CN104217103A - Method for building and digitally expressing grassland vegetation subtypes - Google Patents
Method for building and digitally expressing grassland vegetation subtypes Download PDFInfo
- Publication number
- CN104217103A CN104217103A CN201410397373.7A CN201410397373A CN104217103A CN 104217103 A CN104217103 A CN 104217103A CN 201410397373 A CN201410397373 A CN 201410397373A CN 104217103 A CN104217103 A CN 104217103A
- Authority
- CN
- China
- Prior art keywords
- vegetation
- subtype
- plant
- sampling point
- sociales
- 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.)
- Granted
Links
Abstract
The invention relates to a method for building and digitally expressing grassland vegetation subtypes. The method comprises the following steps of (1) marking a searched area, and searching vegetation of acquired searching sample points; (2) calculating the plant species dominance degree of vegetation of each searching sample point; (3) according to the plant type and the plant species dominance degree of each searching sample point, judging whether the searching sample points have the same vegetation subtype or not, and when the vegetation subtype is changed, actually measuring the boundary coordinates of different vegetation subtypes to obtain the vector base map of each vegetation subtype in the searched area; (4) building an attribute data table of each vegetation subtype, and drawing the digital vector distribution diagram of each vegetation subtype by adjusting and overlapping transparency of diagram layers; (5) placing the obtained digital vector distribution diagrams of the vegetation subtypes together, and finally generating the digital vector distribution diagram which shows the vegetation subtype distribution in the searched area and the formation and distribution of dominant plants in the vegetation subtypes. The method is widely applied to practical applications, such as research on the evolution of grassland vegetation, agricultural production, grassland vegetation resource monitoring.
Description
Technical field
The present invention relates to informationization and the digitized representations method of a kind of grassland vegetation, particularly about a kind of foundation and digitized representations method of grassland vegetation hypotype.
Background technology
In grassland types division, Chinese scholars proposes 7 large classes tens of kinds of methods.China appoints all academicians that continues to propose the comprehensive series classification system in famous meadow, can be divided into according to this system meadow: class-foundation is meteorological factor temperature and precipitation, subclass-foundation is edphic factor, and type-foundation is vegetation factor, below type, be divided into hypotype, miniature etc. according to vegetation characteristics.Since entering 21 century, along with the widespread use of computer technology, network technology, method such as " 3S " technology and Geostatistical etc., the visual and Digital Study that grassland types divides enters the fast-developing stage.
Visual in current meadow class, subclass, type large scale level and Digital Study is more, the digitizing distribution figure in class, subclass, type level and attribute database thereof are established, but also do not set up reliable digitizing solution at the distribution of reflection vegetation subtype, hypotype plant constitution and plant dominance message context, lack clear and definite vegetation subtype nomenclature principle and method simultaneously.If can directly perceived, the quantitative distribution of display vegetation subtype and the plant constitution of each hypotype and distribution in a regional distribution chart, realize the visual and digitizing of vegetation subtype distribution and plant constitution thereof, map data mining platform can provide abundant hypotype and vegetation information thereof for people, can simply, directly perceived, accurately, science for grassland resources assess, the decision-making of scientific research and agriculture and animal husbandry department provides support.
Summary of the invention
For the problems referred to above, the object of this invention is to provide a kind of foundation and the digitized representations method that can react the grassland vegetation hypotype of grassland vegetation subtype distribution, each vegetation subtype plant constitution and plant dominance information.
For achieving the above object, the present invention takes following technical scheme: a kind of foundation of grassland vegetation hypotype and digitized representations method, and it comprises the following steps: 1) mark survey area, carries out vegetation investigation to the investigation sampling point obtained; 2) the plant species dominance of vegetation in each investigation sampling point is calculated; 3) judge whether it is identical vegetation subtype according to the floristics of each investigation sampling point and species dominance, when vegetation subtype changes, the boundary coordinate of actual measurement differ ent vegetation hypotype, by the boundary coordinate of each vegetation subtype input Google Earth and ENVI4.7 program, obtain the vector base map of each vegetation subtype in this survey area; 4) the attribute data table of each vegetation subtype is built, different sociales plant is represented with different colours, according to the dominance of different sociales plant to color assignment, each vegetation subtype vector base map generates the distribution layer of each dominant plant, carries out superposing the vector distribution plan obtaining each vegetation subtype after transparency regulates to each distribution layer; 5) by step 4) in the digital vector figure of each vegetation subtype that obtains put together, the final vector distribution plan generating each sociales plant constitution and distribution in vegetation subtype distribution and each vegetation subtype in display survey area.
