91 CHAPTER 7 Thematic Accuracy Assessment of Regional Scale Land-Cover Data Siamak Khorram, Joseph F. Knight, and Halil I. Cakir CONTENTS 7.1 Introduction 91 7.2 Approach 92 7.2.1 Sampling Design 92 7.2.2 Training 93 7.2.3 Photographic Interpretation 93 7.2.3.1 Interpretation Protocol 93 7.2.3.2 Interpretation Procedures 94 7.2.3.3 Quality Assurance and Quality Control 94 7.3 Results 94 7.3.1 Accuracy Estimates 94 7.3.2 Issues and Problems 99 7.3.2.1 Heterogeneity 99 7.3.2.2 Acquisition Dates 99 7.3.2.3 Location Errors 99 7.4 Further Research 101 Acknowledgments 101 References 101 Appendix A: MRLC Classification Scheme and Class Definitions 102 7.1 INTRODUCTION The Multi-Resolution Land Characteristics (MRLC) consortium, a cooperative effort of several U.S. federal agencies, including the U.S. Geological Survey (USGS) EROS Data Center (EDC) and the U.S. Environmental Protection Agency (EPA), has conducted the National Land Cover Data (NLCD) program. This program used Landsat Thematic Mapper (TM) 30-m resolution imagery as baseline data and successfully produced a consistent and conterminous land-cover (LC) map of the lower 48 states at approximately an Anderson Level II thematic detail. The primary goal of the program was to provide a generalized and regionally consistent LC product for use in a broad range of applications (Lunetta et al., 1998). Each of the 10 U.S. federal geographic regions L1443_C07.fm Page 91 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC 92 REMOTE SENSING AND GIS ACCURACY ASSESSMENT was mapped independently. EPA funded the Center for Earth Observation (CEO) at North Carolina State University (NCSU) to assess the accuracy of the NLCD for federal geographic Region IV. An accuracy assessment is an integral component of any remote sensing-based mapping project. Thematic accuracy assessment consists of measuring the general and categorical qualities of the data (Khorram et al., 1999). An independent accuracy assessment was implemented for each federal geographic region after LC mapping was completed. The objective for this study was specifically to estimate the overall accuracy and category-specific accuracy of the LC mapping effort. Federal geographic Region IV included the states of Kentucky, Tennessee, Mississippi, Alabama, Georgia, Florida, North Carolina, and South Carolina (Figure 7.1). 7.2 APPROACH 7.2.1 Sampling Design Quantitative accuracy assessment of regional scale LC maps, produced from remotely sensed data, involves comparing thematic maps with reference data (Congalton, 1991). Since there were no suitable existing reference data that could be used for all federal regions, a practical and statistically sound sampling plan was designed by Zhu et al. (2000) to characterize the accuracy of common and rare classes for the map product using National Aerial Photography Program (NAPP) photographs as the reference data. The sampling design was developed based on the following criteria: (1) ensure the objectivity of sample selection and validity of statistical inferences drawn from the sample data, (2) distribute sample sites spatially across the region to ensure adequate coverage of the entire region, (3) reduce the variance for estimated accuracy parameters, (4) provide a low-cost approach in terms of budget and time, and (5) be easy to implement and analyze (Zhu et al., 2000). The sampling was a two-stage design. The first stage, the primary sampling unit (PSU), was the size of a NAPP aerial photograph. One PSU (photo) was randomly selected from a cluster of 128 photographs. These clusters were formed using a geographic frame of 30 ¥ 30 m. Randomly selected PSU locations are shown in Figure 7.1. The second stage was a stratified random sample, Figure 7.1 Randomly selected photograph center points. Tennessee Mississippi Kentucky North Carolina South Carolina Georgia Florida Alabama 070140 210 280 Miles N L1443_C07.fm Page 92 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC THEMATIC ACCURACY ASSESSMENT OF REGIONAL SCALE LAND-COVER DATA 93 within the extent of all of the PSUs only, of 100 sample sites per LC class. The selected sites were referred to as secondary sampling units (SSU). The number of sites per photograph ranged from 1 to approximately 70 (Figure 7.2). The total number of sample sites in the study was 1500 (100 per cover classes), although only 1473 sites were interpreted due to missing NAPP photos. This sampling approach was chosen by the Eros Data Center (EDC) over a standard random sample to reduce the cost of purchasing the NAPP photography (Zhu et al., 2000). 