Chapter 5. The P o te n tia l o f Microwave. Remote Sensing In A ssessing. P est In festa tio n o f Wheat

Transcription

1 Chapter 5 The P o te n tia l o f Microwave Remote Sensing In A ssessing P est In festa tio n o f Wheat

2 The Potential of Microwave Remote Sensing In Assessing Pest Infestation of Wheat 5.1 Introduction: In terms of production wheat (Triticum spp.) occupies the prime position among the food crops in the world. In India, it is the second important food crop being next to rice and contributes to the total food grain production of the country to the extent of about 25 percent. It is used in various forms by more than one thousand millions human being around the world. Besides being staple food for human beings, wheat straw is a good source of food for a large population of cattle. Wheat compares well with other cereals in its nutrition value. It has a relatively high content of niacin and thiamine. The increase population has given challenge to the scientists to maintain a closes balance between wheat production and its demands (Singh et al 2002). Therefore, an effective wheat-monitoring programme is necessary at regional, national and international levels. The interaction of microwave with crop canopies depends upon scattering and attenuation of the microwave signal, both of which are dependent on the plant architecture and water content as well as on the radar or parameters. Additionally, if the attenuations in the canopy are low enough, surface scattering from the soil will contribute or even dominate the signal. However, quantitative understanding of contribution by each crop component to scattering and attenuation and the relation magnitude of scattering from the soil and the vegetable is still a matter of debate for most crops. This greatly hinders the recovery of crop characteristics and resolution of these issues is critical for attempt to monitor crop by radar (Crook martin et al, 2000, and Garcia etal 1997). Therefore, it is necessary to investigate the scattering characteristic of various targets on ground, theoretically and experimentally, for the development of algorithm and

3 empirical model which all such crop can derive characteristic and land surface from the space borne data. It is still uncertain that which crop parameter is more effective on microwave scattering. An attempt to correlate the changes in these agronomic parameters with occurrence of pest is also under study. Various scientists are working on this line. (Attema et al; 1978, Ulaby et al, 1990, Singh et al; 1999, 2002; Le Toan et al, 1997 etc). In this chapter, it is attempted to highlight the microwave (X- band 9.5 GHz) interaction with crop parameters (i.e. plant height PH, leaf area index LAI total chlorophyll TC, crop covered soil moisture (SM) and Biomass (BIO)) of wheat for various system parameters (i.e. for angular variation and like polarization, HH-pol and W - pol) by indigenously built X- band ground based scatterometer. This type of ground based scatterometer study is helpful to understand the interaction mechanism of microwave with crop parameters and further provides data for aerial remote sensing radars. As these parameters are correlated with infestation of pest. These data can also be used for monitoring pest infestations. Thus, helping in timely treatment. Wheat is the main Rabi crop. Among the three species of wheat, Triticum aestivum (WH -147) was taken for observation in this study. Wheat was sown in October 1999 and was harvested in March In this experiment the maturation time was around 110 to 120 days. During growing season, temperature was between 20 C and 25 C, while at ripening stage, it was near 25 C to 28 C. The soil had 2 % gravel, 38 % sand, 52 % silt and 8 % clay and it was silt clay loamy type soil. Adequate soil moisture was maintained for normal development of the wheat plant by artificial irrigation throughout the period. observations. An outdoor bed of 5m x 5m was prepared for ground truth

