CN101788553B - Multiscale analysis method for vegetation indexes of refuse dump and soil nutrient space - Google Patents

Abstract

The invention discloses a multiscale analysis method for a vegetation index of a refuse dump and soil nutrient space. In the method, a normalized difference vegetation index (NDVI) is selected as an index of vegetation condition, a pH value, organic matters, NH4N, rapid available phosphorus and rapid available potassium are selected as indexes of soil nutrients, the six indexes are subjected to wavelet six-scale decomposition and reconstruction so as to obtain wavelet approximate reconstruction information of various indexes on different space scales; a partial correlation coefficient of wavelet approximate reconstruction information of NDVI and the wavelet approximate reconstruction information of each index of the soil nutrients on different space scales is respectively calculated; and correlationships of the NDVI and the soil nutrients on different space scales are analyzed. The method can analyze the relationships of the NDVI and the soil nutrients on different space scales, and is favorable for comprehensively understanding relationships of ecological factors of the refuse dump on different space scales and determinants for spatial variability of the NDVI on different scales.

Description

Vegetation indexes of refuse dump and soil nutrient space multiscale analysis method

Technical field

The present invention relates to refuse dump vegetation and soil and concern the field, be specifically related to a kind of vegetation indexes of refuse dump and soil nutrient space multiscale analysis method based on wavelet analysis.

Background technology

The refuse dump is meant that mining dumps the place that thing is concentrated discharging.Mining is meant surface mining and underground mining, comprises peeling off with shaft and drift excavation during the mine capital construction in the open and opens up; Dump thing and generally comprise corruption and plant table soil, weathered rock and soil, solid rock and mixing ground, also comprising sometimes can recoverable unbalanced-ore, lean ore etc.The refuse dump is the ecosystem of a complicacy, and wild plant invasion, vegetation succession etc. have caused the diversity of the ecosystem, make the ecosystem more complicated.Refuse dump, Haizhou, Fuxin area is big, and different plot have the different casting time limits, has more aggravated the complicacy of refuse dump, Haizhou ecosystem.The refuse dump state of ecological environment is limited by multiple factor, and wherein existing mankind's activity factor also has many natural environmental factors, like landforms, landform, soil and vegetation etc.These factors cause the Spatial Variability of ecologic environment and ecoscape jointly through nonlinear compound action, and the determinative of zones of different, its spatial variability of different scale is different.Prior art adopts multiple regression procedure analysis to the research of refuse dump vegetation and soil nutrient relation more, and multiple regression procedure has been ignored scale effect, does not relate to the relation under the different spaces yardstick.

Wavelet transformation is as a kind of mathematical tool of multiscale analysis; In recent years by in more and ecological study with being applied to; Disclosing the multiple dimensioned general layout of nature or ecological factor, is the of paramount importance step of wavelet analysis, promptly selects suitable wavelet function and function to be analyzed to multiply each other; Decompose the wavelet coefficient obtain under the different scale go forward side by side line reconstruction and then the characteristic of analysis factor under different scale.

Summary of the invention

The technical matters that (one) will solve

The technical matters that the present invention will solve provides a kind of vegetation indexes of refuse dump and soil nutrient space multiscale analysis method, the research of refuse dump vegetation and soil nutrient relation is not had the defective of scale effect to solve prior art.

(2) technical scheme

Therefore, a kind of vegetation indexes of refuse dump provided by the invention and soil nutrient space multiscale analysis method comprise:

Step 10, from the remote sensing images of study area, extract NDVI;

Step 20, gather the soil nutrient of said study area, and the geo-statistic interpolation graphs layer of said soil nutrient is carried out rasterizing handle;

Step 30, respectively select a line-transect at the North and South direction and the east-west direction of said study area;

Step 40, from two said line-transects, gather NDVI and soil nutrient data respectively;

Step 50, the said NDVI and the soil nutrient data that adopt small wave converting method that step 40 is extracted are carried out the multiple dimensioned decomposition and reconstruction in space, obtain the approximate reconfiguration information of small echo of the above NDVI of different spaces yardstick and soil nutrient data;

Step 60, the said small echo that obtains according to step 50 are similar to reconfiguration information; Adopt the partial Correlation Analysis method that the relation of said NDVI under the different spaces yardstick and soil nutrient data is analyzed, obtain the partial correlation coefficient of said NDVI and soil nutrient data under the different spaces yardstick.

