CN108537439A - A kind of multiple dimensioned landscape pattern in basin and water quality index relationship research method - Google Patents
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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 invention relates to the technical field of remote sensing technology and landscape ecology, in particular to a watershed multi-scale landscape pattern and water quality index relation research method based on the remote sensing technology.
Background
The space heterogeneous characteristics of the drainage basin determine the complexity of a landscape structure, the complementary advantages of the characteristic information of the 'graph-spectrum' are fused, and the accuracy and the intelligence of extracting the key region of the drainage basin non-point source pollution landscape source convergence pattern can be improved to the greatest extent. In combination with the ecological process of non-point source pollution, the non-point source pollutants are subjected to different source-sink type landscape patterns in the process of collecting the non-point source pollutants into the water body along with the processes of surface runoff and the like, so that the source-sink type composition, the spatial structure, the arrangement mode and the like of the landscape patterns can certainly influence the transmission of the non-point source pollutants, and further influence the water quality. Under the socioeconomic development condition that agricultural pollutant discharge is pressed by intensive agricultural development and reduction is difficult to realize, a river basin needs to be supported by quantitative scientific theories and methods in the aspect of influence relationship of a river basin non-point source pollution landscape source convergence spatial structure and configuration on water quality. In the existing landscape pattern research, the calculation result of the landscape index does not have ecological significance, and compared with gridded source-sink type data, the landscape pattern under the research scale is reflected by a numerical calculation result. In addition, the scale effect in the relationship between the landscape pattern and the water quality index is often neglected in the existing research, and the effect of the landscape pattern on a certain type or a certain types of water quality indexes can be obviously different under different research scales. The research result of the relationship between the landscape pattern and the water quality index based on the above has a series of problems
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a watershed multi-scale landscape pattern and water quality index relation research method based on a remote sensing technology, and the problem that the best research scale is uncertain when the relation between the watershed multi-scale landscape pattern and the water quality index is analyzed by a traditional analysis method is effectively solved. The method comprises the steps of extracting earth surface source sink type information by utilizing a multispectral remote sensing image and a high-resolution remote sensing image, dividing a minimum hydrological response unit by utilizing DEM data and earth surface land utilization data, constructing a comprehensive landscape index and water quality index system with low redundancy by utilizing a rank analysis method on the basis of calculating a landscape space load contrast index based on the minimum hydrological response unit, and finally analyzing the correlation between a landscape pattern index and a water quality index under different space-time scales by adopting a multivariate correlation analysis and canonical correspondence analysis method.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
a watershed multi-scale landscape pattern and water quality index relation research method based on a remote sensing technology comprises the following steps:
step S1, extracting information of the watershed landscape source pattern merging fused with the characteristics of the map;
step S2, calculating the landscape space load contrast index based on the minimum hydrological unit;
step S3, constructing a landscape pattern index and water quality index system, wherein the landscape pattern index and water quality index system is used for constructing a comprehensive landscape index system and water quality index system with low redundancy;
step S4, the landscape pattern index and water quality index selection module and the landscape pattern index and water quality index relation research;
further, step S1 includes:
(1) extracting the characteristic of a remote sensing image 'atlas' for extracting the shape and texture characteristic of a satellite image with high spatial resolution and extracting the spectral band information of a multispectral satellite image with medium resolution;
(2) landscape type extraction fused with the characteristics of the 'atlas' is used for distinguishing according to the special shape and texture information of different landscapes and the change of reflectivity of different wave bands so as to extract landscape source and sink type information;
further, step S2 includes:
(1) dividing a minimum hydrological response unit for dividing a minimum research scale of index calculation;
(2) the source-sink landscape type correction and the geographic influence factor correction are used for measuring the influence degree of the non-point source pollutant on the geographic factor when the non-point source pollutant is transmitted in the flow domain;
(3) the landscape space load contrast index calculation formula is expressed as follows:
HRULCIi＝Wi*Ai
wherein i represents a specific HRU, Ai represents the area of the HRU, Wi represents the generation/inhibition coefficient of the HRU to non-point source pollutants, and Wi is influenced by land utilization mode, soil property, precipitation amount, fertilizer application amount and the like, and different geographic factors need to be corrected in consideration of the process that the non-point source pollutants are generated/attenuated by the HRU and finally enter the water body, and Wi can be represented as:
Wi＝F(L,P,R,D,N,S,F,A)
wherein L represents a land utilization type correction coefficient, P represents a slope correction coefficient, R represents an annual average precipitation correction coefficient, D represents a distance factor correction coefficient, N represents an NDVI correction coefficient, S represents a soil texture correction coefficient, F represents a fertilizer application amount correction coefficient, and A represents a correction coefficient of the effective water content of the soil.
