CN103294902A - Method for determining natural wetland restoration plan based on remote sensing images and GIS (geographic information system) spatial analyses - Google Patents

Abstract

A method for determining a natural wetland restoration plan based on remote sensing images and GIS spatial analysis, involving a method for determining a natural wetland restoration plan, solving the traditional wetland restoration method that focuses on positioning analysis, has not been studied from a quantitative perspective, and has no Fully integrate the problem of GIS spatial analysis technology. Including steps: obtain initial data, and unify the initial data; respectively use the initial data to calculate the landscape structure factor, DEM data to calculate the humidity index, and NPP product data to divide the cultivated land productivity level; for step 2, obtain the landscape structure factor, river/road density data, The landform data, humidity index, and cultivated land productivity are assigned grades, and the wetland restoration is evaluated; Step 4: According to the wetland restoration evaluation results obtained in Step 3, combined with GIS spatial analysis, determine the wetland restoration plan. The invention can be widely applied to the determination of a wide range of wetland restoration schemes.

Description

A Method for Determining Natural Wetland Restoration Scheme Based on Remote Sensing Image and GIS Spatial Analysis

technical field

The invention relates to a method for determining a natural wetland restoration scheme.

Background technique

Due to over-exploitation and utilization, global wetlands have disappeared and degraded, causing serious ecological microcomputer and social problems. Therefore, it is imminent to restore wetlands by combining natural and artificial means. Wetland restoration refers to the restoration or reconstruction of degraded or disappeared wetlands through ecological technology or ecological engineering, to reproduce the structure and function before disturbance, as well as related physical, chemical and biological characteristics, so that it can play its due role. In recent years, with the increase of human activities, the wetland ecosystem has been severely degraded, causing a series of regional environmental problems such as climate drying, groundwater level drop, soil degradation and fertility depletion, animal and plant resources reduction, and environmental pollution intensification. Remote sensing images and GIS spatial analysis are technical means to explore the restoration plan of regional natural wetlands, which is of great significance to the improvement of regional ecological environment.

The traditional methods of wetland restoration focus on the analysis of positioning, but do not conduct research from a quantitative point of view, and do not fully integrate GIS spatial analysis technology.

Contents of the invention

In order to solve the problem that the traditional wetland restoration method focuses on positioning, does not conduct research from a quantitative perspective, and does not fully integrate GIS space analysis technology, the present invention provides a method for determining a natural wetland restoration plan based on remote sensing images and GIS space method.

A method for determining a natural wetland restoration scheme based on remote sensing images and GIS spatial analysis, which includes the following steps:

Step 1: Obtain initial data and unify the initial data;

The initial data includes land use data, DEM data, river/road density data, landform data and NPP product data;

Step 2: Use the initial data to calculate the landscape structure factor, the DEM data to calculate the humidity index, and the NPP product data to divide the cultivated land productivity level;

Step 3: Assign grades to the landscape structure factors, river/road density data, landform data, humidity index, and cultivated land productivity obtained in Step 2, and evaluate wetland restoration;

Step 4: Based on the wetland restoration assessment results obtained in Step 3, combined with GIS spatial analysis, determine the wetland restoration plan.

The invention conducts research from a quantitative point of view, and fully combines GIS space analysis technology to determine a natural wetland restoration plan. Compared with the traditional wetland restoration method, it has the following advantages: firstly, using the powerful spatial analysis technology of GIS for data analysis and processing, more accurate processing results can be obtained; Restoration; Finally, fully combine the influencing factors of wetland restoration, and formulate a wetland restoration plan from a comprehensive perspective. The formulated wetland restoration plan can increase the wetland area by 20% to 40%.

Description of drawings

Fig. 1 is a flow chart of a method for determining a natural wetland restoration scheme based on remote sensing images and GIS spatial analysis of the present invention;

Fig. 2 is a schematic diagram of the Sanjiang Plain wetland priority restoration area described in the specific embodiment;

Fig. 3 is a schematic diagram of the Sanjiang Plain wetland sub-priority restoration area described in the specific examples.

