Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map

Remote Sensing of Environment 81 (2002) 443 – 455 www.elsevier.com/locate/rse

Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map M. Laba*, S.K. Gregory, J. Braden, D. Ogurcak, E. Hill, E. Fegraus, J. Fiore, S.D. DeGloria New York Cooperative Fish and Wildlife Research Unit, Cornell University, Ithaca, NY, USA Cornell Institute for Resource Information Systems, Cornell University, Ithaca, NY, USA Received 15 April 2001; received in revised form 25 January 2002; accepted 2 February 2002

Abstract The accuracy of a regional-scale thematic map of land cover was assessed using conventional and fuzzy methods. The mapping process integrated Landsat Thematic Mapper (TM) data with expert knowledge to map land cover for a 12 million-ha area. Accuracy assessment was based on a stratified random sample of 113 sampling areas. Paired observed and predicted land cover types for 9745 polygons for conventional accuracy and 933 polygons for fuzzy accuracy were collected. Overall map accuracies using conventional methods ranged from 74% to 42% for maps with increasing taxonomic resolution. Fuzzy map accuracies were assessed at low and high taxonomic resolutions resulting in an improvement in map accuracy of 19% and 23%, respectively. The nature, magnitude, and frequency of errors associated with mapping land cover types for large land areas at different levels of taxonomic resolution are reported, and two accuracy assessment methods are compared with respect to information content and opportunities for improving map quality. D 2002 Elsevier Science Inc. All rights reserved.

1. Introduction The mapping of land cover is accomplished by adopting, adapting, or developing a land cover classification scheme, delineating land areas of relative homogeneity for each category of the scheme, identifying and labeling these areas with the appropriate map unit symbol, and assessing accuracy to indicate where the map adequately represents true land cover conditions and where improvements should be made in the next inventory. The objectives of this study were to (a) map land cover types throughout their distribution in New York State in support of the New York Gap Analysis Project (NY-GAP) and (b) assess the accuracy of the region-wide map using conventional and fuzzy accuracy assessment methods. The gap analysis process relies on maps of natural and humaninfluenced land cover types as the most fundamental spatial component of the analysis of biodiversity for terrestrial environments (Scott & Jennings, 1998). * Corresponding author. Cornell Institute for Resource Information Systems, Cornell University, 301 Rice Hall, Ithaca, NY 14853, USA. Tel.: +1-607-255-0841; fax: +1-607-255-4662. E-mail address: [email protected] (M. Laba).

Both conventional and fuzzy accuracy assessment approaches were implemented to evaluate in a more robust manner the nature and magnitude of errors resulting from mapping a large land area using a detailed land cover classification scheme. In such cases, the relationship between a given land cover type and wildlife habitat associations becomes less discrete as classification schemes become more detailed. In these cases, conventional map accuracy assessment where a set of binary choices (correct or incorrect) dictates whether the map is accurate or not accurate is too severe a test to determine the usefulness of a land cover map for modeling these habitat associations.

2. Methods 2.1. Land cover classification The NY-GAP land cover classification scheme integrates the physiognomic hierarchical structure of the National Vegetation Classification System (NVCS) (FGDC, 1997) and modified descriptions of New York State ecological communities (Reschke, 1990). Subclass, group, subgroup, and formation are used to distinguish land cover types on a

0034-4257/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 0 3 4 - 4 2 5 7 ( 0 2 ) 0 0 0 2 0 - 2

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physiognomic basis. Super-alliances are used to distinguish land cover types on a floristic basis (Table 1). The focus of the accuracy assessment described in this paper is the subclass and super-alliance levels of classification. 2.2. Mapping standards and data sources Landsat-5 Thematic Mapper (TM) multispectral digital imagery was used to map the region-wide distribution of land cover types. All or parts of 14 Landsat TM scenes required to provide region-wide coverage of New York State were georeferenced to the Universal Transverse Mercator (UTM) coordinate system, extended Zone 18 (New York Transverse Mercator), North American Datum of 1983, and resampled to a 900-m2 pixel using a cubic convolution resampling algorithm. The reflective TM bands (1– 5, 7) were clustered using the Spectrum image processing program to generate a 240-cluster product (Benjamin, White, Argiro, & Lowell, 1996). Each of the spectral clusters was assigned to one of 29 land cover types using known spectral characteristics of terrestrial and aquatic features, aerial photo interpretation, reference to field data, and local knowledge.

2.3. Accuracy assessment Two methods were employed to assess the accuracy of the regional land cover map. The first method measured the level of agreement between land cover types predicted through the spectral cluster labeling process and land cover types observed at selected sites throughout the study site and through the use of various metrics calculated from data summarized in a contingency table. This method is referred to here as the conventional method, and is described in detail by Congalton and Green (1999). The second method was implemented to experiment with an alternative way to assess land cover maps produced at high taxonomic resolution. This second method is referred to here as the fuzzy method and is implemented, with modifications, as described by Gopal and Woodcock (1995) and a recent application of a fuzzy accuracy assessment method in Alaska (Muller et al., 1998). 2.4. Sampling design Data collection for a regional, stratified random sample of 113 sampling areas was implemented using a combination

Table 1 Land cover classification scheme based on a modified NVCS hierarchy Order

Class

Subclass

Group

Subgroup

Formation

Super-alliances

Tree

forest/woodland

evergreen

temp needle-leaveda

nat/seminatb

deciduous

cold deciduous

cultivated nat/seminat

upland wetland plantation upland

mixed

evergreen/deciduous

cultivated nat/seminat

wetland plantation upland

deciduous

cold deciduous

nat/seminat

wetland upland

spruce-fir evergreen wetland evergreen plantation sugar maple mesic oak successional hardwoods deciduous wetland orchard/vineyard Appalachian oak – pine pitch pine – oak evergreen – northern hardwood mixed wetland successional shrub alpine krummholz shrub swamp salt shrub/maritime shrubland dwarf shrub bog old field/pasture alpine meadow/rock/heath summit emergent marsh/open fen/wet meadow salt marsh golf course/park/lawn cropland sand dunes/flats barren open water roads urban suburban clouds shadows

