CN110378576A - The quantification detection method of urbanization vegetation effect effective distance - Google Patents
The quantification detection method of urbanization vegetation effect effective distance Download PDFInfo
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Abstract
The invention discloses the quantification detection methods of urbanization vegetation effect effective distance, comprising: establishes the data set of city/group of cities many years space and time continuous remote sensing vegetation index；The newest range in city/group of cities built-up areas is extracted based on remotely-sensed data；The buffer area spacing of spatial resolution setting built-up areas based on remote sensing vegetation index；The many years for calculating city/group of cities built-up areas and buffer area are averaged vegetation index；Zoning is averaged vegetation index variation tendency；The vegetation index variation tendency of city/group of cities built-up areas and buffer area is calculated by pixel；Calculate the vegetation index significant changes area ratio of city/group of cities built-up areas and buffer area；Three index dynamic changing curves are established, judge to influence effective distance.The above method can be realized urban construction and detect to the quantification of Vegetation Effect effective distance, easy to use, robustness is good, precision is high, provide direct technology support for the city ecological environment protection, urban landscaping, restoration of the ecosystem, ecological surveillance etc..
Description
Technical field
The present invention relates to a kind of remotely-sensed data data analysis technique fields, have especially with regard to a kind of urbanization vegetation effect
Imitate the quantification detection method of distance.
Background technique
Urbanization produces far-reaching influence to ecosystems services and function, bio-diversity etc., it is considered to be
One of main drive of Ecosystem Changes.There are " space spills-over effects " in influence of the urbanization to the ecosystem, i.e. city
Change process not only will affect ecological environment inside completed region of the city, can also influence built-up areas periphery ecology ring to a certain extent
Border.Therefore, quantification urban construction is on effective influence distance of the ecosystem, for urban development planning, ecological environmental protection
And the work such as town development boundary, ecological protection red line delimited with highly important realistic price and practice significance.
Vegetation is the most important component part of terrestrial ecosystems, formation and performance for ecosystems services and function
With important supporting role.As the primary producer of food chain bottom end, vegetation can change solar energy by photosynthesis
For chemical energy, energy is provided for organism including humans；By respiration, transpiration and change the surface reflection of light
Rate, fundamentally adjust earth's surface energy balance, water circulation and biogeochemical cycle, and for other biological provide food with
Habitat.In recent years, global environmental change is being interrogated changes landing field vegetation state fastly, so as to the function of earth system
Offer with ecosystems services has an impact.As the composite measurement of ecological functions, the change in time and space of vegetation productivity for
Terrestrial Carbon Sink estimation, natural resource management, ecological study are extremely important, and the variation of monitoring vegetation growth has understood its origin cause of formation
Become history one of the critical issue of decades whole world change.NDVI(Normalized Difference Vegetation
Index, normalized site attenuation) it is considered as that vegetation productivity effectively acts on behalf of the best of index and vegetation growth status
Characteristic index is one of key metrics index of ecological degradation, very extensive concern has been obtained from region to Global Scale
And application.EVI (Enhanced Vegetation Index, enhancement mode meta file) is to improve to propose on the basis of NDVI
, not only allow for Soil Background influence, also according to the impact factor that atmospheric correction is included, for example, atmospheric molecule, aerosol,
The problems such as factors such as Bao Yun, steam and ozone carry out comprehensive atmospheric correction, have handled soil, atmosphere and saturation degree, to vegetation
With higher sensitivity and superiority.Therefore, ecosystem situation is represented using vegetation indexs such as NDVI, EVI is more
Current, generally acknowledged method.
Currently, urbanization focuses mostly on to the research of Vegetation Effect raw in fitting urbanization process and vegetation coverage, vegetation
Relationship between force of labor, vegetation phenology, forest loss, revegetation, ecosystems services and its value etc., then judges city
The vegetation of change, ecological effect.Part research thinks that influence of the urban construction to vegetation, ecology is not solely restricted to built-up areas, right
Its periphery also has a certain impact, and has application from completed region of the city periphery 5-10km and 100km, but it is mostly qualitative to influence distance
Judgement.