Described step 2) in, the dominance SDR of plant species is:
SDR＝[(Y'+C'+D')/3]×100％；
Wherein, Y' is the relative biomass of certain plant, and C' is the relative coverage of certain plant, and D' is the relative density of certain plant; In investigation sampling point, the relative biomass Y' of certain plant is:
Wherein, y is certain phytomass; Y is vegetation total biomass in investigation sampling point;
In investigation sampling point, the relative coverage C' of certain plant is:
Wherein, c is certain plant cover degree; C is vegetation total cover-degree in investigation sampling point;
In investigation sampling point, the relative density D' of certain plant is:
Wherein, d is certain plant density, and D is the gross density of vegetation in investigation sampling point.
Described step 3) in, judge that whether each investigation sampling point is that the method for identical vegetation subtype comprises: 1) if there is the species dominance >50% of a Plants in certain investigation sampling point, then there is with this investigation sampling point identical sociales floristic investigation sampling point and all belong to same vegetation subtype, and this vegetation subtype is with a kind of botanical nomenclature; 2) if there is the species dominance sum >75% of two Plants in certain investigation sampling point, then there is with this investigation sampling point identical sociales floristic investigation sampling point and all belong to same vegetation subtype, name this investigation sampling point according to the species dominance size order of two Plants with two Plants; 3) in all the other situations, descending according to plant species dominance, the investigation sampling point with identical plant species dominance size is classified as same vegetation subtype, and former 3 kinds of dominant plants name this vegetation subtype.
Described step 4) in, the method for drafting of the digital vector distribution plan of each vegetation subtype comprises: the attribute data table 1) making each vegetation subtype; After determining the sociales plant constitution of differ ent vegetation hypotype, the difference that gets colors respectively is large, the different colours being beneficial to resolution represents the sociales plant in each vegetation subtype, and the representative color of identical sociales plant in whole survey area in differ ent vegetation hypotype is consistent; 2) with the dominance SDR of sociales plant to color assignment, with the dominance size of sociales plant in investigation sampling point different in this vegetation subtype of performance that the depth of color is quantitative, this vegetation subtype vector base map utilize Krigging interpolation method to generate the distribution layer of each sociales plant in each vegetation subtype; 3) transparency adjustment is carried out to the distribution layer of each sociales plant: the uncomfortable joint transparency of distribution layer of the plant that in each vegetation subtype, species dominance is maximum; The transparency of the distribution layer of all the other sociales plants is according to its average species dominance in this vegetation subtype
regulate, transparency
wherein average species dominance
for the mean value of the dominance of this sociales plant in investigation sampling points all in this vegetation subtype; 4) according to the order that transparency is ascending, the distribution layer of sociales plant different in each vegetation subtype is superposed, what namely transparency was little is placed on internal layer, and what transparency was large is placed on skin, like this by the outer distribution situation that can observe internal layer sociales plant.
The present invention is owing to taking above technical scheme, it has the following advantages: 1, the present invention owing to achieving the digitized representations of grassland vegetation subtype distribution on a vector distribution plan, the formation of sociales plant and distribution in observation grassland vegetation subtype distribution that can be directly perceived, quantitative and each vegetation subtype.2, the present invention to have introduced according to the dominance size of plant the method for the name of differ ent vegetation hypotype, compensate at present to the deficiency of vegetation subtype name.3, the present invention is not because vegetation investigation method is by the restriction of required survey area size, the longitude and latitude spacer unit of investigation sampling point can be selected flexibly according to survey area area and manpower, financial resources, material resources condition, improves the investigation efficiency to investigation sampling point greatly.4, the present invention regulates three primary colors rgb value to represent different plant owing to adopting, and color category is many, chooses flexibly, and plant representative color is easy to differentiate, and can obtain selected color by the rgb value (or HSV value) of adjustable colors, simple to operate.5, the present invention can provide the data message of each side as required due to the attribute data library information set up, and as geographic entity, meteorological factor, sick worm plague of rats information etc., considerably increases its using value.6, digitized process of the present invention is easy, flexible, can be realized by the basic operation of ArcGIS.Thus the present invention can be widely used in the practical applications such as grassland vegetation differentiation research, agricultural production and grassland vegetation monitoring resource.
