CN103258126A - Wetland landscape surface layer cold and wet climatic element GIS space simulation method based on remote sensing data - Google Patents
Wetland landscape surface layer cold and wet climatic element GIS space simulation method based on remote sensing data Download PDFInfo
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Abstract
The invention provides a wetland landscape surface layer cold and wet climatic element GIS space simulation method based on remote sensing data, and relates to the wetland landscape surface layer cold and wet climate space simulation method based on the remote sensing data. The method solves the problem that temperature space distribution with high precision cannot be obtained in an existing space interpolation method. The method includes the steps of obtaining vegetation index data set NDVI of an MODIS remote-sensing image in a research area, a surface temperature data set LST and a precipitable water data set Pw, building an inversion model of surface layer temperatures and relative humidity, obtaining space distribution of internal temperatures and surface layer relative humidity of wetland patches and dry land patches, obtaining an average value of surface layer cold and wet climatic elements of the wetland patches and the dry land patches, building a horizontal changing model of a cold and wet climate element edge effect, and carrying out space simulation on surface layer temperature and humidity under a wetland landscape scale by means of GIS techniques. The method can be widely applied to the space simulation of wetland landscape climate.
Description
Technical field
The present invention relates to the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data and wait the spatial simulation method.
Background technology
In the face of nearly decades of global environmental change; mankind's activity disturbs regional wetland landscape variation issue down just like to become the important content of global change research due, and especially wetland landscape changes and the climatic effect research that brings has become the foundation of research focus and coordination wet land protection and Regional Economic Sustainable Development.Therefore, obtaining cold moisture exactly waits the space distribution of key element and plays a part very important to researchs such as wetlands ecosystems regional climate model, the hydrology and ecological model and global climate responses.
At present, obtaining weather data from weather station observation is one of main source of simulation climate essential factors space distribution; Because the sparse inequality of meteorological site space distribution, the various space interpolation methods of often sampling expand to face to obtain the climatic data of space continuous distribution with limited point.Yet under different terrain and different landscape condition, the scope that weather station can represent has very big difference, even also can't obtain high-precision temperature space distribution by the space interpolation method.At this deficiency, existing research and utilization remote sensing image obtains large-scale Spatial Distribution of Climatic Variables at present.Though remote-sensing inversion has been realized the embodiment of patch internal diversity, but seldom there is research to use it for the zone with complicated earth surface characteristic, particularly consider the surface layer climatic elements distribution situation of marginal belt between the heterogeneous patch, therefore, during the cold wet essential factors space distribution simulation of the present invention's surface layer under based on the wetland landscape yardstick, both utilize the advantage of remotely-sensed data to overcome the sparse shortcoming of traditional weather station data distribution, considered the horizontal changing pattern of cold moisture time key element on marginal belt between the patch again emphatically.
Summary of the invention
The present invention can't obtain high-precision temperature space distribution in order to solve the existing space interpolation method, thereby provides the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data to wait key element GIS spatial simulation method.
The cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data is waited key element GIS spatial simulation method, and it comprises the steps:
Step 1: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, the line data of going forward side by side is handled;
Step 2: utilize step 1 described vegetation index data set NDVI and surface temperature data set LST to set up surface layer temperature inverse model, obtain the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch;
Step 3: utilize step 1 described surface temperature data set LST and precipitable water data set Pw to set up surface layer relative humidity inverse model, obtain the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity;
Step 4: according to the space distribution of step 2 and the described wetland patch of step 3 and the inner temperature of nonirrigated farmland patch and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time of nonirrigated farmland patch surface layer key element, makes up cold moisture and waits the horizontal variation model of key element edge effect; It is temperature and relative humidity that described cold moisture is waited key element;
Step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture that step 4 obtains, utilize the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation.
Described step 1: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, the process that the line data of going forward side by side is handled is:
Step 11: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, three kinds of data sets are carried out projection conversion, radiation calibration and image splicing cut out processing;
Step 12: the data after step 11 handled are carried out the water body mask process and are handled with cloud, and the image data collection that is produced exceptional value by water body and cloud factor affecting has been rejected in acquisition;
Step 13: data set every day to precipitable water data set Pw is averaging, and obtains and the near infrared of the identical time scale of surface temperature data set LST and infrared precipitable water Pw generated data collection;
Step 14: obtain the weather station data; Described weather station data comprise temperature, relative humidity and day water vapour pressure; And according to the geographic position of weather station, the brightness value DN of the corresponding pixel of precipitable water data set Pw that vegetation index data set NDVI, the surface temperature data set LST that extraction step 1 obtains and step 13 obtain.