Shrub

shrub

wetland dwarf shrub herbaceous

Herb

mixed perennial

evergreen/deciduous

nat/seminat nat/seminat

wetland upland wetland

Sparse

sparse vegetation

Water Built environment

Spectral obstructions a b

Temp = temperate. Nat/seminat = natural/seminatural.

annual soil/sand

sand

cultivated cultivated nat/seminat

urban agricultural upland

cultivated

urban

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455

of field observations and aerial photo interpretation. The stratified random sample corresponds closely to the starting points for the Breeding Bird Survey (BBS) routes developed by the United States Fish and Wildlife Service. The routes were established during the 1960s by randomly selecting a number of locations (starting point of the route) within each 1° latitude  1° longitude block (stratum) for selected regions in the USA. All strata have the same dimension (1° latitude  1° longitude) and only one location for each stratum was selected (Robbins & VanVelzen, 1967). Each sampling area was limited to accessible travel corridors. Each of the accuracy assessment sampling areas was defined as a circular, 1600-ha area centered within the effective area of a 1:40,000 scale color infrared aerial photograph acquired by the National Aerial Photography Program (NAPP). The NAPP aerial photographs were selected based on the latitude and longitude coordinate defining the starting point of each BBS route. The center of the NAPP photo was established as the center of the sampling area. The photo center coordinates were used to extract the corresponding circular, 1600-ha area from the regional land cover map for each of the accuracy assessment sampling areas. Each map polygon intersecting the NAPP-based sampling area was assigned a unique numeric code. Except for the unique numeric code, the polygons were unlabeled with respect to the predicted land cover type so as not to bias field observations. Each of the 1600-ha sampling areas contained from 40 to 200 polygons depending on the complexity of the type and distribution of land cover types within the sampling area. Both the predicted (map) and observed (field) polygons were considered point observations for the purposes of both the conventional and fuzzy accuracy assessment of the land cover map. Field data were collected during the 1998 summer and fall seasons by three 2-person field crews. The NAPP aerial photographs were acquired between 1995 and 1997 during the spring season (leaf-off). The satellite imagery used to produce the regional land cover map was acquired between 1991 and 1993. Most imagery was acquired during the spring season of each year to achieve a relatively cloud-free data set throughout the study area. The discrepancies in time among the acquisition of the NAPP aerial photography, satellite imagery, and field data for accuracy assessment can increase error rates significantly due both to land cover changes that occur on an annual basis and to phenological changes of vegetation that occur within seasons of a given year. Field crews were advised that this may occur and to record instances where such land cover changes or phenological variations may have been a factor. Field data collection for fuzzy accuracy assessment was conducted on a 10% subsample of the plots during the summer and fall of 1998 in much the same way as data collected for conventional accuracy assessment. Field plots were initially chosen at random and fuzzy accuracy assessment data were collected only for polygons that could be

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observed thoroughly without accessibility constraints. Given the priority to conduct a conventional accuracy assessment throughout the region and the additional time required to collect data in support of fuzzy accuracy assessment methods, only a subsample of field plots was used, which represented 22 of 29 land cover types. Field observations in support of fuzzy accuracy assessment were more complex and time-consuming in comparison to conventional accuracy assessment. Field crews had to record for each sampled polygon in each sampling area the most suitable integer score on a linguistic scale between 1 and 5 for every land cover type in the classification scheme. The scores were based on those described by Gopal and Woodcock (1995) as follows: 1 = absolutely incorrect, 2 = understandable but incorrect, 3 = reasonable or acceptable answer, 4 = good answer, 5 = absolutely correct. The field crews were responsible for considering every land cover type of the classification scheme at every polygon observed, then assigning a linguistic score to every land cover type that best matched their perception of what they observed for each polygon. The linguistic value or score  3 constituted an acceptable match between predicted and observed land cover types. Conventional accuracy assessment data were summarized using standard format error matrices where the columns represent the observed land cover type and the rows represent the predicted land cover type at a given taxonomic level. Conventional accuracy assessment statistics include overall accuracy, user’s accuracy, and producer’s accuracy by land cover type (Story & Congalton, 1986) and the Kappa coefficient (Congalton & Green, 1999). Conventional accuracy assessment statistics were generated at the super-alliance and subclass level of the classification scheme, and revised subclass level classification similar to Level I of the United States Geological Survey classification scheme (Anderson, Hardy, Roach, & Witmer, 1976). Fuzzy accuracy assessment statistics were generated using the MAX, RIGHT, DIFFERENCE, and MEMBERSHIP functions. The MAX function indicates an overall map accuracy much like that derived from a conventional error assessment. The RIGHT function indicates the frequency that a map class is an acceptable choice as calculated from the linguistic score assigned to each land cover type for each sample point. Those linguistic scores used to calculate MAX could only be of value 5 (absolutely correct or best answer). Those linguistic scores used to calculate RIGHT can be of value 3, 4, or 5 to qualify as a correct match between the observed and predicted land cover type. The quantifiable improvement in accuracy shown by the RIGHT function over the MAX function indicates the percentage of observations that had an acceptable answer (  3) but not the best answer ( = 5) (Gopal & Woodcock, 1995; Muller et al., 1998). For detailed classification schemes, the RIGHT function tends to provide more realistic map accuracy values related to functional map use and interpretation for biological conservation purposes.

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The DIFFERENCE function indicates the magnitude of the error between what land cover type was observed and what was predicted for each sample point based on the differences between linguistic scores. The function is calculated for the set of field observation for each land cover type by subtracting the linguistic score assigned to the predicted land cover type from the highest assigned fuzzy score among all the other land cover types. Ranging from a numeric value of + 4 to a value of 4 when using a linguistic scale of five, high positive or negative values indicate the magnitude of a correct or incorrect classification of a field plot, respectively. The MEMBERSHIP function indicates the frequency of set membership (match, M) or nonmembership (nonmatch, N ) of a land cover type for each field observation. Optimally, only one cover type should receive an acceptable linguistic score (  3) by field observers at each sample point (MEMBERSHIP = 1). In reality, what is observed in the field may fit in a number of classification categories if the differentiating characteristics of each category are not mutually exclusive with other categories. The higher the MEMBERSHIP value, the greater the apparent overlap and ambiguity in the differentiating characteristics within and between classification categories. This ambiguity will likely result in a given field plot assigned an acceptable linguistic score for MEMBERSHIP in more than one land cover type. The fuzzy accuracy assessment data were collected and tabulated at the super-alliance level, then aggregated to produce fuzzy accuracy assessment statistics at the subclass level. This required some aggregation of linguistic scores at

the more general taxonomic level of subclass. A look-up function was used to generate a nested table of land cover types at the super-alliance and subclass taxonomic levels. In this nested table, land cover types at the subclass level were given the highest fuzzy value found in the set of land cover types at the super-alliance level. For example, if two land cover types at the super-alliance level were given linguistic scores of 3 and 4, then the fuzzy score for the subclass within which the two land cover types occurred was recorded as 4.

