Interpretation of Landsat-4 Thematic Mapper and Multispectral Scanner Data for Forest Surveys I Andrew S. Benson* and Stephen D. DeGloria* Remote Sensing Research Program, University of California, Berkeley, CA 94720

ABSTRACT:Landsat-4 Thematic Mapper (TM) and Multispectral Scanner (MSS) data were evaluated by interpreting film and digital products and statistical data for selected forest cover types in California. Significant results were: (1) TM color image products should contain a spectral band in the visible (bands 1, 2, or 3), near infrared (band 4), and middle infrared (band 5) regions for maximizing the interpretability of vegetation types; (2) TM color com- posites should contain band 4 in all cases even at the expense of excluding band 5; and (3) MSS color composites were more interpretable than all TM color composites for certain cover types and for all cover types when we excluded band 4 from the TM composite.

INTRODUCTION are annotated and subjected to an identification pro- ANDSAT MSS FILM AND DIGITAL products have rep- cess by a set of independent interpreters. Our ob- L resented an important part of vegetation sur- jective at the Remote Sensing Research Program veys by serving as a basis for land cover stratifica- (RSRP) in support of the LIDQA Program was to tion. Areas of similar cover types and manazement determine the relative value of composite TM and practices are delimited on shotographic images Multispectral Scanner (MSS) images based on the based on their s~ectraland s~atialcharacteristics in identification of major forest cover types using es- a single-date or-multi-date :ode. Because the de- tablished interpretation techniques (Colwell, 1978; tection and identification of different land cover con- Benson and Dummer, 1983). The interpretation ditions is a critical step in an effective stratification process, as conducted by human analysts, requires procedure, the high quality Thematic Mapper (TM) that the image products allow both the detection image products from the Landsat-4 and Landsat-5 and correct identification of features of interest. De- systems should play an even more valuable role in tection requires, at a minimum, the simple recog- this regard. nition or awareness that a feature is present. Iden- The spectral quality of TM data can be evaluated tification of a detected feature requires a further for survey purposes by determining the extent to synthesis of spectral, spatial, textural, and associa- which natural targets in vegetated environments can tive characteristics (Hay, 1982). By conducting these be discriminated using specially generated and com- interpretation tests some quantitative measure of in- mercially available image products. An approach for terpretability can be generated to compare with the this type of investigation is: (1) extract spectral sta- previous results achieved by these and other inves- tistics for specific land cover types, (2) compare tigators (Williams et al., 1984; Anuta et al., 1984). these data to selected photographic reproductions of the same area representing various spectral band combinations, and (3) qualitatively determine which METHODS color composite provides the most information for stratification purposes. Using image products and statistical summaries of individual agricultural and The Plumas Forest study site is located in Plumas forest cover types, DeGloria (1984) found the TM County, CA, approximately 265 km northeast of San data quality to be quite useful for discriminating Francisco. The area has been defined as part of the land cover conditions for certain land management Sierra Nevada Mixed Conifer forest cover type by and planning activities. the Society of American Foresters (1980). The area A second approach to assessing spectral quality of contains a diversity of forest cover types ranging image data is to conduct quantitative image inter- from stands of red and white fir (Abies magn$ca pretation tests in which selected land cover types and A. concolor, respectively), to stands of mixed conifer dominated by ponderosa pine (Pinus pon- * Both authors are now with the Resource Survey In- derosa), Douglas fir (Pseudotsuga menziesii), andlor stitute, P.O. 423, Benicia, CA 94510. sugar pine (P. lambertiana). Several other cover