7.2.2 Training Before the NAPP photo interpretation for the sample sites could begin, photo interpreters were trained to accomplish the goals of the study. To provide consistency among the interpreters, a comprehensive training program was devised. The program consisted of a full-day training session and subsequent on-the-job training. Two experienced aerial photo interpretation and photogram- metry instructors led the formal classroom training sessions. The training sessions included the following topics: (1) discussion of color theory and photo interpretation techniques, (2) understand- ing of the class definitions, (3) interpretation of over 100 sample sites of different classes during the training sessions followed by interactive discussions about potential discrepancies, (4) creation of sample sites for later reference, and (5) repetition of interpretation practice after the sessions. The focus was on real-world situations that the interpreters would encounter during the project. Each participant was presented with over 100 preselected sites and was asked to provide his or her interpretation of the land cover for these sites. Their interpretations were analyzed and subsequently discussed to minimize any misconceptions. During the on-the-job portion of the training, each interpreter was assigned approximately 500 sites to examine. Their progress was monitored daily for accuracy and proper methodology. The interpreters kept logs of their decisions and the sites for which they were uncertain about the LC classes. On a weekly basis, their questions were addressed by the project photo interpretation supervisor. The problem sites (approximately 400) were discussed until all team members felt comfortable with the class definitions and their consistency in interpre- tation. Agreement analysis between the three interpreters resulted in an average agreement of 84%. 7.2.3 Photographic Interpretation 7.2.3.1 Interpretation Protocol The standard protocol used by the photo interpreters was as follows: Figure 7.2 Sample sites clustered around the photograph center. Photo Center Sample Points 0 0.6 1.2 N Miles L1443_C07.fm Page 93 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC 94 REMOTE SENSING AND GIS ACCURACY ASSESSMENT • Each interpreter was assigned 500 of the 1500 total sites. • Interpretation was based on NAPP photographs. • The sample site locations on the NAPP photos were found by first plotting the sites on TM false- color composite images then finding the same area on the photo by context. • During the interpretation process, cover type and other related information such as site homogeneity were recorded for later analysis. • When there was some doubt as to the correct class or there was the possibility that two classes could be considered correct, the interpreters selected an alternate class in addition to the primary class. • The interpretations were based on the majority of a 3 ¥ 3 pixel window (Congalton and Green, 1999). 7.2.3.2 Interpretation Procedures The Landsat TM images were displayed using ERDAS Imagine. By plotting the site locations on the Landsat TM false-color composite images, the interpreters precisely located each site. Then, based on the context from the image, the interpreters located the site on the photographs as best they could. Clearly, some error was inherent in this location process; however, this was the simplest and most cost-effective procedure available. The use of a 3 ¥ 3 pixel window for interpretation was intended to reduce the effect of location errors. The interpreters examined each site’s characteristics using the aerial photograph and TM image and determined the appropriate LC label for the site according to the classification scheme, then they entered the information into the project database. The following data were entered into the database: site identification number (sample site), coordinates, photography acquisition date, pho- tograph identification code, imagery identification number, primary or dominant LC class, alternate LC class (if any), general site description, unusual observations, general comments, and any temporal site changes between image and photo acquisition dates. The interpreters did not have prior access to the MRLC classification values during the interpretation process. Individual interpreters analyzed 15% ( n = 75) of each of the other interpreters’ sample sites to create an overlap database to evaluate the performance of the interpreters and the agreement among them. Selection of these 75 sites was done through random sampling. This scheme provided 225 sites that were interpreted by all three interpreters. Agreement analysis using these overlap sites indicated an average agreement of 84% among the three interpreters (Table 7.1). 