Utilizing the Landsat spectral-temporal domain for improved mapping and monitoring of ecosystem state and dynamics

Just as the carbon dioxide observations that form the Keeling curve revolutionized the study of the global carbon cycle, free and open access to all available Landsat imagery is fundamentally changing how the Landsat record is being used to study ecosystems and ecological dynamics. This dissertation advances the use of Landsat time series for visualization, classification, and detection of changes in terrestrial ecological processes. More specifically, it includes new examples of how complex ecological patterns manifest in time series of Landsat observations, as well as novel approaches for detecting and quantifying these patterns. Exploration of the complexity of spectral-temporal patterns in the Landsat record reveals both seasonal variability and longer-term trajectories difficult to characterize using conventional bi-temporal or even annual observations. These examples provide empirical evidence of hypothetical ecosystem response functions proposed by Kennedy et al. (2014). Quantifying observed seasonal and phenological differences in the spectral reflectance of Massachusetts’ forest communities by combining existing harmonic curve fitting and phenology detection algorithms produces stable feature sets that consistently out-performed more traditional approaches for detailed forest type classification. This study addresses the current lack of species-level forest data at Landsat resolutions, demonstrating the advantages of spectral-temporal features as classification inputs. Development of a targeted change detection method using transformations of time series data improves spatial and temporal information on the occurrence of flood events in landscapes actively modified by recovering North American beaver (Castor canadensis) populations. These results indicate the utility of the Landsat record for the study of species-habitat relationships, even in complex wetland environments. Overall, this dissertation confirms the value of the Landsat archive as a continuous record of terrestrial ecosystem state and dynamics. Given the global coverage of remote sensing datasets, the time series visualization and analysis approaches presented here can be extended to other areas. These approaches will also be improved by more frequent collection of moderate resolution imagery, as planned by the Landsat and Sentinel-2 programs. In the modern era of global environmental change, use of the Landsat spectral-temporal domain presents new and exciting opportunities for the long-term large-scale study of ecosystem extent, composition, condition, and change

Characterizing urban landscapes using fuzzy sets

Characterizing urban landscapes is important given the present and future projections of global population that favor urban growth. The definition of “urban” on a thematic map has proven to be problematic since urban areas are heterogeneous in terms of land use and land cover. Further, certain urban classes are inherently imprecise due to the difficulty in integrating various social and environmental inputs into a precise definition. Social components often include demographic patterns, transportation, building type and density while ecological components include soils, elevation, hydrology, climate, vegetation and tree cover. In this paper, we adopt a coupled human and natural system (CHANS) integrated scientific framework for characterizing urban landscapes. We implement the framework by adopting a fuzzy sets concept of “urban characterization” since fuzzy sets relate to classes of object with imprecise boundaries in which membership is a matter of degree. For dynamic mapping applications, user-defined classification schemes involving rules combining different social and ecological inputs can lead to a degree of quantification in class labeling varying from “highly urban” to “least urban”. A socio-economic perspective of urban may include threshold values for population and road network density while a more ecological perspective of urban may utilize the ratio of natural versus built area and percent forest cover. Threshold values are defined to derive the fuzzy rules of membership, in each case, and various combinations of rules offer a greater flexibility to characterize the many facets of the urban landscape. We illustrate the flexibility and utility of this fuzzy inference approach called the Fuzzy Urban Index for the Boston Metro region with five inputs and eighteen rules. The resulting classification map shows levels of fuzzy membership ranging from highly urban to least urban or rural in the Boston study region. We validate our approach using two experts assessing accuracy of the resulting fuzzy urban map. We discuss how our approach can be applied in other urban contexts with newly emerging descriptors of urban sustainability, urban ecology and urban metabolism.This research was partially supported by "Boston University Initiative on Cities Early Stage Urban Research Awards 2015-16" (Gopal & Phillips) and the Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University. We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. (Boston University Initiative on Cities Early Stage Urban Research Awards; Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University)https://doi.org/10.1016/j.compenvurbsys.2016.02.002Published versio

Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series

Introduced insects and pathogens impact millions of acres of forested land in the United States each year, and large-scale monitoring efforts are essential for tracking the spread of outbreaks and quantifying the extent of damage. However, monitoring the impacts of defoliating insects presents a significant challenge due to the ephemeral nature of defoliation events. Using the 2016 gypsy moth (Lymantria dispar) outbreak in Southern New England as a case study, we present a new approach for near-real-time defoliation monitoring using synthetic images produced from Landsat time series. By comparing predicted and observed images, we assessed changes in vegetation condition multiple times over the course of an outbreak. Initial measures can be made as imagery becomes available, and season-integrated products provide a wall-to-wall assessment of potential defoliation at 30 m resolution. Qualitative and quantitative comparisons suggest our Landsat Time Series (LTS) products improve identification of defoliation events relative to existing products and provide a repeatable metric of change in condition. Our synthetic-image approach is an important step toward using the full temporal potential of the Landsat archive for operational monitoring of forest health over large extents, and provides an important new tool for understanding spatial and temporal dynamics of insect defoliators

Regional Invasive Species & Climate Change Management Challenge: Preparing for sleeper species

Many naturalized non-native species never become invasive and generally are not prioritized for management due to limited resources. However, climate change could enhance the success of these species, causing some to become invasive. Therefore, we need to reassess the current pool of naturalized species to identify and prioritize management of ‘sleeper’ species

Incorporating climate change into invasive species management: insights from managers

Invasive alien species are likely to interact with climate change, thus necessitating management that proactively addresses both global changes. However, invasive species managers’ concerns about the effects of climate change, the degree to which they incorporate climate change into their management, and what stops them from doing so remain unknown. Therefore, we surveyed natural resource managers addressing invasive species across the U.S. about their priorities, concerns, and management strategies in a changing climate. Of the 211 managers we surveyed, most were very concerned about the influence of climate change on invasive species management, but their organizations were significantly less so. Managers reported that lack of funding and personnel limited their ability to effectively manage invasive species, while lack of information limited their consideration of climate change in decision-making. Additionally, managers prioritized research that identifies range-shifting invasive species and native communities resilient to invasions and climate change. Managers also reported that this information would be most effectively communicated through conversations, research summaries, and meetings/symposia. Despite the need for more information, 65% of managers incorporate climate change into their invasive species management through strategic planning, preventative management, changing treatment and control, and increasing education and outreach. These results show the potential for incorporating climate change into management, but also highlight a clear and pressing need for more targeted research, accessible science communication, and two-way dialogue between researchers and managers focused on invasive species and climate change

Defoliation severity is positively related to soil solution nitrogen availability and negatively related to soil nitrogen concentrations following a multi-year invasive insect irruption

Understanding connections between ecosystem nitrogen (N) cycling and invasive insect defoliation could facilitate the prediction of disturbance impacts across a range of spatial scales. In this study we investigated relationships between ecosystem N cycling and tree defoliation during a recent 2015–18 irruption of invasive gypsy moth caterpillars (Lymantria dispar), which can cause tree stress and sometimes mortality following multiple years of defoliation. Nitrogen is a critical nutrient that limits the growth of caterpillars and plants in temperate forests. In this study, we assessed the associations among N concentrations, soil solution N availability and defoliation intensity by L. dispar at the scale of individual trees and forest plots. We measured leaf and soil N concentrations and soil solution inorganic N availability among individual red oak trees (Quercus rubra) in Amherst, MA and across a network of forest plots in Central Massachusetts. We combined these field data with estimated defoliation severity derived from Landsat imagery to assess relationships between plot-scale defoliation and ecosystem N cycling. We found that trees in soil with lower N concentrations experienced more herbivory than trees in soil with higher N concentrations. Additionally, forest plots with lower N soil were correlated with more severe L. dispar defoliation, which matched the tree-level relationship. The amount of inorganic N in soil solution was strongly positively correlated with defoliation intensity and the number of sequential years of defoliation. These results suggested that higher ecosystem N pools might promote the resistance of oak trees to L. dispar defoliation and that defoliation severity across multiple years is associated with a linear increase in soil solution inorganic N