Described step 2: utilize step 1 described vegetation index data set NDVI and surface temperature data set LST to set up surface layer temperature inverse model, the process that obtains the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch is:
Utilize the surface temperature data set LST of step 14 acquisitions and the DN value of vegetation index data set NDVI and the temperature record of weather station to make up surface layer temperature inverse model by binary linear regression equation, the model of estimation wetland patch and patch inner near stratum, nonirrigated farmland air temperature distribution is:
y＝β
0+β
1x
1+β
2x
2
In the formula, y represents the temperature on average value estimated, x
1Expression surface temperature LST, x
2Expression vegetation index data set NDVI, β
nThe expression regression coefficient.
Described step 3: utilize step 1 described surface temperature data set LST and precipitable water data set Pw to set up surface layer relative humidity inverse model, the process that obtains the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity is:
Step 31: the DN value of the precipitable water data set Pw that obtains according to step 14 and the day water vapour pressure data construct regression equation of weather station are estimated day water vapour pressure:
e＝a+b×pw
In the formula, e represents the actual vapor pressure, and a, b represent regression coefficient;
Step 32: estimation relative humidity;
The process of described estimation relative humidity is: utilize the Magnus experimental formula to determine saturation vapour e
sRelation with temperature:
In the formula, e
sThe expression saturation vapour pressure, T
aExpression surface layer temperature is the temperature inversion result of step 2 acquisition;
Relative humidity is represented actual vapor pressure e and synthermal saturation vapour pressure e down
sThe ratio:
Make up surface layer relative humidity inverse model according to step 31 to step 32, as the input data, estimate the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity according to temperature inversion result, precipitable water data set Pw and vapour pressure data.
Described step 4: according to the space distribution of step 2 and the inner temperature of three described patches and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time of nonirrigated farmland patch surface layer key element, and the process that makes up the horizontal variation model of cold moisture time key element edge effect is:
Step 41: the wetland that obtains according to step 2 and step 3 and the space distribution of nonirrigated farmland patch inner near stratum temperature and relative humidity are divided by wetland and nonirrigated farmland, calculate the mean value of wetland patch inside and nonirrigated farmland patch inside temperature and relative humidity respectively;
Step 42: at wetland patch and nonirrigated farmland patch intersection x axle initial point is set, and serves as that simulation cold moisture in basis is waited key element at the horizontal change procedure of marginal belt with the Logistic model, thereby make up the horizontal variation model of cold wet key element edge effect of wetland landscape:
In the formula, A
wWith A
fRepresent the mean state of wetland patch inside and the inner cold wet key element of nonirrigated farmland patch respectively, dx represents the steepness of curvilinear motion;
Described step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture of step 4, the process of utilizing the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation is:
Step 51: wait the horizontal change curve that the horizontal variation model of key element edge effect obtains to wait at the cold moisture of wetland-nonirrigated farmland marginal belt key element according to the cold moisture that step 4 obtains, and horizontal change curve is carried out the discretize design, obtain edge strip; Calculate the horizontal span of the edge effect of cold wet key element;
Step 52: by GIS buffer zone analysis and overlay analysis, according to the horizontal span of edge effect in the step 51 the band step-length being set, and making that step-length is radius, is the border with wetland patch and nonirrigated farmland patch, makes buffer strip to patch inside respectively;
Step 53: according to the horizontal variation model of the described edge effect of step 4, distance with distance wetland-patch border, nonirrigated farmland is independent variable, calculating realizes the spatial simulation of cold moisture time key element under the wetland landscape yardstick thus according to temperature and the relative humidity of each edge strip of step 52 acquisitions.
The present invention has realized considering the spatial simulation method that cold wet key element is carried out in the level variation of marginal belt based on remotely-sensed data, can obtain the space distribution of microclimate key element accurately, overcome traditional based on the weather station data shortcoming of spot interior details feature corresponsively, radiometric resolution and gross information content are enhanced about more than once, from having improved the spatial simulation effect in essence.