4 5.2 Wheat Growth Stages i. The sowing period: At the time of sowing, the field was irrigated up to 20% of moisture and grains were sown for the normal crop density. ii. The growing period: The wheat sowed in the field, completed life cycle within 110 to 125 days after sowing. The growing period of wheat is defined by vegetative stage, reproduction stage and ripening stage. Vegetative Stage: The vegetative phase (tillering, elongation and booting) was of 70 days duration. The vegetation phase means an increase in plant height and increase in the number of tillers and development of leaves. Tillering started about 30 days after sowing and continues until flowering. After days of sowing the clusters nearly covered the total area between the plants. The plant structure remained mostly erectophile from sowing to heading. Reproduction Stage: The reproduction stage (Heading, panicle initiation and flowering) lasted about days approximately. It started after days. During this phase, the plant was characterized by decrease of number of tillers, the development of panicular leaf, the panicle formation and development. Ripening Stage: The ripening (grain filling, milk and dough stage) starts about days after sowing. In this stage the ear heads start accumulating milk and the filling of grain takes place. 5.3 Pest Infestation in Crop Wheat The crop wheat is mainly affected by Termites and stem borers in Bilaspur, Chattisgarh region. In our study the termite infestation was not seen,

5 as the area was free from them. The crop was infested by stem borers (Sesamia Inference) at ear head stage i.e. after about 50 days of sowing. The pest Sesamia Inference (Walker) belongs to subclass Lepidoptera. The pink stem borer is a polyphagus insect and is distributed throughout India and Pakistan. In some parts of country, it is a minor pest of sugarcane while where as in others it is common on ragi. In northern India, it is recorded on rice, sugarcane, maize, sorghum, and wheat, but its damage is significant on wheat, rice and maize only. The damage is caused by the caterpillars, which are pinkish brown and have stout body. The pest breeds actively from March - April to November on rice and then migrates to wheat crop. The moth are nocturnal and lay eggs on leaves or ground. The eggs hatches in 6-8 days and young caterpillar bore into the epidermal layers of the leaf sheath later on; they bore into stem as a result of which the growing shoot dries up producing dead hearts When the attacked plant dies, the larvae move on to adjoining plants. They are full-fed in 3-4 weeks and pupa inside the stem or in between the stem and leaves. The pupa stage lasts about a week and the life cycle is completed in 6-7 weeks. There are 4-5 generations of a pest in a year. This pest is common during dry period. The attacked young plants show dead hearts and are killed altogether. The older plants are not killed but they produce a few grains only. (Metcalf) In our study the damage caused by the Sesamia Inference varied from a minimum 2% to maximum 8.53%. The total numbers of tiller per square meter were counted and then the number of damaged tiller was observed. The percentage of damage was calculated to provide data for pest infestation. The damage by wheat stem borer was recorded from last week of December. The maximum damage was recorded in last week of January to 1st week of February and on wards.

6 5.4 Analysis Of Field Data The wheat canopy measurements include plant height (PH), leaf area index (LAI), Total chlorophyll (TC), biomass (BIO), soil moisture(sm), and the pest number or the damage caused by pest The field measurements were taken in regular interval of 10 days Figure 5 1 shows the variation of plant height with crop age First height measurement was taken after 23 days of sowing The plant height increased as the crop became older. The sharp increase in height was observed between days after sowing. The plant was in vegetative stage, which comprises mainly of elongation and tillering. The flowering stage or reproductive stage started after days after sowing and from this stage to grain filling stage or ripening stage ( days), the increase in height was very small The variation of leaf area index (LAI) with age of wheat is shown in figure 5 2 The LAI also increases, as the plant grows old But during tillering stage (20-40 days) the rate of increase of LAI was quite high and after that it slowed down It may be because leaves reached mature stage Figure 5.3 shows the variation of total chlorophyll content (TC) with age of wheat It increased initially till 30 days after sowing but after that it decreased gradually up to 50 days of age It again increases up to 60 days and then remains almost constant This sudden fall in the TC can be correlated to the loss due to pest infestation as the stem borer damages the tiller in turn damaging the leaves. The increase in total chlorophyll content can be contributed to growth of new tiller The variation of LAI with plant height is shown in figure 5 4 It is observed that, as the height increases, the LAI also increases but this increase is slowed down after reaching the height of 50 3 cms (maximum) height achieved by plant after which wheat plant entered into fruit filling stage Figure 5 5 shows the studies on vanation of pest damage with age of crop The initiation of stem borer infestation was seen at about 25 days of

