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Land use survey using remote sensing and geographical information systems
Adv. Space Res. Vol. 12, No.7, pp. (7)395—(7)405, 1992 Printed in Great Britain. All rights reserved.
0273—1177/92 $15.00 Copyright © 1992 COSPAR
LAND USE SURVEY USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS Yuzo Suga Departmentof Civil Engineering, Hiroshima Institute of Technology, 2-1-1, Miyake, Saeki-ku, Hiroshima 732-51, Japan
ABSTRACT A hybrid system which integrates Remote Sensing (RS) data and Geographical Information Systems (GIS) information, has been developed for land use survey in Hiroshima city. The system consists of three interrelated subsystems, i.e., a personal computer, a minicomputer and an engineering workstation: The system can handle an image data base consisting of satellite digital images such as Landsat TM and Spot HRV data, a line map data base consisting of topography and land use zoning, and an updating land use information data base consisting of raster and vector data such as remote sensing data and digital mapping data. This paper, describes the implementation of the integration of multiple sensors/multi—temporal remote sensing images with digital mapping data. The application of the system to a land use survey is discussed with respect to a method of extracting land use information based on remote sensing and geographical information systems. INTRODUCTION Remote Sensing has some characteristics that other observation methods do not have, such as collecting data around the Earth in a comparatively short period or performing a large survey over a long space of time. Its utilization spreads in such various spheres such as geosphere, hydrosphere, and atmosphere, showing interdisciplinary applicability. Currently it has become possible to use remote sensing data delivered by several satellites, and multiple satellite sensor analysis and multi—temporal data analysis approach have been developed. For the effective use of these RS data for land development investigation, it is necessary to integrate GIS, that is the processing of geographical information data, into the analysis system. To build such a system, it is necessary to combine the various RS data, maps, and attributive data, to update the data, and to fulfill the function of data base /1/. The first information source of geographical data is the existing maps and the second is the investigation and statistical data obtained by surveys. How to effectively digitize the geographical data of the existing maps has been a problem awaiting solution since the beginning of GIS. At the early stage of GIS, the existing maps and survey/statistical data were the only information source. Introducing RS data turns it into a spatial information system. Considering the ever increasing demand for map data, it has become necessary to implement the basic map data into various geographical information systems, and to operate these systems. Development and utilization of various geographical information data such as map retrieving system and planning/simulation system will improve either public and private fields contributing for instance social welfare. In this paper we describe the development and the application of the integration system of RS and GIS for the survey of land use using a personal computer, a minicomputer, and an exclusive workstation. LAND USE SURVEY The term “Land Use” is defined as a state of land which is used or preserved for a long period of time to maintain human activity or production activity. From another point of view, land use may be considered as a situation of the regional activity projected on the land in a figurative and visible form /2/. In land use survey, it is necessary to determine first a type of land use (7)395
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classification. Since a definition of a classification is different according to the purpose of the survey, analysis, and planning. It is appropriate to consider following items in defining classification. (1) Purpose of use, (2) Area of the investigation subject, (3) Accuracy of survey, that is the scale of map used or the unit of data, (4) Whether the emphasis is put on the functional side of the land use or the physical cover of the land. llmong the present land use surveys in Japan, one of the most multipurpose ones may be to make the Land Use Map on a scale of 1:25,000 by Geographical Survey Institute. This map is most widely used to show the present condition of the land use covering the plain fields. The land use type in this map is classified into three large groups: urban area, farmland, and woodland, respectively divided into 15, 9, and 12 subdivisions. Further, it has become necessary to monitor the time series change of land use, arrange and manage land use information in the original data, and develop a new method to provide flexibly the data according to its use. Moreover the introduction of RS technique is considered for the monitoring of the time series change of land use over a large area. The original data of regional information are generally not adequate. The census and the statistics are generally made based on an administrative areas, such as a metropolis, prefecture, city, town, village etc., however, a smaller division is required for a specific research field. It often happens that the district of aggregation unit of research differs for every research; so the boundaries of research districts are not constant. Moreover, even in the same research activity, the manner of fixing boundaries may vary from time to time. Changes of indication of residence also happen. For that reason, when time series values are compared with different research times, there must be an unification of land use information. Collecting the land use network and boundary data implies solving the above mentioned difficulties. LAND USE SURVEY USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS For land use survey it is necessary to present the items of classification according to the objects of research, analysis, and planning. When the items are presented, it becomes a subject of discussion to put the emphasis whether on the physical cover of the land or the functional side of the land use. A land use survey integrating RS and GIS should be duly considered as a method to solve this problem. For the former the statistical classification of RS digital data and for the latter map/attribute disposition of GIS may satisfy the requirement. There are two methods to integrate RS data with such geographical data as digitized maps and survey/statistical data /3/. The first one consists in integrating the two types of data after separate processing. In this case, geographical data are not necessary and simple data file will be sufficient. The second one is a method where RS data are built in GIS data base and processed with other geographical data, which can be done by two ways. The first one is to insert directly into the data base the result of such preprocessing as the removing of various strains from the image; the second one is to insert the results of such information extraction processings as classification and calculation of vegetation index. The merit of integrating RS data with geographical data is as follows: RS data can be used for analysis as up—to—date land cover, vegetation, as well as land surface temperature data, which is not possible with the existing geographical data. It also becomes possible to make a satellite image map of high accuracy with RS image as a background, however, the following problems may be mentioned: if the resolution or the positional accuracy of RS data is greatly different from the geographical data’s one, using the integration becomes difficult; when RS data is inserted into GIS data, the data volume becomes a critical point. In the past the unit area of GIS application was city, town, village and the whole country at the most. But recently RS can provide thematic images concerning to the whole surface of the Earth, and the subject of GIS has also been expanded to the whole globe, with the importance of the global monitoring of environment as a background. In GIS can be found both infrastructure informations with several centimeters position accuracy to manage water supply, drainage, roadway, electricity and gas, and global monitoring informations related to environment on the whole Earth with a positional accuracy of several kilometers. RS is now suited to GIS applying to comparatively large areas. Although information concerning cities hitherto has been mapped in an analog form, the improvements of information processing techniques lead to spatial digitization and automation.
