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Author: search.filters.author.Dehghan-Shoar, Mohammad HossainSubject: search.filters.subject.Remote sensing

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    Enhancing grassland nitrogen estimation : a multiscale approach through optical reflectance spectroscopy and hybrid modeling techniques : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2025-01-21) Dehghan-Shoar, Mohammad Hossain
    Optical remote sensing technology has emerged as a powerful tool for assessing vegetation characteristics, particularly nitrogen (N) concentration (N%) in heterogeneous grasslands. Accurate estimation of N% is crucial for farmers, as it directly influences grassland productivity and plays a key role in sustainable land management. Accurate N assessments optimize fertilizer use, boosting productivity, lowering costs, and enhancing environmental modeling to address impacts such as N leaching and greenhouse gas emissions. Despite significant progress, challenges and knowledge gaps remain, highlighting the need for continued research to fully harness remote sensing’s potential in agricultural management and its impact on livestock productivity. This thesis aims to advance the accurate estimation of grassland N% by integrating physically-based, empirical-statistical, and hybrid models using optical reflectance spectroscopy data. The research focuses on three primary objectives: 1. To estimate N% in grasslands using optical reflectance spectroscopy, data will be collected across multiple scales, including ground-, leaf-, canopy-, and satellite-scale observations. 2. To improve the universality and adaptability of grassland N% models through a hybrid approach that combines data from various optical sensors across multiple scales. 3. To account for and quantify uncertainties in grassland N% prediction models. The thesis addresses the challenge of uncertainty by conducting a comprehensive analysis of its sources and developing methods, such as Physically Informed Neural Networks (PINN), to account for them. Key strategies include data fusion techniques for integrating diverse data sources and improving atmospheric correction methods. A unified methodology combining empirical-statistical and physically-based approaches is proposed to enhance generalization. Machine learning algorithms play a pivotal role in feature selection and optimization, further improving model accuracy and transferability. The developed methods undergo evaluation using independent validation data collected from heterogeneous grasslands across different periods and locations. Results demonstrate that integrating physically-based and empirical-statistical approaches significantly improves model accuracy and transferability, providing a deeper understanding of the factors influencing vegetation traits. This thesis highlights the importance of advanced techniques, including machine learning, deep learning algorithms, Radiative Transfer Models (RTM), and data fusion methods, for precisely characterizing vegetation traits, contributing to more sustainable and efficient grassland management practices.
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    Use of new generation geospatial data and technology for low cost drought monitoring and SDG reporting solution : a thesis presented in partial fulfillment of the requirement for the degree of Master of Science in Computer Science at Massey University, Manawatū, New Zealand
    (Massey University, 2018) Dehghan-Shoar, Mohammad Hossain
    Food security is dependent on ecosystems including forests, lakes and wetlands, which in turn depend on water availability and quality. The importance of water availability and monitoring drought has been highlighted in the Sustainable Development Goals (SDGs) within the 2030 agenda under indicator 15.3. In this context the UN member countries, which agreed to the SDGs, have an obligation to report their information to the UN. The objective of this research is to develop a methodology to monitor drought and help countries to report their ndings to UN in a cost-e ective manner. The Standard Precipitation Index (SPI) is a drought indicator which requires longterm precipitation data collected from weather stations as per World Meteorological Organization recommendation. However, weather stations cannot monitor large areas and many developing countries currently struggling with drought do not have access to a large number of weather-stations due to lack of funds and expertise. Therefore, alternative methodologies should be adopted to monitor SPI. In this research SPI values were calculated from available weather stations in Iran and New Zealand. By using Google Earth Engine (GEE), Sentinel-1 and Sentinel- 2 imagery and other complementary data to estimate SPI values. Two genetic algorithms were created, one which constructed additional features using indices calculated from Sentinel-2 imagery and the other data which was used for feature selection of the Sentinel-2 indices including the constructed features. Followed by the feature selection process two datasets were created which contained the Sentinel- 1 and Sentinel-2 data and other complementary information such as seasonal data and Shuttle Radar Topography Mission (SRTM) derived information. The Automated Machine Learning tool known as TPOT was used to create optimized machine learning pipelines using genetic programming. The resulting models yielded an average of 90 percent accuracy in 10-fold cross validation for the Sentinel- 1 dataset and an average of approximately 70 percent for the Sentinel-2 dataset. The nal model achieved a test accuracy of 80 percent in classifying short-term SPI (SPI- 1 and SPI-3) and an accuracy of 65 percent of SPI-6 by using the Sentinel-1 test dataset. However, the results generated by using Sentinel-2 dataset was lower than Sentinel-1 (45 percent for SPI-1 and 65 percent for SPI-6) with the exception of SPI-3 which had an accuracy of 85 percent. The research shows that it is possible to monitor short-term SPI adequately using cost free satellite imagery in particular Sentinel-1 imagery and machine learning. In addition, this methodology reduces the workload on statistical o ces of countries in reporting information to the SDG framework for SDG indicator 15.3. It emerged that Sentinel-1 imagery alone cannot be used to monitor SPI and therefore complementary data are required for the monitoring process. In addition the use of Sentinel-2 imagery did not result in accurate results for SPI-1 and SPI-6 but adequate results for SPI-3. Further research is required to investigate how the use of Sentinel-2 imagery with Sentinel-1 imagery impact the accuracy of the models.