7 sowing. The infestation (damage) increases with age up to 60 days after which the damage remains almost constant. The graph explains the occurrence of pest and damage % caused by it. Figure 5.6 and 5.7 shows variation of percentage of Biomass (BIO) with age of plant and with pest damage respectively. The BIO increased up to 40 days after sowing. It then decreased suddenly till the plant reaches the age of 45 days after which it declined very less. The pest damage percentage and BIO are negatively correlated up to the age of 80 days and then the percentage of damage did not fluctuate much. This can be explained by the nature of the damage caused by the pest. The pest stem borer of wheat damages the tiller, which in turn affects the general growth of plant and ear heads. Thus causing reduction of biomass of plant. 5.4.A. Analysis of Pest Infestation on Wheat with Crop Parameter An attempt is made to develop a relation between pest damage and different biological parameters (SM, TC, BIO, LAI, PH). The climatic factors are being taken as constant because there was no climatic stress and the plant was not faced with any adverse condition. The seasonal climatic factor remains constant on an average. Thus, they are taken to be constant and only biological parameters were further studied. The regression analysis was done between pest damage and all other biological parameters. Table 1 shows the result of the analysis. It is observed that the percentage dependence of pest occurrence represented by values of r 2 for TC, LAI, SM, PH are next in order showing decreasing dependence percentage. It is clearly observed that Pest is highly correlated with wheat biomass than other parameters. A second order polynomial curve relation has been obtained between BIO and Pest infestation and written as P = (BIO) (B!0) ( 1 )

8 The regression analysis result led to further study of data collected by scatterometer. 5.5 Study of Scatterometer Data 5.5.A. Temporal Variation of Scattering coefficient and emissivity Figures 5.8 and 5.9 show the temporal variation of scattering coefficient for wheat at incidence angle 20\ 30', 40\ 50 and 60 for HH and W -pol respectively. The dynamic range in scattering coefficient is from 6 db to dB for HH and from 0.22 db to.91 db for W -pol at 20' incidence angle and scattering coefficient is decreasing as the crop parameter increased. The sharp decrease was found in W -pol than HH-pol. It infers that wheat effect is not very significant at this angle of incidence. While the dynamic range of scattering coefficient is higher at 45 and 50' incidence angle for HH-pol. It means, at higher angle of incidence (0>4O) crop effect is prominent. It is due to the reason that the path length through the vegetation maximizing the crop response as the incidence angle increases (Paris, 1986). Figures 5.10 and 5.11 shows the temporal variation of emissivity for wheat at different incidence angle (i.e., 20, 30, 40, 50', 60' and 70' incidence angle) for HH and W -pol respectively. The trend is just opposite to trend of scattering coefficient. Emissivity shows approximately increasing behavior with wheat age for all incidence angles and like polarizations, but increase is quite sharp till 70 days after sowing. It was due to the fact that, wheat covered soil moisture effect was also significant at early age of wheat. The angular dependence is clearly evident till 85 days after sowing, after this angular dependence is quite low. The maximum value of emissivity was observed in between days after sowing. Because wheat was in its full-grown stage up to this age, and after this ripening stage started. The dynamic range varies from 0.2 to0.42 for HH-pol and 0.1 to 0.69 for W -pol. The dynamic range for W -pol is quite high in comparison to HH-pol. It shows that the polarization effect is significant for crop monitoring.

