Henry Pai

Uncooled thermal infrared (TIR) imagers, commonly used on aircraft and small unmanned aircraft systems (UAS, “drones”), can provide high‐resolution surface temperature maps, but their accuracy is dependent on reliable calibration sources. A novel method for correcting surface temperature observations made by uncooled TIR imagers uses observations over melting snow, which provides a constant 0 °C reference temperature. This bias correction method is applied to remotely sensed surface temperature observations of forests and snow over two mountain study sites: Laret, Davos, Switzerland (27 March 2017) in the Alps, and Sagehen Creek, California, USA (21 April 2017) in the Sierra Nevada. Surface temperature retrieval errors that arise from temperature‐induced instrument bias, differences in image resolution, retrieval of mixed pixels, and variable view angles were evaluated for these forest snow scenes. Applying the melting snow‐based bias correction decreased the root‐mean‐square error by about 1 °C for retrieving snow, water, and forest canopy temperatures from airborne TIR observations. The influence of mixed pixels on surface temperature retrievals over forest snow scenes was found to depend on image resolution and the spatial distribution of forest stands. Airborne observations over the forests at Sagehen showed that near the edges of TIR images, at more than 20° from nadir, the snow surface within forest gaps smaller than 10 m was obscured by the surrounding trees. These off‐nadir views, with fewer mixed pixels, could allow more accurate airborne and satellite‐based observations of canopy surface temperatures.

Ploidy level in plants may influence ecological functioning, demography and response to climate change. However, measuring ploidy level typically requires intensive cell or molecular methods. We map ploidy level variation in quaking aspen, a dominant North American tree species that can be diploid or triploid and that grows in spatially extensive clones. We identify the predictors and spatial scale of ploidy level variation using a combination of genetic and ground‐based and airborne remote sensing methods. We show that ground‐based leaf spectra and airborne canopy spectra can both classify aspen by ploidy level with a precision‐recall harmonic mean of 0.75–0.95 and Cohen's kappa of c. 0.6–0.9. Ground‐based bark spectra cannot classify ploidy level better than chance. We also found that diploids are more common on higher elevation and steeper sites in a network of forest plots in Colorado, and that ploidy level distribution varies at subkilometer spatial scales. Synthesis. Our proof‐of‐concept study shows that remote sensing of ploidy level could become feasible in this tree species. Mapping ploidy level across landscapes could provide insights into the genetic basis of species' responses to climate change.

Groundwater discharge into streams helps maintain flows during droughts and provides refugia for thermally sensitive species, as the relatively constant temperature of spring water buffers against instream diurnal and seasonal fluctuations. Unmanned aerial vehicle (UAV)-based thermal imagery represents an attractive option to monitor sites of groundwater-surface water mixing. However, the use of economical thermal sensors presents several challenges to obtaining a stable dataset for mosaicking. Here, we present a method for acquiring and postprocessing thermal infrared imagery at spring discharge sites using an inexpensive, uncooled microbolometer mounted on a small UAV. The procedure involves initial estimation and removal of pixel bias from the sensor output, and then compensation for temporal sensor drift by optimizing the stability of the signal at ground features detected within multiple frames. We illustrate this approach by presenting a case study at the Devils River, a groundwater dependent stream in Texas. Comparison with imagery acquired using a more expensive thermal camera system, designed to compensate for sensor drift at the time of data acquisition using knowledge of internal camera temperature, reveals that our method produces a more consistent final mosaic image. A good linear fit (r² = 0.97) between the signal in the stabilized dataset and ground-based measurements of water temperature underscores the potential for this method to inexpensively produce high quality maps of surface temperature in ecologically important stream reaches.

Core Ideas: IML‐CZO is structured to study system responses through event‐based monitoring. Management legacy has shaped critical zone processes. Management and weather affect landscape heterogeneity and surface–subsurface pathways. In intensively managed landscapes, interactions between surface (tillage) and subsurface (tile drainage) management with prevailing climate/weather alter landscape characteristics, transport pathways, and transformation rates of surface/subsurface water, soil/sediment, and particulate/dissolved nutrients. To capture the high spatial and temporal variability of constituent transport and residence times in the critical zone (between the bedrock and canopy) of these altered landscapes, both storm event and continuous measurements are needed. The Intensively Managed Landscapes Critical Zone Observatory (IML‐CZO) is comprised of three highly characterized, well instrumented, and representative watersheds (i.e., Clear Creek, Iowa; Upper Sangamon River, Illinois; and Minnesota River, Minnesota). It is organized to quantify the heterogeneity in structure and dynamic response of critical zone processes to human activities in the context of the glacial and management (anthropogenic) legacies. Observations of water, sediment, and nutrients are made at nested points of the landscape in the vertical and lateral directions during and between storm events (i.e., continuously). The measurements and corresponding observational strategy are organized as follows. First, reference measurements from surface soil and deep core extractions, geophysical surveys, lidar, and hyperspectral data, which are common across all Critical Zone Observatories, are available. The reference measurements include continuous quantification of energy, water, solutes, and sediment fluxes. The reference measurements are complemented with event‐based measurements unique to IML‐CZO. These measurements include water table fluctuations, enrichment ratios, and roughness as well as bank erosion, hysteresis, sediment sources, and lake/floodplain sedimentation. The coupling of reference and event‐based measurements support testing of the central hypothesis (i.e., system shifts from transformer to transporter in IML‐CZO due to the interplay between management and weather/climate). Data collected since 2014 are available through a data repository and through the Geodashboard interface, which can be used for process‐based model simulations.

