databases.

Database Type Year of validityMinimum

mapping unit Grid cell size Projection Extent No. of categories Source

Field map Vector 2006 0.2 m (ditch)2 m (roads)

– RD (Dutch)‡ Polder ZegveldPolder Oukoop

12 –

Top10Vector Vector 2000–2004 3 m (ditch)2 m (roads) †

– RD (Dutch)‡ Netherlands 50 TDN (2006)

CBS soil use Vector 2000 10,000 m2 § – RD (Dutch)‡ Netherlands 38 CBS (2002)

LGN4 Raster 1999/2000 5000 m2 25 m RD (Dutch)‡ Netherlands 39 GeoDesk (2006)

CLC2000 Raster 2000 250,000 m2 100 m Lambert Azimuthal Europe 43 EEA (2000)

† Vliegen (2000).

‡ RD = Dutch National Grid.

§ Except for roads and railroads, which are all included in the database.

Nol et al.: Eff ect of Land Cover Data on Nitrous Oxide Inventory in Fen Meadows 1213

elements and derive emission inventories using the

various approaches for the larger region. For upscaling

the fen meadow landscape was subdivided into three

diff erent reclamation landscapes: ‘cope’ reclamations,

reclamations with wide ditches, and irregular reclama-

tions. Th is distinction was made because these three

reclamation landscapes diff er in the prevalence of

landscape elements due to diff erences in the shape of

grassland parcels and open water due to diff erences

in reclamation history. All three reclamation types are

common in the western fen meadow landscape. Th e

Top10Vector database was used to assign the type

of reclamation landscape to each of the 315 polders

in the western fen meadow landscape. Th e surface

areas of landscape elements found in the two research

polders were used to estimate the distribution of

these landscape elements in the fen meadows. Polder

Zegveld was considered to be representative for ‘cope’

reclamation patterns with a regular pattern of predom-

inantly rectangular parcels divided by small ditches.

Th e length/width ratio of the parcels is approximately

10:1. Th e selection procedure for this type of reclama-

tion is therefore based on the length/width ratio of the

parcels. Polders where more than 70% of the parcels

have a length/width ratio equal or greater to 10:1

were considered to be ‘cope’ reclamations. Twenty to

thirty percent of the parcels in ‘cope’ reclamations have

smaller width/length ratio, because these are situated

at the edge of the polder or are dissected by a road.

Th e second reclamation landscape can be described as

polders with signifi cant areas of open water. Usually

these polders have wide ditches in between the parcels.

Polder Oukoop was used as a reference polder for this

reclamation landscape. Th e procedure to distinguish

these polders at the regional level was based on the

occurrence of open water and ditches wider than 3 m

in the polders. Note that the Top10Vector database

represents ditches smaller than 3 m as line elements.

Polders with surface areas of water equal to or larger

than 10% of the grassland surface areas, according to

the Top10Vector, were therefore classifi ed as reclama-

tions with wide ditches. Th is percentage was derived

from the standard width of parcels in this area and the

average ditch width. Th e remaining polders were clas-

sifi ed as irregular reclamations. A representative for this

reclamation landscape is polder Menningweer where

Molenaar (unpublished data, 2006) made a detailed

fi eld map. After classifying the polders based on the

three reclamation landscapes and assignment of the

accompanying surface areas of landscape elements,

the total surface areas of grassland parcels, ditches, and

ditch banks were estimated. Th ese surface areas were

used to estimate the total agricultural N2O emission

from the Dutch western fen meadow landscape. For

each land cover database the amount of grassland on

histosols compared to grassland on mineral soil in each Tab

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kg N

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††

† D

efa

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(IP

CC

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97

, 20

00

).

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r te

mp

era

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on

es

(IP

CC

, 19

97

).

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mis

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Ne

the

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ract

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_o

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mis

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Ne

the

rla

nd

s fo

r m

an

ure

inco

rpo

rati

on

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nic

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ils (

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ace

sp

rea

din

g o

f m

an

ure

is f

orb

idd

en

in t

he

Ne

the

rla

nd

s).

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mis

sio

n f

act

or

in t

he

Ne

the

rla

nd

s (K

uik

ma

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00

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††

Em

issi

on

fa

cto

r in

th

e N

eth

erl

an

ds

= F

ract

ion

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rea

(6

5%

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ract

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s (3

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he

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ere

are

no

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mis

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n f

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ors

an

d f

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s fo

r in

dir

ect

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th

ere

fore

th

e IP

CC

de

fau

lt v

alu

es

we

re u

sed

(IP

CC

, 19

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, 20

00

).