2016_ImageryEcology: v0.0.2

Release for Zonodo DO

Landsat-based Gypsy Moth Defoliation Assessment (Southern New England)

<p>The re-analysis dataset hosted here extends the work presented in the the manuscript, "Extensive gypsy moth defoliation in Southern New England characterized using Landsat satellite observations"  to a larger study area that includes Landsat WRS-2 Path/Rows 12/30, 13/30, 1/131, 12/31 and 13/31 and covers all of Southern New England (RI, CT, and MA) and the southern portions of NH and VT.</p> <p>This re-analysis dataset utilizes Landsat Collection 1 data and the stable base period has been adjusted to 2000-2010. Therefore, the products hosted here may differ slightly from our previously published datasets.</p> <p>Both final (season-integrated) and Near-Real-Time (NRT; single-date) results for 2015, 2016, and 2017 are currently available here. All GeoTIFFs are georeferenced and provided in NAD/Conus Albers (EPSG: 5070). Final products are available with and without an NLCD forest mask applied. NRT products have been cloud masked using Fmask results..</p> <p>Please cite as: Pasquarella, Valerie J. (2018). Landsat-based Gypsy Moth Defoliation Assessment (Southern New England) (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1243935</p> <p>For more on the methods used to generate these datasets, see Pasquarella, V.J., Bradley, B.A, & Woodcock, C.E. Near-real-time monitoring of insect defoliation using Landsat time series. Forests 8(8), 275; doi:10.3390/f8080275, available online: http://www.mdpi.com/1999-4907/8/8/275.</p> <p> </p

yatsm: Yet Another Time Series Model (YATSM): v0.6.1

Bug fix release and beginning of v0.6.x maintenance branch. v0.6.1 - 2016-05-12 Version v0.6.x will be backward patched for any bug fixes (for an undetermined amount of time) as version v0.7.0 will introduce backwards incompatible changes in order to enable incorporation of data from multiple sensors and to better link time series models together in a cohesive pipeline. Fixed CCDCesque: Fixed case in which bands not used as "test indices" would not have time series models estimated (i.e., no coef or rmse) if the time series ends immediately after training #88 RLM: Fixed divide by zero error when n == p (number of observations equals number of parameters estimated) #8

Relating Aerial Deposition of Entomophaga maimaiga Conidia (Zoopagomycota: Entomophthorales) to Mortality of Gypsy Moth (Lepidoptera: Erebidae) Larvae and Nearby Defoliation

We collected data on mortality of late-instar gypsy moth, Lymantria dispar (L.), from outbreak populations over 4 wk in June 2017 at 10 sites in the New England region of the United States, along with estimated rainfall at these sites. Deposition of airborne conidia of the fungal pathogen, Entomophaga maimaiga Humber, Shimazu & R.S. Soper, was measured at these same sites as well as at seven other locations in New England. We also quantified the geographical distribution of gypsy moth-caused defoliation in New England in 2017 and 2018 from Landsat imagery. Weekly mortality of gypsy moth larvae caused by E. maimaiga correlated with local deposition of conidia from the previous week, but not with rainfall. Mortality from this pathogen reached a peak during the last 2 wk of gypsy moth larval development and always exceeded that caused by LdNPV, the viral pathogen of gypsy moth that has long been associated with gypsy moth outbreaks, especially prior to 1989. Cotesia melanoscela (Ratzeburg) was by far the most abundant parasitoid recovered and caused an average of 12.6% cumulative parasitism, but varied widely among sites. Deposition of E. maimaiga conidia was highly correlated with percent land area defoliated by gypsy moths within distances of 1 and 2 km but was not significantly correlated with defoliation at distances greater than 2 km. This is the first study to relate deposition of airborne conidia of E. maimaiga to mortality of gypsy moths from that agent