CN107480818A - A kind of method that rapid evaluation human activities of vegetation covering change influences - Google Patents
A kind of method that rapid evaluation human activities of vegetation covering change influences Download PDFInfo
- Publication number
- CN107480818A CN107480818A CN201710676181.3A CN201710676181A CN107480818A CN 107480818 A CN107480818 A CN 107480818A CN 201710676181 A CN201710676181 A CN 201710676181A CN 107480818 A CN107480818 A CN 107480818A
- Authority
- CN
- China
- Prior art keywords
- ndvi
- data
- annual
- human activities
- vegetation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000000694 effects Effects 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000011156 evaluation Methods 0.000 title abstract description 7
- 238000011160 research Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000001556 precipitation Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 6
- 230000002349 favourable effect Effects 0.000 claims description 5
- 230000009286 beneficial effect Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000012732 spatial analysis Methods 0.000 claims description 3
- 238000001308 synthesis method Methods 0.000 claims description 3
- 230000001174 ascending effect Effects 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims description 2
- 238000001151 non-parametric statistical test Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 6
- 238000012417 linear regression Methods 0.000 abstract description 3
- 230000007774 longterm Effects 0.000 abstract 2
- 238000010276 construction Methods 0.000 description 4
- 230000005764 inhibitory process Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000013441 quality evaluation Methods 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000000528 statistical test Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Mathematical Analysis (AREA)
- Quality & Reliability (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Primary Health Care (AREA)
- Algebra (AREA)
- General Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of method that rapid evaluation human activities of vegetation covers change influence, using long-term sequence MOD13Q1 NDVI as data source, analyzing and processing is carried out to data using MRT softwares and forms NDVI data sets, using linear regression model (LRM), least square method trend analysis and residual analysis method, assess mankind's activity influences situation to vegetation variation, the present invention can be based on long-term sequence MODIS NDVI data and meteorological data, quickly, accurately, the objective human activities of vegetation covering change for carrying out large scale influences research, the potentiality that remote sensing satellite data carry out big regional scale eco-environmental impact assessment with meteorological data are fully excavated.
Description
Technical Field
The invention belongs to the field of environmental quality evaluation, relates to an environmental quality evaluation method, and particularly relates to a method for evaluating the influence of human activities on vegetation coverage change by applying long-time sequence MODIS satellite remote sensing data and meteorological data.
Background
The vegetation is the main body of the land ecosystem and is an indicator for monitoring the ecological environment. Vegetation coverage changes are driven by both climate and human activity. With the continuous acceleration of the urbanization process, the influence degree of human activities on vegetation coverage change in a local area even exceeds the climate change, the quantification of the influence of the human activities on the vegetation coverage change can pointedly provide data support for the human activities on the evaluation of the quality of the ecological environment, and meanwhile, technical support can be provided for the economic sustainable development of the area and the construction of the ecological environment.
Most of the traditional research methods are field investigation and qualitative evaluation, and the method is time-consuming and labor-consuming, is not suitable for large-area scale evaluation, and cannot visually reflect the influence area of human activities on vegetation coverage change. The continuous development of the remote sensing technology provides possibility for dynamic monitoring qualitative evaluation in a large scale range. The normalized Vegetation Index (NDVI) is one of the most common indicators for monitoring Vegetation coverage change and is also important information for measuring the ecological environment of a region. The MODIS NDVI data has the characteristics of high time and spatial resolution, wide coverage range, easiness in data acquisition and the like, and provides a solid data guarantee for dynamically evaluating the influence of human activities on vegetation coverage change.
Disclosure of Invention
The invention aims to provide a method for rapidly and accurately evaluating the influence of human activities on vegetation coverage change based on long-time sequence MODIS NDVI data and meteorological data through professional software processing and analysis.