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All

inte

rvie

we

d f

arm

ers

in t

he

re

sea

rch

po

lde

rs u

se a

mm

on

ium

fe

rtili

zers

.

¶¶

Em

issi

on

fa

cto

r fo

r e

utr

op

hic

pe

at

soils

in t

he

Ne

the

rla

nd

s, m

ea

sure

d in

Ze

gve

ld (

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ikm

an

et

al.,

20

05

).

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Em

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on

fa

cto

r d

ep

en

ds

on

loca

l so

il a

nd

hyd

rolo

gic

al c

ha

ract

eri

stic

s.

††

† In

Tie

r 3

N2O

em

issi

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du

e t

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f N

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nd

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nsi

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ire

ct e

mis

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n (

De

Vri

es

et

al.,

20

03

b).

1214 Journal of Environmental Quality • Volume 37 • May–June 2008

agricultural region (CBS, 2007) was calculated to estimate activity

data such as amount of cattle and amount of fertilizer use. When

emission factors derived from the Dutch situation were available

(Klein Goldewijk et al., 2005), these were used. For some N2O

sources, emission factors have not been determined in the Neth-

erlands (i.e., for indirect emissions), and default emission factors

were used.

Th e emission of the western fen meadows was also estimated

using INITIATOR, based on data on soils, hydrology, and land

use data from the STONE database (Wolf et al., 2003).

Results and Discussion

Land Cover RepresentationsRepresentations of landscape structure in the diff erent land

cover databases are shown in Fig. 2 and 3. Th e enlargements in

Fig. 2 and Fig. 3 clarify the diff erence between the fi eld map and

the Top10Vector database. Th e Top10Vector database represented

ditches smaller than 3 m as lines and did not make a distinction

between ditch banks and grassland. Th e fi eld map represented all

ditches as polygons and was the only database that distinguished

between ditch banks and grassland. Th e CBS soil use database

ignored farmyards, farms, and small ditches due to the large

minimum mapping unit compared to the fi eld map and the

Top10Vector. Only the village center of Zegveld was represented

as a residential area. Th e LGN4 and the fi eld map databases distin-

guished between intensively managed grassland and extensive man-

aged nature area. For both polders, the LGN4 database recorded

a considerable surface area of ‘urban in agricultural area’ compared

to the CBS soil use and CLC2000 database. As a result, the LGN4

database recorded a reduced grassland area compared to the other

databases. Raster databases have diffi culties representing point and

line features that are smaller or equal to the pixel size. Th e farms

with farmyard in the polders are features that were represented by

the LGN4 database as square and rectangular shapes, which were

often diff erent from their real shape (i.e., the fi eld map). Th e coarse

CLC2000 raster database showed the entire polder covered with

grassland, except for one pixel in the Zegveld polder and for one

pixel in the Oukoop polder.

Surface areas of (intensively and extensively managed) grassland

parcels, ditches, and ditch banks as calculated using the diff erent

databases are given in Table 4. Except for the fi eld map the land

cover data did not have a separate class for ditch banks. Th ese

were all classifi ed as grassland. Th e fi eld map showed the smallest

surface area of grassland and the largest surface area occupied by

water and ditch banks. Th e grassland surface area increased with

increased minimum mapping unit for vector data and increased

with increased resolution for raster data. Th is can be explained

by the dominance of grassland which, when presented at coarser

Fig. 2. Representations of polder Zegveld using diff erent land cover databases.

Table 3. Nitrous oxide emission from grassland, ditch, and ditch bank. Emissions were non-continuously measured using fl ux chambers.

Treatment N2O loss

kg N2O ha−1 yr−1

Dry grassland parcel

Unfertilized 13.5†

Fertilized 28.4†

Fertilized and grazed 60.5†

Wet grassland parcel

Unfertilized 3.1†

Fertilized 13.8†

Fertilized and grazed 22.9†

Ditch bank negligible‡

Ditch negligible‡

† Velthof (1997).

‡ Schrier (personal communication, 2007). In polder Oukoop, N2O

emissions from ditches and ditch banks were below the detection limit

of the measurement equipment.

Nol et al.: Eff ect of Land Cover Data on Nitrous Oxide Inventory in Fen Meadows 1215

scales, results in a general overestimation of its prevalence (Moody

and Woodcock, 1996; Schmit et al., 2006). In vector data, ditches

are ignored when they are < 3 m (Top10Vector) or have a surface

area < 1 ha (CBS soil use). In raster data, ditches are ignored when

another type of land cover is more abundant within a pixel.