PHOTOGRAMMETRICENGINEERING AND REMOTESENSING, 0099-1112/85/5109-1281$02.25/0 Vol. 51, No. 9, September 1985, pp. 1281-1289. 0 1985 American Society for Photogrammehy and Remote Sensing PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1985 cover types are also prevalent and include low den- IMAGE DATAACQUISITION AND PROCESSING sity Jeffrey pine (P. jeffreyi) stands on soils derived The Landsat-4 TM and MSS data used for from ultramafic parent material; hardwood stands on our analysis were acquired on 12 August 1983 wet and dry sites; dense shrub fields; wet and dry (#84039218143, WRS path 44, row 32). The TM data meadows; bare soil; granitic rock outcrops; and large were transmitted to Goddard Space Flight Center water bodies. (GSFC) via the Tracking and Data Relay Satellite System (TDRSS) and the TDRSS receiving station at White Sands, NM; they were processed by the Based on over 40 years of experience using re- Thematic Mapper Image Processing System at mote sensing imagery for forest cover type mapping GSFC as a "P" tape (NASA, 1983). The MSS data in this study area, eight forest resource categories were acquired in the usual manner from the EROS were defined as important for stratification purposes Data Center. and for which selective photointerpretation keys Large-scale (1:4300-1:13,000) color and color in- were developed: high density conifer, low density frared oblique aerial photography was acquired conifer, hardwood-conifer mix, brush, wet and dry within one hour of the overpass for the Plumas Na- site hardwood, wet and dry meadow, bare ground, tional Forest study site, California, using a dual 35 and rock (Table 1). mm camera system operated from a light aircraft. This photography was used in conjunction with available ground data to document forest canopy, nonforest cover, and understory conditions as well as to document the environmental conditions prev- TABLE1. FORESTRESOURCE CATEGORIES USED TO DEVELOP alent at the time of the overpass. SELECTIVEIMAGE INTERPRETATION KEYS FOR ONE MULTISPECTRALSCANNER AND THREE THEMATIC MAPPER Based on the available ground data and aerial IMAGES FOR THE PLUMASFOREST STUDY SITE, CALIFORNIA photography for this study site, a 1200 x 1200 pixel block of TM data (1,440,000 pixels) and corre- High Density Conqer-Mixed and pure conifer stands. sponding MSS digital data were extracted from the Stands dense to moderately open: dense stands most full scene CCTs. often present on moist north and west slopes, and more Color glossy prints of four color image types, de- open stands present on the other aspects. Rough image rived from the si~nultaneouslyacquired MSS and texture. Low Density Conqer-Open stands of mixed and pure TM data were used to develop the photo interpre- conifer stands. Brush or herbaceous understory may be tation keys for the following four composite image present. Open stands may be the result of poor site types: (1) MSS bands 4, 2, and 1; (2) TM bands 4, quality (serpentinitic soils) or selective logging prac- 3, and 2; (3) TM bands 5,3, and 2; and (4) TM bands tices. Rough to smooth image texture. 5, 4, and 3. For each image type, the three bands HardwoodlConifer-Mixed hardwood and conifer tree were color coded with red, green, and blue color stands. Stands dense to moderately dense. Stands most channels, respectively, on the RSRP color monitor often present on drier sites (south slopes) or may de- using a linear mapping function to simulate color velop ten to twenty years after selective logging. Rough infrared imagery. Because the RSRP color monitor image texture. Hardwood-Pure and mixed evergreen and deciduous can only display a maximum area of 316 samples by hardwood stands. Stands may be on dry or wet sites: 202 lines, each TM image was subdivided into six dry sites present 011 south aspects; wet sites located in separate test images, and the MSS image was sub- water courses or interspersed throughout dense stands divided into four separate images. Each test image of conifer at the higher elevations. Based on area cov- was copied to photographic film using a Matrix color ered, this class is very small. Moderately rough image graphic camera. The resulting TM and MSS copy texture. products were enlarged to scales of 1:75,000 and Brush-Dense stands of evergreen or deciduous shrubs. 1:120,000, respectively. The image specifications Usually present in large contiguous stands on dry sites. are summarized in Table 2. Evidence of vegetation modification may be present. Smooth image texture. The linear mapping functions that were applied Grassland-Vegetative cover predominantly annual or to the four Landsat-4 images tested in this study perennial grasslands. Sites may include dry or wet hab- were computed in the following manner. For each itats or improved pasture lands. Smooth image texture. of the three bands to be used to make an image, a Bare Ground-Nonvegetated soil. Includes recently dis- histogram was made using all of the digital values turbed sites, logging roads (unpaved), and extremely of scene brightness. From this histogram, the arith- poor habitat. Not to be confused with the rock category metic mean (3) and standard deviation (s) were cal- (below). Smooth image texture. culated. The mapping interval was calculated for the Rock-Areas of bare rock which are devoid of vegetation respective color gun intensity levels (eight intensity to include rock outcrops, talus slopes, and gravel bars. Smooth to moderately smooth image texture. levels for each color gun on the RSRP color monitor) as follows: More than 200 dedicated scientists have contributed their efforts to this classic work. Now the 2-volume Second Edition of the Manual of Remote Sensing has been printed and bound and is available for delivery. For treatment of the entire spectrum of remote sensing, this new manual is unparalleled in quality, completeness, and authoritativeness.

