NL2022102B1 - Prediction method for alpine vegetation in a high-altitude permafrost region - Google Patents

Prediction method for alpine vegetation in a high-altitude permafrost region Download PDF

Info

Publication number
NL2022102B1
NL2022102B1 NL2022102A NL2022102A NL2022102B1 NL 2022102 B1 NL2022102 B1 NL 2022102B1 NL 2022102 A NL2022102 A NL 2022102A NL 2022102 A NL2022102 A NL 2022102A NL 2022102 B1 NL2022102 B1 NL 2022102B1
Authority
NL
Netherlands
Prior art keywords
vegetation
parameters
ndvi
precipitation
quarter
Prior art date
Application number
NL2022102A
Other languages
Dutch (nl)
Inventor
Wang Zhiwei
Wu Jiahai
Song Xuelian
Wu Xiaodong
Zhao Lin
Wang Xiaoli
Wang Qian
Zhang Wen
Ruan Xirui
Wang Shuying
Original Assignee
Guizhou Inst Of Prataculture Of Guizhou Academy Of Agricultural Sciences
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Guizhou Inst Of Prataculture Of Guizhou Academy Of Agricultural Sciences filed Critical Guizhou Inst Of Prataculture Of Guizhou Academy Of Agricultural Sciences
Priority to NL2022102A priority Critical patent/NL2022102B1/en
Application granted granted Critical
Publication of NL2022102B1 publication Critical patent/NL2022102B1/en

Links

Classifications

    • 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

  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a prediction method for alpine vegetation in high—altitude permafrost regions, and relates to the field of geography. The method includes the steps that the survey data of characteristics of the vegetation in the permafrost region of Tibetan Plateau is obtained; bio—climatic parameters are obtained; according to an NDVI data set, NDVI parameters are obtained; according to a digital elevation model (DEM), the slope, the slope direction and the profile curvature at each grid pixel element point in the permafrost region of Tibetan Plateau are obtained; the elevations, the slopes, the slope directions and the profile curvatures are used as topography parameters; the parameters with the correlation coefficients larger than 0.8 are selected in the bio—climatic parameters, the NDVI parameters and the topography parameters through a principal component analysis method, and vegetation classification parameters are obtained; according to the survey data of the characteristics of the vegetation, the vegetation classification parameters, climate scenarios data and a climatic system mode, the types of the vegetation in the permafrost region of Tibetan Plateau are obtained through a decision tree classification method. By means of the method, the type distribution prediction, in the mode of ten types of ten categories of climatic systems and four types of climate scenarios in 2050 and 2070, of the vegetation in the permafrost region of Tibetan Plateau can be achieved.