In order to achieve the purpose, the technical scheme of the invention is as follows: the method for rapidly evaluating the influence of human activities on vegetation coverage change is characterized in that a long-time sequence MODIS NDVI is taken as a data source, MRT software is adopted to analyze and process data to obtain a regional time sequence NDVI data set, and a linear regression model, a least square method trend analysis and a residual error analysis method are adopted to evaluate the influence of human activities on vegetation coverage change, and the method comprises the following specific steps:
(1) Downloading and preprocessing NDVI data:
a. downloading MOD13Q1NDVI of a time sequence of a research area, wherein the spatial resolution of data is 250m, the time resolution is 16d, using MRT software to perform mosaic splicing on the data, converting the data into a Geotiff format from HDF, and re-projecting a projection coordinate to a WGS84/Albers coordinate system from Sinusoid;
b. cutting the NDVI data by adopting mask extraction in ArcGIS, and calculating to obtain the annual maximum NDVI value of the research area through a maximum synthesis method to form a NDVI time sequence data set of the research area;
(2) And (3) meteorological data processing of a research area:
a. collecting daily average air temperature and daily precipitation data of all weather stations in a research area, carrying out arithmetic average calculation on the daily average air temperature to obtain annual average air temperature of the weather stations, and summing daily precipitation to obtain annual precipitation of each weather station;
b. performing spatial interpolation on the annual average air temperature and the annual precipitation of each meteorological site in a research area by using a kriging interpolation method in ArcGIS software to generate a time sequence annual average air temperature and annual precipitation grid data set with the spatial resolution of 250m multiplied by 250m, and projecting the time sequence annual average air temperature and annual precipitation grid data set to a WGS84/Albers coordinate system;
(3) Calculating time series predicted NDVI values:
establishing a binary primary regression relation model of the NDVI, the annual average air temperature and the annual precipitation amount, and simulating and calculating the NDVI value on each grid unit by using a formula (1) in ArcGIS; equation (1) is as follows:
NDVI prediction value ＝β 0 +β 1 T+β 2 P (1)
In the formula, NDVI Prediction value NDVI value, beta, predicted from annual precipitation and annual mean temperature factors 0 Is a constant term, β 1 、β 2 The undetermined coefficient of the regression equation can be obtained by calculation by adopting a least square method; t and P are respectively the annual average temperature and the annual precipitation;
(4) Calculating the influence of human activities in the study area on vegetation coverage change:
a. subtracting the predicted NDVI value calculated in the step (3) from the annual maximum NDVI value obtained in the step (1), namely the real NDVI value obtained through remote sensing observation to obtain a time sequence NDVI residual error data set, wherein if the residual error is greater than 0, human activities are shown to have a promotion effect on vegetation growth; if the residual error is less than 0, the human activities are not favorable for vegetation growth; if the residual error is equal to 0, the human activities have weak influence on vegetation change;
b. analyzing the change trend of human activities influenced by coverage change by adopting a non-parametric trend degree and Mann-Kendall inspection method, and calculating the residual NDVI change trend of each pixel in ArcGIS by using a formula (2), wherein the non-parametric trend calculation formula (2) is as follows:
in the formula, beta is the change trend of residual NDVI, i and j are time sequences, NDVI i 、NDVI j Respectively, the NDVI values at i and j times, if beta&0, indicating that the residual NDVI is in an ascending trend, namely that the human activities are favorable for vegetation growth; if beta is&0, indicating that the residual NDVI is in a descending trend, namely that the human activities are not beneficial to vegetation growth; if the beta =0, the residual NDVI sequence has no change trend;
the Mann-Kendall is a non-parameter statistical test method and is used for judging the significance of the change trend; suppose a time series (x) 1 ，x 2 ，…，x n ) N independent samples with the same random variable distribution are used, the test statistic is defined as S, and the calculation formula of S is shown as a formula (3):
wherein sig () is a sign function when X i -X j When is less than, equal to or greater than zero, sig (X) i -X j ) Are respectively-1, 0 or 1;
the formula for calculating the Z statistic is shown in equation (4):
at a given significance level α, when | Z | > μ 1-α/2 When the expression is used, the sequence has a remarkable change at the alpha level, and the alpha is generally 0.05.
c. And (4) counting the spatial distribution and the area proportion of each change trend based on the ArcGIS spatial analysis function.
