Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands

Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916–2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce wetland habitat availability for many species

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry

Reconstructing the past and modeling the future of wetland dynamics under climate change

Thesis (Ph.D.)--University of Washington, 2017-06Abstract Reconstructing the past and modeling the future of wetland dynamics under climate change Meghan Halabisky Chair of Supervisory Committee: Dr. L. Monika Moskal School of Environmental and Forest Sciences Wetland ecosystems are widely considered to be highly sensitive to climate change. However, scientific capacity to model climate impacts to wetlands has been hampered by the lack of accurate maps showing the spatial distribution of wetlands and data on their historical hydrological dynamics. Though these data may exist for particular wetlands, there are no broad scale datasets of wetland location and long-term hydrological dynamics. Remote sensing has been an important vehicle for mapping change to wetlands, but generally at spatial or temporal scales that do not capture the variability necessary for linking climate to wetland hydrodynamics. This data limitation and lack of methods have restricted research on how changes in climate will impact wetland hydrology to explorations of limited scope. The goal of this PhD was to characterize and model historic and future climate impacts to dynamics of wetland hydrology (i.e. inundation quantity, frequency, timing and duration) across the Columbia Plateau ecoregion. To achieve this goal, I developed new remote sensing methods to map and reconstruct wetland dynamics for thousands of individual wetlands at finer temporal and spatial resolutions than previously available (Chapter 1 and 2). In Chapter 1, I combined high-resolution aerial photographic imagery and a time series of Landsat satellite imagery to reconstruct wetland inundation patterns for individual wetlands from 1984 – 2011 in Douglas County, WA, USA. A key component of this method was the ability to measure fine scale changes (<30m) in surface water area using a sub-pixel technique called spectral mixture analysis. In Chapter 2, I adapted these methods so they could be scaled up to large extents without the computer processing requirements and technical challenges of using aerial imagery. In order to do this, I identified wetlands, not from the spectral and spatial characteristics one can derive out of aerial imagery as in Chapter 1, but instead using their temporal pattern of flooding and drying derived from the time series of Landsat satellite imagery. Using the methods developed in Chapter 1 and Chapter 2, I mapped and reconstructed wetland hydrodynamics for wetlands in the Columbia Plateau ecoregion, far surpassing any existing measurements of wetland hydrology in sample size (n= 5,382), temporal richness (~ 23 days), and temporal extent (27 years). Finally, in Chapter 3 I used this novel dataset to map changes in wetland hydrology across the Columbia Plateau identifying areas undergoing change. Additionally, I developed wetland-specific regression models to understand the relationship between climate and wetland hydrology, which I used to forecast changes to wetland hydrology under climate change. Beyond the technical analyses, an additional important part of the process for Chapter 3 was working with wetland practitioners from start to finish to ensure the data developed is both useful and used. The findings of this research suggest that wetlands in the Columbia Plateau are hydrologically variable with each wetland falling along a continuum from those driven primarily by surface water (i.e. precipitation, evaporation, and surface runoff) to those driven primarily by deep groundwater sources. The location of each wetland along this continuum, which I was able to approximate, varies greatly throughout the region, but follows a defined spatial pattern related to underlying geologic processes. Where a wetland falls along the groundwater to surface water continuum largely determined historical changes in inundation levels and how a wetland will respond in the future under climate change. In general, water levels in groundwater driven wetlands have typically decreased since 1984, whereas water levels in surface water driven wetlands have increased or stayed at similar levels over the same period. However, under the climate change scenario selected (ECHAM5 A1B) the results from the wetland-specific regression models suggest that groundwater driven wetlands will increase in water levels and dry less frequently. On the other end of the wetland continuum, surface water wetlands will decrease in surface water levels, dry more frequently, dry earlier in the season, or have little change. The results of this PhD provide an example of how remote sensing can deliver the fine scale detail and broad temporal and spatial extent necessary to model complex ecosystem dynamics. This knowledge is being used to inform the development of strategies to conserve the biodiversity supported by these systems, and prioritize and help stratify wetlands for further study and conservation action in the Columbia Plateau

Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands

Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristic. When multiple images are used in object-based image analysis, it is often constrained to a specific number of images which are selected because they cover the perceived range of temporal variability of the features of interest. Here, we provide a method to identify wetlands using a time series of Landsat imagery by building a Random Forest model using each image observation as an explanatory variable. We tested our approach in Douglas County, Washington, USA. Our approach exploiting the temporal domain classified wetlands with a high level of accuracy and reduced the number of spectrally similar false positives. We explored how sampling design (i.e., random, stratified, purposive) and temporal resolution (i.e., number of image observations) affected classification accuracy. We found that sampling design introduced bias in different ways, but did not have a substantial impact on overall accuracy. We also found that a higher number of image observations up to a point improved classification accuracy dependent on the selection of images used in the model. While time series analysis has been part of pixel-based remote sensing for many decades, with improved computer processing and increased availability of time series datasets (e.g., Landsat archive), it is now much easier to incorporate time series into object-based image analysis classification

Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation

Wetlands provide society with a myriad of ecosystem services, such as water storage, food sources, and flood control. The ecosystem services provided by a wetland are largely dependent on its hydrological dynamics. Constant monitoring of the spatial extent of water surfaces and the duration of flooding of a wetland is necessary to understand the impact of drought on the ecosystem services a wetland provides. Synthetic aperture radar (SAR) has the potential to reveal wetland dynamics. Multitemporal SAR image analysis for wetland monitoring has been extensively studied based on the advances of modern SAR missions. Unfortunately, most previous studies utilized monopath SAR images, which result in limited success. Tracking changes in individual wetlands remains a challenging task because several environmental factors, such as wind-roughened water, degrade image quality. In general, the data acquisition frequency is an important factor in time series analysis. We propose a Gaussian process-based temporal interpolation (GPTI) method that enables the synergistic use of SAR images taken from multiple paths. The proposed model is applied to a series of Sentinel-1 images capturing wetlands in Okanogan County, Washington State. Our experimental analysis demonstrates that the multiple path analysis based on the proposed method can extract seasonal changes more accurately than a single path analysis

Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data

Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC

Revealing the hidden carbon in forested wetland soils

Abstract Inland wetlands are critical carbon reservoirs storing 30% of global soil organic carbon (SOC) within 6% of the land surface. However, forested regions contain SOC-rich wetlands that are not included in current maps, which we refer to as ‘cryptic carbon’. Here, to demonstrate the magnitude and distribution of cryptic carbon, we measure and map SOC stocks as a function of a continuous, upland-to-wetland gradient across the Hoh River Watershed (HRW) in the Pacific Northwest of the U.S., comprising 68,145 ha. Total catchment SOC at 30 cm depth (5.0 TgC) is between estimates from global SOC maps (GSOC: 3.9 TgC; SoilGrids: 7.8 TgC). For wetland SOC, our 1 m stock estimates are substantially higher (Mean: 259 MgC ha−1; Total: 1.7 TgC) compared to current wetland-specific SOC maps derived from a combination of U.S. national datasets (Mean: 184 MgC ha−1; Total: 0.3 TgC). We show that total unmapped or cryptic carbon is 1.5 TgC and when added to current estimates, increases the estimated wetland SOC stock to 1.8 TgC or by 482%, which highlights the vast stores of SOC that are not mapped and contained in unprotected and vulnerable wetlands

Discovering Inclusivity in Remote Sensing: Leaving No One Behind

Innovative and beneficial science stems from diverse teams and authorships that are inclusive of many perspectives. In this paper, we explore the status of inclusivity in remote sensing academic publishing, using an audit of peer-reviewed journal editorial board composition. Our findings demonstrate diversity deficiency in gender and country of residence, limiting the majority of editors to men residing in four countries. We also examine the many challenges underrepresented communities within our field face, such as implicit bias, harsher reviews, and fewer citations. We assert that in the field of remote sensing, the gatekeepers are not representative of the global society and this lack of representation restricts what research is valued and published, and ultimately who becomes successful. We present an action plan to help make the field of remote sensing more diverse and inclusive and urge every individual to consider their role as editor, author, reviewer, or reader. We believe that each of us have a choice to continue to align with a journal/institution/society that is representative of the dynamic state of our field and its people, ensuring that no one is left behind while discovering all the fascinating possibilities in remote sensing.</jats:p