It can be seen that the detection method for influencing effective distance to the ecosystem in relation to urbanization at present extremely lacks, it is different
The result that expert obtains is inconsistent, and accuracy and accuracy are inevitably present uncertainty, can also deposit in application process
In certain problem.
The present invention is based on long-term sequence remote sensing of vegetation data, by establishing multiple indexs, multiple dimensioned, multizone to score
The method of analysis discloses urbanization process to Vegetation Effect with the attenuation process of distance, reaches quantification urbanization to Vegetation Effect
The purpose of effective distance.
The information disclosed in the background technology section is intended only to increase the understanding to general background of the invention, without answering
When being considered as recognizing or imply that the information constitutes the prior art already known to those of ordinary skill in the art in any form.
Summary of the invention
The purpose of the present invention is to provide a kind of quantification detection methods of urbanization vegetation effect effective distance, can
Realize that urban construction detects the quantification of Vegetation Effect effective distance, it is easy to use, robustness is good, precision is high.
To achieve the above object, the present invention provides a kind of quantification detection sides of urbanization vegetation effect effective distance
Method, the quantification detection method are based on long-term sequence remote sensing of vegetation data, comprising the following steps:
S01: according to Remote Sensing Products or remote sensing image establishes city and/or many years space and time continuous remote sensing vegetation of group of cities refers to
Several data sets；
S02: the newest range in built-up areas of city and/or group of cities is extracted based on remotely-sensed data；
S03: the spatial resolution based on remote sensing vegetation index sets the buffer area spacing of the built-up areas, builds described in building
At the buffer area in area, wherein buffer area spacing is greater than the spatial resolution of remote sensing vegetation index；
S04: many years for calculating city and/or group of cities built-up areas from regional scale are averaged the more of vegetation index and buffer area
Annual vegetation index；
S05: from regional scale calculate city and/or group of cities build up the average vegetation index of region variation tendency and
The variation tendency of the average vegetation index in buffer zone；
S06: from grid cell size by calculated by pixel city and/or group of cities built-up areas vegetation index variation tendency and
The vegetation index variation tendency of buffer area；
S07: the vegetation index significant changes area ratio for calculating city and/or group of cities built-up areas from grid cell size is gentle
The vegetation index significant changes area ratio in area is rushed, the calculation method of the vegetation index significant changes area ratio is: calculated
The pixel number that significant changes occur for vegetation index in same area accounts for the ratio of all pixel numbers；
S08: establishing many years is averaged vegetation index, vegetation index variation tendency, vegetation index significant changes area ratio
Three indexs of example, along the dynamic changing curve of period, are asked in city and/or group of cities built-up areas and buffer area by curve matching
It leads or visually judges to influence effective distance.
In a preferred embodiment, in S01, the remote sensing vegetation index is one or both of NDVI, EVI.
In a preferred embodiment, in S02, the remotely-sensed data is nighttime light data, remote Sensing Interpretation soil benefit
With one or both of data.
In a preferred embodiment, institute in the flat mean of mean vegetation index variation tendency in region described in S05 and S06
Vegetation index variation tendency is stated using one in one-variable linear regression trend, F inspection, Sen+Mann-Kendall non-parametric test
Kind or a variety of draftings.
In a preferred embodiment, turned using subset extraction, image mosaic, cut data, format conversion, projection
It changes, the mode of quality inspection handles remote sensing vegetation index NDVI, EVI.
In a preferred embodiment, above-mentioned nighttime light data is selected from DMSP NTL data, NPP-VIIRS night lamp
One or both of light remotely-sensed data.
In a preferred embodiment, above-mentioned DMSP NTL data are revised DMSP NTL data, described to correct
Method include: mutual calibration, synthesis and the calibration of year border sequence in year.
In a preferred embodiment, above-mentioned one-variable linear regression trend is NDVI one-variable linear regression trend.
In a preferred embodiment, above-mentioned (1) is the average vegetation index in region described in S05 when need to draw
When variation tendency, the calculation formula of the NDVI one-variable linear regression trend are as follows:
In formula, Slope indicates that research period region is averaged variation tendency, that is, Annual variations matched curve slope value of NDVI；
N is year；
I is time serial number；
xiFor the average Growing season NDVI value in 1 year region.
