In cooperation with the Alabama Department of Conservation and Natural Resources, 

Mobile Bay National Estuary Program, National Oceanic and Atmospheric Administration, 

and U.S. Environmental Protection Agency 

Comparison of NLCD with NWI Classifications of Baldwin 

and Mobile Counties, Alabama 

By Larry Handley and Chris Wells 

Open-File Report 2009–1058 

U.S. Department of the Interior 

U.S. Geological Survey 

 i
 ii

U.S. Department of the Interior 

KEN SALAZAR, Secretary 

U.S. Geological Survey 

Suzette M. Kimball, Acting Director 

U.S. Geological Survey, Reston, Virginia: 2009 

This and other USGS information products are available at http://store.usgs.gov/ 

U.S. Geological Survey 

Box 25286, Denver Federal Center 

Denver, CO 80225 

To learn about the USGS and its information products visit http://www.usgs.gov/ 

1–888–ASK–USGS 

Suggested citation: 

Handley, L., and Wells, C., 2009, Comparison of NLCD with NWI Classifications of Baldwin and Mobile Counties, 

Alabama: U.S. Geological Survey Open-File Report 2009–1058. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 
 iii

Contents 

Introduction.................................................................................................................................................................... 1

Accuracy of NWI by Ground-Based Check Sites........................................................................................................... 1

Analysis ..................................................................................................................................................................... 3

Results of NWI as Compared to Check Sites.............................................................................................................. 3

Cross-Walking NWI and NLCD Classification Schemas................................................................................................ 4

Comparison of NLCD with Check Sites ......................................................................................................................... 5

NLCD Accuracy as Compared to NWI........................................................................................................................... 5

Polygon Labeling Accuracy......................................................................................................................................... 6

Discussion ..................................................................................................................................................................... 9

Acknowledgments ........................................................................................................................................................10

References ...................................................................................................................................................................11 

Figures 

1. Representation of sampling method .......................................................................................................................... 2 

2. Expanded portion of Mobile Bay indicating the fundamental problem of 

boundary alignment between National Wetlands Inventory and 

National Land Cover Data ...............................................................................................................................10

Tables 

1. Habitat description and cross-walked classification codes for the 

National Land Cover Data and National Wetlands Inventory 

classification systems ....................................................................................................................................... 4 

2. Accuracy of National Land Cover Data map to check sites ....................................................................................... 5

3. Matrix indicating the class tested across the top and the cross-walked 

National Wetlands Inventory category down the left-hand column ................................................................... 7

4. Summary of National Land Cover Data versus National Wetlands Inventory 

in Mobile Bay.................................................................................................................................................... 8

5. Accuracy of National Land Cover Data as compared to National Wetlands 

Inventory maps of Baldwin and Mobile Counties, Ala....................................................................................... 9
Conversion Factors 

SI to Inch/Pound 

Multiply By To obtain 

Length 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

meter (m) 1.094 yard (yd) 

Area 

hectare (ha) 2.471 acre 

square meter (m2) 10.76 square foot (ft2) 

 iv
Comparison of NLCD with NWI Classifications of 

Baldwin and Mobile Counties, Alabama 

By Larry Handley and Chris Wells 

Introduction 

Mobile Bay National Estuary Program (MBNEP) requested an assessment of the 

accuracy of National Land Cover Data 2001 (NLCD 2001, compiled by the MultiResolution Land Characteristics Consortium) as compared to National Wetlands 

Inventory (NWI [Cowardin and others, 1979], to include uplands through incorporation 

of modified Anderson Level III definitions [Anderson and others, 1976]) mapping of 

Mobile and Baldwin Counties conducted by the U.S. Geological Survey’s (USGS) 

National Wetlands Research Center (NWRC). Both classifications were checked against 

stratified randomly selected ground-based sites and with each other to compare the 

accuracy of the NLCD with NWI classification methods. Previous to this comparison, we 

conducted an accuracy assessment of the modified NWI in Louisiana with an overall 

accuracy of 93 percent (Handley and others, 1995). 

