CN103955606B - A kind of grassland based on remote sensing technology gradual Forecasting Methodology of the plague of locusts - Google Patents

Abstract

The present invention announces a kind of grassland based on remote sensing technology gradual Forecasting Methodology of the plague of locusts.The method is obtained by the means such as quantitative remote sensing and meteorological site observation affects the crucial habitat element distribution of grassland grasshopper population development, wherein, the crucial habitat key element of remote-sensing inversion includes Land Temperture, vegetation coverage and soil moisture, by set up evaluation model quantitative analysis locust lay eggs suitability, hatching suitability with growth suitability, build plague of locusts risk early prediction model；Further according to locust hatching and the time shaft grown, utilize incubation period and the remote sensing observations of three ages and the locust density data of fieldwork, revise plague of locusts risk class and predict the outcome, it is thus achieved that the gradual prediction to grassland plague of locusts the condition of a disaster.The technical scheme that the present invention provides can obtain the gradual renewal of sensitive habitat key element by the remotely-sensed data quantitative inversion that temporal resolution is higher, thus improves the precision of prediction of plague of locusts monitoring and forecasting model.

Description

A kind of grassland based on remote sensing technology gradual Forecasting Methodology of the plague of locusts

Technical field

The present invention relates to damage control and earth observation and field of navigation technology, be specifically related to one and utilize remote sensing with geographical Information systems technology, the method setting up pastoral area-agriculture district gradual prediction of the plague of locusts according to the stage of development of grassland grasshopper, for grassland locust The disaster surveillance of calamity and forecast provide the technical scheme with higher precision of prediction.

Background technology

For a long time, the work of locust detecting and reporting pest information is considered as one of main task of Pasture management department, plant protection department, Carrying out locust diaster prevention and control timely and effectively is a task the most urgent.But China's grassland area is the vastest, mainly Being distributed in northwest, North China and southern mountain, traffic is the most inconvenient, control personnel and preventing and treating equipment wretched insufficiency again, so that Bear the character of much blindness and passivity in plague of locusts preventing and treating.Although country and place put into every year prevents and treats fund in a large number, but produce effects not Significantly, Chang Yin affects adversely and prevents and treats best period and cause heavy economic losses.

At present, Grasshopper Population grows with what particularly effective way plague of locusts disaster surveillance does not has, and domestic and foreign literature is looked into Read finding report also few, especially lack the disaster surveillance forecasting procedure for prairie soil locust.Integrate and see, prior art In, observation station, the grassland fixed point relying primarily on ground carries out the observation of population density, according to different periods Grasshopper Population density Change, relies on the method for artificial principal commander's micro-judgment to carry out the prediction of plague of locusts the condition of a disaster.Wherein, that grows Grasshopper Population is pre- Survey is crucial, currently mainly carries out based on biological method: (1) according to the observation of locust insect life habit and experiment, really Determine the growth and development environment factor range (such as humiture) that Grasshopper Population is suitable；(2) sight on the spot of " age " is grown by locust Survey and analyze population development situation, thus judge whether the plague of locusts.Such as, plague of locusts observation station, general grassland can be after entering the winter Select the region that the plague of locusts occurred last year to gather pedotheque, return laboratory and be analyzed locust worm's ovum density, in conjunction with meteorological observation, Infer the ratio of surviving the winter of worm's ovum, then Second Year at the end of spring and the beginning of summer when, resampling observation egg hatch situation, and different " age Phase " development condition, thus " qualitative " judge that certain region occurs the probability (risk) of the plague of locusts and the order of severity thereof.

Existing biological method is counted as the means of mainly observing and predicting of current administration section, but the concept of geographical space is past Toward out in the cold.The purpose that locust is observed and predicted, not only and to grasp the plague of locusts quantitatively with on time of origin on geographical position When, where development occurs, needs to answer it may happen that the plague of locusts of which kind of degree, administration section just can take effectively Damage control means, carry out preventing and reducing natural disasters and the disaster relief.Have ignored spatial information, rely solely on the some position observation (prison limited to very much Survey station and meteorological site) result, carry out observing and predicting of plague of locusts the condition of a disaster, both lack accuracy, also deficiency thinks administration section's offer Hold.It is true that the information that locust relates in observing and predicting is mostly relevant with locus, including the tune of locust itself Look into distribution and population structure, locust density and the Schistosome eggs distribution of data such as locust, and the information of all kinds of habitats key element.Can recognize For, in locust observes and predicts work, the concept in space is permeated in from data acquisition, data analysis to the overall process of result output, Process the information just remote sensing relevant with locus, the speciality of GIS-Geographic Information System.Developing rapidly of remote sensing technology, increasingly Many sensors in orbit, and define star, sky, ground integrated multi-platform remote sense monitoring system, for continuously, dynamically enter Row is monitored and is extracted various environmental information on a large scale and provides strong instrument, and the most ripe.Utilize remote sensing Technology can carry out SS according to the ecological environmental condition that locust depends on for existence, determines the possible position that the plague of locusts occurs, it was predicted that The risk that the plague of locusts occurs, the scientific basis effectively prevented and treated for the plant protection department offer plague of locusts.Meanwhile, GIS-Geographic Information System can be used for sky Between data tissue, manage, analyze and model, provide spatial analysis and modeling tool for plague of locusts monitoring with prediction.Hence with The method that remote sensing technology combines with GIS-Geographic Information System carries out the trend that locust monitoring becomes new.

From Present Domestic, utilization RS & GIS is from the point of view of locust disaster monitoring with the present situation of forecast, its One, it is concentrated mainly on and utilizes remotely-sensed data to provide large range of habitat parameters to portray, but the method being based primarily upon Classification in Remote Sensing Image Distinguishing different Land cover types, the substantial connection then grown with green vegetation according to Grasshopper Population forecasts that the plague of locusts occurs Probability；Its two, only use GIS carry out the factor of the habitat (such as temperature, precipitation) relevant with locust growth management, can Depending on functions such as change and cartography exports.In these methods or lack description to the Grasshopper Population stage of development, or simply profit The functions such as data management, visualization or cartography export are provided, to plague of locusts wind for the forecast of plague of locusts risk by GIS-Geographic Information System Danger forecast is difficult to the decision support technique scheme provided truly.

The Grasshopper Population of cause calamity substantially can be divided into has the Locustamigratoria (Linnaeus) of stronger flight transfer ability (such as Locusta migratoria manilensis (Meyen), the Central Asia Locustamigratoria (Linnaeus) etc.), and the more weak grassland " soil locust " relying primarily on home environment evolved stages of transfer ability.The kind of prairie soil locust is relatively Many, as common in Xinjiang just has more than more than 10 kinds, and the process difference of the life habit of this two classes locust and cause calamity is bigger. Grow due to Grasshopper Population and affected by many factors such as meteorology, soil, vegetation, thus whether the Grasshopper Population in somewhere of a certain year Meeting amount reproduction, so that eventually forming the plague of locusts, has bigger time and spatial location laws.And because of data acquisition difficulty only Input according only to locust habit and disposable habitat key element and build plague of locusts risk model, often there is prediction by mistake Difference, this is also difficult point and the problem of current plague of locusts risk profile.

