CN108537439A - A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method - Google Patents
A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method Download PDFInfo
- Publication number
- CN108537439A CN108537439A CN201810309064.8A CN201810309064A CN108537439A CN 108537439 A CN108537439 A CN 108537439A CN 201810309064 A CN201810309064 A CN 201810309064A CN 108537439 A CN108537439 A CN 108537439A
- Authority
- CN
- China
- Prior art keywords
- landscape
- water quality
- quality index
- index
- research
- 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
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 53
- 238000011160 research Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000000605 extraction Methods 0.000 claims abstract description 19
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 230000004927 fusion Effects 0.000 claims abstract description 8
- 230000004044 response Effects 0.000 claims abstract description 6
- 238000001228 spectrum Methods 0.000 claims abstract description 4
- 238000012937 correction Methods 0.000 claims description 23
- 239000003344 environmental pollutant Substances 0.000 claims description 19
- 231100000719 pollutant Toxicity 0.000 claims description 19
- 238000005516 engineering process Methods 0.000 claims description 10
- 230000003139 buffering effect Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000002310 reflectometry Methods 0.000 claims description 3
- 230000003595 spectral effect Effects 0.000 claims description 3
- 238000005303 weighing Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 7
- 239000002689 soil Substances 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- 239000000126 substance Substances 0.000 description 5
- 239000003337 fertilizer Substances 0.000 description 4
- 238000001556 precipitation Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000035558 fertility Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000002123 temporal effect Effects 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
- G06Q10/06—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
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a kind of multiple dimensioned landscape patterns in the basin based on remote sensing image data and water quality index relationship research method, this method includes 1) utilizing multi-spectrum remote sensing image and high-resolution remote sensing image, fusion " collection of illustrative plates " feature extraction valley sight source remittance pattern information；2) Law of DEM Data (DEM) and land use classes data, after dividing basin minimum Hydrologic response units, the location-weighted landscape contrast index after computed improved are based on；3) by calculating separately the Spearman related coefficients of landscape indices, water quality index, structure is comprehensive and redundancy is low landscape indices system and water quality index system；4) multi-variables analysis and Canonical correspondence analysis method are utilized, studies the relationship of landscape indices and water quality index under multiple space and time scales respectively.The present invention is by improving a kind of landscape index, and the influences of the scales to landscape pattern and non-point pollution relational process such as space-time are considered in subsequent analysis, so that analysis result has more Ecology Action, efficiently solve the problems, such as that traditional analysis best research scale when analyzing the relationship of the two is uncertain.
Description
Technical field
The present invention relates to remote sensing technologies and landscape ecology technical field, it particularly relates to which a kind of being based on remote sensing technology
The multiple dimensioned landscape pattern in basin and water quality index relationship research method.
Background technology
The Spatial Heterogeneous Environment characteristic in basin determines landscape structure complexity, merges the complementary advantage of " figure-spectrum " characteristic information,
Basin non-point pollution landscape source can utmostly be promoted and converge the precision and intelligent of pattern key area extraction.In conjunction with non-dots
Source pollution ecology process, Non-point Source Pollutants will pass through not during by generating being pooled to water body with processes such as rainwashes
Same Yuan Hui types landscape pattern, therefore source remittance type composition, space structure and arrangement mode etc. of landscape pattern will certainly shadows
The transmission of Non-point Source Pollutants is rung, and then influences water quality.It is discharged by Fertility in Intensive Agricultural in face of agricultural pollution substance in basin
The pressure of industry development, it is difficult under the conditions of the socio-economic development for realizing decrement, need in basin non-point pollution landscape source remittance lattice
Water quality impact relationship aspect quantification scientific theory is supported in office's space structure and its configuration with method.Existing landscape pattern grinds
In studying carefully, the result of calculation of landscape index does not have ecological significance, compared to the source remittance categorical data of gridding, with a numerical value
Landscape pattern's feature that result of calculation is come under image study scale is that do not have convincingness, is searched to the bottom, Traditional Landscape index
Result of calculation do not have Spatial Difference.In addition to this, landscape pattern and water quality index relationship are often ignored in existing research
In scale effect, under different Research scales, landscape pattern is to the effect of certain class or a few class water quality index can there were significant differences.
