CN116310798A - High-precision remote sensing estimation method for reasonable livestock loading of natural grasslands - Google Patents
High-precision remote sensing estimation method for reasonable livestock loading of natural grasslands Download PDFInfo
- Publication number
- CN116310798A CN116310798A CN202310106724.3A CN202310106724A CN116310798A CN 116310798 A CN116310798 A CN 116310798A CN 202310106724 A CN202310106724 A CN 202310106724A CN 116310798 A CN116310798 A CN 116310798A
- Authority
- CN
- China
- Prior art keywords
- grazing
- grasslands
- grassland
- sandy
- research area
- 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
- 244000144972 livestock Species 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000009304 pastoral farming Methods 0.000 claims abstract description 195
- 244000025254 Cannabis sativa Species 0.000 claims abstract description 91
- 238000011160 research Methods 0.000 claims abstract description 82
- 230000015556 catabolic process Effects 0.000 claims abstract description 25
- 238000006731 degradation reaction Methods 0.000 claims abstract description 25
- 238000006243 chemical reaction Methods 0.000 claims abstract description 10
- 239000002028 Biomass Substances 0.000 claims description 7
- 241001465754 Metazoa Species 0.000 claims description 7
- 239000004459 forage Substances 0.000 claims description 7
- 241000283690 Bos taurus Species 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 4
- 238000001556 precipitation Methods 0.000 claims description 4
- 230000005855 radiation Effects 0.000 claims description 4
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000005520 cutting process Methods 0.000 description 4
- 241001494479 Pecora Species 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 241001464837 Viridiplantae Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Analysis (AREA)
- Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Computational Mathematics (AREA)
- Animal Husbandry (AREA)
- Tourism & Hospitality (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Computation (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Artificial Intelligence (AREA)
- Mining & Mineral Resources (AREA)
- Marine Sciences & Fisheries (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Agronomy & Crop Science (AREA)
- General Business, Economics & Management (AREA)
Abstract
The invention discloses a natural grassland reasonable livestock-carrying high-precision remote sensing estimation method, which is characterized in that the range of a typical natural grassland and a sandy grassland in a research area is obtained through a medium-high spatial resolution remote sensing image, a CASA model is utilized to estimate the lunar scale NPP data of the research area, the lunar actual grassland yield of the research area is estimated through a grassland yield conversion formula, and the grassland balance and grazing utilization space-time modes of the typical natural grassland and the sandy grassland in the research area are respectively constructed by combining ecological characteristics, grazing suitability and degradation and sandy conditions of the research area, so that the annual scale reasonable livestock-carrying quantity and other parameters of the research area are obtained through estimating the available grassland yield of the quaternary pixels of the research area according to the formula more carefully and accurately. Provides complete data and technical support for grass and livestock balancing technology and scientific management and protection of grassland resources on the premise of ecological restoration, and has extremely important reference value for the grass and livestock balancing.
Description
Technical Field
The invention relates to an estimation method, in particular to a method for estimating reasonable livestock carrying capacity of natural grasslands through high-precision remote sensing images, and belongs to the technical field of livestock raising.
Background
Grasslands are the second largest ecological system worldwide, and play an important role in animal husbandry production, biodiversity maintenance, leisure service and the like. Therefore, a scientific method for estimating reasonable livestock load of natural grasslands is urgently needed, namely, the reasonable utilization rate of grasslands is determined according to the seasonal variation and the spatial distribution characteristics aiming at the problems of grassland degradation and grassland desertification, and reasonable livestock load estimation is carried out, so that grassland resources can be effectively and reasonably utilized and protected, and the balance of grasslands and livestock under the premise of ecological restoration is realized.
Disclosure of Invention
In view of the above existing circumstances, the present invention aims to provide a method for reasonably carrying livestock in natural grasslands by combining corresponding model construction based on high-precision remote sensing images and through targeted analysis of grassland desertification and grassland degradation problems, thereby providing complete data and technical support for scientific management and protection of grassland resources, perfecting and developing a grassland livestock balance technology on the premise of ecological restoration, and realizing sustainable use and development of grasslands.
The invention is realized by the following technical scheme:
the method for reasonably carrying high-precision remote sensing estimation of livestock on natural grasslands comprises the following specific steps:
step 1, based on the medium-high spatial resolution remote sensing image of the current growing season, the range of the typical natural grassland and sandy grassland of the research area is obtained by adopting a method combining object-oriented classification and artificial visual interpretation.
And 2, respectively simulating the high-resolution NDVI data in the annual missing months of typical natural grasslands and sandy grasslands in the research area by using a STARFM model based on the month complete low-spatial resolution MODIS NDVI data and the month incomplete medium-high resolution NDVI data.