Meanwhile, in order to enable the space load contrast indexes between different HRUs to be compared, the pollutant correction coefficients of different landscape source and sink types are subjected to standardization treatment, namely:
wi is a non-point source pollutant correction coefficient of a certain land use landscape, and WMAX is the maximum value of the non-point source pollutant correction coefficient under the land use landscape in the watershed landscape. If there are multiple non-point source contaminants in the HRU, then:
HRULCIXY＝HRULCIX+HRULCIY
wherein HRULCIY is the landscape load index of non-point source pollutant Y in HRU, and HRULCIXY is the landscape load index sum of pollutant X and Y in HRU.
For the sub-watersheds, the non-point source pollution landscape load index is calculated as:
where N represents the total number of HRUs in the sub-stream domain whose landscape type is the source landscape, and i represents the HRU.
Further, step S4 includes:
(1) selecting a multi-time-space research scale, wherein the multi-time-space research scale is used for selecting a representative time scale (vegetation key phenological period) and a representative space scale (drainage basin scale, catchment area scale and buffer area scale) as research scales of relation research according to characteristics of source-sink landscapes and hydrology periods of a research area;
(2) and the landscape pattern index system and the hydrological index body are subjected to relational analysis and are used for researching the correlation between the landscape pattern index system and the hydrological index body under multiple space-time scales.
After the technical scheme is adopted, the invention has the beneficial effects that: the research on the relationship between the landscape pattern and the water quality index is an important application of the remote sensing technology in the research on the watershed non-point source pollution ecological process, the scale effect of the ecological process is considered, and the research on the relationship between the landscape pattern and the water quality index under different space-time scales is beneficial to selecting the optimal scale for research. The invention aims at the characteristic of large space-time difference of non-point source pollution, improves the landscape space load contrast index, and is suitable for basin research under various scales, wherein the construction link of the landscape pattern index and the water quality index solves the problem of high redundancy among indexes. The invention is beneficial to optimizing the non-point source pollution landscape source convergence pattern in the river basin, and has important significance on ecological evaluation and non-point source pollution prevention and control in the river basin.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention. The invention provides a watershed multi-scale landscape pattern and water quality index relation research method based on a remote sensing technology, which adopts the technical scheme that:
a watershed multi-scale landscape pattern and water quality index relation research method based on a remote sensing technology comprises the following steps:
step S1, extracting information of the watershed landscape source pattern merging fused with the characteristics of the map;
step S2, calculating the landscape space load contrast index based on the minimum hydrological unit;
step S3, constructing a landscape pattern index and water quality index system, wherein the landscape pattern index and water quality index system is used for constructing a comprehensive landscape index system and water quality index system with low redundancy;
step S4, the landscape pattern index and water quality index selection module and the landscape pattern index and water quality index relation research;
further, step S1 includes:
(1) extracting the characteristic of a remote sensing image 'atlas' for extracting the shape and texture characteristic of a satellite image with high spatial resolution and extracting the spectral band information of a multispectral satellite image with medium resolution;
(2) landscape type extraction fused with the characteristics of the 'atlas' is used for distinguishing according to the special shape and texture information of different landscapes and the change of reflectivity of different wave bands so as to extract landscape source and sink type information;
further, step S2 includes:
(1) dividing a minimum hydrological response unit for dividing a minimum research scale of index calculation;
(2) the source-sink landscape type correction and the geographic influence factor correction are used for measuring the influence degree of the non-point source pollutant on the geographic factor when the non-point source pollutant is transmitted in the flow domain;
(3) the landscape space load contrast index calculation formula is expressed as follows:
HRULCIi＝Wi*Ai
wherein i represents a specific HRU, Ai represents the area of the HRU, Wi represents the generation/inhibition coefficient of the HRU to non-point source pollutants, and Wi is influenced by land utilization mode, soil property, precipitation amount, fertilizer application amount and the like, and different geographic factors need to be corrected in consideration of the process that the non-point source pollutants are generated/attenuated by the HRU and finally enter the water body, and Wi can be represented as:
Wi＝F(L,P,R,D,N,S,F,A)
wherein L represents a land utilization type correction coefficient, P represents a slope correction coefficient, R represents an annual average precipitation correction coefficient, D represents a distance factor correction coefficient, N represents an NDVI correction coefficient, S represents a soil texture correction coefficient, F represents a fertilizer application amount correction coefficient, and A represents a correction coefficient of the effective water content of the soil.