For this accuracy comparison, numerous visits were made by photointerpreters to 

the Mobile Bay area to establish signatures for the modified NWI mapping. After all of 

the quadrangles in Baldwin and Mobile Counties were mapped, an accuracy assessment 

of those maps was conducted by field-checking the mapped classes with ground-based 

check sites. These same sites were used to check the accuracy of the NLCD and also as 

the basis for cross-walking the NLCD and NWI classification systems for direct 

comparison of the two methods. 

This accuracy assessment focused on the latest of several dates of habitat mapping 

projects of the Mobile Bay area by NWRC using the same photointerpretation 

methodology and classification system. We have used this methodology and NWI 

classification on numerous other dates and regions. Extrapolation beyond this data set is 

appropriate for maps of the same methodology and classification of the Mobile Bay 

region. This accuracy assessment provides analytical information about the classification 

accuracy of 2001/2002 NWI classification and the 2001 NLCD classification. 

Accuracy of NWI by Ground-Based Check Sites 

A purely random sampling of the area was not practical because of time and legal 

constraints on the USGS, which must have written permission to enter private property. 

Thus, all ground-based check sites (hereafter referred to as “check sites”) had to be 

publicly accessible and timely. For these reasons, all check sites were located adjacent to 

public roadways. 

1 
All roads in a geographic information system (GIS) layer of Baldwin and Mobile 

Counties greater than 5 km in length were stratified by county and within counties, by 

north and south of Interstate 10, resulting in four sampling areas. This sampling scheme 

facilitated wide distribution of sampled roads between counties and north (into the hills) 

and south (into the coastal flatland). Thus, the area was roughly subdivided for sampling 

into four quadrants. The interstate was chosen for rapid accessibility across both counties. 

The geographic area is so large that time and funding played a significant role in both 

ground-truthing and accuracy assessment. 

Selection of check sites within quadrants was accomplished by randomly ordering 

the roads greater than 5 km in length and then sequentially reading down the list. 

Removing shorter roads from consideration decreased the probability of entering urban 

areas, which have a disproportionately large number of short roads which would have 

biased the sample toward urban areas. From the beginning, a stop was made every 0.3 mi 

(measured by using the vehicle’s odometer in tenth-mile intervals, 0.1 mi = 0.26 km), and 

the opposite side of the road from the previous stop was evaluated. The side of the road 

for analysis at the beginning was randomly selected. 

Once at the sample point, a Global Positioning System (GPS) position was 

recorded for the position 100 m perpendicular to the road by using a laser distancemeasuring device connected to the GPS receiver (fig. 1). One observer classified this 

point. A second observer paced 100 m parallel to the road going forward, and a third 

observer paced 100 m parallel to the road going back. The second and third observers 

recorded their classifications of the land at their respective points. All three observers 

then evaluated the center position, came to a classification agreement, and recorded it on 

paper. Data for the map position, its polygon designation, all three of the observers’ 

classes, and the final consensual classification were recorded on a laptop. After the 

sample was completed, the observers drove another interval and repeated the procedure 

until the end of the road was reached. 

Figure 1. Representation of sampling method. See description in text for methodology. 

2 
Every evening in the field, the day’s observations were uploaded to the statistician 

at NWRC. The statistician determined whether the 95 percent confidence interval had 

been met for each level of the NWI hierarchy. From this information it could be 

determined how many days of observations in the field were required to attain the 

sampling confidence interval at each level of the classification hierarchy. 

Analysis 

After the requisite number of samples for the 95 percent confidence interval had 

been attained, accuracy was determined by comparing the NWI polygon definition of the 

map with the consensual ground sample. Accuracy was the total number correct of a 

given class divided by the total number in that class. 

