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USGS2012

Storm surge modeling and applications in coastal areas

This chapter introduces the reader to a wide spectrum of storm surge modeling systems used to assess the impact of tropical cyclones, covering a range of numerical methods, model domains, forcing and boundary conditions, and purposes. New technologies to obtain data such as deployment of temporary sensors and remote sensing practices to support modeling are also presented. Extensive storm surge modeling applications have been made with existing modeling systems and some of them are described in this chapter. The authors recognize the importance of evaluating river-ocean interactions in coastal environments during tropical cyclones. Therefore, the coupling of hydraulic (riverine) and storm surge models is discussed. In addition, results from studies performed in the coast of India are shown which generated maps to help emergency managers and reduce risk due to coastal inundation.

Book chapter↗

USGS2009

USGS Science: Addressing Our Nation's Challenges

With 6.6 billion people already living on Earth, and that number increasing every day, human influence on our planet is ever more apparent. Changes to the natural world combined with increasing human demands threaten our health and safety, our national security, our economy, and our quality of life. As a planet and a Nation, we face unprecedented challenges: loss of critical and unique ecosystems, the effects of climate change, increasing demand for limited energy and mineral resources, increasing vulnerability to natural hazards, the effects of emerging diseases on wildlife and human health, and growing needs for clean water. The time to respond to these challenges is now, but policymakers and decisionmakers face difficult choices. With competing priorities to balance, and potentially serious - perhaps irreversible - consequences at stake, our leaders need reliable scientific information to guide their decisions. As the Nation's earth and natural science agency, the USGS monitors and conducts scientific research on natural hazards and resources and how these elements and human activities influence our environment. Because the challenges we face are complex, the science needed to better understand and deal with these challenges must reflect the complex interplay among natural and human systems. With world-class expertise in biology, geology, geography, hydrology, geospatial information, and remote sensing, the USGS is uniquely capable of conducting the comprehensive scientific research needed to better understand the interdependent interactions of Earth's systems. Every day, the USGS helps decisionmakers to minimize loss of life and property, manage our natural resources, and protect and enhance our quality of life. This brochure provides examples of the challenges we face and how USGS science helps decisionmakers to address these challenges.

General Information Product↗

USGS2006

Integration of coral reef ecosystem process studies and remote sensing

Worldwide, local-scale anthropogenic stress combined with global climate change is driving shifts in the state of reef benthic communities from coral-rich to micro- or macroalgal-dominated (Knowlton, 1992; Done, 1999). Such phase shifts in reef benthic communities may be either abrupt or gradual, and case studies from diverse ocean basins demonstrate that recovery, while uncertain (Hughes, 1994), typically involves progression through successional stages (Done, 1992). These transitions in benthic community structure involve changes in community metabolism, and accordingly, the holistic evaluation of associated biogeochemical variables is of great intrinsic value (Done, 1992). Effective reef management requires advance prediction of coral reef alteration in the face of anthropogenic stress and change in the global environment (Hatcher, 1997a). In practice, this goal requires techniques that can rapidly discern, at an early stage, sublethal effects that may cause long-term increases in mortality (brown, 1988; Grigg and Dollar, 1990). Such methods would improve our understanding of the differences in the population, community, and ecosystem structure, as well as function, between pristine and degraded reefs. This knowledge base could then support scientifically based management strategies (Done, 1992). Brown (1988) noted the general lack of rigor in the assessment of stress on coral reefs and suggested that more quantitative approaches than currently exist are needed to allow objective understanding of coral reef dynamics. Sensitive techniques for the timely appraisal of pollution effects or generalized endemic stress in coral reefs are sorely lacking (Grigg and Dollar, 1990; Wilkinsin, 1992). Moreover, monitoring methods based on population inventories, sclerochronology, or reproductive biology tend to myopic and may give inconsistent results. Ideally, an improved means of evaluating reef stress would discriminate mortality due to natural causes from morality to anthropogenic causes (Brown, 1988). Models of coral reef ecosystems, parameterized by process measurements and scaled in time-space using remote sensing, have the potential to address pressing research questions that are central to devising valid management strategies (Grigg el al., 1984; Hatcher, 1997b). To attain this goal, ecosystem-level models that integrate studies of physical and chemical forcing with observed biological and geological responses are required. This interdisciplinary approach to understanding reef biogeochemical dynamics can allow investigations that integrate the scales of time and space (Hatcher, 1997a), thereby enabling prediction of coral reef change (Andréfouët and Payri, 2001). In turn, prediction of holistic ecosystem function within various environmental focusing scenarios has substantial promise in mitigating future disturbance. Indeed, management of coral reefs at the ecosystem level has been suggested as the only meaningful approach to preserving coral reefs (Bohnsack and Ault, 1996; Christensen et al., 1996).