Overestimation of land cover classes with large abundances has

also occurred in other landscapes (Turner et al., 1989; Moody and

Woodcock, 1996; Ellis et al., 2000; Fassnacht et al., 2006; Schmit

et al., 2006). In the fen meadow landscape, grassland has a large

abundance and therefore absorbs the other classes, especially for

the databases with small accuracies and coarse resolutions. Other

landscapes are less sensitive to aggregation errors (e.g., Turner et al.,

1989). Moody and Woodcock (1995) analyzed a mountainous

forested area in California and found an increasing prevalence of

water with increasing resolution due to lakes with a high degree of

aggregation situated sparsely across the landscape. Th e class “coni-

fers” also increased on average by 20% when aggregating from a

resolution of 30 to 100 m. Th ey concluded that the large increase

in this class was due to the spatial structure of moderately large

patches. Th e results found by Moody and Woodcock (1995)

are large compared with the 7 to 8% diff erence in grassland

in our study areas between the LGN4 (25-m resolution) and

CLC2000 (100-m resolution) databases. On the other hand,

Bach et al. (2006) and Fassnacht et al. (2006) found smaller

diff erences between land use classes when aggregating from 25

to 100 m. Van Oort et al. (2004) compared the LGN database

with reference data from randomly chosen areas in the Nether-

lands. Th e reference data were based on cadastral information.

Th e grassland surface area was 2.5% larger for the LGN database

than for the reference data. We found larger diff erences between

the LGN4 and the fi eld map (20–22%). Th is is probably due to

the fact that Van Oort et al. (2004) only estimated areas of grass-

land and crops, where we found a large diff erence due to the

presence of ditches instead of grassland. In research where thematic

errors are small, positional errors can be large (Bach et al., 2006).

Fassnacht et al. (2006) found the class “broadleaf,” which forms

narrow linear features along rivers, to be particularly susceptible to

changes in resolution. Th is is comparable to our fi ndings of ditches

and ditch banks. Ozdogan and Woodcock (2006) also noted that

large landscape elements can support large pixels, but when the

landscape elements of interest are small, fi ne resolution is needed to

correctly estimate surface areas.

Nitrous Oxide Emission EstimatesUsing inventory techniques based on the diff erent IPCC tier

levels we calculated N2O emissions with the calculated surface areas

(Fig. 4). Bias in estimating the area of grassland was propagated in

the calculated emissions. For all Tier levels and for both polders the

most accurate database represented the smallest area of grassland

and accordingly the smallest N2O emission. Th e N

2O emissions

from polder Oukoop are about four times smaller than N2O emis-

sions from polder Zegveld, which is consistent with the diff erence

Fig. 3. Representations of polder Oukoop using diff erent land cover databases.

Table 4. Surface areas land cover types per land cover database.

Database

Total

grassland

Intensively

managed grassland

Extensively

managed grassland Water

Ditch

bank

——————————––——ha——————————––——Polder Zegveld

Field map 513 434 70 70 33

Top10Vector 586 – – 30 –

CBS soil use 640 – – 19 –

LGN4 627 579 47 8 –

CLC2000 669 – – 0 –

Polder Oukoop

Field map 115 86 29 35 10

Top10Vector 142 – – 19 –

CBS soil use 152 – – 15 –

LGN4 155 155 0 8 –

CLC2000 167 – – 1 –

1216 Journal of Environmental Quality • Volume 37 • May–June 2008

in total surface area between the polders. Th e Tier 3 method pro-

duced the highest N2O emissions for both polders and for all land

cover databases (Fig. 4d and 4h). Furthermore, the Tier 3 method

showed the largest diff erences between emission estimates (24% for

polder Zegveld and 33% for polder Oukoop) because this method

was more dependent on spatial data. Estimated N2O emission per

hectare ranged from 20.0 to 47.1 kg N2O ha−1 yr−1, which is com-

parable to the emissions found by Velthof (1997; Table 3).

For polder Zegveld the emissions of N2O estimated with

the Tier 2b method (Fig. 4c), were higher than the emis-

sions estimated with the Tier 1 (Fig. 4a) and Tier 2a (Fig. 4b)

method. From the interviews, it turned out that more cattle

were present in the polder than estimated from the munici-

pality data (Tier 1 and Tier 2a). Another reason is that the

dairy cattle had, according to the local data, spent more time

in the meadow than global and Dutch numbers indicated.

