Using remote sensing in support of environmental management: A framework for selecting products, algorithms and methods

Klerk, Helen Margaret De; Gilbertson, Jason Kane; Lück‐Vogel, Melanie; Kemp, Jaco; Münch, Zahn

doi:10.1016/j.jenvman.2016.07.073

Cited by 10 publications

(11 citation statements)

References 23 publications

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“…First, the algorithm estimates the atmosphere's effects on thermal radiation or absorption transfer at the surface. Next, that part of the total thermal radiation detected by the sensor is eliminated, then the thermal radiation intensity from the surface is calculated, and finally, this intensity is converted to temperatures using the ENVI software kit [17][18]. The details of this process are given in a flowchart in Figure 5.…”

Section: Lst Retrievalmentioning

confidence: 99%

Retrieval of Land Surface Temperature From Landsat 8 Data of the Dandong-liaoyang Geothermal Area

Luan

2020

Preprint

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In recent years, with the intensification of problems like the depletion of traditional fossil fuels and environmental degradation, the development of new energy sources has become a key long-term strategy in China. Geothermal energy has attracted much attention due to its advantages of abundance and low environmental impact. Based on infrared data sensed remotely by the Landsat 8 satellite, this paper reports a verification of the atmospheric-correction method for extracting the surface temperature of the Dandong-Liaoyang geothermal region in all months of 2014. The method combines the abnormal points in the inversion results with the local sites of hot springs, structures, local historical air temperatures, and land surface temperature/emissivity data (MOD11_L2). Results showed that these data sources were spatially distributed in similar ways, which indicates that these results can be used to identify promising geothermal resources from publically available thermal-infrared remote-sensing data.

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Section: Lst Retrievalmentioning

confidence: 99%

Retrieval of Land Surface Temperature From Landsat 8 Data of the Dandong-liaoyang Geothermal Area

Luan

2020

Preprint

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“…Her notable works include an assessment of fire management in Mediterranean-climate shrub lands of South Africa [492], and the development of a Bayesian model of wildfire [493]. Her recent work includes research to understand the reseeder-resprouter dichotomy in Mediterranean-type vegetation [494], and the development of a framework to select remote-sensing products, algorithms, and methods to support environmental management [495].…”

Section: Kendra Mclauchlan Is a Professor At Kansas State University mentioning

confidence: 99%

Recognizing Women Leaders in Fire Science: Revisited

Smith

Strand

2018

Fire

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In August, 2018, an editorial in Fire entitled Recognizing Women Leaders in Fire Science was published. This was intended to ignite a conversation into diversity in fire science by highlighting several women leaders in fire research and development. This editorial was released alongside a new Topical Collection in Fire called Diversity Leaders in Fire Science. The response on social media was fantastic, leading to numerous recommendations of women leaders in fire science that had been inadvertently missed in the first editorial. In this editorial, we acknowledge 145 women leaders in fire science to promote diversity across our disciplines. Fire is continually committed to improving diversity and inclusion in all aspects of the journal and welcomes perspectives, viewpoints, and constructive criticisms to help advance that mission.

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“…imaging spectroradiometer (MODIS) [2] and the medium resolution imaging spectrometer [3], to the expanded spatial resolution of Landsat [4] and Sentinel-2 [5] instruments, multiple types of RS images with different spatio-spectral features have been successfully used in many valuable applications, such as crop and urban mapping [6]- [8], natural hazards discovery [9], [10], environmental management [11], [12], and detailed land-cover analyses [13]- [15]. With these and other important tasks in mind, the spatial resolution of remotely sensed imagery is often a key factor to achieve better performances since the higher the level of spatial details the more visual information is logically available for consideration [16].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-3 Super-Resolution Based on Dense Multireceptive Channel Attention

Fernández

Fernández-Beltrán

Kang

et al. 2021

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

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The unprecedented availability of remote sensing data from different complementary Sentinel missions provides increasing opportunities to alleviate the spatial limitations of Sentinel-3 (S3) from an intersensor perspective. Nonetheless, effectively exploiting such intersensor synergies still raises important challenges for super-resolution (SR) algorithms in terms of operational data availability, sensor alignment and substantial resolution changes, among others. In this scenario, this article sets a new SR framework for spatially enhancing S3 ocean and land color instrument (OLCI) products by taking advantage of the higher spatial resolution of the Sentinel-2 (S2) multispectral instrument (MSI). To achieve this goal, we initially study some of the most important deep learning-based approaches. Then, we define a novel Level-4 SR framework which integrates a new convolutional neural network specially designed for super-resolving OLCI data. In contrast to other networks, the proposed SR architecture (termed as SRS3) employs a dense multireceptive field together with a residual channel attention mechanism to relieve the particularly low spatial resolution of OLCI while extracting more discriminating features for the large spatial resolution differences with respect to MSI. The experimental part of the work, conducted using ten coupled OLCI and MSI operational data, reveals the suitability of the presented Level-4 SR framework within the Copernicus programme context as well as the advantages of the proposed architecture with respect different state-of-the-art models when spatially enhancing OLCI products. The related codes will be publicly available at https://github.com/rufernan/SRS3.

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Using remote sensing in support of environmental management: A framework for selecting products, algorithms and methods

Cited by 10 publications

References 23 publications

Retrieval of Land Surface Temperature From Landsat 8 Data of the Dandong-liaoyang Geothermal Area

Retrieval of Land Surface Temperature From Landsat 8 Data of the Dandong-liaoyang Geothermal Area

Recognizing Women Leaders in Fire Science: Revisited

Sentinel-3 Super-Resolution Based on Dense Multireceptive Channel Attention

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