7.2.3.3 Quality Assurance and Quality Control Quality assurance (QA) and quality control (QC) procedures were vigorously implemented in the study as designated in the interpretation organization chart (Table 7.2). Discussions among the interpreters and project supervisors during the interpretation process provided an opportunity to discuss the problems that occurred and to resolve problems on the spot. The QA and QC plan is shown in Figure 7.3. Upon completion of training, a test was performed to determine how similarly the interpreters would call the same sites. The initial results of the analysis revealed that some misunderstandings about class definitions had remained after the training process. As a result, the interpreters were retrained as a group to “calibrate” themselves. This helped to ensure that calls were more consistent among interpreters. Upon satisfactory completion of the retraining, the interpreters were assigned to complete interpretation of the 1500 sample sites. 7.3 RESULTS 7.3.1 Accuracy Estimates Table 7.3 presents the error matrix for MRLC Level II classes. The numbers across the top and sides of the matrices represent the 15 MRLC classes (Appendix A). Table 7.4 presents the error L1443_C07.fm Page 94 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC THEMATIC ACCURACY ASSESSMENT OF REGIONAL SCALE LAND-COVER DATA 95 Table 7.1 Agreement Analysis Among PIs: Interpreter Call vs. Overlap Consensus for the 225 Overlap Sites Overlap Consensus Interpreted Results MRLC Class 1.1 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 8.1 8.2 8.5 9.1 9.2 Tot % Corr 1.1 18 1 1 20 0.9 18 2.1 21 1 22 1.0 21 2.2 31 4 0.8 3 2.3 9 9 1.0 9 3.1 42 6 0.7 4 3.2 16 7 0.9 6 3.3 16 1 1 18 0.9 16 4.1 214 111 19 0.7 14 4.2 27 9 0.8 7 4.3 3 2126 4 36 0.7 26 8.1 10 1 11 0.9 10 8.2 310 1 14 0.7 10 8.5 1162 19 0.8 16 9.1 2131 16 0.8 13 9.2 15 15 1.0 15 Tot 19 22 3 10 4 6 23 18 8 29 14 14181918225 % 0.9 1 1 0.9 1 1 0.7 0.8 0.9 0.9 0.7 0.7 0.9 0.7 0.8 0.84 Corr 18 21 3 9 4 6 16 14 7 261010161315 188 L1443_C07.fm Page 95 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC 96 REMOTE SENSING AND GIS ACCURACY ASSESSMENT matrix for MRLC Level I classes. The Level II classes were grouped into the following Level I categories: (1) water, (2) urban or developed, (3) bare surface, (4) agriculture and other grasslands, (5) forest (upland), and (6) wetland (woody or nonwoody). The overall accuracies for the Level I and II classes were 66% and 44%, respectively. Table 7.3 illustrates the confusion among low-intensity residential, high-intensity residential, and commercial/transportation categories. Many factors may have contributed to the confusion; however, we believe the complex classification scheme used was a dominant factor. For example, the most ambiguous categories were the three urban classes, which were distinguished only by percentage of vegetation. Technically, it was beyond the methods employed in this study to quantify subpixel vegetation content. As a result, many high-intensity residential areas in the classified image were assigned to low-intensity residential and commercial/transportation classes. This occurred because high-intensity residential classes, which had a median percentage of vegetation, were easily confused with lower-intensity and higher-intensity urban development. Also, many problems were encountered with the interpretation of cropland and pasture/hay since these classes had very similar spectral and spatial patterns that occurred within the same agricultural areas. In addition, cropland was frequently converted to pasture/hay during the interval of two acquisition dates, or vice versa. Confusion also existed within classes of evergreen forest Table 7.2 Interpretation Team Organization Interpreter Organization Photo Interpreters PI #1 (500 pts + 75 pts from PI #2 and 75 pts from PI #3 PI #2 (500 pts + 75 pts from PI #1 and 75 pts from PI #3 PI #3 (500 pts + 75 pts from PI #1 and 75 pts from PI #2 PI supervisor Random checking for consistency, checking 225 overlapped sites, sites