3. Results and discussion 3.1. Land cover mapping Eleven land cover types were mapped at the subclass level and 29 land cover types were mapped at a combined alliance, super-alliance, and formation taxonomic level (Fig. 1). For reporting purposes, this latter taxonomic level is referred to simply as the super-alliance level. We taxonomically aggregated the 29 land cover types into their respective order and subclass taxonomic levels to facilitate data analyses and map production. Area and proportional extent of land cover types were summarized at the order and subclass levels (Table 2) and at the super-alliance level (Table 3). As mapped, the forest/woodland class dominates land cover in the state (63%). Forest cover types include deciduous forest (41%), mixed evergreen – deciduous forest (17%),

Fig. 1. Land cover map with distribution of accuracy assessment sampling areas.

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455 Table 2 Area and percentage of land cover types mapped at the NVCS order and NVCS subclass levels Land cover type

Area (km2)

Forest Forest/woodland evergreen Forest/woodland deciduous Forest/woodland mixed Shrub Shrub deciduous Dwarf shrub mixed Herbaceous Herbaceous perennial Herbaceous annual Sparse Water Built environment Spectral obstruction Total

79,373 6150 51,570 21,652 2175 2174 1 30,963 7363 23,600 116 6540 5666 551 125,384

Order %

Subclass %

63.3 4.9 41.1 17.3 1.7 1.7 <1 24.7

<1 5.2 4.5 <1 100.0

5.9 18.8 <1 5.2 4.5 <1 100.0

and evergreen forest (5%). In all, 12 forested land cover types at the super-alliance level were mapped. Herbaceous land cover types were the next most common, covering 25% of the state. Most of the herbaceous cover type was classified as cropland (19%) and old field/pasture (5%). The remainder of land cover was mapped as open water

Table 3 Area and percentage of 29 land cover types mapped at the super-alliance level Land cover type

Area (km2)

% of state

Spruce-fir Evergreen wetland Evergreen plantation Sugar maple mesic Oak Successional hardwoods Deciduous wetland Orchard/vineyard Appalachian oak – pine Pitch pine – oak Evergreen – northern hardwood Mixed wetland Successional shrub Alpine krummholz Shrub swamp Salt shrub/maritime shrubland Dwarf shrub bog Old field/pasture Alpine meadow/rock/heath summit Emergent marsh/open fen/wet meadow Salt marsh Golf course/park/lawn Cropland Sand dunes/flats Barren Open water Roads Urban Suburban Spectral obstructions (clouds, shadows) Total

4544 1129 478 32,088 7521 9611 2138 212 848 784 19,712 308 1524 <1 634 16 1 6673 <1 479 89 122 23,600 116 72 6540 999 4434 161 551 125,384

3.6 0.9 0.4 25.6 6.0 7.7 1.7 0.2 0.7 0.6 15.7 0.3 1.2 0.0 0.5 < 0.1 < 0.1 5.3 < 0.1 0.4 0.1 0.1 18.9 0.1 0.1 5.2 0.8 3.5 0.1 0.5 100.0

447

Table 4 Proportion of land cover in the study area estimated by four regional studies Land cover

NY-GAP

MRLCa

FIAb

ARMEc

Evergreen forest Deciduous forest Mixed forest Forest subtotal Woody wetland Emergent wetland Wetland subtotal Hay/pasture/old field Row crops/cropland Other herbaceous Herbaceous subtotal Shrub Barren Urban/suburban Water Other Total

4.0 39.5 17.0 60.5 3.4 0.5 3.9 5.3 18.9 0.1 24.3 1.2 0.2 4.4 5.2 0.3 100.0

6.2 38.6 18.2 63.0 2.6 0.3 2.9 19.5 5.5 0.8 25.8 – 0.1 5.4 2.8 – 100.0

5 40 17 62 – – – – – – – – – – – 38 100

– – – 64 – – – 4 17 – 21 – – – – 15 100

a b c

Vogelmann et al. (1998) and Zhu et al. (2000). Alerich and Drake (1995). Stanton and Bills (1996).

(5%), urban and suburban (4%), shrub (2%), and roads or sparsely vegetated land types (1%). The extent of land cover types mapped by this study was compared to three other regional studies that use a similar land cover classification (Table 4). These studies included: (1) the recently completed Multi-Resolution Land Characterization (MRLC) land cover map for the United States Environmental Protection Agency Region 2 (Vogelmann, Sohl, & Howard, 1998; Zhu, Yang, Stehman, & Czaplewski, 2000), (2) the summary of 1993 United States Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) data (Alerich & Drake, 1995), and (3) a study conducted by agricultural economists at Cornell University using county-based agricultural census data (Stanton & Bills, 1996). Overall, the proportion of forested and nonforested land, excluding wetland cover types, for the state derived from our land cover map (61% forested to 39% nonforested) corresponds well with all three regional studies. Evergreen, deciduous, and mixed forest cover types also correspond well between our map and the MRLC map and the FIA sample plot data. The old field/pasture and the cropland cover types were comparable to the Stanton and Bills estimates, but differ significantly from the MRLC data. This difference is likely due to differences in land cover type definitions. The MRLC hay/pasture cover type includes land used to grow seed and hay crops whereas this type of land cover is represented in both our map and Stanton and Bills study as a cropland cover type. 3.2. Conventional accuracy assessment A total of 9745 polygons representing 26 of 29 land cover types at the super-alliance level were surveyed by

