Leveraging NAIP,LiDAR and Sentinel data for accurate multiclass mapping of heterogenous grassland landscapes



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With the advancement in remote sensing (RS), sensors, platforms and data processing, RS data have significantly contributed to science and policy. However, data processing capability has not fully matured to handle high spatial and temporal resolution data to make management decisions. Using high-spatial-resolution (NAIP), temporal (Sentinel), and light detection and ranging (LiDAR) data, I derived land use land cover (LULC) maps using machine learning and data fusion while accounting for spatial autocorrelation in the sample data. Chapter I offers a brief overview of the development and current state-of-the-art practices in accurate LULC mapping using high-spatial-resolution remote sensing data, then introduces a list of questions that were tackled in this dissertation. Finally, Chapter I presents a brief synopsis of subsequent chapters and summarize the limitations. The chapter outlines the process of data ingestion, pre-processing and machine learning to produce accurate LULC mapping. The chapter describes a methodological workflow that can be adapted to regular computing resources available in most office settings. Chapter III evaluated the efficacy and effectiveness of surface features derived from LiDAR data in improving the mapping of grass- and shrub-dominated landscapes. Chapter IV integrated the time-series Sentinel 2A data overhigh-resolution NAIP data in heterogeneous landscapes using stacking ensemble machine learning. Finally, chapter V summarizes each chapter and discusses each case study's overall significance. With data fusion and supervised machine learning, I showed a practical approach to producing accurate land use land cover maps of grass-and-shrub-dominated landscapes of Texas.

Embargo status: Restricted until 09/2027. To request the author grant access, click on the PDF link to the left.



Object-Based Image Analysis, Land Cover Classification, Principal Component Analysis, GLCM, Random Forest, Stacking Ensemble, LiDAR, Machine Learning, Autocorrelation, Semivariance, Spatial Structure