Description of drawings
Fig. 1 waits the process flow diagram of key element GIS spatial simulation method for the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data of the present invention;
Fig. 2 is the spatial distribution map of embodiment one described wetland patch and the inner temperature of nonirrigated farmland patch;
Fig. 3 is the spatial distribution map of embodiment one described wetland patch and patch inner near stratum, nonirrigated farmland relative humidity;
Fig. 4 is embodiment one described marginal belt discretize synoptic diagram;
Fig. 5 is the surface layer temperature spatial mode graphoid under the embodiment one described wetland landscape yardstick;
Fig. 6 is the sample district detail view of Fig. 5;
Fig. 7 is the surface layer humidity spatial mode graphoid under the embodiment one described wetland landscape yardstick;
Fig. 8 is the sample district detail view of Fig. 7.
Embodiment
Embodiment one, illustrate that in conjunction with Fig. 1-Fig. 9 the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data of present embodiment waits key element GIS spatial simulation method, it comprises the steps:
Step 1: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, the line data of going forward side by side is handled;
Step 2: utilize step 1 described vegetation index data set NDVI and surface temperature data set LST to set up surface layer temperature inverse model, obtain the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch;
Step 3: utilize step 1 described surface temperature data set LST and precipitable water data set Pw to set up surface layer relative humidity inverse model, obtain the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity;
Step 4: according to the space distribution of step 2 and the described wetland patch of step 3 and the inner temperature of nonirrigated farmland patch and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time of nonirrigated farmland patch surface layer key element, makes up cold moisture and waits the horizontal variation model of key element edge effect; It is temperature and relative humidity that described cold moisture is waited key element;
Step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture that step 4 obtains, utilize the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation.
Detailed step of the present invention is:
The cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data is waited key element GIS spatial simulation method, and it comprises the steps:
Step 1: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, the line data of going forward side by side is handled;
Described step 1: obtain 16 days vegetation index data set NDVI of study area MODIS remote sensing image, 8 days face of land temperature data collection LST and every day precipitable water data set Pw, the process that the line data of going forward side by side is handled is:
Step 11: obtain vegetation index data set NDVI, surface temperature data set LST and precipitable water data set Pw from US Geological Survey (USGG) website, three kinds of data sets are carried out projection conversion, radiation calibration and image splicing cut out processing;
Step 12: the data after step 11 handled are carried out the water body mask process and are handled with cloud, and the image data collection that is produced exceptional value by water body and cloud factor affecting has been rejected in acquisition;
Step 13: data set every day to precipitable water data set Pw is averaging, obtain near infrared and infrared can be with 8 days generated data collection of water yield Pw, so that consistent with surface temperature data set LST time precision;
Step 14: in order to make up inverse model and to carry out the inversion result checking, obtain weather station data of identical time period (temperature, relative humidity and day water vapour pressure) from China's meteorological science data share service net; And according to the geographic position of weather station, the brightness value DN of the corresponding pixel of precipitable water data set Pw that vegetation index data set NDVI, the surface temperature data set LST that extraction step 1 obtains and step 13 obtain, and the weather station data are divided into two parts, a part is used for inverse model and makes up, and another part is used for result verification.
Step 2: the vegetation index data set NDVI and the surface temperature data set LST that utilize step 1 to obtain set up surface layer temperature inverse model, obtain the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch;
Described step 2: the vegetation index data set NDVI and the surface temperature data set LST that utilize step 1 to obtain set up surface layer temperature inverse model, and the process that obtains the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch is:
Utilize the surface temperature data set LST of step 14 acquisitions and the DN value of vegetation index data set NDVI and the temperature record of a part of weather station to make up surface layer temperature inverse model by binary linear regression equation, estimation wetland patch and patch inner near stratum, nonirrigated farmland air temperature distribution:
y＝β
0+β
1x
1+β
2x
2
In the formula, y represents the temperature on average value estimated, x
1The surface temperature LST that expression is obtained by remote sensing, x
2The vegetation index data set NDVI that expression has remote sensing to obtain, β
nThe expression regression coefficient.
Utilize another part weather station temperature record to carry out inverse model result's checking.