The hierarchy was defined thus, from top to bottom: 

1. Upland versus wetland 

2. System 

3. Class 

4. Subclass 

5. Modifier 

A difficulty with the method was that the NWI photography was flown during the 

2000–2002 time period but that the ground sampling began in 2004 and was completed in 

2008. In that period of time, land was cleared for new houses and new roads; grasslands 

converted to farmland or scrub-shrub and scrub-shrub to forest. We addressed this 

complication by inferring what the area looked like 3–7 years earlier, as well as what we 

would call it at the date of ground observation. In the majority of instances the check sites 

appeared unchanged, and so, the delineation stood on its own. In a few cases a forest may 

have been clear-cut and replaced by a subdivision or other feature. 

Under these circumstances, a choice was made whether to ignore the check site, 

record it as incorrect, or second-guess the determination. The decision was made by 

consensus. Most often the site was skipped and was not included in the accuracy 

assessment. It was not recorded as incorrect unless it was certain that the original 

delineation itself was incorrect before the habitat change. On occasions when the original 

delineation was clearly correct it was recorded as such. 

Results of NWI as Compared to Check Sites 

A total of 195 accuracy assessment field checks were conducted, each with three 

independent observations and one consensual observation. The observations were 

roughly half in each county, and about half of each county was sampled in the north and 

about half in the south to ensure wide coverage of the samples. 

The class “upland versus wetland” consisted of only those two possibilities. By 

definition, all areas designated as urban or agriculture were uplands. The accuracy was 

evaluated at each level of the classification hierarchy. With the uppermost level, the 

analysis was one-way—we looked only at the errors of calling a habitat an upland when 

it was actually a wetland to determine whether we were missing wetlands. For the class 

“upland versus wetland,” accuracy was 98 percent; standard error was 1 percent. 

3 
“System” included all NWI system categories. There were no upland designations 

for “system.” Accuracy was 97 percent; standard error was 1 percent. 

“Class” consisted of 15 NWI and upland categories. Accuracy was 88 percent; 

standard error was 3 percent. 

“Subclass” consisted of eight NWI and upland categories. Accuracy was 88 

percent; standard error was 3 percent. 

“Modifier” is only found in the NWI. These designations resolve differences 

among seasonality, duration of saturation, and tidal frequency. Accuracy was 78 percent; 

standard error was 6 percent. 

Cross-Walking NWI and NLCD Classification Schemas 

Definitions of each category within each classification schema were compared. 

The NLCD categories have definitions that match various levels of NWI. Because of its 

complexity, NWI required collapsing lower levels of the hierarchy until the categories 

were sufficiently general to match the other schema. Even then the correspondence was 

not necessarily one to one, as some definitions defied cross-categorization. The resultant 

classification cross-walk is provided in table 1. 

Table 1. Habitat description and cross-walked classification codes for the National Land Cover 

Data (NLCD) and National Wetlands Inventory (NWI) classification systems. 

Description NLCD code NWI code (modified) 

Open water 11 Most E1, L1, P, R1, R2 with AB and UB) 

Developed, open space 21 UUp 

Developed, all densities 23 UU except UUp 

Barren land 31 UB (all) 

Unconsolidated shore 32 E2US, L2US, MUS, R1US, R2US 

Deciduous forest 41 UF6 (all) 

Evergreen forest 42 UF7 (all) 

Mixed forest 43 UF8 (all) 

Scrub/shrub 52 USS (all) 

Grassland/herbaceous 71 UR (all) 

Cultivated crops/pasture 82 UA 

Palustrine forest 91 PF (all) 

Palustrine scrub/shrub 92 PSS (all) 

Estuarine scrub/shrub 94 E2SS(all) 

Palustrine emergent (persistent) 96 PEM (all) 

Estuarine emergent 97 E2EM (all) 

The NLCD 2001 map was transformed to Universal Transverse Mercator (UTM) 

projection coordinates to match the NWI maps. The NLCD file was converted from a 

raster (*.img) file to a GRID file and then to a polygon (shape) file by using ArcMap 

(ESRI, Inc., Redlands, Calif.) GIS. During conversion, the original cell boundaries were 

maintained so that the derived polygon (shape) file perimeters matched the boundaries of 

the original raster file. In both files an additional column was added for the cross-walk to 

the NLCD. The NLCD classes had to be reduced somewhat in upland categories because 

our modified NWI does not have as many categories for uplands as does NLCD. Also, 

4 
there were some issues with the open-water categories that were resolved by collapsing 

the water categories of both NWI and NLCD. 