448

Table 5 Conventional accuracy assessment of the regional land cover map at the super-alliance level Predicted

Observed data set (field data) Subclass code 1100

Land cover type

3

4

5

1300 6

7

8

9

3100

10

11

12

13

20

5100 22

24 0 0 0

1 3 4 5 6 7 8 9 10 11 12

67 22 0 2 0 0 3 0 0 0 30

13 26 0 2 0 0 2 0 0 0 5

3 2 57 14 2 7 0 0 13 0 142

26 6 0 554 91 167 33 0 21 0 205

1 0 1 186 241 94 17 0 32 25 67

0 1 2 325 94 455 47 0 10 2 119

2 4 4 51 14 46 96 0 7 3 49

0 0 0 0 2 0 0 4 0 0 0

0 0 0 3 4 4 0 0 5 0 3

0 0 0 0 1 1 0 0 0 49 0

195 79 36 196 91 151 35 0 71 3 1205

4 1 0 1 0 1 4 0 0 0 5

0 0 0 37 9 27 3 0 0 1 12

0 5 0 11 0 10 14 0 0 0 15

13 20 22 24

0 0 4 0

0 0 7 0

3 2 0 0

5 17 7 0

0 11 0 0

5 72 12 0

2 8 8 0

0 0 0 0

0 0 0 0

0 2 0 0

44 17 7 0

1 0 1 0

1 42 1 0

30 31

0 1

0 1

2 3

16 0

12 0

38 4

8 10

1 0

0 0

0 0

4 8

0 3

32 36 37 43 45 50 60 61 62

0 0 0 0 0 1 0 0 0 130 52

0 0 2 0 0 0 0 0 0 58 45

0 0 3 0 0 0 0 0 0 253 23

0 1 17 0 0 0 0 0 0 1166 48

0 0 18 0 0 0 0 1 1 707 34

0 0 29 0 0 0 0 0 0 1215 37

0 0 3 0 0 0 0 0 0 315 30

0 0 4 0 0 0 0 0 0 11 36

0 0 0 0 0 0 0 0 0 19 26

0 0 2 1 0 0 0 0 0 56 88

0 0 11 0 0 3 0 1 0 2157 56

0 0 0 0 0 0 0 0 0 21 5

Total correct = 4091; overall accuracy (%) = 42.0; Kappa=.345.

30

31

32

36

5400

6300

37

43

45

7000

8000

50

60

61

62

Row total

User’s (%)

0 0 0 0 0 0 0

2 0 0 172 41 108 22 0 11 0 29

8 6 0 2 1 4 28 0 1 0 9

0 0 0 0 0 0 0 0 0 2 0

0 0 0 1 1 0 0 0 1 0 0

1 0 1 124 25 74 15 0 0 0 13

0 0 0 0 0 0 0 0 0 1 0

0 0 0 1 0 0 0 0 1 0 0

2 7 0 3 2 2 27 0 1 1 5

0 0 0 2 0 0 2 0 0 0 4

0 0 0 13 5 4 1 0 1 9 2

0 1 0 22 23 33 6 0 14 28 10

324 160 101 1722 647 1188 355 4 188 125 1929

21 16 56 32 37 38 27 0 0 40 62

1 0 10 0

0 2 0 0

3 72 12 0

2 1 8 0

0 3 0 0

0 0 0 0

1 55 20 0

0 0 0 0

0 0 2 0

2 1 7 0

0 4 1 0

2 18 2 0

4 32 5 0

76 359 115 0

1 12 9 0

35 5

1 2

5 0

406 13

0 26

0 0

9 0

230 16

2 0

0 2

0 8

6 2

20 1

53 0

848 105

48 25

0 0 21 0 0 0 0 1 0 195 22

0 0 2 0 0 0 0 0 0 71 14

1 0 0 0 0 0 2 1 0 11 0

0 6 334 1 3 0 0 3 1 1240 33

0 1 12 0 0 5 0 0 0 114 23

30 0 0 0 0 0 0 0 0 35 86

1 9 11 0 0 0 0 0 0 33 27

0 0 610 0 0 0 0 0 0 1185 51

0 0 0 7 0 1 0 0 0 11 64

0 0 2 0 1 0 0 0 0 9 0

0 0 1 0 0 121 0 0 0 190 64

0 0 2 1 0 0 12 12 3 51 24

0 0 33 13 8 0 0 35 3 170 21

2 1 39 5 2 0 0 20 22 322 7

34 18 1156 28 14 131 14 74 30 9745

88 0 53 25 7 92 86 47 73

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455

Spruce-fir Evergreen wetland Evergreen plantation Sugar maple mesic Oak Successional hardwoods Deciduous wetland Orchard/vineyard Appalachian oak – pine Pitch pine – oak Evergreen – northern hardwood Mixed wetland Successional shrub Shrub swamp Salt shrub/maritime shrubland Old field/pasture Emergent marsh/open fen/wet meadow Salt marsh Golf course/park/lawn Cropland Sand dunes/flats Barren Open water Road Urban Suburban Column total Producer’s (%)

1

1200

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455

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Table 6 Conventional accuracy assessment of the regional land cover map at the subclass level Predicted data set (map data)

Observed data set (field data)

Land cover type

Code

1100

1200

1300

3100

5100

5400

6300

Evergreen forest Deciduous forest Mixed forest Deciduous shrubland Herbaceous perennial Herbaceous annual Sparse vegetation Water Built environment Column total Producer’s (%)

1100 1200 1300 3100 5100 5400 6300 7000 8000

190 32 193 13 7 5 0 1

47 2517 552 135 90 71 0 0 2 3414 74

315 492 1386 27 15 13 1 3 1 2253 62

5 111 30 55 49 23 0 0 4 277 20

16 380 58 97 501 357 1 5 7 1422 35

2 238 14 75 246 610 0 0 0 1185 51

1 2 2 4 2 7 1 1 20 35

441 43

7000

8000

Row total

User’s (%)

9 34 9 8 8 1 0 121

1 111 74 62 85 74 19 0 117 543 22

585 3916 2318 474 1005 1156 28 131 132 9745

32 64 60 12 50 53 25 92 89

190 64

Total correct = 5504; overall accuracy (%) = 56.5; Kappa = .435.