Detailed ways

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 1. This specific implementation will be described with reference to FIG. 1 . A method for determining a natural wetland restoration scheme based on remote sensing images and GIS spatial analysis, which includes the following steps:

Step 1: Obtain initial data and unify the initial data;

The initial data includes land use data, DEM data, river/road density data, landform data and NPP product data;

Step 2: Use the initial data to calculate the landscape structure factor, the DEM data to calculate the humidity index, and the NPP product data to divide the cultivated land productivity level;

Step 3: Assign grades to the landscape structure factors, river/road density data, landform data, humidity index, and cultivated land productivity obtained in Step 2, and evaluate wetland restoration;

Step 4: Based on the wetland restoration assessment results obtained in Step 3, combined with GIS spatial analysis, determine the wetland restoration plan.

Detailed steps of the present invention are:

A method for determining a natural wetland restoration scheme based on remote sensing images and GIS spatial analysis, which includes the following steps:

Step 1: Obtain initial data and unify the initial data;

The initial data includes land use data, DEM data (mathematical elevation model data), river/road density data, landform data and NPP product data;

Unifying the initial data described in step 1 includes converting the initial data into a grid of the same size and a unified projection;

The grid of the same size is a grid with a size of 30m×30m;

The projection is the Universal Transverse Mercator projection with the central meridian being 105° East.

The process of using the initial data to calculate the landscape structure factor described in step 2 is:

The landscape structure factors include the largest patch index LPI, the aggregation index AI and the dispersion and juxtaposition index IJI;

The calculation method of the maximum plaque index LPI is:

$\frac{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; max</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \%</span>$

The calculation method of the aggregation index AI is:

$<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}}{\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \%</span>$

The calculation method of the dispersion and tie index IJI is:

$\frac{<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; rk</span>}}}{\mathrm{<span\; class="notranslate">\; E.</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; rk</span>}}}{\mathrm{<span\; class="notranslate">\; E.</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span><span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \%</span>$

Among them, A is the patch area; a _ij is the landscape area; g _ij is the number of grid cells adjacent to type i and type j; max is the maximum number of grid cells adjacent to type i and type j; e _rk is The length of the common boundary between adjacent landscapes r and k; E is the total length of the common boundary of all landscape types; n is the total number of types, and m is the total number of patch types in the landscape.

The NPP product data in step 2 is divided into cultivated land productivity grades including low-yield, middle-yield, and high-yield;

The NPP product data is extracted from medium-resolution imaging spectral data using the CASA model. <-10 means low yield; -10-10 means middle yield; >10 means high yield.

The CASA model was proposed by Potter et al. in 1993. The full name is the Carnegie-Ames-Stanford Approach. There is no Chinese meaning for the title at present. It is a method for calculating net primary productivity.

Step 3: Assign grades to the landscape structure factors, river/road density data, landform data, humidity index, and cultivated land productivity obtained in Step 2, and evaluate wetland restoration;

In Step 3, grade the landscape structure factor, river/road density data, landform data, humidity index, and cultivated land productivity obtained in Step 2. The specific value assignment scheme is:

In the landscape structure factor, the maximum patch index LPI grade assignment is: 0-30% assignment value 0, 30%-50% assignment value 3, 50%-100% assignment value 5; aggregation index AI assignment value: 0-30% assignment value 0 , 30%-50% value 3, 50%-100% value 5, the distribution and tie index IJI value is: 0-30% value 5, 30%-50% value 3, 50%-100% value 0;

In the river/road density, the grade assignment of river density is: 0-0.3 assigns 0, 0.3-0.7 assigns 3, >0.7 assigns 5.; the grade assignment of road density: 0-0.3 assigns 5, 0.3-0.7 assigns 3 ,>0.7 assigns 0;

The range of humidity index calculated according to weight 4 is: -10-26.5, and the value assignment scheme is: -10-5.5 is assigned a value of 0, 5.6-12.5 is assigned a value of 3, and 12.5-26.5 is assigned a value of 5;

In the landform data, the floodplain is assigned a value of 5; the depression is assigned a value of 3; the others are assigned a value of 0;

The NPP product classification assignment scheme is as follows: the high-yield assignment is 0; the middle-class assignment is 3; the low-yield assignment is 5.