Step 3: the surface temperature data set LST and the precipitable water data set Pw that utilize step 1 to acquire set up surface layer relative humidity inverse model, obtain the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity;
Described step 3: the surface temperature data set LST and the precipitable water data set Pw that utilize step 1 to acquire set up surface layer relative humidity inverse model, and the process that obtains the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity is:
Step 31: the regression equation of the DN value of the precipitable water data set Pw that obtains according to step 14 and the day water vapour pressure data construct of a part of weather station is estimated day water vapour pressure:
e＝a+b×pw
In the formula, e represents the actual vapor pressure, and a, b represent regression coefficient;
Utilize another part weather station vapour pressure data to carry out inverse model result's checking.
Step 32: estimation relative humidity;
The process of described estimation relative humidity is: utilize the Magnus experimental formula to determine saturation vapour e
sRelation with temperature:
In the formula, e
sThe expression saturation vapour pressure, T
aExpression surface layer temperature is the temperature inversion result of step 2 acquisition.
Relative humidity is represented actual vapor pressure e and synthermal saturation vapour pressure e down
sThe ratio:
Can make up surface layer relative humidity inverse model according to step 31 to three formula of step 32, again with the vapour pressure data of temperature inversion result, precipitable water data set Pw and weather station as the input data, obtain the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity.At last, utilize the relative humidity data of weather station to carry out modelling verification.
Step 4: according to the space distribution of step 2 and the inner temperature of three described patches and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time key element of nonirrigated farmland patch surface layer (temperature and relative humidity), makes up cold moisture and waits the horizontal variation model of key element edge effect;
Described step 4: according to the space distribution of step 2 and the inner temperature of three described patches and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time key element of nonirrigated farmland patch surface layer (temperature and relative humidity), and the process that makes up the horizontal variation model of cold moisture time key element edge effect is:
Step 41: the wetland that obtains according to step 2 and step 3 and the space distribution of nonirrigated farmland patch inner near stratum temperature and relative humidity are divided by wetland and nonirrigated farmland, calculate the mean value of wetland patch inside and nonirrigated farmland patch inside temperature and relative humidity afterwards respectively;
Step 42: at wetland patch and nonirrigated farmland patch intersection x axle initial point is set, and utilizes the cold moisture of Logistic modeling wetland landscape to wait the variation of key element edge effect level:
In the formula, A
wWith A
fRepresent the mean state of wetland patch inside and the inner cold wet key element of nonirrigated farmland patch respectively, dx represents the steepness of curvilinear motion;
With the mean state of the inner cold wet key element of wetland patch as A
w, with the mean state of the inner cold wet key element of nonirrigated farmland patch as A
f, can make up the horizontal variation model of cold wet key element edge effect of wetland landscape according to above-mentioned formula.
Step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture of step 4, utilize the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation.
Described step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture of step 4, the process of utilizing the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation is:
Step 51: wait the horizontal change curve that the horizontal variation model of key element edge effect obtains to wait at the cold moisture of wetland-nonirrigated farmland marginal belt key element according to the cold moisture that step 4 obtains, and horizontal change curve is carried out the discretize design, obtain several edge strip; Calculate the horizontal span of the edge effect of cold wet key element, and design the plan of establishment of edge strip step-length according to this scope;
Step 52: by GIS buffer zone analysis and overlay analysis, be radius with each step-length in the edge strip step-length plan of establishment of step 51 design, be the border with wetland and nonirrigated farmland patch, make buffer strip to patch inside respectively, realize discrete several edge strip that turns to of wetland-nonirrigated farmland marginal belt.
Step 53: according to the horizontal variation model of the described edge effect of step 4, distance with distance wetland-patch border, nonirrigated farmland is independent variable, calculating realizes the spatial simulation of cold moisture time key element under the wetland landscape yardstick thus according to temperature and the relative humidity of each edge strip of step 52 acquisitions.