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As it is shown in Figure 1, this paper describes our study concerning the development of land use survey system to integrate the following informations into a spatial information concerning Hiroshima City. That is, we filed information about land cover into raster data through RS data processing, and converted census’s results and existing ~ap information into vector data through digital mapping process. RS data are two dimensionally raster data stored leaving no space between them, and essentially different from vector data, which represent the position and length of a line segment by only its first and last points. Processing these data with a personal computer, needs to convert vector type data into raster type one. Multi-spectral image data and processed image data classified with the maximum likelihood method can be mentioned as raster data. To process the multi—spectral data with a common personal computer system, it is necessary to use the full color frame memory which can display full colors of 16.77 millions per pictures element in 640 pixels x 400 lines. In this manner, the false color composite image can be generated. The data concerning the administrative and regional boundaries of maps are vector data generated with a digitizer. Type of data concerning these regional boundaries are processed with a coordinate value unit, using a digitizer software. The following operations were performed for raster and vector data integration. For raster data, we developed an identification and registration method based on the spatial resolution concerning information related to land cover and land cover change detection of RS data. As for vector data, we developed a method to digitize various subjects of the ready-made map using a simplified digitizer. Moreover, we investigated the automated management of land use information in the reasonably integrated part of the urban area using both above—mentioned data as a spatial structural model of the city. In this case the system was designed by dividing spatial information into three steps, that is, data management, mapping, and analysis. Fundamental hardware of our equipment consists of a personal computer loaded with frame buffer, a minicomputer system connected with image memory and graphic terminal, and a simplified digitizer, and is available in any ordinary laboratory. The software for this hardware, centering around a modular package, has a phased structure and is able to perform the image processing and graphic processing with the aid of interactive devices.
[~mote Sensing Systems Landsat, Spot _____________
Preprocessing Land Use/Cover [ç~assiflcation
Geographical Information Systems National Census, Conventional Thematic Maps Digitizationi Digital Map I
Raster/Vector Processing Identification, Registration Integrating Remote Sensing and Geographical Information Systems f~~tiaiData Base for Land Use Survey Classification Land Use/ Landscape Land Use Survey of Land Use Cover Change Evaluation for Characteristics Detection ________ Urban Planning Land Use Phenomena Analysis Image Fig. 1.
Color Coded Landscape Grid/Polygon Image Image ___________
Land Use Zoning Image ________________
A Concept for Land Use Survey Using Remote Sensing and Geographical Information Systems in HIT
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APPLICATION IN LAND USE SURVEY Land Use Phenomena Analysis To assess the capacity of this system, a typical city area representing the local urban area was selected and an analysis of land use phenomena in Hiroshima City influenced by the impact of the urban development tendency was attempted. This land use phenomena analysis uses a multivariate model which can take into account the qualitative difference of the land by the graded evaluation method /4/. Here we show, with the aid of a personal computer, some examples of the application of the land use phenomena analysis in the local urban area, using such social statistical data as population combined with the urbanization ratio, the ratio of residential area, and the ratio of vegetation area obtained from RS, as instruction variables. The area of the analysis is proportional to the area shown in the topographical map on a scale of 1:50,000 centering around the city. The study area for analysis is set according to the 1/2 standard areal grid in Japan and consists of 1,600 divisions (40 divisions in the directions of latitude and longitude respectively) For the evaluation factors of land use phenomena, data were prepared concerning two indices such as the population index and the land use index. Plate 1 shows the display of the area classification color image indicating the result of the analysis. For the divisional unit for the evaluation of the type, standard of the areal classification is set on the basis of the standard areal grid which is adopted by the Digital National Land Information of Japan and the type can be classified. For the land use/cover indices, Landsat MSS data taken on 11 Sept 1979 were used. MSS data were geometrically preprocessed with a residual error lower than one pixel to register it to the study area. Identification to the 1/2 standard areal grid was made, setting such classification items as urban area, residential area, vegetation area, and water area. Basing upon the above mentioned analysis data, a new data set was made for study purposes by combining seven variates: total population, primary industry population, secondary industry population, and tertiary industry population of the population index and densely built up area, residential area, and vegetation area of the land use/cover index. In each grid of the study area the characteristics of the land use phenomena were considered using the principal component analysis. The analysis clearly proved that in the first and second principal components, eigenvalue was not less than 1.0, and accumulative contribution ratio was 74.5%; 87.1% of information can be collected adding the third principal component. We could make a land use phenomena analysis in the study area by classifying these three principal components into eight kinds of areal characteristic patterns. Land Use/Cover Change Detection This system allows the use of RS data as a land information data source. The land cover change can be detected from RS data obtained at different dates. First, the data obtained at two different dates are geometrically corrected and classified using the maximum likelihood method. Then the land cover change is detected from the classification result. The data used are RS data of Landsat MSS (1979) and TM (1984); Hiroshima City is selected as a study area. Since these RS data are already geometrically corrected, the sizes of a pixel are 57m x 57m and 28.5m x 28.5m respectively. The image size is 400 pixels x 320 lines. For the detection of the land cover change thanks to the grid type, the grid analysis is based on the 1/2 standard areal grid. The classification images of MSS and TM are read into a video RAM in the personal computer; the images are alternately displayed on the screen and the state of change can be interpreted. Moreover, the integrated classification data consisting of the land cover classification images of MSS and TM are read into CPU memory and the image data for display are read into video RAM, and a cursor is moved on the displayed image as shown in Plate 2 (a). Then a block divided by grid is appointed and extracted, and the magnified images are presented; so, the state of change can be interpreted quantitatively. Images shown on Plate 2 (b) represent the land cover classification images at two different dates in a block of .a 1/2 standard areal grid, with the ratios of possession of each classification item at the bottom of the screen; so, the change of the land cover can be easily interpreted visually. As for the land cover change detection using a method of polygon type, an image is first made for administrative district (for city or ward) as shown in Plate 3 using vector data. Then, based on the results of two different