Comparison of NLCD with Check Sites 

We also compared the sites sampled for the NWI accuracy assessment (described 

above) to the NLCD classification. Of the 16 NLCD classes cross-walked, 14 had check 

sites associated with them that cross-walked to NLCD. For two of the NLCD classes, 

“estuarine scrub/shrub” and “developed open space,” there were no associated check 

sites. NLCD did not classify any “estuarine scrub/shrub” (code 94) or “unconsolidated 

shore” (code 32) in Mobile Bay. 

The NWI classes of the check sites were cross-walked to the NLCD classification 

by using the method in table 1. There was 38 percent agreement between the NLCD 

classes and the cross-walked NWI check site classifications. Agreement came in “open 

water” (9 percent), “developed, all densities” (48 percent), “evergreen forest” (41 

percent), “mixed forest” (15 percent), “scrub/shrub” (36 percent), “cultivated 

crops/pasture” (70 percent), and “palustrine forest” (46 percent). Check site accuracy is 

provided in table 2. 

Table 2. Accuracy of National Land Cover Data (NLCD) map to check sites. See text for 

description of check site sampling methodology. 

Check sites Check site 

Description Code per class accuracy 

Open water 11 11 9% 

Developed, all densities 23 31 48% 

Barren land 31 1 0% 

Deciduous forest 41 4 0% 

Evergreen forest 42 45 41% 

Mixed forest 43 13 15% 

Scrub/shrub 52 11 36% 

Grassland/herbaceous 71 8 0% 

Cultivated crops/pasture 82 10 70% 

Palustrine forest 91 48 46% 

Palustrine emergent (persistent) 96 10 0% 

Estuarine emergent 97 2 0% 

The average accuracy, excluding NLCD classes not sampled, was 27 percent. The 

small sample size in some NLCD classes may have increased the likelihood of sampling 

error having a greater effect. Unlike the NWI design for ground visits, no test was done to 

evaluate the effect of check site sampling size on NLCD classification. Consequently, no 

confidence limits are provided. 

NLCD Accuracy as Compared to NWI 

Two comparisons were made between NWI and NLCD. The first was a strict 

polygon labeling comparison, and the second was polygon labeling weighted by area. 

5 
6 

Polygon Labeling Accuracy 

NWI codes are hierarchical and have the potential for hundreds of combinations 

to describe a given polygon in Mobile Bay. There were a total of about 87,000 NWI 

polygons and 382 unique classifications in the two-county area. By comparison, NLCD 

(16 classes in Mobile Bay and 24,200,000 cells) is a far simpler classification system but 

has about 300 times the number of polygons. We simplified NWI by aggregating 382 

categories upward to match the 16 NLCD classes by using the cross-walk described in 

table 1. Adjacent NWI polygons with identical labels were combined into single 

polygons. Similarly, identical labels in adjacent NLCD polygons were combined into 

single polygons. 

A matrix detailing the origin and disposition of the NLCD area within the NWI 

polygons is provided in table 3. Obviously, there is strong agreement between NWI and 

the NLCD concerning “open water” (class 11). There is low to no agreement between 

NWI and NLCD in the category “unconsolidated shore” (class 32); in NLCD most of that 

category went to “open water.” 
7 

Table 3. Matrix indicating the class tested across the top (the column headers are the particular National Land Cover Data [

the classification) and the cross-walked National Wetlands Inventory (NWI) category down the left-hand column (again, thNLCD classes). 