Accompanying drawing explanation
Fig. 1 is vegetation subtype survey area of the present invention and vegetation investigation sampling spot
Fig. 2 is Stipa capillata type distributed areas of the present invention and Stipa capillata distribution plan
Fig. 3 is alfalfa sheep's hay type region of the present invention and alfalfa distribution plan
Fig. 4 is conventional part RGB color table
Fig. 5 is alfalfa of the present invention, sheep's hay type region and sheep's hay distribution plan
Fig. 6 is alfalfa after the present invention regulates transparency, sheep's hay type region and sheep's hay distribution plan
Fig. 7 is alfalfa of the present invention, sheep's hay type region and alfalfa and sheep's hay distribution plan
Fig. 8 is prairie sagewort of the present invention, hidden son grass, Stipa capillata type region and prairie sagewort, hidden son grass, Stipa capillata distribution plan
Fig. 9 is the distribution of survey area of the present invention vegetation subtype and each hypotype plant constitution and distribution plan
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in detail.
The foundation of grassland vegetation hypotype of the present invention and digitized representations method, comprise the following steps:
1) survey area is marked, vegetation investigation is carried out to the investigation sampling point obtained.
As shown in Figure 1, by Google Earth (Google Maps), survey area is marked, utilize ENVI4.7 (The Environment for Visualizing Images, complete Remote Sensing Image Processing) software obtains the polar plot of survey area, set the tone at polar plot subscript and look into sampling point, by ArcGIS (Geographic Information System) with longitude and latitude 2, " (only as example, but being not limited thereto) is unit acquisition vegetation investigation sampling point.In Fig. 1, each round dot represents an investigation sampling point, and its area is 1m
2, investigate in investigation sampling point the kind of vegetation, cover degree, highly, density and biomass.
2) the plant species dominance of vegetation in each investigation sampling point is calculated.
In investigation sampling point, the species dominance SDR of certain plant represents with centigrade, and the computing method of SDR are:
SDR＝[(Y'+C'+D')/3]×100％
Wherein, Y' is the relative biomass of certain plant, and C' is the relative coverage of certain plant, and D' is the relative density of certain plant.In investigation sampling point, the relative biomass Y' of certain plant is:
Wherein, y is certain phytomass; Y is vegetation total biomass in investigation sampling point;
In investigation sampling point, the relative coverage C' of certain plant is:
Wherein, c is certain plant cover degree; C is vegetation total cover-degree in investigation sampling point;
In investigation sampling point, the relative density D' of certain plant is:
Wherein, d is certain plant density, and D is the gross density of vegetation in investigation sampling point.
3) judge the boundary coordinate of each vegetation subtype, draw the vector base map of each vegetation subtype in this survey area.
Judge whether it is identical vegetation subtype according to the floristics of each investigation sampling point and species dominance, judge that whether each investigation sampling point is that the method for identical vegetation subtype comprises:
If 1. there is the species dominance >50% of a Plants in certain investigation sampling point, then there is with this investigation sampling point identical sociales floristic investigation sampling point and all belong to same vegetation subtype, and this vegetation subtype is with a kind of botanical nomenclature, as: Stipa capillata type (as shown in Figure 2);
If 2. there is the species dominance sum >75% of two Plants in certain investigation sampling point, then there is with this investigation sampling point identical sociales floristic investigation sampling point and all belong to same vegetation subtype, this investigation sampling point is named with two Plants according to the species dominance size order of two Plants, as: alfalfa, sheep's hay type (as shown in Figure 3);
3. in all the other situations, descending according to plant species dominance, the investigation sampling point with identical plant species dominance size is classified as same vegetation subtype, and former 3 kinds of dominant plants name this vegetation subtype.