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Land cover classifications, the land cover change pattern is set up. In this case, according to the purpose of the detection, single or plural classification items are set up for the period, and the classification item for the considered period can be set up. The detection of land cover change is performed, by comparing an item in the former classification with the same item in the latter; the detected image of land cover change is generated as shown in plate 4. The detection of land cover change using high accuracy satellite image map was also attempted. Spot is implemented HRV which is a sensor with higher spatial resolution than Landsat TM. A panchromatic mode observes the visible range with spatial resolution of lOm. Currently such earth observation satellites have been launched which have different individual characteristics so the same area on the Earth can be observed by different sensors. If new image information could be integrated by combining these different sensor data, it would be expected to obtain new remotely sensed image which can’t be collected by one sensor. Merging the image data obtained by different satellites needs to registrate both images geometrically with high accuracy. To make images for each spectral range with lOm spatial resolution, by merging lOm resolution Spot image data collected in the spectral range of 0.5 — 0.73um with the image data of various spectral ranges collected by Landsat TM with spatial resolution of 28.5m, the following procedures should be followed /5/. Using resampling method, Landsat image data with pixel size of lOm, which corresponds to a pixel ratio of one to one to Spot image data, was generated; the nearest neighbour method was adopted for the resampling. Landsat image data with spatial resolution of lOm generated by the above mentioned method contains theoretically only the spatial frequency component with wavelength of 30m. On the other hand, Spot image data contain the spatial frequency component with wavelength of lOm. Therefore, the digital merging of Spot and Landsat data was adopted. Such a method tends to preserve the spectral pattern and the resolution of the image. A true color composite image digitally merged is shown in Plate 5. In this method, the resolution of the image increases with the amount of HRV component. A linear combination method was used to adjust HRV component using weight coefficient. Giving weight to HRV provides good contrast and good color tone results from addition of TM multi-spectral information. Plate 6 shows the integrated image of block road generated by GIS. As we have seen, the land cover change can be detected using satellite data collected from different sensors and at different dates. We also considered the method and its accuracy using Landsat TM (1989) and Spot HRV data (1987) to detect the land cover change. In this study we examined the following three detecting methods. (1) Using the above mentioned digital merging method, we integrated the lOm resolution HRV—PA data collected at two different dates to the 30m resolution TM data to generate a digitally merged image. Applying the maximum likelihood method to this image, land cover change was detected. (2) The normalized images of TM data at two different times were generated. The change detection was performed by level slicing. (3) Change detection was performed by applying the maximum likelihood method independently to TM data collected at two different dates. An inland outskirt area in Hiroshima City where preparation work of housing site had been widely advanced was chosen as a training site. Method (1): A new image of high accuracy with a lOm spatial resolution was generated by applying the digital merge processing to HRV-PA data and TM ch 1 to 4 data. We could detect the change of land cover which occurred during the period between the two different dates using a high accuracy integrated color composite image with ch 3 (red), ch 2 (green), and ch 1 (blue); the changed part was represented in red. With this part as training data in supervised classification, the changed part is extracted by applying the maximum likelihood classification. Method (2): A difference image of two images is generated by applying the biband processing ch 3 and ch 4 to TM data at different two dates. In this case beforehand normalization was made to each channel at each date and the pixel values on the changed part were calculated. Method (3): After the maximum likelihood method classification was applied independently to TM data at different two dates, the land cover changes at two dates were detected. We compared these three methods to detect the land cover change and came to a conclusion that the first one showed the best fit to the research data on the site.
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Landscape Evaluation RS data is a spectral information concerning the Earth surface and the analysis result thereof is also a planar information. The RS data, however, is not only valuable as a planar information, but also by combining with other informations such as topographical information, allowing a topographical analysis with a higher added value. Landscape is an important factor to evaluate the land type from a viewpoint of land use or environment. Generating a landscape image using RS data is performed mathematically using the central projection method. For this purpose, spectral data and topographical data are necessary. When generating a landscape image from RS data, TM data of each image is used as a color data. The elevation corresponding to each pixel (that is a digital terrain model) is used as a topographical data. An area as wide as the prescribed topographical map is selected for landscape image generation and RS image data of the area is corrected geometrically. On the other hand, the digital terrain model is generated using contour data on the topographical map. These two kinds of data are transformed into the landscape image coordinate system using the central projection method. To perform the registration of both data, we tried to make a numerical elevation grid data with higher resolution. In this study, to make a new digital elevation data corresponding to the TM data, we transformed the numerical elevation grid data of 50m, using bilinear method, into the digital terrain model corresponding to a pixel of Landsat TM data with 28.5m spatial resolution. Using TM ch 1 to 3 image geometrically preprocessed data, a false color composite landscape image was generated. A set of landscape images produced with different points of view made it possible to show a continuous representation of the landscape of land use. It is also possible to use the color image corresponding to the maximum likelihood classification. Using the digital terrain model, a color classified image at each optional height value can be produced. It may also be used for topographic analysis such as the slope estimation image made by processing the gradient value calculated from the topographic element of ~the elevation data with the level slicing method and classified with color. An example of application of the landscape image generation, is shown in Plate 7. The landscape image generated with Landsat TM data can be integrated with color coded vector data such as the land use planning area and boundary line representing vector data such as administrative boundary and road network and processed with the raster data of the classification image. The study area was a hill area on the west of Hiroshima City under development. In this case it is required to transform the vector (boundary line data of the land use planning area) read from a map by the digitizer into raster data. In this way, the land use and environment information of the city can be caught spatially and visually. The landscape image using Landsat TM data and DTM data makes it easy to interpret the topographic information, resulting in a good integration effect of the Earth surface information and the topographic information. Land Use Survey for Urban Planning Land use planning should be carried out with appropriate guidance and control. Most of the buildings of a city are individual or company owned. Geographical construction of a city is represented by a combination of block and road network informations, the block being a basic unit zone, and its position being represented by a polygon system with elements such as points, lines and planes and their connections. In Japan land use zoning is the basic system for land use planning. The regulations which intend to control from view points such as vacant land, height, and structure were gradually prepared. Three fundamental elements of land use zoning are residence, commerce and industry. Moreover under the existing system land use zoning is divided into eight sub—districts: first class exclusively residential zone, second class exclusively residential zone, residential zone, neighbourhood commercial zone, commercial zone, semi—industrial zone, industrial zone, exclusively industrial zone. Others than the above mentioned regulations, site coverage, floor space index and slant line restriction due to the building lot were also added. Plate 8 shows an example of Hiroshima city town plan general map digitized using GIS’s exclusive engineering workstation (ARC/INFO) /6/.
