JP2006252529A - Planimetric feature environment condition provision method and program thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a planimetric feature environment condition provision method and a program thereof capable of easily grasping the condition of the whole of a river and the condition of data of foundations from aircraft laser data by quantifying them planely and displaying them in color properly. <P>SOLUTION: The system comprises a river environment condition chart creation part 20, a reflection intensity chart creation part 21 (a program), a land-cover classification chart creation part 22 (a program), a river channel analysis area creation part 23 (a program), and a synthesizing part 24, and generates a sand bar relative elevation image A from an orthophotoimage and laser data, a rapid and deep pool image B from which rapids, deep pools, etc. of a river can be obtained at a glance, or a riverbed material image C from which conditions of a sandbar of a river channel, stones in a sandy area, and gravel can be obtained at a glance, etc. They are obtained at the output part 3. By using an orthophotoimage and laser data, proportions of existence of a rapid and deep pool of a river channel, a sandbar, a sandy area, and a riverbed material can be obtained as numerical data. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention grasps the situation of rivers, coasts, wetlands, etc., and the conditions of rivers, rivers, rivers, or the size of gravel existing in the state, and displays them appropriately in color. The present invention relates to a feature environment situation providing method that provides information visually.

  In 1990, the “National Census of River Waterside” (hereinafter referred to as the “Water Country Survey”) started in the first-class rivers of 109 water systems nationwide and some of the second-class rivers managed by each prefecture. This survey is to grasp the organisms that inhabit and grow in rivers, such as plants, fish, and birds, by field confirmation. The survey has been conducted for 5 years as one cycle, and the survey for the third round is now underway.

  At the beginning of the survey, river managers had no information about living organisms, and began with investigating what organisms were present against the backdrop of growing public opinion on environmental issues. After that, the River Law was revised in 1997, and in addition to the conventional “Flood Control” and “Water Utilization” as the purpose of the river business, “maintenance and conservation of the river environment” was positioned as one of the three pillars of the project. As basic data that bears the burden, surveys have been conducted on rivers (including lakes) nationwide with a large budget each year.

  On the other hand, while the cost reduction is screamed throughout the public works, the value of data accumulated over the past ten years is being questioned. In other words, although the biological information has been accumulated, the fact that it is hardly utilized for river management (management of flood control while maintaining the biological environment) has become apparent.

  Since it is essential for river managers to acquire environmental information, the manual is being revised for the fourth round of surveys starting in 2006. There is no solution to the problem of how to utilize it.

  Factors that make it difficult for current water country survey data to be used for river management include survey sites that are heavily focused on living organisms, such as river channel topography, riverbed materials, trees in Nakashu, Nakashu, and river flows (Hayase, Hirase, Kashiwa). It can be pointed out that surveys on the environment have become very short.

In other words, the objects that can be operated in river management are not individual organisms but physical conditions such as river channel forms and flow conditions.
JP 2000-89664 A Japanese Patent No. 3546349 JP 2003-156330 A

  One of the factors that make it difficult for current water country survey data to be used in river management is that the survey target is heavily focused on living organisms, and the survey of the environment of the field is short.

  In other words, the objects that can be manipulated in river management are not individual organisms, but physical conditions such as river channel forms and flow conditions, etc. The number of torrents, sand gravel deposits, river changes, etc.) has not been acquired, so how to maintain the biological environment while ensuring a safe river channel against flooding There was a problem that it was not managed.

  In addition, the conventional water country survey is conducted only at some survey points set in the target section even at a high cost, so only local information can be obtained. There was a problem that could not be grasped.

  Therefore, there is a water environment information provision method that can easily provide visual information by quantifying and grasping the situation of the entire river and the base data easily with aviation laser data without overwhelming it, and displaying it appropriately in color. It is desirable to obtain.

  The feature environment status providing method using laser data and color image data by an aircraft according to the present invention is used for any band of red (R), green (G), and blue (B) of a mesh of a color orthophoto image. A step of comparing the color intensity with the first threshold and classifying the mesh into the sun and the shade, and the color intensities of the red (R), green (G) and blue (B) bands of the sun mesh respectively Comparing the Hyuga mesh into water, bare land, and vegetation, comparing the surface reflection intensity of the laser data of the section including the shade mesh with the second and third thresholds, Performing a step of classifying a shaded mesh into a water area, the bare land, and the vegetation; and a step of repeatedly performing all the steps for all the meshes of the color orthophoto image to classify the land cover of the target area. The gist.

  The feature environment status providing method includes a step of reading the ground elevation value and surface elevation value of each section included in the laser data, obtaining a vegetation height of the section from the difference, and a vegetation of the section including the mesh of the vegetation The summary is to further perform a step of classifying the mesh of the vegetation into a tree and a grassland by comparing the height with the fourth threshold value.

  Further, the feature environment situation providing method extracts a ground elevation value of the laser data of the section including the mesh for all meshes classified into the water area, and creates a water surface gradient section, and the ground elevation Generate an inequality triangle mesh for any 3 meshes in the value, calculate the elevation value of the reference water surface of the sandbar sandwiched between multiple water areas, and generate arbitrarily on a separately created two-dimensional dike line A given point group is given an elevation value from the ground elevation value nearest to the point, and an inequality triangular network is generated for the 3 meshes of the elevation value and the ground elevation value to be sandwiched between the water area and the dike. And a step of calculating an elevation value of the reference water surface of the sand bar and a step of calculating a difference between the elevation value of the reference water surface and the ground elevation value and creating a specific height distribution of the sand bar. .

  In addition, the feature environment status providing method includes a step of obtaining a water surface gradient for each arbitrary section from the water surface gradient section, and extracting a pixel value of a color orthophoto image corresponding to the section to obtain a high frequency component and a low frequency component. The gist is to further perform a step of obtaining a water surface gradient and a wave surface state of the water surface from the separation result.

  Then, the feature environment status providing method averages the pixel values of the color orthophoto image corresponding to the sand bar and separates them into a low frequency component and a high frequency component, and the particle size of the sand bar region by the standard deviation value of the high frequency component. The gist is to further perform the step of obtaining

  On the other hand, the feature environment situation providing program using the color orthophoto image and laser data of the target area, which is photographed and measured by the aircraft of the present invention, is stored in the computer in red (R) and green ( Means for comparing the color intensity of an arbitrary band of G) and blue (B) with the first threshold value and classifying the mesh into the sun and the shadow; the red (R) and green (G ), Comparing the color intensity of each of the three bands of blue (B), a means for classifying the sunscreen mesh into water, bare land, and vegetation, the surface layer reflection intensity of the laser data of the section including the sunshade mesh, Comparing the second and third thresholds, the function of classifying the shaded mesh into water, bare land, and vegetation is repeatedly executed for all meshes of the color orthophoto image, and the land cover in the target area Min And summarized in that to perform the functions as a means to perform.

  In addition, the feature environment situation providing program reads a ground elevation value and a surface elevation value of each section included in the laser data to a computer, a means for obtaining a vegetation height of the section from the difference, and a section including a vegetation mesh The gist of the present invention is to further execute a function as a means for classifying the mesh of the vegetation into a tree and a grassland by comparing the vegetation height with a fourth threshold value.

  Further, the feature environment situation providing program, on the computer, for all meshes classified into the water area, extracts the ground elevation value of the laser data of the section including the mesh, respectively, and creates a water surface gradient section; On the two-dimensional dike line created separately, means for generating an inequality triangle network for any three meshes in the ground elevation value and calculating the elevation value of the reference water surface of the sandbar sandwiched between a plurality of water areas An arbitrarily generated point group is given an elevation value from the ground elevation value nearest to the point, and an inequality triangular network is generated for the three meshes of the elevation value and the ground elevation value, The function of calculating the altitude value of the reference water level of the sand bar sandwiched between the dikes, and calculating the difference between the altitude value of the reference water surface and the ground altitude value and creating the specific height distribution of the sand bar is executed. thing The gist.

  Still further, the feature environment situation providing program extracts a pixel value of a color orthophoto image corresponding to a section, a means for obtaining a water surface gradient for each arbitrary section from the water surface gradient section, and extracts a high-frequency component and a low-frequency component. The gist is to perform a function as means for obtaining a water surface gradient and a wave surface state of the water surface from the separation result by separating into frequency components.

  Then, the feature environment status providing program averages the pixel values of the color orthophoto image corresponding to the sand bar and separates them into a low frequency component and a high frequency component, and uses the standard deviation value of the high frequency component as a sand bar region. The gist of the present invention is to execute a function as a means for obtaining the particle size of the particles.

  According to the present invention, it is possible to grasp the state of river flow, the state of Nakashuu, the size of gravel deposited in the Nakasu and other areas without numerical costs, and display the color appropriately. By expressing this information visually and clearly, it can be used for river management. The same effect can be expected with respect to the seaside and wetlands.

  FIG. 1 is a hardware configuration diagram of a terrestrial environment status providing system using laser data and color images by an aircraft according to the present invention.

  As shown in FIG. 1, a terrestrial environment situation providing system VPS1 using laser data from an aircraft includes a central information processing device (CPU unit) 1 including an appropriate combination of a processor, a microcomputer, a logic, a register, and the like, and a CPU unit. 1. Information input unit 2 such as a keyboard, mouse, interactive soft switch, external communication channel, etc. for inputting necessary control information and operation information, and display and printer for displaying and transmitting information from the CPU unit 1 in a broad sense The information output unit 3 including an external communication channel, the first storage unit 4 such as a ROM (ROM) in which information such as an operating system and application program read by the CPU unit 1 is stored, and the CPU unit 1 as needed. Second memory such as a RAM (RAM) for storing information to be processed and information from the CPU unit 1 Equipped with a 5 and the like. The first and second storage units 4 and 5 may be appropriately integrated and subdivided.

  The first storage unit 4 includes a water / land environment providing unit 10 that operates on a predetermined application, laser data Ri when a predetermined range of rivers, seasides, lakes, and wetlands are acquired by an aircraft, and color digital data Orthophoto image Pi (hereinafter referred to as orthoimage Pi), analysis area information (river channel area, wetland area, seaside area, etc.) that defines the analysis range of this image, laser data Ri, and orthoimage Pi Land cover classification information is stored. The laser data Ri and the ortho image Pi described above are stored in advance in the second storage unit 5 and stored in the first storage unit 4. Analysis area information, land cover classification information, and the like are generated in the second storage unit 5 and stored in the first storage unit 4.

  As shown in FIG. 2, the water land environment providing unit (program) 10 includes a sandbar relative height distribution calculation program 11 (hereinafter referred to as a state high classification map creating unit 11), a water surface gradient classification, and an image. River environment situation diagram creation consisting of Seto classification program 14 (hereinafter referred to as Seto classification unit 14) combined with analysis, riverbed particle size classification program 12 (hereinafter referred to as riverbed material information creation unit 12) by image analysis, etc. Unit 20, laser data editing unit 200 (program), reflection intensity map creation unit 21 (program), land cover classification map creation unit 22 (program), river channel analysis area creation unit 23 (program), and synthesis unit 24, etc., and canceling the slope of the river shown in FIG. 3 from the ortho image Pi and the laser data Ri, for example, the sand bar specific height image A or the rapids of the river shown in FIG. Etc. is SeraFukashi image B or the Kawado as shown in FIG. 5 sandbank glance, to obtain sand stone, the status of gravel it generates the bed material image C and the like at a glance the information output unit 3. In other words, by using the ortho image Pi and the laser data Ri, it is possible to obtain numerical data indicating the ratio of river channel waterfall, Nakashu, sand, and riverbed material. Therefore, it can be applied to the management and analysis of the river channel environment. The synthesizing unit 24 converts the ortho image Pi into a monochrome image (black and white), synthesizes the monochrome image and the image of the river channel area (may be either one of the images), and displays it on the display unit.

  As shown in FIG. 33, the laser data editing unit 200 includes a laser data classification unit 201, an elevation value data extraction unit 202, a vegetation height calculation unit 204, and a reflection intensity data extraction unit 203. The input laser data Ri includes an X coordinate value, a Y coordinate value, a ground elevation value Zg, a surface elevation value Zt, a ground reflection intensity Ig, and a surface reflection intensity It. Yes.

  The laser data classification unit 201 classifies the laser data Ri into laser ground data Rg (X, Y, Zg, Ig) and laser surface layer data Rt (X, Y, Zt, It), and stores the first storage unit 4. To store.

  Subsequently, the altitude value data extraction unit 202 extracts altitude value data from the classified laser ground data Rg, and the ground mesh data ER (X, Y, Zg) as a digital altitude model (DEM), and the laser surface layer. Elevation value data is extracted from the data Rt, and surface mesh data SR (X, Y, Zt) is generated as a numerical ground model (DSM) and stored in the first storage unit 4.

  Next, the vegetation height calculation unit 204 reads the ground elevation value Zg and the surface elevation value Zt at the same XY coordinate point from the ground mesh data ER and the surface layer mesh data SR, calculates the difference between them, and mainly Find the height of the plant growing at the point (vegetation height h). And it stores in the 1st memory | storage part 4 as vegetation height data HD (X, Y, h) with a coordinate value.

  On the other hand, the reflection intensity data extraction unit 203 extracts the reflection intensity data from the classified laser ground data Rg, obtains the ground reflection intensity data HRg (X, Y, Ig), and the reflection intensity data from the laser surface layer data Rt. The surface layer reflection intensity data HRt (X, Y, It) is generated and stored in the first storage unit 4.

  As shown in FIG. 34, the laser ground data Rg and the laser surface data Rt are respectively obtained when the measurement point when the laser is irradiated is the ground and the surface of an object (plant etc.) protruding from the ground. The data of As can be seen from FIG. 34, the difference between the elevation values (Zg and Zt) at the same point is the vegetation height (h).

  The reflection intensity diagram creating unit 21 assigns a color corresponding to the reflection intensity of each mesh of the surface layer reflection intensity data HRt to obtain the reflection intensity distribution diagram HRi of FIG.

  That is, by having the reflection intensity map creating unit 21, the water / land environment providing unit 10 obtains the reflection intensity distribution map HRi shown in FIG. 7 in addition to the ortho image Pi (RGB in xy) shown in FIG. It will be. In the reflection intensity distribution diagram HRi of FIG. 7, red is assigned to a mesh (pixel) having a high reflection intensity, yellow when the reflection intensity is between medium and strong, yellow with green, blue with weak, and between weak and medium. Is assigned light blue. These color assignments are made by determination based on threshold values. Even if there are shadows on the river surface and sandbar surface due to trees, the reflection intensity is the reflection intensity of the laser, so there is almost no influence of the shadow.

  The land cover classification map creation unit 22 analyzes the color information of the mesh of the ortho image Pi in a land cover map memory (not shown) defined in the orthophoto coordinate system, and classifies it according to this color. Thus, as shown in FIG. 8, a land cover classification map E classified into trees, water (river surface), bare land, grassland, and other features is obtained. At this time, since the shadow portion cannot be distinguished, the reflection intensity distribution diagram HRi of the laser data corresponding to the shadow portion is read and classified into water, bare land, grassland, or trees.

  Moreover, the land cover classification map creation part 22 can also obtain a vegetation height distribution map as shown in FIG. The vegetation height distribution map reads the vegetation height data HD into the vegetation height memory in the coordinate system corresponding to the mesh position in the dark green area of the land cover classification map E, and the color corresponding to the vegetation height (h) value of each mesh Is assigned to the vegetation height memory mesh to obtain the vegetation height distribution map of FIG.

  In the present embodiment, red is assigned to a tree having the highest tree height, yellow is assigned to a tree that is higher than the middle, yellow is assigned to the middle, and blue is assigned to the lower. However, when displaying on the display unit, the ortho image Pi is converted into monochrome, and the vegetation height distribution map information of the vegetation height memory is synthesized to obtain the image of FIG. As shown in FIG. 9, the tree can be clearly seen even if there is a shadow.

  Here, the land cover classification map creation unit 22 will be described in detail with reference to the flowchart of FIG.

  In the present embodiment, as shown in FIG. 11, laser light is emitted downward while flying horizontally over the target area from an airplane, and is calculated from the time required for reciprocation, the position, attitude, and launch angle of the aircraft ( The computer obtains x, y, and z of the ground surface. This laser can be fired at a high frequency of, for example, 33,000 times to 1,000,000 times per second, so that, for example, an elevation point (Rx, Ry, Rz) at a density of about one point from 50 cm to 1 m. Acquisition is possible. In addition, the same range as the above laser is photographed with a digital camera, and a high-resolution color (blue / green / red or green / red / near infrared) image of, for example, 20 cm above the ground is acquired, and is combined with the laser data Ri. An image Pi can be obtained.

  As shown in FIG. 35, for example, the data accuracy is 20 cm × 20 cm indicated by A in the case of the unit mesh section of the ortho image Pi, while 1 m indicated by B in the case of the unit mesh section of the laser data Ri. × 1 m. Thus, the unit mesh of the ortho image Pi is smaller than the unit mesh of the laser data Ri due to the difference in the data collection method. For this reason, information on the precise ortho image Pi with high accuracy is mainly used. However, in the shaded portion or the like, the covering may not be determined by the ortho image Pi. Therefore, by performing various determination processes by treating the information of the unit mesh section B of the laser data Ri including the unit mesh section A of the ortho image Pi as a value common to each of the unit mesh sections A of the ortho image Pi, The accuracy of the obtained topographic information is raised.

<Embodiment 1>
The land cover classification map creation unit 22 analyzes the high-precision ortho image Pi acquired by the digital camera stored in the first storage unit 4 by a decision tree method, and the land cover state (trees) in and around the river channel , Water, bare land, etc.). At this time, since the shadow portion may not be classified by normal image classification, accuracy is ensured by using information on laser reflection intensity (Intensity) acquired simultaneously with photographing. In the image classification, the vegetation height data obtained by laser is used to improve the grassland and trees with large errors.

  The ortho image Pi is obtained by sequentially recording the intensity of light of three bands of red, blue, and green or three bands of green, red, and near-infrared in order of x and y in a sequence of meshes (pixels) at regular intervals. It is.

  Further, the laser data Ri is given as (X, Y, Zg, Ig, Zt, It).

  First, the ortho image Pi in the storage unit is read, and a land cover classification map memory defined by the coordinate system of the ortho image Pi is generated.

  Then, the mesh number of the ortho image Pi is set, and the image data (Px, Py, R, G, B) of the mesh with this mesh number is read (S200).

  Next, the color intensity of the three bands of red (R), green (G), and blue (B) of the read image data is compared with the first threshold value T1 (S201). This threshold value T1 is a threshold value determined from the statistic (brightness distribution) of the entire image. In addition, which color is used for the determination may be appropriately determined from the statistic (brightness distribution) of the entire image.

  If it is determined in step S201 that the color intensity is smaller than the threshold value T1, it is determined to be a shade. In step S201, when it is determined that the value of the color intensity is larger than the threshold value T1, it is determined to be sunny.

  When the read mesh (pixel) is classified into a shaded area, the color intensity cannot determine what the point is, so the corresponding (Px, Py) mesh from the surface reflection intensity data HRt. (X, Y) mesh section data of the laser data including is read (S202).