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Range of values + 1 TABLE2. SUMMARYOF LANDS AT^ IMAGE SPECIFICATIONSFOR Mapping interval = ONEMSS IMAGE AND THREETM IMAGES FOR WHICHPHOTO- Number of intensity levels ~NTERPRETAT~ONTESTS WERE PERFORMEDON THE where: PLUMASFOREST STUDY SITE Range of values = Maximum step value - Band Minimum step value Image Color Band Standard Display Image Maximum I+ 3*s or the maximum step value Sensor Bands Filter Mean Deviation Range Scale step value = observed, whichever is less; and MSS 4 Red 40.93 10.08 10-71 Minimum I- 3*s or the minimum observed 2 Green 15.58 14.63 2-31 1:120,000 step value = step value, whichever is greater. 1 Blue 13.42 15.87 7-26 Based on our interpretation experiences using both TM 4 Red 64.07 15.93 16-111 this enhancement procedure and other comlnonly 3 Green 23.14 8.21 7-47 1: 75,000 used procedures, we prefer the linear mapping en- 2 Blue 23.69 5.22 11-39 hancement. This product uses the available color TM 5 Red 46.30 21.65 0-111 levels efficiently, provides an image that closely sim- 3 Green 23.14 8.21 7-47 1: 75,000 ulates color infrared photography if the appropriate 2 Blue 23.69 5.22 11-39 TM and MSS bands are used, and preserves many TM 5 Red 46.30 21.65 0-111 of the subtleties which occur in transition zones be- 4 Green 64.07 15.93 16-111 1: 75,000 tween and within wildland vegetation types. 3 Blue 23.14 8.21 7-47 Selection of the four image types, shown in Plate 1 and used in our testing procedures, was based on the following reasons: (2) TM bands 4, 3, and 2 had approximately the (1) MSS bands 4, 2, and 1 represented those images same spectral sensitivity of MSS bands 4, 2, and that have been available since the Landsat 1 but with more than twice thespatial resolu- system was first launched in 1972. Through a tion. comparison of the interpretability of this image (3) TM bands 5, 3, and 2 allowed us to compare the to the following TM image types, we can make contribution made to an image if the middle in- some inferences as to improvement in inter- frared band (band 5) is used in place of the more pretability due to increased spatial and spectral traditional photographic infrared band (band 4). resolution of the TM sensor. (4) TM bands 5, 4, and 3 appeared to contain more

PLATE1. Selective photointerpretation key and examples of the four image types tested: C = high density conifer, L = low density conifer, HIC = hardwood conifer mix, H = dry and wet site hardwood, 6 = brush, G = grassland, S = bare soil, and R = rock. TM IMAGE 4, 3 AND 2