[Units are in hectares. Ideally, all of the area should be found along the diagonal (top left to bottom right). The diagonals are indicated ivalues off-diagonal indicate the area (in hectares) misclassified as some habitat type other than the co-occurrence diagonal. The accurac“correct”) of the cross-walked classifications was derived by dividing the diagonal values (bold) by the row sums] 

NLCD vs. modified NWI

11 21 23 31 32 41 42 43 52 71 82 91 92 94 

11 212976 583 460 1409 0 7 491 51 324 187 388 2144 0 0 21 0 404 86 5 0 0 6 1 3 20 65 3 0 0 23 189 25465 21266 1255 0 76 5265 490 6938 2785 14970 3093 0 0 

31 113 217 338 880 0 1 67 10 190 271 638 86 0 0 

32 616 45 31 872 0 0 47 4 23 29 63 116 0 0 41 37 299 27 7 0 103 360 377 533 84 329 1723 0 0 

42 267 10064 937 194 0 1165 183276 20305 26899 3494 4991 45875 0 0 

43 26 1775 176 41 0 334 4387 2081 2544 390 1641 8847 0 0 

52 15 1319 283 101 0 370 8905 1394 18927 6355 2544 3111 0 0 

71 95 3697 1267 608 0 94 3195 452 9446 5023 11869 2305 0 0 

82 23 4336 797 93 0 24 1012 97 4064 3755 66992 1167 0 0 

91 1507 1167 185 57 0 288 28393 3092 1874 589 1186 85840 0 0 92 45 114 27 6 0 16 1910 140 269 100 89 5481 0 0 

94 10 13 10 5 0 0 14 0 3 3 0 254 0 0 

96 85 172 138 75 0 14 590 57 554 455 537 1791 0 0 

97 297 123 56 139 0 0 72 0 65 55 11 440 0 0 
The summary results of polygon labeling of a one-tailed comparison of NWI with 

NLCD are given in table 4. 

Table 4. Summary of National Land Cover Data versus National Wetlands Inventory in Mobile 

Bay. Excluding the absent classes (see text for explanation), accuracy increased to 43 percent. 

[stderr, standard error] 

mean stderr 

0.3186981 0.08551 

0.2914461 0.34595 95% confidence interval 

(95% of the values will lie between 0.2914 and 0.34595) 

There is about 32 percent agreement with 95 percent of the values falling within 

the interval 0.29–0.35. Perfect agreement would have a value of one. A brief scan of the 

“correct” column in table 3 indicates that some categories had very high agreement 

(NLCD versus NWI in category 11, “open water,” has a value of 0.96, very high 

agreement). At the other end, classes 32, 92, 94, and 97 had none correct. This result is 

due to the NLCD of Mobile Bay not classifying “unconsolidated shore,” “wetland 

scrub/shrub,” or “estuarine emergent.” The classification comparison in table 3 calculates 

the mean of the correct values column. Those four absent classes greatly bias the error. 

Removal of those columns from the calculation raises the agreement to about 43 percent 

(95 percent confidence interval was 0.38 to 0.47). 

The fundamental difference between NWI photography and interpretation 

compared to NLCD satellite imagery classification methodology resulted in maps with 

strikingly different lineaments of the boundaries even when they fundamentally agreed 

on a classification. The NLCD boundaries zigzagged, while the NWI boundaries were 

smoothly curved. Thus, line overlap results in abundant sliver polygons as the NWI line 

overlays the zigzagged NLCD line. There are various methods for smoothing NLCD, but 

they result in compounding errors during interpolation. 

The decision was made to leave the boundaries as they were in the original maps 

and deal with the boundary alignment during analysis. In this first analysis, all polygons 

received equal weight. This method resulted in sliver polygons having weight equal to 

large polygons. A review of polygon areas revealed that almost half of the polygons were 

less than 0.1 ha, which is about the size of a 30- by 30-m pixel in NLCD. Thus, about 

half of the pixels were slivers, probably produced as an artifact of the map type 

difference: raster versus vector. The pixels were retained for the polygon labeling 

accuracy assessment. Sliver polygons were created most frequently from polygons with 

complicated boundaries, especially those with large perimeter to area ratios (common 

with rivers and streamside wetlands). 