When vegetation subtype changes, the boundary coordinate of actual measurement differ ent vegetation hypotype, by the boundary coordinate of each vegetation subtype input Google Earth and ENVI4.7 program, draws the vector base map of each vegetation subtype in this survey area.
4) build the vector distribution plan of sociales plant in each vegetation subtype, comprise the following steps:
1. the attribute data table of each vegetation subtype is made.
2. after determining the sociales plant constitution of differ ent vegetation hypotype, the difference that gets colors is large, the different colours being beneficial to resolution represents the sociales plant in each vegetation subtype, and the representative color of identical sociales plant in whole survey area in differ ent vegetation hypotype should be consistent.As shown in Figure 4, by regulating three primary colors rgb value (or HSV value) that selected color can be obtained.
3. with the color assignment of the dominance SDR of sociales plant to selection, with the dominance size (as shown in Figure 2) of sociales plant in investigation sampling point different in this vegetation subtype of performance that the depth of color is quantitative, this vegetation subtype vector base map utilize the higher Kriging of ArcGIS precision (Ke Lijin) interpolation method generate the distribution layer (as shown in Fig. 3, Fig. 5) of each sociales plant in each vegetation subtype.
3. transparency adjustment is carried out to the distribution layer of each sociales plant: the uncomfortable joint transparency of distribution layer of the plant that in each vegetation subtype, species dominance is maximum; The transparency of the distribution layer of all the other sociales plants is according to its average species dominance in this vegetation subtype
carry out regulating (as shown in Figure 6), transparency
wherein average species dominance
for the mean value of the species dominance of this sociales plant in investigation sampling points all in this vegetation subtype.
4. according to the order that transparency is ascending, the distribution layer of sociales plant different in each vegetation subtype is superposed, what namely transparency was little is placed on internal layer, what transparency was large is placed on skin, like this by the outer distribution situation (as shown in Figure 6) that can observe internal layer sociales plant.
5) by step 4) in the vector distribution plan of each vegetation subtype that obtains put together, the final vector distribution plan (as shown in Figure 9) generating each sociales plant constitution and distribution in vegetation subtype distribution and each vegetation subtype in display survey area.
Below for the vegetation investigation in In Xilingol League In Inner Mongolia Xilinhot City western suburb transformer station region in 2014, the foundation of grassland vegetation hypotype of the present invention and digitized representations method are described in detail.
1) survey area is marked, vegetation investigation is carried out to the investigation sampling point obtained.
As shown in Figure 1, by Google Earth (Google Maps), survey area is marked, obtained the polar plot of survey area by ENVI4.7 software registration, set the tone at polar plot subscript and look into sampling point, by ArcGIS with longitude and latitude 2 " for unit obtains vegetation investigation sampling point.In Fig. 1, each round dot represents an investigation sampling point, and its area is 1m
2, investigate in investigation sampling point the kind of vegetation, cover degree, highly, density and biomass.
2) the plant species dominance of vegetation in each investigation sampling point is calculated.
3) by data survey and observation, obtaining being divided into five vegetation subtypes in this survey area, is Stipa capillata type respectively, sheep's hay type, Sievers wormwood type, alfalfa, sheep's hay type, prairie sagewort, hidden son grass, Stipa capillata type.According to vegetation subtype change, the geographic coordinate on actual measurement differ ent vegetation hypotype border, is input to Google Earth and ENVI4.7 program, obtains the vector base map of each vegetation subtype in this survey area.
4) the vector distribution plan of sociales plant in each vegetation subtype is built.
According to division and the nomenclature principle of differ ent vegetation hypotype, the acquisition of its vector distribution plan is divided into following three kinds of situations:
1. Stipa capillata type is drawn, sheep's hay type, the vector distribution plan of Sievers wormwood type vegetation subtype.
