1. Ph.D Theses
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/11
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Item Integrated Surface Water Resource Modeling and Irrigation Productivity in Lower Baro, Ethiopia(National Institute of Technology Karnataka, Surathkal, 2024) DENEKE, FISEHA BEFIKADU; Shetty, Amba; Fufa, Ing. FekaduThe management of surface water resources is hampered in many river basins by a lack of data. The problem is for several of Ethiopia’s river basins increasing the productivity of surface irrigation and scientifically understanding the factors that led to integrated surface water modeling, particularly in Ethiopia's lower Baro is useful. The objectives of this study were (i) to review the land cover (LC) change implications to hydrological variables soil erodibility and yield reduction (ii) to explore statistical and trend analysis of hydrometeorological data, (iii) to quantify the surface water potential and irrigation water demand, and (iv) to investigate the satellite-ET based irrigation performance using Water Productivity Open-access Portal database and to come up with a strategy for quantifying the spatial and temporal increase water use efficiency (WUE) and system water use efficiency (sWUE) in the rainfed and irrigated area of lower Baro watershed. GIS-based multi-criteria evaluation used with the interaction of 8 factoring parameters, to see the low level of irrigation development. In the eight sub-classes, a total of 20, 325 km2 of appropriate pastoral land has been transformed into rainfed rice, sugarcane, maize, and vegetable land. This study used GIS, RS, Water Evaluation and Planning (WEAP), Cropwat8.0, and EasyFit software. The soil moisture rainfall-runoff method was computed using the WEAP hydrological model for the surface water demand and potential simultaneously from 2000-2014 and 2020-2030. This work used systematic reviews and a meta-analysis technique to examine the LC change and its effects on hydrological variables, soil erodibility, and yield reduction. Record identified through Scopus Searching, Web of Science Searches, and Google Scholar. Fully articles were assessed for eligibility and excluded for reasons. In the data search, 85 articles with investigations published between 2007 to 2022 were examined. Then, for surface water potential and irrigation productivity, only 2% of the abstracts that were eventually evaluated for assessment were selected. Four crops were selected to grow in these identified irrigable areas, and their gross irrigation demand was calculated using Cropwat8.0. In addition to annual streamflow variability, using graph pad prism 9.4 the monthly streamflow variability was determined. With the help of the WEAP system's parameter estimation tool weekly, average streamflow calibration was performed. II As a result, of the review, in the lower Baro, built-up/ settlement, agricultural land, water body, bare/ outcrop, and commercial farm rose roughly +195, +48, +35, +35, and +1%, respectively. On the other hand, shrubland, rangeland, forest land, and wetland decreased by 1, 0.5, 5, and 10%, respectively. But the revised universal soil loss equation looks to be a good alternative and soil water conservation measures are crucial for minimizing soil erodibility in lower Baro. As a result, of the water balance obtained the maximum estimated monthly hydropower potential, irrigation water demand, livestock water demand, and surface water potential were 60.3 Mm3 and the corresponding outflow value was 52.4 Mm3. The total estimated mean annual surface runoff leaving each station of the watershed was 462.06 Mm3. The coefficient of determination (R2) result was 0.88 and the Nash efficiency coefficient (NSE) was 0.91 highest at the Baro Gambella station. The anticipated irrigation requirement for the selected crop's driest five months of May, February, March, January, and April was 1, 0.9, 0.78, 0.78, and 0.34 l/s/h. The Baro Gambella sub-catchment had maximum critical values test results of σ = 12.6, μ =11.9, and γ = 0, while the Sor Metu showed the smallest value of 0.80, 1.75, and -0.03. Across the watershed, the sWUE varies with runoff, with a coefficient of variation of 71%. As a result, the overall accuracy of the LC change was 81%, the Landsat 8 images of the soiladjusted vegetation index showed a maximum value of 0.87 and a minimum of -1.5. The normalized vegetation index of 0.58 maximum and -1 minimum was observed. By 2050, the sWUE will be 10% lower temporally, but its spatial variability will be 25% higher. From 2017-2023 an increase in trees (dense forest), scrub/shrub land, flooded vegetation, and bare ground, while there had been a decline in water bodies and crops during the same period. When yield gaps are increased by a factor of 1/3, 2/3, and 3/4, the Baro Gambella sub-catchment has the biggest yield gaps 443.52, 887.04, and 1008.106 kg respectively, while the Gumero Gore sub-catchment experiences the smallest yield gaps 0.01, 0.02, and 0.03 kg respectively. As a result, the crop water productivity, ET, crop index, and temporal fluctuation of the yield gap were investigated. Also, the available dry river flow does not meet the available potential irrigable land. Moreover, to increase the irrigation crop water productivity by 2050 in the lower Baro watershed. Therefore, improve soil infiltration and water storage, which decreases runoff and the water lost by ET and raises sWUE.Item MINERAL IDENTIFICATION On MARTIAN SURFACE USING SUPERVISED LEARNING APPROACH FROM CRISM HYPERSPECTRAL DATA(National Institute of Technology Karnataka, Surathkal, 2024) KUMARI, PRIYANKA; Shetty, Amba; Koolagudi, Shashidhar GThe availability of spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data through NASA’s Planetary Data System has revolutionized the study of the surface mineralogy of Mars. However, building supervised learning models for mineral mapping remains a challenge due to the scarcity of ground-truth training data. In this thesis, an innovative framework is presented that leverages supervised learning to classify spectra within CRISM hyperspectral images. To overcome the data limitation, an augmentation approach is employed that creates the training data by augmenting the minerals available in the MICA spectral library, preserving key absorption signatures of each mineral class while introducing adequate variability. The framework includes a comprehensive pre-processing pipeline, featuring a novel feature extraction method to capture distinctive absorption patterns in the spectra. The approach is validated using CRISM images from diverse Martian locations and interactive mineral maps are also provided for the detected dominant minerals. While this initial framework ensures acceptable accuracy, utilizing more sophisticated learning models and advanced preprocessing techniques can enhance the performance of the framework. Spectra in remotely sensed hyperspectral images are often affected by the presence of continuum, which changes the global curvature of the spectra, although the key absorption signatures are present. The continuum removal process, one of the critical preprocessing steps in the pipeline, is modified from the traditional approach to a novel method named Segmented Curve Fitting, which can identify more absorption shoulder points in a spectrum and thus can detect the absorption features in it more distinctively. Lastly, the thesis introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture tailored for mineral identification using CRISM hyperspectral data. Inspired by Inception-V3 and InceptionResnet-V1 architectures, MICAnet leverages 1-dimensional convolutions for processing spectra at the pixel level. This innovative architecture represents a significant contribution, being the first solely dedicated to this objective. The performance of the mineral mapping framework is assessed using both simulated data of varying complexity and a real CRISM TRDR/MTRDR hyperspectral dataset. In conclusion, this study advances the field of planetary science and remote sensing by providing automated approaches for mineral identification and mapping on Mars, also, enhances the understanding of Martian surface mineralogy, offering valuable insights into the planet’s geological history and habitability.Item Novel Techniques in Hyperspectral Data Analysis for Endmember Extraction, Change Detection and Classification(National Institute of Technology Karnataka, Surathkal, 2024) Yadav, Palla Parasuram; A. V. Narasimhadhan; B. S. Raghavendra; Shetty, AmbaHyperspectral image (HSI) analysis is a powerful technique in remote sensing that involves the acquisition and analysis of images captured across hundreds or even thousands of narrow and contiguous spectral bands. Unlike traditional remote sensing techniques that capture information in just a few broadbands, HSI provides detailed spectral information for each pixel in an image scene. This wealth of spectral data enables a more comprehensive understanding of the Earth’s surface and the objects it contains. By analyzing the unique spectral signatures of different materials, HSI enables the identification and discrimination of various land cover types, vegetation species, soil properties, and even specific minerals. However, the analysis of hyperspectral data presents several challenges that require specialized approaches. Endmember extraction (EE) is one such challenge, involving the identification of pure spectral signatures or reference spectra that represent specific target materials. Spectral matching is a vital component of HSI analysis that involves measuring the degree of closeness between the spectral signatures extracted from the image and those obtained through ground-based spectrometer measurements or known reference spectra. Spectral matching algorithms are useful not only for validating the accuracy of image-based signatures but also for feature extraction. This matching process helps in the identification, analysis, and interpretation of different materials or targets present in the HSI. Through the combination of endmember extraction and spectral matching in HSI, diverse applications in geology and related fields are empowered, enabling tasks such as geological mapping, mineral exploration, environmental monitoring, and land cover analysis with increased accuracy and efficiency. Although hyperspectral imaging was originally developed for mining and geology, mineral identification using hyperspectral data has not been addressed adequately yet and remains a challenging task. HSIs due to advancements in spatial-spectral resolutions and the availability of multi-temporal information are in demand for many applications. Change detection (CD), in particular, is an important and challenging problem in monitoring changes such as deforestation, urban development, and landslides using time series HSI data. Though several endmember extraction algorithms (EEAs) are developed, spectral matching algorithms (SMAs) have not been explored much in the extraction of spectrally distinct signatures. Therefore, in this work, similarity measures based EEAs (SM-EEAs) are proposed to explore the fundamental characteristic, i.e. spectrally distinctive in nature, of endmembers. Experimental results on proposed EEAs i.e., a similarity measures-based subtractive clustering algorithm (SM-SCA) and a similarity measures-based endmember initialization algorithm (SM-EIA) showed the applicability of SMAs in extracting spectrally distinct signatures as the endmembers and also hinted the importance of endmember initialization. The darkest pixel identified as a target pixel of interest (TPOI) in the further investigation on endmember initialization strategies emerges not only as a potential TPOI but also contributes to improving the performance of EEAs when combined with the brightest pixel as TPOIs. Experiments carried out on an improved SM-EIA to test its applicability in extracting pure endmembers present maximin-distance algorithm (MDA) do not able to identify vertices of the simplex with simple metrics like Euclidean distance (ED) and other simple SMAS but with higher dimensionality metrics like volume. The proposed corner-driven iterative clustering algorithm (CDIC) appears to perform better in EE by identifying the corner pixels and thereby providing training samples for HSI classification. Though few already developed spectral matching measures are available, the identification of diagnostic features of spectrally distinct signatures with the existing SMAs to discriminate them effectively is still a challenging task. Therefore, this work presents a gradientbased spectral similarity measure (GSSM) that captures the diagnostic (absorption) features to measure the degree of closeness between spectral signatures. The effectiveness of the proposed GSSM in distinguishing spectrally distinct signatures is studied with that of other spectral matching algorithms in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. The proposed GSSM was not only able to highlight diagnostic features of target signatures but also showed its effectiveness in discriminating spectrally distinct target signatures better than other SMAs. Further study on gradient correlation (GC) incorporated showed improved discrimination power with geometrical SMAs. Further, a meaningful way of measuring RSDPW is proposed. Reformulated RSDPW appears to be more meaningful in discriminating endmembers and obtaining the range of RSDPW values for different levels of discrimination than the former one. The high dimensionality of HSI data and limited availability of hyperspectral CD data sets with ground-truth change map make CD not so easy but a difficult task. Though there are many classical and deep learning (DL) based algorithms to detect changes, either their performance is not so better or the final performance depends on efficiency of pre-detection algorithms. In addition, there is not much comprehensive study on developing CD algorithms that not only simple to use but also as efficient as that of DL based algorithms is available. Therefore, an endmember related feature extraction is proposed for HSI-CD. Proposed ATGP based CD algorithms not only perform better than classical CD algorithms but also able to reach the performance of DL based CD algorithms. additionally, even a minimum number of features around three to five (3-5) also good enough to get high accuracy as that of DL models. Mineral identification remains a challenging task due to the subtle differences among the spectral signatures of minerals and insufficient ground truth. The classical spectral angle measure (SAM) classifier is a simple model and does not yield high accuracy and an expert system for hyperspectral data classification (ExHype) is a binary classifier and therefore complex to train binary classifier modules equal to the number of minerals to be classified and obtain thresholds to gain accuracy. Due to lack of training samples and sufficient data with ground truth to be tested, DL models have not been explored much in mineral classification so far. To overcome this, a virtual sample generation to be able to generate more training samples that provide a chance to explore DL models that need variations in training samples in mineral classification is proposed. Further, a one-dimensional convolutional neural network (1-D CNN) model, trained on training samples generated by virtual sample generation, designed to classify minerals performed well in classifying the tested mineral classes with high accuracy.