Florida↗

USGS2022

The Coastal Imaging Research Network (CIRN)

The Coastal Imaging Research Network (CIRN) is an international group of researchers who exploit signatures of phenomena in imagery of coastal, estuarine, and riverine environments. CIRN participants develop and implement new coastal imaging methodologies. The research objective of the group is to use imagery to gain a better fundamental understanding of the processes shaping those environments. Coastal imaging data may also be used to derive inputs for model boundary and initial conditions through assimilation, to validate models, and to make management decisions. CIRN was officially formed in 2016 to provide an integrative, multi-institutional group to collaborate on remotely sensed data techniques. As of 2021, the network is a collaboration between researchers from approximately 16 countries and includes investigators from universities, government laboratories and agencies, non-profits, and private companies. CIRN has a strong emphasis on education, exemplified by hosting annual “boot camps” to teach photogrammetry fundamentals and toolboxes from the CIRN code repository, as well as hosting an annual meeting for its members to present coastal imaging research. In this review article, we provide context for the development of CIRN as well as describe the goals and accomplishments of the CIRN community. We highlight components of CIRN’s resources for researchers worldwide including an open-source GitHub repository and coding boot camps. Finally, we provide CIRN’s perspective on the future of coastal imaging.

Remote Sensing↗

USGS2021

Developing bare-earth digital elevation models from structure-from-motion data on barrier islands

Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy . Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.

Alabama↗

USGS1993

Relations of ammonium minerals at several hydrothermal systems in the western U.S.

Ammonium bound to silicate and sulfate minerals has recently been located at several major hydrothermal systems in the western U.S. utilizing newly-discovered near-infrared spectral properties. Knowledge of the origin and mineralogic relations of ammonium minerals at known hydrothermal systems is critical for the proper interpretation of remote sensing data and for testing of possible links to mineralization. Submicroscopic analysis of ammonium minerals from two mercury- and gold-bearing hot-springs deposits at Ivanhoe, Nevada and McLaughlin, California shows that the ammonium feldspar, buddingtonite, occurs as fine-grained euhedral crystals coating larger sulfide and quartz crystals. Ammonium feldspar seems to precipitate relatively late in the crystallization sequence and shows evidence for replacement of NH4+ by K+ or other monovalent cations. Some buddingtonite is observed in close association with mercury, but not with gold. Ammonioalunite is found in a variety of isolated crystal forms at both deposits. Nitrogen isotopic values for ammonium-bearing minerals show a 14??? range in composition, precluding assignment of a specific provenance to the nitrogen. The correlations of nitrogen isotopic values with depth and ammonium content suggest some loss of nitrogen in the oxidizing supergene environment, possibly as a metastable mineral. The high ammonium content in these hydrothermal systems, the close association to mercury, and the small crystal size of the ammonium-bearing minerals all suggest that ammonium may be transported in a late-stage vapor phase or as an organic volatile. Such a process could lead to the formation of a non-carbonaceous organic aureole above a buried geothermal source. The discovery of a 10-km outcrop of ammonium minerals confirms that significant substitution of ammonium in minerals is possible over an extensive area and that remote sensing is a feasible means to detect such aureoles. ?? 1993.

