scholarly journals Modelling the Spatial Distribution of Culicoides imicola: Climatic versus Remote Sensing Data

2014 ◽  
Vol 6 (7) ◽  
pp. 6604-6619 ◽  
Author(s):  
Jasper Van Doninck ◽  
Bernard De Baets ◽  
Jan Peters ◽  
Guy Hendrickx ◽  
Els Ducheyne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Culicoides Imicola

scholarly journals Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

2020 ◽  
Vol 10 (14) ◽  
pp. 4919
Author(s):  
Guoqing Li ◽  
Alona Armstrong ◽  
Xueli Chang
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Time Interval ◽  
Sensing Data ◽  
Significant Linear Correlation ◽  
Sebal Model ◽  
Moderate Resolution Imaging Spectroradiometer

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.

scholarly journals Modeling the Spatial Distribution of Swordfish (Xiphias gladius) Using Fishery and Remote Sensing Data: Approach and Resolution

2020 ◽  
Vol 12 (6) ◽  
pp. 947
Author(s):  
Nan-Jay Su ◽  
Chia-Hao Chang ◽  
Ya-Ting Hu ◽  
Wei-Chuan Chiang ◽  
Chen-Te Tseng
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Pacific Ocean ◽  
Remote Sensing Data ◽  
Additive Models ◽  
Temporal Data ◽  
Sensing Data ◽  
Data Resolution ◽  
Xiphias Gladius ◽  
Spatio Temporal

Swordfish, Xiphias gladius (Linnaeus, 1758), is a commercially important species that is widely distributed throughout three oceans. This species inhabits oceanic waters with preferred environmental ranges and migrates vertically to the surface layer for feeding. However, the spatial distribution pattern and habitat preferences of swordfish have been rarely studied in the Pacific Ocean due to the wide geographic range of this species. This study examined the spatial distribution and preferred ranges of environmental variables for swordfish using two approaches, generalized additive models and habitat suitability index methods, with different spatio-temporal data resolution scales. Results indicated that sea surface temperature is the most important factor determining swordfish spatial distribution. Habitat spatial pattern and preferred environmental ranges, estimated using various modeling approaches, were robust relative to the spatio-temporal data resolution scales. The models were validated by examining the consistency between predictions and untrained actual observations, which all predicted a high relative density of swordfish in the tropical waters of the central Pacific Ocean, with no obvious seasonal movement. Results from this study, based on fishery and remote sensing data with wide spatial coverage, could benefit the conservation and management of fisheries for highly migratory species such as swordfish and tuna.

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