(2) when need to draw is vegetation index variation tendency described in S06, the NDVI one-variable linear regression trend
Calculation formula are as follows:
In formula, Slope indicates the research period by the variation tendency of pixel NDVI；
N is year；
I is time serial number；
xI, jkIt is the year maximum NDVI value of jth row kth column pixel on 1 year NVDI image.
It is to be noted that needing to comprehensively consider institute in the building process of the buffer area of city and/or group of cities built-up areas
It selects the spatial resolution of remotely-sensed data and influences the required precision of effective distance, the spacing of buffer area should be greater than remotely-sensed data sky
Between resolution ratio, to guarantee that different buffer areas include different vegetation index pixels.
Compared with prior art, it has the following beneficial effects: according to the present invention
(1) a kind of urbanization process provided by the invention is applicable in the quantification detection method of Vegetation Effect effective distance
In single city or Region urbanization's identification urban construction to the effective distance of Vegetation Effect, can be realized urban construction to plant
It is affected the quantification detection of effective distance, has the characteristics that easy to use, robustness is good, with high accuracy.
(2) present invention by using long-term sequence data and for many years be averaged vegetation index, vegetation index variation tendency and
Three indexs of vegetation index significant changes area ratio, can easy, intuitively detect city and/or group of cities urbanization into
The activity such as urban sprawl measures the effective distance of Vegetation Effect with 36 cities and group of cities in following embodiments the most in journey
Object is obtained as drawn a conclusion: 36 cities and group of cities built-up areas and the variation slope difference of buffer area vegetation index are obvious；36
Spatially there is notable difference, vegetation refers to coupling relationship trend between a city and group of cities built-up areas and each buffer area
The ratio that the pixel of number significant changes accounts for whole pixels changes fairly obvious and regular between built-up areas and each buffer area
It is relatively strong；With the continuous expansion of buffer area distance, built-up areas vegetation index is in first to increase the variation tendency to tend to be steady afterwards,
Amplitude of variation significantly reduces after the buffer area 30-40km；It is slow that vegetation index change rate then becomes 0-10km from the negative value of built-up areas
The positive value in area is rushed, and is gradually expanded, until being held essentially constant after the buffer area 30-40km；Vegetation index significant changes pixel institute
What the variation of accounting example showed to dramatically increase obviously increases, the pixel ratio substantially reduced as ratio is increased along buffer area distance
Example then becomes remote with buffer area distance and is obviously reduced, and variation reduces or tends towards stability after the buffer area 30-40km.Cause
This, which can be the city ecological environment protection, urban landscaping, restoration of the ecosystem, ecological monitoring and assessment, ecology
The work such as supervision determine that bounds, engineering construction point etc. provide direct technology support, can produce important social economy's effect
Benefit.
Definition or paraphrase
NDVI:Normalized Difference Vegetation Index, normalized differential vegetation index, standard difference are planted
By index, calculation formula are as follows:Wherein, ρNIRAnd ρREDRespectively represent near infrared band and feux rouges wave
The reflectivity of section, the value of NDVI is between -1 and 1.
EVI:Enhanced Vegetation Index, enhancement mode meta file, calculation formula are as follows:Wherein, ρNIR、ρREDAnd ρBLUERespectively near infrared band, feux rouges wave
The reflectivity of section and blue wave band.
The spatial resolution of remote sensing vegetation index: refer to the most narrow spacing for the two adjacent atural objects that can be identified on remote sensing image
From the size or size of the minimum unit that can be distinguished in detail on remote sensing image.
The variation tendency of the average vegetation index in region: the average of all pixel vegetation indexs is used as area in completed region of the city
The trend that increases or decreases of the domain average value by index, in calculation interval.
By pixel: all pixels in traversal region.
Robustness: robustness, insensitivity of the characterization control system to characteristic or parameter perturbation.
Detailed description of the invention
Fig. 1 is the process of the quantification detection method of the urbanization vegetation effect effective distance of embodiment according to the present invention
Figure.
Fig. 2 is that the Chinese Growing season vegetation of the nineteen eighty-two of embodiment, 1992,2002 and 2013 refers to according to the present invention
Number NDVI spatial framework.