Specific embodiment:
Step 1: obtain MODIS (Moderate Imaging Spectroradiomete from US Geological Survey (USGG) website, Moderate-resolution Imaging Spectroradiometer,) 16 days vegetation index data set NDVI, 8 days face of land temperature data collection LST and every day precipitable water data set Pw, the line data of going forward side by side is handled;
Utilize MRT and HEG software, the HDF file of all MODIS is carried out image splicing and projection conversion, select the Albers projection, reference ellipsoid is WGS84, carries out radiation calibration one and collects study area and cut out;
Obtain the water body space distribution by land use data, vegetation index data set NDVI, surface temperature data set LST and three kinds of data of precipitable water data set Pw are carried out the water body mask, and remove exceptional value and invalid value; Subsequently, carry out cloud and handle exceptional value and the invalid value of rejecting by the cloud influence.
With 8 days served as at interval to day the precipitable water data set the near infrared in the morning and infrared data carry out superposed average Pw and average, obtain the precipitable water data set Pw at 8 days synthetic daytimes and night, so that consistent with surface temperature data set LST time precision;
Obtain weather station data of identical time period (temperature, relative humidity and day water vapour pressure) from China's meteorological science data share service net; And according to the geographic position of weather station, extract the brightness value DN of vegetation index data set NDVI, surface temperature data set LST and the corresponding pixel of precipitable water data set Pw; By interpolation obtain daytime 11:00 and night 23:00 the weather station data, and these data are divided into two parts, a part is used for inverse model and makes up, another part is used for result verification.
Step 2: utilize vegetation index data set NDVI, the surface temperature data set LST of step 1 acquisition and the temperature record of weather station to set up surface layer temperature inverse model, obtain the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch;
Obtain weather station temperature and the vegetation index data set NDVI of corresponding pixel and the DN value of surface temperature data set LST, every group observations is specified a sequence number at random, data based sequence number random division is two parts: a part is used for model construction, another part is used for modelling verification, utilize binary linear regression model assessment patch inner near stratum air temperature distribution, the result as shown in Figure 2.
Step 3: utilize precipitable water data set Pw that step 1 obtains, temperature inversion result that step 2 acquires and the relative humidity data of weather station to set up surface layer relative humidity inverse model, obtain the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity
In conjunction with the per 8 days surface water vapour pressure mean value of the vapour pressure data computation of weather station, vapour pressure data when passing by according to the time utilization interpolation acquisition of passing by at MODIS daytime and night, extract corresponding precipitable water data from 8 days average of MODIS precipitable water according to collection, after rejecting invalid value, make up the regression model of vapour pressure and precipitable water; The day water vapour pressure of recycling regression model inverting and the temperature of step 2 inverting, estimation daytime and relative humidity at night, the space distribution of acquisition patch internal relative humidity.
Step 4: according to the space distribution of step 2 and the inner temperature of three patches that obtain and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time key element of nonirrigated farmland patch surface layer (temperature and relative humidity), makes up cold moisture and waits the horizontal variation model of key element edge effect;
Utilize the spatial clustering method to ask respectively to calculate temperature that step 2 and three remote-sensing inversions obtain and relative humidity at the mean value of wetland patch inside and patch inside, nonirrigated farmland, then, suppose that the curvilinear motion rate in the horizontal model of cold wet edge effect is constant, make up five study area daytimes and night cold moisture and wait the horizontal variation model of key element edge effect, as table 1:
The cold moisture of table 1 is waited the horizontal variation model of key element edge effect
Step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture of step 4, utilize the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation;
Formula according to table 1 obtains the horizontal change curve of marginal belt; Be wetland inner area, nonirrigated farmland inner area with a wetland landscape dividing elements, and the wetland marginal belt between wetland and the nonirrigated farmland and nonirrigated farmland marginal belt; Marginal belt in the certain limit on the change curve is carried out discretize, form several edge strip, as Fig. 4; Calculate the horizontal span of the edge effect of cold wet key element, and design the plan of establishment of edge strip step-length according to this scope;
The step-length of different edge strip is set according to different cold wet key element daytime and the horizontal span at night, the size of step-length is determined at the Changing Pattern of marginal belt according to cold wet key element, because temperature and relative humidity present S type curvilinear characteristic at marginal belt, and the matched curve on daytime changes more violent, so be step-length with the cumulative spacing of 20m, 40m, and matched curve variation at night is milder, and arranging with cumulative spacings such as 20m, 40m, 60m is step-length, as shown in table 2:
The plan of establishment (the unit: m) of table 2 wetland and nonirrigated farmland edge strip
By GIS buffer zone analysis and overlay analysis, be the border with wetland and nonirrigated farmland patch, be that the unit radius is as buffer strip to patch inside with each step-length respectively, the number that generates the buffering band is set by corresponding marginal belt horizontal span, according to the horizontal variation model of edge effect, be independent variable with the space length, calculate temperature and the relative humidity of each edge strip at each wetland study area daytime and night, realize that thus cold moisture under the wetland landscape yardstick waits the spatial simulation of key element.Spatial simulation result such as Fig. 5-shown in Figure 8.
