CN103914755A - Method and system for determining spatial scales of field investigation and field management - Google Patents
Method and system for determining spatial scales of field investigation and field management Download PDFInfo
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Abstract
The invention provides a method and system for determining spatial scales of field investigation and field management. The method includes the steps of (1) obtaining remote sensing images containing a to-be-processed land parcel, and carrying out preprocessing, (2) obtaining vegetation parameters from the remote sensing images, (3) respectively constructing semi-variance variation functions of all pixel pairs with fixed step lengths in the to-be-processed land parcel in the direction along ridges and the direction perpendicular to the ridges according to the vegetation parameters, (4) calculating the minimum fixed step length in the direction along the ridges when a first-order derivative value of the semi-variance variation function in the direction along the ridges is smaller than a first preset threshold value, and calculating the minimum fixed step length in the direction perpendicular to the ridges when the first-order derivative value of the semi-variance variation function in the direction perpendicular to the ridges is smaller than a second preset threshold value, and (5) determining the spatial scales of field investigation and field management according to the optimal step length in the direction along the ridges and the optimal step length in the direction perpendicular to the ridges. By means of the method and system, the proper spatial scales of field investigation and field management can be rapidly and accurately determined.
Description
Technical field
The present invention relates to agricultural technology field, relate in particular to the method and system of the space scale of a kind of definite field investigation and field management.
Background technology
For a long time, China's agricultural production taking field as basis, is substantially all used the seed of equivalent and fertilizer, herbicide, growth stimulator etc. always on all farmlands, areal.Due to consumption blindly, often drug on the market causes outside waste, also caused the pollution of soil and groundwater.Field sampling shows, even in the identical region of the soil texture, soil characteristic attribute (physics, chemistry, biological nature etc.) also has notable difference in different spatial, and crop growing state information is not only relevant to soil characteristic, simultaneously also closely related with farmland underground hydrologic condition, agricultural tillage level, also there is complex space variability.Accurate agricultural is exactly the difference for the condition such as soil, geography of field inside zones of different, drop in position by suitable method a kind of farming method that appropriate material (as seed, agricultural chemicals, chemical fertilizer etc.) is implemented, the Characteristics of spatial variability of understanding the envirment factors such as Soil Nutrients in Farmland and the growing way of crop own is the basis of accurate agricultural variable management implementation.The developing direction of precision agriculture is will be according to meter level or more the soil property, the spatial variability of plant growth parameter of fine dimension are adjusted the operation such as field water fertilizer, sowing, to truly reflect the spatial variations of Farmland Soil, crop nutrition content level and growing way, need a large amount of sampling point Data supports.Secondly, in the process of implementing in demand of precision agriculture of variable rate, the size of fertilizing management unit, directly effect and the economic benefit of impact fertilising, administrative unit is excessive, and impact is the effect of management precisely, and administrative unit is too small, can affect again efficiency of the practice.In prior art, also determine the method for the space scale of the interior different croplands investigation of regional extent and field management.
Summary of the invention
The invention provides the method and system of the space scale of a kind of definite field investigation and field management, can determine fast and accurately the suitable space yardstick of field investigation and field management.
The method that the invention provides the space scale of a kind of definite field investigation and field management, the method comprises:
S1: obtain the remote sensing image that comprises pending plot and described remote sensing image is carried out to pre-service and obtain pretreated remote sensing image;
S2: obtain vegetation parameter from described pretreated remote sensing image;
S3: construct the right semivariance variation function of all pixels of fixed step size in pending plot from suitable ridge direction with vertical ridge direction respectively according to described vegetation parameter;
S4: the minimum fixed step size that calculates the suitable ridge direction while being less than the first predetermined threshold value along the first derivative values of the semivariance variation function of ridge direction, the minimum fixed step size of the vertical ridge direction when first derivative values that calculates the semivariance variation function of vertical ridge direction is less than the second predetermined threshold value, wherein, the described minimum fixed step size along ridge direction is that the minimum fixed step size of described vertical ridge direction is the optimal step size of vertical ridge direction along the optimal step size of ridge direction;
S5: the space scale of determining field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
Further, described described remote sensing image is carried out to pre-service, comprising:
Described remote sensing image is carried out to image co-registration;
And/or, described remote sensing image is carried out to radiation correcting;
And/or, described remote sensing image is carried out to Atmospheric Correction;
And/or, described remote sensing image is carried out to geometric correction.