For both polders, the smallest N2O emissions were obtained

from the Tier 1 method (Fig. 4a and 4e). Th e emission factors in

the Tier 2 methods were larger and caused higher emission estima-

tions. In polder Oukoop the diff erence between Tier 2a (Fig. 4f)

and Tier 2b (Fig. 4g) was small, indicating that the activity data

from the CBS database were close to the activity data estimated

from the interviews. Results from the Tier 3 method (Fig. 4d and

4h) were high for both polders compared to the other methods.

Th e INITIATOR estimates for N2O emission are based on the

amount of denitrifi cation and nitrifi cation. Because the peat soils

in the western fen meadows landscape have excellent conditions

for nitrifi cation and denitrifi cation, the emissions estimated by

INITIATOR were much higher than the emissions estimated by

other inventory methods. As shown in Fig. 4, diff erences between

inventory methods were larger than diff erences between land cover

databases, which was also confi rmed by analysis of variance (ANO-

VA). For both polders, the emission estimates diff ered signifi cantly

between all inventory methods, except for polder Oukoop between

methods Tier 2a and Tier 2b. Due to the high emissions estimated

by the Tier 3 (INITIATOR) method, the diff erences between land

cover databases were not signifi cant, except for polder Zegveld be-

tween the LGN4 and CBS soil database.

Regional ExtrapolationTh e surface area distribution of grassland parcels, ditches, and

ditch banks from the research polders were used to scale up to the

entire western fen meadow landscape (Table 5). Th e three polders

(Oukoop, Zegveld, and Menningweer) were assumed to be rep-

resentative for all Dutch fen meadow polders. Th is is assumed to

be correct for the purpose of examining the impact of scale bias

in land cover data for estimating regional N2O emissions.

Th e reclamation landscape with wide ditches contained about

twice as much open water as the other two landscape types. Th e

irregular reclamation landscape had the smallest share in ditch

banks, which can be explained by the large abundance of square

parcels compared to more elongated parcels in the other reclama-

tion landscapes. Figure 5 shows a map of the western fen meadow

including a classifi cation of the polders in reclamation landscapes.

Intersecting rivers and disappearance of peat due to peat

excavation caused fragmentation of the western fen meadow

landscape. In the southern part ‘cope’ reclamations were

abundant. Th e irregular reclamation landscape is common

in the northern part of the western fen meadow landscape,

where there was no common strategy during the reclamation

period. Th e reclamations with wide ditches were most abun-

dant near locations where the peat was excavated for fuel use.

Th e area of grassland based on the fi eld map was considerably

smaller than the other estimated areas of grassland (Table 6).

Th e fi eld map was used to estimate the extent of ditches

in the western fen meadow landscape, where other databases

reported larger areas of grassland. According to the Dutch Soil

map 36% of the Dutch peat soils are situated in the western

fen meadow landscape. In the current national inventory, the

total surface area of grassland on organic soils equals 231,000

ha (Klein Goldewijk et al., 2005). Assuming that there are no

meaningful nationwide diff erences between the proportion

of land on peat soils occupied by grassland, this suggests that

83,000 ha of grassland is located in the western fen meadow

landscape. Th is estimate is smaller than the estimates from the

Table 5. Surface area distribution of landscape elements in research polders used as reference for upscaling.

Landscape elements

Research polderReclamation

landscapeGrassland

parcels DitchesDitch banks

————— %—————Polder Zegveld ‘Cope’ reclamation 76.6 10.5 4.9

Polder Oukoop Reclamation with wide ditches

68.8 20.7 6.0

Polder Menningweer Irregular reclamation 80.6 12.0 2.0

Fig. 4. N2O emission from agriculture using diff erent land cover data

and diff erent inventory methods.

Nol et al.: Eff ect of Land Cover Data on Nitrous Oxide Inventory in Fen Meadows 1217

land cover databases, except for the fi eld map estimate, which

is 11% smaller.

In general, vector data are more suitable in representing

distinct boundaries and clear landscape elements, whereas raster

data are assumed to better represent natural phenomena with

gradual boundaries, such as soils, vegetation types, and slopes

(Star and Estes, 1990). Th e landscape structure of the western

fen meadow landscape with predominantly sharp boundaries

between landscape elements and with long narrow ditches was

therefore best represented by vector data. Poor representation of

line elements—especially ditches—in this landscape was a large

source of bias by both vector and raster data. Note that the bias

would be much smaller for landscapes with fewer line elements

and larger patches of the same land use.