  Then, the surface reflection intensity (It) of the section is compared with the second and third thresholds T21 and T22 determined from the statistics of the laser reflection intensity data (S203). If it is smaller than the threshold T21, the mesh (pixel) is set as category C = 1 (water area) and stored in the land cover classification map E (S204). If the value is between the threshold values T21 and T22, the category (pixel) is set to category C = 2 (bare ground) and stored in the land cover classification map E (S205). On the other hand, when larger than threshold value T22, it shifts to processing (S209-S212) for judging vegetation height (h) mentioned below.

  In step S201, when it is determined that the value of the color intensity is larger than the threshold value T1, and the read mesh (pixel) is classified into the sunny area, three bands of red (R), green (G), and blue (B) The correlation between the color intensities is determined (S206).

  For example, if the color intensity of blue (B) is greater than the values of other red (R) and green (G), set the mesh (pixel) as category C = 1 (water area), and the land cover classification map E is stored (S208). Also, if the color intensity of red (R) is greater than the values of other blue (B) and green (G), the category C = 2 (bare) is set for the mesh (pixel), and the land cover classification Save in FIG. E (S207).

  Here, meshes (pixels) determined that the color intensity of green (G) is greater than the values of other blue (B) and red (R) are determined as vegetation, and are classified further finely. The data of the (X, Y) mesh section of the laser data including the corresponding (Px, Py) mesh is read from the data HD (S209).

  Then, the vegetation height (h) of the section is compared with the fourth threshold value T3 of the height for classifying the plant (S210). For example, the threshold T3 is set to 50 when classifying as a tree if there is a height of 50 cm or more.

  If it is determined in step S210 that the vegetation height (h) is larger than the threshold value T3, category C = 3 (tree) is set in the mesh (pixel) and stored in the land cover classification map E (S211). . On the other hand, when it is determined that the vegetation height (h) is smaller than the threshold value T3, category C = 4 (grassland) is set in the mesh (pixel) and stored in the land cover classification map E (S212).

  Needless to say, the conditional expression of the classification based on the correlation of the color intensities of the three bands differs depending on the color of the photographed terrain. Therefore, the conditional expression is appropriately determined from the statistics (color intensity distribution) of the entire image.

  The above processing is repeated for all the mesh ortho images Pi by counting up and repeating the mesh numbers, and the entire land cover classification map E is created. By setting the value of category C in this way, for example, when classified as a water area (C = 1), blue is classified when classified as a tree (C = 3), and grassland (C = 4 ) And yellow when it is classified as bare (C = 2), and so on. This coloring can be performed by using a lookup table. The land cover classification map information generated in this way is stored in the first storage unit 4 and output to the output unit 3 to obtain the land cover classification map E shown in FIG. Since this land cover classification map E has no shadow portion, when the river channel is shaded by a mountain or the like, it is possible to determine the situation of the shadow location that is difficult to see with the orthophoto. In addition, about the above flow, you may change suitably the decision branch of a decision tree, the color to be used, etc. according to the color ortho image to be used.

  Furthermore, as shown in FIG. 12, it is possible to enclose a predetermined area, enlarge the predetermined area, extract an image of the predetermined area from the orthophoto image Pi, and simultaneously display it in the vicinity of the enlargement frame. In this way, the environment of the shadow area can be grasped more.

   For this reason, it can be seen at a glance whether there are high trees or low trees on the dike in the Nakasu of the vegetation height distribution map. For example, if there are trees in Nakasu, it will be covered more and more in the future, and as a result, the entire Nakasu is expected to be fixed. In other words, in the event of a flood, it can be used as a flood control measure because it can be expected to be a factor that stops river flow.

Next, the river channel analysis area creation unit 23 will be described.
The river channel analysis area creation unit 23 stores the trajectory traced by the operator in the land cover classification map E (FIG. 8) on the screen as a river channel area Fa (also referred to as a quay area: xy coordinates) (FIGS. 12 and 8). .

  Then, the image information of the land cover classification map E in the river channel area Fa is read (extracted from the land cover classification memory), and the uncovered land bare land area Fb (sand state in Nakashu, bare land on both banks) is extracted (river channel area) (The yellow area of the land cover classification map) is stored (FIG. 13b). Also, the image information of the land cover classification map E in the river channel area Fa (FIG. 13a) is read (extracted from the land cover classification memory), and the water surface area Fc is extracted (light blue area of the land cover classification map of the river channel area). And memorize (not shown).

(Calculation of specific height distribution of sandbar relative to water surface)
Next, the calculation of the specific height distribution of the sand bar relative to the water surface will be described.

  In calculating the specific height distribution of the sand bar with respect to the water surface, the water surface elevation is calculated at arbitrary intervals along the flow path from the laser data Ri, and is adjusted to a certain reference surface. That is, the river channel elevation data is corrected so that the water surface gradient is virtually zero, the specific distribution of the sand bar with respect to the reference surface is calculated, and the stepwise display is performed as necessary. In a typical stage chart without this correction, the slight relative height of the sand bar relative to the water surface is difficult to discern due to the slope of the river, but with this method, the relative height of the sand bar from the water surface at all points on the flow path is difficult. Since it is expressed, it is possible to know the flooding frequency at the time of flooding that defines the river environment (see FIG. 3).

  FIG. 14 is a flowchart for explaining the outline of calculating the specific height distribution of the sand bar with respect to the water surface.

  As shown in FIG. 14, first, data is read from the land cover classification map E (S400). Then, a mesh (Px, Py) of category C = 1 is extracted to obtain a water area (S401).

  Next, the mesh section information of the laser data including the water area mesh is read from the ground mesh data (DEM) ER (S402). Thereby, the elevation value (Zg) of the water area mesh is obtained.

  An elevation value (Zg) is set as the coordinate value of the water area mesh, and a water surface gradient division map M is created (S403).

  Subsequently, an inequality triangular network (TIN) is generated from the water area mesh, and the elevation value (Zs) of the reference water surface is calculated (S404). An image of a specific calculation method is shown in the water section S. Three arbitrary points of the ridge whose actual elevation is known (in the water section S, only two points are displayed because of the cross section) are extracted, and a triangle having the three points as vertices is created. The altitude at each point on the triangular surface is the reference elevation value (Zs) of the water surface. FIG. 32 shows a state where a TIN is generated on an actual image.

  Then, the difference between the altitude value (Zs) of the reference water surface and the altitude value (Zg) of the water area mesh is calculated (S405), and a sandbar specific height distribution map A is created (S406). This tells you how high the sandbar is from the surface of the water.

  FIG. 15 is a flowchart for explaining the calculation of the specific height distribution of the sand bar relative to the water surface according to another embodiment.