And 3, based on complete medium-high resolution lunar NDVI data of typical natural grasslands and sandy grasslands in the research area, and combining contemporaneous lunar air temperature, precipitation and solar radiation data and contemporaneous land utilization and/or land coverage data, respectively estimating medium-high spatial resolution lunar scale NPP data of the typical natural grasslands and sandy grasslands in the research area by using a CASA model.
Step 4, respectively estimating the actual grass yield of the moon pixel level of the typical natural grassland and the sandy grassland of the research area through a grass yield conversion formula, wherein the grass yield conversion formula is as follows:
wherein Q represents the actual monthly grass yield of a typical natural grass or sandy grass of the study area; a is the available grass area; y is the ratio of grassland biomass to total biomass, typical natural grassland is 1:3.8, sandy grass is 1:5.25.
step 5, aiming at the degradation condition of typical natural grasslands in a research area, taking the average value of actual grasslands in any three years in the eighties of the twentieth century in the local area as a standard, taking the actual grassland annual grassland actual grassland yield lowering level in the research area as a grassland degradation evaluation index, and calculating the grassland yield change rate:
wherein Q is 1 The annual actual grass yield of the typical natural grassland in the research area, namely the sum of 12 months of actual grass yield; q (Q) 2 The average value of actual grass yield in any three years in the twentieth eighties of the typical natural grassland of the research area; p is the grass yield change rate. And (3) carrying out grassland degradation grade division on typical natural grasslands in the research area according to the grass yield change rate and the national relevant standard, and simultaneously, adopting a pixel bipartite model to estimate pixel-scale vegetation coverage of the sandy grasslands in the research area in the last three years respectively aiming at sandy grasslands in the research area to obtain average vegetation coverage in the last three years, and carrying out sandy grassland sandy grade division on the sandy grasslands in the research area according to the national relevant standard.
Setting 4-5 months of each year as spring grazing period, 6-9 months as summer grazing period and 10-2 nd 3 months as autumn and winter grazing period, constructing typical natural grassland and livestock balance and grazing utilization space-time modes of a research area according to grazing period and grassland degradation grade division, respectively setting quarter grazing utilization rate, constructing the sandy grassland and livestock balance and grazing utilization space-time modes of the research area according to grazing period and sandiness grade division, and respectively setting quarter grazing utilization rate.
Step 7, respectively estimating the quarter pixel level available grass yield of the typical natural grassland and the sandy grassland of the research area according to the set quarter grazing utilization rate, wherein the formula is as follows:
wherein AY is the quarter available grass yield of typical natural grasslands or desertification grasslands in kg/ha; w is the actual yield of the grasslands in the quarters of the typical natural grasslands or sandy grasslands in the research area, wherein the unit is kg/ha, namely the actual yield of the grasslands in the corresponding months is divided and summarized in the quarters; PU is the quarter grazing utilization rate.
Step 8, calculating annual reasonable livestock carrying quantity of typical natural grassland and sandy grassland resources in the research area, wherein the formula is as follows:
wherein CC is the annual reasonable livestock carrying amount of typical natural grasslands or desertification grasslands in the research area, and the unit is bovine units/ha; FI is the daily forage intake in siemens per unit of kg/(day ∙ newton); GD is grazing days;the actual grass yield in spring; />The actual grass yield in summer; />Is the actual grass yield in autumn and winter.
In the step 5, the grassland degradation grade of the typical natural grassland of the research area is divided into: undegraded, mildly degraded, moderately degraded and severely degraded.
In the step 6, the typical natural grassland livestock balance of the research area and grazing are constructed by using a space-time mode, which comprises the following steps:
A. grazing suitability of public welfare forests distributed in typical natural grasslands: the utilization rate of summer grazing is 0.3, and the grass cutting in the forests is carried out in the period of summer grazing, and the grazing is forbidden all the year round.
B. Grazing suitability of undegraded grasslands: the utilization rate of grazing in summer is 0.5, the utilization rate of grazing in autumn and winter is 0.32, and grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
C. Grazing suitability of mildly degraded grasslands: the grazing utilization rate in summer is 0.4, the grazing utilization rate in autumn and winter is 0.24, the grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
D. Grazing suitability of moderately degraded grasslands: the grazing utilization rate in summer is 0.2, the grazing utilization rate in autumn and winter is 0.16, the grazing period in summer and autumn and winter is controlled grazing, the grazing is forbidden in spring, and the slightly degraded grassland is utilized five years later.
E. Grazing suitability of severely degraded grasslands: the pasture is forbidden all year round, and is utilized according to moderate degraded grasslands after three years and according to mild degraded grasslands after five years.
In the step 5, the desertification grades of the desertification grasslands in the research area are divided into: slightly sandy grasslands, moderately sandy grasslands, heavily sandy grasslands, and extremely heavily sandy grasslands.