field crews who succeeded in observing over 95% of the originally selected polygons either visually (61%) or through aerial photo interpretation (34%). A total of 933 polygons representing 22 of 29 land cover types at the superalliance level were observed and assigned linguistic scores for the fuzzy accuracy assessment process. The distribution of accuracy assessment sampling areas is shown in Fig. 1. Regional conventional accuracy assessments for land cover types reported at super-alliance, subclass, and revised subclass are presented in Tables 5, 6, and 7, respectively. Overall accuracies of land cover types mapped at superalliance, subclass, and revised subclass were 42.0% (Kappa = .345), 56.5% (Kappa=.435), and 74.4% (Kappa = .549), respectively. Data summarized at subclass and revised subclass levels were aggregated from data in the super-alliance table. The type and pattern of land cover mapping errors for the regional land cover map are characterized by examining the data tabulated in the off-diagonal cells of the error matrices. The pattern of these errors is similar at super-alliance, subclass, and revised subclass levels due to data aggregation methods and the hierarchical structure of the classification scheme. Because of these similar error patterns, only those associated with the super-alliance classification level will be discussed.

At each taxonomic level, mapping errors occurred due to the limited ability of image analysts to consistently and unambiguously discriminate (1) evergreen forest types from mixed forest types, (2) deciduous forest types from each other, (3) deciduous forest types from mixed forest types, and (4) successional forest types from old fields, pastures, and cropland. These mapping errors result directly both from the mislabeling of spectral clusters due to a phenomenon known as spectral confusion and from the variation in time when satellite imagery, ancillary data, and field observations were acquired. Spectral confusion arises either when several land cover types have a similar multispectral response, or reflectance, or when one land cover type has a wide variety of multispectral responses in different locations due to regional differences in phenological development, biophysical conditions, or cultural land use practices. Satellite imagery was acquired during the early 1990s during the spring season when there was much variation in phenological development of vegetative cover types. Image analysts attempted to compensate for variations in phenological development by assuming land cover types within or in proximity to forested areas exhibiting high albedo were deciduous vegetation types under leaf-off conditions. In reality, many of these areas could have been old fields, pastures, cropland, or early successional vegetation types

Table 7 Conventional accuracy assessment of the regional land cover map summarized at the revised subclass level Predicted data set (map data)

Observed data set (field data)

Land cover type Agricultural lands Developed lands Forested lands Wetlands Shrub/barren Water Column total Producer’s (%)

1 2 3 4 5 6

Agricultural

Developed

Forest

Wetlands

Shrub

Water

Row total

User’s (%)

1589 10 603 103 131 0 2436 65

173 117 173 30 83 0 576 20

152 4 5073 348 122 4 5703 89

28 1 270 298 12 5 614 49

65 4 89 15 52 1 226 23

1 0 16 51 1 121 190 64

2008 136 6224 845 401 131 9745

79.1 86.0 81.5 35.3 13.0 92.4

Total correct = 7250; overall accuracy (%) = 74.4; Kappa=.549.

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Table 8 Comparison of producer’s accuracies for selected NY-GAP and MRLC land cover types Land cover type (NY-GAP)

NY-GAP

MRLCa

Class (MRLC)

Evergreen forest/woodland Deciduous forest/woodland Mixed forest/woodland Cropland Old field/pasture Golf course/park/lawn Sand dunes/flats

43.1 73.7 61.5 51.5 32.7 27.3 63.6

38.6 79.9 72.3 51.4 45.3 27.5 18.4

conifer deciduous mixed forest row crop hay/pasture other grass sand beach

a

the color infrared aerial photos used for cluster labeling within the Adirondack Park were acquired during leaf-off or just as leaves were appearing. The image analysts typically assumed that areas on the aerial photos light red and pink in color were evergreen forests (e.g., spruce-fir land cover type). In contrast, when the accuracy assessment field crew visited the same site during the summer, the deciduous trees were more conspicuous and they were more inclined to label such areas as the mixed forest land cover types. During the cluster labeling process, more clusters were labeled as the sugar maple mesic forest land cover type because the deciduous forest land cover types can be comprised of 10– 15% conifers such as eastern white pine. The field crews were more apt to label these forest stands as one of the mixed forest types if conifer trees were present. This same phenomenon occurred between evergreen –northern hardwood and successional hardwood land cover types.

From Table 3 of Zhu et al. (2000).

dominated by herbs, forbs, and shrubs that characteristically exhibit high albedo at various stages of development. Much of the spectral confusion between forest cover types can be attributed to the variability in an image analyst’s perception of a land cover type and how that perception may change with time and in space. For example,

Table 9 Results of the MAX and RIGHT functions at the subclass and super-alliance levels MAX (M )

best answer

RIGHT (R)

correct

Subclass number

Subclass

Sites

Sample %

Matches

Mismatches

Matches

Mismatches

Improvement (R M )

1100 1200 1300 3100 5100 5400 6300 7000 8000 Total

forest/woodland evergreen forest/woodland deciduous forest/woodland mixed shrub deciduous herbaceous perennial herbaceous annual sparse vegetation water built environment

71 335 262 44 103 92 3 17 6 933

7.6 35.9 28.1 4.7 11.0 9.9 0.3 1.8 0.6 100.0

40 213 142 8 63 45 3 15 6 535

31 122 120 36 40 47 0 2 0 398

45 244 181 10 75 55 3 15 6 634

26 91 81 34 28 37 0 2 0 299

5 31 39 2 12 10 0 0 0 99

Super-alliance

Sites

Sample %

Matches

spruce-fir evergreen wetland evergreen plantation sugar maple mesic oak successional hardwoods deciduous wetland Appalachian oak – pine pitch pine – oak evergreen – northern hardwood mixed wetland successional shrub shrub swamp old field/pasture emergent marsh/open fen/wet meadow salt marsh cropland sand dunes/flats open water roads urban suburban

32 24 15 155 66 83 31 17 28 215

3.4 2.6 1.6 16.6 7.1 8.9 3.3 1.8 3.0 23.0

10 10 10 49 28 23 6 0 11 121

2 35 9 66 17

0.2 3.8 1.0 7.1 1.8

20 92 3 17 3 1 2 933

2.1 9.9 0.3 1.8 0.3 0.1 0.2 100.0

Super-alliance number 1 3 4 5 6 7 8 10 11 12 13 20 22 30 31 32 37 43 50 60 61 62 Total