Using the GIS geographic information system spatial overlay analysis technology, the landscape structure factors, river/road density data, landform data, humidity index, and cultivated land productivity grade assigned data are overlaid to obtain the distribution results of wetland restoration evaluation values.

After obtaining the landscape structure factors, river/road density data, landform data, humidity index, and cultivated land productivity factors, it is necessary to use GIS spatial analysis technology to superimpose each factor to obtain the final wetland restoration results; in addition, the river/road density data is also Extracted from the original river/road vector data by GIS spatial analysis function.

Step 4: Based on the wetland restoration assessment results obtained in Step 3, combined with GIS spatial analysis, determine the wetland restoration plan.

Step 4: According to the wetland restoration assessment results obtained in step 3, combined with GIS spatial analysis, the process of determining the wetland restoration plan is as follows:

Step 4 1: Obtain the wetland restoration assessment results in Step 3;

Step 4 2: Determine whether the evaluation score is within 32-42 points; if so, perform regional recovery, otherwise go to Step 4 3;

Step 4 3: Determine whether the evaluation score is within 22-32 points; if so, perform regional recovery, otherwise go to Step 4 4;

Step 4: Obtain the remaining wetland information, which will not be recovered for now.

Specific embodiment: this specific embodiment is described in conjunction with FIG. 2 and FIG. 3 .

Using the method of natural wetland restoration combined with remote sensing and GIS to realize the spatial restoration of natural wetland in Sanjiang Plain. The specific operation steps are as follows:

Obtain plain data and process the original data. The data involved include land use data; DEM data; river and road density data; landform data and Modis NPP product data. The land use data is obtained from TM remote sensing images through manual visual interpretation; the river and road vector data are extracted from the land use data. Under the ArcGIS9.3 data processing platform, the Line Density command is used to generate the river and road density maps respectively. Geomorphic data is generated by scanning, digitizing and cartographically integrated 1:250,000 geomorphic map. According to the analysis requirements in this paper, the geomorphic types are divided into: floodplain, depression and other types. Cultivated land productivity uses the commonly used NPP index to reflect cultivated land productivity. NPP refers to the organic dry matter that green plants can accumulate per unit time and per unit area. It can reflect the productivity of ecosystems with a uniform scale standard and is a good measure of cultivated land productivity. index.

The above data are unified under the same coordinate system and projection. The projection adopted is the Universal Transverse Mercator projection, and adopts the unified central meridian of the whole country. The central meridian is 105°E.

Five wetland restoration index factors, landscape structure factor, river and road density, humidity index, landform conditions, and cultivated land productivity, were selected respectively, and a wetland restoration plan was designed using a spatial analysis model. Among them, the landscape structure reflects the landscape ecological characteristics of the region. Combined with the characteristics of landscape indicators, the largest patch index LPI, the aggregation index AI, and the dispersion and juxtaposition index IJI are selected as landscape structure factors, as shown in Table 1.

Table 1 Landscape index and its ecological meaning

The humidity index can quantitatively simulate the dry and wet state of soil moisture in the watershed. It is the most commonly used indicator of static soil moisture content and can be used as an important reference index for wetland restoration. The humidity index is extracted from DEM data. The NPP data is extracted from the Modis data using the CASA model. According to the calculation results, the NPP calculation results are converted into three grades: low-yield, middle-yield, and high-yield.

Combined with the results of index factor analysis and their classification, a spatial decision-making model for wetland restoration was constructed; the classification of each index factor is shown in Table 2.