Further, described S2 comprises:
Process for irrigating, from described pretreated remote sensing image, obtain normalization moisture vegetation index NDWI and/or vegetation moisture absorption index W BI;
For fertilizing management, from described pretreated remote sensing image, obtain ratio vegetation index RVI and/or green normalized differential vegetation index GNDVI and/or nitrogen reflection index NRI and/or photochemistry reflection index PRI and/or improved chlorophyll absorptance value index number MCARI and/or chlorophyll absorption index TCARI.
Further, described S4 comprises:
From described pretreated remote sensing image, extract pending plot.
Further, described S3 comprises:
Construct the right semivariance variation function of all pixels of fixed step size in pending plot according to following formula:
Wherein, γ (h) is called semivariance variation function, Z (x
i) be the pixel x of described pretreated remote sensing image
icorresponding vegetation parameter value, Z (x
i+ h) with pixel x
idistance is the corresponding vegetation parameter value of pixel of h, 1≤h≤k, k is the half of the suitable ridge direction of pretreated remote sensing image or the transversal section pixel sum of vertical ridge direction, m=n-h, the pixel sum of the suitable ridge direction that n is pretreated remote sensing image or vertical ridge direction.
Further, described S5 comprises:
From
in scope, choose arbitrarily a numerical value as the space scale along ridge direction;
From
in scope, choose arbitrarily the space scale of a numerical value as vertical ridge direction;
Wherein, a
v=h
v× p, a
h=h
h× p, a
hfor the border yardstick along ridge direction, a
vfor the border yardstick of vertical ridge direction, h
hfor the optimal step size of described suitable ridge direction, h
vfor the optimal step size of described vertical ridge direction, the spatial resolution that p is described pretreated remote sensing image.
On the other hand, the invention provides the system of the space scale of a kind of definite field investigation and field management, this system comprises:
Pretreatment module, obtains pretreated remote sensing image for obtaining the remote sensing image that comprises pending plot and described remote sensing image being carried out to pre-service;
Acquisition module, for obtaining vegetation parameter from described pretreated remote sensing image;
Build module, the vegetation parameter obtaining according to described acquisition module is respectively from constructing the right semivariance variation function of all pixels of fixed step size in pending plot along ridge direction with vertical ridge direction;
Computing module, the minimum fixed step size of the suitable ridge direction while being less than the first predetermined threshold value for calculating along the first derivative values of the semivariance variation function of ridge direction, and the minimum fixed step size of the vertical ridge direction of the first derivative values that calculates the semivariance variation function of vertical ridge direction while being less than the second predetermined threshold value, wherein, the described minimum fixed step size along ridge direction is that the minimum fixed step size of described vertical ridge direction is the optimal step size of vertical ridge direction along the optimal step size of ridge direction;
Determination module, for determining the space scale of field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
Further, described pretreatment module, for described remote sensing image is carried out to image co-registration, and/or carries out radiation correcting to described remote sensing image, and/or described remote sensing image is carried out to Atmospheric Correction, and/or described remote sensing image is carried out to geometric correction.
Further, described acquisition module for processing for irrigating, obtains normalization moisture vegetation index NDWI and/or vegetation moisture absorption index W BI from described pretreated remote sensing image; For fertilizing management, from described pretreated remote sensing image, obtain ratio vegetation index RVI and/or green normalized differential vegetation index GNDVI and/or nitrogen reflection index NRI and/or photochemistry reflection index PRI and/or improved chlorophyll absorptance value index number MCARI and/or chlorophyll absorption index TCARI.
Further, described computing module, for extracting pending plot from described pretreated remote sensing image.
Further, described structure module, for constructing the right semivariance variation function of all pixels of fixed step size in pending plot according to following formula:
Wherein, γ (h) is called semivariance variation function, Z (x
i) be the pixel x of described pretreated remote sensing image
icorresponding vegetation parameter value, Z (x
i+ h) with pixel x
idistance is the corresponding vegetation parameter value of pixel of h, 1≤h≤k, k is the half of the suitable ridge direction of pretreated remote sensing image or the transversal section pixel sum of vertical ridge direction, m=n-h, the pixel sum of the suitable ridge direction that n is pretreated remote sensing image or vertical ridge direction.
Further, described determination module, for from
in scope, choose arbitrarily a numerical value as the space scale along ridge direction, from
in scope, choose arbitrarily the space scale of a numerical value as vertical ridge direction, wherein, a
v=h
v× p, a
h=h
h× p, a
hfor the border yardstick along ridge direction, a
vfor the border yardstick of vertical ridge direction, h
hfor the optimal step size of described suitable ridge direction, h
vfor the optimal step size of described vertical ridge direction, the spatial resolution that p is described pretreated remote sensing image.