9 5.5.B. Variation of Scattering Coefficient with Biomass (BIO) of wheat The field analysis shows that pest is highly correlated with wheat biomass. Now, it is possible to assess the wheat biomass with microwave remote sensing. This section represents for the same. Table 2 shows the linear regression result of o' with respect to Biomass (BIO) of wheat at different incidence angles for both like polarizations. The value of correlated coefficient (r2) for HH-pol varies from to for HH-pol and to for W -pol. The maximum value for r2 is higher for W -pol than HH- pol. It suggests more effective observation of biomass possible on W - pol than HH-pol. SE is also illustrated in table 2, its value varies from to and to for HH and W -pol respectively. It also confirms that W -pol is better than HH-pol for observing the wheat biomass Variation of scattering coefficient with biomass is shown in fig at 30 for HH-pol and 55" for W -pol (dotted line for W -pol and solid line for HHpol). The empirical relation between wheat biomass and scattering coefficient are or* = (BIO) , at 30, HH-pol (2) a = (BIO) , at 55",W -pol (3) Inverting these equations BIO can be assessed remotely. The inversion relation can be written as following BIO = 5.06 a at 30, HH-pol (4) BIO = 9.25o at 55, W -p ol (5) It is observed that W -pol has better precision than HH-pol for estimating the wheat BIO. Fig shows the comparison with observed values of BIO

10 with estimated values of BIO (with eq.5) for W -pol. A quite good agreement is noticed in observed and calculated values of BIO. 5.5.C. Variation of Emissivity with Biomass of wheat Table 3 shows the linear regression result of emissivity (e) with respect to Biomass (BIO) of crop wheat. The value of r is positive for both HH and W -pol. It shows that emissivity increases as the BIO increases, which confirm the experimental finding. It varies from to and to for HH and W -pol respectively. The value of slope is negative for both like pol and it varies from to for HH-pol and from to for W -pol. The dynamic range varies from to for HH-pol and 0.04 to 0.69 for W -pol. A good dynamic variation in emissivity with BIO shows better sensitivity of microwave with crop wheat. The minimum value of SE is at 30 for HH-pol and for W - pol at 50 incidence angle. Figure 5.14 shows the variation of emissivity with Biomass (BIO) of wheat for both like polarizations. The regression lines are drawn and empirical relations are following: e = BIO , at 30, HH-pol (6) e = BIO at 50, W -pol (7) The inversion of these equation for assessment of BIO with emissivity are as following: BIO = e at 30 HH-pol (8) BIO = e at 55, W -pol (9)

11 Estimated values of BIO with equation 9 are shown in fig Microwave emission at X-band also shows good results for estimating wheat BIO. 5.6 Pest Estimation With Remote Sensing Technique (I.E., With X-Band Scatterometer) 5.6.A Estimation of pest infestation by microwave scattering: As one of the main objectives of this chapter are to estimate the number of pests by scatterometer (or remote sensing techniques). The BIO can be estimated by scatterometer through equation number 5 for W -pol. W -pol has been considered because; it gives better precision than HH-pol for estimating wheat BIO at X-band. The occurrence of pest infestation can be written as (using eq. 1 and 5) P= -0.34a 2-5a" (10) Using equation 10, pest infestation can be directly estimated by scatterometer or satellite/air-borne sensor data. The observed number of pest and estimated number of pest (using equation 10) with microwave scattering has been shown in figure 5.16, where it is clearly noticed that the percentage of agreement between observed and estimated number of pest is good. 5.6.B Interpretation Of Pest Infestation By Microwave Emissivity The estimated BIO with microwave emission for W -pol is shown in fig W -pol has been considered because; it gives better precision than HH-pol for estimating wheat BIO at X-band. These analyses reveal the following equations for pest occurrence P = e* e-3.49 (11)

12 This equation can directly estimate the pest infestation by scatterometer or satellite/air-borne sensor data. The observed number of pest and estimated number of pest (using equation 11) with microwave emission has been shown in figure 5.17, where it is clearly indicated that the percentage of agreement between observed and estimated number of pest are good. 5.7 Concluding Remarks: Wheat parameter effects on the microwave scattering/emission at X- band for various sensor parameters have been studied with reference to occurrence of pest infestation on wheat. The study involved the field data and scatterometer data analysis and modeling approach for estimation of pest occurrence. The following conclusions are drawn by these studies: - 1. The Pest stem borer was found prominently in our field, which is one of the main pests of wheat in Chattisgarh area. 2. Pest characteristics are to bore in tiller of wheat damaging them so as hindering the development of healthy inflorescence. Thus affecting the crop yield. 3. Among the several plant parameters this pest is mainly correlated with wheat biomass. 4. A second order polynomial empirical relation has been obtained between pest damage and Biomass. 5. Microwave emissivity/scattering is well sensitivity to observe the wheat biomass at X-band (9.5 GHz). 6. The W-polarization is more sensitive than HH-polarization for observing wheat biomass at X- band. 7. Biomass is well estimated by scatterometer data using microwave scattering and emission.