Description

Prediction method for alpine vegetation in a high-altitude permafrost region
TECHNICAL FIELD The invention pertains to the field of geography; to be more specific, a prediction method for alpine vegetation in a high-altitude permafrost region.
BACKGROUND TECHNOLOGY The Tibetan Plateau is a formed by crushing of plates in multiple stages with long-term and complicated geological processes. It is the world's largest high altitude area and is known as "the Third Pole." The plateau is covered with various kinds of alpine vegetation and is dubbed "the Gene Pool of Alpine Plants." With the incessant rising temperature in the past century, the pervasive permafrost on the Tibetan Plateau is now receding at a disturbing pace. Statistics suggest that the phenomenon is showing no sign of mitigation in the 21 century. The receding of the permafrost will cause the thickening of the active layer and change the water and thermal environment in the soil.
These changes will have some critical influences on the growth and distribution of vegetation in the permafrost regions on the plateau. Changes of the vegetation, water, and thermal conditions of the area will react with the underlying surface and the bottom environment of the atmospheric shell and further impact the surrounding regions and the global climate.
As an important natural resource, vegetation is regarded as the sensitive indicator that reflects changes in the biological environment. Apart from being a factor that impacts the underlying surface of the land surface scheme, vegetation is also bestowed with the power to affect the global climate for its interactive relation with carbon. As the warmer climate becomes routine, changes in surface properties occur due to the transformation of vegetation. The changes in surface properties include albedo, the penetration rate of precipitation, and the transpiration and vaporization in the soil. These changes will not only have significant impacts on the cycle rate of hydrology and the climate system, but also transform the water and thermal conditions of the permafrost. Information on the vegetation types is integral when using the earth system modelling (ESM) to analyze the relation between vegetation and conditions of the surface. it is used as the initial parameter for the model such as the surface model, the hydrology model, the biochemistry model, and the global vegetation model.
According to the fourth assessment report (AR4} conducted by the first working group of the Intergovernmental Panel on Climate Change (IPCC), over the past century, the global surface temperature is rising continuously at a speed of 0.78+0.18°C. Global warming will then cause changes in the density, constitution, and distribution of vegetation. Changes happen to the growth of vegetation will eventually react with environmental factors such as the atmosphere and soil.
1
Simulating future climate changes has a significant meaning to the study of the growth and distribution of vegetation, and similarly, the analysis of the future growth condition of vegetation can also serve as strong evidence for the precision of climate prediction. Scrutiny on the current distribution of vegetation in permafrost regions on the Tibetan Plateau is an issue of importance.
Other than providing theoretic support for the research of climate change, a critical database for the carbon recycle of permafrost regions on the Tibetan Plateau and even the globe itself can be provided.
Climatic system mode can effectively and quantitatively show changing patterns of climate systems in number, and is one of the tools that can be used to predict future climate changes. Though we can use biographic models to predict vegetation types; however, the dynamic global vegetation models (DGVM} has generalized vegetation on the Tibetan Plateau into only a few simple types. These types include BIOME1, which is based on biogeography, TEM and CENTURY, which are based on biogeochemistry, MAPSS, BIOME3, and BIOME4, which is based on the combination of the two fields, and the two dynamic models called IBIS and LPJ, which are based on the ecosystem process. Also, there is a classification for grassland called the integrated orderly classification, which is invented by member Ren, Ji-Zhou based on his observation of the process of water and heat. Many vegetation models predict the vegetation on the Tibetan Plateau to be the ones found in tundra, glacier, and ice/polar desert; however, they do not specifically distinguish the endemic vegetation in the alpine environment. In addition, in the five land uses determined by MCD12, MODIS, the proper distinguishment for vegetation types that are endemic to plateaus is absent. We can only see shrublands, grasslands, and savannas in most of the classification projects. There, it is imperative that we find a suitable prediction model for vegetation types on the Tibetan Plateau and use climates system models to predict the vegetation types in alpine permafrost regions.
In a nutshell, current prediction methods for vegetation types are unable to distinguish vegetation endemic to the alpine environment.