The method and system of the space scale by a kind of definite field investigation provided by the invention and field management, can determine the suitable sampling scale of field investigation and the suitable management yardstick of field management fast and accurately.
Brief description of the drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the method flow diagram of the space scale of a kind of definite field investigation of providing of one embodiment of the invention and field management;
Fig. 2 is the curve that the semivariance variation function of the suitable ridge direction in the pending plot that provides of one embodiment of the invention fits to;
Fig. 3 is the curve that the semivariance variation function of the vertical ridge direction in the pending plot that provides of one embodiment of the invention fits to;
Fig. 4 is the system architecture schematic diagram of the space scale of a kind of definite field investigation of providing of one embodiment of the invention and field management.
Embodiment
For making object, technical scheme and the advantage of the embodiment of the present invention clearer; below in conjunction with the accompanying drawing in the embodiment of the present invention; technical scheme in the embodiment of the present invention is clearly and completely described; obviously; described embodiment is the present invention's part embodiment, instead of whole embodiment, based on the embodiment in the present invention; the every other embodiment that those of ordinary skill in the art obtain under the prerequisite of not making creative work, belongs to the scope of protection of the invention.
The spatial variations situation of field-crop growth information need to be accurately obtained in demand of precision agriculture of variable rate management decision, mostly be to adopt at present the method for Field sampling and lab analysis for Soil Nutrients in Farmland, crop growing state and the condition survey of correlative environmental factors spatial variability, sampled the restriction of the conditions such as manpower and materials cost, data-analysis time, sampled point quantity is limited, both cannot truly reflect the spatial variations of crop alimentary level and growing way in farmland, also cannot support for demand of precision agriculture of variable rate management provide the most effectively.And blindly obtain the spatial character of fine dimension field crops growing way and envirment factor more by increasing sampling density, not only can greatly increase financial cost, also can be because of the excessive generation data of laboratory data amount of analysis hysteresis quality, cannot meet the time requirement of field farming management, and then affect the large scale application of precision agriculture technology.In prior art, also determine the method for the space scale of the interior different field investigations of regional extent and field management.In order to solve the problems of the technologies described above, the embodiment of the present invention provides the method for the space scale of a kind of definite field investigation and field management, and referring to Fig. 1, the method comprises:
S1: obtain the remote sensing image that comprises pending plot and described remote sensing image is carried out to pre-service and obtain pretreated remote sensing image;
S2: obtain vegetation parameter from described pretreated remote sensing image;
S3: construct the right semivariance variation function of all pixels of fixed step size in pending plot from suitable ridge direction with vertical ridge direction respectively according to described vegetation parameter;
S4: the minimum fixed step size that calculates the suitable ridge direction while being less than the first predetermined threshold value along the first derivative values of the semivariance variation function of ridge direction, the minimum fixed step size of the vertical ridge direction when first derivative values that calculates the semivariance variation function of vertical ridge direction is less than the second predetermined threshold value, wherein, the described minimum fixed step size along ridge direction is that the minimum fixed step size of described vertical ridge direction is the optimal step size of vertical ridge direction along the optimal step size of ridge direction;
S5: the space scale of determining field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
The method of a kind of definite field investigation providing by the embodiment of the present invention and the space scale of field management, can determine the suitable space yardstick of field investigation and field management fast and accurately.
Wherein, the first predetermined threshold value and the second predetermined threshold value be not less than 0 close to 0 numerical value, can value be more than or equal to 0, be less than or equal to 0.001.
In step S1, described described remote sensing image is carried out to pre-service, comprising: described remote sensing image is carried out to image co-registration; And/or, described remote sensing image is carried out to Atmospheric Correction; And/or, described remote sensing image is carried out to geometric correction.After processing, step S1 can obtain Reflectivity for Growing Season image data.
Wherein, remote sensing image comprises: high score satellite image, near-earth aviation flight image data, unmanned plane image data etc.In the method providing in the embodiment of the present invention, need to carry out pre-service to the remote sensing image obtaining, pre-service comprises: image co-registration, radiation correcting, Atmospheric Correction, geometric correction etc.Image co-registration is the image processing techniques that the single band image resampling of the multispectral image of low spatial resolution or high-spectral data and high spatial resolution is generated to a secondary high-resolution multi-spectral image remote sensing, make the existing higher spatial resolution of image after treatment, there is again multispectral feature.Image co-registration is general direct carries out on the raw data layer collecting, and general remote sensing image processing software generally has corresponding functional module.The object of radiation correcting and Atmospheric Correction is to eliminate the impact of the various factorss such as sensor self that remote sensing image is subject to while obtaining, atmosphere, sun altitude, obtain earth's surface real reflectance data, geometric correction is to utilize ground control point to correct the geometry deformation of the remote sensing image that causes of various factors, remote sensing image is carried out to geographic coordinate location, integrate for how much of realization and standard picture, map and normal vector data, geometric correction can be realized by the geometric correction function of relevant speciality software.
