CN102323974A - Method for evaluating degradation degree of alpine grassland based on visual vegetation indexes - Google Patents
Method for evaluating degradation degree of alpine grassland based on visual vegetation indexes Download PDFInfo
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Abstract
The invention discloses a method for evaluating degradation degree of alpine grassland based on visual vegetation indexes by using a grassland degradation index. The grassland degradation index (GDI) comprises an integer bit and a significant figure behind the decimal point and has a structure that GDI=(100-C)*28 percent+(100-P)*39 percent+(70-E)*26 percent+(25-H)*7 percent, wherein C is a cover degree, P is a grass yield, E is an edible grass proportion, and H is an edible grass height. During actual application, the cover degree, the biomass liveweight, the edible grass quantity and the edible grass height of a plant community are only determined, so that the GDI can be calculated. The degradation degree of the alpine grassland is judged according to the GDI, when the GDI is less than 13.9, the grassland is non-degraded grassland; when the GDI is 13.9-33.5, the grassland is lightly-degraded grassland; when the GDI is 33.5-52.4, the grassland is medium-degraded grassland; when the GDI is 52.4-68.4, the grassland is seriously-degraded grassland; and when the GDI is more than 68.4, the grassland is extremely-degraded grassland. According to the invention, the degradation condition information of the grassland can be completely expressed, and the degradation degree of the alpine grassland is quantitatively and qualitatively evaluated.
Description
Technical field
The present invention is a kind of based on the high and cold grassland degeneration degree methods of visual vegetation index evaluation, is a kind of new high and cold grassland degeneration degree evaluation method.This invention belongs to high and cold grassland degeneration degree diagnosis and assessment technique field, is important basic work during meadow child care and deteriorate grassland are administered.Based on the field study data high and cold grassland degeneration situation is carried out rational evaluation, the child care of the corresponding formulation science of ability and recovery planning take effective measures.We can say the directly influence decision-making of rationality of high and cold grassland degeneration situation diagnosis, to such an extent as to influence the ecological safety of the development of extremely frigid zones animal husbandry economy and local and surrounding area.
Background technology
The Qinghai-Tibet Platean is the world's " the 3rd utmost point ", is the important ecological protective screen in the China and even the world.It is the cradle in the Changjiang river, the Yellow River, the Lancang River, is called as " Chinese water tower ".This zone all is coated with high and cold meadow more than 85%, be one of main natural pasture of China, and ecosystem service functions such as water and soil conservation, soil nutrient are kept, gene child care are provided.Yet owing to influences such as climate change and artificial interferences, high and cold grassland degeneration was serious day by day in the last few years.The grassland degeneration area increases with 1.2-7.44% every year.In this case, the recovery on the high and cold meadow of degenerating is particularly urgent and important, and the reasonable formulation that recovers policy then need be the basis with the reliable diagnosis to the grassland degeneration degree.
The diagnosis and evaluation of meadow health status is the prerequisite and the basis of deteriorate grassland ecological recovery.At present existing scholar classifies to Chinese grassland degeneration level and defines.In these researchs, the grassland degeneration grade mainly is the vegetation index that can be investigated by some, like species diversity, and the height of plant, vegetation coverage, meadow net primary productivity and meadow quality etc. are divided; Or divide according to the NDVI single index that remote sensing images extract.But high and cold grassland degeneration degree evaluation method commonly used at present mainly is that degree of degeneration is carried out qualitative description.These methods have played positive effect to the health degree evaluation of high and cold meadow, but also come with some shortcomings, and they can not the high and cold meadow of quantitative description health degree.Up to the present, the visible index of with good grounds grassland degeneration evaluation method that the grassland degeneration degree is quantized not also.Original appraisement system regular meeting occurs carrying out degree of degeneration diagnosis, the phenomenon that evaluation result is different according to each single index.And the weight of each index in the also not clear and definite system of original appraisement system, thereby cause uncertainty for the grassland degeneration degree evaluation.