Table 2 Grade assignment table of wetland restoration indicators in Northeast China

According to the scores calculated by each index level, combined with the characteristics of natural wetlands, the decision-making model for the spatial restoration of natural wetlands is determined.

According to the established wetland restoration decision-making model, combined with GIS spatial analysis technology, determine the priority and sub-priority restoration plans for regional wetlands. On the basis of data analysis, combined with the processing of GIS spatial analysis technology, the spatial distribution of wetland restoration in Northeast China is obtained, and graded according to the scores in Table 2, combined with the decision-making of natural wetland space restoration, the priority and secondary priority of natural wetland in the region can be constructed Restoration areas, among which, the priority level is the near-term wetland restoration area, and the second priority is the mid-to-long-term wetland restoration plan to improve the connectivity of the priority restoration wetland patches. Finally, the Sanjiang Plain wetland priority and sub-priority restoration areas are obtained as shown in Figures 2 and 3.

It can be seen from the figure that the restoration of wetlands is mainly aimed at the plain areas with relatively low altitudes, mainly in the northeast and central areas of the Sanjiang Plain. Figure 2 shows that priority wetland restoration is distributed throughout the Sanjiang Plain, mainly in the following two locations. First, it is located around open water bodies such as rivers and lakes. These areas have poor natural environments and relatively low land utilization rates. Close to water bodies, wetland restoration is relatively easy, and another part of the priority restoration areas is located in the cultivated land and grassland in plain areas. The productivity of cultivated land in these areas is relatively low, and restoration of wetlands is conducive to the coordination of large regional ecosystems; a few priority restoration areas are grasslands ; The area of the sub-priority restoration area is larger than that of the priority level, mainly to increase the connectivity of the priority level wetland restoration and optimize the wetland landscape pattern. Compared with the priority level of restoration, the sub-priority restoration patch is larger. Among them, the priority restoration area is 1.08×10 ⁵ hm ² , and the sub-priority restoration area is 1.21×10 ⁶ hm ² , accounting for 1.29% and 3.67% of the total area of the Sanjiang Plain respectively. 30.58%, which can provide data reference for the implementation of Sanjiang Plain wetland restoration.

Claims (7)

1. determine the method for natural wetland recovery scheme to it is characterized in that it comprises the steps: based on remote sensing image and GIS spatial analysis for one kind

Step 1: obtain primary data, and unified to primary data;

Described primary data comprises land use data, dem data, river and roading density data, relief data and NPP product data;

Step 2: utilize primary data to calculate the landscape structure factor respectively, dem data calculates humidity index, and the NPP product data are divided arable land yield-power grade;

Step 3: step 2 is obtained the landscape structure factor, river and roading density data, relief data, humidity index, arable land yield-power carry out the grade assignment, and wetland is recovered assessment;

Step 4: the wetland that obtains according to step 3 recovers assessment result, in conjunction with the GIS spatial analysis, determines the wetland recovery scheme.

2. according to claim 1ly a kind ofly determine the method for natural wetland recovery scheme based on remote sensing image and GIS spatial analysis, it is characterized in that described unification comprises the grid and unified projection that primary data is changed into same size to step 1 to primary data;

The grid of described same size is the grid of 30m * 30m size;

The described Universal Transverse Mercator Projection that is projected as, wherein central meridian is 105 ° of east longitudes.