In the step 6, the desertification grassland and livestock balance and grazing of the research area are constructed by using a space-time mode, and the method comprises the following steps:
A. grazing suitability of public welfare forests distributed in desertification grasslands: the utilization rate of summer grazing is 0.3, and the grass cutting in the forests is carried out in the period of summer grazing, and the grazing is forbidden all the year round.
B. Grazing suitability of slightly sanded grasslands: the utilization rate of grazing in summer is 0.5, the utilization rate of grazing in autumn and winter is 0.32, and grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
C. Grazing suitability of light sandy grasslands: the utilization rate of grazing in summer is 0.4, the utilization rate of grazing in autumn and winter is 0.32, and grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
D. Grazing suitability of moderately sanded grasslands: the utilization rate of grazing in summer is 0.3, the utilization rate of grazing in autumn and winter is 0.24, and the grazing period in summer and autumn and winter adopts a mode of combining grazing with controlling grazing, and grazing is forbidden in spring.
E. Grazing suitability of severe sandy grasslands: forbidden to graze all the year round; the medium sandy grassland is utilized after three years of ecological restoration, the light sandy grassland is utilized after five years of ecological restoration, and otherwise, the pasture is forbidden.
F. Grazing suitability of extremely severe sandy grasslands: forbidden to graze all the year round; the medium sandy grassland is utilized after five years of ecological restoration, the light sandy grassland is utilized after seven years of ecological restoration, otherwise, the pasture is forbidden.
The high-precision remote sensing estimation method for reasonable livestock loading of the natural grassland has the beneficial effects that:
1. the monitoring of large-area natural grassland resources is rapidly and accurately realized by using the medium-high spatial resolution remote sensing image, and the method has the advantages of macroscopic, rapid, labor-saving, time-saving and the like;
2. the method is characterized in that the actual lunar yield of the typical natural grassland or the sandy grassland in the research area is accurately estimated through a grass yield conversion formula, meanwhile, according to the ecological characteristics, grazing suitability and typical natural grassland degradation and sandy grassland sandy conditions of the typical natural grassland and the sandy grassland in the research area, the grass-livestock balance and grazing utilization space-time mode of the typical natural grassland and the sandy grassland are respectively constructed, the available grass yield of the quaternary pixel levels of the typical natural grassland and the sandy grassland in the research area and the reasonable annual livestock carrying amount are estimated through the formula more carefully and accurately, and complete data and technical support are provided for grass-livestock balance technology and scientific management and protection grassland resources on the premise of ecological restoration, so that the method has extremely important reference value on the local grass-livestock balance.
Detailed Description
The high-precision remote sensing estimation method for reasonable livestock loading of the natural grassland is further described in detail as follows:
the invention relates to a high-precision remote sensing estimation method for reasonable livestock loading of natural grasslands, which comprises the following specific steps:
step 1, based on the medium-high spatial resolution remote sensing image of the current growing season, the range of the typical natural grassland and sandy grassland of the research area is obtained by adopting a method combining object-oriented classification and artificial visual interpretation.
Wherein, the remote sensing image with medium and high spatial resolution is Landsat-8/9, GF-1/6 or Sentinel-2. By splicing the medium-high spatial resolution remote sensing images, remote sensing images of typical natural grasslands and sandy grasslands of a research area in a required range can be formed.
And 2, respectively simulating the high-resolution NDVI data in the annual missing months of typical natural grasslands and sandy grasslands in the research area by using a STARFM model based on the month complete low-spatial resolution MODIS NDVI data and the month incomplete medium-high resolution NDVI data.
MODIS (Moderate-resolution Imaging Spectroradiometer) is a short for medium resolution imaging spectrometer, NDVI (NomralizedDifference Vegeattion Idnex) is normalized vegetation index, also called normalized vegetation index. Because medium-high resolution NDVI data is typically month incomplete data, due to time and climate effects, missing month data simulation by STARFM model is required. Of course, the low spatial resolution MODIS NDVI data can generally maintain a full month status and provide for downloading, which is convenient.
And 3, based on complete medium-high resolution lunar NDVI data of typical natural grasslands and sandy grasslands in the research area, and combining contemporaneous lunar air temperature, precipitation and solar radiation data and contemporaneous land utilization and/or land coverage data, respectively estimating medium-high spatial resolution lunar scale NPP data of the typical natural grasslands and sandy grasslands in the research area by using a CASA model.
NPP (Net Primary Productivity) is the net primary productivity of vegetation, i.e., the amount of organic matter accumulated by green plants per unit time per unit area. The medium-high spatial resolution month scale NPP data of typical natural grasslands and sandy grasslands of a research area can be estimated respectively by a CASA model and combining contemporaneous month temperature, precipitation and solar radiation data and contemporaneous land utilization and/or land coverage data. Of course, if desired, annual scale NPP data can be summarized from monthly scale NPP data.