The present invention proposes to consider that based on remotely-sensed data cold wet key element changes and the spatial simulation method of carrying out can be obtained the space distribution of microclimate key element exactly in the level of marginal belt, overcome traditional shortcoming that can't reflect ground patch interior details feature based on the weather station data, radiometric resolution and gross information content are enhanced about more than once, from having improved the spatial simulation effect in essence.The present invention provides a kind of new approaches for the space distribution of simulation landscape scale climatic elements, also provides effective way for the conversion of landscape ecological functional space yardstick and Mechanism Study.
Claims (6)
1. the cold moisture of the wetland landscape surface layer based on remotely-sensed data is waited key element GIS spatial simulation method, it is characterized in that it comprises the steps:
Step 1: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, the line data of going forward side by side is handled;
Step 2: utilize step 1 described vegetation index data set NDVI and surface temperature data set LST to set up surface layer temperature inverse model, obtain the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch;
Step 3: utilize step 1 described surface temperature data set LST and precipitable water data set Pw to set up surface layer relative humidity inverse model, obtain the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity;
Step 4: according to the space distribution of step 2 and the described wetland patch of step 3 and the inner temperature of nonirrigated farmland patch and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time of nonirrigated farmland patch surface layer key element, makes up cold moisture and waits the horizontal variation model of key element edge effect; It is temperature and relative humidity that described cold moisture is waited key element;
Step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture that step 4 obtains, utilize the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation.
2. the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data according to claim 1 is waited key element GIS spatial simulation method, it is characterized in that described step 1: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, the process that the line data of going forward side by side is handled is:
Step 11: obtain vegetation index data set NDVI, surface temperature data set LST and the precipitable water data set Pw of study area MODIS remote sensing image, three kinds of data sets are carried out projection conversion, radiation calibration and image splicing cut out processing;
Step 12: the data after step 11 handled are carried out the water body mask process and are handled with cloud, and the image data collection that is produced exceptional value by water body and cloud factor affecting has been rejected in acquisition;
Step 13: data set every day to precipitable water data set Pw is averaging, and obtains and the near infrared of the identical time scale of surface temperature data set LST and infrared precipitable water Pw generated data collection;
Step 14: obtain the weather station data; Described weather station data comprise temperature, relative humidity and day water vapour pressure; And according to the geographic position of weather station, the brightness value DN of the corresponding pixel of precipitable water data set Pw that vegetation index data set NDVI, the surface temperature data set LST that extraction step 1 obtains and step 13 obtain.
3. the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data according to claim 2 is waited key element GIS spatial simulation method, it is characterized in that described step 2: utilize step 1 described vegetation index data set NDVI and surface temperature data set LST to set up surface layer temperature inverse model, the process that obtains the space distribution of the inner temperature of wetland patch and nonirrigated farmland patch is:
Utilize the surface temperature data set LST of step 14 acquisitions and the DN value of vegetation index data set NDVI and the temperature record of weather station to make up surface layer temperature inverse model by binary linear regression equation, the model of estimation wetland patch and patch inner near stratum, nonirrigated farmland air temperature distribution is:
y＝β
0+β
1x
1+β
2x
2
In the formula, y represents the temperature on average value estimated, x
1Expression surface temperature LST, x
2Expression vegetation index data set NDVI, β
nThe expression regression coefficient.