with question from three PIs Project supervisor Checking sites with question from PI supervisor, random checking of overall sites, overall QA/QC Project director Procedure establishment, discussions on issues, random checking, overall QA/QC Figure 7.3 Training, photo interpretation (PI), and quality assurance and quality control (QA/QC) procedures. Classroom photo interpretation training Independent and supervised photo interpretation for each interpreter Interpretation of 225 overlap points Overlap satisfactory? Photo interpretation of the 1500 random sample points Interpreters work through overlap points as a group to resolve differences Accuracy analysis MRLC region 4 classified data Yes No L1443_C07.fm Page 96 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC THEMATIC ACCURACY ASSESSMENT OF REGIONAL SCALE LAND-COVER DATA 97 Table 7.3 Error Matrix for the Level II MRLC Data (15 Classes) PI Results Classified MRLC Data MRLC Class 1.1 2.1 2.2 2.3 3.1 3.2 3.3 4.1 4.2 4.3 8.1 8.2 8.5 9.1 9.2 Tot % Corr 1.1 87 3352 242 108 0.8 87 2.1 47 49 22 122151241 155 0.3 47 2.2 1 10 2 42 19 0.5 10 2.3 322 32 15 1 4 1 69 0.5 32 3.1 236 33 18 1 2 1 1 2 69 0.5 33 3.2 13 34 38 0.9 34 3.3 113 33 421234 51 78 0.4 33 4.1 63 86 46 373719 99 0.5 46 4.2 1111 7 34 4 264 61 0.6 34 4.3 24 3 7 2 6 16 29 42 62 944164 228 0.3 62 8.1 2 1215 4114 4 28 18 11 1 2 103 0.3 28 8.2 1311 1 631137 57 322 128 0.4 57 8.5 110111320 3 741384 41 23 131 0.3 41 9.1 42 11282104 31 43 15 96 0.4 43 9.2 41 2101211 9 60 91 0.7 60 Tot98 100 100 98 100 100 100 94 99 98 93 99 97 99 98 1473 % 0.9 0.5 0.1 0.3 0.3 0.3 0.3 0.5 0.3 0.6 0.3 0.6 0.4 0.4 0.6 0.44 Corr 87 47 10 32 33 34 33 46 34 62 28 57 41 43 60 647 L1443_C07.fm Page 97 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC 98 REMOTE SENSING AND GIS ACCURACY ASSESSMENT and mixed forest, deciduous forest and mixed forest, barren ground and other grassland, low- intensity residential and mixed forest, and transitional and all other classes. The difference between image classification and photo interpretation is that image classification is mostly based on the spectral values of the pixels, whereas photo interpretation incorporates color (tones), pattern recognition, and background context in combination. These issues are inherent in any accuracy assessment project using aerial photos as the reference data (Ramsey et al., 2001). For this project, however, aerial photos were the only reasonable reference data source. The interpretation process is not the only component of the accuracy assessment process (Congalton and Green, 1999). Additional factors that should be considered are positional and correspondence error. To account for these errors, the following additional criteria for correct classification were considered in this project: (1) primary matches classified pixel, (2) primary or alternate matches classified pixel, (3) primary is most common in classified 3 ¥ 3 pixel areas, (4) primary matches any pixel in a classified 3 ¥ 3 pixel area, (5) primary is most common in classified 3 ¥ 3 pixel area, and (6) primary or alternate matches any pixel in a 3 ¥ 3 pixel area. “Interpreted” refers to the classes chosen during the aerial photo interpretation process, “primary” and “alternate” are the most probable LC classes for a particular site, and “classified” refers to the MRLC classification result for that site. The analysis results for each cover class in six cases are presented in Table 7.5 and Table 7.6. The overall accuracies under various scenarios ranged from 44% to 79.4% ( n = 1473) for cases “a” and “f,” respectively. Table 7.4 Error Matrix for the Level I MRLC Data PI Results MRLC data 123489Tot%Corr 187 310026 108 0.81 87 2 0 188 94384 243 0.77 188 3 112 134 2189 185 0.72 134 4 14645 227 30 39 388 0.59 227 8 1437821 207 12 362 0.57 207 9 862418 4 127 187 0.68 127 Tot98 298 300 291 289 197 1473 % 0.89 0.63 0.45 0.78 0.72 0.64 0.66 Corr 87 188 134 227 207 127 970 Table 7.5 Summary of Further Accuracy Analysis by Interpreted Cover Class: Number of Sites Class No. Primary PI Matches MRLC Prim or Alt Matches MRLC Primary PI Is Mode of 3 ¥ 3 Primary PI Matches Any 3 ¥ 3 Prim or Alt PI Is Mode of 3 ¥ 3 Prim or Alt PI Matches Any 3 ¥ 3 1.1 108 87 95 84 92 94 100 2.1 155 47 69 60 81 124 135 2.2 19 10 11 8 11 15 16 2.3 69 32 39 35 41 44 49 3.1 69 33 35 27 30 34 42 3.2 38 34 36 34 36 35 37 3.3 78 33 44 33 42 40 52 4.1 99 46 55 60 68 79 83 4.2 61 34 39 44 48 52 54 4.3 228 62 98 68 110 148 187 8.1 103 