Summary of the invention

For above-mentioned the deficiencies in the prior art, overcome and be currently based on website observation and difficulty in the locust forecast of biological method Change during to obtain each factor of the habitat empty, it was predicted that result lacks enough space orientation, and precision of prediction is the highest is difficult for The problems such as administration section's application, the present invention provide a kind of gradual, there is the Forecasting Methodology of self regulation, mainly for China's grass The main cause calamity person prairie soil locust of former locust disaster carries out plague of locusts the condition of a disaster Risk Monitoring and forecast: it is fixed first to make full use of Amount remote-sensing inversion technology sets up the spatial and temporal distributions of the crucial habitat key element affecting grassland grasshopper population development, in conjunction with Grasshopper Population Up-to-date observation data that in growth course, remote sensing obtains and meteorological site data, set up a kind of based on the Grasshopper Population stage of development The plague of locusts forecast of gradual grassland and the condition of a disaster risk evaluating method.Gradual referring mainly to, the prediction of the method is not disposable Provide final result, and be as the change of habitat conditions and different critical stages that Grasshopper Population is grown, model can be carried out Revise and adjust.This invention can obtain the gradual renewal of sensitive habitat key element by the remote-sensing inversion of high time resolution, thus Improve the precision that predicts the outcome of plague of locusts monitoring and forecasting model.

The technical scheme is that

A kind of grassland based on remote sensing technology gradual Forecasting Methodology of the plague of locusts, the method by quantitative remote sensing technology and Meteorological site observation data obtain affecting the spatial and temporal distributions of the crucial habitat key element of grassland grasshopper population development, by building the plague of locusts Risk forecast model obtains locust calamity source index, then is modified locust calamity source index, thus obtains grassland The gradual prediction of plague of locusts the condition of a disaster, comprises the steps:

1) Grasshopper Population that complete is divided into the stage of laying eggs, incubation period and growth stage, for locust growth phase The three phases in worm population development cycle, obtains, by quantitative remote sensing, the crucial habitat key element that Grasshopper Population is grown, including Land Temperture, vegetation coverage and soil moisture, respectively obtain suitable index (Oviposition of laying eggs by calculating Suitability Index, OSI), hatch suitable index (Incubation Suitability Index, ISI) and growth is suitable Preferably index (Development Suitability Index, DSI)；

2) by suitable index of laying eggs, hatch suitable index and grow suitable Index for Calculation obtain locust calamity source refer to Number, as plague of locusts risk profile result；The gradual prediction that the present invention provides includes that initial predicted, incubation period are revised and three ages Revise:

2.1) when locust egg not yet starts to hatch, initial plague of locusts risk profile is carried out, by this Grasshopper Population growth phase In suitable index of laying eggs, hatched the suitable Index for Calculation of growth in suitable index and a upper cycle, it is thus achieved that initial locust disaster wind Danger index, as initial plague of locusts risk profile result；

2.2) at the end of Locust ovum hatching, carrying out incubation period correction, the remote sensing monitoring data stylish by this obtain new Hatch suitable index, more initial plague of locusts risk index is modified, thus obtain incubation period revised plague of locusts risk profile Result；

2.3) in three ages of locust, carry out the correction of three ages, by after Locust ovum hatching to three ages actual remote sensing supervise Survey data and meteorological rainfall spatial interpolation data, obtain the suitable index of new growth, further to step 2.2) plague of locusts wind that obtains Danger index is modified, as finally predicting the outcome.

In above-mentioned Forecasting Methodology, step 1) to obtain the method for soil moisture by quantitative remote sensing be that the present invention proposes Segmentation inversion method.First ground mulching state is divided into three types by the method, respectively bare soil, sparse vegetation Soil under Fu Gaiing and the soil under airtight vegetative coverage；Then using pixel as elementary cell, by remote sensing normalization vegetation The threshold value of index divides distinguishes above-mentioned three kinds of vegetation cover types, thus covers complicated earth surface and simplified；For three kinds Vegetation cover type, then carry out soil moisture retrieval and optimization by different inverse models, particularly as follows: for bare soil, Soil moisture is obtained by thermal inertia model inversion method；For the soil under airtight vegetative coverage, by temperature vegetation arid Index inverting obtains soil moisture；Soil under covering for sparse vegetation, is referred to temperature vegetation arid by heat of mixing inertia Number model inversion obtains soil moisture.

Step 1 of the present invention) to obtain the method for vegetation coverage by quantitative remote sensing be based on the pixel two points improved Model inversion method, the method is by the vegetative coverage degrees of data by a fieldwork sample prescription for Land cover types from different places Carry out statistical regression with from the calculated normalized differential vegetation index of remotely-sensed data, obtain with method of least square theoretic pure Vegetation and the normalized differential vegetation index value of pure soil pixel, then substitute into Pixel scrambling inverting and obtain the vegetation of this area and cover Cover degree.

In above-mentioned Forecasting Methodology, step 1) obtain suitable index of laying eggs be according to locust lay eggs the stage vegetation coverage, The soil texture and soil moisture status, build suitable index of laying eggs, for representing the suppression or suitable that locust is laid eggs by habitat key element Preferably situation, characterizes locust and lays eggs successful probability, and detailed process includes:

C1) obtain sandy loam index by amass with clay content index of calculating soil content index, and carry out normalizing Change, it is thus achieved that the soil types factor (Soil Type Factor, STF)；

C2) actual measurement soil moisture data and the temperature vegetation drought index obtained by remotely-sensed data inverting are utilized (Temperature Vegetation Dryness Index, TVDI) carries out statistical analysis, and the remote sensing setting up soil moisture is anti- Drill model, and be used for calculating Soil moisture factor (Soil Moisture Factor, SMF)；

C3) utilize remotely-sensed data inverting to obtain the vegetation coverage in egg-laying season, build the egg-laying season vegetative coverage factor (Vegetation Factor for OSI, VFO)；

C4) the soil types factor, Soil moisture factor and the egg-laying season vegetative coverage factor are first normalized, then lead to Cross weigthed sums approach to calculate acquisition and lay eggs suitable index.

Above-mentioned acquisition lay eggs suitable index weigthed sums approach in, in one embodiment of the invention, to soil types The weight value respectively of the factor, Soil moisture factor and the egg-laying season vegetative coverage factor is 0.3,0.5 and 0.2, for expression one The difference of three factor significance levels in region.

In above-mentioned Forecasting Methodology, step 1) obtain that to hatch suitable index be first acquisition Locust ovum overwintering survival rate, incubation period The soil moisture factor and humidity factor, then hatch suitable index by calculating to obtain, be used for characterizing severe winter and incubation period habitat The impact that locust egg is successfully hatched by condition, detailed process includes:

F1) time arrived according to first time cold wave is estimated by empirical statistics and to obtain Locust ovum overwintering survival rate；

F2) close on the Land Temperture of period by the locust incubation period in this cycle and be calculated temperature factor, recycle remote sensing Data inversion obtains temperature vegetation drought index (TVDI), is calculated the humidity factor before the incubation period；

F3) Locust ovum overwintering survival rate, the temperature factor of incubation period and humidity factor are first normalized, then by taking advantage of Area method calculates to obtain hatches suitable index.

In above-mentioned Forecasting Methodology, step 1) obtain that to grow suitable index be the geographical height above sea level factor first obtaining estimation range (Elevation Factor, EF), the locust trophophase vegetation pattern factor (Vegetation Type Factor, VTF) and plant The capped factor (Vegetation Cover Factor2, VCF₂), then by calculating the life obtaining locust growth and development stage Long suitable index (DSI), for quantitative description locust inhibitory action by surrounding habitat key element after egg hatching success, specifically Process includes:

S1) altitude ranges suitably lived according to geographical height above sea level and locust, is calculated height above sea level by elevation segmentation The factor；

S2) remotely-sensed data inverting is utilized to obtain the vegetation coverage of locust growth stage, according to applicable locust growth promoter Vegetation coverage scope, be calculated the vegetative coverage factor of locust growth stage；

S3) the vegetation pattern factor of locust growth stage is obtained according to vegetation pattern data；

S4) the height above sea level factor, the vegetative coverage factor of locust growth stage and the vegetation pattern factor are normalized, then lead to Cross weigthed sums approach and be calculated the suitable index of growth.