CONCLUSION
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Through this study we proved that RS/GIS integrated system which was developed using a personal computer and a minicomputer systems was effective as a supporting system for the land use survey. Raster/vector data integrated treatment system manufactured by combining the geographical information with processed RS data, allows to digitize and present with color codes the areal grid and polygon system representing the land use information system. Combination of RS data and social statistical data makes it possible to analyze synthetically the land use structure characteristics and social/ economical characteristics, and can be used as the supporting system for the land use phenomena analysis in land use planning. As a result it becomes also possible to classify the land use phenomena in the study area and analyze the trend and content of the land use. Moreover with the rearrangement of analytical data, the classification of the areal characteristics of the study area can be done quickly, the system is effective for making the basic documents of land use planning. When the digital map is prepared, multivariate analysis with variables which are the explanatory items for land use and environmental phenomena becomes possible. Changing the variables, simulation in various phenomena analysis will also become possible. The development of this system with a personal computer allows to detect the land use/cover change of the specified administrative area, and proves the usefulness of RS data for administrative information. In particular it is proved that the calculation of the detected area of the land use/cover change made by high accuracy satellite image maps and processed with the maximum likelihood method gives the most approximate value to the survey data on the site. For the registration of multisensor and multitemporal data, it is necessary to perform a local detailed geometrical correction. If the detection of the land cover change is conducted using some past TM data, the detection accuracy is expected to improve by using the digital merging method, which will be efficiently used for the time series analysis of land use/cover change. With the generating of the landscape image, it become possible to merge three-dimensionally the geographical position and the state of land use of the development planning area. By combining RS data with GIS data such as the land use landscape simulation which uses the digital terrain model data, it will be possible to develop utilizations which will produce higher value informations. The existing map information will be integrated with various data base and measuring systems in the future. Integration of RS and GIS for the purpose of land use survey allows to apply the system to various fields such as land use planning and environmental planning, based not on the existing idea of a mere map, but on the map data base which is a spatial information system merging satellite information, map data, and attribute information such as the administrative information corresponding to the considered application. In the future the development of RS/GIS integrated system, with a strong link between the exclusive GIS workstation and the computer system for RS data analysis and the development of its application, is expected to advance. ACKNOWLEDGMENTS This research has been supported in part by grants from Tsuru Foundation for Advancement of Education and Research. The author would like to thank Dr. Sotaro Tanaka and Toshiro Sugimura of Remote Sensing Technology Center of Japan for their helpful comments and suggestions in this research. REFERENCES 1. O.Zhou, A Method for Integrating Remote Sensing and Geographic Information Systems, Photogranunetric Engineering and Remote Sensing, Vol.LV, No.5, Parti, 591—596 (1989). 2. H.Nakamura ed., National Land Survey, Gihoudou Publishers, 1984. 3. K.Tsuchiya ed., Introduction of Remote Sensing, Asakurashoten Publishers, 1990. 4. Y.Suga, S.Tanaka and T.Sugimura, Land Use Phenomena Analysis Using Spatial Data Base, International Archives of Photograinmetry and Remote Sensing, Vol.27, Part B4, 374—383 (1988). 5. S.Tanaka and T.Sugimura, Digital Merging of Spot HRV and Landsat TM Data in the Small and Low Undulation Area, J.The Remote Sensing Society of Japan, Vol.8, No.1, 51—58 (1988). 6. ESRI, The Geographic Information System Software, Users Guide ARC/INFO Volume 1, 1989. JASR 12:7—Z
Plate 1.
Color Coded Grid Image of Land Use Phenomena Classification (Hiroshima City)
(a) Land Cover Classification Image and the 1/2 Standard Areal Grid
•
~
_
:-•-~
-~
(b) Magnified Image of a Grid Assigned by a Cursor Plate 2.
Land Cover Change Detection Image (7)403
Plate
3.
I
Plate 5.
Land Cover Classification Image of Administrative Area of Hiroshima City
Plate 4.
Land Cover Change Detection Image in Administrative Area of Hiroshima City
Plate
Composite Image using by Digital Merged Image and GIS Data
ii ~:“~i~.
1:50,000 Satellite Image Map Generated from Digital Merging of Landsat TM and Spot HRV Data (Hiroshima and Vicinity) ©CNES 1987, SPOT®
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6.
Plate 7.