28 39 27 38 46 64 8.2 128 57 82 56 83 83 102 8.5 131 41 61 33 53 56 91 9.1 96 43 53 47 59 68 84 9.2 91 60 68 58 67 67 74 Totals 1473 647 824 674 859 985 1170 L1443_C07.fm Page 98 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC THEMATIC ACCURACY ASSESSMENT OF REGIONAL SCALE LAND-COVER DATA 99 7.3.2 Issues and Problems 7.3.2.1 Heterogeneity The heterogeneity of many areas caused confusion in assigning an exact class label to the sites. Since the spatial resolution of the Landsat TM data was 30 ¥ 30 m, pixel heterogeneity was a common problem (Plate 7.1a). For example, a site on the image frequently contained a mixture of trees, grassland, and several houses. Thus, the reflectance of the pixel was actually a combination of different reflectance classes within that pixel. This factor contributed to confusion between evergreen forest and mixed forest, deciduous forest and mixed forest, low-intensity residential and other grassland, and transitional and several classes. 7.3.2.2 Acquisition Dates Temporal discrepancies between photograph and image acquisition dates, if not reconciled, would negatively affect the classification accuracy (Plate 7.1b). For example, to interpret early forest growth areas, the interpreter had to decide whether the site was a transitional or a forested area. If the photograph was acquired before the image (e.g., as much as 6 years earlier), it was clear that those early forest growth sites would show up as forest cover on the satellite image. In this case, the interpreters decided the appropriate cover class based on satellite imagery. 7.3.2.3 Location Errors Locating the reference site on the photo was sometimes problematic. This frequently occurred when: (1) the LC had changed between the image and photo acquisition dates, (2) there were few clearly identifiable features for positional reference, and (3) the reference site was on the border of two or more classes (boundary pixel problem). When the LC had changed between acquisition dates, locating reference sites was difficult because the features surrounding the reference site were also changed. Similarly, when a reference site fell in an area with few identifiable features for positional reference, the interpreter had to approximate the location of the reference site. For Table 7.6 Summary of Further Accuracy Analysis by Interpreted Cover Class: Percentage of Sites for Each Class Class Percentage Primary PI Matches MRLC Prim or Alt PI Matches MRLC Primary PI Is Mode of 3 ¥ 3 Primary PI Matches Any 3 ¥ 3 Prim or Alt PI Is Mode of 3 ¥ 3 Prim or Alt PI Matches Any 3 ¥ 3 1.1 100.0 80.6 88.0 77.8 85.2 87.0 92.6 2.1 100.0 30.3 44.5 38.7 52.3 80.0 87.1 2.2 100.0 52.6 57.9 42.1 57.9 78.9 84.2 2.3 100.0 46.4 56.5 50.7 59.4 63.8 71.0 3.1 100.0 47.8 50.7 39.1 43.5 49.3 60.9 3.2 100.0 89.5 94.7 89.5 94.7 92.1 97.4 3.3 100.0 42.3 56.4 42.3 53.8 51.3 66.7 4.1 100.0 46.5 55.6 60.6 68.7 79.8 83.8 4.2 100.0 55.7 63.9 72.1 78.7 85.2 88.5 4.3 100.0 27.2 43.0 29.8 48.2 64.9 82.0 8.1 100.0 27.2 37.9 26.2 36.9 44.7 62.1 8.2 100.0 44.5 64.1 43.8 64.8 64.8 79.7 8.5 100.0 31.3 46.6 25.2 40.5 42.7 69.5 9.1 100.0 44.8 55.2 49.0 61.5 70.8 87.5 9.2 100.0 66.3 73.9 63.0 72.8 72.8 80.4 Total Percentage 100.0 44.0 55.9 45.7 58.3 66.8 79.4 L1443_C07.fm Page 99 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC 100 REMOTE SENSING AND GIS ACCURACY ASSESSMENT example, when the reference site was on the shadowy side of a mountain, it was impossible to see the reference features except the ridgeline of the mountain; thus, the interpreter was required to locate the reference site based on the approximate distance to and the direction of the ridgeline. The third case was the most common source of confusion in the interpretation process. Reference sites were frequently on the border of two or more classes. In these situations, the interpreter Plate 7.1 (See color insert following page 114.) (a) Heterogeneity problem: reference site consists of several classes. (b) LC class changed between acquisition dates in reference site. (c) Ambiguity of class definitions; it is difficult to differentiate between high-density and commercial class according to definition. B&W Aerial Photo LANDSAT TM Image LANDSAT TM ImageCIR Aerial Photo LANDSAT TM ImageCIR Aerial Photo (a) (b) (c) L1443_C07.fm Page 100 Friday, June 25, 2004 10:14 AM © 2004 by Taylor & Francis Group, LLC [...]