Monitoring and understanding plant phenology is becoming an increasingly important way to identify and model global changes in vegetation life cycle events. Although numerous studies have used synoptically sensed data to study phenological patterns at the continental and global scale, relatively few have focused on characterizing the land surface phenology of specific ecosystems. Mountain environments provide excellent examples of how variations in topography, elevation, solar radiation, temperature, and spatial location affect vegetation phenology. High elevation biomes cover twenty percent of the Earth's land surface and provide essential resources to both the human and non-human population. These areas experience limited resource availability for plant growth, development, and reproduction, and are one of the first ecosystems to reflect the harmful impact of climate change. Despite this, the phenology of mountain ecosystems has historically been understudied due to the rough and variable terrain and inaccessibility of the area. Here, we use two MODIS/Terra satellite 16-day products, Vegetation Index and Nadir BRDF Adjusted Reflectance, to assess start of season (SOS) for the 2007 calendar year. Independent data for elevation, slope, aspect, solar radiation, and temperature as well as longitude and latitude were then related to the SOS output. Based on the results of these analyses, we found that SOS can be predicted with a significant R² (0.55-0.64) for each individual zone as well as the entire western mountain range. While both elevation and latitude have significant influences on the timing of SOS for all six study areas. When examined at the regional scale and accounting for aspect, SOS follows closely with Hopkins' findings in regard to both elevation and latitude. / Master of Science

Droughts are a recurrent part of our climate, and are still considered to be one of the most complex and least understood of all natural hazards in terms of their impact on the environment. In recent years drought has become more common and more severe across the world. For more than a decade, the US southwest has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources. The focus of this work is achieving a better understanding of the impact of drought on the lands of the Hopi Tribe and Navajo Nation, situated in the Northeastern corner of Arizona. This research explores the application of remote sensing data and geospatial tools in two studies to monitor drought impacts on vegetation productivity. In both studies we used land surface phenometrics as the data tool. In a third related study, I have compared satellite-derived land surface phenology (LSP) to field observations of crop stages at the Maricopa Agricultural Center to achieve a better understanding of the temporal sensitivity of satellite derived phenology of vegetation and understand their accuracy as a tool for monitoring change. The first study explores long-term vegetation productivity responses to drought. The paper develops a framework for drought monitoring and assessment by integrating land cover, climate, and topographical data with LSP. The objective of the framework is to detect long-term vegetation changes and trends in the Normalized Difference Vegetation Index (NDVI) related productivity. The second study examines the major driving forces of vegetation dynamics in order to provide valuable spatial information related to inter-annual variability in vegetation productivity for mitigating drought impacts. The third study tests the accuracy of remote sensing-derived LSP by comparing them to the actual seasonal phases of crop growth. This provides a way to compare and validate the various LSP algorithms, and more crucially, helps to characterize the remote sensing-based metrics that contrast with the actual biological phenophases of the crops. These studies demonstrate how remote sensing data and simple statistical tools can be used to assess drought effects on vegetation productivity and to inform about land conditions, as well as to better understand the accuracy of satellite derived LSP.

Vegetation phenology controls seasonal variation in ecosystem processes and exerts important controls on land-atmosphere exchanges of carbon, water, and energy. However, the ecological processes and interactions between climate and vegetation that control phenology and associated feedbacks to the atmosphere are not fully understood. In this dissertation, I use remote sensing in combination with climate and ecological data to improve understanding of biophysical controls and feedbacks between vegetation phenology and the atmosphere in temperate forest ecosystems of North America. In the first part of this dissertation, I evaluate the agreement and characterize the similarities and differences between land surface phenology products from two remote sensing instruments (MODIS and VIIRS) that are designed to provide long-term continuity of land surface phenology measurements at global scale. Results from this analysis indicate that the VIIRS land surface phenology product provides excellent continuity with the MODIS record despite subtle differences between each instrument and the algorithms used to generate each product. In the second part of this dissertation, a state-space Bayesian modeling framework is applied to seventeen years of MODIS and daily weather data to improve understanding of what controls the timing of springtime phenology in deciduous forests of temperate and boreal North America. Results show that photoperiod is more important in warmer regions than in colder regions, which contradicts a widely held hypothesis that photoperiod provides a key safety mechanism preventing early leaf-out during springtime. In the final part of this dissertation, I use a physically-based attribution method to quantify the relative importance of covarying surface biophysical and atmospheric variables in modifying the surface energy balance during springtime. Results show that the widely observed decrease in the Bowen ratio that occurs with leaf emergence is not solely attributable to changes in surface resistance caused by increasing leaf area during spring. Rather, observed changes in the Bowen ratio reflect the combined effects of changes in surface properties and atmospheric conditions. The results from this dissertation provide an improved foundation for long-term studies focused on observing and modeling springtime vegetation phenology and associated feedbacks to the atmosphere in deciduous forest ecosystems at local-to-continental scales.