Journal of Volcanology and Geothermal Research↗

USGS2001

Transboundary movement of airborne pollutants — A methodology for integrating spaceborne images and ground based data

The availability of relevant and accurate environmental information is essential for environmental policy-makers. Recent improvements in satellite remote sensing technologies, groundbased monitors, and data access have resulted in the ability to observe and assess major atmospheric and ecological events around the world on a timely basis Each of these monitoring technologies reveals different and useful information, yet rarely are the resulting data sets used together in an integrated manner. The U.S. Environmental Protection Agency (EPA) and the United Nations Environment Programme (UNEP) Global Resource Information Database (GRID) office in Sioux Falls identified an environmental issue of global interest as a test case for applying an integrated approach: the transboundary movement of atmospheric pollutants. Transboundary movement of atmospheric pollutants has ramifications for human and environmental health, as well as economic impacts. As a result, it is the focus of many bilateral, regional, and international policy efforts. A central question with atmospheric pollutant transport is how to monitor pollutant movement and how to merge different monitoring datasets into useful information. Highly visible regional plumes of dust, smoke, and urban haze can be seen with satellite sensors, while ground-based monitoring of air pollutants such as fine particulates, SO2 , and toxics occurs at the local level. Integration of these two kinds of measurements allows the user to remotely observe large environmental effects in many areas of the world, while obtaining more detailed information from ground-based monitors. Hence, the combination of satellite-based sensor data and ground-based monitoring data promotes greater understanding of the movement of pollutants than either data set alone. Combined data sets are important for use by both scientists and international policy-makers. A standard methodology did not exist to guide and encourage integrated use of satellite images and ground-based data to monitor and understand major pollution events, such as air pollution. Thus, a small team was assembled to develop a methodology for the integration of satellite images and ground-based data. First, we conducted a literature and project review covering past and current integrated remote and ground-based data projects, a literature search of published work, and a search of data sets and technologies that could be used in a combined form. Second, based on this search and documentation, a general methodology was developed for using integrated spaceborne and ground-based data sets, intended as a guide for general scientists and policy-makers. Third, we found an existing project that was willing to be a pilot for testing the methodology: a U.S. EPANOAA project that was using aerial and ground-based sampling to learn more about the airborne sources of mercury deposition in the Florida Everglades. This document presents the results of the literature and project review, the complete methodology, and the outcome of the Florida Everglades pilot project.

Report↗

USGS2024

Intra-urban variations in land surface phenology in a semi-arid environment

Urban vegetation is growing in importance as cities use 'green infrastructure' to mitigate the impacts of climate change, reduce extreme heat, and improve human health and comfort. However, due to the heterogeneity of city landscapes, urban vegetation experiences a diverse range of environmental conditions, potentially leading to differences in growing season timing and length within cities. Here, we investigate physical drivers of urban land surface phenology and timing within a semi-arid city (Denver, CO, USA) using four years (2018–2021) of remotely sensed vegetation indices, modelled air temperature, and land cover datasets. Within the metropolitan region study area, satellite-based vegetation index measurements indicate that growing season length is variable on sub-neighborhood spatial scales. This variability is largely due to differences in the timing of fall senescence, as opposed to early season growth. Areas with substantial fractions of irrigated land cover tend to remain greener for longer, while unirrigated and cooler areas are correlated with an earlier end to the growing season (up to ∼two months shorter). These findings complement those from non-arid cities where surface and air temperature are the dominant environmental control on phenological timing. Results here indicate the importance of soil moisture for phenology in semi-arid regions and suggest unique semi-arid urban growing season dynamics and temperature-vegetation feedbacks. These interactions have implications for water, heat, and vegetation management strategies to maximize ecosystem services in water-limited environments.