The method for rapidly evaluating the influence of human activities on vegetation coverage change has the following beneficial effects:
1. the method can rapidly, accurately and objectively develop the research on the influence of large-scale human activities on vegetation coverage change based on the long-time sequence MODIS NDVI data and meteorological data, and fully excavate the potential of the remote sensing satellite data and the meteorological data for large-area scale ecological environment influence evaluation.
2. The invention applies computer graphic processing technology and spatial simulation technology, and can quantitatively evaluate the spatial distribution characteristics and the influence degree of the human activities on the vegetation coverage change.
3. The method overcomes the limitation of qualitative research on vegetation cover change of human activities in time and labor consumption of the traditional method, and realizes low-cost, high-efficiency and quantification of large-scale human activities on vegetation cover change.
Drawings
FIG. 1 is a spatial distribution diagram of NDVI predicted in Hainan island 2001-2015;
FIG. 2 is a NDVI residual spatial distribution plot from 2001-2015;
FIG. 3 is a graph of NDVI residual variation trend in Hainan island 2001-2015;
FIG. 4 is a chart of significance test of Mann-Kendall residual variation trend of NDVI in Hainan island;
Detailed Description
The present invention will be described in detail with reference to the embodiments and the accompanying drawings.
The Hainan island is an important ecological conserving functional area in China, and the good ecological environment is a prominent advantage for the construction of the international tourist island. With the promotion of international travel island construction, the novel industrialization and urbanization construction pace accelerates the speed increase, human activities have great influence on the change of the ecological environment of the Hainan island, and the accurate and objective analysis of the influence of the human activities on the vegetation coverage change of the Hainan island can provide scientific data support for the reasonable planning and layout of relevant departments and the sustainable development of the Hainan island.
The method for rapidly evaluating the influence of human activities on the vegetation coverage change of the Hainan island comprises the following steps:
(1) Downloading and preprocessing NDVI data:
a. MOD13Q1NDVI data are downloaded from NASA website of the national aerospace agency of China, and the research regions are numbered as h28v06 and h28v07 in the global sinusoidal projection, and the time span is 15 years from 2001 to 2015, which totals 690 scene data. Using MRT software to perform mosaic splicing on data, converting the data from HDF into Geotiff format, and projecting coordinates from Sinussoidal to WGS84/Albers coordinate system;
b. cutting MODIS NDVI data based on a research area vector file by adopting a mask extraction function in ArcGIS, and calculating to obtain a annual maximum NDVI value of a research area through a maximum synthesis method to form a Hainan island annual NDVI time sequence dataset;
(2) And (3) meteorological data processing of a research area:
a. downloading the daily average air temperature and daily precipitation data of all weather sites in the Hainan island in a China weather data sharing service network, carrying out arithmetic average calculation on the daily average air temperature to obtain the annual average air temperature of the weather sites, and summing the daily precipitation to obtain the annual precipitation of each weather site;
b. performing spatial interpolation on the annual average air temperature and the annual precipitation of each meteorological site in a research area by using a kriging interpolation method in ArcGIS software to generate a time sequence annual average air temperature and annual precipitation grid data set with the spatial resolution of 250m multiplied by 250m, and projecting the time sequence annual average air temperature and annual precipitation grid data set to a WGS84/Albers coordinate system;
(3) Calculating time series predicted NDVI values:
establishing a binary primary regression relation model of NDVI, the annual average air temperature and the annual precipitation, and simulating and calculating the NDVI value on each grid unit to form a 2001-2015 year time sequence prediction NDVI data set. FIG. 1 is a spatial distribution diagram of NDVI predicted in Hainan island 2001-2015;
(4) Calculating the effect of human activity in the study area on vegetation coverage change:
a. subtracting the predicted NDVI value calculated in the step (3) from the maximum NDVI value obtained in the step (1), namely the real NDVI value obtained through remote sensing observation to obtain a time sequence NDVI residual error data set, wherein if the residual error is greater than 0, the human activity is shown to have a promotion effect on vegetation growth; if the residual error is less than 0, the human activities are not favorable for vegetation growth; if the residual equals 0, it indicates that human activities have a weak impact on vegetation change. FIG. 2 is a NDVI residual spatial distribution plot from 2001-2015;
b. analyzing the variation trend of the influence of the human activities on the coverage variation by adopting unparameterized trend, and dividing the variation trend into 7 stages: beta is less than-0.036 and is divided into serious vegetation inhibition, beta is between-0.036 and-0.015 and is divided into moderate vegetation inhibition, beta is between-0.015 and-0.001 and is divided into mild vegetation inhibition, beta is between-0.001 and is divided into regions which are kept unchanged, beta is between 0.001 and 0.015 and is divided into mild vegetation promotion, beta is between 0.015 and-0.036 and is divided into moderate vegetation promotion, and beta is more than 0.036 and is divided into obvious vegetation promotion. FIG. 3 is a graph showing the spatial distribution of the NDVI residual variation trend in Hainan island 2001-2015. Based on the ArcGIS spatial analysis function, the spatial distribution and the area ratio of each variation trend are counted, as shown in Table 1.