In one embodiment of the invention, above-mentioned steps S4) obtain grow suitable index linear weighting method be right The weight value respectively of the height above sea level factor, the vegetative coverage factor of locust growth stage and the vegetation pattern factor is 0.2,0.5 and 0.3, for representing the difference of three factor significance levels in a region.

In the Forecasting Methodology that the present invention provides, step 2.3) carry out, in three age makeover process, needing to recalculate growth Suitable index, and add the rainfall factor (Rainfall Factor, RF), reflect the rainfall impact on locust growth promoter. Detailed process is as follows:

X1) altitude ranges suitably lived according to geographical height above sea level and locust, is calculated height above sea level by elevation segmentation The factor；

X2) remotely-sensed data inverting is utilized to obtain the vegetation coverage of locust growth stage, according to applicable locust growth promoter Vegetation coverage scope, be calculated the vegetative coverage factor of locust growth stage；

X3) the vegetation pattern factor of locust growth stage is obtained according to vegetation pattern data；

X4), after the rainfall product data obtained according to meteorological site observation carries out space interpolation, the rainfall factor (RF) is calculated；

X5) the height above sea level factor, the vegetative coverage factor of locust growth stage, the vegetation pattern factor and the rainfall factor are returned One changes, then recalculates the suitable index of the growth after being revised by weigthed sums approach.

Wherein, X1), X2) and the vegetative coverage factor and the vegetation class of X3) the height above sea level factor in step, locust growth stage The computational methods of the type factor are with step S1), S2) and S3).Step X4) in the rainfall factor be according to fall maximum in region, minimum Rainfall carries out calculated.In one embodiment of the invention, what above-mentioned acquisition three age was revised grows suitable index Linear weighting method is to the height above sea level factor, the vegetative coverage factor of locust growth stage, the vegetation pattern factor and the rainfall factor Weight value respectively is 0.15,0.40 and 0.2 and 0.25, for representing the difference of four factor significance levels in a region.Fall The rain factor is only in step 5) three ages revise and just introduce because at this moment rainfall will limit the activity of locust, locust cause calamity is risen To certain inhibitory action.In step 3) initial plague of locusts risk profile and step 4) incubation period revise in, all without introduce The rainfall factor.

Further, step S1) and step X1) the intermediate altitude factor is according to a region highest elevation value, minimum height value Be best suitable for the elevation upper limit and lower limit that locust grows to calculate acquisition with this region, described in be best suitable on the elevation that locust grows Limit and lower limit need to be obtained by field investigation research.In one embodiment of the invention, Xinjiang region is suitable for grassland grasshopper The elevation upper and lower limit of growth promoter is respectively 2300 meters and 600 meters.

In one embodiment of the invention, at the beginning of above-mentioned grassland based on the remote sensing technology gradual Forecasting Methodology of the plague of locusts is carried out The time of beginning plague of locusts risk profile is the annual last ten-days period in April；The time carrying out incubation period correction is the last ten-days period in May；Carried out for three ages The time that phase is revised is the last ten-days period in June.

Beneficial effects of the present invention:

The present invention makes full use of the earth observation technology of advanced person, by quantitative remote sensing means quick obtaining locust habitat The spatial and temporal distributions information of key element, three critical stages grown further according to locust, a kind of gradual plague of locusts risk profile side is proposed Method, its result precision, higher than predicting the outcome that tradition dependence biological method and meteorological site observation data obtain, can be locust The prevention and control of plague of insects evil provide early warning support.Concrete advantage is as follows:

(1) utilize satellite remote sensing date and the quantitative inversion algorithm can be with the space of rapid extraction locust habitat key element on a large scale Distributed intelligence, by setting up quantitative evalution model, it is possible to achieve the suitability degree evaluation that locust is grown by certain habitat；

(2) three critical periods that locust is grown: egg-laying season, incubation period and trophophase are as prediction Grasshopper Population change Critical period, can preferably portray the coupling relation of each ecological factor and locust life habit, build plague of locusts risk and comment Valency model and method；

(3) owing at incubation period and trophophase, the change of habitat conditions can affect the change of Grasshopper Population density, or in quick-fried Hairdo increases, or develops round about, and therefore, the present invention proposes a kind of gradual strategy to predict locust hatching, growth shape Condition, the probability of prediction plague of locusts generation more accurately and risk, it is to avoid rely on the error once inputting data prediction result, can send out The advantage waving the remote sensing technology energy quick obtaining every day of habitat key element on a large scale.

Accompanying drawing explanation

Fig. 1 is embodiment of the present invention technical scheme flow chart (three late Aprils critical period, late May and June in figure The determination in the last ten-days period is the investigation acquisition according to Xinjiang region recent years, and different regions and different year may be because of habitat key elements Coupling there are differences).

Fig. 2 is that Acridoid From Xinjiang in 2011 grows suitability exponential figure (A: suitability index of laying eggs；B: hatching suitability Index；C: growth suitability index).

Fig. 3 is that grassland, Xinjiang region in 2011 plague of locusts occurrence risk predicts the outcome figure (A: predicting the outcome of the middle ten days and the last ten days in April； B: the last ten-days period in May revised result).

Detailed description of the invention

Below in conjunction with the accompanying drawings, by embodiment, the present invention is expanded on further, but limits the model of the present invention never in any form Enclose.

The present embodiment, as a example by the Xinjiang region that the grassland plague of locusts frequently occurs, uses the gradual plague of locusts wind that the present invention provides Danger Forecasting Methodology, carries out risk profile to locust the condition of a disaster.

As it is shown in figure 1, according to the interaction relationship of grassland grasshopper population development Yu habitat key element, the present invention is by locust kind Mass-sending is educated and is divided into three phases: stage of laying eggs, incubation period and growth stage, builds as locust the condition of a disaster risk forecast model Basis.The these three stage is to determine that Grasshopper Population grows the key that finally whether may be constructed disaster.Based on the these three stage RS & GIS technology is used to build suitable index of laying eggs, hatch suitable index and grow suitable index, then Observe and predict the field inspection at station by each department locust and report the pattern collected, and the renewal of remote sensing monitoring data, to for the first time Predict the outcome (P in Fig. 1) carry out the modified twice (M in Fig. 1₁With M₂) set up " gradual " prediction side of locust calamity source Method, makes to predict the outcome more accurately and reliably.Overall technological scheme flow process of the present invention is as it is shown in figure 1, P, M in figure₁、M₂Need basis The particular geographic location of grassland grasshopper disaster determines the division time of incubation period, three ages.In figure, three critical periods 4 are below the moon The determination of ten days, late May and late June is the investigation acquisition according to Xinjiang region recent years, different regions and different year May there are differences because of the coupling of habitat key element.Xinjiang region in this example, according to this cycle lay eggs suitable index and Hatch suitable index, start the risk to the plague of locusts from annual late April and carry out initial predicted (P), close on before using the incubation period and incubate Up-to-date LST and the TVDI data of the remotely-sensed data inverting of change phase (this is because before Locust ovum hatching also can be started hatching The impact of the conditions such as the temperature of a period of time, moisture), in conjunction with temperature record every day in winter in this cycle, build plague of locusts risk Index, carries out initial prediction (P in Fig. 1).In the late May in locust hatching later stage, the remotely-sensed data according to the incubation period is anti- Drill the up-to-date Land Temperture and TVDI data obtained, recalculate the suitable index of hatching and plague of locusts risk index, to initial predicted Result is modified, and improves the precision of plague of locusts risk profile, and this revised (see the M in Fig. 1 for the incubation period₁).According to Xinjiang of China The condition survey result in area shows, late June is the period terminated three ages of grassland grasshopper, is that locust grows and locust of going out Critical period, according to the space interpolation data of rainfall of meteorological site, the vegetative coverage number of degrees of remote-sensing inversion in June in this cycle According to, recalculating the suitable index of growth and plague of locusts risk index, carry out the correction again of plague of locusts risk, this is to revise three ages (the M in Fig. 1₂)。