Colorado↗

USGS2012

Burn severity mapping in Australia 2009

In 2009, the Victoria Department of Sustainability and Environment estimated approximately 430,000 hectares of Victoria Australia were burned by numerous bushfires. Burned Area Emergency Response (BAER) teams from the United States were deployed to Victoria to assist local fire managers. The U.S. Geological Survey Earth Resources Observation and Science Center (USGS/EROS) and U.S. Forest Service Remote Sensing Applications Center (USFS/RSAC) aided the support effort by providing satellite-derived "soil burn severity " maps for over 280,000 burned hectares. In the United States, BAER teams are assembled to make rapid assessments of burned lands to identify potential hazards to public health and property. An early step in the assessment process is the creation of a soil burn severity map used to identify hazard areas and prioritize treatment locations. These maps are developed primarily using Landsat satellite imagery and the differenced Normalized Burn Ratio (dNBR) algorithm.

Conference Paper↗

USGS2022

The effects of prolonged drought on vegetation dieback and megafires in southern California chaparral

Drought contributed to extensive dieback of southern California chaparral, and normalized difference vegetation index before drought and near the end of the drought was used to estimate this dieback, after accounting for other disturbances recorded in aerial photographs. Within the perimeters of two megafires that occurred after the drought, the 2017 Thomas Fire and the 2018 Woolsey Fire, there had been extensive areas of dieback. Comparing dieback with Monitoring Trends in Burn Severity measures of fire severity, there was a highly significant negative relationship between drought-caused shrub dieback and fire-caused dieback as measured by fire severity. We interpret this as further support for our remote sensing methodology for prefire dieback. Models of fire behavior suggest that one means by which dieback contributes to fire size is through increasing the density and distance of spot fires, particularly under extreme wind conditions. Lower elevation chaparral associations appear to be most vulnerable and are closer to urban environments, which should be a concern to fire managers in regions subjected to extended droughts.

California↗

USGS2026

A methods framework for evaluating measurement consistency across spectrometers for multispectral uncrewed aerial system vegetation mapping applications

The U.S. Geological Survey collects remote sensing data to support national scientific assessments of natural resources, hazards, and landscape change. Spectrometers and spectroradiometers are essential for gathering point-based spectral measurements used in applications such as uncrewed aerial systems (UAS) multispectral image calibration, validation, and analysis. Evaluating how different instruments perform in laboratory and field environments helps determine whether they provide consistent, interoperable measurements. Such verification can expand access to spectral ground data during UAS operations by allowing scientists to use alternative instruments when budgets, logistics, or field conditions limit options. We propose and test a methodological framework for evaluating spectrometers for measurement consistency during UAS multispectral vegetation mapping applications. There are three central evaluation components to the framework: laboratory, field, and relative to UAS multispectral imagery. By evaluating the instruments in both relatively controlled and uncontrolled environments, we thoroughly examine measurement consistency and when/why measurements may differ. We opportunistically selected two instruments for a case study in a coastal marsh setting: a compact laboratory spectrometer we modified for field use and a field-ready spectroradiometer. The instruments produced consistent measurements in both environments. We found differences between the field spectra and UAS spectra that likely reflect the perspectives of ground vs. aerial data and indicate that further radiometric calibration may be needed.

Massachusetts↗

USGS2014

Terrestrial reference standard sites for postlaunch sensor calibration

In an era when the number of Earth observation satellites is rapidly growing and measurements from satellite sensors are used to address increasingly urgent global issues, often through synergistic and operational combinations of data from multiple sources, it is imperative that scientists and decision-makers are able to rely on the accuracy of Earth observation data products. The characterization and calibration of these sensors, particularly their relative biases, are vital to the success of the developing integrated Global Earth Observation System of Systems (GEOSS) for coordinated and sustained observations of the Earth. This can only reliably be achieved in the postlaunch environment through the careful use of observations by multiple sensor systems over common, well-characterized terrestrial targets (i.e., on or near the Earth's surface). Through greater access to and understanding of these vital reference standard sites and their use, the validity and utility of information gained from Earth remote sensing will continue to improve. This paper provides a brief overview of the use of reference standard sites for postlaunch sensor radiometric calibration from historical, current, and future perspectives. Emphasis is placed on optical sensors operating in the visible, near-infrared, and shortwave infrared spectral regions.