We completed another analysis in which polygon area weighted the results (table 

5). In this case, polygons with larger areas had a proportionately larger influence on the 

comparison than did smaller polygons. By accounting for area, the overall accuracy 

doubled to about 63 percent. Though the analysis of the individual contributions of the 

habitats is incomplete, we expect that they will indicate increased accuracy for the 

8 
polygons that typically have large perimeter to area ratios, as the slivers from wetland 

areas lose influence in the analysis. 

Table 5. Accuracy of National Land Cover Data (NLCD) as compared to National Wetlands 

Inventory (NWI) maps of Baldwin and Mobile Counties, Ala. This analysis was weighted by the 

area of each polygon. 

Observations Accuracy Variance Standard deviation 

1 0.62786 0.23365 0.48338 

Discussion 

The Multi-Resolution Land Characteristics Consortium (MRLC) planned for an 

NLCD accuracy assessment of regional classifications for the 2001 classification 

(Vogelmann and others, 1998; see also http://www.epa.gov/mrlc/accuracy-2001.html), 

but according to the MRLC Web site this has not been conducted. Thus, there is no 

objective, independent accuracy comparison of the 2001 NLCD. It should be 

remembered that NLCD is intended as an analysis of national and regional coverage for 

any given date and as input for time-series changes over large areas. The classification 

system of NLCD was developed for small-scale classification aggregated by general 

cover classes that are reasonably accurate at those scales. 

This accuracy assessment is dependent on a methodology and analysis developed 

for the unique characteristics and properties of NWI classification, which is mapped to 

polygon, as compared to NLCD, which uses raster classification and mapping. This 

cartographic presentation results in fundamentally different classification boundaries. 

Overlaying these map types cannot prevent boundary differences resulting in a 

tremendous proliferation of sliver polygons (fig. 2). The intersection of both maps 

resulted in three-quarters of a million polygons, nearly half of which were less than the 

900-m2

 pixel size used in NLCD mapping (the smaller of the minimum mapping units of 

NWI and NLCD versus NWI mapping was about 0.5 ha). It is the authors’ opinion that 

this cartographic difference needs to be more fully explored before accepting the 

accuracy difference between NWI and NLCD presented here at face value. 

9 
Figure 2. Expanded portion of Mobile Bay indicating the fundamental problem of boundary 

alignment between National Wetlands Inventory (vector, yellow line) and National Land Cover 

Data (raster blocks in shades of blue). 

The authors are satisfied that the cross-walking matrix was appropriate, given the 

constraints of the two classification methods. Some differences in classes are simply 

irreconcilable because they do not exist in one classification system or the other. These 

missing classes greatly contribute to accuracy differences. 

Acknowledgments 

This project was supported wholly or in part by the Mobile Bay National Estuary 

Program as part of a grant from the Alabama Department of Conservation and Natural 

Resources by the Coastal Zone Management Act of 1972, as amended and administered 

by the Office of Ocean and Coastal Management, National Oceanic and Atmospheric 

Administration. Geospatial data analyzed in this report conformed with Federal 

Geospatial Data Center Standards developed under Executive Order 12906, 

“Coordinating Geographic Data Acquisition and Access: The National Spatial Data 

Infrastructure” (Federal Register, v. 59, no. 71, p. 17671–17674). 

10 
References 

Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E., 1976, A land use and land 

cover classification system for use with remote sensor data: U.S. Geological Survey 

Professional Paper 964. 

Cowardin, L.M., Carter, V., Golet, F.C., and LaRoe, E.T., 1979, Classification of 

wetlands and deepwater habitats of the United States: U.S. Fish and Wildlife Service 

FWS/OBS-79/31. 

Handley, L., Fuhrmann, M.J., Fuller, D.A., and Blackmon, J.H., Jr., 1995, An accuracy 

assessment—the 1988 Louisiana habitat maps: Charlotte, N.C., American Society of 

Photogrammetry and Remote Sensing. 

Vogelmann, J.E., Sohl, T.L., Campbell, P.V., and Shaw, D.M., 1998, Regional land cover 

characterization using Landsat Thematic Mapper data and ancillary data sources: 

Environmental Monitoring and Assessment, v. 51, p. 415–428. 

11