Landscape Image Generated from Landsat TM and Digital Terrain Model Data (Western Part of Hiroshima City)
Plate
Land Use Zoning Image of Hiroshima City Produced by GIS
8.
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0273—1177/92 $15.00 Copyright © 1992 COSPAR
LAND USE SURVEY USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS Yuzo Suga Departmentof Civil Engineering, Hiroshima Institute of Technology, 2-1-1, Miyake, Saeki-ku, Hiroshima 732-51, Japan
ABSTRACT A hybrid system which integrates Remote Sensing (RS) data and Geographical Information Systems (GIS) information, has been developed for land use survey in Hiroshima city. The system consists of three interrelated subsystems, i.e., a personal computer, a minicomputer and an engineering workstation: The system can handle an image data base consisting of satellite digital images such as Landsat TM and Spot HRV data, a line map data base consisting of topography and land use zoning, and an updating land use information data base consisting of raster and vector data such as remote sensing data and digital mapping data. This paper, describes the implementation of the integration of multiple sensors/multi—temporal remote sensing images with digital mapping data. The application of the system to a land use survey is discussed with respect to a method of extracting land use information based on remote sensing and geographical information systems. INTRODUCTION Remote Sensing has some characteristics that other observation methods do not have, such as collecting data around the Earth in a comparatively short period or performing a large survey over a long space of time. Its utilization spreads in such various spheres such as geosphere, hydrosphere, and atmosphere, showing interdisciplinary applicability. Currently it has become possible to use remote sensing data delivered by several satellites, and multiple satellite sensor analysis and multi—temporal data analysis approach have been developed. For the effective use of these RS data for land development investigation, it is necessary to integrate GIS, that is the processing of geographical information data, into the analysis system. To build such a system, it is necessary to combine the various RS data, maps, and attributive data, to update the data, and to fulfill the function of data base /1/. The first information source of geographical data is the existing maps and the second is the investigation and statistical data obtained by surveys. How to effectively digitize the geographical data of the existing maps has been a problem awaiting solution since the beginning of GIS. At the early stage of GIS, the existing maps and survey/statistical data were the only information source. Introducing RS data turns it into a spatial information system. Considering the ever increasing demand for map data, it has become necessary to implement the basic map data into various geographical information systems, and to operate these systems. Development and utilization of various geographical information data such as map retrieving system and planning/simulation system will improve either public and private fields contributing for instance social welfare. In this paper we describe the development and the application of the integration system of RS and GIS for the survey of land use using a personal computer, a minicomputer, and an exclusive workstation. LAND USE SURVEY The term “Land Use” is defined as a state of land which is used or preserved for a long period of time to maintain human activity or production activity. From another point of view, land use may be considered as a situation of the regional activity projected on the land in a figurative and visible form /2/. In land use survey, it is necessary to determine first a type of land use (7)395
(7)396
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classification. Since a definition of a classification is different according to the purpose of the survey, analysis, and planning. It is appropriate to consider following items in defining classification. (1) Purpose of use, (2) Area of the investigation subject, (3) Accuracy of survey, that is the scale of map used or the unit of data, (4) Whether the emphasis is put on the functional side of the land use or the physical cover of the land. llmong the present land use surveys in Japan, one of the most multipurpose ones may be to make the Land Use Map on a scale of 1:25,000 by Geographical Survey Institute. This map is most widely used to show the present condition of the land use covering the plain fields. The land use type in this map is classified into three large groups: urban area, farmland, and woodland, respectively divided into 15, 9, and 12 subdivisions. Further, it has become necessary to monitor the time series change of land use, arrange and manage land use information in the original data, and develop a new method to provide flexibly the data according to its use. Moreover the introduction of RS technique is considered for the monitoring of the time series change of land use over a large area. The original data of regional information are generally not adequate. The census and the statistics are generally made based on an administrative areas, such as a metropolis, prefecture, city, town, village etc., however, a smaller division is required for a specific research field. It often happens that the district of aggregation unit of research differs for every research; so the boundaries of research districts are not constant. Moreover, even in the same research activity, the manner of fixing boundaries may vary from time to time. Changes of indication of residence also happen. For that reason, when time series values are compared with different research times, there must be an unification of land use information. Collecting the land use network and boundary data implies solving the above mentioned difficulties. LAND USE SURVEY USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS For land use survey it is necessary to present the items of classification according to the objects of research, analysis, and planning. When the items are presented, it becomes a subject of discussion to put the emphasis whether on the physical cover of the land or the functional side of the land use. A land use survey integrating RS and GIS should be duly considered as a method to solve this problem. For the former the statistical classification of RS digital data and for the latter map/attribute disposition of GIS may satisfy the requirement. There are two methods to integrate RS data with such geographical data as digitized maps and survey/statistical data /3/. The first one consists in integrating the two types of data after separate processing. In this case, geographical data are not necessary and simple data file will be sufficient. The second one is a method where RS data are built in GIS data base and processed with other geographical data, which can be done by two ways. The first one is to insert directly into the data base the result of such preprocessing as the removing of various strains from the image; the second one is to insert the results of such information extraction processings as classification and calculation of vegetation index. The merit of integrating RS data with geographical data is as follows: RS data can be used for analysis as up—to—date land cover, vegetation, as well as land surface temperature data, which is not possible with the existing geographical data. It also becomes possible to make a satellite image map of high accuracy with RS image as a background, however, the following problems may be mentioned: if the resolution or the positional accuracy of RS data is greatly different from the geographical data’s one, using the integration becomes difficult; when RS data is inserted into GIS data, the data volume becomes a critical point. In the past the unit area of GIS application was city, town, village and the whole country at the most. But recently RS can provide thematic images concerning to the whole surface of the Earth, and the subject of GIS has also been expanded to the whole globe, with the importance of the global monitoring of environment as a background. In GIS can be found both infrastructure informations with several centimeters position accuracy to manage water supply, drainage, roadway, electricity and gas, and global monitoring informations related to environment on the whole Earth with a positional accuracy of several kilometers. RS is now suited to GIS applying to comparatively large areas. Although information concerning cities hitherto has been mapped in an analog form, the improvements of information processing techniques lead to spatial digitization and automation.