Step 4, respectively estimating the actual grass yield of the moon pixel level of the typical natural grassland and the sandy grassland of the research area by using a grass yield conversion formula, wherein the grass yield conversion formula is as follows:
wherein Q represents the actual monthly grass yield of a typical natural grass or sandy grass of the study area; a is the available grass area; y is the ratio of grassland biomass to total biomass, typical natural grassland is 1:3.8, sandy grass is 1:5.25; and 0.47 is the conversion coefficient of NPP to grassland biomass.
Step 5, aiming at the degradation condition of typical natural grasslands in a research area, taking the average value of actual grasslands in any three years in the eighties of the twentieth century in the local area as a standard, taking the actual grassland annual grassland actual grassland yield lowering level in the research area as a grassland degradation evaluation index, and calculating the grassland yield change rate:
wherein Q is 1 The annual actual grass yield of the typical natural grassland in the research area, namely the sum of 12 months of actual grass yield; q (Q) 2 The average value of actual grass yield in any three years in the twentieth eighties of the typical natural grassland of the research area; p is the grass yield change rate. Grading grassland degradation of typical natural grasslands in a research area according to national related standards and the change rate of grass yield, wherein the grassland degradation grading comprises the following steps: undegraded (-10 < grass yield rate), slightly degraded (-20% < grass yield rate of less than or equal to-10%), moderately degraded (-50% < grass yield rate of less than or equal to-20%) and severeDegradation (the change rate of grass yield is less than or equal to-50%). Meanwhile, aiming at the desertification condition of the desertification grasslands in the research area, respectively estimating the pixel-scale vegetation coverage of the desertification grasslands in the research area in the last three years by adopting a pixel bipartite model to obtain the average vegetation coverage in the last three years, and classifying the desertification grades of the desertification grasslands in the research area according to the national relevant standards, wherein the method comprises the following steps: slightly sandy grasslands (vegetation coverage of 70% or less), slightly sandy grasslands (vegetation coverage of 50% or less)<70 percent of medium sandy grassland (vegetation coverage of 30 percent is less than or equal to)<50 percent of severe desertification grassland (vegetation coverage of 10 percent is less than or equal to)<30%) and extremely severe sandy grasslands (vegetation coverage<10%)。
In the above process, typical natural grasslands are graded for grassland degradation at the rate of change of yield in the case of degradation, and sanded grasslands are graded for grassland sandiness at the vegetation coverage in the case of sandiness. In order to avoid the influence of annual climate fluctuation on the growth of grassland vegetation, the average value of the grassland pixel scale vegetation coverage of the desertification grassland in the latest three years of research areas is adopted as a standard vegetation coverage value to carry out desertification grading, the typical natural grassland degradation condition of the research areas is adopted as a standard by taking the average value of the actual grassland yield of the typical natural grassland in any three years of the research areas in the eighties of the twenty century accepted in China, so that the actual grassland yield lowering level of the typical natural grassland in the current research areas is formed as a grassland degradation evaluation index, and the accuracy, reliability and scientificity of grading are ensured.
Step 6, constructing a grass and livestock balance and grazing utilization space-time mode, wherein 4-5 months are set as spring grazing period, 6-9 months are set as summer grazing period, 10-2 nd year 3 months are set as autumn and winter grazing period each year, and the method comprises the following steps:
1. typical natural grassland livestock balance and grazing are constructed using spatiotemporal patterns as follows:
the concrete explanation is as follows:
1. the public welfare forest area distributed in the typical natural grassland is smaller, and the utilization mode is mainly summer woodland mowing. Grazing suitability of public welfare forests distributed in typical natural grasslands: the utilization rate of summer grazing is 0.3, and the grass cutting in the forests is carried out in the period of summer grazing, and the grazing is forbidden all the year round.
2. The undegraded grassland vegetation has high coverage and vigorous growth. Grazing suitability of undegraded grasslands: the utilization rate of grazing in summer is 0.5, the utilization rate of grazing in autumn and winter is 0.32, and grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
3. The slightly degraded grassland vegetation has high coverage and vigorous growth. Grazing suitability of mildly degraded grasslands: the grazing utilization rate in summer is 0.4, the grazing utilization rate in autumn and winter is 0.24, the grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
4. Moderately degraded grassy vegetation cover is also relatively high, but the degradation is severe. Grazing suitability of moderately degraded grasslands: the grazing utilization rate in summer is 0.2, the grazing utilization rate in autumn and winter is 0.16, the grazing period in summer and autumn and winter is controlled grazing, the grazing is forbidden in spring, and the slightly degraded grassland is utilized five years later.
