Study on the utility of IRS-P6 AWiFS SWIR band for crop discrimination and classification

Panigrahy, Rabindra K.; Ray, S. S.; Panigrahy, Sushma

doi:10.1007/s12524-009-0026-6

Cited by 32 publications

(12 citation statements)

References 14 publications

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“…The topographical maps (1:250 000) scale obtained from Survey of India (SOI) were used for selection of ground control points (GCPs) for georeferencing purpose and also for verification of data acquired at the field [29], [30]. The satellite images were downloaded and imported in ERDAS for further analysis such as Layer Stacking, Resampling etc.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…3 shows that most of the wavebands selected (seven out nine) were within the domain of SWIR2 (1900-2500 nm), which is negatively related to leaf water content. However, the only waveband within the NIR domain (700-1300 nm) was almost independent of the variation in water content (Knipling 1970, Tucker 1980, Raymond Hunt et al 1987, Ceccato et al 2001, Panigrahy et al 2009). …”

Section: Plsr Analysis Of Reflectance To Trace Leaf Transpirationmentioning

confidence: 98%

“…3 -The number of wavebands selected for transpiration estimation PLSR modeling within the visible (VIS), near-infrared (NIR), short-wave 1 (SWIR1), and short-wave 2 (SWIR2) spectral regions. Wang thermore, increased reflectance of SWIR wavelengths is the most consistent response of leaf reflectance to plant stress, including water stress (Carter 1994, Panigrahy et al 2009). As the SWIR band is known to provide information on the water content of plants, it has been widely used to assess the water stress of plants (Tucker 1980, Ahlrichs & Bauer 1983, Raymond Hunt et al 1987, Baret et al 1988, Chuvieco et al 2002.…”

Section: Superiority Of Derivative Spectra Over Original Spectramentioning

confidence: 99%

Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis

Wang¹,

Jin²

2016

iForest

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A clear understanding of plant transpiration is a crucial step for water cycle and climate modeling, especially for arid ecosystems in which water is one of the major constraints. Traditional field measurements of leaf scale transpiration are always time-consuming and often unfeasible in the context of large spatial and temporal scales. This study focused on a dominant native plant in the arid land of central Asia, Haloxylon ammondendron, with the aim of deriving the leaf-scale transpiration through hyperspectral reflectance using Partial Least Squares Regression (PLSR) analysis. The results revealed that the PLSR model based on the first-order derivative spectra at wavelengths selected through stepwise regression analysis can closely trace leaf transpiration with a high accuracy (R 2 = 0.78, RMSE = 1.62 µmol g -1 s -1 ). The accuracy is also relatively stable even at a spectral resolution of 10 nm, which is very close to the bandwidths of several running satellite-borne hyperspectral sensors such as Hyperion. The results also proved that the first-order derivative spectra within the shortwave infrared (SWIR) domain, especially at 2435, 2440, 2445, and 2470 nm, were critical for PLSR models to predict leaf transpiration. These findings highlight a promising strategy for developing remote sensing methods to potentially characterize transpiration at broad scales.

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“…It lies in the Satmala and Ajantha hill ranges of the Western Ghats. The wildlife sanctuary was established in 1986 in the existing reserved forest area [7,8].…”

Section: Study Areamentioning

confidence: 99%

“…A total of 90 ground truth points were collected. The coordinates of the ground truth points were recorded with the help of handheld Mobile [10,8].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Estimation of Crop and Forest Areas using Expert System based Knowledge Classifier Approach for Aurangabad District

Thorat¹,

Rajendra²,

Kale³

et al. 2015

IJCA

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The present study demonstrates the application of remote sensing for the estimation of areas corresponding to crop and forest lands covered in the district of Aurangabad, (Maharashtra), India. The data acquired by IRS-P6 Advanced Wide Field Sensors (AWiFS) having 56m spatial resolution for the months of October & December 2012 which covered complete study areas with its swath of 740Km has been used for the study. The Maximum Likelihood Classification (MLC) and Knowledge Classification (KC) techniques based on Decision Tree approach were applied. It has basically two elements, knowledge engineering and knowledge classifier. Knowledge engineering provides an interface to build up decision tree which defines the rules and variables represented by three parameters, i.e. Normalized Difference Vegetation Index (NDVI), Soil Adjust Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI) threshold value of each class. Knowledge classifier generates the required output classification. The objective of this research work is to perform classification of crop and forest acreage estimation from the AWiFS data and comparing it to the supervised classification techniques, MLC and KC. The result shows that values of overall classification accuracy were 82% and 84% for the months of December and October 2012 respectively using MLC, whereas corresponding values of accuracy were found to be 85% and 87% based on KC. Thus the classification results based on KC provide better results than corresponding results based on the MLC.

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Study on the utility of IRS-P6 AWiFS SWIR band for crop discrimination and classification

Cited by 32 publications

References 14 publications

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

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

Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis

Estimation of Crop and Forest Areas using Expert System based Knowledge Classifier Approach for Aurangabad District

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