SUMMARY OF THE INVENTION An exemplary embodiment of the invention provides a prediction method for alpine vegetation in a permafrost region to solve the problem that current technologies are unable to distinguish vegetation endemic to the alpine environment.
The exemplary embodiment of the invention provides a prediction method for alpine vegetation in a permafrost region, which includes: obtaining survey data of characteristics of the vegetation in the permafrost region of Tibetan Plateau; in which the data covers 490 vegetation spots for characteristics investigation, which are set on alpine swamp meadows, alpine meadows, alpine steppes, alpine deserts, and bare soils with no vegetation; 2obtaining 19 bioclimatic parameters, in which each bioclimatic parameter comprise the annual mean temperature, mean diurnal range, isothermality, temperature seasonality, max temperature of warmest month, min temperature of coldest month, temperature annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter; obtaining four NDVI parameters based on an NDVI dataset, in which each NDVI parameter comprises: the mean value of NDVI, the max value of NDVI, the min value of NDVI, and the range of NDVI; obtaining slope, slope direction and profile curvature at each grid pixel element point in the permafrost region of the Tibetan Plateau based on a digital elevation model (DEM.), and using the elevations, the slopes, the slope directions and the profile curvatures as topography parameters; obtaining 12 vegetation classification parameters by selecting parameters with correlation coefficients larger than 0.8 from the bioclimatic parameters, NDVI parameters, and topography parameters via a principal component analysis method, in which the vegetation classification parameter comprises: the annual mean temperature, isothermality, temperature annual range, annual precipitation, precipitation of driest month, precipitation of wettest quarter, precipitation of driest quarter, precipitation of coldest quarter, the mean value of NDVI, the max value of NDVI, the min value of NDVI, and elevation; and obtaining types of vegetation in the permafrost region of the Tibetan Plateau by using a decision tree classification method to classify the survey data of the characteristics of the vegetation, the vegetation classification parameters, survey data of four climate scenarios, and 10 climatic system modes, in which the data of each climatic system mode comprises: RCP2.6, RCP4.5, RCP6.0, and RCP8.0, and each climatic system mode comprises: BCC-CSM1-1, CCSM4, GISS-E2-R, HadGEM2-A0, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM, MIROC5, MRI-CGCM3, and NorESM1-M.
In a preferred embodiment, the method includes: selecting the NDVI data dataset from 1982 to 2015; obtaining the annual change rate of NDVI in every quadrant point; and obtaining the NDVI parameters in 2050 and 2070 via the NDVI images and the annual change rate of NDVi in 2015.
In a preferred embodiment, the method includes: using the data mining tool called software See5.0 to carry out the classification rule extraction of the decision tree classification method for ten times with the use of 90% of the data of vegetation characteristics investigation; and using the remaining 10% of the data of vegetation characteristics investigation to verify the data precision.
The exemplary embodiment of the invention provides a prediction method for alpine vegetation in a permafrost region, which, when compared with current technologies, contains the following 3benefits: the invention uses public and free products (which reveal vegetation characteristics), data of topography characteristics, and parameters of future climate changes to classify alpine grasslands (alpine swamp meadows, alpine meadows, alpine steppes, and alpine deserts} endemic to the Tibetan Plateau. The invention can realize the prediction of the distribution of different types of vegetation in permafrost regions on the Tibetan Plateau in 2050 and 2070 under four climate scenarios and ten climatic system modes. With successful predictions, the method provided by the present invention can provide an effective prediction model for vegetation in permafrost regions on the Tibetan Plateau. The invention can solve the technical problem of conventional classification methods, which is unable to distinguish vegetation endemic to the alpine environment. Other than providing theoretic support for the research of climate change, a critical database for the carbon recycle of permafrost regions on the Tibetan Plateau and even the globe itself can be provided.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart illustrating a prediction method for alpine vegetation in a high-altitude permafrost region according to an exemplary embodiment provided by the present invention.
FIG. 2 is a schematic diagram illustrating the distribution of vegetation under the scenario of four concentration pathways in permafrost regions on the Tibetan Plateau in 2020 and 2070 with a model called BCC-CSM1-1 an exemplary embodiment provided by the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the following passages, we will elaborate on the exemplary embodiment of the invention in a clear and throughout manner with pictures. Obviously, the exemplary embodiment described here is only partial. Based on the exemplary embodiment of the invention, all non-creative exemplary embodiments conducted by the average technical staff in the field are within the protection scope of the invention.