Th e N2O emission estimates for the Dutch western fen mead-

ows are shown in Table 7. Th e largest source of agricultural N2O

emissions was the cultivation of histosols, which demonstrates the

importance of this source. Th e highest emissions from this source

were obtained with the CLC2000 database because of the larger

estimated surface area grassland (Table 6). Th e total emissions were

larger for Tier 2a than for Tier 1 largely due to larger ammonium

losses according to the Tier 1 method. For the western fen meadow

landscape the maximum diff erence between the land cover data-

bases was almost twice as large as between the inventory methods.

Th is diff erence was largely due to two sources of error. Th e fi rst

error was the varying activity data used for the fen meadow land-

scape. For the research polders most activity data were relatively

constant for all land cover databases. Many Dutch activity data

(e.g., number of cows) were reported per agricultural region with-

out information about the distribution (e.g., the amount of cows

grazing on mineral soils vs. grazing on organic soils). To estimate

these activity data for the western fen meadow landscape estimates

about the proportion of organic soils compared to the proportion

of mineral soils in the agricultural regions from the land cover da-

tabases were used. Th ese activity data, which varied between land

cover databases, caused some diff erences in emission estimates. Th e

second source of error was the bias in representation of landscape

elements by the land cover databases.

Th e N2O emission was also calculated using Tier 3 (INITIA-

TOR) and input from the STONE database. Th e estimated emis-

sions were about twice the emissions estimated with the Tier 1 and

2a method (data not shown). Th is was partly due to the high deni-

trifi cation and nitrifi cation estimated by INITIATOR, which was

also identifi ed at polder scale, and partly due to the use of STONE,

which is a very coarse database (with a resolution of 250 m) com-

pared to the other databases used for the Tier 1 and 2a methods.

ConclusionsIn this research the surface area of grassland was overesti-

mated when using the land cover databases. When moving

to a coarser resolution for raster data or to a larger minimum

mapping unit for vector data, classes with large abundances

‘absorbed’ classes with small abundances. Th e choice of a cer-

tain land cover database can have drastic eff ects on N2O in-

ventories, because diff erences between estimated surface areas

Table 6. Surface area of grassland in western fen meadow landscape.

Database Surface area of grassland

ha

Field map 74,049

Top10Vector 86,891

CBS soil use 92,692

LGN4 87,461

CLC2000 96,391

Fig. 5. Reclamation landscapes in the western fen meadow landscape.

Table 7. Emission of N2O estimated for the western fen meadows

using the IPCC Tier 1 and Tier 2a method from diff erent sources.

Cultivation of histosols Total emission from agriculture

Database Tier 1 Tier 2a Tier 1 Tier 2a

––––––———— 103 kg N2O yr−1––––––————

Field map 582 547 1517 1685

Top10Vector 683 642 1725 1910

CBS soil use 728 685 1747 1979

LGN4 687 646 1788 1987

CLC2000 757 712 1902 2106

1218 Journal of Environmental Quality • Volume 37 • May–June 2008

sometimes exceed 20% and diff erent surfaces have diff erent

emissions. Such diff erences do not only apply to our study

sites; at the regional level the amount of diff erence is similar.

For the Zegveld and Oukoop polders, the diff erences in es-

timated N2O emissions were larger between the inventory tech-

niques than between land cover databases. For the western fen

meadow landscape as a whole, the reverse applied because errors

in land cover data were mainly systematic errors (bias) and er-

rors from the inventory techniques were mainly random. Bias is

consistently in the same direction and does not cancel out when

estimates are scaled up to larger regions; therefore, these systematic

errors became more distinct for larger areas compared to random

errors in emission factors. Th e eff ect of using a more detailed land

cover database had the opposite eff ect of using a more detailed in-

ventory method. Highest emissions were estimated using the coars-

est land cover database and the most detailed inventory method

and vice versa. Although focusing on the reduction of uncertainty

by improving emission inventory methods may be effi cient at the

local scale, this study has shown that for large-scale inventories the

careful selection, inventory, and use of land cover data may be as

important in reducing inventory uncertainties. While signifi cant

eff ort has gone into improving emission factors and improving in-

ventory techniques, this research demonstrated that with relatively

little eff ort emission inventories can be improved by improving

land cover data input.

AcknowledgmentsTh is work was funded by the Dutch project “Climate

Changes Spatial Planning (KvR ME1)”. We want to

thank Wim de Vries and Hans Kros for making the model

INITIATOR available for this research and Jan-Cees Voogd

for his help using INITIATOR. We also thank Arina Schrier,

Matheijs Pleijter, and Gerard Velthof for making their valuable

fi eldwork data in the research polders available for this study.

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