4. the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data according to claim 3 is waited key element GIS spatial simulation method, it is characterized in that described step 3: utilize step 1 described surface temperature data set LST and precipitable water data set Pw to set up surface layer relative humidity inverse model, the process that obtains the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity is:
Step 31: the DN value of the precipitable water data set Pw that obtains according to step 14 and the day water vapour pressure data construct regression equation of weather station are estimated day water vapour pressure:
e＝a+b×pw
In the formula, e represents the actual vapor pressure, and a, b represent regression coefficient;
Step 32: estimation relative humidity;
The process of described estimation relative humidity is: utilize the Magnus experimental formula to determine saturation vapour e
sRelation with temperature:
In the formula, e
sThe expression saturation vapour pressure, T
aExpression surface layer temperature is the temperature inversion result of step 2 acquisition;
Relative humidity is represented actual vapor pressure e and synthermal saturation vapour pressure e down
sThe ratio:
Make up surface layer relative humidity inverse model according to step 31 to step 32, as the input data, estimate the space distribution of wetland patch and patch inner near stratum, nonirrigated farmland relative humidity according to temperature inversion result, precipitable water data set Pw and vapour pressure data.
5. wait key element GIS spatial simulation method according to claim 1 or the cold moisture of 3 and 4 described a kind of wetland landscape surface layers based on remotely-sensed data, it is characterized in that described step 4: according to the space distribution of step 2 and the inner temperature of three described patches and relative humidity, the usage space polymerization obtains the mean value of wetland patch and the cold moisture time of nonirrigated farmland patch surface layer key element, and the process that makes up the horizontal variation model of cold moisture time key element edge effect is:
Step 41: the wetland that obtains according to step 2 and step 3 and the space distribution of nonirrigated farmland patch inner near stratum temperature and relative humidity are divided by wetland and nonirrigated farmland, calculate the mean value of wetland patch inside and nonirrigated farmland patch inside temperature and relative humidity respectively;
Step 42: at wetland patch and nonirrigated farmland patch intersection x axle initial point is set, and serves as that simulation cold moisture in basis is waited key element at the horizontal change procedure of marginal belt with the Logistic model, thereby make up the horizontal variation model of cold wet key element edge effect of wetland landscape:
In the formula, A
wWith A
fRepresent the mean state of wetland patch inside and the inner cold wet key element of nonirrigated farmland patch respectively, dx represents the steepness of curvilinear motion.
6. the cold moisture of a kind of wetland landscape surface layer based on remotely-sensed data according to claim 5 is waited key element GIS spatial simulation method, it is characterized in that described step 5: wait the horizontal variation model analog result of key element edge effect according to the cold moisture of step 4, the process of utilizing the GIS technology that the surface layer temperature under the wetland landscape yardstick and humidity are carried out spatial simulation is:
Step 51: wait the horizontal change curve that the horizontal variation model of key element edge effect obtains to wait at the cold moisture of wetland-nonirrigated farmland marginal belt key element according to the cold moisture that step 4 obtains, and horizontal change curve is carried out the discretize design, obtain edge strip; Calculate the horizontal span of the edge effect of cold wet key element;
Step 52: by GIS buffer zone analysis and overlay analysis, according to the horizontal span of edge effect in the step 51 the band step-length being set, and making that step-length is radius, is the border with wetland patch and nonirrigated farmland patch, makes buffer strip to patch inside respectively;