It is embodied as step as follows:

1) suitable index (OSI) of laying eggs is built: lay eggs the vegetative coverage in stage, the soil texture with native according to locust upper one year The situations such as earth humidity, to the soil types factor (Soil Type Factor, STF), Soil moisture factor (Soil Moisture Factor, SMF) and three factors of the egg-laying season vegetative coverage factor (Vegetation Factor for OSI, VFO) return One change, then use linear weighted model obtain suitable index of laying eggs (Oviposition Suitability Index, OSI), in order to represent suppression locust laid eggs habitat or suitable situation, characterize locust and lay eggs successful probability.Specifically include Following process:

1.1) obtain the soil types factor (STF) by calculating: locust spawning habitat is optimum with sandy loam, sandy loam i.e. between Soil between sandy soil and clay.Clay content is about 40%, and silt content is about 60%, and optimum locust lays eggs, and utilizes Amass with clay content index (CI) of silt content index (SI) obtains sandy loam index, and is normalized.Computing formula is as follows:

$\mathrm{SI}=\left\{\begin{array}{cc}(S_{\max }-S)/(S_{\max }-60)& (60\&le;S)\\ (S-S_{\min })/(60-S_{\min })& (S<60)\end{array}\right.---\left(1\right)$

$\mathrm{CI}=\left\{\begin{array}{cc}(C_{\max }-C)/(C_{\max }-40)& (40\&le;C)\\ (C-C_{\min })/(40-C_{\min })& (C<40)\end{array}---\left(2\right)\right.$

STF=SI*CI (3)

In formula, SI is silt content index, and CI is clay content index, and STF is the soil types factor；S and C represents respectively and contains Husky amount data and clay content data, for dimensionless, in terms of percent；S_max,S_minThe respectively maximum of silt content in study area Value and minima；C_max,C_minIt is respectively the maxima and minima of clay content.

1.2) Soil moisture factor (SMF) is obtained by inverting: the growth of locust egg needs to absorb moisture, and soil moisture is too high Or the too low growth that all can affect locust egg.Temperature vegetation drought index (TVDI) obtained by remotely-sensed data inverting and soil moisture It is inversely proportional to, is used for representing the change of soil moisture herein, by actual measurement soil moisture data and the statistical analysis of TVDI, set up soil The inversion formula of earth humidity is as follows.Wherein, when soil moisture optimum locust between 10%～20% lays eggs, therefore corresponding TVDI scope is 0.71～0.86.

$\mathrm{SMF}=\left\{\begin{array}{cc}({\mathrm{TVDI}}_{\max }-\mathrm{TVDI})/({\mathrm{TVDI}}_{\max }-0.86)& (\mathrm{TVDI}>0.86)\\ 1& (0.71\&le;\mathrm{TVDI}<0.86)\\ (\mathrm{TVDI}-{\mathrm{TVDI}}_{\min })/(0.71-{\mathrm{TVDI}}_{\min })& (\mathrm{TVDI}<0.71)\end{array}\right.---\left(4\right)$

In formula, SMF is Soil moisture factor, and TVDI is temperature vegetation drought index, for dimensionless, value 0～1 it Between；TVDI_max,TVDI_minThe maximum that respectively in survey region, remote-sensing inversion obtains and minimum TVDI value.

1.3) by calculating the acquisition vegetative coverage factor (VFO): locust typically the most just lays eggs at vegetative coverage, because of This, vegetation coverage is the biggest more is not suitable for locust and lays eggs.Here FVC1 the is locust vegetative coverage in (7～August) when laying eggs Degree.

$\mathrm{VFO}=\frac{{\mathrm{FVC}1}_{\max }-\mathrm{FVC}1}{{\mathrm{FVC}1}_{\max }-{\mathrm{FVC}1}_{\min }}---\left(5\right)$

In formula, VFO is the vegetative coverage factor in egg-laying season；FVC1 is planting of egg-laying season of utilizing remote sensing images inverting to obtain Capped degrees of data, for dimensionless variable, value is between 0～1；FVC1_max,FVC1_minIt is on egg-laying season remote sensing images respectively Vegetation coverage maximum and minima.

1.4) calculate acquisition by linear weighting method to lay eggs suitable index (OSI): owing to different factor pair locusts lay eggs Impact is different, by linear weighted function and, locust can be calculated and lay eggs suitable index.

OSI=b₁×nSTF+b₂×nSMF+b₃×nVFO (6)

In formula, b₁、b₂、b₃Being respectively weight number, in this example, value is 0.3,0.5 and 0.2；N represents and carries out index The normalization of 1～10 intervals.

2) the suitable index of hatching (ISI) is built: carry out structure by the temperature and humidity factor of Locust ovum overwintering survival rate, incubation period Build the suitable index of hatching (Incubation Suitability Index, ISI), characterize severe winter with incubation period habitat conditions to locust The impact that ovum is successfully hatched；Method is, is first normalized three indexes, then takes linear weighted model to build ISI；Specifically include following process:

2.1) Locust ovum overwintering survival rate it is calculated: the calculating Main Basis of Locust ovum overwintering survival rate cold wave for the first time arrives Time carry out estimating, concrete calculation procedure is as follows: Step1: according on JIUYUE, upper 1 to the every per day temperature in Second Year March Degree and every max. daily temperature data, select mean daily temperature reduction by more than 10 DEG C, max. daily temperature to be less than 5 DEG C, and day the earliest Phase is as the time of cold wave first.Step2: calculating the value of time X: first 5 days of early September was 1, and latter 5 days is 2, first 5 days of the middle ten days was 3, latter 5 days is 4, the like ...；Step3: be calculated as motility rate, first cold wave time and the relation such as formula (7) of survival rate,

K=-0.06X²+4.2X+7 (7)

In formula, K is survival rate, and X is time value, and computational methods are shown in above-mentioned Step2.

2.2) temperature factor (TF) and humidity factor (MF) it are calculated:

$\mathrm{TF}=\left\{\begin{array}{c}\frac{{\mathrm{LST}}_{\max }-\mathrm{LST}}{{\mathrm{LST}}_{\max }-15}(\mathrm{LST}\&GreaterEqual;15)\\ \frac{\mathrm{LST}-{\mathrm{LST}}_{\min }}{15-{\mathrm{LST}}_{\min }}(\mathrm{LST}<15)\end{array}\right.---\left(8\right)$

In formula, TF and MF is respectively the soil moisture factor and Soil moisture factor；LST is the face, land obtained by remote-sensing inversion Temperature, unit is degree Celsius, LST_max,LST_minIt is respectively the maximal and minmal value of LST.

Remote sensor MODIS Land Temperture (LST) that (late April) acquisition is the last before the incubation period then closes on Data and TVDI data, be used for building the soil moisture factor (TF).Soil moisture factor (MF) directly represents by TVDI value.

2.3) the acquisition suitable index of Locust ovum hatching (ISI) is calculated: after temperature factor and humidity factor being normalized, press Such as following formula

Son calculating ISI:

ISI=K*n (TF*MF) (9)

In formula, K expression (7) result of calculation, n represents that the product to variable TF Yu MF carries out the normalization in 1～10 intervals.