There will be a series of problem based on this landscape pattern and water quality index relationship result of study
Invention content
For above-mentioned technical problem in the related technology, it is multiple dimensioned that the present invention proposes a kind of basin based on remote sensing technology
Landscape pattern and water quality index relationship research method, efficiently solve traditional analysis best research when analyzing the relationship of the two
The uncertain problem of scale.Utilize multi-spectrum remote sensing image and high-resolution remote sensing image extraction earth's surface source remittance type information, profit
Minimum Hydrologic response units are divided with dem data and earth's surface land use data, are being calculated based on minimum Hydrologic response units
On the basis of location-weighted landscape contrast index, comprehensive and low redundancy landscape index and water quality are built using rank analysis method
Index system finally analyzes landscape lattice from Canonical correspondence analysis method using multivariate correlation analysis under different spatial and temporal scales
Correlativity between office's index and water quality index.
To realize the above-mentioned technical purpose, the technical proposal of the invention is realized in this way：
A kind of multiple dimensioned landscape pattern in basin based on remote sensing technology and water quality index relationship research method, including following step
Suddenly：
Step S1, the valley sight source remittance pattern information extraction of fusion " collection of illustrative plates " feature；
Step S2, the location-weighted landscape contrast index based on minimum water unit calculate；
Step S3, the landscape indices and water quality index system construction, for building comprehensive and low redundancy scape
See index number system and water quality index system；
Step S4, the landscape indices choose module and landscape indices and water quality index relationship with water quality index
Research；
Further, step S1 includes：
(1) remote sensing image " collection of illustrative plates " feature extraction, carries for the shape to high spatial resolution satellite image, textural characteristics
It takes and the spectral band information extraction of centering resolution multi-spectral satellite image works；
(2) fusion " collection of illustrative plates " feature landscape types extraction, for according to the distinctive shape of different landscape, texture information with
And landscape source remittance type information is extracted in turn to distinguish in the variation of different-waveband reflectivity；
Further, step S2 includes：
(1) minimum Hydrologic response units divide, the minimum Research scale calculated for division index；
(2) source remittance landscape types amendment and geographic influence factor correction, pass for weighing Non-point Source Pollutants in basin
By the influence degree of geographic factor when defeated；
(3) location-weighted landscape contrast index calculation formula is expressed as：
HRULCIi=Wi*Ai
Wherein, i indicates that a certain specific HRU, Ai indicate that the area of the HRU, Wi indicate productions of the HRU to Non-point Source Pollutants
Life/rejection coefficient, Wi are influenced by Land-Use, soil property, precipitation and applying quantity of chemical fertilizer etc., consider non-point source
Pollutant need to be modified different geographic factors, Wi is represented by by the process of HRU generations/decay and eventually enter into water body：
Wi=F (L, P, R, D, N, S, F, A)
Wherein, L represents land use pattern correction factor, and P represents gradient correction factor, and R represents average annual precipitation amendment
Coefficient, D represent distance factor correction factor, and N represents NDVI correction factors, and S represents soil texture correction factor, and F represents chemical fertilizer
Using quantity correction coefficient, A represents the correction factor of Effective Soil Water Content.
Meanwhile to allow the space load correlation index between different HRU to compare, the dirt to different landscape source remittance type
Dye object correction factor is standardized, i.e.,：
Wherein Wi is the Non-point Source Pollutants correction factor of certain land use landscape, and WMAX refers to soil in valley sight
Utilize the maximum value of Non-point Source Pollutants correction factor under landscape.If Non-point Source Pollutants there are many in HRU,：
HRULCIXY=HRULCIX+HRULCIY
Wherein, HRULCIY is landscape load factors of the Non-point Source Pollutants Y in HRU, and HRULCIXY is pollutant X and Y
Landscape load factor summation in HRU.
For sub-basin, the calculating of non-point pollution landscape load factor is expressed as：
Wherein N represents landscape types in sub-basin as the HRU total numbers of source landscape, and i represents HRU.
Further, step S4 includes：
(1) multi-space Research scale selects, for according to research area source remittance landscape and hydrology period feature, selection to have generation
The time scale (vegetation key phenological period) of table and space scale (Watershed Scale, catchment scale, buffering area scale) conduct
The Research scale of relationship research；
(2) landscape indices system and hydrology index body relationship analysis, for being studied between the two under multiple space and time scales
Correlativity.
After adopting the above technical scheme, beneficial effects of the present invention：The relationship research of landscape pattern and water quality index is distant
Important application of the sense technology in basin non-point pollution ecological process research considers that the scale effect of ecological process, research exist
Landscape pattern contributes to the relationship of water quality index to choose the best scale studied under different time and space scales.The present invention is directed to non-dots
The big feature of spatio-temporal difference is polluted in source, improves location-weighted landscape contrast index, is ground suitable for the basin under various scales
Study carefully, the structure links of wherein landscape indices and water quality index solve the problems, such as between index redundancy it is high this.The present invention has
Help optimize remittance pattern in non-point pollution landscape source in basin, while ecological evaluation and non-point pollution prevention and control have in watershed
Significance.