Canadian Journal of Remote Sensing↗

USGS2017

Biological soil crusts: Diminutive communities of potential global importance

Biological soil crusts (biocrusts) are widespread, diverse communities of cyanobacteria, fungi, lichens, and mosses living on soil surfaces, primarily in drylands. Biocrusts can locally govern primary production, soil fertility, hydrology, and surface energy balance, with considerable variation in these functions across alternate community states. Further, these communities have been implicated in Earth system functioning via potential influences on global biogeochemistry and climate. Biocrusts are easily destroyed by disturbances and appear to be exceptionally vulnerable to warming temperatures and altered precipitation inputs, signaling possible losses of dryland functions with global change. Despite these concerns, we lack sufficient spatiotemporal data on biocrust function, cover, and community structure to confidently assess their ecological roles across the extensive dryland biome. Here, we present the case for cross-scale research and restoration efforts coupled with remote-sensing and modeling approaches that improve our collective understanding of biocrust responses to global change and the ecological roles of these diminutive communities at global scales.

Frontiers in Ecology and the Environment↗

USGS2012

Thermal infrared remote sensing of water temperature in riverine landscapes

Water temperature in riverine landscapes is an important regional indicator of water quality that is influenced by both ground- and surface-water inputs, and indirectly by land use in the surrounding watershed (Brown and Krygier, 1970; Beschta et al., 1987; Chen et al., 1998; Poole and Berman, 2001). Coldwater fishes such as salmon and trout are sensitive to elevated water temperature; therefore, water temperature must meet management guidelines and quality standards, which aim to create a healthy environment for endangered populations (McCullough et al., 2009). For example, in the USA, the Environmental Protection Agency (EPA) has established water quality standards to identify specific temperature criteria to protect coldwater fishes (Environmental Protection Agency, 2003). Trout and salmon can survive in cool-water refugia even when temperatures at other measurement locations are at or above the recommended maximums (Ebersole et al., 2001; Baird and Krueger, 2003; High et al., 2006). Spatially extensive measurements of water temperature are necessary to locate these refugia, to identify the location of ground- and surface-water inputs to the river channel, and to identify thermal pollution sources. Regional assessment of water temperature in streams and rivers has been limited by sparse sampling in both space and time. Water temperature has typically been measured using a network of widely distributed instream gages, which record the temporal change of the bulk, or kinetic, temperature of the water (Tk) at specific locations. For example, the State of Washington (USA) recorded water quality conditions at 76 stations within the Puget Lowlands eco region, which contains 12,721 km of streams and rivers (Washington Department of Ecology, 1998). Such gages are sparsely distributed, are typically located only in larger streams and rivers, and give limited information about the spatial distribution of water temperature (Cherkauer et al., 2005).

Book chapter↗

USGS2012

Thermal infrared remote sensing of water temperature in riverine landscapes

Water temperature in riverine landscapes is an important regional indicator of water quality that is influenced by both ground- and surface-water inputs, and indirectly by land use in the surrounding watershed (Brown and Krygier, 1970; Beschta et al., 1987; Chen et al., 1998; Poole and Berman, 2001).Coldwater fishes such as salmon and trout are sensitive to elevated water temperature; therefore, water temperature must meet management guidelines and quality standards, which aim to create a healthy environment for endangered populations (McCullough et al., 2009). For example, in the USA, the Environmental Protection Agency (EPA) has established water quality standards to identify specific temperature criteria to protect coldwater fishes (Environmental Protection Agency, 2003). Trout and salmon can survive in cool-water refugia even when temperatures at other measurement locations are at or above the recommended maximums (Ebersole et al., 2001; Baird and Krueger, 2003; High et al., 2006). Spatially extensive measurements of water temperature are necessary to locate these refugia, to identify the location of ground- and surface-water inputs to the river channel, and to identify thermal pollution sources. Regional assessment of water temperature in streams and rivers has been limited by sparse sampling in both space and time. Water temperature has typically been measured using a network of widely distributed instream gages, which record the temporal change of the bulk, or kinetic, temperature of the water (Tk) at specific locations. For example, the State of Washington (USA) recorded water quality conditions at 76 stations within the Puget Lowlands eco region, which contains 12,721 km of streams and rivers (Washington Department of Ecology, 1998). Such gages are sparsely distributed, are typically located only in larger streams and rivers, and give limited information about the spatial distribution of water temperature.

