CN105787457A - Evaluation method for improving vegetation classified remote sensing precision through integration of MODIS satellite and DEM - Google Patents

Abstract

The invention discloses an evaluation method for improving vegetation classified remote sensing precision through integration of MODIS satellite and a DEM. The evaluation method is characterized by comprising the following steps of a first step, establishing a normalized vegetation index (NDVI) and an enhanced vegetation index (EVI); a second step, respectively calculating by means of a MODIS NDVI index and a MODIS EVI index for obtaining required vegetation information; a third step, calculating an interpolation elevation of each net point by means of a method of discrete point moving fitting distance weighting and average interpolation, thereby obtaining the digital elevation model (DEM) of an experiment area; a fourth step, extracting ground gradient information from the digital elevation model (DEM) based on a landform elevation digital map; and a fifth step, extracting the ground gradient information from the digital elevation model (DEM) and combining two front wave bands of two time phase MODISs and the MODIS vegetation index, and performing vegetation classified remote sensing identification by means of a pixel identification classification basic method which mainly comprises maximum likelihood. The evaluation method has advantages of high precision and high practicability. Furthermore the evaluation method can be used for extracting vegetation information of a complicated landform area without damage in real time.

Description

A kind of integrated DEM of MODIS satellite improves the evaluation method of vegetation classification remote sensing precision

Technical field

The present invention relates to a kind of integrated DEM of MODIS satellite and improve estimating of vegetation classification remote sensing precision Calculation method.

Background technology

In the nearest more than ten years, researcher both domestic and external is to how utilizing multiple geographical supplementary number According to, based on Geo knowledge, from image, extract relevant information carried out numerous studies, Such as: by present landuse map, the image information of possible for paddy rice planting area is first extracted Out, then classify, greatly reduce class object, improve nicety of grading.The Yan is waited quietly People utilizes neural net method that multi-source data both can be provided to input, the most not by data distributional assumption The feature limited, from NOAA image zooming-out NDVI and day and night temperature value, by its resampling, then Add soil types, land use pattern and the elevation that paddy growth region is had a major impact to divide The information such as cloth, obtain ideal Hubei Province's late-seaon rice cultivated area；Extraction saline-alkali soil is believed During breath, according to the formation condition of saline-alkali soil and geomorphic feature, and morphologic region and salinization and alkalization Dependency relation, determine identify saline-alkali soil region parameter, improve nicety of grading；By expert it is The method of system is applied to the remote sensing investigation of grass resources, employs MSS4,5,7 three wave band and enters Row biomass calculates and chroma space, and uses geographic assistant data, answering of multi-source information Closing the degree of isolation (Du Mingyi etc., 2002) being greatly improved between sample, nicety of grading ratio is simple Remotely-sensed data and maximum likelihood method is used to improve.

The bibliography of the present patent application:

[1] Yan Jing, Wang Wen, Li Xiangge. utilize neural net method to extract Monitoring of Paddy Rice Plant Area with lake North saves as a example by late-seaon rice [J]. remote sensing journal, and 2001 (03).

[2] horse speeds. pine the Liao Dynasty Plain saline Land detection mechanism and technique study [D]. and Jilin is big Learn .2011.

[3] Zhang Youshui, Feng Xuezhi. BP neutral net classification of remote-sensing images based on GIS research [J]. Nanjing University journal .2003 (06).

[4] competing rosy clouds, Wang Jin ground, Wang Jihua, Huang Wenjiang. based on subregion and the mountain of multi-temporal remote sensing data District's vegetation classification research [J]. remote sensing technology and application .2008 (04).

[5] Chen Jianyu, Zhan Yuanzeng, Mao Zhihua, Lu Lizhen. seas based on multi-source multi-temporal remote sensing data Island land cover pattern super-resolution reconstruction [A]. the 17th Chinese remote sensing conference summary collection [C].2010。

[6] Zhu Changbai. Land_use change based on RS with GIS technology covers investigation--with buddhist city, Foshan City As a example by district [J]. scientific and technological information .2008 (18).

