Geology topics

Shirley Qiu

Publications and source records attributed to Shirley Qiu.

4 recordsLinked to original sources

USGS2022

Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management . Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km 2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km 2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.

Remote Sensing of Environment↗

USGS2021

Can Landsat 7 preserve its science capability with a drifting orbit?

Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–75 0 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.

Science of Remote Sensing↗

USGS2020

Quantifying the contribution of habitats and pathways to a spatially structured population facing environmental change

The consequences of environmental disturbance and management are difficult to quantify for spatially structured populations because changes in one location carry through to other areas as a result of species movement. We develop a metric, G , for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different from other contribution metrics, as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences the ordering of node contribution. We find that habitats within which all individuals reside during one season are the most important to short-term network growth under complete removal scenarios, whereas the central breeding region contributes most to population growth over all but the strongest disturbances. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations. use changes in one location carry through to other areas due to species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different than other contribution metrics as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences ordering of node contribution. We find that habitats through which all migrants flow are the most important to short-term network growth under complete-removal scenarios. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.

American Naturalist↗

USGS2019

Quantifying source and sink habitats and pathways in spatially structured populations: A generalized modelling approach

The ability to classify habitats and movement pathways as sources or sinks is an important part of the decision making process for the conservation of spatially structured populations. Diverse approaches have been used to quantify the importance of habitats and pathways in a spatial network, however these approaches have been limited by a lack of general applicability across life histories and movement strategies. In this paper we develop a generalized per-capita contribution metric, the C-metric, for quantifying habitat and pathway quality. This metric is novel in that it can be applied broadly to both metapopulations and migratory species. It allows for any number of age and sex classes, unlimited number of seasons or time intervals within the annual cycle, and for density-dependent parameters. We demonstrate the ﬂexibility of the metric with four case studies: a hypothetical metapopulation, elk of the Greater Yellowstone Ecosystem, northern pintail ducks in North America, and the eastern population of the monarch butterﬂy. General computer code to calculate the per-capita contribution metric is provided. We demonstrate that the C-metric is useful for identifying source and sink habitats in a network and suggest that the C-metric could be supplemented by some measure of network structure for a more robust description of habitat or pathway importance.

Ecological Modelling↗