FIG. 1 is a flow chart illustrating a prediction method for alpine vegetation in a high-altitude permafrost region according to an exemplary embodiment provided by the present invention. As shown in FIG. 1, the method includes: S101: Obtaining survey data of characteristics of the vegetation in a permafrost region on the Tibetan Plateau, in which the data covers 490 vegetation spots for characteristics investigation, which are set on alpine swamp meadows, alpine meadows, alpine steppes, alpine deserts, and bare soils with no vegetation.
What needs to be pointed out here is that there are two aspects to the investigation, one is the vegetation types and the other is the species of plant in the frame quadrant. The investigation also involves calculating the height and vegetation cover. The investigation can provide classification 4standards for future predictions and use current data to set up rules for classifications to be applied to later predictions.
What needs to be pointed out here is that the investigation data of alpine vegetation in a permafrost region on the Tibetan Plateau used in the invention is collected from 2009 to 2013.
S102: Obtaining 19 bioclimatic parameters that include the annual mean temperature, mean diurnal range, isothermality, temperature seasonality, max temperature of warmest month, min temperature of coldest month, temperature annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter.
What needs to be pointed out here is that the data used to assess the current result are the bioclimatic parameters from the WorldClim database. There are 19 bioclimatic parameters, which are listed below in FIG. 1. The resolution is 1 km x 1 km. GIMMS NDVI's dataset collected from 1982 to 2015 is selected and used to calculate four relevant NDVI parameters that can be used in the prediction model for future vegetation in a permafrost region on the Tibetan Plateau. These four parameters are the mean value of NDVI (Bio20}, the max value of NDVI (Bio21), the min value of NDVI (Bio22), and the range of NDVI (BI023{Bl021-Bio22}).
Chart 1: Bioclimatic variable used in the prediction model for future vegetation in permafrost regions on the Tibetan Plateau Bioclimatic Variable WorldClim Code Annual mean temperature Biol Mean diurnal Bio2 range(Mean of monthly [max temp - min temp]) Isothemality ([Bio2/bio7]x100) Bio3 Temperature seasonality Bio4 (Standard deviationx100) Max temperature of warmest month Bio5 Min temperature of coldest month Bio6 5
Temperature annual range (Bio5-Bi06) Bio7 Mean temperature of wettest quarter Bio8 Mean temperature of driest quarter Bio9 Mean temperature of warmest quarter Bio10 Mean temperature of coldest quarter Bio11 Annual precipitation Bio12 Precipitation of wettest month Bio13 Precipitation of driest month Bio14 Precipitation seasonality (coefficient of variation) Bio15 Precipitation of wettest quarter Bio16 Precipitation of driest quarter Bio17 Precipitation of warmest quarter Bio18 Precipitation of coldest quarter Bio19 $103: Obtaining four NDVI parameters based on the NDVI dataset. The four NDVI parameters include the mean value of NDVI, the max value of NDVI, the min value of NDVI, and the range of
NDVI What needs to be pointed out here is that in order to lower uncertainty of future NDVI predictions, the NDVI dataset collected from 1982 to 2015 is selected to calculate the annual change rate of NDVI in every quadrant point. Then, by means of the NDVI images and annual changing statistics of NDVI in 2015, the mean value of NDVI (Bio20), the max value of NDVI (Bio21}, the min value of NDVI (Bio22), and the range of NDVI (BI023[BI021-Bi022}} can be obtained. The resolution is 1/12°x1/12. S104: Obtaining slope, slope direction and profile curvature at each grid pixel element point in the permafrost region of the Tibetan Plateau based on a digital elevation model (DEM) What needs to be pointed out here is that due to the fact that the DEM data lacks long period and continuous dataset collected from the same source, current topography statistics need to be served as the future topography factors to achieve the prediction. These factors include elevation 6
(Bio24), slope (Bio25}, slope direction (Bio26), and profile curvature (Bio27.) All statistics come from statistics centers in the western part of China and the resolution is 1 kmx1 km. S105: Obtaining 12 vegetation classification parameters by selecting parameters with correlation coefficients larger than 0.8 from the bioclimatic parameters, NDVI parameters, and topography parameters via a principal component analysis method, in which the vegetation classification parameters includes the annual mean temperature, isothermality, temperature annual range, annual precipitation, precipitation of driest month, precipitation of wettest quarter, precipitation of driest quarter, precipitation of coldest quarter, the mean value of NDVI, the max value of NDVI, the min value of NDVi, and elevation.
