Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

damen, m. c. j.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856641294

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: IX Seiten, Seiten 551-956

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A,. A. Balkema

Identifier (digital):: 856641294

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,2)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 7 Human settlements: Urban surveys, human settlement analysis and archaeology. Chairman: W. G. Collins, Co-chairman: B. C. Forster, Liaison: P. Hofstee

Write comment:: Wegen zu enger Bindung kommt es teilweise im Original zu Textverlust.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Spectral characterization of urban land covers from Thematic Mapper data. Douglas J. Wheeler

Document type:: Multivolume work

Structure type:: Chapter

thresholds within the 67 spectral classes in order to reclassify the data into more definitive land cover categories. After working with the thermal data it was decided to add two more land cover categories to the classification. The light vegetation class was divided into a class of sparse vegetation with soil showing through, and another class of mostly soil with a little green vegetation remaining. The mixed pixels were divided into three groups instead of two. These categories include mixed pixels with a low percentage of vegetation (10-40 percent), medium vegetation response (around 0.50) , and mixed pixels with a dominant vegetation component (over 0.75). These two additional categories raised the number of land cover types that were identified to 14. Fran a thermal channel 6 histogram it was often possible to observe "breaks," or places where bimodal distribution curves crossed. It was mentioned, for example, that class 54 of the classification map represented both open water and coal. The mean multispectral signatures for these two cover types were nearly identical yet the thermal band demonstrated a clear distinction between the two categories. Other class separations were not as clear as in class 54. A more typical situation is class 56, where stubble fields were classed the same as the mixed cover in several trailer courts. The thermal channel indicted that agricultural fields were slightly cooler than their urban residential counterparts. After changes were made the map was again reclassified and grouped into the 14 cover types. The reclassified map with 122 classes is referred to as THRM67. 4 ACCURACY EVALUATION OF LAND COVER MAPS In order to compare the utility of the two land cover maps (THRM67 and CLUS67) an accuracy assessment was made on each. The first step was to geometrically correct the maps so that each classified pixel corresponded to a particular Universal Transverse Mercator (UTM) coordinate on the ground. A random sample stratified by map categories was selected as the procedure for assessing map accuracy. An ELAS module named "RANS" (uniform random sample) created a new data file from the geometrically corrected THRM67 classification map. This file contained the original classification data minus 350 sample locations (25 samples each from the 14 land cover categories). A cursor was trained on each sample pixel to determine the precise UTM coordinates for each sample. These coordinates were then plotted onto USGS 1:24,000 topographic maps. When all 350 samples were plotted, an investigation ccnmenced to determine the "ground truth" category for each of the sample sites. In an attempt to eliminate the bias, the map categories were not included on the maps taken to the field for ground investigation. When the "ground truth" determination was canpleted, the observed cover categories were compared to the map categories and a confusion matrix was constructed. Percentages were calcuated for the number of pixels correctly classified, pixels that were classified in the wrong category (errors of catmission), and pixels that were not placed in the correct categories (errors of omission). The map category marginal proportions were calculated in order to remove the over-represenation of small categories from the stratified randan sample (Card 1982). These marginal proportions were multiplied by the percentages of correctly classified pixels and then surtmed to provide the overall map classification accuracy (estimated probability correct). The same 350 samples derived from the THRM67 classification were also used to build a confusion matrix for the CLUS67 map. The CLUS67 map demonstrated an overall map accuracy of 80.2 percent for the fourteen land cover categories, with a 0.05 confidence interval between 0.7581 - 0.8459. The THRM67 map, on the other hand, showed an accuracy of 91.6 percent with a 0.05 confidence interval of 0.8866 - 0.9434. The overall accuracy assessments fran each map were compared using a "difference of proportions" test to determine if there was a significant difference between them. There definately was a significant improvement in classification accuracy using thermal channel 6 data The mapping accuracies for each class were also compared and tested for significant differences at 0.05 level. In these particular cases a "t" statistic was computed rather than a "z" value since the number of sample sites per class was lower than 50. The results of these tests indicate that there is a significant improvement in detecting asphault, coal, water, and sparse vegetation by using thermal thresholds. The most dramatic improvement was observed in the water and inert classes. CLUS67 maintained a high degree of classification error between water and coal. Water was also carrmonly classified as dark inert material and asphault. These descrepancies were almost entirely eliminated by use of the thermal data. Another significant advantage of using thermal thresholds was in discriminating between cool agricultural areas and the warmer urban and natural grass cover types. Even though a minor confusion of classes is still evident after using a combination of multispectral and thermal classification techniques, the land cover map of the Salt Lake study area derived from this study is a substantial improvement over previous TM, IMS, and MSS classifications of urban areas. REFERENCES Card, D.H. 1982. Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogranmetric Engineering and Remote Sensing 48:431-439. Carlson, T.N., & F.E. Boland 1978. Analysis of rural-urban canopy using a surface heat flux/temperature model. Journal of Applied Meteorology 17:998-1013. Chavez, P., Jr., S.C. Guptill, & J.A. Bowell 1984. Image processing techniques for Thematic Mapper data. ASP-ACSM Technical Papers Annual Meeting, p. 728-743. Clark, J. 1980. Ihe Effect of Resolution in Simulated Satellite Imagery of Spectral Characteristics and Computer-Assisted Land Use Classification. NASA, Jet Propulsion Laboratory Report 715-22, Pasadena, CA. Quattrochi, D.A. 1983. Analysis of Landsat-4 Thematic Mapper data for classification of the Mobile, Alabama metropolitan area. Proceedings 17th International Symposium on Remote Sensing of Environment. Ann Arbor, MI, p. 1393-1402. Sheffield, C. 1985. Selecting band combinations from multispectral data. Photogranmetric Engineering and Remote Sensing 51:681-687. Todd, W.J. 1978. A Selective Bibliography: Remote Sensing Applications in Land Cover Inventory Tasks. Technicolor Graphics Services, Sioux Falls, SD. Wang. S.C. 1985. Ihe potential of Landsat Thematic Mapper data for applied research. University of New Orleans, New Orleans, LA. (Manuscript for article) . Ward, J.H., Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58:236-244. Welch, R. 1982. Spatial resolution requirements for urban studies. International Journal of Remote Sensing. 3:139-146.

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