Fig. 3 is 1992 and 2013 Chinese night remote sensing light datas of embodiment according to the present invention.
Fig. 4 is 36 main cities of China and group of cities built-up areas and 10 buffer areas of embodiment according to the present invention.
Fig. 5 is Chinese main cities and the variation of group of cities 1982-2013 vegetation index of embodiment according to the present invention
Trend.
Fig. 6 be embodiment according to the present invention Chinese main cities and group of cities built-up areas and buffer area many years it is average
Vegetation index.
Fig. 7 is the Chinese main cities of embodiment according to the present invention and the vegetation index of group of cities built-up areas and buffer area
Variation tendency.
Fig. 8 be embodiment according to the present invention Chinese main cities and group of cities built-up areas and buffer area vegetation index it is aobvious
Write variation pixel scale.
Specific embodiment
With reference to the accompanying drawing, specific embodiments of the present invention will be described in detail, it is to be understood that guarantor of the invention
Shield range is not limited by the specific implementation.
Survey region:
Select GDP in 2015 be more than 500,000,000,000 yuan 34 cities and other 12 provincial capitals, based on totally 46 cities
Study city, wherein the Yangtze River Delta, Pearl River Delta group of cities contain 12 cities altogether, therefore, analyze in total 36 cities and
Group of cities.
Note: above-mentioned 36 cities and group of cities include: Harbin City, Urumqi City, Changchun, Shenyang City, exhale with it is great
Special city, Beijing, Tianjin, Tangshan City, Daliang City, Yinchuan City, Shijiazhuang City, Taiyuan City, Yantai City, Weifang City, Jinan City,
Xining, Qingdao City, Lanzhou, Zhengzhou City, Xi'an, Xuzhou City, Hefei City, Chongqing City, Wuhan City, Chengdu, Lhasa,
Nanchang City, Changsha, Guiyang, Kunming, Fuzhou City, Quanzhou City, Nanning City, Haikou City and Pearl River Delta group of cities and long three
Angle group of cities.
Choose data:
For a wide range of group of cities such as the whole world, each continent, the whole nation or megapolis, optional AVHRR (The Advanced
Very High Resolution Radiometer, very high resolution radiometer), GIMMM (Global Inventory
Modeling and Mapping Studies, the whole world detection with model study group), MODIS (Moderate Resolution
Imaging Spectroradiometer, Moderate Imaging Spectroradiomete), SPOT (Systeme Probatoired '
Observation dela Terre, earth observation satellite system) etc. with continuous time series satellite remote sensing date, extract
Vegetation index data；For single megalopolis, optional MODIS, Landsat (land satellite, the land of U.S. NASA
Satellite) etc. image datas, extract vegetation index data.
Embodiment
In conjunction with attached drawing 1, the quantification detection method of urbanization vegetation effect effective distance is described in detail, it is specific to walk
It is rapid as follows:
S01: the data set of many years space and time continuous remote sensing vegetation index of city and group of cities is established
It is specific: what the 15d maximum of selection NASA Ge Dade space center global monitoring and the production of analog study group was combined to
The AVHRR GIMMMS NDVI of 8km3g(3g: referring to third generation AVHRR GIMMS data set) data set as vegetation index data,
Time span is 1982-2013, wherein NDVI3gData are by subset extraction, image mosaic, cut data, format conversion, throwing
The preprocessing process such as shadow conversion and quality inspection；The moon Value Data collection that MVC method obtains vegetation index is synthesized by maximum value, it is raw
The NDVI composite value of the long season 4-10 month each monthly average characterizes annual vegetation growth, with the vegetative coverage situation of reflecting regional；
Gained nineteen eighty-two, 1992,2002 and 2013, China's Growing season vegetation index NDVI spatial framework was referring to attached
Fig. 2.2 it is found that the comparison between the Growing season NDVI of nineteen eighty-two, 1992,2002 and 2013 shows that vegetation refers to reference to the accompanying drawings
Number changes obviously, for example, being located at Ordos City south, the Mu Us Shadi vegetative coverage in Yulin City the north restores trend and shows
It writes.Meanwhile also illustrate NDVI data can accurately reflect China's Macroscopic scale land surface vegetation growth conditions and its
Spatial framework is that vegetation productivity and the effective of vegetative coverage act on behalf of one of index.