What needs to be pointed out is that we winnow out the 27 current bioclimatic, NDVI, topography variables to 12 by selecting variables with correlation coefficients larger than 0.8 via a principal component analysis method while carrying out the classification rule extraction via the decision tree classification method. The remaining variables used to establish the prediction model for vegetation types are Biol, Bio3, Bio7, Bio12, Bio14, Bio16, Bio17, Bio19, Bio20, Bio21, Bio22, and Bio24, corresponding respectively to the annual mean temperature, isothermality, temperature annual range, annual precipitation, precipitation of driest month, precipitation of wettest quarter, precipitation of driest quarter, precipitation of coldest quarter, the mean value of NDVI, the max value of NDVI, the min value of NDVI, and elevation.
In a preferred embodiment, the method includes: using the data mining tool called software Seeb5.0 to carry out the classification rule extraction of the decision tree classification method for ten times with the use of 90% of the data of vegetation characteristics investigation, and using the remaining 10% of the data of vegetation characteristics investigation to verify the data precision. The data precision of the ten classification are respectively 65 %, 76 %, 76 %, 69 %, 62 %, 79 %, 70 %, 60 %, 63 %, and 67 %. The average precision is 69 %.
What needs to be pointed out here is that the purpose of the principal component analysis method is to delete repeated variables by processing multiple parameters. According to statistics, most of the time, the variables chosen in the data processing to explain natural pattern contain a great extent of repeated "explanatory power." To eliminate these repeated variables, researchers often use dimension reduction statistics methods such as principal component analysis method and factor analysis method.
$106: Obtaining types of vegetation in a permafrost region of the Tibetan Plateau by using the decision tree classification method to classify the data of vegetation characteristics, classification parameters for vegetation, data of four climate scenarios, and 10 climatic system modes, in which the data of climate scenarios include RCP2.6, RCP4.5, RCP6.0, and RCP8.0, and the climatic system modes include BCC-CSM1-1, CCSM4, GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM, 7
MIROC5, MRI-CGCM3, and NorESM1-M.
What needs to be pointed out here is that the reason of using a decision tree classification method (software See5.0} to distinguish vegetation types in a permafrost region on the Tibetan Plateau is that it produces simple, fast, and precise classification results that are pervasively applied to vegetation classification. The operation involves conducting a dimension reduction process to all free data collected. Then we use the variables that have gone through the reduction process and contain less “repeated variables" to set up classification rules. After the vegetation investigation, only a few "points" contain the data of vegetation types and therefore, when we reflect the result to the "surface" of the permafrost regions, we will have to process data from different sensors according to the rules to complete the "inversion" of using "classification rules" from "point" to "surface." What needs to be pointed out here is that the development of scenarios of representative concentration pathways (RCPs) is accomplished in a parallel manner, which can organically combine climate, climate modelings (CMs), and integrated assessment models (IAMs} to provide analysis on the impacts, adaption, fragility, and carbon reduction in the research region. IPCC ARS representative concentration pathway scenarios include the following four RCPs: One high pathway where the radioactive forcing value reaches 8.5 W/m? in 2100 and lasts for a period of time; two steady "pathways" where the radioactive forcing value reaches 6 W/m? and 4.5 W/m? prospectively; one low pathway where the radioactive forcing reaches peak value of 3 W/m? before 2100 and then decline. For the invention, we choose ten climatic system modes in which data of RCP2.6, RCP4.5, RCP6.0, and RCP8.0 can all be found to accomplish the future distribution of vegetation on the Tibetan Plateau. The ten climatic system modes include BCC-CSM1-1, CCSM4, GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM, MIROCS, MRI-CGCM3, and NorESM1-M.
What needs to be pointed out here is that statistics about future temperature and precipitations can be obtained by outputs of the downscaled AOGCM and the final resolution is 1 kmx1 km, including the 19 parameters listed in Chart 1. The years of future prediction are 2050 (the mean value from 2041 to 2060) and 2070 (the mean value from 2061 to 2080.) The exemplary embodiment disclosed by the description is only partial. The technical staff in the field can modify and make changes to the invention so long as these modifications and changes do not digress from its intention and scope. if these modifications and changes belong to the claims of the invention or fall within the scope of the equivalent technology, then the invention intends to incorporate these changes and modifications.
8