Wherein, said step 20 comprises:

The soil nutrient of said study area is carried out the geo-statistic credit analyse, utilize the Ordinary Kriging Interpolation method of interpolation to obtain the spatial distribution map of said soil nutrient;

Said spatial distribution map is carried out rasterizing, obtain the grating image identical with the vegetation index spatial resolution.

Said soil nutrient comprises pH value, organic matter, NH ₄_ N, rapid available phosphorus and available potassium.

The wavelet basis function of choosing when adopting small wave converting method in the said step 50 is DB4, and decomposing the number of plies is 6.

(3) beneficial effect

Vegetation indexes of refuse dump provided by the invention and soil nutrient space multiscale analysis method are selected the index of NDVI as vegetation state, select pH value, organic matter, NH ₄_ N, rapid available phosphorus, available potassium are as the index of soil nutrient; Through these 6 indexs are carried out small echo 6 yardstick decomposition and reconstructions; Obtain the approximate reconfiguration information of small echo of each index under the different spaces yardstick; Calculate the partial correlation coefficient of the approximate reconfiguration information of NDVI small echo and the approximate reconfiguration information of each index small echo of soil nutrient under the different spaces yardstick respectively, the correlationship of NDVI and soil nutrient under the analysis different spaces yardstick.This method has multiple dimensioned effect, can analyze NDVI and Relationship with Soil Nutrients from different yardsticks, helps fullying understand the determinative of NDVI spatial variability on relation and the different scale on the ecological factor different spaces yardstick of refuse dump.

Description of drawings

Fig. 1 is vegetation indexes of refuse dump of the present invention and soil nutrient space multiscale analysis method process flow diagram.

Embodiment

Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.

As shown in Figure 1, be vegetation indexes of refuse dump of the present invention and soil nutrient space multiscale analysis method process flow diagram, present embodiment may further comprise the steps:

Step 10, from the remote sensing images of study area, extract normalized differential vegetation index (Normalized Difference Vegetation Index, be called for short NDVI);

The computing method of extracting NDVI are:

$\mathrm{NDVI}=\frac{{\mathrm{\ρ}}_{\mathrm{NIR}}-{\mathrm{\ρ}}_R}{{\mathrm{\ρ}}_{\mathrm{NIR}}+{\mathrm{\ρ}}_R}$

Wherein, ρ _NIR, ρ _RBe respectively the earth surface reflection rate of near-infrared band and visible red wave band.

Because receive the influence of factors such as air, light; Remote sensing images that photograph and actual value have certain error, and therefore, preferably, at first the remote sensing images to study area carry out treatment for correcting; Comprise geometry correction and radiant correction, to obtain remote sensing images more accurately;

Particularly, be reference diagram with topomap through geometrical registration, in Erdas Imagine 9.2, choose 15 reference mark; Choose 5 checkpoints simultaneously; Adopt method for resampling that remote sensing images are carried out geometry correction, wherein method for resampling is selected nearest neighbor algorithm, and correction error RMS is 0.3633.

When adopting radiant correction, adopt the radiation calibration method in the present embodiment, divide two steps, convert pixel DN value into waveband integral radiance (Band-integrated radiance) earlier, calculate spectral radiance then.

Step 20, gather the soil nutrient of study area, and the geo-statistic interpolation graphs layer of soil nutrient is carried out rasterizing handle;

Particularly, this step comprises: gather the soil nutrient of study area, and soil nutrient is carried out the geo-statistic credit analyse, utilize the space distribution information of Ordinary Kriging Interpolation method of interpolation acquisition soil nutrient, i.e. the geo-statistic interpolation graphs layer of soil nutrient; Soil nutrient spatial distribution map after the geo-statistic interpolation is carried out rasterizing in ArcGIS9.2, obtain the grating image identical with the vegetation index spatial resolution;

Soil nutrient in the present embodiment comprises pH value, organic matter, NH ₄_ N, rapid available phosphorus and available potassium.

In this step, it is theoretical according to regionalized variable that soil nutrient is carried out the main method that the geo-statistic credit analyses.Regard each factor of soil nutrient as regionalized variable Z (x), it has structural and randomness simultaneously, can represent with semi-variance function

$\mathrm{\γ}\left(h\right)=\frac{1}{2N\left(h\right)}\underset{i=1}{\overset{N\left(h\right)}{\mathrm{\Σ}}}{[Z(x_i+h)-Z\left(x_i\right)]}^2$

H representes the sampled point spacing, claims potential difference again; N (h) is for being the paired number of all observation stations of spacing with h.