S02: the newest range in city and the built-up areas of group of cities in recent years is extracted based on remotely-sensed data
Specific: selection is using revised DMSP (Defense Meteorological Satellite Program, beauty
State's Defence Meteorological Satellite Project) NTL (night time light, night steady light data) remote sensing nighttime light data, is obtained
Take above-mentioned Urban Datas in 2013 and built-up areas spatial dimension；Wherein, DMSP is completed according to the method for Elvidge etc. and Liu et al.
NTL data correct work, specifically include mutually calibration, synthesis and the calibration of year border sequence in year；
Chinese night remote sensing light data is referring to attached drawing 3 within gained 1992 and 2013.3 it is found that in China with reference to the accompanying drawings
Eastern cityization area and western main cities night lights brightness value are larger, form striking contrast, energy with surrounding area
Enough built-up areas spatial dimensions for extracting selected city.More phase comparisons also indicate that night remote sensing light data is able to reflect cities and towns and builds
At the spatial expansion process in area, the especially megalopolis of the central and east, group of cities.
S03: the buffer area in building city and group of cities built-up areas
Specific: the NDVI data spatial resolution of use is about 8km, and using 10km as buffer area spacing, totally 10 are delayed
Rush area, i.e. 100km other than outermost to completed region of the city outer boundary, and reject the water body in buffer area, other cities are built up
The interference regions such as area；
36 cities of gained and the buffer area of group of cities are referring to attached drawing 4.4 it is found that 36 cities and group of cities with reference to the accompanying drawings
And its buffer area is distributed in China four corners of the world everywhere, wherein central and east urbanization process is very fast, horizontal higher regional city
Distribution is also more, and buffer area area is also larger, has stronger representativeness.
S04: calculating city and the built-up areas of group of cities from regional scale and many years of buffer area are averaged vegetation index
Firstly, obtaining 1982-2013 year-by-year using each city and group of cities built-up areas and buffer area vector boundary
The average Growing season vegetation index in region；Then, the long-time average annual value of built-up areas, buffer area NDVI is calculated separately；
The average Growing season vegetation index ginseng in the region in the built-up areas 1982-2013 of gained city and group of cities each year
It is shown in Table 1-1 to table 1-3, also there is similar tables of data in each buffer area.
The many years of the built-up areas and buffer area of city and group of cities are averaged vegetation index referring to table 2.As shown in Table 2, same
Bu Tong annual region is averaged city and group of cities built-up areas, and vegetation index difference is obvious, and built-up areas and 10 buffer areas are mutual
Between for many years be averaged vegetation index difference it is obvious.
Each city table 1-1 1982-1992 and group of cities build up the average Growing season vegetation index of region
Each city table 1-2 1993-202 and group of cities build up the average Growing season vegetation index of region
Each city table 1-3 2003-2013 and group of cities build up the average Growing season vegetation index of region
Each city of table 2 and group of cities built-up areas average production many years, vegetation index season
S05: the built-up areas in city and group of cities and the average vegetation index variation tendency of buffer area are calculated from regional scale
Vegetation index variation tendency is calculated using one-variable linear regression trend and the F method of inspection, wherein linear regression slope
For NDVI variable quantity, it is used to examine the conspicuousness of this kind of variation tendency with the correlation in time；According to Principle of Statistics and generally acknowledge
Convention, according to significance test as a result, variation tendency is divided into 2 grades: significant (P < 0.05) and not significant (P >=0.05)；
When carrying out linear fit analysis using the NDVI numerical value changed over time, slope be can reflect out in the period
Coupling relationship trend.Wherein, the calculation formula of NDVI one-variable linear regression trend are as follows:
In formula, Slope indicates between 1982-2013 that region is averaged variation tendency (the i.e. Annual variations matched curve of NDVI
Slope value)；
N is research period year, is 32, indicates 1982-2013 year sequence length；
I is 1-32, indicates 1982-2013 time serial number；
xiFor the average Growing season NDVI value in 1 year region.