Claims (3)

ConclusiesConclusions 1. Voorspellingswerkwijze voor alpiene vegetatie in een hoger gelegen permafrostgebied omvattende: het bekomen van onderzoeksgegevens van vegetatiekenmerken in het permafrostgebied van het Tibetaanse Plateau, waarbij de gegevens 490 vegetatieplaatsen voor het onderzoek van kenmerken beslaan, vegetatieplaatsen die op alpiene moerassige weiden, alpiene weiden, alpiene steppen, alpiene woestijnen en naakte gronden zonder begroeiing gelegen zijn; het bekomen van 19 bioklimatologische parameters, waarbij elke bioklimatologische parameter de gemiddelde jaarlijkse temperatuur, het gemiddelde dagbereik, het isotherme gedrag, de seizoensgevoeligheid van de temperatuur, de maximale temperatuur van de warmste maand, de minimale temperatuur van de koudste maand, het jaarlijkse temperatuurbereik, de gemiddelde temperatuur van het vochtigste kwartaal, de gemiddelde temperatuur van het droogste kwartaal, de gemiddelde temperatuur van het warmste kwartaal, de gemiddelde temperatuur van het koudste kwartaal, de jaarlijkse neerslag, de neerslag van de vochtigste maand, de neerslag van de droogste maand, de seizoensgevoeligheid van de neerslag, de neerslag van het vochtigste kwartaal, de neerslag van het droogste kwartaal, de neerslag van het warmste kwartaal, de neerslag van het koudste kwartaal omvat; het bekomen van vier NDVI-parameters op basis van een NDVI-dataset, waarbij elke NDVI-parameter omvat: de gemiddelde NDVi-waarde, de maximale NDVl-waarde, de minimale NDVI-waarde, en het NDVI-bereik; het bekomen van de helling, de hellingsrichting en de profielkromming op elk punt van een rasterpixelelement in het permafrostgebied van het Tibetaanse Plateau op basis van een digitaal hoogtemodel (DEM - Digital Elevation Model} en het gebruik van de hoogten, de hellingen, de hellingsrichtingen en de profielkrommingen als topografische parameters; het bekomen van 12 vegetatieindelingsparameters door parameters te selecteren met correlatiecoéfficiénten groter dan 0,8 vanuit de bioklimatologische parameters, de NDVi-parameters, en vanuit de topografische parameters bij middel van een methode van de hoofdcomponentenanalyse, waarbij de vegetatieindelingsparameter omvat: de jaarlijkse gemiddelde temperatuur, het isotherme gedrag, het jaarlijkse temperatuurbereik, de jaarlijkse neerslag, de neerslag van de droogste maand, de neerslag van het vochtigste kwartaal, de neerslag van het droogste kwartaal, de neerslag van het koudste kwartaal; de gemiddelde NDVi-waarde, de maximale NDVI-waarde, de minimale NDVI-waarde, en de hoogte; en 9het bekomen van vegetatietypes in het permafrostgebied van het Tibetaanse Plateau bij gebruik van een indelingsmethode met beslissingsboom ten einde de onderzoeksgegevens van de vegetatie- kenmerken, de vegetatieindelingsparameters, onderzoekgegevens van vier klimaatscenario's en van 10 klimaatsysteemmodi in te delen, waarbij de gegevens van elk klimaatsysteemmodus omvatten ; RCP2.6, RCP4.5, RCP6.0 en RCP8.0, en elk klimaatsysteemmodus omvat; BCC-CSM1-1, CCSM4, GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM, MIROC5, MRI-CGCM3, en NorESM1-M.A prediction method for alpine vegetation in an elevated permafrost area comprising: obtaining survey data of vegetation traits in the permafrost region of the Tibetan Plateau, the data covering 490 vegetation sites for the study of traits, vegetation sites on alpine marshy pastures, alpine pastures, alpine steppes, alpine deserts and bare soils are located without vegetation; obtaining 19 bioclimatic parameters, with each bioclimatic parameter the average annual temperature, the average day range, the isothermal behavior, the seasonal sensitivity of the temperature, the maximum temperature of the hottest month, the minimum temperature of the coldest month, the annual temperature range, the average temperature of the wettest quarter, the average temperature of the driest quarter, the average temperature of the