TABLE 1 statistical analysis of area occupied by NDVI residual variation trend in Hainan island
The significance of the NDVI residual variation trend is analyzed by adopting a Mann-Kendall test method, the result of the significance test of the Mann-Kendall test on a confidence level of 0.05 is divided into significant variation (| Z | < + > 1.96) and insignificant variation (-1.96 is less than or equal to Z and less than or equal to 1.96), and a graph 4 is a Mann-Kendall significance test graph of the NDVI residual variation trend. Z < -1.96 shows that human activities have significant inhibition effect on vegetation growth change, and Z > 1.96 shows that human activities have significant promotion effect on vegetation growth change.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, therefore, the present invention is not limited by the appended claims.
Claims (1)
1. A method for rapidly assessing the effect of human activity on vegetation coverage change, comprising the steps of:
(1) Downloading and preprocessing NDVI data:
a. the method comprises the steps of downloading MOD13Q1NDVI of a time sequence of a research area, enabling the spatial resolution of data to be 250m and the time resolution to be 16d, carrying out mosaic splicing on the data by using MRT software, converting the data into a Geotiff format from HDF, and re-projecting a projection coordinate to a WGS84/Albers coordinate system from Sinusoidal;
b. cutting the NDVI data by adopting mask extraction in ArcGIS, and calculating to obtain the annual maximum NDVI value of the research area through a maximum synthesis method to form a NDVI time sequence data set of the research area;
(2) And (3) meteorological data processing of a research area:
a. collecting daily average air temperature and daily precipitation data of all weather sites in a research area, carrying out arithmetic average calculation on the daily average air temperature to obtain annual average air temperature of the weather sites, and summing the daily precipitation to obtain annual precipitation of each weather site;
b. carrying out spatial interpolation on the annual average air temperature and the annual precipitation of each meteorological site in a research area by using a kriging interpolation method in ArcGIS software to generate a time series annual average air temperature and annual precipitation grid data set with the spatial resolution of 250m multiplied by 250m, and projecting the time series annual average air temperature and annual precipitation grid data set to a WGS84/Albers coordinate system;
(3) Calculating time series predicted NDVI values:
establishing a binary primary regression relation model of the NDVI, the annual average air temperature and the annual precipitation amount, and simulating and calculating the NDVI value on each grid unit by using a formula (1) in ArcGIS; equation (1) is as follows:
NDVI prediction value ＝β 0 +β 1 T+β 2 P (1)
In the formula, NDVI Prediction value NDVI value, beta, predicted from annual precipitation and annual average air temperature 0 Is a constant term, β 1 、β 2 The undetermined coefficient of the regression equation can be obtained by calculation by adopting a least square method; t and P are respectively the annual average temperature and the annual precipitation;
(4) Calculating the influence of human activities in the study area on vegetation coverage change:
a. subtracting the predicted NDVI value calculated in the step (3) from the annual maximum NDVI value obtained in the step (1), namely the real NDVI value obtained through remote sensing observation to obtain a time sequence NDVI residual error data set, wherein if the residual error is greater than 0, human activities are shown to have a promotion effect on vegetation growth; if the residual error is less than 0, the human activities are not favorable for vegetation growth; if the residual error is equal to 0, the human activities have weak influence on vegetation change;