The region of the built-up areas and buffer area of the city and group of cities vegetation index variation tendency that is averaged the results are shown in Table 3.By table 3
As it can be seen that city and group of cities built-up areas and buffer zone are averaged, the variation slope difference of vegetation index is obvious.
3 city 1982-2013 of table and group of cities build up region and are averaged the variation tendency (10 of vegetation index-4/ year)
S06: the built-up areas in city and group of cities and the vegetation index variation tendency of buffer area are calculated by pixel
In grid cell size, i.e., NDVI variation tendency is calculated using one-variable linear regression trend and the F method of inspection by pixel；
The built-up areas and buffer area of city and group of cities use one-variable linear regression trend by the vegetation index variation of pixel,
Calculation formula is as follows:
In formula, by the variation tendency of pixel NDVI between Slope expression 1982-2013；
N is research period year, is 32, indicates 1982-2013 year sequence length；
I is 1-32, indicates 1982-2013 time serial number；
xI, jkIt is the year maximum NDVI value of jth row kth column pixel on 1 year NVDI image.
The result that the city 1982-2013 and group of cities built-up areas and buffer area are calculated by pixel vegetation index variation tendency
Referring to attached drawing 5.5 it is found that coupling relationship trend is in space between city and group of cities built-up areas and each buffer area with reference to the accompanying drawings
On there is notable differences.
S07: the built-up areas in city and group of cities and the vegetation index significant changes area ratio of buffer area are calculated
On grid cell size, dramatically increases, substantially reduces, increases and (wrap in built-up areas, each buffer area statistics NDVI respectively
Include and dramatically increase and do not dramatically increase two parts) pixel scale；
National scale, Changjiang Delta urban agglomeration and Pearl River Delta group of cities built-up areas and each buffer area vegetation index significant changes
Pixel occupied area ratio, referring to table 4.As shown in Table 4, either key cities such as national scale or the Yangtze River Delta, Pearl River Delta
Group, it is clearly demarcated that the ratio that the pixel of vegetation index significant changes accounts for whole pixels changes ten between built-up areas and each buffer area
It is aobvious, and regularity is stronger.
4 national scale of table, Changjiang Delta urban agglomeration and Pearl River Delta group of cities vegetation index are in the pixel institute of different variation tendencies
Accounting example (%)
S08: establishing change curve, influences effective distance by curve matching derivation or visually judgement
Be averaged for many years three vegetation index, vegetation index variation tendency, significant changes area ratio indexs are established in city
Built-up areas, multiple buffering area change curve, by visual observation i.e. can determine whether above-mentioned city (group) urbanization process to Vegetation Effect
Effective distance be 30-40km.
National scale and each city and group of cities many years averagely vegetation index and 1982-2013 vegetation index change rate
Variation along 10 buffer areas is shown in Table 5.National scale averagely vegetation index, 1982-2013 vegetation index change rate, plant for many years
Attached drawing 6, attached drawing 7 and attached are shown in variation by index significant changes pixel occupied area ratio along built-up areas, buffer area distance respectively
Fig. 8.
By table 5, attached drawing 6, attached drawing 7 and attached drawing 8 it is found that with buffer area distance continuous expansion, built-up areas are average for many years
Vegetation index is in the variation tendency to tend to be steady afterwards is first increased, and amplitude of variation significantly reduces after the buffer area 30-40km；
1982-2013 vegetation index change rate then becomes the positive value of the buffer area 0-10km from the negative value of built-up areas, and is gradually expanded, until
It is held essentially constant after the buffer area 30-40km；The variation of vegetation index significant changes pixel proportion shows to dramatically increase
As ratio along buffer area distance increase and obviously increase, the pixel scale substantially reduced then with buffer area distance become it is remote and obvious
Reduce, and amplitude of variation reduces or tends towards stability after the buffer area 30-40km.Vegetation index is (including aobvious in increase trend
Write and increase and do not dramatically increase two parts) pixel proportion be also in the trend that increases far from built-up areas, and also in 30-
It tends towards stability after the buffer area 40km, also demonstrates the above results.
