Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data

Singh, Nawab; Kudrat, M.; Jain, Kamal; Pandey, Kamal

doi:10.1080/01431161.2010.489061

Cited by 31 publications

(19 citation statements)

References 22 publications

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“…Before 2000, approximately 50% farmers in the study area used to grow ground nut but now only few farmers in few villages grow ground nut and in most of the villages people do not grow this crop. Cropping pattern of study area along with whole state is dependent on the monsoon rainfall and water availability (Singh et al, 2011). The major cropping pattern of study area observed during the survey is Rice-wheat and sugarcane.…”

Section: Change In Cropping Patternmentioning

confidence: 94%

A case study on changing pattern of agriculture and related factors at Najibabad region of Bijnor, India

Bhutiani¹,

Ahamad²

2019

Contaminants in Agriculture and Environment: Health Risks and Remediation

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The study was conducted to assess the changing pattern of agriculture and related factors at Najibabad in Bijnor district of Uttar Pradesh. Survey was conducted in the study area for data Chapter contents Introduction …………………………………………………………………………………………………….. Materials and methods ………………………………………………………………………………………... Study area ……………………………………………………………………………………………………. Study design and data collection ………………………………………………………………………….. Results and discussion ………………………………………………………………………………………… Change in the rainfall ………………………………………………………………………………………. Change in temperature ……………………………………………………………………………………...

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Section: Change In Cropping Patternmentioning

confidence: 94%

A case study on changing pattern of agriculture and related factors at Najibabad region of Bijnor, India

Bhutiani¹,

Ahamad²

2019

Contaminants in Agriculture and Environment: Health Risks and Remediation

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“…Due to the lack of availability of sensors providing both high spatial and high temporal resolution imagery, most crop mapping studies using multi-temporal optical data had to be based on low to medium spatial resolution images with pixel sizes > 30 m [3], [6], [7], [8], [9], [10]. However, in highly fractured landscapes with heterogeneous cropping patterns of smaller fields, images with higher spatial resolution are needed [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Crop identification by means of seasonal statistics of RapidEye time series

Zillmann¹,

Weichelt²

2014

2014 the Third International Conference on Agro-Geoinformatics

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Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye's high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km² in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.

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“…It may be observed that values of NDVI for sugarcane and maize after the month of October always remain less as compared to corresponding values for the month of September. During the two temporal sets, it shows the difference between the growth pattern of sugarcane and maize crop [41], [20]. Computed values of NDVI images from Landsat 8 Band OLI for the study area were used for creating the crop growth profile.…”

Section: Crop Spectral Growth Profilementioning

confidence: 99%

“…[17] have done studies on fuzzy classifier for mapping forestry, urban planning and savanna woodlands using supervised fuzzy convolution filter to reduce the ambiguity of natural land covers where they disappear and dominated by medium to tall grasslands [19]. Three cities in the state of Sao Paulo, Brazil used 12 images of Landsat satellite from 2002 to 2004 for classification of the agricultural crop (Sugarcane, Soybeans, and Corn) based on Hidden Markov Model (HMM) and achieved 93% accuracy on the identification of correct crops [20]. Mapping of sugarcane planted area using artificial intelligence; objectbased image analysis and Data mining were used in a study area located in Sao Paulo state, which is well representative of the agriculture of large regions of Brazil and other countries [21].…”

Section: Introductionmentioning

confidence: 99%

Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation

Thorat¹,

Kale²,

Mehrotra³

et al. 2017

IJCA

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Estimation of specific crop and acreage plays a vital role in the field of crop planning, monitoring, crop condition, yield forecasting and acreage estimation. There have been several studies conducted to classify the crops at continental to the regional level, but still, work is needed to map small area covered by different crops using Remote Sensing technology. The main objective of the present study is to explore whether the Fuzzy classifier can improve the accuracy of crop classification as compared to other traditional Classifiers, such as Maximum likelihood, Mahalanobis etc. The attempt has been done to classify different crops at a smaller scale. The Landsat time series 8 band OLI data was used to investigate multiple crop phenomena. Two scenes were acquired in Kharif seasons (September 28 and October 30, 2014). Three indices such as NDVI, SAVI, and RVI, were used to know vegetation condition. The Spectral signatures generated from data for the residues of Sugarcane and Maize based on prior knowledge of the field work. Four techniques based on Maximum Likelihood, Mahalanobis Classifier, Knowledge classifier and fuzzy classification techniques were used to extract the crops information based on the signatures. The resulting overall classification accuracy was calculated using stratified random sampling method. The corresponding performance efficiency of these four methods was found to be 84%, 85%, 87% and 90.67%, respectively, indicating the fuzzy method to be the most efficient as compared with other classification techniques.

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Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data

Cited by 31 publications

References 22 publications

A case study on changing pattern of agriculture and related factors at Najibabad region of Bijnor, India

A case study on changing pattern of agriculture and related factors at Najibabad region of Bijnor, India

Crop identification by means of seasonal statistics of RapidEye time series

Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation

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