  The state classification classification creating unit 11 of the river environment situation diagram creating unit 20 corresponds to an area Fd (bare land, grassland, tree area; hereinafter referred to as a specific height distribution providing area Fd) other than the river surface area Fc of the river channel area Fa. Read Z of laser data having information. Then, the color value corresponding to the Z value is assigned to the mesh of the specific height distribution providing area memory having the same coordinate system as that of the ortho image Pi. Red is assigned when the threshold value a is equal to or greater than the threshold value a, yellow is assigned between the threshold value a and the middle threshold value b, green is assigned when the threshold value is lower than the threshold value b and the threshold value c is greater than the threshold value c, and blue is assigned.

  That is, by displaying the image information of the specific height distribution providing area memory, the stage chroma H before gradient cancellation shown in FIG. 16 is obtained. In this figure, as the river goes upstream, the river becomes more red and the downstream becomes more blue. As shown in FIG. 16, although the height of the state area is not known, it will become clear if it is as follows. That is, as shown in FIG. 32, from the DEM (reference plane) of the river surface of the ground mesh data ER, TIN is generated for the region other than the river surface, and X, Y, and Z of these TINs are extracted (the river surface Reference DEM).

  Then, the elevation of the bare vegetation on the riverbank in the state shown in FIG. 3 is extracted by obtaining the difference between each DEM Z value (laser data) in the river channel area and each DEM Z value based on the river surface.

  In FIG. 3, colors corresponding to the Z values of states and the like are assigned so that the height of the state can be visually recognized.

  Further, as shown in the flowchart of FIG. 15, as another embodiment, it may be obtained as follows.

  In FIG. 15, the operator draws “Miosuji” in the river of the stage chart before the slope cancellation, reads the “water area” laser data Mpi in the land cover classification map E of this “Miosuji”, and at the inputted interval Mti, The Z value is read (S51).

  In addition, the Z value of the laser data at the downstream end of “Miosuji” on the screen is read and used as a reference value BZi (S52).

  Then, a correction function βi for obtaining the Z value of each interval Mti as the reference value Bzi is obtained (S53).

  Next, the mesh number Kmpi of the stage chroma H (specific height distribution providing area memory) before the gradient cancellation is set (S54).

  Next, a water surface gradient zero diagram memory for obtaining a water surface gradient zero diagram is defined in the same coordinate system as the stepped chroma diagram H before the gradient cancellation (S55).

  Then, the Z value of the stage chroma H (priority distribution providing memory) before the gradient cancellation of the mesh number Kmpi set in step S54 is read, and this Z value is corrected to the reference value Bzi based on the correction function βi (FIG. 17). Data Rbi is obtained (S56).

  Next, the data Rbi is stored in the river channel area memory (S57). That is, data with the gradient as the reference value is created in the mesh number Kmpi of the stage chroma H before the gradient cancellation.

  Next, it is determined whether or not the Z value of the last mesh Kmpi in the stage chart H before the gradient cancellation has been corrected to the reference value (S58). If the correction for the last mesh Kmpi has not been completed, the mesh Kmpi is determined. Is updated to the next mesh, and the process returns to step S54 (S59).

  If it is determined in step S58 that the Z values of all the meshes Kmpi in the stage chart H before the gradient cancellation have been corrected to the reference value, the region data kpi other than the river surface area from the water surface gradient map (memory for the river channel area diagram). Is extracted (S60).

  That is, only bare land, grassland, and tree areas in the river channel area are extracted.

  Next, the z value of the extracted data kpi and its mesh number Kmpi are read and assigned to the river area memory of this mesh number (defined in the water surface gradient zero diagram) (S61).

  Next, the Z value of each mesh Kmpi is read, a color corresponding to the Z value is allocated (memory for the river channel area) (S62), and is synthesized into an ortho and output to the screen (S63). Red is assigned when the Z value is greater than or equal to the threshold value Ab, yellow is assigned when the threshold value is greater than or equal to abc and less than or equal to Ab, yellow and green are assigned when the value is less than abc and greater than or equal to acd, and blue is assigned. That is, the specific height distribution diagram A shown in FIG. 3 can be obtained. This coloring can be performed by using a lookup table.

(Se / Kashiwa)
Next, the Se / Kashiwa classification unit 14 will be described. FIG. 18 is a flowchart for explaining the outline of Seto image generation.

  The Seze classification part 14 classifies the water surface gradient from the elevation value (Z value in the river surface area Fa) of the water surface of the laser data Ri acquired by the aviation laser. Furthermore, the wave surface state of the water surface is evaluated based on the texture of the orthophoto image Pi, and by superimposing these, the steep water surface waved part of the rapids and the smooth water surface of the ridge and the smooth water surface are accurately distributed. Express well (see Figure 4).

  Regarding the gradient of the water surface, it may be difficult to obtain the water surface elevation directly depending on the laser measuring instrument. In that case, the elevation value of the shoreline (the frame of the water surface area) may be substituted.

  In addition, when evaluating the ripples on the water surface from an aerial image, the wave condition is not known simply by evaluating the brightness of the image because it is affected by water quality, bottom sediment, and reflection of sunlight, but in this embodiment, The image of the water part that has been converted into a single band by averaging three bands (RGB) is separated into high frequency / low frequency components.

  By calculating the distribution of the dispersion value using the moving window, it is possible to extract only the ripples reflected in the high-frequency components of the image and to perform relative classification (if low-frequency / high-frequency separation must be obtained). , Picking up changes in luminance information due to other than waves). This processing flow is shown in FIG.

  The Seto classification unit 14 reads the mesh of the ortho image Pi in the river surface area Fc (S70), and sequentially obtains the average of these three bands (RGB) to obtain a monochrome image (S71) and (S72). Then, the local average is sequentially obtained by applying the window wi to the monochrome image, and these low frequency component images are obtained (S74).

  Next, the difference between the monochrome image obtained in step S71 and the low frequency component image obtained in step S74 is obtained (S75) to obtain a high frequency component image (S76). That is, the wave component is extracted.

  Next, by obtaining the local standard deviation of the high-frequency component image, a raw image (also referred to as a roughness image) representing the water surface ripple condition is obtained (S78). Then, an average value (μ) and a standard deviation value (σ) of the whole drawing image are calculated (S79). Next, with reference to these values, the value of each pixel in the drawing is equally divided within a certain range (for example, 0 to μ + 2σ) to obtain a wave condition division diagram in which the wave condition is relatively divided into 16 gradations ( S80), (S81).

  In order to obtain the Hirose image, first, the wave condition is discriminated. FIG. 19 is a flowchart for explaining the swell state determination.

