Optical medium spatial resolution satellite constellation data for monitoring woodland in the UK

The aim of this study was to test the potential of a constellation of remote sensing satellites, the Disaster Monitoring Constellation (DMC), for retrieving a temporal record of forest leaf area index (LAI) in the United Kingdom (U.K.). Ground-based LAI measurements were made over a 12-month period in broadleaf woodland at Risley Moss Nature Reserve, Lancashire, U.K. The ground-based LAI varied between zero in January to a maximum of 4.5 in July. Nine DMC images, combining data from UK-DMC and NigeriaSat-1, were acquired, and all images were cross-calibrated and atmospherically corrected. The spectral reflectance of the test site was extracted, and a range of vegetation indices were then computed and correlated with the ground measurements of LAI. The soil adjusted vegetation index (SAVI) had the strongest correlation, and this was used to derive independent estimates of LAI using the “leave-one-out” method. The root mean square error of the LAI estimates was 0.47, which was close to that calculated for the ground-measured LAI. This study shows, for the first time, that data from a constellation of high temporal, medium spatial resolution optical satellite sensors may be used to map seasonal variation in woodland canopy leaf area index (LAI) in cloud-prone areas, like the U.K

SATELLITE MICROWAVE MEASUREMENT OF LAND SURFACE PHENOLOGY: CLARIFYING VEGETATION PHENOLOGY RESPONSE TO CLIMATIC DRIVERS AND EXTREME EVENTS

The seasonality of terrestrial vegetation controls feedbacks to the climate system including land-atmosphere water, energy and carbon (CO2) exchanges with cascading effects on regional-to-global weather and circulation patterns. Proper characterization of vegetation phenology is necessary to understand and quantify changes in the earthÆs ecosystems and biogeochemical cycles and is a key component in tracking ecological species response to climate change. The response of both functional and structural vegetation phenology to climatic drivers on a global scale is still poorly understood however, which has hindered the development of robust vegetation phenology models. In this dissertation I use satellite microwave vegetation optical depth (VOD) in conjunction with an array of satellite measures, Global Positioning System (GPS) reflectometry, field observations and flux tower data to 1) clarify vegetation phenology response to water, temperature and solar irradiance constraints, 2) demonstrate the asynchrony between changes in vegetation water content and biomass and changes in greenness and leaf area in relation to land cover type and climate constraints, 3) provide enhanced assessment of seasonal recovery of vegetation biomass following wildfire and 4) present a method to more accurately model tropical vegetation phenology. This research will establish VOD as a useful and informative parameter for regional-to-global vegetation phenology modeling, more accurately define the drivers of both structural and functional vegetation phenology, and help minimize errors in phenology simulations within earth system models. This dissertation also includes the development of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) vegetation health climate indicators as part of a NASA funded project entitled Development and Testing of Potential Indicators for the National Climate Assessment; Translating EOS datasets into National Ecosystem Biophysical Indicators

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

Observing giant panda habitat and forage abundance from space

Giant pandas are obligate bamboo grazers. The bamboos favoured by giant pandas are typical forest understorey plants. Therefore, the availability and abundance of understorey bamboo is a key factor in determining the quantity and quality of giant panda food resources. However, there is little or no information about the spatial distribution or abundance of bamboo underneath the forest canopy, due to the limitations of traditional ground survey and remote sensing classification techniques. In this regard, the development of methods that can predict the understorey bamboo spatial distribution and cover abundance is critical for an improved understanding of the habitat, foraging behaviour and distribution of giant pandas, as well as facilitating an optimal conservation strategy for this endangered species. The objectives of this study were to develop innovative methods in remote sensing and GIS for estimating the giant panda habitat and forage abundance, and to explain the altitudinal migration and the spatial distribution of giant pandas in the fragmented forest landscape. It was concluded that 1) the vegetation indices derived from winter (leaf-off) satellite images can be successfully used to predict the distribution of evergreen understorey bamboo in a deciduous-dominated forest, 2) winter is the optimal season for quantifying the coverage of evergreen understorey bamboo in a mixed temperate forest, regardless of the classification methods used, 3) a higher mapping accuracy for understorey bamboo in a coniferous-dominated forest can be achieved by using an integrated neural network and expert system algorithm, 4) the altitudinal migration patterns of sympatric giant pandas and golden takins are related to satellite-derived plant phenology (a surrogate of food quality) and bamboo abundance (a surrogate of food quantity), 5) the driving force behind the seasonal vertical migration of giant pandas is the occurrence of bamboo shoots and the temperature variation along an altitudinal gradient, 6) the satellite-derived forest patches occupied by giant pandas were significantly larger and more contiguous than patches where giant pandas were not recorded, indicating that giant pandas appear sensitive to patch size and isolation effects associated with forest fragmentation. Overall, the study has been shown the potential of satellite remote sensing to map giant panda habitat and forage (i.e., understorey bamboo) abundance. The results are important for understanding the foraging behaviour and the spatial distribution of giant pandas, as well as the evaluation and modelling of giant panda habitat in order to guide decision-making on giant panda conservation. <br/