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As it is shown in Figure 1, this paper describes our study concerning the development of land use survey system to integrate the following informations into a spatial information concerning Hiroshima City. That is, we filed information about land cover into raster data through RS data processing, and converted census’s results and existing ~ap information into vector data through digital mapping process. RS data are two dimensionally raster data stored leaving no space between them, and essentially different from vector data, which represent the position and length of a line segment by only its first and last points. Processing these data with a personal computer, needs to convert vector type data into raster type one. Multi-spectral image data and processed image data classified with the maximum likelihood method can be mentioned as raster data. To process the multi—spectral data with a common personal computer system, it is necessary to use the full color frame memory which can display full colors of 16.77 millions per pictures element in 640 pixels x 400 lines. In this manner, the false color composite image can be generated. The data concerning the administrative and regional boundaries of maps are vector data generated with a digitizer. Type of data concerning these regional boundaries are processed with a coordinate value unit, using a digitizer software. The following operations were performed for raster and vector data integration. For raster data, we developed an identification and registration method based on the spatial resolution concerning information related to land cover and land cover change detection of RS data. As for vector data, we developed a method to digitize various subjects of the ready-made map using a simplified digitizer. Moreover, we investigated the automated management of land use information in the reasonably integrated part of the urban area using both above—mentioned data as a spatial structural model of the city. In this case the system was designed by dividing spatial information into three steps, that is, data management, mapping, and analysis. Fundamental hardware of our equipment consists of a personal computer loaded with frame buffer, a minicomputer system connected with image memory and graphic terminal, and a simplified digitizer, and is available in any ordinary laboratory. The software for this hardware, centering around a modular package, has a phased structure and is able to perform the image processing and graphic processing with the aid of interactive devices.
[~mote Sensing Systems Landsat, Spot _____________
Preprocessing Land Use/Cover [ç~assiflcation
Geographical Information Systems National Census, Conventional Thematic Maps Digitizationi Digital Map I
Raster/Vector Processing Identification, Registration Integrating Remote Sensing and Geographical Information Systems f~~tiaiData Base for Land Use Survey Classification Land Use/ Landscape Land Use Survey of Land Use Cover Change Evaluation for Characteristics Detection ________ Urban Planning Land Use Phenomena Analysis Image Fig. 1.
Color Coded Landscape Grid/Polygon Image Image ___________
Land Use Zoning Image ________________
A Concept for Land Use Survey Using Remote Sensing and Geographical Information Systems in HIT
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Y. Suga
APPLICATION IN LAND USE SURVEY Land Use Phenomena Analysis To assess the capacity of this system, a typical city area representing the local urban area was selected and an analysis of land use phenomena in Hiroshima City influenced by the impact of the urban development tendency was attempted. This land use phenomena analysis uses a multivariate model which can take into account the qualitative difference of the land by the graded evaluation method /4/. Here we show, with the aid of a personal computer, some examples of the application of the land use phenomena analysis in the local urban area, using such social statistical data as population combined with the urbanization ratio, the ratio of residential area, and the ratio of vegetation area obtained from RS, as instruction variables. The area of the analysis is proportional to the area shown in the topographical map on a scale of 1:50,000 centering around the city. The study area for analysis is set according to the 1/2 standard areal grid in Japan and consists of 1,600 divisions (40 divisions in the directions of latitude and longitude respectively) For the evaluation factors of land use phenomena, data were prepared concerning two indices such as the population index and the land use index. Plate 1 shows the display of the area classification color image indicating the result of the analysis. For the divisional unit for the evaluation of the type, standard of the areal classification is set on the basis of the standard areal grid which is adopted by the Digital National Land Information of Japan and the type can be classified. For the land use/cover indices, Landsat MSS data taken on 11 Sept 1979 were used. MSS data were geometrically preprocessed with a residual error lower than one pixel to register it to the study area. Identification to the 1/2 standard areal grid was made, setting such classification items as urban area, residential area, vegetation area, and water area. Basing upon the above mentioned analysis data, a new data set was made for study purposes by combining seven variates: total population, primary industry population, secondary industry population, and tertiary industry population of the population index and densely built up area, residential area, and vegetation area of the land use/cover index. In each grid of the study area the characteristics of the land use phenomena were considered using the principal component analysis. The analysis clearly proved that in the first and second principal components, eigenvalue was not less than 1.0, and accumulative contribution ratio was 74.5%; 87.1% of information can be collected adding the third principal component. We could make a land use phenomena analysis in the study area by classifying these three principal components into eight kinds of areal characteristic patterns. Land Use/Cover Change Detection This system allows the use of RS data as a land information data source. The land cover change can be detected from RS data obtained at different dates. First, the data obtained at two different dates are geometrically corrected and classified using the maximum likelihood method. Then the land cover change is detected from the classification result. The data used are RS data of Landsat MSS (1979) and TM (1984); Hiroshima City is selected as a study area. Since these RS data are already geometrically corrected, the sizes of a pixel are 57m x 57m and 28.5m x 28.5m respectively. The image size is 400 pixels x 320 lines. For the detection of the land cover change thanks to the grid type, the grid analysis is based on the 1/2 standard areal grid. The classification images of MSS and TM are read into a video RAM in the personal computer; the images are alternately displayed on the screen and the state of change can be interpreted. Moreover, the integrated classification data consisting of the land cover classification images of MSS and TM are read into CPU memory and the image data for display are read into video RAM, and a cursor is moved on the displayed image as shown in Plate 2 (a). Then a block divided by grid is appointed and extracted, and the magnified images are presented; so, the state of change can be interpreted quantitatively. Images shown on Plate 2 (b) represent the land cover classification images at two different dates in a block of .a 1/2 standard areal grid, with the ratios of possession of each classification item at the bottom of the screen; so, the change of the land cover can be easily interpreted visually. As for the land cover change detection using a method of polygon type, an image is first made for administrative district (for city or ward) as shown in Plate 3 using vector data. Then, based on the results of two different