  The river surface area Fc and the ortho image Pi are compared, and the ortho image Pci of the river surface area Fc is read (S71). Next, average processing of three bands (RGB) is performed for each mesh position (pixel) Mpi of the ortho image pci (S72) to obtain a monochrome image Pm in the river surface area.

  Next, a window wi of a predetermined size is applied to the monochrome image Pm in the river surface area (S73), and the monochrome luminance value of the mesh in this window is averaged (low wave frequency component extraction) (S74). The results are sequentially stored in the memory (S75). In the present embodiment, this is called a river surface low frequency component image EWi.

  Next, it is determined whether or not the averaging process has been performed on the monochrome image of the last-numbered window wi (S76). When it is determined that the averaging process is not performed on the monochrome image of the last window, the position (i, j) of the window wi is updated to the next position (S77), and the process returns to step S73.

  If it is determined in step S76 that the averaging process has been performed on the monochrome image in the last window, the mesh position Mpi is set (S78).

  Then, by subtracting the luminance value of the low frequency component image EWi of the mesh Mpi from the luminance value of the orthophoto image Pcim of the river surface of the mesh Mpi, a high frequency component image of the mesh is obtained (S79), and this is stored in the memory. Store sequentially (S80). In other words, an image of the rippling part of the river surface was obtained.

  Next, as shown in the flowchart of FIG. 19, it is determined whether or not the last wave component of the mesh Mpi has been extracted (S81). If it is determined in step S81 that the last wave component of the mesh Mpi has not been extracted, the process returns to step S78.

  If it is determined in step S81 that the wave component of the last mesh Mpi has been extracted, the wave component image Qi stored in step S80 is multiplied by the window wi (S83), and the standard of each pixel in the window wi is determined. A deviation (roughness map image) is obtained (S84) and stored in memory (roughness image RWi) (S85).

  Next, it is determined whether or not a drawing image of the window at the last position has been obtained (S86). When the drawing image of the last window is not obtained, the window position is updated to the next position, and the process returns to step S83 (S87).

  If it is determined in step S86 that the base image of the window at the last position has been obtained, the roughness image RWi in the memory is multiplied by the window wi (S88), and the standard deviation is obtained by averaging (S89). Conversion is performed (S90). The value of each pixel of the raw map is equally divided within a certain range (for example, 0 to μ + 2σ) to obtain a wave condition division diagram in which the wave condition is relatively divided into 16 gradations.

  Next, it is determined whether or not the wave condition QRi of the last window wi has been obtained (S91). If it is determined in step S91 that it is not the last, the window position is updated to the next position, and the process returns to step S89.

  Next, the color separation processing of the Se and Hanga classification chart QRi will be described with reference to FIGS. 21, 22, and 23. FIG.

  As shown in FIG. 21, the mesh position Mpi of the orthoimage Pci of the river surface is set (S100). Next, the luminance eh of the ripple component image Qi of the mesh Mpi is read (S101), and this eh is compared with the ripple threshold Ni (S102).

  In step S102, if eh is larger than the threshold value na, which indicates that the ripple is high, the presence of the ripple is identified in the ripple distribution map memory (same as the coordinate system of the river surface area, which is generated at the time of instructing the calculation of the stream / soil). After defining (S103), the process proceeds to step S107 described later.

  If it is determined in step S102 that eh is less than or equal to na, it is determined whether or not nb <eh <na (S104).

  If it is determined in step S104 that eh is nb <eh <na, a medium wave identification code is defined in the area of the wave distribution map memory at the position corresponding to the mesh (S105).

  If it is determined in step S104 that eh is not nb <eh <na, an identification code for no ripple is defined in the area of the ripple distribution memory at a position corresponding to the mesh (S106).

  In step S107, it is determined whether or not the last mesh Mpi has been identified (S107). If it is determined that identification has not been performed, the mesh Mpi is updated and the process returns to step 100 (S108).

  Next, as shown in FIG. 22, the mesh number Mpi is set for the elevation data (DEM) of the river surface generated based on the laser data (S110).

  Next, the laser data Zi of the mesh Mpi is read from the elevation data (DEM) of the river surface (S111).

  Next, based on the flow direction, the z value of the laser data at the next mesh position is read from the elevation data (DEM) of the river surface (S112), the difference between the two is obtained, and this is used as the water surface gradient KGi (S113). .

  Then, the water surface gradient KGi is compared with the threshold value Li (S114). When it is determined that KGi is equal to or greater than the threshold value Li, a steep slope identification code is assigned to EPi in the water surface gradient map memory (corresponding to the river surface area: created at the time of Seto processing) (S115), and processing is performed in step S117 described later Move.

  If it is determined in step S114 that KGi is equal to or smaller than Li, a mesh that is not steep is assigned to the water surface gradient map Epi (S118).

  Then, it is determined whether or not the last mesh MPi has been assigned. If no assignment has been made, the mesh position is updated, and the process returns to step S110.

  Next, as shown in FIG. 23, the mesh ei of the wave component image Qi (wave component information memory) generated based on the ortho image, and the water surface gradient map Si (water surface gradient memory) generated based on the laser data The mesh epi is set (S121).

  Next, the identification code assigned to the meshes epi and ei is read (S122). Then, it is determined whether or not the mesh ei has a ripple and the epi has a steep slope (S123).

  If it is determined in step S123 that ei is rippled and identification is assigned to epi as abrupt, it is determined as Hayase (S124).

  Next, Hayase's identification is assigned to the area of the memory for the map and map at the position corresponding to ei or eip (generated in response to the command to generate the map image) (S125), and the process proceeds to step S126. Transfer.

  If it is determined in step S123 that ei has a wave and epi does not have an abrupt identification, it is determined whether an identification that is waved and epi is not abrupt is assigned (S128).

  If it is determined in step S128 that there is a wave and it is not abrupt, Hirase's identification is assigned to the memory for the map (S129).

  If it is determined in step S128 that there is no undulation and it is not abrupt, the identification of the cocoon is assigned to the memory for drawing the map (S130).

  In step S126, it is determined whether or not a determination result has been assigned to the last kei in the memory for drawing images (S126). If it is not the last time, kei is updated and the process returns to step S121 (S127).

  If it is determined in step S126 that an identification result has been assigned to the last kei, this Hirose chart table is read and displayed on the screen (S131). That is to say, the Seto classification diagram of FIG. 24 is obtained by this processing.

  FIG. 24 shows a case where a photo is assigned to a specific area. In other words, a photo (or a moving image) is allocated to a specific area of the set / 淵 image and stored in the memory, and when the specific area is selected, the photo (or moving image) is displayed together with the set / set classification chart from the memory. As a result, as shown in FIG. 25, it is possible to understand the situation of the rivers such as the rapids, the hills, and the rivers, which is effective in grasping the river environment.