The Krieger method of interpolation is to use the widest optimum interpolation method during geo-statistic is learned.It is utilize raw data and semi-variance function structural, sampled point is not had a kind of method of optimum partially valuation.N the sampling point measured value valuation that is located at a certain variable in the zone is Z (x _i) (i=1,2,3 ..., n), existing asking through the linear combination of this n measured value waits to estimate an X ₀The estimated value Z at place ^*(x ₀), promptly

$Z^{*}\left(x_0\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_{i}Z\left(x_i\right)$

In the formula, λ _iFor with Z (x _i) the relevant weighting coefficient in position.

When carrying out soil nutrient geo-statistic interpolation graphs rasterizing; At first export as polar plot to the geo-statistic interpolation graphs; Adopt the conversion tools in the Arctoolbox to convert polar plot to the resolution grating image identical with the vegetation index image resolution ratio then, picture format is ERISGRID.

Step 30, selection study area line-transect are selected two line-transects in thing, North and South direction;

Can be referred to as thing line-transect and north and south line-transect respectively;

In the present embodiment, get 1073 with the pixel count of north and south line-transect, the pixel count of thing line-transect gets 626, and north and south line-transect longitude is got 121 ° 39 ' 6 ", thing line-transect latitude is got 41 ° 57 ' 22 " be example;

The selection of line-transect needs fully to represent the study area characteristic, because this study area area is less relatively, therefore on North and South direction and east-west direction, respectively selects one;

Step 40, from two line-transects, gather NDVI and soil nutrient data respectively;

This step is accomplished under the support of ArcGIS 9.2 and Erdas 9.2;

Step 50, based on Matlab 7.0 platforms, select female small echo DB4 for use, decomposition level 6, promptly decomposition scale is 2.44 * 2 ¹M ², 2.44 * 2 ²M ², 2.44 * 2 ³M ², 2.44 * 2 ⁴M ², 2.44 * 2 ⁵M ², 2.44 * 2 ⁶M ², NDVI on the different line-transects and soil nutrient are carried out multiple dimensioned decomposition and reconstruct, obtain the approximate reconfiguration information of small echo of NDVI and soil nutrient data on the different scale;

Step 60, the approximate reconfiguration information that obtains according to step 50 are calculated NDVI and the partial correlation coefficient of soil nutrient under the different spaces yardstick, draw its correlationship under the different spaces yardstick.

If the spatial sequence of NDVI and soil nutrient is x (h), its multi-scale wavelet is decomposed into

$c_{j,k}={\&Integral;}_{-\&infin;}^{+\&infin;}x\left(h\right){\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_{j,k}\left(h\right)\mathrm{dh}=<x,{\mathrm{\&psi;}}_{j,k}>$

Be reconstructed into

$x\left(h\right)=c\underset{j=-\&infin;}{\overset{+\&infin;}{\mathrm{\&Sigma;}}}\underset{k=-\&infin;}{\overset{+\&infin;}{\mathrm{\&Sigma;}}}{c}_{j,k}{\mathrm{\&psi;}}_{j,k}\left(h\right)$

NDVI and soil nutrient space different scale partial correlation computing method are: at situational variables x ₁And during the partial correlation coefficient between the y, when having controlled x ₂Linear action after, x ₁And the single order partial correlation coefficient between the y is:

$r_{y\mathrm{1,2}}=\frac{r_{y1}-r_{y2}{r}_{12}}{\sqrt{(1-r_{y2}^2)(1-r_{12}^2)}}$

Wherein, r _Y1, r _Y2, r ₁₂Be respectively y and x ₁Related coefficient, y and x ₂Related coefficient, x ₁And x ₂Related coefficient, the Probability p value is used in the check of partial Correlation Analysis usually, if the p value is less than given level of significance, then partial correlation property is remarkable, otherwise the correlativity between two variablees is not remarkable.When the partial correlation coefficient to north and south line-transect soil nutrient envirment factor and NDVI calculated, organic control variable was rapid available phosphorus and available potassium, NH in the present embodiment ₄The control variable of _ N is chosen for available potassium, and the control variable of rapid available phosphorus is organic, and the control variable of available potassium is NH ₄_ N and organic matter.In the thing line-transect, its control variable is organic matter and rapid available phosphorus during the partial correlation coefficient of calculating pH value and NDVI, and organic control variable is pH value and rapid available phosphorus, NH ₄The control variable of _ N is decided to be rapid available phosphorus and available potassium, and the control variable of rapid available phosphorus has three, is respectively pH value, organic matter and NH ₄_ N, the control variable of available potassium is NH ₄_ N.For there not being the strong especially envirment factor of correlativity with it, then get the related coefficient of itself and NDVI.