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Land cover classifications, the land cover change pattern is set up. In this case, according to the purpose of the detection, single or plural classification items are set up for the period, and the classification item for the considered period can be set up. The detection of land cover change is performed, by comparing an item in the former classification with the same item in the latter; the detected image of land cover change is generated as shown in plate 4. The detection of land cover change using high accuracy satellite image map was also attempted. Spot is implemented HRV which is a sensor with higher spatial resolution than Landsat TM. A panchromatic mode observes the visible range with spatial resolution of lOm. Currently such earth observation satellites have been launched which have different individual characteristics so the same area on the Earth can be observed by different sensors. If new image information could be integrated by combining these different sensor data, it would be expected to obtain new remotely sensed image which can’t be collected by one sensor. Merging the image data obtained by different satellites needs to registrate both images geometrically with high accuracy. To make images for each spectral range with lOm spatial resolution, by merging lOm resolution Spot image data collected in the spectral range of 0.5 — 0.73um with the image data of various spectral ranges collected by Landsat TM with spatial resolution of 28.5m, the following procedures should be followed /5/. Using resampling method, Landsat image data with pixel size of lOm, which corresponds to a pixel ratio of one to one to Spot image data, was generated; the nearest neighbour method was adopted for the resampling. Landsat image data with spatial resolution of lOm generated by the above mentioned method contains theoretically only the spatial frequency component with wavelength of 30m. On the other hand, Spot image data contain the spatial frequency component with wavelength of lOm. Therefore, the digital merging of Spot and Landsat data was adopted. Such a method tends to preserve the spectral pattern and the resolution of the image. A true color composite image digitally merged is shown in Plate 5. In this method, the resolution of the image increases with the amount of HRV component. A linear combination method was used to adjust HRV component using weight coefficient. Giving weight to HRV provides good contrast and good color tone results from addition of TM multi-spectral information. Plate 6 shows the integrated image of block road generated by GIS. As we have seen, the land cover change can be detected using satellite data collected from different sensors and at different dates. We also considered the method and its accuracy using Landsat TM (1989) and Spot HRV data (1987) to detect the land cover change. In this study we examined the following three detecting methods. (1) Using the above mentioned digital merging method, we integrated the lOm resolution HRV—PA data collected at two different dates to the 30m resolution TM data to generate a digitally merged image. Applying the maximum likelihood method to this image, land cover change was detected. (2) The normalized images of TM data at two different times were generated. The change detection was performed by level slicing. (3) Change detection was performed by applying the maximum likelihood method independently to TM data collected at two different dates. An inland outskirt area in Hiroshima City where preparation work of housing site had been widely advanced was chosen as a training site. Method (1): A new image of high accuracy with a lOm spatial resolution was generated by applying the digital merge processing to HRV-PA data and TM ch 1 to 4 data. We could detect the change of land cover which occurred during the period between the two different dates using a high accuracy integrated color composite image with ch 3 (red), ch 2 (green), and ch 1 (blue); the changed part was represented in red. With this part as training data in supervised classification, the changed part is extracted by applying the maximum likelihood classification. Method (2): A difference image of two images is generated by applying the biband processing ch 3 and ch 4 to TM data at different two dates. In this case beforehand normalization was made to each channel at each date and the pixel values on the changed part were calculated. Method (3): After the maximum likelihood method classification was applied independently to TM data at different two dates, the land cover changes at two dates were detected. We compared these three methods to detect the land cover change and came to a conclusion that the first one showed the best fit to the research data on the site.
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Landscape Evaluation RS data is a spectral information concerning the Earth surface and the analysis result thereof is also a planar information. The RS data, however, is not only valuable as a planar information, but also by combining with other informations such as topographical information, allowing a topographical analysis with a higher added value. Landscape is an important factor to evaluate the land type from a viewpoint of land use or environment. Generating a landscape image using RS data is performed mathematically using the central projection method. For this purpose, spectral data and topographical data are necessary. When generating a landscape image from RS data, TM data of each image is used as a color data. The elevation corresponding to each pixel (that is a digital terrain model) is used as a topographical data. An area as wide as the prescribed topographical map is selected for landscape image generation and RS image data of the area is corrected geometrically. On the other hand, the digital terrain model is generated using contour data on the topographical map. These two kinds of data are transformed into the landscape image coordinate system using the central projection method. To perform the registration of both data, we tried to make a numerical elevation grid data with higher resolution. In this study, to make a new digital elevation data corresponding to the TM data, we transformed the numerical elevation grid data of 50m, using bilinear method, into the digital terrain model corresponding to a pixel of Landsat TM data with 28.5m spatial resolution. Using TM ch 1 to 3 image geometrically preprocessed data, a false color composite landscape image was generated. A set of landscape images produced with different points of view made it possible to show a continuous representation of the landscape of land use. It is also possible to use the color image corresponding to the maximum likelihood classification. Using the digital terrain model, a color classified image at each optional height value can be produced. It may also be used for topographic analysis such as the slope estimation image made by processing the gradient value calculated from the topographic element of ~the elevation data with the level slicing method and classified with color. An example of application of the landscape image generation, is shown in Plate 7. The landscape image generated with Landsat TM data can be integrated with color coded vector data such as the land use planning area and boundary line representing vector data such as administrative boundary and road network and processed with the raster data of the classification image. The study area was a hill area on the west of Hiroshima City under development. In this case it is required to transform the vector (boundary line data of the land use planning area) read from a map by the digitizer into raster data. In this way, the land use and environment information of the city can be caught spatially and visually. The landscape image using Landsat TM data and DTM data makes it easy to interpret the topographic information, resulting in a good integration effect of the Earth surface information and the topographic information. Land Use Survey for Urban Planning Land use planning should be carried out with appropriate guidance and control. Most of the buildings of a city are individual or company owned. Geographical construction of a city is represented by a combination of block and road network informations, the block being a basic unit zone, and its position being represented by a polygon system with elements such as points, lines and planes and their connections. In Japan land use zoning is the basic system for land use planning. The regulations which intend to control from view points such as vacant land, height, and structure were gradually prepared. Three fundamental elements of land use zoning are residence, commerce and industry. Moreover under the existing system land use zoning is divided into eight sub—districts: first class exclusively residential zone, second class exclusively residential zone, residential zone, neighbourhood commercial zone, commercial zone, semi—industrial zone, industrial zone, exclusively industrial zone. Others than the above mentioned regulations, site coverage, floor space index and slant line restriction due to the building lot were also added. Plate 8 shows an example of Hiroshima city town plan general map digitized using GIS’s exclusive engineering workstation (ARC/INFO) /6/.