Specific implementation mode
The technical scheme in the embodiments of the invention will be clearly and completely described below.Based on the reality in the present invention
Example is applied, the every other embodiment that those of ordinary skill in the art are obtained shall fall within the protection scope of the present invention.The present invention carries
The multiple dimensioned landscape pattern in a kind of basin based on remote sensing technology gone out and water quality index relationship research method, technical solution is in this way
It realizes：
A kind of multiple dimensioned landscape pattern in basin based on remote sensing technology and water quality index relationship research method, including following step
Suddenly：
Step S1, the valley sight source remittance pattern information extraction of fusion " collection of illustrative plates " feature；
Step S2, the location-weighted landscape contrast index based on minimum water unit calculate；
Step S3, the landscape indices and water quality index system construction, for building comprehensive and low redundancy scape
See index number system and water quality index system；
Step S4, the landscape indices choose module and landscape indices and water quality index relationship with water quality index
Research；
Further, step S1 includes：
(1) remote sensing image " collection of illustrative plates " feature extraction, carries for the shape to high spatial resolution satellite image, textural characteristics
It takes and the spectral band information extraction of centering resolution multi-spectral satellite image works；
(2) fusion " collection of illustrative plates " feature landscape types extraction, for according to the distinctive shape of different landscape, texture information with
And landscape source remittance type information is extracted in turn to distinguish in the variation of different-waveband reflectivity；
Further, step S2 includes：
(1) minimum Hydrologic response units divide, the minimum Research scale calculated for division index；
(2) source remittance landscape types amendment and geographic influence factor correction, pass for weighing Non-point Source Pollutants in basin
By the influence degree of geographic factor when defeated；
(3) location-weighted landscape contrast index calculation formula is expressed as：
HRULCIi=Wi*Ai
Wherein, i indicates that a certain specific HRU, Ai indicate that the area of the HRU, Wi indicate productions of the HRU to Non-point Source Pollutants
Life/rejection coefficient, Wi are influenced by Land-Use, soil property, precipitation and applying quantity of chemical fertilizer etc., consider non-point source
Pollutant need to be modified different geographic factors, Wi is represented by by the process of HRU generations/decay and eventually enter into water body：
Wi=F (L, P, R, D, N, S, F, A)
Wherein, L represents land use pattern correction factor, and P represents gradient correction factor, and R represents average annual precipitation amendment
Coefficient, D represent distance factor correction factor, and N represents NDVI correction factors, and S represents soil texture correction factor, and F represents chemical fertilizer
Using quantity correction coefficient, A represents the correction factor of Effective Soil Water Content.
Meanwhile to allow the space load correlation index between different HRU to compare, the dirt to different landscape source remittance type
Dye object correction factor is standardized, i.e.,：
Wherein Wi is the Non-point Source Pollutants correction factor of certain land use landscape, and WMAX refers to soil in valley sight
Utilize the maximum value of Non-point Source Pollutants correction factor under landscape.If Non-point Source Pollutants there are many in HRU,：
HRULCIXY=HRULCIX+HRULCIY
Wherein, HRULCIY is landscape load factors of the Non-point Source Pollutants Y in HRU, and HRULCIXY is pollutant X and Y
Landscape load factor summation in HRU.
For sub-basin, the calculating of non-point pollution landscape load factor is expressed as：
Wherein N represents landscape types in sub-basin as the HRU total numbers of source landscape, and i represents HRU.
Further, step S4 includes：
(1) multi-space Research scale selects, for according to research area source remittance landscape and hydrology period feature, selection to have generation
The time scale (vegetation key phenological period) of table and space scale (Watershed Scale, catchment scale, buffering area scale) conduct
The Research scale of relationship research；
(2) landscape indices system and hydrology index body relationship analysis, for being studied between the two under multiple space and time scales
Correlativity.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
With within principle, any modification, equivalent replacement, improvement and so on should all be included in the protection scope of the present invention god.