The exchange of groundwater and surface water (GW‐SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here we describe the use of a suite of high spatial resolution remote sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW‐SW exchange. sUAS provided centimeter‐scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS‐based vegetation imagery, vegetation‐top elevation, and normalized difference vegetation index mapping indicated GW‐SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite‐based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.

Delineating pollutant reactive transport pathways that connect local land use patterns to surface water is an important goal. This work illustrates high‐resolution river mapping of salinity or specific conductance (SC) and nitrate ( ) as a potential part of achieving this goal. We observed longitudinal river SC and nitrate distributions using high‐resolution synoptic in situ sensing along the lower Merced River (38 river km) in Central California (USA) from 2010 to 2012. We calibrated a distributed groundwater‐surface water (GW‐SW) discharge model for a conservative solute using 13 synoptic SC sampling events at flows ranging from 1.3 to 31.6 m³ s⁻¹. Nitrogen loads ranged from 0.3 to 1.6 kg N d⁻¹ and were greater following an extended high flow period during a wet winter. Applying the distributed GW‐SW discharge estimates to a simplistic reactive nitrate transport model, the model reproduced observed river nitrate distribution well (RRMSE = 5–21%), with dimensionless watershed‐averaged nitrate removal (kt) ranging from 0 to 0.43. Estimates were uncertain due to GW nitrate data variability, but the resulting range was consistent with prior removal estimates. At the segment scale, estimated GW‐SW nitrate loading ranged from 0 to 17 g s⁻¹ km⁻¹. Local loading peaked near the middle of the study reach, a location that coincides with a shallow clay lens and with confined animal feed operations in close proximity to the river. Overall, the results demonstrate the potential for high‐resolution synoptic monitoring to support GW‐SW modeling efforts aimed at understanding and managing nonpoint source pollution.

Quantifying distributed lateral groundwater contributions to surface water (GW-SW discharges) is a key aspect of tracking nonpoint-source pollution (NPSP) within a watershed. In this study, we characterized distributed GW-SW discharges and associated salt loading using elevated GW specific conductance (SC) as a tracer along a 38 km reach of the Lower Merced River in Central California. High resolution longitudinal surveys for multiple flows (1.3–150 m³ s^–1) revealed river SC gradients that mainly decreased with increasing flow, suggesting a dilution effect and/or reduced GW-SW discharges due to hydraulic gradient reductions. However, exceptions occurred (gradients increasing with increasing flow), pointing to complex spatiotemporal influences on GW-SW dynamics. The surveys revealed detailed variability in salinity gradients, from which we estimated distributed GW-SW discharge and salt loading using a simple mixing model. Modeled cumulative GW discharges for two surveys unaffected by ungauged SW discharges were comparable in magnitude to differential gauging-based discharge estimates and prior GW-SW studies along the same river reach. Ungauged lateral inlets and sparse GW data limited the study, and argue for enhancing monitoring efforts. Our approach provides a rapid and economical method for characterizing NPSP for gaining rivers in the context of integrated watershed modeling and management.

Four members of Engineers without Borders UCLA (EWB-UCLA), constituting the Thinklab! team, collected eleven laptops from various personal and corporate donors to establish the Thinklab! computer laboratory. The project aspired to provide an education resource to encourage learning and creativity for a small children's center called El Buen Samaraitano, in Jocotenango, Guatemala. The team members tested each computer individually for functionality and installed Linux and OpenOffice, free alternatives to Microsoft Windows and Office. After setting up the units, approximately 25 students attended several weeks of typing, computing, and lab maintenance courses. Meanwhile, to address cultural impacts and logistical concerns, the Thinklab! team developed a close partnership with El Buen Samaritano directors. Design and implementation of the project covered eight main areas of focus: location, hardware and software, transportation, customs, Internet, classroom, benchmarking, and recycling. These main areas of focus, or modules, will be discussed in detail in this paper.