5. The serious degradation grass yield is large in reduction amplitude, serious in degradation and low in grass yield. Grazing suitability of severely degraded grasslands: the pasture is forbidden all year round, and is utilized according to moderate degraded grasslands after three years and according to mild degraded grasslands after five years.
2. The sandy grassland livestock balance and grazing were constructed using a spatiotemporal pattern as follows:
the concrete explanation is as follows:
1. the public welfare forest Lin Jian in the desertification grassland is large in empty space, and the forage grass yield is high, and the grazing suitability of public welfare forests distributed in the desertification grassland is high: the utilization rate of summer grazing is 0.3, and the grass cutting in the forests is carried out in the period of summer grazing, and the grazing is forbidden all the year round.
2. The slightly sandy grassland forest grass vegetation is luxuriant and is a main grazing utilization type of the sandy grassland, but the grazing intensity management needs to be enhanced to prevent re-desertification. Grazing suitability of slightly sanded grasslands: the utilization rate of grazing in summer is 0.5, the utilization rate of grazing in autumn and winter is 0.32, and grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
3. The light desertification grassland forest grass vegetation is luxuriant and is the main grazing utilization type of the desertification grassland, but the grazing intensity management needs to be enhanced to prevent the desertification again. Grazing suitability of light sandy grasslands: the utilization rate of grazing in summer is 0.4, the utilization rate of grazing in autumn and winter is 0.32, and grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring.
4. The medium-sandy grassland forest grass vegetation coverage is also larger, and the quicksand fixation degree is high. Grazing suitability of moderately sanded grasslands: the utilization rate of grazing in summer is 0.3, the utilization rate of grazing in autumn and winter is 0.24, and the grazing period in summer and autumn and winter adopts a mode of combining grazing with controlling grazing, and grazing is forbidden in spring.
5. The heavy sandy grassland has low vegetation coverage and large quicksand area, and is not suitable for grazing. Grazing suitability of severe sandy grasslands: forbidden to graze all the year round; the medium sandy grassland is utilized after three years of ecological restoration, the light sandy grassland is utilized after five years of ecological restoration, and otherwise, the pasture is forbidden.
6. The extremely severe desertification grassland has low vegetation coverage and large quicksand area, and is not suitable for grazing. Grazing suitability of extremely severe sandy grasslands: forbidden to graze all the year round; the medium sandy grassland is utilized after five years of ecological restoration, the light sandy grassland is utilized after seven years of ecological restoration, otherwise, the pasture is forbidden.
Step 7, respectively estimating the available grass yield of the quarter pixel levels of the typical natural grassland and the sandy grassland in the research area, wherein the formula is as follows:
wherein AY is the quarter available grass yield of typical natural grasslands or desertification grasslands in kg/ha; w is the actual yield of the grasslands in the quarters of the typical natural grasslands or sandy grasslands in the research area, wherein the unit is kg/ha, namely the actual yield of the grasslands in the corresponding months is divided and summarized in the quarters; PU is the quaternary grazing utilization in a spatial-temporal pattern of grass-livestock balance and grazing utilization.
Because grazing utilization rate is divided into quarters, the actual grass yield of the corresponding quarters is obtained by summarizing the actual grass yield of the corresponding months in spring, summer and autumn and winter. And (3) setting the quarter grazing utilization rate of the typical natural grassland and the sandy grassland of the research area in the step (6), further adjusting and controlling the grass yield estimation process, and forming the calculated quarter available grass yield data after the actual grass yields of the corresponding months are summarized and divided into the standard real grass yields more accurately in the quarter. Such as: the actual grass yield in spring is the sum of the actual grass yields of 4 and 5 months; the actual grass yield in summer is the sum of the actual grass yield in four months of 6, 7, 8 and 9.
Step 8, calculating annual reasonable livestock carrying quantity of typical natural grassland and sandy grassland resources in the research area, wherein the formula is as follows:
wherein CC is the annual reasonable livestock carrying amount of typical natural grasslands or desertification grasslands in the research area, and the unit is bovine units/ha; FI is the daily forage intake in siemens per unit of kg/(day ∙ newton); GD is grazing days;the actual grass yield in spring; />The actual grass yield in summer; />Is the actual grass yield in autumn and winter.
Annual available grass yield includes spring available grass yield, summer available grass yield and autumn and winter available grass yield. The annual reasonable livestock carrying amount of typical natural grasslands or sandy grasslands in a research area can be easily calculated by summarizing the ratio of the annual available grass yield to the daily forage grass intake of the Siemendall cattle units and the grazing days, so that the method provides help for reasonable grazing and scientific management. Of course, the annual reasonable livestock carrying amount of the standard sheep unit can be obtained according to a conversion formula of the daily forage grass intake of the Simoda cattle unit and the daily forage grass intake of the standard sheep unit, and the description is omitted here.