[7] Peng Dailiang. paddy rice yield estimation method research [D] based on statistics with MODIS data. Zhejiang is big Learn .2009.

Summary of the invention

It is an object of the invention to provide a kind of integrated DEM of MODIS satellite of utilization and improve vegetation classification The evaluation method of remote sensing precision, precision is high, and feasibility is strong, it is possible to real-time, lossless to complexity Shaped area vegetation information extracts.

For achieving the above object, the present invention can take following technical proposals:

A kind of integrated DEM of MODIS satellite improves the evaluation method of vegetation classification remote sensing precision, bag Include following steps:

Step one: gather geography information and remotely-sensed data

By choosing corresponding MODIS vegetation index wave band 1,2 and 3, set up normalization vegetation Index (NDVI) and enhancing vegetation index (EVI)；

Step 2: calculate and obtain vegetation information

It is utilized respectively MODIS-NDVI index and MODIS-EVI index is calculated required vegetation Information；

Step 3: set up digital elevation model (DEM)

The method using discrete point to move the distance weighted average interpolation of matching calculates each mesh point Interpolation elevation, it is thus achieved that the digital elevation model (DEM) of test block；

Step 4: extract ground line gradient information

On the basis of landform altitude digitalized maps, from digital elevation model (DEM), extract ground Face grade information；

Step 5: utilize digital elevation model (DEM) to extract ground line gradient information, in conjunction with two Phase MODIS the first two wave band and MODIS vegetation index, utilizing maximum likelihood is main pixel Identify the basic skills of classification, carry out vegetation classification remote sensing recognition.

Collection geography information described in step one should expire with the wavelength band selected by remotely-sensed data Foot table 1；MODIS data should be corrected through sun altitude, projective transformation and radiant correction, then Carrying out rigid registrations under GIS supports, registration error should be respectively less than 0.5 pixel.

The different vegetation index of table 1 and the wave band applied

Calculating described in step 2 obtains vegetation information, and concrete formula is:

MODIS-NDVI computing formula is as follows:

$NDVI=\frac{NIR-R}{NIR+R}---\left(1\right)$

In formula: NIR and R is respectively near-infrared and red spectral band；

MODIS-EVI computing formula is as follows:

$EVI=\frac{{\mathrm{\ρ}}_{NIR}-{\mathrm{\ρ}}_{RED}}{{\mathrm{\ρ}}_{NIR}+C_1{\mathrm{\ρ}}_{RED}-C_2{\mathrm{\ρ}}_{BLUE}+L}(1+L)---\left(2\right)$

Wherein, ρ_NIR、ρ_REDAnd ρ_BLUEIt is corresponding MODIS satellite near-infrared the second ripple respectively Section, ruddiness first band and the spectral reflectance values of blue light the 3rd wave band, L is that background adjusts item, C₁And C₂It is fitting coefficient, L=1, C₁=6, and C₂=7.5.

Set up digital elevation model (DEM) described in step 3, use discrete point move matching away from Method from interpolation by weighted average calculates the interpolation elevation of each mesh point, and DEM regards as one Or the sum of multiple function, utilize this or these function to derive terrain factor, concrete formula is:

Set up an office p (x_p,y_p) it is mesh point to be interpolated, draw by 45 azimuthal spacings centered by p point Article eight, direction line.These eight direction lines are respectively with the distance of p point immediate contour intersection point d₁,d₂,......d₈.The elevation of these points is Z₁,Z₂.....Z₈.If d₂=0, (l=1,2,3 ... .., 8), Then p point is positioned on a certain contour, the elevation Z of this point_pIt is required gridded elevation；Otherwise P point is not on contour, for mesh point to be interpolated；When d ≠ 0, if required grid Elevation is Z_i,j, then