For Stipa capillata type vegetation subtype, first set up its attribute database (as shown in table 1), because sampling point is more, only list part sampling point in table 1, the content of form can need to increase according to user's application.Stipa capillata is represented with [RGB, (0,255,0)]; As shown in Figure 2, with the dominance of Stipa capillata to this color assignment, show the size of Stipa capillata dominance in each investigation sampling point with the depth of this color quantitatively, by the Krigging interpolation method that precision in ArcGIS is higher, the vector base map of Stipa capillata type vegetation subtype generates the distribution layer of Stipa capillata; The distribution layer of Stipa capillata does not do transparency process, can obtain the vector distribution plan of Stipa capillata property vegetation subtype.After setting up the attribute database of Sievers wormwood type and sheep's hay type, with [RGB, (255,0,0)] represent sheep's hay, with [RGB, (255,225,0)] represent Sievers wormwood, adopt same procedure to process Sievers wormwood type and sheep's hay type vegetation subtype, obtain the vector distribution plan in Sievers wormwood type and sheep's hay type hypotype region.
Table 1 Stipa capillata type meadow attribute list
2. the vector distribution plan of alfalfa, sheep's hay type vegetation subtype is drawn.
Set up the attribute database (as shown in table 2) of alfalfa, sheep's hay type vegetation subtype, because sampling point is more, only list part sampling point in table 2, table content can need to increase according to user's application.Represent alfalfa with [RGB, (160,32,240)], represent sheep's hay (1. middle sheep's hay representative color is identical with step) with [RGB, (255,0,0)].As shown in Fig. 3, Fig. 5, in the vector base map of alfalfa, sheep's hay type vegetation subtype, generate the distribution layer of alfalfa and the distribution layer of sheep's hay respectively.The distribution layer of alfalfa does not do transparency process, according to the average superiority degree of sheep's hay in alfalfa, sheep's hay type vegetation subtype
regulate the transparency of sheep's hay distribution layer
i.e. P=61% (as shown in Figure 6).As shown in Figure 7, the distribution layer of alfalfa and the distribution layer of sheep's hay are superposed, the alfalfa distribution layer not doing transparency process is placed on internal layer, the sheep's hay distribution layer of adjusted transparency is placed on skin and carries out map overlay, by Fig. 7 can be directly perceived, quantitative observe plant constitution in alfalfa, sheep's hay type vegetation subtype and distribution.
Table 2 alfalfa, sheep's hay type meadow attribute list
3. the digital vector distribution plan of prairie sagewort, hidden son grass, Stipa capillata type vegetation subtype is drawn.
Set up the attribute database (as shown in table 3) of prairie sagewort, hidden son grass, Stipa capillata type vegetation subtype, because sampling point is more, only list part sampling point in table 3, the content in form can need to increase according to the application of user.Represent prairie sagewort with [RGB, (255,97,0)], represent hidden son grass with [RGB, (0,0,255)], represent Stipa capillata (color synchronization suddenly 1. in Stipa capillata solid colour) with [RGB, (0,255,0)].Prairie sagewort distribution layer is generated respectively, hidden son grass distribution layer and Stipa capillata distribution layer in the vector base map of prairie sagewort, hidden son grass, Stipa capillata type vegetation subtype.Dominance maximum prairie sagewort distribution layer do not do transparency process, to hidden son grass distribution layer (P=65%)) and Stipa capillata distribution layer (P=77%) carry out transparency adjustment, 2. the distribute control method of layer of middle sheep's hay is identical for control method and step.As shown in Figure 8, the order ascending according to transparency outwards carries out map overlay successively, namely the distribution layer of prairie sagewort is at innermost layer, the distribution layer of hidden son grass is in middle layer, the distribution layer of Stipa capillata superposes at outermost layer, obtain the vector distribution plan of prairie sagewort, hidden son grass, Stipa capillata type vegetation subtype, by Fig. 8 can be directly perceived, quantitative observe prairie sagewort, hidden son grass, the forming and distribute of sociales plant prairie sagewort in Stipa capillata type vegetation subtype, hidden son grass and Stipa capillata.