3) build and grow suitable index (DSI): according to the geographical height above sea level factor of estimation range (Elevation Factor, EF), the locust trophophase vegetation pattern factor (Vegetation Type Factor, VTF) and the vegetative coverage factor (Vegetation Cover Factor2, VCF₂), first three indexes are normalized, then take linear weighted function Model builds the suitable index (Development Suitability Index, DSI) of locust growth and development stage.This refers to Count quantitative description locust inhibitory action by surrounding habitat key element after egg hatching success.Specifically include following process:

3.1) by being calculated the height above sea level factor (EF): geographical height above sea level be key affect locust growth habitat conditions because of Son, according to the region characteristic in Xinjiang, takes height above sea level 2300m with 600m as separation.If applied to other survey regions, The value of this separation to adjust according to the locust life habit of study area and to determine.

$\mathrm{EF}=\left\{\begin{array}{cc}\frac{{\mathrm{DEM}}_{\max }-\mathrm{DEM}}{({\mathrm{DEM}}_{\max }-H_1)}& (\mathrm{DEM}>H_1)\\ 1& (H_2\&le;\mathrm{DEM}\&le;H_1)\\ \frac{\mathrm{DEM}-{\mathrm{DEM}}_{\min }}{H_2-{\mathrm{DEM}}_{\min }}& (\mathrm{DEM}<H_2)\end{array}\right.---\left(10\right)$

Formula, EF is the height above sea level factor, and DEM is Law of DEM Data, and unit is m, DEM_max,DEM_minIt is respectively research Maximum and the minimum height value in region, unit is m；H₁With H₂Represent respectively study area be best suitable for locust grow the elevation upper limit with under Limit, needs to determine span according to concrete region and locust life habit.

3.2) by being calculated the vegetative coverage factor (VFD) of locust growth stage, concrete grammar is as follows:

$\mathrm{VFD}=\left\{\begin{array}{c}\frac{{\mathrm{FVC}2}_{\max }-\mathrm{FVC}2}{{\mathrm{FVC}2}_{\max }-0.5}(\mathrm{VFD}>0.5)\\ 1(0.25\&le;\mathrm{VFD}\&le;0.5)\\ \frac{\mathrm{FVC}2-{\mathrm{FVC}2}_{\min }}{0.25-{\mathrm{FVC}2}_{\min }}(\mathrm{VFD}<0.25)\end{array}\right.---\left(11\right)$

In formula, VFD is the vegetation coverage factor of locust growth stage, and FVC2 is that the locust according to remotely-sensed data inverting is raw The vegetation coverage in long stage, for dimensionless, value 0～1, FVC2_max, FVC2_minRepresent locust growth stage study area respectively The maximum and minimum value of interior each remote sensing pixel vegetation coverage.Coefficient 0.50 and 0.25 represents applicable locust growth promoter respectively The minimum and maximum threshold value of vegetation coverage, vegetation coverage is too high can be limited locust activity and obtain enough illumination, and too low The most it is not provided that the food of abundance.

3.3) determine the assignment of each vegetation pattern according to vegetation pattern data, obtain the vegetation pattern of locust growth stage The factor (VTF)；.

3.4) above three index is normalized, is calculated growth by linear weighted model and suitably refers to Number (DSI):

DSI=d₁×nEF+d₂×nVFD+d₃×nVTF (12)

In formula, d₁、d₂、d₃It is respectively three factors by the weight coefficient after 1～10 normalization, respectively 0.2,0.5 He 0.3。

4) carry out initial plague of locusts risk profile: structure lay eggs suitable index, hatch suitable index and grow suitable index On the basis of, the method that taking counts is multiplied builds locust calamity source index (Locust Risk Indicator, LRI), fixed The risk size of amount prediction grassland plague of locusts outburst.Utilize historical data to the empirical relation between LRI and risk class, set up district Territory plague of locusts risk class partitioning standards, makes plague of locusts risk rating scheme.Due to predicting the outcome based on upper one year of this step Historical data is as input, because being referred to herein as " initial plague of locusts risk profile ", shown in the P stage in Fig. 1；

5) revised beginning plague of locusts risk profile result (M for the first time is obtained by incubation period correction₁): incubation period correction is According to the actual remote sensing monitoring data of locust incubation period then, mainly include Land Temperture, TVDI data, recalculate hatching suitable Preferably index and plague of locusts risk index, in conjunction with the locust density data of ground investigation, adjusts risk class and divides, to initial risks Predict the outcome and be modified；

6) final result (M of plague of locusts risk profile is obtained by the correction of three ages₂): it is to utilize for three ages that three ages were revised Actual habitat key element (rainfall, vegetation coverage) monitoring result, as entering data to recalculate the suitable index of growth With plague of locusts risk index, observe in conjunction with ground locust density, plague of locusts risk class is modified.Revised through three ages Output result, can be as the foundation of department's decision-making of preventing and reducing natural disasters as the final result of this method plague of locusts risk profile.In three ages Growth and development stage after phase, can further rely on the variable density of ground observation locust, as depending on of " closing on " early warning According to.

Step 5) and step 6) in, it is to the correction predicted the outcome respectively, concrete grammar is to utilize step 1)～step 4) Method, using remote-sensing inversion actual then and ground observation data replace the data of previous stage as input, respectively to incubating Change suitable index and the suitable index of growth recalculates.Additionally, in step 6) three ages revise, calculate validation period Needing to add the rainfall factor (RF) when of growth suitability index, its computational methods are as follows:

$\mathrm{RF}=\left(\frac{P_{\max }-P}{P_{\max }-P_{\min }}\right)$

In formula, P is rainfall, and unit is mm；P_max, P_minThe respectively maxima and minima of rainfall in region.

Then, above-mentioned steps 3.4) in formula (12) just become:

DSI=d₁×nEF+d₂×nVFD+d₃×nVTF+d₄×nRF

In formula, d₁、d₂、d₃、d₄It is respectively the height above sea level factor (EF), the vegetative coverage factor of three ages (VFD), vegetation pattern The factor (VTF) and four factors of the rainfall factor (RF) by the weight coefficient after 1～10 normalization, respectively 0.15,0.4 and 0.2 With 0.25.

Due to fluctuation and the change of annual each habitat key element, plague of locusts risk profile result precision the most backward is naturally more Height, but the time to department's emergency response of preventing and reducing natural disasters is the shortest, therefore, present invention employs the prediction side of one " gradual " Formula, meets the demand of plague of locusts defence.

In the plague of locusts Risk Forecast Method of above-mentioned grassland based on remote sensing technology, relate to the Grasshopper Population habitat of multiple key The remote-sensing inversion of the factor, periodically obtains the quantitative data of large-scale locust habitat key element by remote sensing technology.Wherein, in State's terrestrial climate data earning in a day data set (mean temperature, maximum temperature, minimum temperature) relies on surface weather observation to obtain, can be from National Meteorological Bureau shares and downloads on website, and administrative division, dem data obtain from local Mapping departments, the soil texture, silt content number According to obtaining from agricultural sector.In addition, the crucial habitat key element relying on remote-sensing inversion to obtain in the present invention is: face, land temperature Degree, vegetation coverage, soil moisture, these three factor change in time and space is obvious, maximum to locust development impact, and is difficult to pass through Conventional means obtains.The inversion algorithm of Land Temperture is the most ripe, and inversion accuracy is also enough, and relatively difficult is to plant Coating cover degree and the remote-sensing inversion of soil moisture.The vegetation coverage factor and the inverting of Soil moisture factor the two key factor Extracting method is as follows:

A) the segmentation inversion method of soil moisture based on remote sensing:

