Landsat and Thermal Infrared Imaging

Arvidson, Terry; Barsi, Julia A.; Jhabvala, M.; Reuter, Dennis C.

doi:10.1007/978-94-007-6639-6_9

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…SNR primarily varies with range, and thus the variance in SNR too is low. The small variance in range is further attributed to the relatively small off-nadir (+/− 7.5°cross-track FOV [8], [9]) allowed for imaging. The trend in both Figs.…”

Section: B Uniform Random Sampling Methodsmentioning

confidence: 99%

“…For accurate access calculations, the propagation time step should be a fraction of the time taken to go over the ground pixel (smaller fraction is higher fidelity but computationally more expensive). It is thus a computational burden to simulate narrow AT FOV sensors like the Landsat-8 TIRS pushbroom sensor where the AT FOV is 142 urad [8], [9] and at an altitude of 705 km, the time required to go over a ground pixel at nadir is 14.8 ms. Moreover, accurately computed access information is needed to make a fair estimate of the observational data metrics (e.g., view zenith, view azimuth, and bidirectional reflectance), which depend on the observation geometry.…”

Section: A Backgroundmentioning

confidence: 99%

“…Rectangular narrow along-track (AT) field of view (FOV) sensors are fairly common in EO, examples being pushbroom imagers such as the Thermal InfraRed Sensor (TIRS) [8], [9] on Landsat-8, Operational Land Imager [10] on Landsat-8 (30 m along track swath), Multi-Spectral Imager for Sentinel-2 (10-60 m along track swath); panchromatic and multispectral matrix imagers such as WorldView-3 satellite sensor by DigitalGlobe, Ball Global Imaging System on Quickbird (16.5 km steerable swath) [11]; synthetic aperture radars (SARs) such as C-SAR on Sentinel-1 (80 km nadir swath) [12], PALSAR-2 of ALOS-2 [13]. Conical FOV sensors too are popular in EO, examples being the precipitation profiling radar on RainCube [14], and the MicroWave Radiometers on Sentinel 3 [15].…”

Section: Introductionmentioning

confidence: 99%

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Fast Methods of Coverage Evaluation for Tradespace Analysis of Constellations

Ravindra

Nag

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Simulation of distributed space missions (DSMs) for the purpose of phase-A mission design studies and general tradespace analysis is computationally challenging owing to the necessity of evaluating thousands of architecture options. Machine learning and evolutionary optimization methods have enabled intelligent search of the architectural tradespace of DSMs, including spacecraft and instrument design specifications. A critical computational bottleneck in evaluating architectures is the ability to rapidly simulate orbits of many DSMs with varying parameters for global earth observation and compare their coverage-related performance. When design variables include heterogeneous payload types and characteristics, orbital characteristics, areas of interest, and user constraints, the parameter space may be in thousands. In this article, we describe the difficulty of coverage calculations for narrow field of view (FOV) and conical FOV sensors, and propose a novel algorithm, called quick search and correction (QSC), to overcome it. We also propose new temporal evaluation metrics to characterize the coverage performance of DSMs, as well as a uniform random sampling technique for fast evaluation of overall performance of DSMs. Performance of the proposed methods and metrics are verified on an example Landsat-derived DSM, showing ∼100x improvement in computational speed due to the QSC algorithm and ∼10-250x due to the sampling technique.

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Section: B Uniform Random Sampling Methodsmentioning

confidence: 99%

Section: A Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Methods of Coverage Evaluation for Tradespace Analysis of Constellations

Ravindra

Nag

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A false-color map was also created (RGB:843) to highlight the vegetation (in red due to the NIR band) and distinguish from areas with anthropic changes (such as streets, industries, residences, etc.) (Figure 3b) [39].…”

Section: Satellite Datamentioning

confidence: 99%

An Interactive WebGIS Integrating Environmental Susceptibility Mapping in a Self-Burning Waste Pile Using a Multi-Criteria Decision Analysis Approach

et al. 2022

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Mining activities promote resulting wastes, so coal mines are prone to release contaminants to the environment, namely to the soil and water. Therefore, the analysis of this type of risk is crucial in waste pile management. The São Pedro da Cova (Porto, Portugal) coal waste pile has been studied in recent years, with several data acquired from 2019–2021 under a research project using distinct methodologies. These results are now combined in a multi-approach method to estimate the environmental impacts of the waste pile and identify the contamination. With the integration of all the data in a Geographical Information System (GIS) environment, and to fulfill a scientific gap, this study aims: (i) to create a susceptibility map of contamination in the areas surrounding the self-burning coal waste pile in São Pedro da Cova, using Analytical Hierarchy Process (AHP) and Fuzzy AHP approaches; and (ii) to develop a webGIS application incorporating all the information acquired that can be useful for the residents of São Pedro da Cova and also to the decision-making public entities and researchers. The results obtained show that the contamination susceptibility is higher surrounding the abandoned mine, particularly along the waste piles and the corresponding runoff areas, which can be especially sensitive.

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“…La banda verde es muy reflexiva a la nieve en comparación con la banda SWIR que es poco reflexiva para la nieve y altamente reflexiva para las nubes, permitiendo discriminar las nubes de la nieve. Para el cálculo de la TSS se utilizó el valor de la temperatura TOA de las bandas térmicas de los sensores TM, ETM+ y TIRS de resolución espacial 120, 60 y 100 m respectivamente, cuyas características se detallan en la Tabla 1 (Arvidson et al, 2013; USGS, 2019).…”

Section: Ndvi Nir Rojo Nir Rojounclassified

Spatial and temporal analysis of surface temperature in the Apacheta micro-basin using Landsat thermal data

Moncada

Willems²

2020

Rev. Teledetec.

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<p>High Andean ecosystems, such as grasslands and peatlands, are fragile and, due to the effects of climate change, their sustainability is being jeopardized. A key factor hampering sustainable management efforts from the government and communities, is the lack or scarcity of in-situ eco-hydrological and climate data. In that sense, remote sensing techniques offers a powerful alternative for the assessment of the evolution of these ecosystems, by providing a holistic view of the territory. The objective of this work is to determine both the spatial and temporal evolution of the local atmospheric temperature of the Apacheta micro-basin in Ayacucho over the past 34 years, using the soil surface temperature (SST) as a proxy. For this, thermal data of Landsat series (TM, ETM+ and TIRS sensors), covering the period from 1985 to 2018, were used. The TSS estimates were made from the emissivity correction of the brightness temperatures at the top of the atmosphere, considering the negligible atmospheric effect due to the conditions of high atmospheric transmissivity in the study area. The results show a positive trend of the SST with an increase of 4.9 °C, equivalent to 27.5% of the SST. Trends are higher (5.8 °C) in the snowy areas (equivalent to 35.3% of the TSS in the whole micro-basin). The SST in the snow area explains the 83.6% of the behavior of the snow cover derived by the NDSI, with a decreasing surface as SST increase.</p>

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Landsat and Thermal Infrared Imaging

Cited by 6 publications

References 16 publications

Fast Methods of Coverage Evaluation for Tradespace Analysis of Constellations

Fast Methods of Coverage Evaluation for Tradespace Analysis of Constellations

An Interactive WebGIS Integrating Environmental Susceptibility Mapping in a Self-Burning Waste Pile Using a Multi-Criteria Decision Analysis Approach

Spatial and temporal analysis of surface temperature in the Apacheta micro-basin using Landsat thermal data

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