3. according to claim 1 and 2ly a kind ofly determine the method for natural wetland recovery scheme to it is characterized in that the described process of utilizing primary data to calculate the landscape structure factor of step 2 is based on remote sensing image and GIS spatial analysis:

The described landscape structure factor comprises maximum patch index LPI, concentration class Index A I and scatters and column index IJI also;

The computing method of described maximum patch index LPI are:

$\frac{\underset{j=1}{\overset{n}{\max }}\left(a_{\mathrm{ij}}\right)}{A}\left(100\right)\%$

The computing method of aggregate index AI are:

$\left(\frac{g_{\mathrm{ij}}}{\max -g_{\mathrm{ij}}}\right)\left(100\right)\%$

Scatter with and the computing method of column index IJI be:

$\frac{-\underset{r=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{k=r+1}{\overset{m}{\mathrm{\&Sigma;}}}[\left(\frac{e_{\mathrm{rk}}}{E}\right)\ln \left(\frac{e_{\mathrm{rk}}}{E}\right)}{\ln \left(0.5\right[m(m-1)\left]\right)}\left(100\right)\%$

Wherein, A is plaque area; a _IjIt is the view area; g _IjBe the type i grid unit number adjacent with type j; Max is the maximum number of the type i grid unit adjacent with type j; e _RkBe the length of the common boundary between adjacent view r, the k; E is the total length of all view type common boundaries; N is the type sum, and m is the sum of plaque type in the view.

4. describedly a kind ofly determine the method for natural wetland recovery scheme based on remote sensing image and GIS spatial analysis according to claim 1 or 3, it is characterized in that the described NPP product data of step 2 divide arable land yield-power grade and comprise low yield, middle product, high yield;

The NPP product data are to utilize the CASA model to extract from the intermediate-resolution imaging spectrometer data to obtain, and＜-10 is low yield;-10-10 is middle product;＞10 is high yield.

5. a kind of method of determining the natural wetland recovery scheme based on remote sensing image and GIS spatial analysis according to claim 4, it is characterized in that step 3 is described obtains the landscape structure factor, river/roading density data, relief data, humidity index, arable land yield-power to step 2 and carries out the grade assignment, and concrete valuation scheme is:

In the landscape structure factor, maximum patch index LPI grade assignment is: 0-30% assignment 0,30%-50% assignment 3,50%-100% assignment 5; Aggregate index AI assignment is: 0-30% assignment 0,30%-50% assignment 3,50%-100% assignment 5, scatter with and column index IJI assignment be: 0-30% assignment 5,30%-50% assignment 3,50%-100% assignment 0;

In river/roading density, the grade assignment of river density is: 0-0.3 assignment 0,0.3-0.7 assignment 3,＞0.7 assignment 5.; The grade assignment of roading density is: 0-0.3 assignment 5,0.3-0.7 assignment 3,＞0.7 assignment 0;

The humidity index value scope of calculating according to power 4 is :-10-26.5, and valuation scheme is :-10-5.5 assignment is that 0,5.6-12.5 assignment is that 3,12.5-26.5 assignment is 5;

In relief data, the valley flat assignment is 5; The depression assignment is 3; Other equal assignment is 0;

The NPP product hierarchy is divided valuation scheme: the high yield assignment is 0; Middle product assignment is 3; The low yield assignment is 5.

6. according to claim 5ly a kind ofly determine the method for natural wetland recovery scheme to it is characterized in that described step 3 based on remote sensing image and GIS spatial analysis: obtain the landscape structure factor, river/roading density data, relief data, humidity index, arable land yield-power grade according to step 2 and carry out the process that wetland recovers assessment and be:

Data after view structure factor, river/roading density data, relief data, humidity index, the arable land yield-power grade assignment are superposeed, obtain the distribution results that wetland recovers assessed value.

7. a kind of method of determining the natural wetland recovery scheme based on remote sensing image and GIS spatial analysis according to claim 6, it is characterized in that described step 4: the wetland that obtains according to step 3 recovers assessment result, in conjunction with the GIS spatial analysis, determine that the process of wetland recovery scheme is:

Step 41: wetland recovers assessment result in the obtaining step three;

Step 42: judge that point value of evaluation is whether within the 32-42 mark; Recover if then carry out the zone, otherwise enter step 43:

Step 43: judge that point value of evaluation is whether within the 22-32 mark; Recover if then carry out the zone, otherwise enter step 44;

Step 44: obtain residue wetland information, wouldn't recover.

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