hottest quarter, the average temperature of the coldest quarter, the annual precipitation, the precipitation of the wettest month, the precipitation of the driest month, seasonal sensitivity of precipitation, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the hottest quarter, precipitation of the coldest quarter; obtaining four NDVI parameters based on an NDVI data set, each NDVI parameter comprising: the mean NDVi value, the maximum NDV1 value, the minimum NDVI value, and the NDVI range; obtaining the slope, slope direction and profile curvature at each point of a raster pixel element in the permafrost region of the Tibetan Plateau based on a digital elevation model (DEM) and use of the heights, slopes, slope directions and the profile curves as topographic parameters; obtaining 12 vegetation classification parameters by selecting parameters with correlation coefficients greater than 0.8 from the bioclimatic parameters, the NDVi parameters, and from the topographic parameters using a method of the main component analysis, which includes the vegetation classification parameter : the annual average temperature, the isothermal behavior, the annual temperature range, the annual precipitation, the precipitation of the driest month, the precipitation of the wettest quarter, the precipitation of the driest quarter, the precipitation of the coldest quarter; the average NDVi- value, the maximum NDVI value, d e minimum NDVI value and height; and 9 obtaining vegetation types in the permafrost region of the Tibetan Plateau using a decision tree classification method to classify the survey data of the vegetation characteristics, the vegetation classification parameters, survey data of four climate scenarios and of 10 climate system modes, the data of each climate system mode include; RCP2.6, RCP4.5, RCP6.0 and RCP8.0, and each climate system mode includes; BCC-CSM1-1, CCSM4, GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM, MIROC5, MRI-CGCM3, and NorESM1-M. 2. Voorspellingswerkwijze voor alpiene vegetatie in een hoger gelegen permafrostgebied volgens conclusie 1, omvattende het selecteren van de NDVl-datasets van 1982 tot 2015, het bekomen van het jaarlijkse veranderingsritme van NDVI in elk kwadrantpunt; en het bekomen van de NDVi-parameters in 2050 en 2070 door middel van NDVl-beelden en het jaarlijkse NDVi-veranderingsritme in 2015.The alpine vegetation prediction method in a higher permafrost region according to claim 1, comprising selecting the NDV1 data sets from 1982 to 2015, obtaining the annual rate of change of NDVI at each quadrant point; and obtaining the NDVi parameters in 2050 and 2070 by means of NDVl images and the annual NDVi change rate in 2015. 3. Voorspellingswerkwijze voor alpiene vegetatie in een hoger gelegen permafrostgebied volgens conclusie 1, omvattende het gebruik van het dataminingsinstrument dat software See5.0 genoemd is, om de extractie van de indelingsregel van de indelingsmethode met beslissingsboom gedurende tien keren met gebruik van 90 % van de gegevens van het onderzoek naar de vegetatiekenmerken uit te voeren, en het gebruik van de overige 10 % van de gegevens van het onderzoek naar de vegetatiekenmerken om de gegevensnauwkeurigheid na te gaan.The alpine vegetation prediction method in an elevated permafrost region according to claim 1, comprising using the data mining tool called software See5.0 to extract the classification rule of the decision method classification method ten times using 90% of the perform data from the vegetation characteristics study, and use the remaining 10% of the vegetation characteristics study data to verify data accuracy. 1010
NL2022102A 2018-11-30 2018-11-30 Prediction method for alpine vegetation in a high-altitude permafrost region NL2022102B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
NL2022102A NL2022102B1 (en) 2018-11-30 2018-11-30 Prediction method for alpine vegetation in a high-altitude permafrost region