$\begin{array}{ccc}{Z}_{i,j}=\frac{\underset{i=1}{\overset{8}{\Σ}}\left(Z_l{d}_l\right)}{\underset{i=1}{\overset{8}{\Σ}}(1/d_l)}& d_l\≠0& (l=1,2,3,\mathrm{.....},8)\end{array}---\left(3\right)$

Wherein, d_l=| x_l-x_p| (l=1,2)；d_l=| y_l-y_p| (l=3,4)；

Extraction ground line gradient information described in step 4 the steps include:

Step 5: by the 1:250000 ten thousand engineer's scale (form of National Foundation Geography Information Center EOO) Zhejiang Province's contour line data be read into ARC/INFO software, contour interval is 50 meters, the method for step 3 is used to generate 100 meters of * 100 meters of Grid squares；

Step 6: obtain the DEM of 250 meters of * 250 meters of grids through resampling, the grid map of generation As depositing with data set, its form and positional information match with Value of Remote Sensing Data；

Step 7: on the basis of landform altitude digitalized maps, obtain digital elevation model (DEM) the ground line gradient factor (grid size 250 is produced, and from digital elevation model (DEM) * 250 meters of rice).

Compared with prior art the invention has the beneficial effects as follows: use technique scheme,

1, utilize terrain slope information, multi-temporal remote sensing data and MODIS provided a large amount of many time The data product of phase, for research object, fully excavates geographic information data and studies；

2, grade information that DEM produces and two phase MODIS image datas and vegetation is used to refer to Number compound collecting area, geography information and remotely-sensed data multi-source information synthesis utilize relative to simple Single scape image data can significantly improve the precision of area reckoning.

Accompanying drawing explanation

Fig. 1 is study area DEM striograph；

Fig. 2 is study area ground line gradient striograph；

Fig. 3 is divergence analysis figure between sample (first three wave band of MODIS and NDVI)；

Fig. 4 is (MODIS two first three wave band of phase, NDVI, the EVI of divergence analysis figure between sample And the gradient).

Detailed description of the invention

The present invention be a kind of use MODIS data integration DEM improve vegetation classification remote sensing precision Evaluation method, comprises the following steps:

Collection geography information and the step of remotely-sensed data:

By choosing corresponding MODIS vegetation index wave band 1,2 and 3, set up normalization vegetation Index (Normalized Vegetation Indices is called for short NDVI) and enhancing vegetation index (Enhance Vegetation Indices is called for short EVI)；

Described collection geography information vegetation index different from selected by remotely-sensed data and being answered Wave band should meet table 1:

The different vegetation index of table 1 and the wave band applied

MODIS data should be corrected through sun altitude, projective transformation and radiant correction, then GIS carries out strict registration under supporting, registration error should be respectively less than 0.5 pixel.

It is utilized respectively NDVI index and MODIS-EVI index is calculated required vegetation information,

MODIS-NDVI index can use computing formula (1),

$NDVI=\frac{NIR-R}{NIR+R}---\left(1\right)$

In formula, NIR and R is respectively near-infrared and red spectral band；

MODIS-EVI index can use computing formula (2),

$EVI=\frac{{\mathrm{\ρ}}_{NIR}-{\mathrm{\ρ}}_{RED}}{{\mathrm{\ρ}}_{NIR}+C_1{\mathrm{\ρ}}_{RED}-C_2{\mathrm{\ρ}}_{BLUE}+L}(1+L)---\left(2\right)$

ρ in formula_NIR、ρ_REDAnd ρ_BLUEIt is corresponding MODIS near-infrared 2 wave band, ruddiness 1 respectively Wave band and the spectral reflectance values of blue light 3 wave band, L is that background adjusts item, C₁And C₂It is to intend Syzygy number, L=1, C₁=6, and C₂=7.5.