b. analyzing the change trend of human activities influenced by coverage change by adopting a non-parametric trend degree and Mann-Kendall inspection method, and calculating the residual NDVI change trend of each pixel in ArcGIS by using a formula (2), wherein the non-parametric trend calculation formula (2) is as follows:
in the formula, beta is the change trend of residual NDVI, i and j are time sequences, NDVI i 、NDVI j Respectively, the NDVI values at i and j times, if beta&0, indicating that the residual NDVI is in an ascending trend, namely that the human activities are favorable for vegetation growth; if beta is&0, indicating that the residual NDVI is in a descending trend, namely that the human activities are not beneficial to vegetation growth; if the beta =0, the residual NDVI sequence has no change trend;
the Mann-Kendall is a nonparametric statistical test method and is used for judging the significance of the change trend; suppose a time series (x) 1 ，x 2 ，…，x n ) N independent samples with the same random variable distribution are used, the test statistic is defined as S, and the calculation formula of S is shown as a formula (3):
wherein sign () is a sign function when X i -X j When the value is less than, equal to or greater than zero, sign (X) i -X j ) Are respectively-1, 0 or 1;
the formula for calculating the Z statistic is shown in equation (4):
at a given significance level α, when | Z | > μ 1-α/2 It indicates that there is a significant change in the sequence at the α level, which is typically 0.05.
c. And (4) counting the spatial distribution and the area proportion of each change trend based on the ArcGIS spatial analysis function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710676181.3A CN107480818A (en) | 2017-08-09 | 2017-08-09 | A kind of method that rapid evaluation human activities of vegetation covering change influences |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710676181.3A CN107480818A (en) | 2017-08-09 | 2017-08-09 | A kind of method that rapid evaluation human activities of vegetation covering change influences |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107480818A true CN107480818A (en) | 2017-12-15 |
Family
ID=60599880
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710676181.3A Pending CN107480818A (en) | 2017-08-09 | 2017-08-09 | A kind of method that rapid evaluation human activities of vegetation covering change influences |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107480818A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108509527A (en) * | 2018-03-14 | 2018-09-07 | 中国科学院海洋研究所 | A kind of ice concentration variation tendency towards IDL language seeks calculation algorithm |
| CN109190810A (en) * | 2018-08-16 | 2019-01-11 | 天津大学 | The prediction technique of grassland in northern China area NDVI based on TDNN |
| CN109272144A (en) * | 2018-08-16 | 2019-01-25 | 天津大学 | The prediction technique of grassland in northern China area NDVI based on BPNN |
| CN109615215A (en) * | 2018-12-06 | 2019-04-12 | 西安理工大学 | A kind of characteristic analysis method that regional vegetation restores |
| CN110378576A (en) * | 2019-07-01 | 2019-10-25 | 中国环境科学研究院 | The quantification detection method of urbanization vegetation effect effective distance |
| CN111582688A (en) * | 2020-04-28 | 2020-08-25 | 中国环境科学研究院 | Vegetation historical change automatic analysis system |
| CN111754096A (en) * | 2020-06-17 | 2020-10-09 | 河南大学 | Method for acquiring human influence degree of ecological space |
| CN112464167A (en) * | 2020-11-26 | 2021-03-09 | 中国科学院地理科学与资源研究所 | Method for analyzing influence of oil and gas field development on vegetation coverage and landscape pattern |
| CN112613347A (en) * | 2020-12-03 | 2021-04-06 | 应急管理部国家自然灾害防治研究院 | Automatic recognition method for fire passing range and burning degree of forest fire |