56% 64% 54% 18% 61% 49% 100% 88% 100% 57%

MAX (M )

44% 36% 46% 82% 39% 51% 0% 12% 0% 43%

best answer

63% 73% 69% 23% 73% 60% 100% 88% 100% 68%

RIGHT (R)

37% 27% 31% 77% 27% 40% 0% 12% 0% 32%

correct Mismatches

7% 9% 15% 5% 12% 11% 0% 0% 0% 11%

Improvement (R M )

Mismatches

Matches

31% 42% 67% 32% 42% 28% 19% 0% 39% 56%

22 14 5 106 38 60 25 17 17 94

69% 58% 33% 68% 58% 72% 81% 100% 61% 44%

14 11 11 62 33 29 9 0 17 151

44% 46% 73% 40% 50% 35% 29% 0% 61% 70%

18 13 4 93 33 54 22 17 11 64

56% 54% 27% 60% 50% 65% 71% 100% 39% 30%

4 1 1 13 5 6 3 0 6 30

13% 4% 7% 8% 8% 7% 10% 0% 21% 14%

0 3 1 36 5

0% 9% 11% 55% 29%

2 32 8 30 12

100% 91% 89% 45% 71%

0 5 1 47 6

0% 14% 11% 71% 35%

2 30 8 19 11

100% 86% 89% 29% 65%

0 2 0 11 1

0% 6% 0% 17% 6%

17 45 3 15 3 1 2 399

85% 49% 100% 88% 100% 100% 100% 43%

3 47 0 2 0 0 0 534

15% 51% 0% 12% 0% 0% 0% 57%

17 55 3 15 3 1 2 492

85% 60% 100% 88% 100% 100% 100% 53%

3 37 0 2 0 0 0 441

15% 40% 0% 12% 0% 0% 0% 47%

0 10 0 0 0 0 0 93

0% 11% 0% 0% 0% 0% 0% 10%

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455

451

albedo. This high albedo of a surface can also be interpreted as being an urban or built-up area on a satellite image during leaf-off conditions. The very small sample size could also lead to poor precision in the estimate of error. The successional shrub land cover type was spectrally confused with several deciduous forest land cover types resulting in a producer’s accuracy of only 22%. This low producer’s accuracy is due, in part, to the nature of field data collection methods used by our accuracy assessment field crews. The difficulties associated with assessing the dominant land cover type of a 4-ha polygon in a landscape in which land cover types change at high spatial frequency are amplified when the field crews are unable to observe the

The suburban and urban land cover types both had extremely low producer’s accuracies of 7% and 21%, respectively. This error is due not only to the difficulties the image analysts faced in separating wooded urban areas from forested areas but also to the amount of time that lapsed between the acquisition dates for the Landsat imagery (1991 – 1993) and the accuracy assessment data (1995 –1999). Areas that the field crew labeled urban or suburban in 1999 could have been forest, shrub, or agriculture in 1991. The effect of phenological stage of vegetative development may have played a role as well. When deciduous vegetation is in leaf-off condition as when the satellite imagery was acquired, the surface has relatively high

Table 10 Results of the DIFFERENCE function at the subclass and super-alliance levels Mismatches Subclass number Subclass 1100 1200 1300 3100 5100 5400 6300 7000 8000 Total % of Total Super-alliance number 1.0 3.0 4.0 5.0 6.0 7.0 8.0 10.0 11.0 12.0 13.0 20.0 22.0 30.0 31.0 32.0 37.0 43.0 50.0 60.0 61.0 62.0 Total % of Total

forest/woodland evergreen forest/woodland deciduous forest/woodland mixed shrub deciduous herbaceous perennial herbaceous annual sparse vegetation water built environment

Matches 2

1

4

3

71

21

5

3

2

2

10

10

9

9

3.45

2.33

335

43

32

19

28

13

39

34

66

61

2.74

2.58

262 44 103 92 3 17 6 933

28 16 12 13 0 1 0 134 14.4

2.57 3.25 2.73 2.81 0.00 3.50 0.00 2.80

2.64 2.88 2.87 2.38 3.00 3.73 3.33 2.64

Sites

spruce-fir 32 evergreen wetland 24 evergreen plantation 15 sugar maple mesic 155 oak 66 successional hardwoods 83 deciduous wetland 31 Appalachian oak – pine 17 pitch pine – oak 28 evergreen – northern 215 hardwood mixed wetland 2 successional shrub 35 shrub swamp 9 old field/pasture 66 emergent marsh/open 17 fen/wet meadow salt marsh 20 cropland 92 sand dunes/flats 3 open water 17 roads 3 urban 1 suburban 2 933

1

2

3

4

38 28 26 4 18 35 53 32 14 5 1 0 0 1 7 0 11 11 6 5 8 9 9 32 15 16 3 1 13 11 8 12 0 0 0 0 1 0 0 0 1 0 0 0 0 2 0 13 0 0 0 0 0 2 0 4 116 82 66 25 89 104 152 165 12.4 8.8 7.1 2.7 9.5 11.1 16.3 17.7

Mismatches Super-alliance

0

Arithmetic mean Arithmetic mean of mismatches of matches

Sites

Matches

4

3

2

15 6 3 37 16 22 10 11 5 22

2 6 1 46 14 21 10 6 4 28

5 1 0 9 4 14 2 0 3 25

1 10 6 8 8

1 16 2 7 2

0 5 0 9 2

1

1

0 1 1 14 4 3 3 0 5 19

1 0 0 3 1 5 1 0 11 4

6 6 3 16 8 8 2 0 0 17

3 4 1 7 7 3 1 0 0 32

0 0 1 10 7 3 1 0 0 40

0 0 5 13 5 4 1 0 0 28

3.45 3.21 3.20 3.00 3.11 3.03 3.08 3.65 2.53 2.56

1.20 1.40 2.80 2.29 2.25 1.70 1.83 0.00 0.00 2.59

0 1 0 6 0

0 2 0 5 0

0 0 1 6 0

0 1 0 6 2

0 0 0 9 0

0 0 0 10 3

3.50 3.09 3.75 2.57 3.50

0.00 0.67 1.00 2.36 3.20

0 11 0 2 1 0 1 82 8.8

16 8 0 0 0 1 0 96 10.3

0 12 2 13 2 0 1 99 10.6

3.00 2.81 0.00 3.50 0.00 0.00 0.00 2.96

2.88 2.38 3.00 3.73 3.33 3.00 3.00 2.35

0 3 0 0 0 1 13 15 16 3 1 13 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 194 185 95 60 34 88 20.8 19.8 10.2 6.4 3.6 9.4