Set up the step of digital elevation model (DEM):

The method using discrete point to move the distance weighted average interpolation of matching calculates each mesh point Interpolation elevation, it is thus achieved that the digital elevation model (DEM) of test block, concrete formula is:

Set up an office p (x_p,y_p) it is mesh point to be interpolated, draw by 45 azimuthal spacings centered by p point Article eight, direction line.These eight direction lines are respectively with the distance of p point immediate contour intersection point d₁,d₂,......d₈.The elevation of these points is Z₁,Z₂.....Z₈.If d₂=0, (l=1,2,3 ... .., 8), Then p point is positioned on a certain contour, the elevation Z of this point_pIt is required gridded elevation；Otherwise P point is not on contour, for mesh point to be interpolated.When d ≠ 0, if required grid Elevation is Z_i,j, then

$\begin{array}{ccc}{Z}_{i,j}=\frac{\underset{i=1}{\overset{8}{\Σ}}\left(Z_l{d}_l\right)}{\underset{i=1}{\overset{8}{\Σ}}(1/d_l)}& d_l\≠0& (l=1,2,3,\mathrm{.....},8)\end{array}---\left(3\right)$

Wherein, d_l=| x_l-x_p| (l=1,2)；d_l=| y_l-y_p| (l=3,4)；

Calculated the elevation of each mesh point by above-mentioned formula, finally obtain the DEM of whole trial zone.

The step of extraction ground line gradient information:

On the basis of landform altitude digitalized maps, from digital elevation model (DEM), extract ground Face grade information, the steps include:

By 1:250000 ten thousand engineer's scale (form is EOO) of National Foundation Geography Information Center Zhejiang Province's contour line data be read into ARC/INFO software, contour interval 50 meters, use The above-mentioned method setting up digital elevation model (DEM) generates 100 meters of * 100 meters of Grid squares；

Obtain the DEM of 250 meters of * 250 meters of grids through resampling, the grating image of generation is with data Collection is deposited, and its form and positional information match with Value of Remote Sensing Data；

On the basis of landform altitude digitalized maps, obtain digital elevation model (DEM), and from Digital elevation model (DEM) produces the ground line gradient factor (grid size 250 meters * 250 meters), As shown in Figure 1, 2.

The step of classification extraction vegetation information:

Digital elevation model (DEM) is utilized to extract ground line gradient information, in conjunction with two phases MODIS the first two wave band and MODIS vegetation index are combined, and utilizing maximum likelihood is main pixel Identify the basic skills of classification, carry out vegetation classification remote sensing recognition；Contrast does not accounts for DEM number According to, the most single scape MODIS image is classified.

Under the same conditions, at only one scape MODIS image classification, nicety of grading ratio is relatively low 53.3%；Digital elevation model (DEM) is utilized to extract ground line gradient information, in conjunction with two phases MODIS the first two wave band and MODIS vegetation index combined entry precision bring up to 79.8%.This Illustrate the introducing of ground line gradient and multi_temporal images data to improve area information extraction accuracy and Classification quality has obvious effect.

Claims (5)

1. the integrated DEM of MODIS satellite improves an evaluation method for vegetation classification remote sensing precision, and its feature includes Following steps:

Step one: gather geography information and remotely-sensed data

By choosing corresponding MODIS vegetation index wave band 1,2 and 3, set up normalized differential vegetation index (NDVI) and Strengthen vegetation index (EVI)；

Step 2: calculate and obtain vegetation information

It is utilized respectively MODIS-NDVI index and MODIS-EVI index is calculated required vegetation information；

Step 3: set up digital elevation model (DEM)

The method using discrete point to move the distance weighted average interpolation of matching calculates each mesh point interpolation elevation, obtains Obtain the digital elevation model (DEM) of test block；

Step 4: extract ground line gradient information

On the basis of landform altitude digitalized maps, from digital elevation model (DEM), extract ground line gradient information；

Step 5: utilize digital elevation model (DEM) to extract ground line gradient information, in conjunction with two phases MODIS The first two wave band and MODIS vegetation index, the basic skills utilizing maximum likelihood to be main pixel identification classification,

Carry out vegetation classification remote sensing recognition.