Therefore; Recognizing that high and cold Grassland ecosystems degree of degeneration assessment indicator system is on the circumscribed basis that is had aspect science, the rationality; The objective characteristics that degeneration is appeared in view of Grassland ecosystems; Set up based on visual grassland vegetation index evaluation method, made up high and cold grassland degeneration index (GDI) and estimate high and cold grassland degeneration degree.Visual grassland vegetation index promptly refers to can be through the direct meadow structure of community index that obtains of field study.
Summary of the invention
The object of the invention is to provide a kind of new method of high and cold grassland degeneration degree evaluation, promptly utilizes visual vegetation index to make up the grassland degeneration index, and high and cold grassland degeneration situation is carried out the analysis-by-synthesis evaluation.Evaluation method is simple and practical, is easy in China's high and cold grassland degeneration degree evaluation work, promote, and can directly go out the grassland degeneration grade according to visual vegetation index evaluation, and can quantize the grassland degeneration degree, is easier to contrast the grassland degeneration situation.
The primary link that high and cold grassland degeneration degree evaluation system is set up is the foundation of index system.For the science and the rationality that guarantee that index is selected, index choice should be followed following principle: the availability of data and representativeness; Index is to changing the susceptibility of response; The independence of index and comparability.Grassland degeneration directly causes meadow group to form and structure changes, and vegetation structure is the direct reflection of ecosystem state.The vegetation investigation is carried out on a large amount of high and cold meadows; And deteriorate grassland vegetation data are carried out correlation analysis; Filter out four vegetation indexs that best embody the grassland degeneration situation, comprising: cover degree, grass yield ratio, edible herbage ratio, edible herbage height are as the index of comprehensive evaluation grassland degeneration degree.During all can investigating in the open air, these four indexs directly obtain, and all can be by accurate quantification.The weight of each index is confirmed according to the ratio that this index of different degradation level descends.
Grassland degeneration is the result that reverse succession takes place vegetation.According to Theory on Succession (no matter being top theory in unit or polynary top theory); For the evaluation of high and cold grassland degeneration degree need be reference system with deteriorate grassland not; The measured data of utilization grassland vegetation situation contrasts with deteriorate grassland system not, thereby analyzes the grassland degeneration degree.
Grassland degeneration index (DGI) makes up as follows:
GDI＝(100-C)×28％+(100-P)×39％+(70-E)×26％+(25-H)×7％
E=E wherein
1/ P
1* 100
C is cover degree (%), and P is grass yield ratio (%), and E is an edible herbage ratio (%), and H is an edible herbage height (%), E
1For surveying edible forage volume, P
1Be the actual measurement grass yield.
Consider the seasonal fluctuation of grass yield, should do following calculating to the grass yield ratio according to different mensuration months:
P＝(P
1×γ
t1)/(P
o×γ
to)×100
P wherein
1Be actual measurement grass yield, P
0Grass yield for selected reference system (not deteriorate grassland).γ
T1Be actual measurement phase grass yield scale-up factor, γ
T0For selected reference system t grass yield scale-up factor during the month, when t is γ=4 May; When t is June, γ=2; When t is in 8-10 during the month, γ=1.
Only need cover degree, biomass, edible forage volume and the edible herbage height of the high and cold meadow of practical measurement plant community in the practical application; And measure and select reference system (not deteriorate grassland) grass yield; Can calculate according to above-mentioned formula, draw the grassland degeneration index, to estimate its degree of degeneration.The concrete criteria for classifying is as shown in table 1.This evaluation method can not only be divided into five types with the appearance ground of being investigated, and promptly degenerates, slightly degenerates, moderate is degenerated, severe is degenerated and extremely degeneration, the difference of the degree of degeneration between can also contrasting not likewise more accurately according to the GDI index.
