Ajay D. Nagne

Comparative Analysis of Different Land Use–Land Cover Classifiers on Remote Sensing LISS-III Sensors Dataset

Determination and identification of land use–land cover (LULC) of urban area have become very challenging issue in planning a city development. In this paper, we report application of four classifiers to identify LULC using remote sensing data. In our study, LISS-III image dataset of February 2015, obtained from NRSC Hyderabad, India, for the region of Aurangabad city (India) has been used. It was found that all classifiers provided similar results for water body, whereas significant differences were detected for regions related to residential, rock, barren land and fallow land. The average values from these four classifiers are satisfactory in agreement with Toposheet obtained from the Survey of India.

Digital Assessment of Spatial Distribution of the Surface Soil Types Using Spatial (Texture) Features with MLC and SVM Approaches

In the present work, the effort has been made to identify and distribute of surface soil types using high spatial resolution multispectral (HSRM) image investigated in two ways. First, multispectral data is classified based on conventional approaches. Second, a method based on gray level co occurrence matrix (GLCM) as spatial objects extraction of the multispectral data is proposed. In this view, various texture parameters of the co-occurrence matrix method were used to highlight and extract the textures in the image. The method was computed on increasing matrix window size starting from original one. The Resourcesat-II Linear Imaging Self Scanning (LISS-IV) sensor multispectral image was used for testing the algorithms of the study area Phulambri Tehsil of Aurangabad region of Maharashtra state, India. The proposed approach was used as an input for Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) approaches for identification and distribution of surface soil types and other patterns. The experimental outcomes of the present research were appraised on the basis of classification accuracy of methods. The overall accuracy of classification by MLC and SVM after spatial feature extraction was 92.82 and 97.32% with kappa value of 0.90 and 0.96 respectively. It was found that, the accuracy of the classification has increased after considering spatial features based on co-occurrence matrix. The results were promising to extract the mixed features for classification of soil type objects.

Fuzzy convolution tactic for classification of spatial pattern and crop area

In this work we have used data as acquired by IRS P6 LISS III sensor to classify crop area and spatial pattern of Vaijapur Tehsil. The supervised classification approach with fuzzy convolution technique and Maximum likelihood classifier has been used for analysis of satellite image for the month of February 2015 and ground truth data collected during field visit for testing and training purpose. Fuzzy convolution has been applied on the fuzzy layers obtained after fuzzy classification and compared these result with Maximum Likelihood classifier. It is found that Maximum likelihood has given better result for overall classification but for crop area class; fuzzy convolution gives more truthful results.

Performance Analysis of Spectral Features Based on Narrowband Vegetation Indices for Cotton and Maize Crops by EO-1 Hyperion Dataset

The objective of this paper is to estimate and analyze the selected narrowband vegetation indices for cotton and maize crops at canopy level, generated by using EO-1 Hyperion dataset. EO-1 Hyperion data of the date 15th October 2014 has been collected from United States Geological Survey (USGS) Earth Explorer by data aquisition request (DAR). After performing atmospheric corrections by using Quick Atmospheric Correction (QUAC), we have applied selected narrowband vegetation indices specifically those which are based on greenness/leaf pigments namely NDVI, EVI, ARVI, SGI, and red-edge indices such as RENDVI and VOG-I. Statistical analysis has been done by using the statistical t-test, it is found that there is a more significant difference in the mean of the responses of cotton and maize to NDVI, ARVI and VOG-1 than EVI & RENDVI, whereas, the response to SGI for both the crops is very close to each other.

Application of Remote Sensing for Assessing Forest Cover Conditions of Aurangabad, (MS), India

The Remote Sensing has been playing an important role in mapping spatial and temporal behavior of forest cover. The mapping results are largely dependent on the user’s preferences because it is location and application specific. The study deals with the use of RS techniques to know the present status of forest area undertaken in the Gautala Wildlife Sanctuary, and Bird Sanctuary, Aurangabad region. Forest cover is depleting very fast due to the conversion of forest region into agricultural or other land use. The forest cover estimation of these protected areas has been derived from forest cover map generated from LISS III satellite images of the year 1997 and 2015 using digital image classification and processing approach. The temperature of the Aurangabad district is increasing and rainfall is reducing which indicates that deforestation can be one of the associated causes for it. The classification result shows that there is a significant conversion, loss in forest cover.

Performance analysis of fuzzy convolution tactic for classification of spatial pattern and crop area

In this work we have used data as acquired by IRS P6 LISS III sensor to classify crop area and spatial pattern of Vaijapur Tehsil. The supervised classification approach with fuzzy convolution technique and Maximum likelihood classifier has been used for analysis of satellite image for the month of February 2015 and ground truth data collected during field visit for testing and training purpose. Fuzzy convolution has been applied on the fuzzy layers obtained after fuzzy classification and compared these result with Maximum Likelihood classifier. It is found that Maximum likelihood has given better result for overall classification but for crop area class; fuzzy convolution gives more truthful results.

Understanding the dynamics of Gautala Autramghat forest: A digital image classification approach

Gautala Autramghat Wildlife Sanctuary is a protected forest area of Aurangabad, Maharashtra state, India. It lies in the Ajanta hill ranges of the Western Ghats; it is administrated by Forest Authority of Aurangabad and Jargon District. In 1986, it was established as the wildlife sanctuary in an existing forest area. The forest area is southern tropical dry deciduous forest, with interspersed shrubs and grasslands. The study Forest ecosystem is very important due to its role in the global carbon cycle. This study highlights the potential of LISSIII sensor temporal data of the year 2003 and 2015 for monitoring the precious natural resource. The remote sensing techniques have been employed to study the dynamic nature of the natural resource and also focus on the changes that have occurred naturally or artificially in its current state.