Table 3 prairie sagewort, hidden son grass, Stipa capillata type meadow attribute list
5) by step 4) in the vector distribution plan of each vegetation subtype that obtains put together, finally generate subtype distribution and each hypotype dominant plant in display survey area and form and the digital vector distribution plan of distribution.As shown in Figure 9, can be directly perceived, quantitative observe that in survey area, grassland vegetation can be divided into 5 hypotypes: a-quadrant is Stipa capillata type, take Stipa capillata as Dominant Species; B region is prairie sagewort, hidden son grass, Stipa capillata type, and plant dominance size is the careless > Stipa capillata of the hidden son of prairie sagewort >; C region is Sievers wormwood type, take Sievers wormwood as Dominant Species; D region is alfalfa, sheep's hay type, and plant dominance size is alfalfa > sheep's hay; E region is sheep's hay type, take sheep's hay as Dominant Species.
The various embodiments described above are only for illustration of the present invention, and every equivalents of carrying out on the basis of technical solution of the present invention and improvement, all should not get rid of outside protection scope of the present invention.
Claims (4)
1. the foundation of grassland vegetation hypotype and a digitized representations method, it comprises the following steps:
1) survey area is marked, vegetation investigation is carried out to the investigation sampling point obtained;
2) the plant species dominance of vegetation in each investigation sampling point is calculated;
3) judge whether it is identical vegetation subtype according to the floristics of each investigation sampling point and species dominance; When vegetation subtype changes, the boundary coordinate of actual measurement differ ent vegetation hypotype, by the boundary coordinate of each vegetation subtype input Google Earth and ENVI4.7 program, obtains the vector base map of each vegetation subtype in this survey area;
4) the attribute data table of each vegetation subtype is built, different sociales plant is represented with different colours, according to the dominance of different sociales plant to color assignment, each vegetation subtype vector base map generates the distribution layer of each sociales plant, carries out superposing the vector distribution plan obtaining each vegetation subtype after transparency regulates to each distribution layer;
5) by step 4) in the digital vector figure of each vegetation subtype that obtains put together, the final vector distribution plan generating each sociales plant constitution and distribution in vegetation subtype distribution and each vegetation subtype in display survey area.
2. the foundation of a kind of grassland vegetation hypotype as claimed in claim 1 and digitized representations method, is characterized in that: described step 2) in, the species dominance SDR of plant is:
SDR＝[(Y'+C'+D')/3]×100％；
Wherein, Y' is the relative biomass of certain plant, and C' is the relative coverage of certain plant, and D' is the relative density of certain plant; In investigation sampling point, the relative biomass Y' of certain plant is:
Wherein, y is certain phytomass; Y is vegetation total biomass in investigation sampling point;
In investigation sampling point, the relative coverage C' of certain plant is:
Wherein, c is certain plant cover degree; C is vegetation total cover-degree in investigation sampling point;
In investigation sampling point, the relative density D' of certain plant is:
Wherein, d is certain plant density, and D is the gross density of vegetation in investigation sampling point.
3. the foundation of a kind of grassland vegetation hypotype as claimed in claim 1 and digitized representations method, is characterized in that: described step 3) in, judge that whether each investigation sampling point is that the method for identical vegetation subtype comprises:
1) if there is the species dominance >50% of a Plants in certain investigation sampling point, then there is with this investigation sampling point identical sociales floristic investigation sampling point and all belong to same vegetation subtype, and this vegetation subtype is with a kind of botanical nomenclature;
2) if there is the species dominance sum >75% of two Plants in certain investigation sampling point, then there is with this investigation sampling point identical sociales floristic investigation sampling point and all belong to same vegetation subtype, name this investigation sampling point according to the species dominance size order of two Plants with two Plants;
3) in all the other situations, descending according to plant species dominance, the investigation sampling point with identical plant species dominance size is classified as same vegetation subtype, and former 3 kinds of dominant plants name this vegetation subtype.