The remote sensor detection spectral coverage that vegetation cover situation is different, select is different, and the soil moisture of foundation is with distant Information chain between sense information is also the most different.In general, the research of Remote Sensing of Soil Moisture inverse model is all by ground table-like State is defined as two kinds of perfect conditions, and one is bare area, under the conditions of another one is then vegetative coverage.The principle of thermal inertia method is base In the heat exchange on earth's surface Yu air, the theoretical model obtained by road radiation transmission process is derived, at vegetative coverage relatively High earth's surface can produce the biggest error due to the interference of vegetation information；And surface temperature of based on energy-balance equation combines and plants By the method for index, such as: temperature vegetation drought index (Temperature Vegetation Dryness Index, TVDI), Vegetation coverage less close in the case of bare area as vegetation information impact is significantly increased by Soil Background so that Scholar's earth true water content obtains the estimation of mistake.Therefore, a kind of method that the present invention proposes segmentation inversion soil moisture. First ground mulching state is divided into three types by the method: bare soil, sparse vegetation cover under soil and airtight vegetation Soil under Fu Gaiing；Then using pixel as elementary cell, vegetation index threshold value is used to distinguish above-mentioned three kinds of vegetative coverage classes Type, thus complicated earth surface is covered (unrelated with Growing season) and is simplified, reselection suitable Optimization inversion model, whole to improve The topsoil remote-sensing humidity inversion accuracy in individual region and automatization level.Specific as follows:

A1)In the case of bare soil: build thermal inertia model.Soil moisture content is the highest, and the thermal inertia of soil is the biggest, Soil moisture luffing is the least；Otherwise, antecedent soil moisture hydropenia, Soil thermal intertia is the least, and soil moisture luffing is the biggest.Utilize heat The infrared remote sensing soil moisture changes, and obtains Soil thermal intertia, reaches to estimate the purpose of soil moisture content.Thermal infrared sensor Inverting surface temperature on daytime, is synthesized it by maximum synthetic method.For the soil moisture content inverting in big region, The product that a couple of days synthesizes can be used, to eliminate the impact of cloud to greatest extent, ensure the seriality of data space simultaneously.Synthesis side Method is formula (13):

LST_MAX=MAX (LST_DAY1,LST_DAY2,LST_DAY3……,LST_DAYk) (13)

Wherein MAX () is that band math takes max function, LST_MAXFor function return maximum synthesis after image, LST_DAY1,LST_DAY2,LST_DAY3……,LST_DAYkBeing respectively the surface temperature on daytime of every day in k days, unit is degree Celsius.K is For the natural law of temperature value of synthesis, utilize formula (14), calculate all band albedo α (be with MODIS data instance here, Different sensors has difference),

α=0.160 ρ₁+0.291ρ₂+0.243ρ₃+0.116ρ₄+0.112ρ₅+0.081ρ₇-0.0015 (14)

In formula, α is all band albedo, dimensionless；ρ_iFor the reflectance of the 1st～7 wave bands of MODIS sensor, immeasurable Guiding principle, value 0～1.

Formula in recycling formula (15) calculates apparent thermal inertia ATI, can obtain under the support of ground measured data ATI and the statistical relationship of soil moisture, predict the soil humidity information of unknown pixel with this.

ATI=(1-α)/△ T (15)

In formula, ATI is apparent thermal inertia, dimensionless；α is all band albedo, dimensionless；△ T is maximum temperature difference round the clock, Unit is Kelvin.

A2)In the case of airtight vegetative coverage: build TVDI model.Utilize the Ts-that Land Temperture is constituted with vegetation index NDVI feature space, can derive index TVDI representing water stress.At the feature space simplified, by wet limit (Ts_min) it is processed as the straight line parallel with NDVI axle, dry limit (Ts_max) linear with NDVI, TVDI=1 on dry limit, TVDI=0 on wet limit.TVDI is the biggest, and soil moisture is the lowest, and TVDI is the least, and soil moisture is the highest.Vegetation cover situation is used NDVI replaces, and in the two-dimensional space of surface temperature Ts and NDVI, TVDI is expressed as formula (16):

$\mathrm{TVDI}=\frac{\mathrm{Ts}-{\mathrm{Ts}}_{\min }}{{\mathrm{Ts}}_{\max }-{\mathrm{Ts}}_{\min }}=\frac{\mathrm{Ts}-(a_2+b_2\mathrm{NDVI})}{a_1+b_1\mathrm{NDVI}-(a_2+b_2\mathrm{NDVI})}$

In formula, TVDI is temperature vegetation drought index, dimensionless, value 0～1, and Ts-gives the surface temperature of pixel, single Position is Kelvin, and NDVI-gives the normalized site attenuation of pixel, dimensionless；Ts_maxAnd Ts_minIt is respectively Ts-NDVI Dry limit and the temperature value on wet limit in feature space, unit is Kelvin.TVDI asks at last with Ts-NDVI feature space as base Plinth, with the effective water content of study area upper soll layer between wilting point and field capacity as qualifications, wet limit TVDI is minimum, is 0, and soil moisture content is close to field capacity；The TVDI on dry limit is maximum, is 1, and soil moisture content is close to the system that wilts Number.a₁,a₂,b₁,b₂It is to dry limit (Ts respectively according to ground measured data_max) and wet limit (Ts_min) correction coefficient.

A)Under sparse vegetation covers: build hybrid mean value model.For sparse vegetation area of coverage soil moisture retrieval, it should In view of the impact of vegetation, using normalized differential vegetation index as a simple vegetation state evaluating, it is introduced into recurrence Model, in low vegetation-covered area, utilizes the meansigma methods of two kinds of model inversion results, inverting topsoil humidity value, as such, it is possible to Avoid the error that simple thermal inertia or TVDI method are brought.

Soil under covering according to bare soil, sparse vegetation and the three kinds of different situations of soil under airtight vegetative coverage, Use different models, carry out soil moisture retrieval.Soil and airtight vegetation under covering for bare soil, sparse vegetation are covered Three kinds of different land covers types of soil under Gai design Remote Sensing of Soil Moisture inversion methods, by the optimization of inverse model and Segmentation selects, and final integration also sets up the segmentation inversion model being applicable to different cover situations.Segmentation composite model is in fortune In row, the selection of submodel is that the statistical relationship of the NDVI value according to survey region determines, finds out thermal inertia model and TVDI The flex point that model accuracy changes with NDVI, is the diacritical point of three kinds of cover types.So, the operation of composite model is not required to very important person For getting involved, it is only necessary to survey region is analyzed, find out the threshold limit value of the NDVI that suitable model segment selects.

B) vegetation coverage inversion method based on the Pixel scrambling improved:

Pixel scrambling is that a pixel is regarded as the mixed pixel being made up of vegetation and soil two parts, it is assumed that plant The capped area ratio i.e. vegetation coverage of this pixel is f_C, then the area ratio that soil covers is then 1-f_C.If by vegetation Remote sensing information obtained by the pure pixel covered is S_veg, then information S contributed of vegetation part in mixed pixel_VCan be with table It is shown as S_vegAnd f_cProduct.With normalized differential vegetation index (Normalized Difference Vegetation Index, NDVI) vegetation state of each remote sensing image picture element is described, the inversion algorithm of available vegetation coverage, such as formula (17):

Fc=(NDVI-NDVI_soil)/(NDVI_veg-NDVI_soil) (17)

Wherein, f_CFor the pixel vegetation coverage of remote-sensing inversion, dimensionless, value is 0～1；NDVI is this pixel NDVI value, dimensionless, between value-1～+1；NDVI_vegFor the NDVI value of pure vegetation pixel, NDVI_soilFor pure soil pixel NDVI value.

In actual applications, conventional NDVI maximum and minima replace pure vegetation and the vegetation index of pure soil.But It is difficult to ensure that the pixel found is pure pixel in remote sensing image.Therefore, in invention, propose to utilize by fieldwork sample prescription Data carry out statistical regression and ask for pure vegetation pixel and the NDVI value of pure soil pixel, counter push away NDVI_vegAnd NDVI_soil Value.