CONCLUSION
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Through this study we proved that RS/GIS integrated system which was developed using a personal computer and a minicomputer systems was effective as a supporting system for the land use survey. Raster/vector data integrated treatment system manufactured by combining the geographical information with processed RS data, allows to digitize and present with color codes the areal grid and polygon system representing the land use information system. Combination of RS data and social statistical data makes it possible to analyze synthetically the land use structure characteristics and social/ economical characteristics, and can be used as the supporting system for the land use phenomena analysis in land use planning. As a result it becomes also possible to classify the land use phenomena in the study area and analyze the trend and content of the land use. Moreover with the rearrangement of analytical data, the classification of the areal characteristics of the study area can be done quickly, the system is effective for making the basic documents of land use planning. When the digital map is prepared, multivariate analysis with variables which are the explanatory items for land use and environmental phenomena becomes possible. Changing the variables, simulation in various phenomena analysis will also become possible. The development of this system with a personal computer allows to detect the land use/cover change of the specified administrative area, and proves the usefulness of RS data for administrative information. In particular it is proved that the calculation of the detected area of the land use/cover change made by high accuracy satellite image maps and processed with the maximum likelihood method gives the most approximate value to the survey data on the site. For the registration of multisensor and multitemporal data, it is necessary to perform a local detailed geometrical correction. If the detection of the land cover change is conducted using some past TM data, the detection accuracy is expected to improve by using the digital merging method, which will be efficiently used for the time series analysis of land use/cover change. With the generating of the landscape image, it become possible to merge three-dimensionally the geographical position and the state of land use of the development planning area. By combining RS data with GIS data such as the land use landscape simulation which uses the digital terrain model data, it will be possible to develop utilizations which will produce higher value informations. The existing map information will be integrated with various data base and measuring systems in the future. Integration of RS and GIS for the purpose of land use survey allows to apply the system to various fields such as land use planning and environmental planning, based not on the existing idea of a mere map, but on the map data base which is a spatial information system merging satellite information, map data, and attribute information such as the administrative information corresponding to the considered application. In the future the development of RS/GIS integrated system, with a strong link between the exclusive GIS workstation and the computer system for RS data analysis and the development of its application, is expected to advance. ACKNOWLEDGMENTS This research has been supported in part by grants from Tsuru Foundation for Advancement of Education and Research. The author would like to thank Dr. Sotaro Tanaka and Toshiro Sugimura of Remote Sensing Technology Center of Japan for their helpful comments and suggestions in this research. REFERENCES 1. O.Zhou, A Method for Integrating Remote Sensing and Geographic Information Systems, Photogranunetric Engineering and Remote Sensing, Vol.LV, No.5, Parti, 591—596 (1989). 2. H.Nakamura ed., National Land Survey, Gihoudou Publishers, 1984. 3. K.Tsuchiya ed., Introduction of Remote Sensing, Asakurashoten Publishers, 1990. 4. Y.Suga, S.Tanaka and T.Sugimura, Land Use Phenomena Analysis Using Spatial Data Base, International Archives of Photograinmetry and Remote Sensing, Vol.27, Part B4, 374—383 (1988). 5. S.Tanaka and T.Sugimura, Digital Merging of Spot HRV and Landsat TM Data in the Small and Low Undulation Area, J.The Remote Sensing Society of Japan, Vol.8, No.1, 51—58 (1988). 6. ESRI, The Geographic Information System Software, Users Guide ARC/INFO Volume 1, 1989. JASR 12:7—Z
Plate 1.
Color Coded Grid Image of Land Use Phenomena Classification (Hiroshima City)
(a) Land Cover Classification Image and the 1/2 Standard Areal Grid
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(b) Magnified Image of a Grid Assigned by a Cursor Plate 2.
Land Cover Change Detection Image (7)403
Plate
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Plate 5.
Land Cover Classification Image of Administrative Area of Hiroshima City
Plate 4.
Land Cover Change Detection Image in Administrative Area of Hiroshima City
Plate
Composite Image using by Digital Merged Image and GIS Data
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1:50,000 Satellite Image Map Generated from Digital Merging of Landsat TM and Spot HRV Data (Hiroshima and Vicinity) ©CNES 1987, SPOT®
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Plate 7.
Landscape Image Generated from Landsat TM and Digital Terrain Model Data (Western Part of Hiroshima City)
Plate
Land Use Zoning Image of Hiroshima City Produced by GIS
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