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Browsing by Author "Williams, Paige T."

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    Combining remotely sensed estimates of structure and function to improve quantification of forest productivity
    Williams, Paige T. (Virginia Tech, 2024-09-19)
    Gross primary productivity (GPP) describes the total photosynthesis (i.e., carbon fixation) for an ecosystem and is an important component of the global land carbon budget. Accurate measurements of forest carbon sequestration are crucial, as climate, soils, and management practices influence carbon uptake. Disturbances such as wildfires, diseases, insect infestations, and extreme weather events significantly impact forest health and functionality. Remote sensing is useful for monitoring global ecosystems and estimating forest vitality. High-resolution images and lidar (Light Detection and Ranging) data offer enhanced insights into forest light utilization. Combining lidar with spectral data provides a comprehensive view of forest ecosystems, integrating both structural and physiological information. This work includes three separate studies under a common objective to establish an improved understanding of forest productivity in different forest compositions using a combination of physiological function from optical imagery and morphological structure from lidar. The first study investigates the photochemical reflectance index (PRI) and how illumination affects the diurnal and vertical distributions of two managed pine stands with varying ages and row orientations, using airborne hyperspectral and lidar data. We developed a novel method to classify canopy illumination into sunlit, shaded, and mixed light areas using the hyperspectral data and a simulated panchromatic band. PRI values varied between sunlit and shaded foliage throughout the day, reflecting differences in foliage efficiency depending on light conditions and forest structure. The second study evaluates how well remotely quantified plant functional traits predict GPP across U.S. forests. Using data from NEON's airborne remote sensing and in situ flux tower measurements, the study assessed hyperspectral optical indices as physiological traits and lidar-derived products as morphological and environmental traits. By developing multiple linear regression models with separated and combined trait groups, the best prediction model combined morphological, environmental, and physiological traits with a PRESS R2 of 0.75. This underscores the importance of integrating various functional traits for accurate forest productivity predictions. The last study detects insect-induced tree mortality with separated and combined models of structure from lidar and physiology from satellite imagery. Although all models tend to overestimate tree mortality, integrating lidar data enhances predictions by 6% offering valuable structural context. A central theme of this work is that lidar-derived structural measurements were crucial in every chapter.
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    Mapping Smallholder Forest Plantations in Andhra Pradesh, India using Multitemporal Harmonized Landsat Sentinel-2 S10 Data
    Williams, Paige T. (Virginia Tech, 2020-01-27)
    The objective of this study was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India using multitemporal (intra- and inter-annual) visible and near-infrared (VNIR) bands from the Sentinel-2 MultiSpectral Instruments (MSIs). Dependency on and scarcity of wood products have driven the deforestation and degradation of natural forests in Southeast Asia. At the same time, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) being the focus of this study. The ecosystem services provided by natural forests are different from those of plantations. As such, being able to separate natural forests from plantations is important. Unfortunately, there are constraints to accurately mapping planted forests in Andhra Pradesh (and other similar landscapes in South and Southeast Asia) using remotely sensed data due to the plantations' small size (average 2 hectares), short rotation ages (often 4-7 years for timber species), and spectral similarities to croplands and natural forests. The East and West Godavari districts of Andhra Pradesh were selected as the area for a case study. Cloud-free Harmonized Landsat Sentinel-2 (HLS) S10 data was acquired over six dates, from different seasons, as follows: December 28, 2015; November 22, 2016; November 2, 2017; December 22, 2017; March 1, 2018; and June 15, 2018. Cloud-free satellite data are not available during the monsoon season (July to September) in this coastal region. In situ data on forest plantations, provided by collaborators, was supplemented with additional training data representing other land cover subclasses in the region: agriculture, water, aquaculture, mangrove, palm, forest plantation, ground, natural forest, shrub/scrub, sand, and urban, with a total sample size of 2,230. These high-quality samples were then aggregated into three land use classes: non-forest, natural forest, and forest plantations. Image classification used random forests within the Julia Decision Tree package on a thirty-band stack that was comprised of the VNIR bands and NDVI images for all dates. The median classification accuracy from the 5-fold cross validation was 94.3%. Our results, predicated on high quality training data, demonstrate that (mostly smallholder) forest plantations can be separated from natural forests even using only the Sentinel 2 VNIR bands when multitemporal data (across both years and seasons) are used.