This index is characterised in that and has quantized high and cold grassland degeneration degree, is used in four visual indexs that can directly obtain in the field study, calculates grassland degeneration index (GDI).Compare with evaluation index before, the GDI index has the following advantages:
(1) this method adopts four visual indexs that can directly obtain through field study; That is: cover degree, grass yield ratio, edible herbage ratio, edible herbage are highly; The grassland degeneration degree is estimated; These four indexs are easy to obtain and make that this index is easier in practical application, accepted, and can effectively promote it to bring into play its function in practice;
(2) this index considers that for the first time the season of meadow structure of community is dynamic, investigates period of living in difference according to vegetational type and converts, thereby the grassland degeneration degree is carried out unified evaluation;
(3) the grassland degeneration degree that quantizes for the first time of this method can be diagnosed the health status on meadow more accurately.
Table 1 is according to the criteria for classifying of GDI index to the grassland degeneration degree
Embodiment
The present invention proposes a kind of brand-new high and cold grassland degeneration degree aggregative index evaluation assessment, and it specifically is applied in the health status comprehensive evaluation of high and cold meadow.Use two kinds of methods of original evaluation criterion and comprehensive grassland degeneration index to estimate grassland degeneration degree result and see table 2.Through present embodiment, further specify grassland degeneration index among the present invention can expressed intact the growth information on high and cold meadow, to overall meadow health status can qualitative evaluation also can quantitative evaluation; Neither can remedy the not confirmability of original evaluation criterion, can make rational evaluation to high and cold meadow health status again, for the improvement of high and cold deteriorate grassland and recover policy making basic data is provided because of indivedual relatively poor negative meadows of index health status.
Comprehensive health index assessment method of the present invention has overcome the deficiency of evaluation method at present commonly used, and computing method are simple, and analysis result is directly perceived, and evaluation conclusion is reasonable, is easy in the health degree appraisal of high and cold meadow, promote.
Table 2 practical implementation case
Annotate:---expression can't adapt to appraisement system fully
Claims (2)
1. one kind based on the high and cold grassland degeneration degree methods of visual vegetation index evaluation; It is characterized in that can be through the visual grassland vegetation information of field study acquisition based on one group; Comprise height, grass yield, edible forage volume, edible herbage average height; Make up the grassland degeneration index, estimate high and cold grassland degeneration degree according to the exponential size that calculates.Described grassland degeneration index GDI by integer-bit and radix point after a position effective digital form, its structure is: GDI=(100-C) * 28%+ (100-P) * 39%+ (70-E) * 26%+ (25-H) * 7%.Wherein C is a cover degree, and P is the grass yield ratio, and E is edible herbage ratio, and H is edible herbage height.
2. high and cold grassland degeneration degree aggregative index evaluation method according to claim 1 is characterized in that, for high and cold meadow, its GDI index is less than 13.9 o'clock, and the meadow is deteriorate grassland not; When the GDI index is between 13.9-33.5, be slight deteriorate grassland; When the GDI index is between 33.5-52.4, be the moderate deteriorate grassland; When the GDI index is between 52.4-68.4, be the severe deteriorate grassland; When the GDI index is deteriorate grassland extremely greater than 68.4 the time.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2536056C2 (en) * | 2013-02-01 | 2014-12-20 | Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Поволжский государственный технологический университет" | Method of analysis of species composition of meadow grass on dynamics of mass of sample parts |