| CN112907113A (en) * | 2021-03-18 | 2021-06-04 | 中国科学院地理科学与资源研究所 | Vegetation change cause identification method considering spatial correlation |
| CN113269464A (en) * | 2021-06-10 | 2021-08-17 | 中国科学院地理科学与资源研究所 | Ecological restoration evaluation method and ecological restoration evaluation device |
| CN114545528A (en) * | 2022-03-09 | 2022-05-27 | 北京墨迹风云科技股份有限公司 | Meteorological numerical model element forecasting and post-correcting method and device based on machine learning |
| CN114677020A (en) * | 2022-03-30 | 2022-06-28 | 河北省科学院地理科学研究所 | Quantitative evaluation method for regional scale soil wind erosion change driving mechanism |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102122370A (en) * | 2011-03-07 | 2011-07-13 | 北京师范大学 | Method for predicting river basin climatic change and analyzing tendency |
| CN103778241A (en) * | 2014-02-10 | 2014-05-07 | 中国科学院南京地理与湖泊研究所 | Method for identifying large-scale vegetation degeneration area by remote sensing |
| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN106484973A (en) * | 2016-09-28 | 2017-03-08 | 太原理工大学 | A kind of method quantifying human activities of vegetation consumption |
-
2017
- 2017-08-09 CN CN201710676181.3A patent/CN107480818A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102122370A (en) * | 2011-03-07 | 2011-07-13 | 北京师范大学 | Method for predicting river basin climatic change and analyzing tendency |
| CN103778241A (en) * | 2014-02-10 | 2014-05-07 | 中国科学院南京地理与湖泊研究所 | Method for identifying large-scale vegetation degeneration area by remote sensing |
| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN106484973A (en) * | 2016-09-28 | 2017-03-08 | 太原理工大学 | A kind of method quantifying human activities of vegetation consumption |
Non-Patent Citations (2)
| Title |
|---|
| 易浪: "黄土高原植被覆盖变化与气候和人类活动的关系", 《资源科学》 * |
| 王佃来: "基于Sen Mann Kendall的北京植被变化趋势分析", 《计算机工程与应用》 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108509527A (en) * | 2018-03-14 | 2018-09-07 | 中国科学院海洋研究所 | A kind of ice concentration variation tendency towards IDL language seeks calculation algorithm |
| CN109190810B (en) * | 2018-08-16 | 2021-05-04 | 天津大学 | TDNN-based prediction method for NDVI (normalized difference vegetation index) of northern grassland area of China |
| CN109190810A (en) * | 2018-08-16 | 2019-01-11 | 天津大学 | The prediction technique of grassland in northern China area NDVI based on TDNN |
| CN109272144A (en) * | 2018-08-16 | 2019-01-25 | 天津大学 | The prediction technique of grassland in northern China area NDVI based on BPNN |
| CN109272144B (en) * | 2018-08-16 | 2021-05-04 | 天津大学 | BPNN-based prediction method for NDVI (normalized difference of variance) in northern grassland area of China |
| CN109615215A (en) * | 2018-12-06 | 2019-04-12 | 西安理工大学 | A kind of characteristic analysis method that regional vegetation restores |
| CN109615215B (en) * | 2018-12-06 | 2022-11-29 | 西安理工大学 | Feature analysis method for regional vegetation recovery |
| CN110378576A (en) * | 2019-07-01 | 2019-10-25 | 中国环境科学研究院 | The quantification detection method of urbanization vegetation effect effective distance |
| CN111582688A (en) * | 2020-04-28 | 2020-08-25 | 中国环境科学研究院 | Vegetation historical change automatic analysis system |
| CN111754096B (en) * | 2020-06-17 | 2022-09-20 | 河南大学 | Method for acquiring human influence degree of ecological space |
| CN111754096A (en) * | 2020-06-17 | 2020-10-09 | 河南大学 | Method for acquiring human influence degree of ecological space |
| CN112464167A (en) * | 2020-11-26 | 2021-03-09 | 中国科学院地理科学与资源研究所 | Method for analyzing influence of oil and gas field development on vegetation coverage and landscape pattern |
| CN112613347A (en) * | 2020-12-03 | 2021-04-06 | 应急管理部国家自然灾害防治研究院 | Automatic recognition method for fire passing range and burning degree of forest fire |
| CN112613347B (en) * | 2020-12-03 | 2021-07-27 | 应急管理部国家自然灾害防治研究院 | Automatic recognition method for fire passing range and burning degree of forest fire |