2

3

4

Arithmetic mean Arithmetic mean of mismatches of matches

0

452

Table 11 Results of the MEMBERSHIP function at the subclass and super-alliance levels 0 Subclass

1100

forest/woodland evergreen forest/woodland deciduous forest/woodland mixed shrub deciduous herbaceous perennial herbaceous annual sparse vegetation water built environment

1200 1300 3100 5100 5400 6300 7000 8000 Total % of Total Sites Super-alliance number 1.0 3.0 4.0 5.0 6.0 7.0 8.0 10.0 11.0 12.0 13.0 20.0 22.0 30.0 31.0 32.0 37.0 43.0 50.0 60.0 61.0 62.0 Total % of Total Sites

1 N

2 M

N

M

71

0

0

0

49

25

24

21

14

7

1

1

333

2

2

0

217

153

64

107

55

52

9

262

0

0

0

166

107

59

85

34

51

11

44 101 92 3 17 6 929 0.4%

0 2 0 0 0 0 4 0.4%

0 2 0 0 0 0 4 0.0%

0 0 0 0 0 0 0 63.1%

25 62 46 2 15 4 586 40.3%

3 45 22 2 13 4 374 22.8%

22 17 24 0 2 0 212 33.6%

N

M

N

1

16 33 45 1 2 2 312 15.7%

M

N

T

3 12 23 1 2 2 146 17.9%

13 21 22 0 0 0 166 3.2%

3 5 1 0 0 0 30 1.1%

M

N

T

2

M

N

T

M

N

0

0

0

0

3

6

0

0

0

1

10

0

0

0

2 3 0 0 0 0 10 2.2%

1 2 1 0 0 0 20 0.1%

0 1 0 0 0 0 1 0.1%

0 1 0 0 0 0 1 0.0%

0 0 0 0 0 0 0

M

N

T

M

N

3

Sites

T

M

spruce-fir evergreen wetland evergreen plantation sugar maple mesic oak successional hardwoods deciduous wetland Appalachian oak – pine pitch pine – oak evergreen – n. hardwood mixed wetland successional shrub shrub swamp old field/pasture emergent marsh/open fen/wet meadow salt marsh cropland sand dunes/flats open water roads urban suburban

32 24 15 153 66 83

0 0 0 2 0 0

0 0 0 2 0 0

0 0 0 0 0 0

15 7 10 74 34 38

0 0 6 26 16 9

15 7 4 48 18 29

16 15 5 69 27 40

10 9 4 17 10 14

6 6 1 52 17 26

1 2 0 8 5 5

0 1 0 2 2 0

1 1 0 6 3 5

0 0 0 2 0 0

0 0 0 2 0 0

0 0 0 0 0 0

31 17

0 0

0 0

0 0

14 12

2 0

12 12

15 5

3 0

12 5

2 0

1 0

1 0

0 0

0 0

0 0

28 215

0 0

0 0

0 0

13 120

9 88

4 32

10 83

2 31

8 52

5 9

0 2

5 7

0 3

0 1

0 2

2 35 9 64 17

0 0 0 2 0

0 0 0 2 0

0 0 0 0 0

1 17 6 31 13

0 1 0 21 4

1 16 6 10 9

1 16 2 26 3

0 1 1 9 1

1 15 1 17 2

0 2 1 5 1

0 1 0 3 0

0 1 1 2 1

0 0 0 2 0

0 0 0 1 0

0 0 0 1 0

0 0 0 0 0 0 0 4 0.43%

0 0 0 0 0 0 0 4 0.00%

0 0 0 0 0 0 0 7 0.43%

0 0 0 0 0 0 0 4 0.32%

0 0 0 0 0 0 0 3

0 0 0 0 0 0 0 0 52.74%

18 46 2 15 2 1 1 490 25.73%

16 22 2 13 2 1 1 239 27.02%

T = total number of sites in each group; M = number of matched sites; N = number of mismatched sites.