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
NL2022102A NL2022102B1 (en) 2018-11-30 2018-11-30 Prediction method for alpine vegetation in a high-altitude permafrost region

Publications (1)

Publication Number Publication Date
NL2022102B1 true NL2022102B1 (en) 2020-06-26

Family

ID=71515190

Family Applications (1)

Application Number Title Priority Date Filing Date
NL2022102A NL2022102B1 (en) 2018-11-30 2018-11-30 Prediction method for alpine vegetation in a high-altitude permafrost region

Country Status (1)

Country Link
NL (1) NL2022102B1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117571658A (en) * 2024-01-16 2024-02-20 航天宏图信息技术股份有限公司 VBFP-based plateau Gao Hancao ground object waiting period monitoring method and device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117571658A (en) * 2024-01-16 2024-02-20 航天宏图信息技术股份有限公司 VBFP-based plateau Gao Hancao ground object waiting period monitoring method and device
CN117571658B (en) * 2024-01-16 2024-03-26 航天宏图信息技术股份有限公司 VBFP-based plateau Gao Hancao ground object waiting period monitoring method and device

Similar Documents

Publication Publication Date Title
US10831949B2 (en) Nonlinear method for area-wide near surface air temperature precision retrieval
Zhang et al. Using species distribution modeling to delineate the botanical richness patterns and phytogeographical regions of China
Zhao et al. Demand for multi-scale weather data for regional crop modeling
Cui et al. Climate impacts of anthropogenic land use changes on the Tibetan Plateau
Tickle et al. Assessing forest productivity at local scales across a native eucalypt forest using a process model, 3PG-SPATIAL
Mohr Interannual, monthly, and regional variability in the wet season diurnal cycle of precipitation in sub-Saharan Africa
Hashim et al. Effect of land use land cover changes on land surface temperature during 1984–2020: A case study of Baghdad city using landsat image
Chen et al. Changing spatial structure of summer heavy rainfall, using convection‐permitting ensemble
Jia et al. High emissions could increase the future risk of maize drought in China by 60–70%
Pimentel et al. Study of snow dynamics at subgrid scale in semiarid environments combining terrestrial photography and data assimilation techniques
An et al. Investigating the influence of synoptic circulation patterns on regional dry and moist heat waves in North China
Wu et al. Spatial estimation of monthly mean daily sunshine hours and solar radiation across mainland China
Sellevold et al. Surface mass balance downscaling through elevation classes in an Earth system model: Application to the Greenland ice sheet
Mohammed et al. Assessing land cover/use changes in Karbala city (Iraq) using GIS techniques and remote sensing data
Daniel Performance assessment of bias correction methods using observed and regional climate model data in different watersheds, Ethiopia
NL2022102B1 (en) Prediction method for alpine vegetation in a high-altitude permafrost region
Demeke et al. Application of remote sensing for evaluation of land use change responses on hydrology of Muga Watershed, Abbay River Basin, Ethiopia
Reuter et al. Optimisation of relief classification for different levels of generalisation
Banerjee et al. Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India
Han et al. Dynamic change, driving mechanism and spatiotemporal prediction of the normalized vegetation index: a case study from Yunnan Province, China
Schlager et al. Generation of high-resolution wind fields from the WegenerNet dense meteorological station network in southeastern Austria
Häring et al. Regionalizing indicator values for soil reaction in the Bavarian Alps–from averages to multivariate spectra
Cheng et al. 30-year average monthly/1-km climate variables dataset of China (1951-1980, 1981-2010)
Ryazanova et al. Bias-corrected monthly precipitation data over South Siberia for 1979-2019
Hibino et al. Objective estimate of future climate analogues projected by an ensemble AGCM experiment under the SRES A1B scenario

Legal Events

Date Code Title Description
MM Lapsed because of non-payment of the annual fee

Effective date: 20221201