MSS IMAGE 4, 2 AND 1 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1985

spectral information than the other TM image TABLE3. CORRELATIONMATRIX OF THE SEVEN THEMATIC types based on a qualitative evaluation of film MAPPERBANDS FOR DATAEXTRACTED FOR THE PLUMAS products and of the correlation matrix shown in FORESTRYSTUDY SITE (DEGLORIA, 1984) Table 3. Band1 2 3 4 5 7 6 - 0.96 0.15 0.74 0.84 0.30 We developed selective interpretation keys for 0.96 0.24 0.80 0.87 0.77 the four Landsat-4 images for the following two rea- 1.00 0.18 0.81 0.90 0.81 1.00 0.43 0.18 0.06 sons. Because of the relatively poor spatial resolu- 1.00 0.93 0.85 tion of satellite imagery, association and location are 1.00 0.97 important image characteristics that must be used 1.00 to identify a forest resource category; these two characteristics are presented most easily in selective = 40,000 pixels keys. Secondly, the primary use of these keys was to familiarize skilled interpreters with the expected spectral responses of the resource categories on the If the Delta Kappa calculated between two matrices different image types; again, the range of these re- exceeded 1.96, we concluded that the Kappa values sponses can be presented most effectively in selec- were significantly different at the 95 percent confi- tive keys. dence level; if the calculated value was less than 1.96 we concluded that the Kappa values were not significantly different and that the image types rep- resented by those matrices were equally interpret- On each of the four image types, ten points were able. A complete description of the use of the Kappa randomly allocated for each of the eight resource statistic can be found in Congalton et al. (1984a, b). categories resulting in 80 test points per image type. The points were randomly located on each of the RESULTS AND DISCUSSION image types so that interpreter-recall would be minimized. Six interpreters were given the photo- Summaries of the interpretation test results are interpretation tests during one 60-minute session. given in Tables 4 and 5. Table 4 lists the ranked After the testing was completed, the answer image types based on the Kappa statistics calculated sheets were corrected and error matrices were con- for all eight resource categories, and Table 5 lists structed for each interpreter by image type. The the ranked image types based on the individual re- resulting 24 matrices were then aggregated to pro- source categories. In these two tables, lines have duce an error matrix based on 480 interpreter re- been drawn to the right of the Kappa-ranked images sponses for each image type. For each interpreta- which represent those image types that are not sig- tion error matrix, percent correct and percent com- nificantly different at the 95 percent confidence mission error were calculated as follows: level. In Table 5, the percent correct (%C) and per-

Number of correct interpretation responses for a resource category Percent Correct = x 100 (2) Total number of that resource category present - Number of incorrect interpretation responses for a resource category Percent x 100 ~~mmissionh-~r - Total number of that resource category indicated by the interpreter(s) In addition, for each matrix, a Kappa statistic was calculated as follows: 8 DiagonaVN - S(row total x column total)lN2 Kappa = 1 - 8(row total x column tota1)lP The Kappa statistic, which is a nonparametric cent commission error (%CE) associated with each measure of agreement between "ground truth" and image type have been included for each resource photointerpretation labels, was used to rank the category. These two types of error matrices were error matrices. The rankings were considered to be used to calculate the values given in Table 4. Ex- significantly different, or not, based on the values amples of the error matrices for TM bands 5, 4, and obtained through the use of the following relation- 3 are given in Tables 6 and 7. 'hble 6 represents the ship: interpretability of the TM bands 5, 4, and 3 based

IKappai - K~PP~J Delta Kappa = [Variance Kappa, + Variance KappaJlt2 MSS DATA FOR FOREST SURVEYS