For the purposes of the crop of different times and different space scale, select suitable vegetation parameter to process, can reach better effect.
In step S2, specifically comprise: process for irrigating, from described pretreated remote sensing image, obtain NDWI(Normalized Difference water Index, normalization moisture vegetation index) and/or WBI (Water Band Index, vegetation moisture absorption index);
For fertilizing management, from described pretreated remote sensing image, obtain RVI (Ratio Vegetation Index, ratio vegetation index) and/or GNDVI(Green Normalized Difference Vegetation Index, green normalized differential vegetation index) and/or NRI (Nitrogen Reflectance Index, nitrogen reflection index) and/or PRI (Photochemical reflectance index, photochemistry reflection index) and/or MCARI (Modified chlorophyll Absorption Ratio Index, improved chlorophyll absorptance value index number) and/or TCARI (Transformed Chlorophyll Absorption in Reflectance Index, chlorophyll absorption index).
In addition, in vegetation index, conventionally select the strong visible red wave band absorbing of green plants (chlorophyll causes) and the near-infrared band to the high reflection of green plants (leaf inner tissue causes).These two wave bands are not only the most typical wave band in plant spectral, and they completely contradict to the spectral response of same biophysics phenomenon, therefore their multiple combination will be favourable to strengthening or disclosing implicit information.
Crop, at different growth stage, is subject to the impact of environmental factor and management factors, and vine growth and development state there are differences, and its overall space variation situation also there are differences simultaneously.
The present invention is directed to concrete Making Agricultural Management Decisions measure (fertilising, irrigation etc.) demand, in conjunction with crops breeding time and remotely-sensed data band setting feature, select different spectrum parameters to carry out suitable space dimensional analysis evaluation.
Early stage at crop growth, plant individuality is less, Soil Background is larger on the impact of crop growing state spectrum parameter value, vegetation index is mainly the size of its coverage to the reflection of field-crop growth information, therefore, the general spectrum parameter NDVI(Normalized Difference Vegetation Index that selects reflection crop groups growing way, normalized differential vegetation index), or select the vegetation index that can reduce Soil Background impact as SAVI (Soil Adjusted Vegetation Index, soil regulates vegetation index), OSAVI (Optimized Soil Adjusted Vegetation Index, optimize soil and regulate vegetation index), MSAVI (Modified Soil-Adjusted Vegetation Index, improved soil regulates vegetation index) etc. carry out correlation analysis.
In the crop growth middle and later periods, particularly behind crop envelope ridge, select specific spectrum parameter according to agricultural management measure demand, if carry out fertilizing management, can select crop nitrogen, chlorophyll changes than more sensitive spectrum parameter as RVI (Ratio Vegetation Index, ratio vegetation index), GNDVI(Green Normalized Difference Vegetation Index, green normalized differential vegetation index), NRI (Nitrogen Reflectance Index, nitrogen reflection index), PRI (Photochemical reflectance index, photochemistry reflection index), MCARI (Modified chlorophyll Absorption Ratio Index, improved chlorophyll absorptance value index number), TCARI (Transformed Chlorophyll Absorption in Reflectance Index, chlorophyll absorption index) etc. spectrum parameter, if irrigate processing, select vegetation moisture to change than more sensitive vegetation index as NDWI(Normalized Difference water Index, normalization moisture vegetation index), WBI (Water Band Index, vegetation moisture absorption index) etc. carries out follow-up correlation analysis.
Step S3 comprises: construct the right semivariance variation function of all pixels of fixed step size in pending plot according to following formula:
Wherein, γ (h) is called semivariance variation function, Z (x
i) be the pixel x of described pretreated remote sensing image
icorresponding vegetation parameter value, Z (x
i+ h) with pixel x
idistance is the corresponding vegetation parameter value of pixel of h, 1≤h≤k, k is the half of the suitable ridge direction of pretreated remote sensing image or the transversal section pixel sum of vertical ridge direction, m=n-h, the pixel sum of the suitable ridge direction that n is pretreated remote sensing image or vertical ridge direction.
In step S2, obtained the vegetation parameter of remote sensing image, in step S3, the vegetation parameter obtaining from S2 extracts the vegetation parameter value of each pixel in pending plot, constructs semivariance variation function according to this vegetation parameter value.