Claims (5)
1. a kind of multiple dimensioned landscape pattern in basin based on remote sensing technology and water quality index relationship research method, including fusion " figure
Converge pattern information extraction modules, the location-weighted landscape contrast index based on minimum water unit in the valley sight source of spectrum " feature
Computing module, landscape indices and water quality index choose module and landscape indices and water quality index relationship research module；
Wherein,
The valley sight source remittance pattern information extraction modules of described fusion " collection of illustrative plates " feature, for extraction source remittance landscape types letter
Breath；
The location-weighted landscape contrast index computing module based on minimum water unit, for calculating a kind of improved scape
See space load correlation index；
The landscape indices choose module with water quality index and choose comprehensive and independent index and index system for establishing；
The landscape indices are with water quality index relationship research module for studying landscape pattern and water under multi-space angle
Correlativity between matter index.
2. the multiple dimensioned landscape pattern in the basin according to claim 1 based on remote sensing image data grinds with water quality index relationship
Study carefully method, the valley sight source remittance pattern information extraction modules of described fusion " collection of illustrative plates " feature specifically include：
Remote sensing image " collection of illustrative plates " feature extraction unit, for shape, the texture feature extraction to high spatial resolution satellite image
And the spectral band information extraction work of centering resolution multi-spectral satellite image；
Merge " collection of illustrative plates " feature landscape types extraction unit, for according to the distinctive shape of different landscape, texture information and
It is distinguished in the variation of different-waveband reflectivity and then extracts landscape source remittance type information.
3. the multiple dimensioned landscape pattern in the basin according to claim 1 based on remote sensing image data grinds with water quality index relationship
Study carefully method, the location-weighted landscape contrast index computing module based on minimum water unit specifically includes：
Minimum Hydrologic response units division unit, the minimum Research scale calculated for division index；
Converge landscape types amendment and geographic influence factor correction unit in source, when being transmitted in basin for weighing Non-point Source Pollutants
By the influence degree of geographic factor.
4. the multiple dimensioned landscape pattern in the basin according to claim 1 based on remote sensing image data grinds with water quality index relationship
Study carefully method, the landscape indices are chosen module with water quality index and specifically included：
Key index system construction unit, for building comprehensive and low redundancy landscape index system and water quality index system.
5. the multiple dimensioned landscape pattern in the basin according to claim 1 based on remote sensing image data grinds with water quality index relationship
Study carefully method, the landscape indices choose module and landscape indices and water quality index relationship research module with water quality index
Including：
Multi-space Research scale selecting unit is represented for according to research area source remittance landscape and hydrology period feature, selecting to have
Property time scale (vegetation key phenological period) and space scale (Watershed Scale, catchment scale, buffering area scale) as pass
It is the Research scale of research；
Landscape indices system and hydrology index body relationship analysis unit, for studying phase between the two under multiple space and time scales
Pass relationship.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810309064.8A CN108537439A (en) | 2018-04-09 | 2018-04-09 | A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810309064.8A CN108537439A (en) | 2018-04-09 | 2018-04-09 | A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108537439A true CN108537439A (en) | 2018-09-14 |
Family
ID=63483420
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810309064.8A Pending CN108537439A (en) | 2018-04-09 | 2018-04-09 | A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108537439A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111898896A (en) * | 2020-07-24 | 2020-11-06 | 中国科学院城市环境研究所 | Watershed non-point source pollution loss intensity evaluation method considering soil attributes |
| CN112990684A (en) * | 2021-03-09 | 2021-06-18 | 中国科学院城市环境研究所 | Method and system for determining accessibility of green land to ecological effect of building energy consumption carbon emission reduction |
| CN112990661A (en) * | 2021-02-07 | 2021-06-18 | 华中农业大学 | Small watershed ecological space health assessment system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831480A (en) * | 2011-06-13 | 2012-12-19 | 同济大学 | Ecological assessment information processing method suitable for regulating polluted river comprehensively |
| CN103425890A (en) * | 2013-08-24 | 2013-12-04 | 王海丰 | Landscape water quality analysis algorithm |
| CN106295121A (en) * | 2016-07-21 | 2017-01-04 | 天津大学 | Landscape impoundments Bayes's water quality grade Forecasting Methodology |