TABLE4. SUMMARYOF IMAGE RANKINGSFROM THE PLUMAS indicates that any image type for this mixed conifer FORESTSTUDY SITE LANDSAT-4 IMAGE ~NTERPRETAT~ONTESTS. forest environment must contain a band that is sen- TESTRESULTS BASED ON EIGHTRESOURCE CATEGORIES: sitive to the photographic infrared region of the (1) HIGHDENSITY CONIFER, (2) LOW DENSITYCONIFER, electromagnetic spectrum. Even the MSS image, (3) HARDWOOD/CONIFER,(4) HARDWOOD, (5) BRUSH, despite its relatively poor spatial resolution, proved (6) GRASSLAND,(7) BARESOIL, AND (8) ROCK. THELINES TO to be more interpretable than the higher spatial res- THE RIGHTOF THE RANKEDKAPPA VALUE~ND~CATE THOSE IMAGE TYPESTHAT ARE NOT SIGNIFICANTLYDIFFERENT olution TM 5, 3, and 2 image which did not include AT THE 95 PERCENTCONFIDENCE LEVEL a band sensitive to the photographic infrared region. Of the four images tested, all six interpreters com- Percent Percent mented that they considered TM 5, 4, and 3 to be Image Kappa and Correct Correct the most easily interpretable. They stated that this Rank Type Significance Range Mean was particularly true when they attempted to sep- 1 TM 5,4&3 41.3-73.8 56.5 arate the brush fields from hardwood stands in this 46.3-70.0 55.0 image, for many more subtle differences are present 3 TM 4,3&2 ,467 30.0-71.3 53.3 than with conventional color infrared image (TM 4, 3, and 2). These comments are confirmed when you 4 TM5,3&2 .388 1 23.8-63.0 46.5 examine Table 5 in which TM 5, 4, and 3 was sta- tistically more interpretable than the other image types for the Brush category. on the six interpreters; and Table 7 represents the Of interest is that for four of the individual re- interpretability of the image type based on the source categories, the MSS 4, 2, and 1 image sur- "best" photointerpreter. The data from this second passed in absolute terms the higher spatial resolu- matrix should be used when evaluating the infor- tion TM images: low-density conifer, hardwood, mation content of the respective image types, for in bare soil, and rock. Why the MSS image was more operational applications of these images, the most interpretable than the others for the hardwood cat- able interpreter would be given the task of infor- egory remains an enigma, for this category is typi- mation extraction. cally located in riparian zones which should be more By examining the rankings in Table 4 we con- easily detected and identified on the higher reso- cluded that image types TM 5, 4, and 3; MSS 4, 2, lution imagery. The other three categories, how- and 1; and TM 4, 3, and 2 were statistically more ever, represent large, contiguous nonvegetated cat- interpretable than image type TM 5, 3, and 2 This egories. Perhaps the interpreters were better able

TABLE5. SUMMARYOF IMAGE RANKINGSFROM THE PLUMASFOREST STUDY SITE LANDSAT-4 ~NTERPRETATION TESTS. RESULTSARE BASEDON INDIVIDUAL RESOURCECATEGORIES. THELINES TO THE RIGHTOF THE RANKEDPERCENT CORRECT CORRECT/PERCENTCOMMISSION ERROR VALUES (%C/%CE) ARE NOT SIGNIFICANTLYDIFFERENT AT THE 95 PERCENT CONFIDENCELEVEL BASED ON THE KAPPASTATISTIC Rank Cateeorv %CI%CE Rank Catezory %C/%CE - High-Density Conifer Low-Density Conifer TM 4,3&2 MSS 4,2&1 TM 5,3&2 TM 5,4&3 TM 5,4&3 TM 4,3&2 MSS 4,2&1 TM 5,3&2 liardwoodlCon~er Hardwood TM 4,3&2 MSS 4.28~1 TM 5,4&3 TM 5,4&3 MSS 4,2&1 TM 4,3&2 TM 5,3&2 TM 5,3&2 Rncsh Crassland TM 5,4&3 TM 5,3&2 TM 4,3&2 TM 5,4&3 MSS 4,2&1 MSS 4,2&1 TM 5,3&2 TM 4,3&2 Bare Soil Rock MSS 4,2&1 MSS 4,2&1 TM 5,3&2 TM 5,3&2 TM 5,4&3 TM 4,3&2 TM 4.3&2 TM 5,4623 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1985

TABLE6. ERRORMATRIX BASED ON A POOLOF SIX~NTERPRETERS FROM THE PLUMASFOREST STUDY SITE FOR IMAGE TYPETM 5,4, AND 3 Ground Truth Percent Co~nlnission P.I. Results 1 2 3 4 5 6 7 8 Total Error 1 Hi Conifer 2 Lo Conifer 3 HardJCon 4 Hardwood 5 Brush 6 Grassland 7 Bare Soil 8 Rock

Total 60 60 60 60 60 60 60 60 480 Percent Correct 62 77 52 37 80 63 62 20 Total Correct = 271 Percent Correct = 56.5 Total Error = 209 Percent Error = 43.5 Estimated Kappa = 0.502 Estimated St. Dev. = 0.026