The above description is only of the preferred embodiments of the present invention and the description of the technical principles applied is not intended to limit the scope of the invention as claimed, but merely represents the preferred embodiments of the present invention. It will be appreciated by persons skilled in the art that the scope of the invention referred to in the present invention is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Claims (5)
1. The high-precision remote sensing estimation method for reasonable animal carrying capacity of natural grasslands is characterized by comprising the following specific steps of:
step 1, based on the middle-high spatial resolution remote sensing image of the current growing season, obtaining the range of typical natural grasslands and sandy grasslands of a research area by adopting a method combining object-oriented classification and artificial visual interpretation;
step 2, respectively simulating the annual missing month medium-high resolution NDVI data of typical natural grasslands and sandy grasslands in a research area by using a STARFM model based on the month complete low-spatial resolution MODIS NDVI data and the month incomplete medium-high resolution NDVI data;
step 3, based on complete medium-high resolution lunar NDVI data of typical natural grasslands and sandy grasslands in a research area, and combining contemporaneous lunar air temperature, precipitation and solar radiation data and contemporaneous land utilization and/or land coverage data, respectively estimating medium-high spatial resolution lunar scale NPP data of the typical natural grasslands and sandy grasslands in the research area by using a CASA model;
step 4, respectively estimating the actual grass yield of the moon pixel level of the typical natural grassland and the sandy grassland of the research area through a grass yield conversion formula, wherein the grass yield conversion formula is as follows:
wherein Q represents the actual monthly grass yield of a typical natural grass or sandy grass of the study area; a is the available grass area; y is the ratio of grassland biomass to total biomass, typical natural grassland is 1:3.8, sandy grass is 1:5.25;
step 5, aiming at the degradation condition of typical natural grasslands in a research area, taking the average value of actual grasslands in any three years in the eighties of the twentieth century in the local area as a standard, taking the actual grassland annual grassland actual grassland yield lowering level in the research area as a grassland degradation evaluation index, and calculating the grassland yield change rate:
wherein Q is 1 The annual actual grass yield of the typical natural grassland in the research area, namely the sum of 12 months of actual grass yield; q (Q) 2 The average value of actual grass yield in any three years in the twentieth eighties of the typical natural grassland of the research area; p is the grass yield change rate;
carrying out grassland degradation grading on typical natural grasslands in a research area according to the grass yield change rate and the national related standard;
at the same time, the method comprises the steps of,
aiming at the desertification condition of the desertification grasslands of the research area, respectively estimating the pixel scale vegetation coverage of the desertification grasslands of the research area in the last three years by adopting a pixel bipartite model to obtain the average vegetation coverage of the desertification grasslands of the research area in the last three years, and classifying the desertification grades of the desertification grasslands of the research area according to the national relevant standards;
step 6, setting 4-5 months of each year as spring grazing period, 6-9 months as summer grazing period and 10-2 nd 3 months as autumn and winter grazing period, constructing typical natural grassland and livestock balance and grazing utilization space-time modes of a research area according to grazing period and grassland degradation grade division, respectively setting quarter grazing utilization rate, constructing the sandy grassland and livestock balance and grazing utilization space-time modes of the research area according to grazing period and sandiness grade division, and respectively setting quarter grazing utilization rate;
step 7, respectively estimating the quarter pixel level available grass yield of the typical natural grassland and the sandy grassland of the research area according to the set quarter grazing utilization rate, wherein the formula is as follows:
wherein AY is the quarter available grass yield of typical natural grasslands or desertification grasslands in kg/ha; w is the actual yield of the grasslands in the quarters of the typical natural grasslands or sandy grasslands in the research area, wherein the unit is kg/ha, namely the actual yield of the grasslands in the corresponding months is divided and summarized in the quarters; PU is quarter grazing utilization rate;
step 8, calculating annual reasonable livestock carrying quantity of typical natural grassland and sandy grassland resources in the research area, wherein the formula is as follows:
wherein CC is the annual reasonable livestock carrying amount of typical natural grasslands or desertification grasslands in the research area, and the unit is bovine units/ha; FI is the daily forage intake in siemens per unit of kg/(day ∙ newton); GD is grazing days;the actual grass yield in spring; />The actual grass yield in summer; />Is the actual grass yield in autumn and winter.
2. The method for reasonably loading animal capacity high-precision remote sensing estimation of natural grasslands according to claim 1, wherein in the step 5, grassland degradation grades of typical natural grasslands of a research area are divided into: undegraded, mildly degraded, moderately degraded and severely degraded.