... Louisiana, J Coastal Res., 17, 53 71 , 2001 Zhu, Z.,L Yang, S.V Stehman, and R.L Czaplewski, Accuracy assessment for the U.S Geological Survey regional land cover mapping program: New York and New Jersey region, Photogram Eng Remote Sens., 66, 1425–1438, 2000 © 2004 by Taylor & Francis Group, LLC L1443_C 07. fm Page 102 Friday, June 25, 2004 10:14 AM 102 REMOTE SENSING AND GIS ACCURACY ASSESSMENT APPENDIX A... the accuracy of classifications of remotely sensed data, Remote Sens Environ., 37, 35–46, 1991 Congalton, R and K Green, A practical look at the sources of confusion in error matrix generation, Photogram Eng Remote Sens., 59, 641–644, 1999 Khorram, S., G.S Biging, N.R Chrisman, D.R Colby, R.G Congalton, J.E Dobson, R.L Ferguson, M.F Goodchild, J.R Jensen, and T.H Mace, Accuracy Assessment of Remote Sensing- Derived...L1443_C 07. fm Page 101 Friday, June 25, 2004 10:14 AM THEMATIC ACCURACY ASSESSMENT OF REGIONAL SCALE LAND-COVER DATA 101 decided between two or more classes by determining which class covered the majority of the 3 ¥ 3 pixel window 7. 4 FURTHER RESEARCH The results of this study point to numerous opportunities for further research to improve accuracy assessment methods for regional scale assessments,... Accuracy Assessment of Remote Sensing- Derived Change Detection, monograph, American Society of Photogrammetry and Remote Sensing (ASPRS), Bethesda, MD, 1999 Lunetta, R S., J.G Lyon, B Guidon, and C.D Elvidge, North American landscape characterization dataset development and data fusion issues, Photogram Eng Remote Sens., 64, 821–829, 1998 Ramsey, E., G Nelson, and K Sapkota, Coastal change analysis... classes in the accuracy assessment, (2) evaluating and analyzing the effect of positional errors on accuracy assessment, (3) collecting field data for the 225 overlapping sample sites to validate the interpretation, and (4) analyzing satellite data with a higher temporal resolution to better identify changes between the acquisition of TM data and NAPP photography (e.g., using NOAA-AVHRR and MODIS data)... Grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops © 2004 by Taylor & Francis Group, LLC L1443_C 07. fm Page 103 Friday, June 25, 2004 10:14 AM THEMATIC ACCURACY ASSESSMENT OF REGIONAL SCALE LAND-COVER DATA 5.2 6.0 103 Row Crops: All areas used for the production of crops such as corn, soybeans, vegetables, tobacco, and cotton 5.3 Other grasses:... debris, beach, and other accumulations of rock and/ or sand without vegetative cover 3.2 Quarries/strip mines/gravel pits: Areas of extractive mining activities with significant surface expression 3.3 Transitional: Areas dynamically changing from one land cover to another, often because of land use activities Examples include forestlands cleared for timber and may include both freshly cleared areas as... heavily built-up urban centers where people reside Examples include apartment complexes and row houses Vegetation occupies less than 25% of the landscape Constructed materials account for 80 to 100% of the total area Typically, population densities will be quite high in these areas 2.3 High-intensity commercial/industrial/transportation: Includes all highly developed lands not classified as “high-intensity... industrial, and transportation Barren: Bare rock, sand, silt, gravel, or other earthen material with little or no vegetation regardless of its inherent ability to support life Vegetation, if present, is more widely spaced and scrubby than that in the vegetated categories 3.1 Bare Rock/Sand: Includes areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, beach, and other... 2.1 Low-intensity residential: Land includes areas with a mixture of constructed materials and vegetation or other cover Constructed materials account for 30 to 80% of the total area These areas most commonly include single-family housing areas, especially suburban neighborhoods Generally, population density values in this class will be lower than in highintensity residential areas 2.2 High-intensity . LLC 98 REMOTE SENSING AND GIS ACCURACY ASSESSMENT and mixed forest, deciduous forest and mixed forest, barren ground and other grassland, low- intensity residential and mixed forest, and transitional. 94384 243 0 .77 188 3 112 134 2189 185 0 .72 134 4 14645 2 27 30 39 388 0.59 2 27 8 14 378 21 2 07 12 362 0. 57 2 07 9 862418 4 1 27 1 87 0.68 1 27 Tot98 298. 110 148 1 87 8.1 103 28 39 27 38 46 64 8.2 128 57 82 56 83 83 102 8.5 131 41 61 33 53 56 91 9.1 96 43 53 47 59 68 84 9.2 91 60 68 58 67 67 74 Totals 1 473 6 47 824 674 859 985 1 170 L1443_C 07. fm Page