TABLE7. ERRORMATRIX BASED ON THE "BEST"~NTERPRETER FROM THE PLUMASFOREST STUDY SITE FOR IMAGE TM 5, 4, AND 3 Ground Truth Percent Commission P.I. Results 1 2 3 4 5 6 7 8 Total Error 1 Hi Conifer 2 Lo Conifer 3 HardICon 4 Hardwood 5 Brush 6 Grassland 7 Bare Soil 8 Rock Total Percent Correct 90 100 100 20 70 70 90 50 Total Correct = 59 Percent Correct = 73.8 Total Error = 21 Percent Error = 26.3 Estimated Kappa = 0.700 Estimated St. Dev. = 0.056 to exploit associative characteristics on the indi- be either TM bands 5, 4, and 2 or TM bands 5, vidual MSS test images which covered larger areas 4, and 3 where both the reflective and absorptive than did the individual TM test images. properties of the diverse cover types can be ex- ploited. CONCLUSIONS Based on our analysis of image products, statis- ACKNOWLEDGMENTS tical data, and interpretation test results, the fol- lowing two conclusions are drawn regarding the This research was supported under the Landsat spectral quality of Landsat-4 TM and MSS data: Image Data Quality Assessment (LIDQA) Program, Contract #NAS5-27377, R. N. Colwell, Principal Color composites which include TM band 5 with Investigator, National Aeronautic and Space Admin- other visible bands (TM bands 1-3) are inferior istration, Goddard Space Flight Center, Greenbelt, for discriminating important forest types which MD. Grateful acknowledgment is extended to the are highly reflective in the photographic infrared staff of the Remote Sensing Research Program and (TM band 4). to Mr. Darrel Williams, Science Representative, The optimum combination of reflective TM NASA-GSFC for their continued scientific and tech- bands for discriminating forest cover types would nical support throughout this project. MSS DATA FOR FOREST SURVEYS

REFERENCES DeGloria, S. D., 1984. Spectral variability of Landsat-4 Anuta, P. E., Bartolucci, L. A., Dean, M. E., Valdes, Thematic Mapper and Multispectral Scanner data for J. A., and Valenzuela, C. R., 1984. Landsat-4 MSS selected crop and forest cover types: IEEE Transac- and Thematic Mapper design quality and information tions on Geoscience and Retnote Sensing, v. GE-22, content analysis: ZEEE Transactions on Geoscience no. 3, pp. 308-311. and Remote Sensing, v. GE-22, no. 3, pp. 222-236. Hay, C. M., 1982. Remote sensing measurement tech- Benson, A. S., and Dummer, K. J., 1983. Analysis of nique for use in crop inventories: in Remote Sensing photo interpretation test results for seven aerospace for Resource Management, (C. Johannsen and J. image types of the San Juan National Forest, Colo- Sanders, editors): Soil Conservation Society of rado: Proceedings, 49th American Societv of Photo- America, Ankeny, Iowa, pp. 420-433. grammetry Meeting, Washington, D.C., National Aeronautics and Space Administration (NASA), 121-130. 1983. Thematic Mapper computer compatible tape Colwell, R. N., 1978. Interpretability of vegetation re- (CCT-AT, CCT-PT): Interface Control Doc. LSD- sources on various image types acquired from earth- ICD-105, Rev A. NASA, Goddard Space Flight orbiting spacecraft: Journal of Applied Plzotogram- Center, Greenbelt, MD, December, 184 pp. metric Engineering, v. 4, no. 3, pp. 107-117. Society of American Foresters, 1980. Forest cover types Congalton, R. G., and Mead, R. A., 1983. A quantitative of the United States: Society of American Foresters, method to test for consistency and correctness in pho- Washington, D. C. tointerpretation: Photograintnetric Engineering and Williams, D. L., Irons, J. R., Markham, B. L., Nelson, Remote Sensing, v. 49, no. 1, pp. 69-74. R. F., Toll, D. L., Latty, R. S., and Stauffer, M. L., Congalton, R. G., Odenvald, R. G., and Mead, R. A., 1984. A statistical evaluation of the advantages of 1983. Assessing Landsat classification accuracy using Landsat Thematic Mapper data in comparison to mul- discrete multivariate analysis statistical techniques: tispectral scanner data: ZEEE Transactions on Geo- Photogram~netricEngineering and Reinote Sensing, v. science and Reinote Sensing, v. GE-22, no. 3, pp. 294- 49, no. 12, pp. 1671-1678. 302.

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