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Die Brasilianische Savanne, auch bekannt als der Cerrado, bedeckt ca. 24% der Landoberfläche Brasiliens. Der Cerrado ist von einer einzigartigen Biodiversität und einem starken Gradienten in der Vegetationsstruktur gekennzeichnet. Großflächige Landnutzungsveränderungen haben dazu geführt, dass annähernd die Hälfte der Cerrado in bewirtschaftetes Land umgewandelt wurde. Die Kartierung ökologischer Prozesse ist nützlich, um naturschutzpolitische Entscheidungen auf räumlich explizite Informationen zu stützen, sowie um das Verständnis der Ökosystemdynamik zu verbessern. Neue Erdbeobachtungssensoren, frei verfügbare Daten, sowie Fortschritte in der Datenverarbeitung ermöglichen erstmalig die großflächige Erfassung saisonaler Vegetationsdynamiken mit hohem räumlichen Detail. In dieser Arbeit wird der Mehrwert von Landsat-basierten Landoberflächenphänologischen (LSP) Metriken, für die Charakterisierung der Cerrado-Vegetation, hinsichtlich ihrer strukturellen und phänologischen Diversität, sowie zur Schätzung des oberirdischen Kohlenstoffgehaltes (AGC), analysiert. Die Ergebnisse zeigen, dass LSP-Metriken die saisonale Vegetatiosdynamik erfassen und für die Kartierung von Vegetationsphysiognomien nützlich sind, wobei hier die Grenzen der Einteilung von Vegetationsgradienten in diskrete Klassen erreicht wurden. Basierend auf Ähnlichkeiten in LSP wurden LSP Archetypen definiert, welche die Erfassung und Darstellung der phänologischen Diversität im gesamten Cerrado ermöglichten und somit zur Optimierung aktueller Kartierungskonzepte beitragen können. LSP-Metriken ermöglichten die räumlich explizite Quantifizierung von AGC in drei Untersuchungsgebieten und sollten bei zukünftigen Kohlenstoffschätzungen berücksichtigt werden. Die Erkenntnisse dieser Dissertation zeigen die Vorteile und Nutzungsmöglichkeiten von LSP Metriken im Bereich der Ökosystemüberwachung und haben demnach direkte Implikationen für die Entwicklung und Bewertung nachhaltiger Landnutzungsstrategien. / The Brazilian savanna, known as the Cerrado, covers around 24% of Brazil. It is characterized by a unique biodiversity and a strong gradient in vegetation structure. Land-use changes have led to almost half of the Cerrado being converted into cultivated land. The mapping of ecological processes is, therefore, an important prerequisite for supporting nature conservation policies based on spatially explicit information and for deepening our understanding of ecosystem dynamics. New sensors, freely available data, and advances in data processing allow the analysis of large data sets and thus for the first time to capture seasonal vegetation dynamics over large extents with a high spatial detail. This thesis aimed to analyze the benefits of Landsat based land surface phenological (LSP) metrics, for the characterization of Cerrado vegetation, regarding its structural and phenological diversity, and to assess their relation to above ground carbon. The results revealed that LSP metrics enable to capture the seasonal dynamics of photosynthetically active vegetation and are beneficial for the mapping of vegetation physiognomies. However, the results also revealed limitations of hard classification approaches for mapping vegetation gradients in complex ecosystems. Based on similarities in LSP metrics, which were for the first time derived for the whole extent of the Cerrado, LSP archetypes were proposed, which revealed the spatial patterns of LSP diversity at a 30 m spatial resolution and offer potential to enhance current mapping concepts. Further, LSP metrics facilitated the spatially explicit quantification of AGC in three study areas in the central Cerrado and should thus be considered as a valuable variable for future carbon estimations. Overall, the insights highlight that Landsat based LSP metrics are beneficial for ecosystem monitoring approaches, which are crucial to design sustainable land management strategies that maintain key ecosystem functions and services.