Step S4 comprises: from described pretreated remote sensing image, extract pending plot.
Wherein, extracting pending plot can utilize remote sensing image processing software cutting plot image or utilize area-of-interest to delineate corresponding region.
In step S4, obtain after the optimal step size of suitable ridge direction and the optimal step size of vertical ridge direction, can calculate the space scale in real plot according to optimal step size.
Step S5 comprises: from
in scope, choose arbitrarily a numerical value as the space scale along ridge direction;
From
in scope, choose arbitrarily the space scale of a numerical value as vertical ridge direction;
Wherein, a
v=h
v× p, a
h=h
h× p, a
hfor the border yardstick along ridge direction, a
vfor the border yardstick of vertical ridge direction, h
hfor the optimal step size of described suitable ridge direction, h
vfor the optimal step size of described vertical ridge direction, the spatial resolution that p is described pretreated remote sensing image.
In addition, can also obtain by the following method space scale:
Calculate the right semivariance variation function value of all pixels of fixed step size in pending plot from suitable ridge direction with vertical ridge telegoniometer respectively according to described vegetation parameter;
According to fitting to a smooth continuous curve along the semivariance variation function value of ridge direction, calculate the range of this curve, this range is the optimal step size along ridge direction;
Fit to a smooth continuous curve according to the semivariance variation function value of vertical ridge direction, calculate the range of this curve, this range is the optimal step size of vertical ridge direction;
Determine the space scale of field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
Wherein, above-mentioned continuous curve adopts y axle to represent average variance, and the brightness value unit of γ (h) after with remote sensing image calibration represent, x axle represents different fixed step sizes.Semivariance variation function refers to the half of difference variance between two measurement points, and within the specific limits, semivariance variation function value increases with the increase of fixed step size, but while exceeding certain distance, semivariance variation function value tends towards stability.Total base station value, refers to the stationary value of appearance when semi-variance function increases to a certain degree with distance, total represent the variation of system.Base station value refers to the poor of total variation and piece gold number, is the variation being caused by the non-artificial factor such as parent soil material, weather.Range is the maximal correlation distance of certain specific character, and what it represented is under certain observation yardstick, the coverage of spatial coherence, and its size is relevant with observation yardstick.In the time that between two observation stations, distance exceedes range, between them, be independently; If be less than range, between them, there is spatial coherence.When Existential Space correlationship, the right difference of point of density should be very little, and generally speaking, between point pair, mutually from away from must be more, the difference of two squares be just larger.Range is the distance that sample possesses spatial autocorrelation, that is to say, the variable that exceeds this distance only has very little correlativity or there is no correlativity, and this scope has autocorrelation with interior place data, and place does not in addition have autocorrelation.In the investigation of farmland, if sampling scale is greater than range, just cannot correctly reflect the spatial variability situation of agricultural land information; In like manner, for the variable management of precision agriculture fertilizer, water, agricultural chemicals, this distance is also the important references value that variable is implemented yardstick, and the yardstick of enforcement should be less than or equal to this value, could ensure that variable enforcement shoots the arrow at the target, and be greater than the effect that this value meeting variation is implemented.
Winter wheat plot below by different growing ways describes one embodiment of the present of invention in detail.
The first step: obtain high spatial resolution satellite remote-sensing image and this remote sensing image is carried out to pre-service;
Wherein, in the remote sensing image obtaining by high spatial resolution satellite, there is the Panchromatic image of 0.61 meter of spatial resolution, four band images of 2.4 meters of spatial resolutions, four wave band datas have comprised blue wave band, green light band, red spectral band and near-infrared band.This remote sensing image is carried out to image co-registration, after fusion, carry out Atmospheric Correction, finally obtain 0.61 meter of spatial resolution atural object Reflectivity for Growing Season image.
Second step: obtain NDVI from pretreated remote sensing image;
Wherein, according to winter wheat in the feature breeding time in boot stage, and remote sensing image band setting feature, the present embodiment is selected NDVI, wherein, the NDVI of the remote sensing image in the present embodiment is calculated by its B4 wave band and B3 band combination, and formula is as follows:
wherein B3 is remote sensing image triband, is also the wave band that red light wavelength is corresponding, and B4 is remote sensing image the 4th wave band, is also the wave band that near-infrared wavelength is corresponding.