  In addition, by applying this wave condition classification technique to the coast as well as the river, it is possible to grasp the water depth distribution in the coastal area.

  In combination with laser measurement at low tide, it can be applied to grasp coastal erosion, which has become a problem in various places in recent years.

<Embodiment 2>
(River bed material)
FIG. 26 is a flowchart for explaining the display of the classification image of the riverbed material. The riverbed material information creation unit takes advantage of the fact that the particle size distribution of the riverbed material is reflected in the texture of the image, and uses the average of the three bands of the color image to produce a monochrome image (hereinafter referred to as a single band image). The particle size of the riverbed material is relatively divided by calculating the dispersion value using the moving window.

  Furthermore, by selecting representative points for each classified hierarchy and confirming the particle size composition on site, the particle size distribution of the riverbed material over a wide area will be clarified.

  Similar to the festival and Seto processing, the image is separated into high and low frequency components, and only the former is targeted, resulting in changes in texture due to factors other than fine particle size (such as differences in wet and dry conditions and shadows of trees). Has reduced the impact.

  In addition, the present invention can be applied not only to rivers but also to coastal areas, and the particle size distribution of the beach may be grasped and compared with the river that is the supply source.

  That is, the riverbed material information creation unit 41 reads the ortho image Pi of the bare area, performs an average of the three bands (S131, S132), and obtains a single band image (monochrome image) (S133).

  Next, a local average (filter size 1 m) of the single band image is obtained, and a low frequency component image is created (S135).

  Then, a difference between the single band image and the low frequency component image is obtained (S136), and a high frequency component image is obtained (S137).

  Next, a local standard deviation (filter size is, for example, 1 m) of the high-frequency component image is obtained (S138), and a riverbed material map image is created (S139). Next, a raw image is used to linearly convert a certain range (for example, [0, μ + σ]) to [0, 15] (S140, 141), and the riverbed material (particle size) image (μ) shown in FIG. And [sigma] create a roughness image average and standard deviation (S142). The color values are assigned by the same processing as that shown in FIGS. 21 and 22 described in the above-mentioned steps for setting the color distribution for obtaining the riverbed material image.

  When applying normal filters, shadows and boundaries (edges) between different land covers are emphasized, but this effect is reduced by removing low-frequency components.

  By such processing, as shown in FIG. 27, the color is displayed according to the particle size of the material of the sand bar, and when the photograph is linked to this point, the photograph of this place is displayed as shown in FIG. It will be.

  The sandbar wet-wet plot drawing section assumes that the riverbed material is uniform, and uses the fact that the wetter sandbar has lower lightness, averages the three bands, and clarifies the water status of the sandbar by the brightness of the single band.

   For this reason, as shown in FIG. 29, the sandbar wet / dry diagram creation unit 42 reads the ortho image of bare ground (S151), averages RGB (three bands) of this image (S152), and obtains a single band image. (S153). Next, the average and standard deviation of the sand bar are calculated (S154), linearly deformed (S155), and the sand bar moisture image shown in FIG. 30 is obtained (S155). The value of each pixel of the raw map is equally divided within a certain range (for example, 0 to μ + 2σ) to obtain a wave condition division diagram in which the wave condition is relatively divided into 16 gradations.

  The image shown in FIG. 30 is obtained by performing such processing. In the image shown in FIG. 30, the orthophoto is converted into black and white except for the sandbar area, and is displayed by the compositing unit with the color image of the sandbar.

<Embodiment 3>
The above results are comprehensively managed and analyzed by GIS, and the items shown in Fig. 31 are aggregated for the following indicators for each unit distance along the flow path. Clarify sections where deterioration is progressing.

・ Ratio of riverbanks and forest land in the river channel ・ Representative grain size of riverbed materials ・ Forestation index (forest value area x tree height)
-Water surface area-Forestry potential distribution (overlay of sandbar specific height and particle size distribution)
-Distribution of habitat potential of various organisms The present invention naturally includes various embodiments not described herein. The technical scope of the present invention is determined only by the invention specifying matters according to the scope of claims reasonable from the above description.

It is a hardware block diagram of the terrestrial environment condition provision system using the laser data and color image by an aircraft. It is a schematic block diagram explaining this invention. It is explanatory drawing of the sand bar specific height image which canceled the gradient of the river. It is explanatory drawing of the Seto image which understands Hayase of a river, a kite, etc. at a glance. It is explanatory drawing of a riverbed material image. It is explanatory drawing of an ortho image. It is explanatory drawing of a reflection intensity distribution figure. It is explanatory drawing of a land cover classification | category map. It is explanatory drawing of a vegetation height distribution map. It is a flowchart explaining a land cover classification map creation part in detail. It is explanatory drawing explaining collection of the laser data by an aircraft, and acquisition of an ortho image. It is explanatory drawing explaining the attached image in a land cover classification | category figure. It is explanatory drawing explaining a bank-outside area mask (river channel area) and a bank-outside bare field (river surface area). It is a flowchart explaining the outline of specific height distribution calculation of the sand bar with respect to the water surface. It is a flowchart explaining the specific height distribution calculation of the sand bar with respect to the water surface of other embodiment. It is explanatory drawing explaining the stage chroma before a gradient cancellation. It is explanatory drawing explaining cancellation of a water surface gradient. It is a flowchart explaining the outline of a Seto image generation. It is a flowchart explaining a ripple condition discrimination. It is a flowchart explaining a ripple condition discrimination. It is a flowchart explaining the color classification process of a stream and a haze image. It is a flowchart explaining the color classification process of a stream and a haze image. It is a flowchart explaining the color classification process of a stream and a haze image. It is explanatory drawing of a Seto image. It is explanatory drawing explaining the photographic image (a moving image is included) attached to the Seto image. It is a flowchart explaining the display of the classification image of a riverbed material. It is explanatory drawing explaining a riverbed material classification | category figure. It is explanatory drawing explaining the photograph image (a moving image is included) attached to the riverbed material classification | category figure. It is explanatory drawing explaining the process of the sandbar dry / wet map preparation part. It is explanatory drawing explaining a sandbar dry / wet classification chart. 10 is an explanatory diagram for explaining an analysis screen according to Embodiment 3. FIG. It is explanatory drawing explaining TIN provided with respect to a water surface. It is a block diagram explaining the detailed structure of a laser data editing part. It is explanatory drawing explaining ground laser data (DEM) and surface layer laser data (DSM). It is a comparison figure of the mesh division of an ortho image and laser data.