| CN109845589A (en) * | 2019-03-18 | 2019-06-07 | 兰州大学 | A kind of stage division of dogstail/Trifolium repense Grassland degradation degree and application |
| CN110132343A (en) * | 2018-02-02 | 2019-08-16 | 中国科学院寒区旱区环境与工程研究所 | A kind of measuring method of high and cold upland meadow degree of degeneration |
| CN110245867A (en) * | 2019-06-18 | 2019-09-17 | 青海大学 | A kind of grassland degeneration stage division based on bp neural network |
| CN110348108A (en) * | 2019-07-08 | 2019-10-18 | 青海大学 | A method of evaluation Grassland degradation degree |
| CN112070152A (en) * | 2020-09-07 | 2020-12-11 | 中国农业科学院农业环境与可持续发展研究所 | Evaluation method for grazing recovery effect of degenerated alpine meadow |
| CN112381288A (en) * | 2020-11-13 | 2021-02-19 | 西北民族大学 | Ecological management system for grassland in alpine regions |
| CN114639012A (en) * | 2022-02-10 | 2022-06-17 | 成都理工大学 | Grassland degradation evaluation method based on unmanned aerial vehicle hyperspectral remote sensing |
| CN115294460A (en) * | 2022-10-08 | 2022-11-04 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN116012733A (en) * | 2022-12-14 | 2023-04-25 | 兰州大学 | Method for repairing severe degradation alpine grassland bare spot by using species combination of native grass |
| CN116431952A (en) * | 2023-03-22 | 2023-07-14 | 中国地质大学（北京） | Grassland ecological monitoring method and system based on artificial intelligence |
-
2011
- 2011-06-08 CN CN201110151084A patent/CN102323974A/en active Pending
Non-Patent Citations (3)
| Title |
|---|
| L.WEN .ETC: "The construction of Grassland Degradation Index for Alpine Meadow in Qinghai-Tibetan Plateau", 《PROCEDIA ENVIRONMENTAL SCIENCES》 * |
| 崔庆虎等: "青藏高原草地退化原因评述", 《草业科学》 * |
| 谢高地等: "青藏高原高寒草地生态系统服务价值评估", 《山地学报》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2536056C2 (en) * | 2013-02-01 | 2014-12-20 | Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Поволжский государственный технологический университет" | Method of analysis of species composition of meadow grass on dynamics of mass of sample parts |
| CN110132343A (en) * | 2018-02-02 | 2019-08-16 | 中国科学院寒区旱区环境与工程研究所 | A kind of measuring method of high and cold upland meadow degree of degeneration |
| CN110132343B (en) * | 2018-02-02 | 2020-08-07 | 中国科学院西北生态环境资源研究院 | Method for measuring degradation degree of grassland in alpine mountain region |
| CN109845589A (en) * | 2019-03-18 | 2019-06-07 | 兰州大学 | A kind of stage division of dogstail/Trifolium repense Grassland degradation degree and application |
| CN110245867A (en) * | 2019-06-18 | 2019-09-17 | 青海大学 | A kind of grassland degeneration stage division based on bp neural network |
| CN110348108A (en) * | 2019-07-08 | 2019-10-18 | 青海大学 | A method of evaluation Grassland degradation degree |
| CN112070152B (en) * | 2020-09-07 | 2023-11-21 | 中国农业科学院农业环境与可持续发展研究所 | Evaluation method for forbidden pasture recovery effect of degraded alpine meadow |
| CN112070152A (en) * | 2020-09-07 | 2020-12-11 | 中国农业科学院农业环境与可持续发展研究所 | Evaluation method for grazing recovery effect of degenerated alpine meadow |
| CN112381288A (en) * | 2020-11-13 | 2021-02-19 | 西北民族大学 | Ecological management system for grassland in alpine regions |
| CN112381288B (en) * | 2020-11-13 | 2022-07-05 | 西北民族大学 | Ecological management system for grassland in alpine regions |
| CN114639012A (en) * | 2022-02-10 | 2022-06-17 | 成都理工大学 | Grassland degradation evaluation method based on unmanned aerial vehicle hyperspectral remote sensing |
| CN115294460B (en) * | 2022-10-08 | 2023-01-17 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN115294460A (en) * | 2022-10-08 | 2022-11-04 | 杭州领见数字农业科技有限公司 | Method for determining degradation degree of phyllostachys praecox forest, medium and electronic device |
| CN116012733A (en) * | 2022-12-14 | 2023-04-25 | 兰州大学 | Method for repairing severe degradation alpine grassland bare spot by using species combination of native grass |
| CN116012733B (en) * | 2022-12-14 | 2023-09-29 | 兰州大学 | Method for repairing degenerated alpine grassland bare spot by utilizing species combination of native grass |
| CN116431952A (en) * | 2023-03-22 | 2023-07-14 | 中国地质大学（北京） | Grassland ecological monitoring method and system based on artificial intelligence |
| CN116431952B (en) * | 2023-03-22 | 2024-01-30 | 中国地质大学（北京） | Grassland ecological monitoring method and system based on artificial intelligence |
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