<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>
Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing original information through a process known as dimensionality reduction (DR). Redundant bands portray the fact that neighboring bands are highly correlated, sharing similar information. The benefits of utilizing dimensionality reduction include the ability to slacken the complexity of data during processing and transform original data to remove the correlation among bands. In this paper, two DR methods, principal component analysis (PCA) and minimum noise fraction (MNF), are applied to the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset of Kalaburagi for discussion.
Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data.
<p><strong>Abstract.</strong> In this present world, due to the increasing adverse effect of anthropological activities on the natural environment causes a large scale environmental degradation which directly reduces the suitable natural environment for human habitation. As a consequence, in recent years, human realised the need for a favourable natural environment which is adoptable for habitation. In this present study, some of the following five criterions such as Land Surface Temperature (LST), vegetation coverage, impervious surface, wetness and water condition derived from the remotely sensed data were used to evaluate the Natural Human habitation Environment Suitability Index (NHESI) along the coastal taluks of Tamil Nadu. Landsat-7 (ETM+) images and Landsat-8 (OLI/TIRS) images with a spatial resolution of 30m have been used to derive the evaluation factors of NHESI for the year of 2000 and 2018. Multi Criteria Evaluation (MCE) based Analytical Hierarchical Process (AHP) and fuzzy linear membership has been used in this study to evaluate the weighs and ratings of each criterion and its classes. The best NHESI is seen in 2000 where a total area of about 13902.9<span class="thinspace"></span>km<sup>2</sup> comes under the habitable region, against an area of 7726.9<span class="thinspace"></span>km<sup>2</sup> in 2018. The study area is further classified into moderately habitable, marginally habitable and uninhabitable regions. This study clearly indicates the degradation of the natural environmental conditions for human habitation. This kind of habitability study will help the researchers, decision makers and government agencies in creating awareness and adopting policies in the spatial planning of human land utilization for habitability.</p>
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