2 24 0 2 0 0 0 251 41.33%

T

4

Super-alliance

20 92 3 17 3 1 2 929 0.43%

T

T

4

T

0

T

3

Sites

1 45 1 2 1 0 1 384 15.07%

0 23 1 2 1 0 1 140 26.26%

1 22 0 0 0 0 0 244 5.17%

1 1 0 0 0 0 0 48 1.40%

1 0 0 0 0 0 0 13 3.77%

0 1 0 0 0 0 0 35 0.75%

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455

Subclass number

M. Laba et al. / Remote Sensing of Environment 81 (2002) 443–455

full extent of a sample unit. Continuous successional transitions of land cover types exist throughout the region at various spatial scales. The aggregations of these land cover types from an original minimum mapping unit of 30 meters square (900 m2) to a maximum of 4 ha could be a source of error as well. At the revised subclass level, producer’s accuracies for agriculture, forested lands, and water were generally acceptable. However, producer’s accuracies for developed lands and wetlands were low. The table shows that developed lands were mislabeled as both agricultural lands and forested lands while wetlands were misclassified as forested lands. As described previously, these errors result directly from spectral confusion and variations in the phenological development of vegetated land cover types throughout the state due to the different times the satellite imagery, ancillary data, and field observations were acquired. The recently conducted accuracy assessment of the MRLC land cover map for New York and New Jersey (Zhu et al., 2000) permitted a general comparison of map accuracies for selected land cover types (Table 8). The classification schemes are different in many respects, but they do permit comparisons for those cover types mapped by both studies. The dates of satellite imagery used by MRLC were similar but analytical and accuracy assessment methods varied somewhat from those employed by our study. Despite these analytical and methodological differences, the range of producer’s accuracies for these major land cover types was very similar. Both projects were relatively successful in accurately mapping deciduous and mixed forest cover types, but experienced difficulty in mapping evergreen forest, agricultural, and some herbaceous cover types. Many of these errors can be attributed to spectral confusion as previously discussed. In addition, some error can be attributed to those sample units in which both projects used aerial photo interpretation as the primary information source for field observations. The accuracy assessments of several recently completed regional-scale land cover mapping projects indicate that producer’s and user’s accuracies are stabilizing in the 50 –70% range, independent of level of taxonomic detail or methodological approaches (Edwards, Moisen, & Cutler, 1998; Ma, Hart, & Redmond, 2001; Zhu et al., 2000). In our view, additional improvements in accuracy are not likely, and that only through the use of sensors with high spectral, spatial, and temporal resolution will map accuracies approach 80%. In addition, the traditional, and artificial, target of 85% overall percent correct should not be used as a criterion to measure success or failure of a land cover mapping project. 3.3. Fuzzy accuracy assessment At the subclass level, the MAX function indicates an overall map accuracy of 57% while the RIGHT function shows a 19% improvement to 68% (Table 9). Individual

453

categories improved from 0% to 15%. At the super-alliance level, the MAX function indicates an overall map accuracy of 43% while the RIGHT function shows a 23% improvement to 53%. Individual categories improved from 0% to 21%. If the sample size was equal for both conventional and fuzzy accuracy assessment, the MAX function would yield the same value as conventional accuracy assessment for overall map accuracies at both the subclass and superalliance levels, 57% and 42%, respectively (Gopal & Woodcock, 1995). The MAX function indicates an overall map accuracy much like that derived from a conventional error matrix while the RIGHT function indicates what would be acceptable to a person using the map in the field for conservation biology purposes. The improvement in accuracy shown by the RIGHT function over the MAX function indicates the percentage of observations that had an acceptable, but not the best, answer. If the degree of improvement is considerable even though the MAX function indicates a land cover type difficult to map, then the RIGHT function indicates that the effect on the map user may not be as large as the MAX function suggests (Gopal & Woodcock, 1995). The results of the DIFFERENCE function at the subclass and super-alliance levels are shown in Table 10. Optimally, the goal is to have DIFFERENCE values of + 4 for each land cover type over all sample site occurrences of that land cover type (mean of matches = 4.0) that would indicate that there are clear distinctions between land cover types as observed in the field and as predicted, or mapped, using satellite imagery. At the subclass level, 57% of the field sites are matches, or 57% of the field sites were given fuzzy scores that were equal to or higher than any other fuzzy score given to that particular site. Conversely, the large number of maximum mismatches ( 4 scores) for major land cover types is disconcerting. The magnitude of these mismatches indicates that there was a significant analyst error in cluster labeling, in georeferencing field observations with the satellite spectral data, or lack of understanding of the relationships between land cover type and prevailing biophysical conditions at selected locations. The nature of the mismatches also indicates that there was little ambiguity or uncertainty in the appropriate land cover conditions at each plot. What is not obvious in the interpretation of the DIFFERENCE metric is whether the mismatch occurred between two similar or dissimilar land cover types independent of the magnitude of the difference. The results from the MEMBERSHIP function at the subclass and super-alliance levels are shown in Table 11. At the subclass level, over 96% of fuzzy accuracy field observations had MEMBERSHIP values of 2 or less indicating land cover types were clearly defined and presented little confusion to field crews when assigning a linguistic score. Sixty-three percent of all samples were members of only one land cover type while 33% were

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members of two land cover types. There were four field sites that were not members in any land cover types, which results when the field site is assigned a linguistic score of 2 or less for all land cover types. The remaining 4% were divided between three and four land cover types. At the super-alliance level, over 94% of fuzzy accuracy field observations had MEMBERSHIP values of 2 or less. Fifty-three percent of all samples were members of only one land cover type while 41% were members of two land cover types. The remaining 6% were divided between three and four land cover types.

4. Summary Based on this study, further interpretation and discussion of fuzzy accuracy assessment methods need to occur within the remote sensing community to better understand their significance and value beyond the metrics provided by conventional accuracy assessment methods. Based on the initial experience in this study, conventional accuracy assessment should serve as the primary source of information on the quality of land cover maps. If the taxonomic detail of the classification scheme exceeds the information requirement for a given application or there is uncertainty on the quality and ambiguity associated with a given classification scheme, then fuzzy accuracy assessment methods should be considered. Both conventional and fuzzy accuracy assessment statistics presented for this regional-scale land cover map are disappointing and indicate that significant improvements are needed in the characterization and mapping of several land cover types in the region using satellite imagery. Visual inspection of the spatial distribution of land cover types throughout the region appear to represent well the occurrence of these types as do the data indicating the proportional extent by major cover types in comparison to other regional studies. In the future, allocation and location of samples should involve more actively project stakeholders who have a greater level of field-based knowledge concerning environmental resources under variable edaphic conditions. Though beyond the scope of this study, the propagation of errors from the land cover mapping process through the habitat modeling and gap analysis process should be investigated to convey fully the error status of GAP-related maps. Given the general habitat requirements of many vertebrates in the study area, the low land cover map accuracies at high taxonomic resolution should not adversely affect nor lower the quality of habitat prediction models based in part on the land cover mapping effort. The major errors of omission and commission were for those land cover types that provide similar habitat values to many vertebrates in the study area, thus minimizing somewhat the impact of low overall map accuracies at the three classification levels used in this study.

Acknowledgments This study was supported by the New York Gap Analysis Project, Research Work Orders 16 and 35, National Gap Analysis Program, Biological Resources Division, United States Geological Survey. Grateful acknowledgment is extended to Stephen D. Smith and staff, Cornell Institute for Resource Information Systems, Anna M. Stalter, Jason Beecher, Robert Elliot, and Joseph T. Weber for their technical assistance, and Charles R. Smith and Milo E. Richmond, New York Cooperative Fish and Wildlife Research Unit, Cornell University, for their leadership, scientific and technical contributions, and guidance throughout the course of this study.

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