3. The method for high-precision remote sensing estimation of reasonable natural grassland animal load according to claim 2, wherein in step 6, the research area is constructed by using a space-time model of typical natural grassland animal balance and grazing, and the method comprises the following steps:
A. grazing suitability of public welfare forests distributed in typical natural grasslands: the utilization rate of summer grazing is 0.3, the mowing of the forests is carried out in the period of summer grazing, and the grazing is forbidden all the year round;
B. grazing suitability of undegraded grasslands: the grazing utilization rate in summer is 0.5, the grazing utilization rate in autumn and winter is 0.32, the grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring;
C. grazing suitability of mildly degraded grasslands: the grazing utilization rate in summer is 0.4, the grazing utilization rate in autumn and winter is 0.24, the grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring;
D. grazing suitability of moderately degraded grasslands: the grazing utilization rate in summer is 0.2, the grazing utilization rate in autumn and winter is 0.16, the grazing period in summer and autumn and winter is controlled grazing, the grazing is forbidden in spring, and the slightly degraded grassland is utilized five years later;
E. grazing suitability of severely degraded grasslands: the pasture is forbidden all year round, and is utilized according to moderate degraded grasslands after three years and according to mild degraded grasslands after five years.
4. The method for high-precision remote sensing estimation of reasonable livestock load of natural grasslands according to claim 1, wherein in the step 5, the desertification level of the desertification grasslands of the research area is divided into: slightly sandy grasslands, moderately sandy grasslands, heavily sandy grasslands, and extremely heavily sandy grasslands.
5. The method for high-precision remote sensing estimation of reasonable grassland animal load according to claim 4, wherein in step 6, the research area is constructed by using a space-time model for balancing and grazing grassland animal load, and the method comprises the following steps:
A. grazing suitability of public welfare forests distributed in desertification grasslands: the utilization rate of summer grazing is 0.3, the mowing of the forests is carried out in the period of summer grazing, and the grazing is forbidden all the year round;
B. grazing suitability of slightly sanded grasslands: the grazing utilization rate in summer is 0.5, the grazing utilization rate in autumn and winter is 0.32, the grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring;
C. grazing suitability of light sandy grasslands: the grazing utilization rate in summer is 0.4, the grazing utilization rate in autumn and winter is 0.32, the grazing is balanced in the grazing period in summer and autumn and winter, and the grazing is forbidden in spring;
D. grazing suitability of moderately sanded grasslands: the grazing utilization rate in summer is 0.3, the grazing utilization rate in autumn and winter is 0.24, and the grazing control mode is adopted in the grazing period in summer and autumn and winter, so that the grazing is forbidden in spring;
E. grazing suitability of severe sandy grasslands: forbidden to graze all the year round; after three years of ecological restoration, the medium-degree sandy grassland is utilized, after five years of ecological restoration, the light-degree sandy grassland is utilized, otherwise, the grazing is forbidden;
F. grazing suitability of extremely severe sandy grasslands: forbidden to graze all the year round; the medium sandy grassland is utilized after five years of ecological restoration, the light sandy grassland is utilized after seven years of ecological restoration, otherwise, the pasture is forbidden.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310106724.3A CN116310798A (en) | 2023-02-13 | 2023-02-13 | High-precision remote sensing estimation method for reasonable livestock loading of natural grasslands |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310106724.3A CN116310798A (en) | 2023-02-13 | 2023-02-13 | High-precision remote sensing estimation method for reasonable livestock loading of natural grasslands |
Publications (1)
Publication Number | Publication Date |
---|---|
CN116310798A true CN116310798A (en) | 2023-06-23 |
Family
ID=86785972
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310106724.3A Pending CN116310798A (en) | 2023-02-13 | 2023-02-13 | High-precision remote sensing estimation method for reasonable livestock loading of natural grasslands |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116310798A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117313961A (en) * | 2023-12-01 | 2023-12-29 | 首都师范大学 | Grassland ecological livestock information space-space cooperative sensing and intelligent prediction platform |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018107245A1 (en) * | 2016-12-16 | 2018-06-21 | The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development | Detection of environmental conditions |