The 3rd step: calculate respectively the minimum fixed step size while being less than predetermined threshold value along the semivariance variation function of ridge direction with the first derivative values of the semivariance variation function of vertical ridge direction, wherein, described two minimum fixed step sizes are respectively along the optimal step size of ridge direction and the optimal step size of vertical ridge direction;
Referring to table 1, table 1 is the semivariance variation function value of suitable ridge direction and the semivariance variation function value of vertical ridge direction in pending plot, wherein, h is fixed step size, H-r (h) is that V-r (h) is the semivariance variation function value of vertical ridge direction along the semivariance variation function value of ridge direction.
Table 1
The 4th step: fit to one article of smooth continuous curve according to the semivariance variation function value of vertical ridge direction, calculate the range of this curve, this range is the optimal step size of vertical ridge direction; Referring to Fig. 2, the curve that the semivariance variation function of the suitable ridge direction that Fig. 2 is pending plot fits to.
Fit to a smooth continuous curve according to the semivariance variation function value of vertical ridge direction, calculate the range of this curve, this range is the optimal step size of vertical ridge direction, referring to Fig. 3, and the curve that the semivariance variation function of the vertical ridge direction that Fig. 3 is pending plot fits to.
Wherein, the first predetermined threshold value is that 0.000002, the second predetermined threshold value is 0.00000038.
Referring to table 2, table 2 is the semivariance variation function of suitable ridge direction and the first order derivative table of the semivariance variation function of vertical ridge direction in pending plot, wherein, h is fixed step size, H-r'(h) be the semivariance variation function value along ridge direction, V-r'(h) be the semivariance variation function value of vertical ridge direction.
Table 2
As can be drawn from Table 2, be 20 along the optimal step size of ridge direction, the optimal step size of vertical ridge direction is 22.
The 5th step: the space scale of determining field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
Wherein, the spatial resolution position 0.61m of above-mentioned remote sensing image,
a
v=22*0.61m=13.42m；
a
h=20*0.61m=12.20m。
Can select 6.71m and 6.10m as optimal spatial yardstick, to carry out field information acquisition and variable enforcement with the interval of 6.71*6.10 rice in the direction of suitable ridge with in the direction of vertical ridge, it is optimum selection, consider cost and concrete field management performance constraint, with the space interval of 6.71 meters-13.42 meters, vertical ridge direction, 6.10 meters-12.20 meters, suitable ridge direction, all can meet investigation and the Production requirement in target plot, exceed sampling and the management of 13.42*12.20 rice space scale, may produce certain error, yardstick is larger, and the error of generation is larger.
The embodiment of the present invention provides the system of the space scale of a kind of definite field investigation and field management, and referring to Fig. 4, this system comprises:
Pretreatment module 401, obtains pretreated remote sensing image for obtaining the remote sensing image that comprises pending plot and described remote sensing image being carried out to pre-service;
Acquisition module 402, for obtaining vegetation parameter from described pretreated remote sensing image;
Build module 403, the vegetation parameter obtaining according to described acquisition module is respectively from constructing the right semivariance variation function of all pixels of fixed step size in pending plot along ridge direction with vertical ridge direction;
Computing module 404, the minimum fixed step size of the suitable ridge direction while being less than the first predetermined threshold value for calculating along the first derivative values of the semivariance variation function of ridge direction, and the minimum fixed step size of the vertical ridge direction of the first derivative values that calculates the semivariance variation function of vertical ridge direction while being less than the second predetermined threshold value, wherein, the described minimum fixed step size along ridge direction is that the minimum fixed step size of described vertical ridge direction is the optimal step size of vertical ridge direction along the optimal step size of ridge direction;
Determination module 405, for determining the space scale of field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
Described pretreatment module 401, for described remote sensing image is carried out to image co-registration, and/or carries out radiation correcting to described remote sensing image, and/or described remote sensing image is carried out to Atmospheric Correction, and/or described remote sensing image is carried out to geometric correction.
Described acquisition module 402 for processing for irrigating, obtains NDWI and/or WBI from described pretreated remote sensing image; For fertilizing management, from described pretreated remote sensing image, obtain RVI and/or GNDVI and/or NRI and/or PRI and/or MCARI and/or TCARI.
Described computing module 404, for extracting pending plot from described pretreated remote sensing image.
Described structure module 403, for constructing the right semivariance variation function of all pixels of fixed step size in pending plot according to following formula:
Wherein, γ (h) is called semivariance variation function, Z (x
i) be the pixel x of described pretreated remote sensing image
icorresponding vegetation parameter value, Z (x
i+ h) with pixel x
idistance is the corresponding vegetation parameter value of pixel of h, 1≤h≤k, k is the half of the suitable ridge direction of pretreated remote sensing image or the transversal section pixel sum of vertical ridge direction, m=n-h, the pixel sum of the suitable ridge direction that n is pretreated remote sensing image or vertical ridge direction.
Described determination module 405, for from
in scope, choose arbitrarily a numerical value as the space scale along ridge direction, from
in scope, choose arbitrarily the space scale of a numerical value as vertical ridge direction, wherein, a
v=h
v× p, a
h=h
h× p, a
hfor the border yardstick along ridge direction, a
vfor the border yardstick of vertical ridge direction, h
hfor the optimal step size of described suitable ridge direction, h
vfor the optimal step size of described vertical ridge direction, the spatial resolution that p is described pretreated remote sensing image.
The content such as information interaction, implementation between each module, submodule in the said equipment, due to the inventive method embodiment based on same design, particular content can, referring to the narration in the inventive method embodiment, repeat no more herein.
The technical scheme that the embodiment of the present invention proposes takes into full account crop growing state and agricultural land soil characteristic in farmland, farmland underground hydrologic condition, agricultural tillage levels etc. are closely related, there is the feature of complex space variability, utilize the remote sensing image can be instantaneous, the harmless feature of obtaining on a large scale " planar " object spectrum information, fully excavate remote sensing image crop growing state sensor information, in district, counties etc. are compared with in large scale, realized for farmland massif fast, accurately, the extraction of real-time crop growing state Characteristics of spatial variability, provide the suitable space yardstick that different field agricultural land information investigation and variable are implemented simultaneously, increase work efficiency on ground, when alleviating working strength, also effectively improve accuracy and the precision of crop growing state field investigation, simultaneously, be implemented in spread in regional extent for precision agriculture acquisition of information and variable, significant.
It should be noted that, in this article, relational terms such as first and second is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply and between these entities or operation, have the relation of any this reality or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby the process, method, article or the equipment that make to comprise a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or be also included as the intrinsic key element of this process, method, article or equipment.The in the situation that of more restrictions not, the key element that " comprises " and limit by statement, and be not precluded within process, method, article or the equipment that comprises described key element and also have other same factor.
One of ordinary skill in the art will appreciate that: all or part of step that realizes said method embodiment can complete by the relevant hardware of programmed instruction, aforesaid program can be stored in the storage medium of embodied on computer readable, this program, in the time carrying out, is carried out the step that comprises said method embodiment; And aforesaid storage medium comprises: in the various media that can be program code stored such as ROM, RAM, magnetic disc or CD.
Finally it should be noted that: the foregoing is only preferred embodiment of the present invention, only, for technical scheme of the present invention is described, be not intended to limit protection scope of the present invention.All any amendments of making within the spirit and principles in the present invention, be equal to replacement, improvement etc., be all included in protection scope of the present invention.
Claims (12)
1. a method for the space scale of definite field investigation and field management, is characterized in that, the method comprises:
S1: obtain the remote sensing image that comprises pending plot and described remote sensing image is carried out to pre-service and obtain pretreated remote sensing image;
S2: obtain vegetation parameter from described pretreated remote sensing image;
S3: construct the right semivariance variation function of all pixels of fixed step size in pending plot from suitable ridge direction with vertical ridge direction respectively according to described vegetation parameter;
S4: the minimum fixed step size that calculates the suitable ridge direction while being less than the first predetermined threshold value along the first derivative values of the semivariance variation function of ridge direction, the minimum fixed step size of the vertical ridge direction when first derivative values that calculates the semivariance variation function of vertical ridge direction is less than the second predetermined threshold value, wherein, the described minimum fixed step size along ridge direction is that the minimum fixed step size of described vertical ridge direction is the optimal step size of vertical ridge direction along the optimal step size of ridge direction;
S5: the space scale of determining field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
2. method according to claim 1, is characterized in that, described described remote sensing image is carried out to pre-service, comprising:
Described remote sensing image is carried out to image co-registration;
And/or, described remote sensing image is carried out to radiation correcting;
And/or, described remote sensing image is carried out to Atmospheric Correction;
And/or, described remote sensing image is carried out to geometric correction.
3. method according to claim 1, is characterized in that, described S2 comprises:
Process for irrigating, from described pretreated remote sensing image, obtain normalization moisture vegetation index NDWI and/or vegetation moisture absorption index W BI;
For fertilizing management, from described pretreated remote sensing image, obtain ratio vegetation index RVI and/or green normalized differential vegetation index GNDVI and/or nitrogen reflection index NRI and/or photochemistry reflection index PRI and/or improved chlorophyll absorptance value index number MCARI and/or chlorophyll absorption index TCARI.
4. method according to claim 1, is characterized in that, described S4 comprises:
From described pretreated remote sensing image, extract pending plot.
5. method according to claim 1, is characterized in that, described S3 comprises:
Construct the right semivariance variation function of all pixels of fixed step size in pending plot according to following formula:
Wherein, γ (h) is called semivariance variation function, Z (x
i) be the pixel x of described pretreated remote sensing image
icorresponding vegetation parameter value, Z (x
i+ h) with pixel x
idistance is the corresponding vegetation parameter value of pixel of h, 1≤h≤k, k is the half of the suitable ridge direction of pretreated remote sensing image or the transversal section pixel sum of vertical ridge direction, m=n-h, the pixel sum of the suitable ridge direction that n is pretreated remote sensing image or vertical ridge direction.
6. method according to claim 1, is characterized in that, described S5 comprises:
From
in scope, choose arbitrarily a numerical value as the space scale along ridge direction;
From
in scope, choose arbitrarily the space scale of a numerical value as vertical ridge direction;
Wherein, a
v=h
v× p, a
h=h
h× p, a
hfor the border yardstick along ridge direction, a
vfor the border yardstick of vertical ridge direction, h
hfor the optimal step size of described suitable ridge direction, h
vfor the optimal step size of described vertical ridge direction, the spatial resolution that p is described pretreated remote sensing image.
7. a system for the space scale of definite field investigation and field management, is characterized in that, this system comprises:
Pretreatment module, obtains pretreated remote sensing image for obtaining the remote sensing image that comprises pending plot and described remote sensing image being carried out to pre-service;
Acquisition module, for obtaining vegetation parameter from described pretreated remote sensing image;
Build module, the vegetation parameter obtaining according to described acquisition module is respectively from constructing the right semivariance variation function of all pixels of fixed step size in pending plot along ridge direction with vertical ridge direction;
Computing module, the minimum fixed step size of the suitable ridge direction while being less than the first predetermined threshold value for calculating along the first derivative values of the semivariance variation function of ridge direction, and the minimum fixed step size of the vertical ridge direction of the first derivative values that calculates the semivariance variation function of vertical ridge direction while being less than the second predetermined threshold value, wherein, the described minimum fixed step size along ridge direction is that the minimum fixed step size of described vertical ridge direction is the optimal step size of vertical ridge direction along the optimal step size of ridge direction;
Determination module, for determining the space scale of field investigation and field management according to the described optimal step size along ridge direction and the optimal step size of described vertical ridge direction.
8. system according to claim 7, it is characterized in that, described pretreatment module, for described remote sensing image is carried out to image co-registration, and/or described remote sensing image is carried out to radiation correcting, and/or described remote sensing image is carried out to Atmospheric Correction, and/or described remote sensing image is carried out to geometric correction.
9. system according to claim 7, is characterized in that, described acquisition module, for processing for irrigating, obtains normalization moisture vegetation index NDWI and/or vegetation moisture absorption index W BI from described pretreated remote sensing image; For fertilizing management, from described pretreated remote sensing image, obtain ratio vegetation index RVI and/or green normalized differential vegetation index GNDVI and/or nitrogen reflection index NRI and/or photochemistry reflection index PRI and/or improved chlorophyll absorptance value index number MCARI and/or chlorophyll absorption index TCARI.
10. system according to claim 7, is characterized in that, described computing module, for extracting pending plot from described pretreated remote sensing image.
11. systems according to claim 7, is characterized in that, described structure module, for constructing the right semivariance variation function of all pixels of fixed step size in pending plot according to following formula:
Wherein, γ (h) is called semivariance variation function, Z (x
i) be the pixel x of described pretreated remote sensing image
icorresponding vegetation parameter value, Z (x
i+ h) with pixel x
idistance is the corresponding vegetation parameter value of pixel of h, 1≤h≤k, k is the half of the suitable ridge direction of pretreated remote sensing image or the transversal section pixel sum of vertical ridge direction, m=n-h, the pixel sum of the suitable ridge direction that n is pretreated remote sensing image or vertical ridge direction.
12. systems according to claim 7, is characterized in that, described determination module, for from
in scope, choose arbitrarily a numerical value as the space scale along ridge direction, from
in scope, choose arbitrarily the space scale of a numerical value as vertical ridge direction, wherein, a
v=h
v× p, a
h=h
h× p, a
hfor the border yardstick along ridge direction, a
vfor the border yardstick of vertical ridge direction, h
hfor the optimal step size of described suitable ridge direction, h
vfor the optimal step size of described vertical ridge direction, the spatial resolution that p is described pretreated remote sensing image.
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