For pixel each on remote sensing images, there is following relation:

NDVI_veg·fc+NDVI_soil(1-fc)=NDVI (18)

I.e.

$\left[\begin{array}{cc}{\mathrm{fc}}_1& 1-{\mathrm{fc}}_1\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\mathrm{fc}}_n& 1-{\mathrm{fc}}_n\end{array}\right]\left[\begin{array}{c}{\mathrm{NDVI}}_{\mathrm{veg}}\\ {\mathrm{EDVI}}_{\mathrm{soil}}\end{array}\right]=\left[\begin{array}{c}{\mathrm{NDVI}}_1\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ {\mathrm{NDVI}}_n\end{array}\right]---\left(19\right)$

In formula, fc_iProfit for surveying the vegetative coverage angle value obtained, NDVI by ground sample_iRemote sensing vegetation for this pixel Exponential quantity, NDVI_soilWith NDVI_vegIt is the NDVI value of pure soil pixel and pure vegetation pixel, all dimensionless.Utilize Method of least square, can solve its least square solution NDVI_veg、NDVI_soil.For every kind of land use pattern, calculate respectively NDVI_vegAnd NDVI_soilValue.The most just establish the vegetation coverage inverse model for specific region.This method solve picture In unit's two sub-models, pure vegetation and a determination difficult problem for the NDVI value of pure soil pixel, take into full account specific region in research Priori, can improve the precision utilizing Multi-spectral Remote Sensing Data inverting regional vegetation coverage, it is to avoid in existing method Think and determine the disadvantage of this two parameter.

In the present embodiment, as a example by the Xinjiang region that the grassland plague of locusts frequently occurs, to crucial habitat key element vegetation therein In the step of coverage and the remote-sensing inversion of soil moisture and plague of locusts risk profile, it is preferable that the determination of some key parameters is such as Under:

First, utilize and on TERRA and the AQUA satellite that NASA (NASA) launches, carry intermediate-resolution imaging Spectrogrph (MODIS) sensing data, 2010 6, region, inverting Xinjiang, July and 2011 3,8 days land surface temperatures in April (LST), vegetation coverage and soil moisture data.Wherein soil moisture and the statistical model such as following formula of TVDI: y=-66.662x + 69.939, x, y are respectively TVDI and the soil moisture value of certain pixel.According to correlational study, region, Xinjiang exists when soil moisture Between 10%～20%, optimum locust lays eggs.The inversion method of vegetation coverage have employed formula (17)～(19) carry out inverting and estimate Calculate.Land Temperture data directly have employed the product that US Geological Survey utilizes the Thermal infrared bands of MODIS remotely-sensed data to produce Product.

Then, suitability index of laying eggs, hatching suitability index and growth suitability index are calculated.Concrete steps are the most attached Shown in Fig. 1, Grasshopper Population is grown and is divided into three phases: stage of laying eggs, incubation period and growth stage, first pass through calculating Obtain laying eggs suitable index, hatch suitable index and grow suitable index, result of calculation is as shown in Figure 2.As a example by Xinjiang, Calculating formula lay eggs suitability index time, the value of b1, b2, b3 coefficient in above-mentioned formula (6) is respectively 0.3,0.5,0.2, represents The weight difference of three factors；Coefficient H in the formula (10) calculating growth suitability index₁And H₂According to field investigation and locust Worm habit result of study value respectively is 2300m and 600m, the coefficient d in formula (12)₁、d₂、d₃Take 0.2 respectively, 0.5, 0.3, the assignment of the vegetation pattern factor is shown in Table 1.

Table 1 vegetation pattern factor value and corresponding vegetation pattern

Then, it is thus achieved that plague of locusts risk index.Lay eggs suitability index, hatching suitability index and life having obtained locust After long suitability index, it is possible to the mode taking each index to be multiplied builds plague of locusts risk index, divide plague of locusts risk class i.e. Predicting the outcome first of the available outburst risk of the plague of locusts then.As a example by Xinjiang, plague of locusts risk index is entered according to index height Row risk stratification: high (>200), medium higher (150～200), medium (100～150) on the low side, low (<100) four grades, see Fig. 3, A be predict the outcome mid or late April (this year April, humiture rose very fast, Locust ovum hatching somewhat partially early, the MODIS8 of employing It generated data is across the middle ten days and the last ten days)；B is late May revised result.This result shows which region occurs the risk of the plague of locusts Height, which area risk is relatively low, can be that the plant protection department prevention and control plague of locusts provides early warning support.

The table 2 Xinjiang plague of locusts in 2011 actually occurs situation

Finally, carry out gradual correction to predict.The sociales of grassland in Xinjiang locust mainly have Italian locust, red shin halberd stricture of vagina Locust, secret note dolly locust, Siberia locust etc., the incubation period the earliest in normal time occurs in the first tenday period of a month in May, and late May is basically completed incubates Change.Now can be observed and predicted station by each locust and proceed by incubation period field investigation, investigation content mainly has geographical position, locust kind Class, locust density, aerial temperature and humidity, upper soll layer humiture.Elapsed time is mainly in the locust incubation period (under the first tenday period of a month in May arrive Ten days), three ages (mid or late June), three locust developmental stages of adult stage (after by the end of June).Fig. 3 B show according to 2011 5 The habitat key element of moon remotely-sensed data inverting in the last ten-days period, the plague of locusts risk index obtained after mode input is adjusted.Table 2 is 2011 A situation arises, from accompanying drawing 3B it can be seen that the method utilizing the present invention to provide is pre-to grassland, the Xinjiang region plague of locusts for year actual plague of locusts The result of report with 2011 the last actual plague of locusts that a situation arises is the most identical, it was demonstrated that the effectiveness of the method.The most visible, 2011 The result (Fig. 3 A) of mid or late April in year prediction and the statistical of practical situation in table 2, differ greatly, and the plague of locusts occurs generally Risk is relatively low, and the most high risk region is on the low side；But in late May, 2011 prediction result (Fig. 3 B) with actually occur situation It coincide preferably, illustrate that the gradual Forecasting Methodology that the present invention proposes can be greatly enhanced model prediction accuracy.

Claims (12)

1. grassland based on the remote sensing technology gradual Forecasting Methodology of the plague of locusts, is characterized in that, described method passes through quantitative remote sensing Inversion technique and meteorological site observation data obtain affecting the spatial and temporal distributions of the crucial habitat key element of grassland grasshopper population development, logical Cross structure plague of locusts risk forecast model and obtain locust calamity source index, then locust calamity source index is modified, thus Obtain the gradual prediction to grassland plague of locusts the condition of a disaster, comprise the steps:

1) Grasshopper Population that complete is divided into the stage of laying eggs, incubation period and growth stage, for locust kind growth phase Group growth phase three phases, by quantitative remote sensing obtain Grasshopper Population grow crucial habitat key element, including face, land Temperature, vegetation coverage and soil moisture, respectively obtained suitable index of laying eggs, hatch suitable index and grow suitably by calculating Index；

2) by suitable index of laying eggs, hatch suitable index and grow suitable Index for Calculation acquisition locust calamity source index, making For plague of locusts risk profile result；Described gradual prediction includes that initial predicted, incubation period are revised and three ages were revised:

2.1) when locust egg not yet starts to hatch, initial plague of locusts risk profile is carried out, by this Grasshopper Population growth phase Lay eggs suitable index, hatched the suitable Index for Calculation of growth in suitable index and a upper cycle, it is thus achieved that initial locust calamity source refers to Number, as initial plague of locusts risk profile result；

2.2) at the end of Locust ovum hatching, carrying out incubation period correction, the remote sensing monitoring data stylish by this obtain new hatching Suitable index, more initial plague of locusts risk index is modified, thus obtain incubation period revised plague of locusts risk profile result；

2.3) in three ages of locust, the correction of three ages is carried out, by actual remote sensing monitoring number to three ages after Locust ovum hatching According to meteorological rainfall spatial interpolation data, obtain the suitable index of new growth, further to step 2.2) plague of locusts risk that obtains refers to Number is modified, as finally predicting the outcome.