| CN106354940A (en) * | 2016-08-30 | 2017-01-25 | 天津大学 | Landscape water quality simulation and early warning method based on water quality model uncertainty input |
| KR20170068900A (en) * | 2015-12-10 | 2017-06-20 | 임병을 | It based on quality, standardization and quality assurance of gardening Distribution System |
-
2018
- 2018-04-09 CN CN201810309064.8A patent/CN108537439A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831480A (en) * | 2011-06-13 | 2012-12-19 | 同济大学 | Ecological assessment information processing method suitable for regulating polluted river comprehensively |
| CN103425890A (en) * | 2013-08-24 | 2013-12-04 | 王海丰 | Landscape water quality analysis algorithm |
| KR20170068900A (en) * | 2015-12-10 | 2017-06-20 | 임병을 | It based on quality, standardization and quality assurance of gardening Distribution System |
| CN106295121A (en) * | 2016-07-21 | 2017-01-04 | 天津大学 | Landscape impoundments Bayes's water quality grade Forecasting Methodology |
| CN106354940A (en) * | 2016-08-30 | 2017-01-25 | 天津大学 | Landscape water quality simulation and early warning method based on water quality model uncertainty input |
Non-Patent Citations (1)
| Title |
|---|
| ZHANG XIN 等: "Geo-cognitive computing method for identifying "source-sink" landscape patterns of river basin non-point source pollution", 《INT J AGRIC & BIOL ENG》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111898896A (en) * | 2020-07-24 | 2020-11-06 | 中国科学院城市环境研究所 | Watershed non-point source pollution loss intensity evaluation method considering soil attributes |
| CN112990661A (en) * | 2021-02-07 | 2021-06-18 | 华中农业大学 | Small watershed ecological space health assessment system and method |
| CN112990661B (en) * | 2021-02-07 | 2023-04-07 | 华中农业大学 | Small watershed ecological space health assessment system and method |
| CN112990684A (en) * | 2021-03-09 | 2021-06-18 | 中国科学院城市环境研究所 | Method and system for determining accessibility of green land to ecological effect of building energy consumption carbon emission reduction |
| CN112990684B (en) * | 2021-03-09 | 2022-09-13 | 中国科学院城市环境研究所 | Method and system for determining accessibility of green land to ecological effect of building energy consumption carbon emission reduction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fijani et al. | Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran | |
| Tong et al. | Modeling the relationship between land use and surface water quality | |
| Chen et al. | Evaluating performance of macroinvertebrate-based adjusted and unadjusted multi-metric indices (MMI) using multi-season and multi-year samples | |
| Li et al. | On the relationship between landscape ecological patterns and water quality across gradient zones of rapid urbanization in coastal China | |
| Li et al. | The integrated eco-environment assessment of the red soil hilly region based on GIS—A case study in Changsha City, China | |
| Gou et al. | Mapping potential groundwater‐dependent ecosystems for sustainable management | |
| Fathizad et al. | Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques | |
| CN109740956A (en) | A kind of land ecology quality automatic evaluation method | |
| Wu et al. | Evaluation for the ecological quality status of coastal waters in East China Sea using fuzzy integrated assessment method | |
| Szoszkiewicz et al. | Parameters structuring macrophyte communities in rivers and lakes–results from a case study in North-Central Poland | |
| Cortes et al. | Tools for bioindicator assessment in rivers: the importance of spatial scale, land use patterns and biotic integration | |
| CN108537439A (en) | A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method | |
| Herlihy et al. | Using multiple approaches to develop nutrient criteria for lakes in the conterminous USA | |
| Japelaghi et al. | Spatio-temporal analysis and prediction of landscape patterns and change processes in the Central Zagros region, Iran | |
| Mahan et al. | Using ecological indicators and a decision support system for integrated ecological assessment at two national park units in the mid-Atlantic region, USA | |
| Chen et al. | A risk assessment method for remote sensing of cyanobacterial blooms in inland waters | |
| Liao et al. | Exploring the database of a soil environmental survey using a geo-self-organizing map: A pilot study | |
| Chamanehpour et al. | The potential evaluation of groundwater pollution based on the intrinsic and the specific vulnerability index | |
| Liu et al. | The establishment of an eco-environmental evaluation model for southwest China and eastern South Africa based on the DPSIR framework | |
| Herlihy et al. | The response of wetland quality indicators to human disturbance indicators across the United States | |
| Wang et al. | Analysis of oasis land ecological security and influencing factors in arid areas | |
| Shakeri et al. | Comparison of the DRASTIC+ L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods | |
| Ablat et al. | Spatiotemporal variations and underlying mechanism of the floodplain wetlands in the meandering Yellow River in arid and semi-arid regions | |
| Das et al. | Assessment of wetland health dynamics: Comparing fuzzy-AHP and composite indexing methods in an urban agglomeration in east India | |
| Nguyen et al. | Assessing groundwater vulnerability and addressing salinization in the coastal region of Ba Ria–Vung Tau province, Vietnam: an enhanced DRASTIC model approach |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180914 |