JOURNAL OF MODERN BIOTECHNOLOGY, VOL. 1, NO. 1, pp 26 30, September 2012 Copyright 2012, by Madras Institute of Biotechnology. All Right Reserved. www.thebiotech.org Research Article Abstract PADDY YIELD ESTIMATION USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEM Manickam Gopperundevi* and Vijayaraghavan Kannan Center for Advanced Studies in Botany, University of Madras, Maraimalai Campus, Guindy, Chennai 600 025, Tamil Nadu, India *Corresponding Author e-mail: cukdevi2@yahoo.com Received: 28 June 2012; Revised: 12 July 2012; Accepted: 28 August 2012 Agriculture is the basis of Indian economy. The rapid development of Remote Sensing technology serves as the solid technological foundation for the in-depth application of Indian agricultural statistics. Satellite remote sensing techniques can provide resource managers an efficient and economical means of acquiring timely data for the development and management of our natural resources. Remote Sensing (RS) data, acquired repetitively over agricultural land help in identification and mapping of crops and also in assessing crop vigour. This paper reports on a study that has been carried out to estimate paddy yield from the Landsat-5 Thematic Mapper (TM) data in Krishnagiri taluk. In this study, the Landsat image is classified using supervised classification and then vegetation index (VI) namely the Normalized Difference Vegetation Index (NDVI) have been used together with some field data to derive relationships between NDVI and yield. A linear relationship has been obtained between NDVI with yield. The yield estimated derived from the satellite data have been found to be accurate. Keywords: Remote Sensing, Normalised Difference Vegetation Index, Vegetation Index, Yield INTRODUCTION Remote sensing and its associated image analysis technology provide access to spatial information on a planetary scale. New detectors and imaging technologies are increasing the capability of remote sensing to acquire digital spatial information at very fine resolutions in an efficient manner (Ehlers et al. 1989). Up-to-date information of features and phenomena on the earth can be derived in a short duration of time. Crop identification and prediction of yield are the main concern of remote sensing application in agriculture. Various studies have demonstrated the usefulness of satellite remote sensing data for generating the information on total irrigated area and area under different crops (Kenneth and Lee, 1984, Nageswar Rao and Mohan Kumar, 1994), condition of crops (Singh et al., 1985; Lloyd, 1989) and crop production (Hatfield 1983; Jasinski, 1990; Murthy et.al, 1996). Researchers have investigated different spectral bands for vegetation sensitivity. Remote sensing has potential not only in identifying crop classes but also in the estimation of crop area and yield. The examination of the relationships between vegetation indices and yield has been frequently studied over the years and has been shown the useful for yield prediction purposes. For example, Major et al (1986) have shown usefulness of some VIs to estimate some leaf area index (LAI), biomass and grain production for cereals from radiometric measurements. Tucker et al (1980) found a strong relationship between specific spectral data and yield. The resulting plots of spectral data against time give a distinctive pattern which when integrated could be related to yield. All these relationships have a high correlation with yield. In another study, Green and Invins (1985) also proved that VIs relate strongly to biomass, and have been found to relate directly to yield. Rudoff and Batista (1990) have indicated that spectral data when transformed into VIs have great potential to be used in wheat yield prediction models tropical regions. Since VIs have been shown to be useful for yield estimation, in this study NDVI is used for paddy yield estimation. 26 JOURNAL OF MODERN BIOTECHNOLOGY VOLUME 1 NUMBER 1 SEPTEMBER 2012
Gopperundevi and Kannan Paddy yield estimation using remote sensing The NDVI used in this study is commonly preferred because it reduces the effects of atmospheric conditions and topographical variations. The NDVI is the ratio between the difference in the infrared and red bands and the sum of bands, i.e. the infrared bands used in the study are band 4 (0.76-0.90mm) and band 5 (1.55-1.75mm) whilst the red band is 3 (0.63-0.69mm) of the Landsat-5 TM data. MATERIAL AND METHODS Study area Krishnagiri district covers an area of 5143 km². Krishnagiri district is bound by Vellore and Thiruvannamalai districts to the East, State of Karnataka to the west, State of Andhra Pradesh to the North and Dharmapuri District to the south. This district is elevated from 300m to 1400m above the mean sea level. It is located between 11º 12'N to 12º 49'N Latitude, 77º 27'E to 78º 38'E Longitude (Figure 1). Methodology This study is aimed at evaluating the possibility of remotely sensed data to estimate paddy acreage as a component of crop production fore casting process. Figure 2 shows the methodology for paddy yield prediction. Combination of digital image processing and classification technique namely supervised classification; extraction of paddy by binary coding, NDVI calculation for yield estimation is performed in this study. Then Regression analysis is done between vegetation indices and yield collected in training sites. Initially the satellite image is classified using Maximum likelihood classification technique. Training sets were given to different land use/land cover classes like paddy, forest/scrub, fallow land, water bodies etc., and statistical parameters like mean, standard deviation etc. were generated for these categories. From the study of statistical parameters of crop category, two or three major crop classes could be identified. Preliminary digital analysis was carried out by using maximum likelihood classification. The training sets given during preliminary digital analysis were purified with the available ground truth for different crops and other land use/land cover classes. Once again signature sets were generated for the above mentioned classes. Subsequently maximum likelihood classification program was run for classifying the data. Figure1: Satellite image of the study area The important crops of Krishnagiri District are paddy, maize, ragi, banana, sugarcane, cotton, tamarind, coconut, mango, groundnut, vegetables and flowers. The district has an excellent scope for agri-business. Data used Satellite data were selected based on the crop calendar and cloud free data availability of the study area and it is shown in figure 1. Field visits were made for the ground truth collection for study area so as to correlate the tones and textures of different crops and other land use/land cover categories with the image interpretation key. In the predetermined test sites on toposheets, detailed information of different crops grown and other ancillary information were collected. Figure 2: Flowchart for the methodology 27 JOURNAL OF MODERN BIOTECHNOLOGY VOLUME 1 NUMBER 1 SEPTEMBER 2012
Paddy yield estimation using remote sensing Gopperundevi and Kannan In order to relate the yield and vegetation index field data were obtained by conducting field survey and enquiring the agronomists. The average yield of paddy for healthy, moderate healthy and stressed crops in Krishnagiri area are 9000kg/ha, 7000 kg/ha and 5000 kg/ha respectively and in Kodaivasal it ranges from 10500 kg/ha, 8000 kg/ha and 7000kg/ha respectively. The 0.4-1.0 µm part of the electromagnetic spectrum contains the red edge feature of the green vegetation reflectance spectrum which is exploited by standard vegetation indices. The VI values obtained from the sample plot were plotted against the grain yield. A regression fit was made to determine the relationship between yield and NDVI. RESULTS AND DISCUSSION Supervised classification The attempt was made to classify the various land uses in the study area using ERDAS image processing software by Maximum Likelihood supervised classification technique. In supervised classification, spectral signatures are developed from specified locations in the image. These specified locations are given the generic name 'training sites' and knowledge of training sites is obtained by field survey. The land Use Land Cover (LULC) images of the study area are shown in figure 3. The accuracy of classification of images of Krishnagiri is 87.50% respectively. Training areas, usually small and discrete compared to the full image, are used to train the classification algorithm to recognize land cover classes based on their spectral signatures, as found in the image. The training areas for any one land cover class need to fully represent the variability of that class within the image. There are numerous factors that can affect the training signatures of the land cover classes. Environmental factors such as differences in soil type, varying soil moisture, and health of vegetation, can affect the signature and affect the accuracy of the final thematic map. Acreage estimation Remote sensing has potential in estimation of crop area and yield in to addition to identify the different crop classes. After classifying the image using supervised classification technique, aggregation is carried out to get the crop area for the study area. Areas of different land uses are calculated initially and the details are given the Table 1. Figure 3: Classified output of Krishnagiri District Table 1: Area of Land use Land Cover in Krishnagiri SL NO. Land use Area hectares 1 Rocky area 66052.3 2 Water body 1216.10 3 Paddy 2818.56 4 Other 25521.40 5 vegetation Waste land 31781.10 TOTAL AREA 127389.46 NDVI estimation One of the most widely used indices for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI), because vegetation differential absorbs visible incident solar radiant and reflects much if the infrared (NIR), data on vegetation biophysical characteristics can be derived from visible and NIR and mid- infrared portions of the electromagnetic spectrum (EMS). The NDVI approach is based on the fact that healthy vegetation has low reflectance in the visible portion of the EMS due to chlorophyll and other pigment absorption and has high reflectance in the NIR because of the internal reflectance by the mesophyll spongy tissue of green leaf. NDVI can be calculated as a ratio of red and the NIR bands of a sensor system. NDVI values range from -1 to +1, because of high reflectance in the NIR portion of the EMS, vegetation is in 28 VOLUME 1 NUMBER 1 SEPTEMBER 2012 JOURNAL OF MODERN BIOTECHNOLOGY
Gopperundevi and Kannan Paddy yield estimation using remote sensing represented by high NDVI values between 0.1 and 1. Conversely, non- vegetated surfaces such as water bodies yield negative values of NDVI because of the electromagnetic absorption quality of water. Bare soil areas represent NDVI values which are closest to 0 due to high reflectance in both the visible and NIR portions of the EMS. NDVI is related to the absorption of photo synthetically active radiation (PAR) and basically measures the photosynthetic capability of leaves, which is related to vegetative canopy resistance and water vapour transfer. Figure 4 shows the image of NDVI. yield estimation equation. The yield estimation equation is as follows. Yield (Kg/pixel) for Krishnagiri = 1459.9 x (NDVI) + 221.15 Figure 5: NDVI vs. yield The average yield for healthy, moderate and stressed paddy crop and over all yield of paddy are quantified and tabulated in Table 2. Table 2: Estimated yield of paddy Scatter plot Figure 4: NDVI image The VI values and paddy yield obtained for different sample points and scatter plot is plotted between them. Numerous techniques have been applied for modeling the relationship between crop yields and measured vegetation parameters. Linear regression is the most popular technique to perform the relationship significance and predictive ability. A linear analysis of investigating yield response consists of empirical analysis of large, spatial, and multivariate data sets have often been reported in the literature. Several authors have found that linear correlations between yield and vegetation properties, soil properties, vary greatly. A regression fit was made to determine the relationship between yield and VIs in this study. Yield in kg/ha is converted in yield kg/pixel and related to corresponding NDVI. A linear relationship was obtained from ten samples that were taken. Figure 5 shows the relationship and correlation for the area for NDVI versus yield. The results show there is a linear relationship between each index and yield. Thus the NDVI versus data have been selected for obtaining the SL NO. Name of the Paddy Area 1 Krishnagiri CONCLUSIONS Yield tonne ) Healthy 8394.20 Moderate 12338.09 Stressed 1315.18 Total (MetricYield Metric tonne 22047.47 The NDVI values obtained from a combination of bands 3 and 4 of the Landsat-5 TM show better correlation with yield. This is because the effects of soil and atmosphere have been considerably reduced in the NDVI. The error in the satellite yield estimate is due to the difference in the paddy phenology cycle at the time the satellite data were acquired and the time the field measurement were taken. This study indicates the usefulness of satellite remote sensing techniques in deriving agriculture information over large areas in a cost effective manner that can benefit related agencies. REFERENCES Lloyd D. 1989. A phenological description of Iberian vegetation using short wave vegetation index image, International Journal of Remote Sensing 10:827 833. 29 JOURNAL OF MODERN BIOTECHNOLOGY VOLUME 1 NUMBER 1 SEPTEMBER 2012
Paddy yield estimation using remote sensing Gopperundevi and Kannan Hatfield JK. 1983. Remote sensing estimators of potential and actual crop yield. Remote Sensing of Environment, 13:301 311. Kenneth KE and Lee HC. 1984. The identification of irrigated crop types and estimation of acreages from Landsat imagery. Photogrammetric Engineering and Remote Sensing 50:1479 1490. Murthy CS, Thiruvengadachari S, Raju PV and Jonna S. 1996. Improved ground sampling and crop yield estimation using satellite data, International Journal of Remote Sensing 17:945 956. Nageswar Rao PP and Mohan Kumar A. 1994. Crop land inventory in the command area of Krishnarajasagar project using satellite data, International Journal of Remote Sensing 15:1295 1305. Ehlers M, Edward G and Bedard Y. 1989. Integration of remote sensing with geographic information systems: A necessary evolution. Photogrammetric Engineering and Remote Sensing 55:1619 1627. Green CF and Invins JD. 1985. Time of sowing and yields of winter wheat. Journal of Agricultural Science 104:235 238. Jasinski MF. 1990. Sensitility of the normalized difference vegetation index to sub-pixel canopy cover, soil albedo and pixel scale. Remote Sensing of Environment 32:169 187. Major DG, Schaalje GB, Asrar G Kanemasu ET. 1986. Estimation of whole plant biomass and grain yield from spectral reflectance of cereals. Canadian Journal of Remote Sensing 12:47 54. Singh PNK, Sahai TP and Patel MS. 1985. Spectral responses of rice crop and its relation to yield and yield attributes. International Journal of Remote Sensing 6:657 664. Rudorff BFT and Batista GT. 1990. Spectral response of wheat and its relationship to agronomic variable in the tropical region. Remote Sensing of Environment 31:53 63. Tucker CJ, Holben BN, Elgin JH and Murtey JE. 1980. Relation of spectral to grain yield variation. Photogrammetric Engineering and Remote Sensing 46:43 66. 30 VOLUME 1 NUMBER 1 SEPTEMBER 2012 JOURNAL OF MODERN BIOTECHNOLOGY