Can find out that by above embodiment the embodiment of the invention is selected pH value, organic matter, NH through selecting the index of NDVI as vegetation state ₄_ N, rapid available phosphorus, available potassium are as the index of soil nutrient; Through 6 indexs being carried out small echo 6 yardstick decomposition and reconstructions; Obtain the approximate reconfiguration information of small echo of each index under the different spaces yardstick; Calculate the partial correlation coefficient of the approximate reconfiguration information of NDVI small echo and the approximate reconfiguration information of each index small echo of soil nutrient under the different spaces yardstick respectively, the correlationship of vegetation index and soil nutrient under the analysis different spaces yardstick.This method has multiple dimensioned effect, can analyze NDVI and Relationship with Soil Nutrients from different yardsticks, helps fullying understand the determinative of NDVI spatial variability on relation and the different scale on the ecological factor different spaces yardstick of refuse dump.

The above only is a preferred implementation of the present invention; Should be pointed out that for those skilled in the art, under the prerequisite that does not break away from know-why of the present invention; Can also make some improvement and modification, these improve and modification also should be regarded as protection scope of the present invention.

Claims (4)

1. vegetation indexes of refuse dump and soil nutrient space multiscale analysis method is characterized in that, comprising:

Step 10, from the remote sensing images of study area, extract normalized differential vegetation index (NDVI); Specifically comprise: the remote sensing images to study area carry out treatment for correcting; Comprise geometry correction and radiant correction; Topomap with through geometrical registration is a reference diagram, in Erdas Imagine 9.2, chooses a plurality of reference mark and checkpoint, adopts method for resampling that remote sensing images are carried out geometry correction; Wherein method for resampling is selected nearest neighbor algorithm, and correction error RMS is 0.3633;

When adopting radiant correction, adopt the radiation calibration method, convert pixel DN value into waveband integral radiance (Band-integrated radiance) earlier, calculate spectral radiance then;

Step 20, gather the soil nutrient of said study area, and the geo-statistic interpolation graphs layer of said soil nutrient is carried out rasterizing handle;

Step 30, respectively select a line-transect at the North and South direction and the east-west direction of said study area;

Step 40, from two said line-transects, gather NDVI and soil nutrient data respectively;

Step 50, the said NDVI and the soil nutrient data that adopt small wave converting method that said step 40 is extracted are carried out the multiple dimensioned decomposition and reconstruction in space, obtain that the small echo of NDVI and soil nutrient data is similar to reconfiguration information on the different spaces yardstick;

Step 60, the small echo that obtains according to said step 50 are similar to reconfiguration information; Adopt the partial Correlation Analysis method that the relation of said NDVI under the different spaces yardstick and soil nutrient data is analyzed, obtain the partial correlation coefficient of said NDVI and soil nutrient data under the different spaces yardstick.

2. vegetation indexes of refuse dump as claimed in claim 1 and soil nutrient space multiscale analysis method is characterized in that said step 20 comprises:

The soil nutrient of said study area is carried out the geo-statistic credit analyse, utilize the Ordinary Kriging Interpolation method of interpolation to obtain the spatial distribution map of said soil nutrient;

Said spatial distribution map is carried out rasterizing, obtain the grating image identical with the vegetation index spatial resolution.

3. vegetation indexes of refuse dump as claimed in claim 1 and soil nutrient space multiscale analysis method is characterized in that said soil nutrient comprises pH value, organic matter, NH ₄_ N, rapid available phosphorus and available potassium.

4. vegetation indexes of refuse dump as claimed in claim 1 and soil nutrient space multiscale analysis method is characterized in that, the wavelet basis function of choosing when adopting small wave converting method in the said step 50 is DB4, and decomposing the number of plies is 6.

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