2. as claimed in claim 1 Forecasting Methodology, is characterized in that, step 1) described obtain soil by quantitative remote sensing method The method of humidity is segmentation inversion method, and first ground mulching state is divided into three types by described method, the most naked Soil under dew soil, sparse vegetation covering and the soil under airtight vegetative coverage；Then using pixel as elementary cell, pass through The threshold value of the normalized differential vegetation index calculated by remote sensing divides and distinguishes above-mentioned three kinds of vegetation cover types, thus to complicated earth surface Cover and simplified；Again for three kinds of vegetation cover types, carry out soil moisture retrieval and optimization by different inverse models, Particularly as follows: for bare soil, obtain soil moisture by thermal inertia model inversion method；For the soil under airtight vegetative coverage Earth, obtains soil moisture by temperature vegetation drought index model inverting；Soil under covering for sparse vegetation, by mixing Thermal inertia obtains soil moisture with temperature vegetation drought index model inverting.

3. as claimed in claim 1 Forecasting Methodology, is characterized in that, step 1) described obtain vegetation by quantitative remote sensing method Coverage is that described method is by planting the fieldwork sample prescription in an area based on the Pixel scrambling inversion method improved It is capped degrees of data and carries out statistical regression from the calculated normalized differential vegetation index of remotely-sensed data, obtaining with method of least square Theoretic pure vegetation and the normalized differential vegetation index value of pure soil pixel, then substitute into Pixel scrambling inverting and be somebody's turn to do The vegetation coverage in area.

4. as claimed in claim 1 Forecasting Methodology, is characterized in that, step 1) to obtain suitable index of laying eggs be to lay eggs rank according to locust The vegetation coverage of section, the soil texture are calculated with soil moisture status, are used for representing that what locust laid eggs by habitat key element presses down System or suitable situation, characterize locust and lay eggs successful probability, and detailed process includes:

C1) obtain sandy loam index by amass with clay content index of calculating soil content index, and be normalized, obtain Obtain the soil types factor；

C2) actual measurement soil moisture data and the temperature vegetation drought index obtained by remotely-sensed data inverting is utilized to carry out statistical Analysis, sets up the remote sensing estimation model of soil moisture, is used for being calculated Soil moisture factor；

C3) utilize remotely-sensed data inverting to obtain the vegetation coverage in egg-laying season, thus obtain the egg-laying season vegetative coverage factor；

C4) the soil types factor, Soil moisture factor and the egg-laying season vegetative coverage factor are first normalized, then pass through line Property weighting method calculate acquisition and lay eggs suitable index.

5. as claimed in claim 4 Forecasting Methodology, is characterized in that, step C4) described acquisition lays eggs the linear weighted function of suitable index In method, the weight value respectively to the soil types factor, Soil moisture factor and the egg-laying season vegetative coverage factor is 0.3,0.5 and 0.2, for representing the difference of three factor significance levels in a region.

6. as claimed in claim 1 Forecasting Methodology, is characterized in that, step 1) obtain that to hatch suitable index be to become according to Locust ovum overwintering Motility rate, the soil moisture factor of incubation period and humidity factor are calculated, and are used for characterizing severe winter with incubation period habitat conditions to locust The impact that ovum is successfully hatched, detailed process includes:

F1) time arrived according to first time cold wave is estimated by empirical statistics and to obtain Locust ovum overwintering survival rate；

F2) Land Temperture before being hatched by this cycle locust is calculated temperature factor, and recycling remotely-sensed data inverting obtains To temperature vegetation drought index, it is calculated the humidity factor before the incubation period；

F3) Locust ovum overwintering survival rate, the temperature factor of incubation period and humidity factor are first normalized, then pass through product method Calculate to obtain and hatch suitable index.

7. Forecasting Methodology as claimed in claim 1, is characterized in that, step 1) obtain the suitable index of growth according to estimation range The geographical height above sea level factor, the locust trophophase vegetation pattern factor and the vegetative coverage factor are calculated, for quantitative description locust By the inhibitory action of surrounding habitat key element after egg hatching success, detailed process includes:

S1) altitude ranges suitably lived according to geographical height above sea level and locust, is calculated the height above sea level factor by elevation segmentation；

S2) remotely-sensed data inverting is utilized to obtain the vegetation coverage of locust growth stage, according to planting of applicable locust growth promoter Coating cover degree scope, is calculated the vegetative coverage factor of locust growth stage；

S3) the vegetation pattern factor of locust growth stage is obtained according to vegetation pattern data；

S4) the height above sea level factor, the vegetative coverage factor of locust growth stage and the vegetation pattern factor are normalized, then pass through line Property weighting method be calculated growth suitable index.

8. Forecasting Methodology as claimed in claim 7, is characterized in that, step S4) obtain the linear weighting method growing suitable index Be the weight value respectively to the height above sea level factor, the vegetative coverage factor of locust growth stage and the vegetation pattern factor be 0.2,0.5 With 0.3, for representing the difference of three factor significance levels in a region.

9. as claimed in claim 1 Forecasting Methodology, is characterized in that, step 2.3) described three ages revise that to obtain new growth suitable Index be the geographical height above sea level factor according to estimation range three age, the locust trophophase vegetation pattern factor, the vegetative coverage factor and The rainfall factor obtains, for describing the trophophase inhibitory action by surrounding habitat key element；Detailed process includes:

X1) the absolute elevation scope suitably lived according to geographical height above sea level and locust, is calculated height above sea level by elevation segmentation The factor；

X2) remotely-sensed data inverting is utilized to obtain the vegetation coverage of locust growth stage, according to planting of applicable locust growth promoter Coating cover degree scope, is calculated the vegetative coverage factor of locust three age；

X3) the vegetation pattern factor of locust growth stage is obtained according to vegetation pattern data；

X4) the rainfall factor of locust three age is calculated according to the rainfall space interpolation data of meteorological site observation；

X5) the height above sea level factor, the vegetative coverage factor of locust growth stage, the vegetation pattern factor and the rainfall factor are carried out normalizing Change, then be calculated the suitable index of growth by weigthed sums approach.

10. Forecasting Methodology as claimed in claim 9, is characterized in that, step X5) obtain the linear weighting method growing suitable index It it is the value respectively of the weight to the height above sea level factor, the vegetative coverage factor of locust growth stage, the vegetation pattern factor and the rainfall factor It is 0.15,0.4,0.2 and 0.25.

11. as arbitrary in claim 7 or 9 as described in grassland based on the remote sensing technology gradual Forecasting Methodology of the plague of locusts, it is characterized in that, institute State the height above sea level factor be the highest elevation value according to a region, minimum height value and region be suitable for the elevation upper limit that locust grows with under Limit calculates acquisition, and the elevation upper limit that described applicable locust grows is obtained by field investigation research with lower limit needs.

12. Forecasting Methodologies as claimed in claim 1, is characterized in that, step 2.1) time of carrying out initial plague of locusts risk profile is The annual last ten-days period in April；Step 2.2) time of carrying out incubation period correction is the last ten-days period in May；Step 2.3) carry out the correction of three ages Time be the last ten-days period in June.