Average NDVI are with 1982-2013 NDVI change rate along the variation of built-up areas and 10 buffer areas within table more than 5 years
The aforementioned description to specific exemplary embodiment of the invention is in order to illustrate and illustration purpose.These descriptions
It is not wishing to limit the invention to disclosed precise forms, and it will be apparent that according to the above instruction, can much be changed
And variation.The purpose of selecting and describing the exemplary embodiment is that explaining specific principle of the invention and its actually answering
With so that those skilled in the art can be realized and utilize a variety of different exemplary implementation schemes of the invention and
Various chooses and changes.The scope of the present invention is intended to be limited by claims and its equivalents.
Claims (9)
1. a kind of quantification detection method of urbanization vegetation effect effective distance, which is characterized in that the quantification monitoring side
Method is based on long-term sequence remote sensing of vegetation data, comprising the following steps:
S01: many years space and time continuous remote sensing vegetation index of city and/or group of cities is established according to Remote Sensing Products or remote sensing image
Data set；
S02: the newest range in built-up areas of city and/or group of cities is extracted based on remotely-sensed data；
S03: the spatial resolution based on remote sensing vegetation index sets the buffer area spacing of the built-up areas, constructs the built-up areas
Buffer area, wherein buffer area spacing be greater than remote sensing vegetation index spatial resolution；
S04: be averaged many years of vegetation index and buffer area many years for calculating city and/or group of cities built-up areas from regional scale put down
Equal vegetation index；
S05: city is calculated from regional scale and/or group of cities builds up the variation tendency and buffering of the average vegetation index of region
The variation tendency of the average vegetation index of region；
S06: pass through the vegetation index variation tendency and buffering by pixel calculating city and/or group of cities built-up areas from grid cell size
The vegetation index variation tendency in area；
S07: vegetation index significant changes area ratio and the buffer area of city and/or group of cities built-up areas are calculated from grid cell size
Vegetation index significant changes area ratio, the calculation method of the vegetation index significant changes area ratio is: calculate it is same
The pixel number that significant changes occur for vegetation index in area accounts for the ratio of all pixel numbers；
S08: establishing many years is averaged vegetation index, vegetation index variation tendency, vegetation index significant changes area ratio three
A index in city and/or group of cities built-up areas and buffer area along the dynamic changing curve of period, by curve matching derivation or
Visually judgement influences effective distance.
2. quantification detection method according to claim 1, which is characterized in that in S01, the remote sensing vegetation index is
One or both of NDVI, EVI.
3. quantification detection method according to claim 1, which is characterized in that in S02, the remotely-sensed data is night lamp
One or both of light data, remote Sensing Interpretation land use data.
4. quantification detection method according to claim 1, which is characterized in that the average vegetation in region described in S05 refers to
Vegetation index variation tendency described in variation tendency and S06 is counted using one-variable linear regression trend, F inspection, Sen+Mann-
One of Kendall non-parametric test or a variety of draftings.
5. quantification detection method according to claim 2, which is characterized in that use subset extraction, image mosaic, cutting
Data, format conversion, projection transform, quality inspection mode handle remote sensing vegetation index NDVI, EVI.
6. quantification detection method according to claim 3, which is characterized in that the nighttime light data is selected from DMSP
One or both of NTL data, NPP-VIIRS night lights remotely-sensed data.
7. quantification detection method according to claim 6, which is characterized in that the DMSP NTL data are revised
DMSP NTL data, the method corrected include: mutual calibration, synthesis and the calibration of year border sequence in year.
8. quantification detection method according to claim 4, which is characterized in that one-variable linear regression trend is NDVI unitary
Linear regression trend.
9. quantification detection method according to claim 8, which is characterized in that
(1) when need to draw is the flat mean of mean vegetation index variation tendency in region described in S05, the NDVI unitary line
The calculation formula of property regressive trend are as follows:
In formula, Slope indicates that region between each year is averaged the variation tendency i.e. Annual variations matched curve slope value of NDVI；
N is year；
I is time serial number；
xiFor the average Growing season NDVI value in 1 year region.
(2) when need to draw is vegetation index variation tendency described in S06, the meter of the NDVI one-variable linear regression trend
Calculate formula are as follows:
In formula, Slope indicates the variation tendency between each year by pixel NDVI；
N is year；
I is time serial number；
xI, jkIt is the year maximum NDVI value of jth row kth column pixel on 1 year NVDI image.
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