Meanwhile, in order to enable the space load contrast indexes between different HRUs to be compared, the pollutant correction coefficients of different landscape source and sink types are subjected to standardization treatment, namely:
wi is a non-point source pollutant correction coefficient of a certain land use landscape, and WMAX is the maximum value of the non-point source pollutant correction coefficient under the land use landscape in the watershed landscape. If there are multiple non-point source contaminants in the HRU, then:
HRULCIXY＝HRULCIX+HRULCIY
wherein HRULCIY is the landscape load index of non-point source pollutant Y in HRU, and HRULCIXY is the landscape load index sum of pollutant X and Y in HRU.
For the sub-watersheds, the non-point source pollution landscape load index is calculated as:
where N represents the total number of HRUs in the sub-stream domain whose landscape type is the source landscape, and i represents the HRU.
Further, step S4 includes:
(1) selecting a multi-time-space research scale, wherein the multi-time-space research scale is used for selecting a representative time scale (vegetation key phenological period) and a representative space scale (drainage basin scale, catchment area scale and buffer area scale) as research scales of relation research according to characteristics of source-sink landscapes and hydrology periods of a research area;
(2) and the landscape pattern index system and the hydrological index body are subjected to relational analysis and are used for researching the correlation between the landscape pattern index system and the hydrological index body under multiple space-time scales.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A watershed multi-scale landscape pattern and water quality index relation research method based on remote sensing technology comprises a watershed landscape source convergence pattern information extraction module fusing map features, a landscape space load comparison index calculation module based on minimum hydrological units, a landscape pattern index and water quality index selection module and a landscape pattern index and water quality index relation research module; wherein,
the watershed landscape source convergence pattern information extraction module fused with the map features is used for extracting source convergence landscape type information;
the landscape space load comparison index calculation module based on the minimum hydrological unit is used for calculating an improved landscape space load comparison index;
the landscape pattern index and water quality index selection module is used for establishing and selecting a comprehensive and independent index and index system;
the landscape pattern index and water quality index relation research module is used for researching the correlation relation between the landscape pattern and the water quality index under multiple space-time angles.
2. The method for researching relationship between watershed multi-scale landscape pattern and water quality index based on remote sensing image data as claimed in claim 1, wherein the watershed landscape source convergence pattern information extraction module fusing the map features specifically comprises:
the remote sensing image 'map' feature extraction unit is used for extracting the shape and texture features of the high-spatial-resolution satellite image and extracting the spectral band information of the medium-resolution multispectral satellite image;
the landscape type extraction unit fused with the map features is used for distinguishing according to the special shapes and texture information of different landscapes and the change of reflectivity of different wave bands so as to extract landscape source and sink type information.
3. The method for researching relationship between watershed multi-scale landscape pattern and water quality index based on remote sensing image data as claimed in claim 1, wherein the landscape space load contrast index calculation module based on the minimum hydrological unit specifically comprises:
the minimum hydrologic response unit dividing unit is used for dividing the minimum research scale of index calculation;
and the source-sink landscape type correction and geographic influence factor correction unit is used for measuring the influence degree of the non-point source pollutant on the geographic factor during transmission in the flow domain.
4. The method for researching the relationship between the watershed multi-scale landscape pattern and the water quality index based on the remote sensing image data as claimed in claim 1, wherein the landscape pattern index and water quality index selecting module specifically comprises:
and the key index system construction unit is used for constructing a comprehensive landscape index system and a water quality index system with low redundancy.
5. The method for researching the relationship between the watershed multi-scale landscape pattern and the water quality index based on the remote sensing image data as claimed in claim 1, wherein the landscape pattern index and water quality index selecting module and the landscape pattern index and water quality index relationship researching module comprise:
the multi-space-time research scale selection unit is used for selecting representative time scale (vegetation key phenological period) and space scale (drainage basin scale, catchment area scale and buffer area scale) as research scales of relation research according to characteristics of source gathering landscape and hydrological period of a research area;
and the landscape pattern index system and hydrological index body relation analysis unit is used for researching the correlation between the landscape pattern index system and the hydrological index body under multiple space-time scales.
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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 |
| CN112990661A (en) * | 2021-02-07 | 2021-06-18 | 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 |
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 |
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