4. the foundation of a kind of grassland vegetation hypotype as claimed in claim 1 and digitized representations method, is characterized in that: described step 4) in, the method for drafting of the digital vector distribution plan of each vegetation subtype is:
1) the attribute data table of each vegetation subtype is made; After determining the sociales plant constitution of differ ent vegetation hypotype, the difference that gets colors respectively is large, the different colours being beneficial to resolution represents the sociales plant in each vegetation subtype, and the representative color of identical sociales plant in whole survey area in differ ent vegetation hypotype is consistent;
2) with the dominance SDR of sociales plant to color assignment, with the dominance size of sociales plant in investigation sampling point different in this vegetation subtype of performance that the depth of color is quantitative, this vegetation subtype vector base map utilize Krigging interpolation method to generate the distribution layer of each sociales plant in each vegetation subtype;
3) transparency adjustment is carried out to the distribution layer of each sociales plant: the uncomfortable joint transparency of distribution layer of the plant that in each vegetation subtype, species dominance is maximum; The transparency of the distribution layer of all the other sociales plants is according to its average species dominance in this vegetation subtype
regulate, transparency
wherein average species dominance
for the mean value of the dominance of this sociales plant in investigation sampling points all in this vegetation subtype;
4) according to the order that transparency is ascending, the distribution layer of sociales plant different in each vegetation subtype is superposed, what namely transparency was little is placed on internal layer, and what transparency was large is placed on skin, like this by the outer distribution situation that can observe internal layer sociales plant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410397373.7A CN104217103B (en) | 2014-08-13 | 2014-08-13 | A kind of foundation of grassland vegetation hypotype and digitized representations method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410397373.7A CN104217103B (en) | 2014-08-13 | 2014-08-13 | A kind of foundation of grassland vegetation hypotype and digitized representations method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104217103A true CN104217103A (en) | 2014-12-17 |
| CN104217103B CN104217103B (en) | 2017-03-29 |
Family
ID=52098585
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410397373.7A Active CN104217103B (en) | 2014-08-13 | 2014-08-13 | A kind of foundation of grassland vegetation hypotype and digitized representations method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104217103B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105409612A (en) * | 2015-10-26 | 2016-03-23 | 黑龙江省科学院自然与生态研究所 | Method for screening dominant species of water purification plants for riparian wetlands |
| CN108564021A (en) * | 2018-04-08 | 2018-09-21 | 兰州大学 | A method of deserta cover degree is extracted based on digital photo |
| CN109145072A (en) * | 2018-08-10 | 2019-01-04 | 中国农业科学院农业资源与农业区划研究所 | A kind of grassland biomass remote sensing monitoring partition method and device |
| CN110706142A (en) * | 2019-09-24 | 2020-01-17 | 华南农业大学 | Boundary determining method for ecological system boundary of yin-yang mountain |
| CN113111672A (en) * | 2021-04-13 | 2021-07-13 | 中国科学院东北地理与农业生态研究所 | Method for judging true wetland plants |
| CN113537174A (en) * | 2021-09-16 | 2021-10-22 | 中国科学院烟台海岸带研究所 | Coral reef habitat survey video analysis method |
| US11206771B2 (en) | 2017-03-31 | 2021-12-28 | Nec Corporation | Vegetation effect calculation device, vegetation effect calculation system, and storage medium storing vegetation effect calculation program |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646155A (en) * | 2013-12-26 | 2014-03-19 | 中国农业科学院植物保护研究所 | RGB (red, green and blue) chromatographic overlay map digitalized display method for grassland vegetation |
-
2014
- 2014-08-13 CN CN201410397373.7A patent/CN104217103B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646155A (en) * | 2013-12-26 | 2014-03-19 | 中国农业科学院植物保护研究所 | RGB (red, green and blue) chromatographic overlay map digitalized display method for grassland vegetation |
Non-Patent Citations (4)
| Title |
|---|
| LIANG T G ET.: "A GIS expert system for pastoral agricultural development in Gansu Province, P R China", 《NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH》 * |
| 刘朝阳: "草原蝗虫生态经济阈值参数拟合及模型构建", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
| 徐吉宏等: "基于ArcGIS的天祝草地综合顺序分类研究", 《草业科学》 * |
| 陈芙蓉等: "不同干扰对黄土区典型草原物种多样性和生物量的影响", 《生态学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105409612A (en) * | 2015-10-26 | 2016-03-23 | 黑龙江省科学院自然与生态研究所 | Method for screening dominant species of water purification plants for riparian wetlands |
| US11206771B2 (en) | 2017-03-31 | 2021-12-28 | Nec Corporation | Vegetation effect calculation device, vegetation effect calculation system, and storage medium storing vegetation effect calculation program |
| CN108564021A (en) * | 2018-04-08 | 2018-09-21 | 兰州大学 | A method of deserta cover degree is extracted based on digital photo |
| CN109145072A (en) * | 2018-08-10 | 2019-01-04 | 中国农业科学院农业资源与农业区划研究所 | A kind of grassland biomass remote sensing monitoring partition method and device |
| CN109145072B (en) * | 2018-08-10 | 2020-08-11 | 中国农业科学院农业资源与农业区划研究所 | Remote sensing monitoring partition method and device for grassland biomass |
| CN110706142A (en) * | 2019-09-24 | 2020-01-17 | 华南农业大学 | Boundary determining method for ecological system boundary of yin-yang mountain |
| CN110706142B (en) * | 2019-09-24 | 2023-05-16 | 华南农业大学 | Boundary determining method for yin-yang mountain ecological system boundary |
| CN113111672A (en) * | 2021-04-13 | 2021-07-13 | 中国科学院东北地理与农业生态研究所 | Method for judging true wetland plants |
| CN113537174A (en) * | 2021-09-16 | 2021-10-22 | 中国科学院烟台海岸带研究所 | Coral reef habitat survey video analysis method |
| CN113537174B (en) * | 2021-09-16 | 2021-12-28 | 中国科学院烟台海岸带研究所 | Coral reef habitat survey video analysis method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104217103B (en) | 2017-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104217103A (en) | Method for building and digitally expressing grassland vegetation subtypes | |
| Homer et al. | Completion of the 2011 National Land Cover Database for the conterminous United States–representing a decade of land cover change information | |
| Li et al. | An automatic approach for urban land-cover classification from Landsat-8 OLI data | |
| CN108153861B (en) | River mouth fishery resource cluster distribution analysis method based on GIS | |
| Li et al. | Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data | |
| Ippoliti et al. | Defining ecological regions in Italy based on a multivariate clustering approach: A first step towards a targeted vector borne disease surveillance | |
| CN108959661A (en) | A kind of soil nutrient grade separation drawing generating method and its Accuracy Assessment based on 3S technology | |
| CN109977991A (en) | Forest resourceies acquisition method based on high definition satellite remote sensing | |
| CN111222536A (en) | City green space information extraction method based on decision tree classification | |
| Mishra et al. | Urban heat island in Kathmandu, Nepal: Evaluating relationship between NDVI and LST from 2000 to 2018 | |
| CN103955909A (en) | Method and system for manufacturing thematic map by fusing images based on MapGISK9 | |
| Kajisa et al. | Object-based forest biomass estimation using Landsat ETM+ in Kampong Thom Province, Cambodia | |
| Tormos et al. | Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data | |
| Wondie et al. | Modelling the dynamics of landscape transformations and population growth in the highlands of Ethiopia using remote-sensing data | |
| CN113793023A (en) | Regional green development index evaluation method based on satellite remote sensing | |
| Simms et al. | Image segmentation for improved consistency in image-interpretation of opium poppy | |
| Ngunjiri et al. | Predicting soil types and soil properties with limited data in the Uasin Gishu Plateau, Kenya | |
| CN103646155A (en) | RGB (red, green and blue) chromatographic overlay map digitalized display method for grassland vegetation | |
| Jande et al. | Assessment of land use and land cover changes and urban expansion using remote sensing and GIS in Gboko, Benue State, Nigeria | |
| Thorat et al. | Estimation of crop and forest areas using expert system based knowledge classifier approach for Aurangabad district | |
| Serrano et al. | GIS design application for “Sierra Morena Honey” designation of origin | |
| CN109460700B (en) | Crop classification-oriented remote sensing data processing method and device | |
| Mohammadi | A Review on GIS in Irrigation and Water Management | |
| Hu et al. | Multi-Source Data Interpretation For Field Scale Precision Management In Healthcare Industry. | |
| Walsh et al. | Approaches for linking people, place, and environment for human dimensions research |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |