1. Ph.D Theses

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    Estimation and Mapping of Vertisols Soil Nutrients by Geostatistics and Remote Sensing Approach
    (National Institute of Technology Karnataka, Surathkal, 2022) Vinod,Tamburi; Shrihari, S.; Amba,Shetty
    he status of soil fertility is a concern, especially in the Deccan plateau vertisols of India. Vertisols are productive if they are managed well. Understanding the spatial variability of soil nutrients is necessary for agriculture to maintain sustainability. The objective of the present study is to characterize the status of soil nutrients, spatial variability of selected soil nutrients, and the estimation of the presence of these soil nutrients by spaceborne Hyperion data in scattered small-size fields of Gulbarga taluk, northern Karnataka, India. This region is known as the "pigeon pea vessel" of the state. The geostatistical analysis is carried out in SpaceStat 4.0® to find the spatial variability of all the selected nutrients. The coefficient of variation monitors the variation in the nutrients of the soil. The variogram analysis has shown that all the selected nutrients are the best fit for the spherical model except nitrogen, organic carbon, and phosphorus. The nugget/sill ratio is utilized to know the spatial dependence of soil nutrients. Using the best fit model, surface maps are generated by the ordinary kriging method. The estimation of soil nutrients from Hyperion data with statistical regression is measured as an alternative technique. The spectral information of the visible near infrared and short wave infrared range (400-2500 nm) is utilized to characterize soil nutrients. The potential of the Hyperion data has not yet been exploited completely due to noisy atmospheric components in spectral signatures especially in fields of smaller size. Sixty-eight random topsoil samples were collected from small farms, which are less than two acres in size. The systematic sampling of soil was conducted in the month (third week) of November 2016. This duration is also synchronized with the passage of the Hyperion satellite above the study area. The atmospheric (FLASSH) and geometric corrections is carried out and then the spectral reflectances are extracted. The PLS_Toolbox is used for filtering (Savitzky Golay), and the Partial Least square regression (PLSR) technique is applied for the estimation of soil nutrients by Hyperion data. The variable importance in projection (VIP) is identified, which reduces the non-significant wavelengths for the PLSR model. Two indices are ii used to assess the prediction accuracy, Coefficient of determination (R2), and root mean square error (RMSE). From analysis of soil nutrients, it is observed that the spatial variability maps exhibited a heterogeneous pattern of soil nutrients because of individual farming methods. The spatial variability maps are used as initial regulation by policymakers for site nutrient management, including fertilization in vertisols. This is essential for sustainable management of the fields, which are aimed at increasing the productivity of the crops; low productivity vertisols are to be used in cultivation on a global scale due to the current shortage of food supplies and agricultural resources land. The utilization of Hyperion data and PLSR technique showed that it has the low to moderate potential to estimate certain vertisols nutrients such as iron (R2=0.40), potassium (R2=0.45), and Copper (R2=0.41), and moderate estimation for nitrogen (R2=0.54) even though vertisols have less reflectance values compared to other soil types. The vertisols of India exhibit low reflectance, which are deficient in humus, nitrogen, phosphorus, and potassium due to low permeability and moisture stress throughout the drought. Hence the presence of soluble nutrients concentration is low compared to other soil. Generally, the white color contributes to higher reflectance in all wavelengths, so the grey-brown color is natural in the vertisols fields and along with less organic matter, which leads to the low reflectance. Hyperion data can be inventively utilized to estimate vertisols soil nutrients with reasonable accuracy in heterogeneous and small size fields.
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    Hyperspectral Vegetation Indices for Arecanut Crop Monitoring
    (National Institute of Technology Karnataka, Surathkal, 2017) B. E, Bhojaraja; Shetty, Amba; Nagaraj, M. K
    Arecanut (Areca catechu L.) is one of the major profitable plantation crop grown in few regions of the World. Karnataka state in India produces almost half of the world’s total production, in that contribution from Shivamogga district and Coastal Karnataka is significant. The production per unit area in Karnataka is considerably less. The major reasons may be improper irrigation practices, poor soil maintenance, lack of technical knowledge on irrigation water quality, quantity, fertilizers used and frequent occurrence of diseases, small size and spatially scattered farms. These reasons were very typical in Chennagiri region of Karnataka. Farmers’ practice adding tank silt lifted from nearby tanks to their farms followed by drip irrigation in the form of flooding. In this region a typical disorder called crown choke harmed an adult plant’s life. The objective of this research is: to explore the potential of advanced tools for Arecanut crop monitoring and to demonstrate it on portion of Chennagiri region of Karnataka. Advanced technological tools used include GPS, Hyperspectral remote sensing data and GIS. Hyperspectral remote sensing is one of the fastest growing techniques in the field of remote sensing due to its vast applications with improved accuracy over conventional method. Spectral library was built separately for different age group and stressed crops using spectroradiometer. Care was taken to match field data with the Hyperion data acquisition time. Hyperion hyperspectral data was classified into stressed versus healthy and different age group crops using developed spectral library. Stressed versus healthy crop classification revealed 10% crops were under stress in patches. To find a scientific reason for crown choke disease affected crops inflated in study area, grid wise soil and water samples were collected, and subjected to standard physico-chemical analysis. Potential evapotranspiration (ETo) was computed using Normalized Difference Vegetation Index (NDVI) based crop coefficient (Kc) method due to non-availability of weather parameters. ETo, Integrated with Hargreaves Samani method was adopted to compute the crop water requirement of different age crops. Narrow bands in hyperspectral data facilitate computation of several spectral indices and can facilitate improved classification accuracy. Indices developed being Disease Index (DI) to identify disease severity in Arecanut crop, Age Index (AI) to segregate the Arecanut crops into different age groups and Arecanut Crop Water Requirement Index (ACWRI) was built to compute age based crop water requirement.ii Important wavelengths were identified among the hundreds of bands to compute the crop water requirement using statistical techniques. Stepwise Multi Linear Regression (SMLR), Partial Least Square Regression (PLSR), and Variable Importance for Projection (VIP) were the techniques of choice. These techniques also facilitated construction of simple models to predict the Arecanut crop water requirement. On the basis of diseased v/s healthy crop classification, it was inferred that more than 10% of plantation under study was affected by crown choke disease. The physico-chemical analysis revealed that improper soil management is the main cause for crown choke disorder. Soil characterization and water quality analysis infers soil is poorly graded (82% of silt content) with very low hydraulic conductivity of 3.2×10-7 cm/sec, and high bulk density of 2.12 g/cm3. This impervious nature caused water logging and lead to salinity. Age based classification results revealed Arecanut crop can be classified into different age groups; below 3 years, 5 to 7 years, 8 to 15 years and above 25 years. And within class classification accuracy of 72% was observed for Support Vector Machine (SVM) classification with linear kernel. Age based Arecanut crop water requirement map reveals that crop water requirement varies with age of the crop, below 7 years of crop it is 19 and for above 15 years it is 25 liter/day/plant. The derived ACWRI, DI, AI indices to monitor Arecanut crop ranges from 0 to 1 to indicate the age based crop water requirement, disease severity, and age of crop respectively. From the hyperspectral data significant wavelengths were identified: (i) to map the stressed Arecanut crops (750, 550 and 675nm), (ii) Arecanut crop age predication (540, 680 and 780nm). (iii) And to predict the age wise crop water requirement using statistical models: SMLR revealed that 681 and 721nm are significant. PLSR also in agreement with SMLR i.e 681,721 and 548nm are important. Whereas a VIP technique revealed wavelengths 1043, 1053, 1033, 1083, 1023, 1013, 1104, and 854nm are important. This study concludes that, hyperspectral remote sensing data processed with standard procedures with appropriate atmospheric corrections algorithms and integrated with field studies along with statistical models can be effectively used for Arecanut crop monitoring. This study also demonstrates that, how advanced technological tools can be used to address societal problems say crop monitoring. The output of the research is useful to the farming community to actively plan their agriculture water requirement, and also improves water use efficiency.