13 8. The developed algorithm between microwave scattering/emission with pest infestation by satellite and airborne microwave data. Present chapter emphasizes to develop a relation between pest damage and remote sensing data. The developed model and analysis will be quite helpful in near future to monitor the wheat infestation as well as the wheat yield, which will provide a great help to society.

14 TABLE 1 LINEAR REGRESSION RESULTS OF PEST WITH SM.PH. TC.LAI.BIO SM PH TC LAI BIO R*A (Coefficient of Determination) R (Correlation Coefficient) SE=SQRT(VarR) TABLE - 2 Linear regression results o f Scattering Coefficient (o ) w ith Biom ass o f W heat fo r different angle of incidence (6) a t X-Battd (9.5GHz). A ngle Slope Intercept r r2 SE POL H H H H H H H H H H HH HH H H H H HH VV w VV VV VV VV VV VV VV w

15 Linear regression results o f E m issivity (e) w ith Biom ass o f W heat fo r different angle o f incidence (6 ) a t X-Band (9.5GHz). A ngle Slope Intercept r r2 SE POL H H H H H H H H H H H H H H H H H H H H VV VV VV VV w VV w VV VV VV

16 i i. i. i Age of plant (days) Fig5.1: Variation of plant height with age of crop Leaf Area Index Age of plant (days)

17 Leaf Area Index Total chlorophyll (mg/lit) i i i i i Age of plant in days Fig5.3: Variation of total chlorophyll with age of plant Plant Height in cms Fig5.4: Variation of leaf area index with plant height

18 &Cl. Age of Crop (days) Fig5.5: Variation of pest damage with age of crop Biomass % Age of Crop in days

19 7 Pest damage (%) Biomass% Fig5.7: Variation of pest damage with biomass

20 'o <D o U c cso on -O - 20 degree - # - 30 degree -O - 40 degree 50 degree - A - 60 degree - A - 70 degree Age o f plant (days) Fig5.8: Temporal variation of scattering coefficient with age of paddy for HH pol at 9.5 GHz for different incidence angles -O - 20 degree 30 degree -O - 40 degree 50 degree -A - 60 degree -A - 70 degree Age of plant (days) Fig5.9: temporal variation of scattering coefficient with age of paddy for W pol at 9.5 GHz for different incidence angles

21 -O - 20 degree 30 degree -O - 40 degree 50 degree -A - 60 degree -A - 70 degree Age of plant (days) Fig5.10: Temporal variation of emissivity with age of paddy for HH pol at 9.5 GHz for different incidence angles C/5 <75 tu -O - 20 degree 30 degree -Q - 40 degree 50 degree -A - 60 degree -A - 70 degree Age of plant (days) Fig5.11: Temporal variation of emissivity with age of paddy for W pol at 9.5 GHz for different incidence angles

22 Scattering Coefficient dd ( I ^I 0 H ! * 30 degree HH 55 degree VV -Linear (30-6 -] Biomass (% ) Fig5.12: Variation of scattering coefficient with biomass (BIO) of wheat for both like polarizations Observed Biomass (%)

23 f 08 I 0.6 nl degree HH 50 degree VV lin ea r (30 0 J Biomass (%) Fig5.14: Variation of emissivity with biomass (BIO) of Wheat for both like polarizations C3 B o s 0J JS 3 U Observed Biomass (%)

24 Observed Pest Fig5.16: Observed vs. calculated pest number with BIO by scattering coefficient (/) 0) Q. "O CD *-* m3o (0 O Observed Pest