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:: 856343064

Title:: Remote sensing for resources development and environmental management

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

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Classification of land features, using Landsat MSS data in a mountainous terrain. H. Taherkia & W. G. Collins

Document type:: Multivolume work

Structure type:: Chapter

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 87 Classification of land features, using Landsat MSS data in a mountainous terrain H.Taherkia & W.G.Collins Remote Sensing Unit, Civil Eng. Dept., Aston University, UK ABSTRACT - This paper evaluates Landsat MSS data in a hostile terrain located in central Alborz, north of Iran. Correlation of the Three hands of the image were evaluated, and separation of differnt ground features was examined. Training areas of different categories were located on the image using field work. Data from the training areas then were manipulated to eliminate unwanted pixels. Mean and standard deviation of the categories were calculated and data which fell in the range of three standard deviations was selected. For the execution of the training areas a selected subscene was chosen and then classified. 1. INTRODUCTION The study area is located in central Alborz, north of Iran in a very hostile terrain, where the Haraz Road traverses through. Two Landsat MSS secenes recorded at 4th September 1972 and 13th July 1977 were used for this study. After applying geometric and radiometric corrections, resampling at 50x50 metres, registerig two multitemporal images together on the DIPIX, 35 subscenes (e each of 40x40 pixels) were chosen along the road for further investigation. A FORTRAN program was developed to enable plotting of the subscenes in eight colours using a Tekronix ink jet colour plotter. The package developed can perform mathematical manipulation of the data to produce a variety of combinations viz: additions, subtractions, ratios, and vegetation index/formulae etc. and plots the output data in the form of a histogram. These statistical analyses have been applied on a subscene which consists of landslide, coarse lava, scree, vegetation cover and limestone. In processing the subscene, plots of single bands in eight colours (from black to white) were found to be most acceptable to the human eye. However, due to low values of vegetation in band 5 and higher values of them in band 7, different combinations of the bands showed further improvement in the identification of ground features. Subtraction of the two bands (B7-B5) showed clearly the boundaries of ground vegetation (Figure la). The high reflectivities of screes and bare soil in both bands 7 and 5 when they were combined in vegetation index (B7-B5)/(B7+B5) caused low values in the resultant image (Figure lb). Hence all unvegetated areas appeared dark in the vegetation index. The results of the different combinations were compared against the ground truths at same the scale (1:23,000). The outcome is shown in Table 1 comparing results of different combinations of different bands and the ground truths derived from existing maps of the study area. As can be seen from the eight different combinations in Table 1, the best results are obtained after subtraction which allows discrimination of vegetation covered areas (units 1, 2, 3 and 4). The worst combination appears to result from addition of the bands, which provides no basis for significant discrimination of the vegetated areas. The vegetation index, shown in Table 1 and Figure 2 highlights vegetated areas, but some areas of dark shadow are also classified in the vegetation class. Hence the use of the vegetation index in unknown areas produces imperfect results. Despite the recommendation of many Remote Sensing texts that a vegetation index should be used as a tool to distinguish vegetated areas in the Landsat MSS images, it seems, on the basis of this research that the best results are obtained by subtracting band 5 from band 7 (B7-B5). Subtraction of band 4 from band 7 (B7-B4) in both September and July images (figure 2) resulted in over- classification in three vegetated areas and under-classification in one area located on the west facing slope of the Valley (Table 1). The shadow is responsible for misclassification in the number 3 vegetated area, whereas mixed pixels of high reflection of vegetation in boundaries are responsible for over-classification. Where band 5 was subtracted from band 7 (B7-B5), two sunfaced vegetated areas (units 1 and 2) were found to match the base map (Figures 1 and 4) whereas two low illuminated areas (units 3 and 4) mismatched (Table 1 and Figure 3). Additions of different bands in each of the September and July images revealed no more information in this case. Only by the additions of both band 5 and band 7 (B7+B5) and band 7 and band 4 (B7+B4) does there seem to be slight improvement in landslide discrimination. It should be mentioned here that addition of two bands in image processing systems in a vast area showed a better result in linear features recognition. In addition to conventional image procesing techniques, the use of either a vegetation index or subtraction can assist the interpreter in the identification of unstable areas between bare soil and in the classification of vegetated areas. Both procedures should be performed after applying radiometric correction to the MSS image. Without haze removal, any mathemtical manipulation on the data will not produce a reliable result.

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