Geology topics

Kevin Gallo

Publications and source records attributed to Kevin Gallo.

9 recordsLinked to original sources

USGS2022

Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data

Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Assessments of surface urban heat island (UHI) usually focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain period. However, the mechanisms and complex interactions in landscape dynamics and land surface thermal features are still being assessed. In this study, we developed and implemented a novel approach to characterize landscape thermal conditions by focusing on UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature and land cover change products. We analyzed land surface temperature changes in urban and surrounding non-urban lands to quantify the UHI intensity and landscape thermal conditions in the Atlanta and Minneapolis metropolitan areas of the United States. Our results revealed that UHI intensities had averages of 3.4 °C and 3.3 °C in the Atlanta and Minneapolis metropolitan areas, respectively. The dominant land cover type in rural areas and urban imperviousness cover determines the UHI intensity. Increasing trends of 0.04 °C/year and 0.01 °C/year in UHI intensity between 1985 and 2018 were found in Atlanta and Minneapolis, respectively. The UHI intensity variations in 1985 and 2018 suggest that the magnitudes and temporal variations of UHI intensity averaged from all urban land cover classes are close to the UHI intensity estimated from the low intensity urban area only while the UHI intensities are more than 2 °C larger in medium to high and high intensity urban areas. The UHI intensities estimated from the maximum temperature that have statistically significant increasing trends suggest that the maximum temperature is a good element for measuring UHI effect. Urban land cover dynamics play an important role in controlling temporal variation of UHI and the UHI hotspots. Our findings support the scientific value of implementing the prototype approach as an objective framework to quantify and monitor UHI intensity at a large geographic extent.

Remote Sensing of Environment↗

USGS2021

Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Land↗

USGS2021

The effects of urban land cover dynamics on urban heat Island intensity and temporal trends

Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.

South Dakota↗

USGS2021

Monitoring multi-decadal variations of urban heat island intensity

Urban development and associated land cover transitions alter the thermal and physical properties of the land surface, resulting the temperature in urban area higher than in rural area or urban heat island (UHI). Remote sensing and land cover data is usually used to assess UHI intensity and temporal change trends. In this study, we implemented a prototype approach to characterize the UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature products and annual land change information. We analyzed land surface temperature change in urban and surrounding non-urban lands to quantify the UHI intensity and change in the Atlanta and Sioux Falls metropolitan areas of the United States. Our results suggested that the land cover type in rural areas and urban imperviousness cover determine UHI intensity and the urban land cover dynamics plays a major role in controlling temporal trend of UHI.

Conference Paper↗

USGS2017

A land product characterization system for comparative analysis of satellite data and products

A Land Product Characterization System (LPCS) has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature). The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS), and simulated data for the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA) Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.

Remote Sensing↗

USGS2017

Grand challenges in understanding the interplay of climate and land changes

Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.

Earth Interactions↗

USGS2014

Application of spatially gridded temperature and land cover data sets for urban heat island analysis

Two gridded data sets that included (1) daily mean temperatures from 2006 through 2011 and (2) satellite-derived impervious surface area, were combined for a spatial analysis of the urban heat-island effect within the Dallas-Ft. Worth Texas region. The primary advantage of using these combined datasets included the capability to designate each 1 × 1 km grid cell of available temperature data as urban or rural based on the level of impervious surface area within the grid cell. Generally, the observed differences in urban and rural temperature increased as the impervious surface area thresholds used to define an urban grid cell were increased. This result, however, was also dependent on the size of the sample area included in the analysis. As the spatial extent of the sample area increased and included a greater number of rural defined grid cells, the observed urban and rural differences in temperature also increased. A cursory comparison of the spatially gridded temperature observations with observations from climate stations suggest that the number and location of stations included in an urban heat island analysis requires consideration to assure representative samples of each (urban and rural) environment are included in the analysis.

Texas↗

USGS2013

Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data

This study provides a baseline quality check on provisional Landsat Surface Reflectance (SR) products as generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software. Characterization of the Landsat SR products leveraged comparisons between aerosol optical thickness derived from LEDAPS and measured by Aerosol Robotic Network (AERONET), as well as reflectance correlations with field spectrometer and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Results consistently indicated similarity between LEDAPS and alternative data products in longer wavelengths over vegetated areas with no adjacent water, while less reliable performance was observed in shorter wavelengths and sparsely vegetated areas. This study demonstrates the strengths and weaknesses of the atmospheric correction methodology used in LEDAPS, confirming its successful implementation to generate Landsat SR products.

Remote Sensing of Environment↗

USGS2006

An agreement coefficient for image comparison

Combination of datasets acquired from different sensor systems is necessary to construct a long time-series dataset for remotely sensed land-surface variables. Assessment of the agreement of the data derived from various sources is an important issue in understanding the data continuity through the time-series. Some traditional measures, including correlation coefficient, coefficient of determination, mean absolute error, and root mean square error, are not always optimal for evaluating the data agreement. For this reason, we developed a new agreement coefficient for comparing two different images. The agreement coefficient has the following properties: non-dimensional, bounded, symmetric, and distinguishable between systematic and unsystematic differences. The paper provides examples of agreement analyses for hypothetical data and actual remotely sensed data. The results demonstrate that the agreement coefficient does include the above properties, and therefore is a useful tool for image comparison.

Photogrammetric Engineering and Remote Sensing↗