Explanation of symbols

1 Central information processing device (CPU section)
2 Information input unit 3 Information output unit 4 First storage unit 5 Second storage unit 10 Water and land environment provision unit 11 State high classification map generation unit 12 River bed material information generation unit 14 Seto classification unit 20 River environment situation diagram Creation unit 21 Reflection intensity map creation unit 22 Land cover classification map creation unit 23 River channel analysis area creation unit 24 Composition unit 200 Laser data editing unit 201 Laser data classification unit 202 Elevation value data extraction unit 203 Reflection intensity data extraction unit 204 Vegetation height calculation Part

Claims (14)

In the feature environment situation providing method using the color orthophoto image and laser data of the target area photographed and measured by the aircraft,
The color intensity of an arbitrary band of red (R), green (G), and blue (B) of the mesh of the color orthophoto image is compared with the first threshold value, and the mesh is classified into the sun and the shade. And a process of
Comparing the color intensity of each of the three bands of red (R), green (G), and blue (B) of the Hyuga mesh, and classifying the Hyuga mesh into water, bare land, and vegetation;
The surface layer reflection intensity of the laser data of the section including the shade mesh is compared with the second and third threshold values, and the shade mesh is classified into the water area, the bare land, and the vegetation. Process,
A method for providing a feature environment state, comprising: repeatedly performing all the steps for all meshes of the color orthophoto image, and performing a land cover classification of the target area. Reading the ground elevation value and surface elevation value of each section included in the laser data, and obtaining the vegetation height of the section from the difference;
Comparing the vegetation height of the section containing the vegetation mesh with a fourth threshold and classifying the vegetation mesh into trees and grassland;
Further,
The land environment classification method according to claim 1, wherein land cover classification of the target area is performed. For all meshes classified into the water area, extracting the ground elevation value of the laser data of the section including the mesh, respectively, and creating a water surface gradient section;
Generating an inequality triangle network for any 3 mesh in the ground elevation value, and calculating the elevation value of the reference water surface of the sandbar sandwiched between a plurality of water areas;
A point group arbitrarily generated on a two-dimensional dike line created separately is given an altitude value from the ground elevation value nearest to that point, and 3 meshes of the altitude value of the point and the ground elevation value are insignificant. Generating an equilateral triangle net to calculate an elevation value of a reference water surface of a sandbar sandwiched between a water area and a dike;
The feature environment status providing method according to claim 1, further comprising: calculating a difference between the altitude value of the reference water surface and the ground altitude value and creating a specific height distribution of the sand bar. Obtaining a water surface gradient for each arbitrary section from the water surface gradient section;
A step of extracting a pixel value of the color orthophoto image corresponding to the section, separating the pixel value into a high frequency component and a low frequency component, and obtaining a water surface ripple state from the water surface gradient and the separation result. The feature environment situation providing method according to claim 3.   Further performing a step of averaging pixel values of the color orthophoto image corresponding to the sand bar to separate low frequency components and high frequency components, and obtaining a particle size of the sand bar region based on a standard deviation value of the high frequency components. The terrestrial environment situation providing method according to claim 3 or 4, characterized in that: ,   3. The method according to claim 1, further comprising the step of assigning an arbitrary color to the mesh according to the classification result of the mesh of the color orthophoto image and creating a color land cover classification map of the target area. The feature environment status providing method described.   4. The method of assigning an arbitrary color to the mesh according to the difference value of each mesh in the sand bar specific height distribution and creating a color sand bar specific height distribution map of the water area is further performed. The feature environment status providing method described. A feature environment situation providing program using a color orthophoto image and laser data of a target area photographed and measured by an aircraft,
On the computer,
The color intensity of an arbitrary band of red (R), green (G), and blue (B) of the mesh of the color orthophoto image is compared with the first threshold value, and the mesh is classified into the sun and the shade. Means to
Means for comparing the color intensity of each of the three bands of red (R), green (G), and blue (B) of the Hyuga mesh and classifying the Hyuga mesh into water, bare land, and vegetation;
The surface layer reflection intensity of the laser data of the section including the shade mesh is compared with the second and third threshold values, and the shade mesh is classified into the water area, the bare land, and the vegetation. A feature environment situation providing program that repeatedly executes a function as a means for all meshes of the color orthophoto image and executes a function as a means for performing land cover classification of the target area. In the computer,
Means for reading the ground elevation value and the surface elevation value of each section included in the laser data, and obtaining the vegetation height of the section from the difference;
Comparing the vegetation height of the section including the vegetation mesh with a fourth threshold value, further executing a function as a means for classifying the vegetation mesh into trees and grassland, and classifying the land cover classification of the target area 9. The feature environment situation providing program according to claim 8, wherein a function as means for performing is executed. In the computer,
Means for extracting the ground elevation value of the laser data of the section including the mesh for all meshes classified into the water area, and creating a water surface gradient section;
Means for generating an inequality triangular net for any three meshes in the ground elevation value, and calculating an elevation value of a reference water surface of a sand bar sandwiched between a plurality of water areas;
A point group arbitrarily generated on a two-dimensional dike line created separately is given an altitude value from the ground elevation value nearest to that point, and 3 meshes of the altitude value of the point and the ground elevation value are insignificant. Means for generating an equilateral triangle net to calculate the altitude value of the reference water surface of the sandbar sandwiched between the water area and the dike,
The feature environment situation according to claim 8 or 9, further comprising the function of calculating a difference between the elevation value of the reference water surface and the ground elevation value and creating a specific height distribution of a sand bar. Offer program. In the computer,
Means for determining a water surface gradient for each arbitrary section from the water surface gradient section;
The pixel value of the color orthophoto image corresponding to the section is extracted, separated into a high-frequency component and a low-frequency component, and a function as means for obtaining a water surface swell state from the water surface gradient and the separation result is further provided. 11. The feature environment situation providing program according to claim 10, wherein the feature environment situation providing program is executed. In the computer,
The pixel value of the color orthophoto image corresponding to the sand bar is averaged and separated into a low frequency component and a high frequency component, and a function as a means for obtaining a particle size of the sand bar region by a standard deviation value of the high frequency component. 12. The feature environment situation providing program according to claim 10, further comprising executing the program. In the computer,
The function as a means for assigning an arbitrary color to the mesh according to the mesh classification result of the color orthophoto image and creating a color land cover classification map of the target area is further executed. The feature environment situation providing program according to 8 or 9. In the computer,
According to the value of the difference of each mesh in the sand bar specific height distribution, an arbitrary color is assigned to the mesh to further execute a function as means for creating a color sand bar specific height distribution map of the water area. The feature environment situation providing program according to claim 10.