Utilization MODIS data the most according to claim 1 improve the evaluation method of vegetation classification remote sensing precision, It is characterized in that, the collection geography information described in step one should meet table 1 with the wavelength band selected by remotely-sensed data； MODIS data should be corrected through sun altitude, projective transformation and radiant correction, then carries out strict under GIS supports Registration, registration error should be respectively less than 0.5 pixel.

The different vegetation index of table 1 and the wave band applied

The utilization integrated DEM of MODIS satellite the most according to claim 1 improves vegetation classification remote sensing precision Evaluation method, it is characterised in that the calculating described in step 2 obtains vegetation information, and concrete formula is:

MODIS-NDVI computing formula is as follows:

$NDVI=\frac{NIR-R}{NIR+R}---\left(1\right)$

In formula: NIR and R is respectively near-infrared and red spectral band；

MODIS-EVI computing formula is as follows:

$EVI=\frac{{\mathrm{\ρ}}_{NIR}-{\mathrm{\ρ}}_{RED}}{{\mathrm{\ρ}}_{NIR}+C_1{\mathrm{\ρ}}_{RED}-C_2{\mathrm{\ρ}}_{BLUE}+L}(1+L)---\left(2\right)$

Wherein, ρ_NIR、ρ_REDAnd ρ_BLUEIt is corresponding MODIS satellite near-infrared second band, ruddiness first respectively Wave band and the spectral reflectance values of blue light the 3rd wave band, L is that background adjusts item, C₁And C₂It is fitting coefficient, L= 1,C₁=6, and C₂=7.5.

Utilization MODIS data the most according to claim 1 improve the evaluation method of vegetation classification remote sensing precision, It is characterized in that, described in step 3, set up digital elevation model (DEM), use discrete point to move matching distance and add The method of weight average interpolation calculates the interpolation elevation of each mesh point, and DEM regards as the sum of one or more function, Utilizing this or these function to derive terrain factor, concrete formula is:

Set up an office p (x_p,y_p) it is mesh point to be interpolated, centered by p point, draw eight direction lines by 45 azimuthal spacings, These eight direction lines are respectively d with the distance of p point immediate contour intersection point₁,d₂,......d₈, the elevation of these points For Z₁,Z₂.....Z₈.If d₂=0, (l=1,2,3 ... .., 8), then p point is positioned on a certain contour, the elevation Z of this point_p It is required gridded elevation；Otherwise p point is not on contour, for mesh point to be interpolated.When d ≠ 0, if The elevation of required grid is Z_i,j, then

$\begin{array}{cc}{Z}_{i,j}=\frac{\underset{i=1}{\overset{8}{\Σ}}\left(Z_l{d}_l\right)}{\underset{i=1}{\overset{8}{\Σ}}(1/d_l)}& d_l\≠0\end{array},(l=1,2,3,...\mathrm{..},8)---\left(3\right)$

Wherein, d_l=| x_l-x_p| (l=1,2)；d_l=| y_l-y_p| (l=3,4)；

Utilization MODIS data the most according to claim 1 improve the evaluation method of vegetation classification remote sensing precision, It is characterized in that, the extraction ground line gradient information described in step 4 the steps include:

Step 5: by 1:250000 ten thousand engineer's scale (form is EOO) of National Foundation Geography Information Center Zhejiang Province's contour line data is read into ARC/INFO software, and contour interval is 50 meters, uses the side of step 3 Method generates 100 meters of * 100 meters of Grid squares；

Step 6: obtain the DEM of 250 meters of * 250 meters of grids through resampling, the grating image of generation is deposited with data set Putting, its form and positional information match with Value of Remote Sensing Data；

Step 7: on the basis of landform altitude digitalized maps, obtains digital elevation model (DEM), and from numeral Elevation model (DEM) produces the ground line gradient factor (grid size 250 meters * 250 meters).