CN109933873A (en) * | 2018-11-02 | 2019-06-25 | 中国林业科学研究院资源信息研究所 | A kind of method of desertificated area Soil Organic Carbon Density high spatial remote sensing estimation |
CN110647786A (en) * | 2018-06-27 | 2020-01-03 | 中国科学院地理科学与资源研究所 | Non-growing season grass and livestock balance assessment method based on unmanned aerial vehicle LIDAR aerial survey technology |
CN112906628A (en) * | 2021-03-15 | 2021-06-04 | 新疆农业大学 | Remote sensing method for quantifying grazing intensity of alpine meadow |
CN113222453A (en) * | 2021-05-28 | 2021-08-06 | 中国农业科学院草原研究所 | Beidou navigation-based natural grassland and livestock balance assessment method, system, equipment and storage medium |
-
2023
- 2023-02-13 CN CN202310106724.3A patent/CN116310798A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018107245A1 (en) * | 2016-12-16 | 2018-06-21 | The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development | Detection of environmental conditions |
CN110647786A (en) * | 2018-06-27 | 2020-01-03 | 中国科学院地理科学与资源研究所 | Non-growing season grass and livestock balance assessment method based on unmanned aerial vehicle LIDAR aerial survey technology |
CN109933873A (en) * | 2018-11-02 | 2019-06-25 | 中国林业科学研究院资源信息研究所 | A kind of method of desertificated area Soil Organic Carbon Density high spatial remote sensing estimation |
CN112906628A (en) * | 2021-03-15 | 2021-06-04 | 新疆农业大学 | Remote sensing method for quantifying grazing intensity of alpine meadow |
CN113222453A (en) * | 2021-05-28 | 2021-08-06 | 中国农业科学院草原研究所 | Beidou navigation-based natural grassland and livestock balance assessment method, system, equipment and storage medium |
Non-Patent Citations (3)
Title |
---|
PENGYAO QIN 等: "Estimation of Grassland Carrying Capacity by Applying High Spatiotemporal Remote Sensing Techniques in Zhenglan Banner, Inner Mongolia, China", 《MDPI》, pages 2 - 5 * |
吕鑫 等: "基于遥感估产的2006―2015年青海果洛与玉树地区草畜平衡分析", 自然资源学报 * |
王瑞杰 等: "呼伦贝尔草原产草量及载畜平衡研究", 东北大学学报(自然科学版) * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117313961A (en) * | 2023-12-01 | 2023-12-29 | 首都师范大学 | Grassland ecological livestock information space-space cooperative sensing and intelligent prediction platform |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Huang et al. | The conservation patterns of grassland ecosystem in response to the forage-livestock balance in North China | |
Hill et al. | Estimation of pasture growth rate in the south west of Western Australia from AVHRR NDVI and climate data | |
Kern et al. | Conservation tillage impacts on national soil and atmospheric carbon levels | |
Ying et al. | Comparison of high-yield rice in tropical and subtropical environments: I. Determinants of grain and dry matter yields | |
Van Rossum | Reproductive success and pollen dispersal in urban populations of an insect-pollinated hay-meadow herb | |
CN116310798A (en) | High-precision remote sensing estimation method for reasonable livestock loading of natural grasslands | |
CN114190264B (en) | Method and system for determining accurate irrigation scheme and terminal equipment | |
Seydack et al. | Large herbivore population performance and climate in a South African semi-arid savanna | |
Defossé et al. | Above-ground phytomass dynamics in a grassland steppe of Patagonia, Argentina. | |
CN116862270B (en) | Vegetation ecological service evaluation method, system and storage medium covered by solar panel | |
Vepsäläinen | Energy crop cultivations of reed canary grass—An inferior breeding habitat for the skylark, a characteristic farmland bird species | |
Gardiner | Macropterism of Roesel's bushcricket Metrioptera roeselii in relation to climate change and landscape structure in eastern England | |
Ma et al. | Experiment of meteorological disaster monitoring on unmanned aerial vehicle | |
Feng et al. | Population density and habitat utilization of ibex (Capra ibex) in Tomur National Nature Reserve, Xinjiang, China | |
Xu et al. | Identifying areas suitable for cultivation of Medicago sativa L. in a typical steppe of Inner Mongolia | |
Zhao et al. | Agricultural adaptation to drought for different cropping systems in southern china under climate change | |
Boone et al. | Large-Scale Movements of Large Herbivores Livestock following changes in seasonal forage supply | |
Keswick | Ecology and morphology of the Kalahari tent tortoise, Psammobates oculifer, in a semi-arid environment | |
Chen et al. | Spatial heterogeneity and diversity analysis of macrovegetation in the Xilingol region, Inner Mongolia, China, using the beta distribution | |
Gallo et al. | Araucaria araucana forest genetic resources in Argentina | |
Hu et al. | Effects of Enclosure on Grassland Ecological and Economic Benefits in Northern China | |
Lansigan | Coping with climate variability and change in rice production systems in the Philippines | |
Loucks et al. | The northeast | |
Bischoff | Aspects of Breeding of the Osprey'Pandion haliaetus' on the Mid-north Coast of New South Wales | |
Shegwe et al. | Land Use/Land Cover Change in the Western Highlands of Cameroon: Case of the Sabga-Bamunka Area (1980-2020) |
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 |