Step 53: according to the horizontal variation model of the described edge effect of step 4, distance with distance wetland-patch border, nonirrigated farmland is independent variable, calculating realizes the spatial simulation of cold moisture time key element under the wetland landscape yardstick thus according to temperature and the relative humidity of each edge strip of step 52 acquisitions.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462739A (en) * | 2014-03-13 | 2015-03-25 | 中国科学院遥感与数字地球研究所 | Ecological environment parameter ground sampling method suitable for wide-range multi-scale satellite remote sensing data inversion |
| CN104537222A (en) * | 2014-12-18 | 2015-04-22 | 中国科学院东北地理与农业生态研究所 | Remote-sensing-based method for estimating influences of area vegetation cover on earth surface air temperature |
| CN104657935A (en) * | 2015-03-09 | 2015-05-27 | 广西壮族自治区气象减灾研究所 | Near-surface air temperature inversion method |
| CN106530170A (en) * | 2016-09-21 | 2017-03-22 | 环境保护部南京环境科学研究所 | Method for boundary optimization of regional ecological protection red line |
| CN106909899A (en) * | 2017-02-24 | 2017-06-30 | 中国农业大学 | A kind of analysis method and analysis system of wetland landscape evolution process |
| CN107688712A (en) * | 2017-09-04 | 2018-02-13 | 电子科技大学 | A kind of temperature NO emissions reduction method based on DEM and NDVI |
| WO2018145229A1 (en) * | 2017-02-10 | 2018-08-16 | 广西壮族自治区气象减灾研究所 | Accurate large-area inversion method for near-surface air temperature |
| CN108520231A (en) * | 2018-04-04 | 2018-09-11 | 农业部环境保护科研监测所 | A kind of analysis system and method for intelligence wetland landscape evolution process |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1790052A (en) * | 2005-12-19 | 2006-06-21 | 武汉大学 | Area feature variation detection method based on remote sensing image and GIS data |
| CN102565810A (en) * | 2011-12-30 | 2012-07-11 | 武汉大学 | Method for extracting land utilization landmark boundary outline on remote sensing image |
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1790052A (en) * | 2005-12-19 | 2006-06-21 | 武汉大学 | Area feature variation detection method based on remote sensing image and GIS data |
| CN102565810A (en) * | 2011-12-30 | 2012-07-11 | 武汉大学 | Method for extracting land utilization landmark boundary outline on remote sensing image |
Non-Patent Citations (9)
| Title |
|---|
| KLAUS JOSEF HENNENBERG等: "detection of seasonal variability in microclimatic borders and ecotones between forest and savanna", 《BASIC AND APPLIED ECOLOGY》, vol. 9, no. 3, 12 May 2008 (2008-05-12), pages 275 - 285, XP022613306, DOI: doi:10.1016/j.baae.2007.02.004 * |
| SHIGETO KAWASHIMA等: "relations between surface temperature and air temperature on a local scale during winter nights", 《JOURNAL OF APPLIED METEOROLOGY》, vol. 39, no. 9, 30 September 2000 (2000-09-30), pages 1570 - 1579 * |
| 张倩倩: "基于MODIS数据的雾霾检测方法研究", 《中国优秀硕士学位论文全文数据库基础科学辑》, no. 3, 15 March 2013 (2013-03-15), pages 009 - 14 * |
| 张小伟等: "基于GIS的浙江省土地利用/覆盖与地表温度的关系", 《中国农业气象》, vol. 31, no. 2, 28 February 2010 (2010-02-28), pages 295 - 299 * |
| 彭光雄等: "利用MODIS大气廓线产品计算空气相对湿度的方法研究", 《热带气象学报》, vol. 23, no. 6, 31 December 2007 (2007-12-31), pages 611 - 616 * |
| 徐永明等: "基于MODIS数据的长江三角洲地区近地表气温遥感反演", 《农业工程学报》, vol. 27, no. 9, 30 September 2011 (2011-09-30), pages 63 - 68 * |
| 朱海燕: "gis空间分析方法在热带气旋研究中的应用", 《中国优秀博硕士学位论文全文数据库（硕士）基础科学辑》, no. 5, 15 September 2005 (2005-09-15), pages 009 - 9 * |
| 盛裴轩等: "《大气物理学》", 1 January 2003, article "无", pages: 20-22 * |
| 齐述华等: "利用MODIS遥感影像获取近地层气温的方法研究", 《遥感学报》, vol. 9, no. 5, 30 September 2005 (2005-09-30), pages 570 - 575 * |
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|---|---|---|---|---|
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| CN104462739B (en) * | 2014-03-13 | 2017-08-11 | 中国科学院遥感与数字地球研究所 | A kind of environmental parameters ground method of sampling suitable for large-range multi-dimension satellite remote sensing date inverting |
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| CN106530170B (en) * | 2016-09-21 | 2020-11-06 | 生态环境部南京环境科学研究所 | Boundary optimization method for regional ecological protection red line |
| WO2018145229A1 (en) * | 2017-02-10 | 2018-08-16 | 广西壮族自治区气象减灾研究所 | Accurate large-area inversion method for near-surface air temperature |
| US10831949B2 (en) | 2017-02-10 | 2020-11-10 | Guangxi Institute Of Meteorological Disaster-Reducing Research | Nonlinear method for area-wide near surface air temperature precision retrieval |
| CN106909899A (en) * | 2017-02-24 | 2017-06-30 | 中国农业大学 | A kind of analysis method and analysis system of wetland landscape evolution process |
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