| CN112907113A (en) * | 2021-03-18 | 2021-06-04 | 中国科学院地理科学与资源研究所 | Vegetation change cause identification method considering spatial correlation |
| CN113269464A (en) * | 2021-06-10 | 2021-08-17 | 中国科学院地理科学与资源研究所 | Ecological restoration evaluation method and ecological restoration evaluation device |
| CN113269464B (en) * | 2021-06-10 | 2024-04-23 | 中国科学院地理科学与资源研究所 | Ecological restoration assessment method and ecological restoration assessment device |
| CN114545528A (en) * | 2022-03-09 | 2022-05-27 | 北京墨迹风云科技股份有限公司 | Meteorological numerical model element forecasting and post-correcting method and device based on machine learning |
| CN114545528B (en) * | 2022-03-09 | 2024-02-06 | 北京墨迹风云科技股份有限公司 | Machine learning-based correction method and device after meteorological numerical mode element forecast |
| CN114677020A (en) * | 2022-03-30 | 2022-06-28 | 河北省科学院地理科学研究所 | Quantitative evaluation method for regional scale soil wind erosion change driving mechanism |
| CN114677020B (en) * | 2022-03-30 | 2023-09-08 | 河北省科学院地理科学研究所 | Quantitative evaluation method for regional scale soil wind erosion change driving mechanism |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107480818A (en) | A kind of method that rapid evaluation human activities of vegetation covering change influences | |
| Tian et al. | Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities | |
| Michanowicz et al. | A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2. 5 | |
| Ma et al. | Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities | |
| Plouffe et al. | Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka | |
| Baker et al. | Evapotranspiration in the Amazon: spatial patterns, seasonality, and recent trends in observations, reanalysis, and climate models | |
| Zandler et al. | Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia | |
| Liang et al. | Feasibility of a new-generation nighttime light data for estimating in-use steel stock of buildings and civil engineering infrastructures | |
| Li-Juan et al. | Progress in research on homogenization of climate data | |
| Zhang et al. | Quantitatively distinguishing the impact of climate change and human activities on vegetation in mainland China with the improved residual method | |
| Cimburova et al. | The potential of geospatial analysis and Bayesian networks to enable i-Tree Eco assessment of existing tree inventories | |
| Wei et al. | Spatial–temporal variations of NDVI and its response to climate in China from 2001 to 2020 | |
| CN113313296A (en) | Regional soil erosion quantitative evaluation method based on RUSLE | |
| CN109543907B (en) | Complex terrain wind resource assessment method and device | |
| Chen et al. | Combined use of WEPS and Models-3/CMAQ for simulating wind erosion source emission and its environmental impact | |
| CN116822185B (en) | Daily precipitation data space simulation method and system based on HASM | |
| Ouarda et al. | Non-stationary statistical modelling of wind speed: A case study in eastern Canada | |
| CN108764527B (en) | Screening method for soil organic carbon library time-space dynamic prediction optimal environment variables | |
| Tye et al. | Simulating multimodal seasonality in extreme daily precipitation occurrence | |
| Zhang et al. | Relationship between the geographical environment and the forest carbon sink capacity in China based on an individual-tree growth-rate model | |
| Hofer et al. | A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments | |
| Dai et al. | Impact of gauge representative error on a radar rainfall uncertainty model | |
| Fan et al. | Spatio-temporal distribution of vascular plant species abundance on Qinghai-Tibet Plateau | |
| Sun et al. | Impacts of the data quality of remote sensing vegetation index on gross primary productivity estimation | |
| Liang et al. | Spatiotemporal change and non-stationarity of air temperature in China from 1959 to 2018 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171215 |