Book chapter↗

USGS2007

Color shaded-relief and surface-classification maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

The northeastern part of the National Petroleum Reserve in Alaska (NPRA) has become an area of active petroleum exploration during the past five years. Recent leasing and exploration drilling in the NPRA requires the U.S. Bureau of Land Management (BLM) to manage and monitor a variety of surface activities that include seismic surveying, exploration drilling, oil-field development drilling, construction of oil-production facilities, and construction of pipelines and access roads. BLM evaluates a variety of permit applications, environmental impact studies, and other documents that require rapid compilation and analysis of data pertaining to surface and subsurface geology, hydrology, and biology. In addition, BLM must monitor these activities and assess their impacts on the natural environment. Timely and accurate completion of these land-management tasks requires elevation, hydrologic, geologic, petroleum-activity, and cadastral data, all integrated in digital formats at a higher resolution than is currently available in nondigital (paper) formats. To support these land-management tasks, a series of maps was generated from remotely sensed data in an area of high petroleum-industry activity (fig. 1). The maps cover an area from approximately latitude 70°00' N. to 70°30' N. and from longitude 151°00' W. to 153°10' W. The area includes the Alpine oil field in the east, the Husky Inigok exploration well (site of a landing strip) in the west, many of the exploration wells drilled in NPRA since 2000, and the route of a proposed pipeline to carry oil from discovery wells in NPRA to the Alpine oil field. This map area is referred to as the "Fish Creek area" after a creek that flows through the region. The map series includes (1) a color shaded-relief map based on 5-m-resolution data (sheet 1), (2) a surface-classification map based on 30-m-resolution data (sheet 2), and (3) a 5-m-resolution shaded relief-surface classification map that combines the shaded-relief and surface-classification data (sheet 3). Remote sensing datasets that were used to compile the maps include Landsat 7 Enhanced Thematic Mapper+ (ETM+), and interferometric synthetic aperture radar (IFSAR) data. In addition, a 1:250,000-scale geologic map of the Harrison Bay quadrangle, Alaska (Carter and Galloway, 1985, 2005) was used in conjunction with ETM+ and IFSAR data.

Alaska↗

USGS2023

High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature

Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf-level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush ( Artemisia tridentata ), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf-level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies-cytotype relate to leaf-level temperature. When considered across the whole season, stomatal conductance was negatively, non-linearly correlated with crown-level temperature derived from UAS. Subspecies identity best explained crown-level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf-level temperature. These results show how fine-scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate-change-induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene-by-environment interactions.

Idaho↗

USGS2007

The use of a vegetation index for assessment of the urban heat island effect

A vegetation index and radiative surface temperature were derived from NOAA-11 Advanced Very High Resolution Radiometer (AVHRR) data for the Seattle, WA region from 28 June through 4 July 1991. The vegetation index and surface temperature values were computed for locations of weather observation stations within the region and compared to observed minimum air temperatures. These comparisons were used to evaluate the use of AVHRR data to assess the influence of the urban environment on observed minimum air temperatures (the urban heat island effect). AVHRR derived normalized difference vegetation index (NDVI) and radiant surface temperature data from a one week composite product were both related significantly to observed minimum temperatures, however, the vegetation index accounted for a greater amount of the spatial variation observed in mean minimum temperatures. The difference in the NDVI between urban and rural regions appears to be an indicator of the difference in surface properties (i.e., evaporation and heat storage capacity) between the two environments that are responsible for differences in urban and rural minimum temperatures.

International Journal of Remote Sensing↗