Responses of Land Surface Phenology to Wildfire Disturbances in the Western United States Forests

Land surface phenology (LSP) characterizes the seasonal dynamics in the vegetation communities observed for a satellite pixel and it has been widely associated with global climate change. However, LSP and its long-term trend can be influenced by land disturbance events, which could greatly interrupt the LSP responses to climate change. Wildfire is one of the main disturbance agents in the western United States (US) forests, but its impacts on LSP have not been investigated yet. To gain a comprehensive understanding of the LSP responses to wildfires in the western US forests, this dissertation focused on three research objectives: (1) to perform a case study of wildfire impacts on LSP and its trend by comparing the burned and a reference area, (2) to investigate the distribution of wildfire impacts on LSP and identify control factors by analyzing all the wildfires across the western US forests, and (3) to quantify the contributions of land cover composition and other environmental factors to the spatial and interannual variations of LSP in a recently burned landscape. The results reveal that wildfires play a significant role in influencing spatial and interannual variations in LSP across the western US forests. First, the case study showed that the Hayman Fire significantly advanced the start of growing season (SOS) and caused an advancing SOS trend comparing with a delaying trend in the reference area. Second, summarizing \u3e800 wildfires found that the shifts in LSP timing were divergent depending on individual wildfire events and burn severity. Moreover, wildfires showed a stronger impact on the end of growing season (EOS) than SOS. Last, LSP trends were interrupted by wildfires with the degree of impact largely dependent on the wildfire occurrence year. Third, LSP modeling showed that land cover composition, climate, and topography co-determine the LSP variations. Specifically, land cover composition and climate dominate the LSP spatial and interannual variations, respectively. Overall, this research improves the understanding of wildfire impacts on LSP and the underlying mechanism of various factors driving LSP. This research also provides a prototype that can be extended to investigate the impacts on LSP from other disturbances

Investigation of North American vegetation variability under recent climate: a study using the SSiB4/TRIFFID biophysical/dynamic vegetation model

PublishedJournal ArticleThis is the final version of the article. Available from AGU via the DOI in this record.Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60-years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S. on vegetation are also evident from decreases in the simulated and satellite-derived LAIs. Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring reflecting the effect of these climatic variables on photosynthesis and phenological processes. Meanwhile, in southwestern dry lands, negative correlations appear due to the heat and moisture stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which increases from spring to summer. The present study shows both the current improvements and remaining weaknesses in dynamical vegetation models. It also highlights large continental-scale variations that have occurred in NA vegetation over the past six decades and their potential relations to climate. With more observational data availability, more studies with different models and focusing on different regions will be possible and are necessary to achieve comprehensive understanding of the vegetation dynamics and climate interactions. Key Points Climate forcing and spatial and temporal variability of North American ecosystem Evaluate a 2-D biophysical model/dynamic vegetation using satellite data Mechanisms affecting vegetation/climate interactio

Investigation of North American Vegetation Variability under Recent Climate: A Study Using the SSiB4/TRIFFID Biophysical/Dynamic Vegetation Model

Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S. on vegetation are also evident from decreases in the simulated and satellite-derived LAIs. Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring reflecting the effect of these climatic variables on photosynthesis and phenological processes. Meanwhile, in southwestern dry lands, negative correlations appear due to the heat and moisture stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which increases from spring to summer. The present study shows both the current improvements and remaining weaknesses in dynamical vegetation models. It also highlights large continental-scale variations that have occurred in NA vegetation over the past six decades and their potential relations to climate. With more observational data availability, more studies with differentmodels and focusing on different regions will be possible and are necessary to achieve comprehensive understanding of the vegetation dynamics and climate interactions

Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams

Grasslands of the Australian Southern Tablelands represent a patchwork of native and exotic systems, occupying a continuum of C3-dominated to C4-dominated grasslands where composition depends on disturbance factors (e.g., grazing) and climate. Managing these complex landscapes is both challenging and critical for maintaining the security of Australia's pasture industries, and for protecting the biodiversity of native remnants. Differentiating C3 from C4 vegetation has been a prominent theme in remote sensing research due to distinct C3/C4 seasonal productivity patterns (phenology) and high uncertainty about how C3/C4 vegetation will respond to a changing climate. Phenology is used in northern hemisphere ecosystems for a range of purposes but has not been widely adopted in Australia, where dynamic climate often results in non-repetitive seasonal vegetation patterns. We employed time-lapse cameras (phenocams) to study the phenology of twelve grassland areas dominated by cool season (C3) and warm season (C4), native or exotic grasses near Canberra, Australia. Our aims were to assess phenological characteristics of the functional types and to determine the drivers of phenological variability. We compared the fine-scale phenocam seasonal profiles with field sampling and MODIS/Landsat satellite products to assess paddock-to-landscape functioning. We found C3/C4 species dominance to be the primary driver of phenological differences among grassland types, with C3 grasslands demonstrating peak greenness in spring, and senescing rapidly in response to high summer temperatures. In contrast, C4 grasslands showed peak activity in Austral summer and autumn (January-March). Some sites displayed primary and secondary peaks dependent on rainfall and species composition. We found that the proportion of dead vegetation is an important biophysical driver of grassland phenology, as were grazing pressures and species-dependent responses to rainfall and temperature. The satellite and field datasets were in general agreement with the phenocam results. However, the higher temporal fidelity of the cameras captured changes in vegetation not observed in the coarser satellite or field results. Our phenocam data shows consistent periods of increasing and decreasing greenness over as little as 5 days. Applications for management of grasslands in temperate Australia include the identification of remnant native grasslands, tracking biosecurity issues, and assessing productivity responses to climate variability

characterizing responses of land surface phenology to urbanization, climate change, and extreme weather events using remote sensing and Bayesian models

Land surface phenology (LSP) is the intra-annual rhythm of vegetation dynamic from dormancy to activity and back to dormancy over the landscape. Shifts of LSP have cascading effects on food production, carbon sequestration, water consumption, biodiversity, and public health. There are three major knowledge gaps in understanding the impacts of urbanization, climate change, and extreme weather events on LSP. (1) Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing the phenological metrics between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of LSP. (2) Conventional methods usually model key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g. mean temperature, growing-degree days), ignoring the fact that LSP is a dynamic and continuous process which responds to daily weather conditions continuously. (3) Current projection of LSP shifts under future environmental changes relies heavily on species-level observations and degree-day models. It is challenging to produce a set of future LSP metrics in a temporally consistent manner at regional-to-continental scales. To fill these knowledge gaps, this three-part dissertation built a data-model synthesis framework by integrating remotely sensed data and climate data into a state-of-the-art Bayesian model. First, I established a framework to separate the temporal shifts of phenology driven by climate change from those caused by urbanization. Second, I developed and evaluated a Bayesian Hierarchical Space-Time model for Land Surface Phenology (BHST-LSP) to synthesize remotely sensed vegetation greenness with climate covariates at a daily scale from 1981 to 2014 across the entire conterminous United States. Finally, I used the BHST-LSP to project vegetation phenology from 2020 to 2099 under two climate change scenarios. This dissertation contributed in-depth understanding of the LSP and its environmental cues in both natural and human dominated ecosystems. Results from this dissertation provide strong evidences for adoption of climate change mitigation policies and immediate management measures to prevent severe adverse impacts of global warming and urbanization from disrupting vital ecosystem services and functions.Doctor of Philosoph

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects