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SPATIO-TEMPORAL WATER QUALITY MAPPING USING SATELLITE DATA AROUND A MANGROVE PLANTATION IN CAGSAO, CALABANGA, CAMARINES SUR

K. C. M. Saddi
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Spatio-temporal maps of chlorophyll and dissolved oxygen were generated using Linear Regression (LR) models which were derived from the train set and satellite images of the SMB.  ...  a two-color raster image, and 3) generation of the spatio-temporal maps from the analysis.  ...  Spatio-temporal mapping is the key to understanding the variations in water quality parameters and maintaining the health and safety of both the water and aquatic ecosystems (Chu, Kong, & Chang, 2018)  ... 
doi:10.5194/isprs-archives-xlvi-4-w6-2021-257-2021 fatcat:yxbc4t27enfdzb45fenbop4gfm

An Introduction to the Spatio-Temporal Analysis of Satellite Remote Sensing Data for Geostatisticians [chapter]

A. F. Militino, M. D. Ugarte, U. Pérez-Goya
2018 Handbook of Mathematical Geosciences  
provide routinely high quality images with different temporal and spatial resolutions.  ...  Here, we analyze the current use of some of the geostatistical tools in satellite image analysis, and provide an introduction to this subject for potential researchers.  ...  Satellite Images Satellite images are available since more than four decades ago, and since then there has been a notable improvement in quality, quantity, and accessibility of these images, making it  ... 
doi:10.1007/978-3-319-78999-6_13 fatcat:e3xzehllirc4xj24n7htw4yclm

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

Yang Bai, Lixin Wu, Kai Qin, Yufeng Zhang, Yangyang Shen, Yuan Zhou
2016 Remote Sensing  
This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM 2.5 concentrations from satellite-derived 500 m AOD.  ...  The common land use regression (LUR) variables and meteorological factors were then employed to extend the linear mixed effects model to provide spatio-temporally resolved ground PM 2.5 estimations of  ...  In this article, a geographically and temporally weighted regression (GTWR) model based on a satellite-derived AOD is proposed to estimate hourly ground-level PM 2.5 concentration images with fine spatial  ... 
doi:10.3390/rs8030262 fatcat:zfd7s4myo5a5pi3ngbcpjnjk6m

Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network [article]

Rui Li, Shunyi Zheng, Chenxi Duan, Ce Zhang
2020 arXiv   pre-print
In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.  ...  The effectiveness of 3D MSFCN is verified using two Gaofen 2 (GF2) spatio-temporal satellite images [36] , which can be seen in Fig. 9 .  ...  However, when utilizing the 2D CNN to extract features from spatio-temporal satellite images, the temporal dimensions of the extracted features generated by the convolution layer must be averaged and devastated  ... 
arXiv:2008.00168v2 fatcat:zwllbq2hpvg6pimhkhlk6gi7zu

High-resolution air temperature mapping in urban areas: A review on different modelling techniques

Hamid Taheri-Shahraiyni, Sahar Sodoudi
2017 Thermal Science  
Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas.  ...  However, some of the shortcomings and limitations for development of high-resolution spatio-temporal maps in urban areas have not been properly addressed yet.  ...  They also thank Chris Engert and David Mottram for their valuable proof-readings of this paper.  ... 
doi:10.2298/tsci150922094t fatcat:rcejiv6rtjdurf27eqwzosdu3q

A New Fusion Algorithm for Simultaneously Improving Spatio-temporal Continuity and Quality of Remotely Sensed Soil Moisture over the Tibetan Plateau

Yaokui Cui, Chao Zeng, Xi Chen, Wenjie Fan, Haijiang Liu, Yuan Liu, Wentao Xiong, Cong Sun, Zengliang Luo
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The last step is predicting the spatio-temporally continuous and high-quality SM using the trained GRNN derived by the spatio-temporal continuity reference data.  ...  This spatio-temporally continuous and high-quality SM of the TP will help advance our understanding of global and regional changes in water cycle and climate.  ...  They also would like to thank the National Satellite Meteorological Center of China (http://data.nsmc.org.cn), European Space Agency (https://esa-soilmoisture-cci.org) and National Aeronautics and Space  ... 
doi:10.1109/jstars.2020.3043336 fatcat:ltsgne3dpnhmff7jzg5uk4u23e

Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic

Quoc-lap Kieu, Tien-thanh Nguyen, Anh-huy Hoang
2021 Geography, Environment, Sustainability  
In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between  ...  In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19.  ...  From a different angle, using GIS-based approaches such as spatial lag and spatial error models to investigate spatial dependence and multiscale geographically weighted regression models to locally examine  ... 
doi:10.24057/2071-9388-2021-054 fatcat:juel5xrngjhxhalrzltio53udq

Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

Yang Bai, Man Wong, Wen-Zhong Shi, Li-Xin Wu, Kai Qin
2015 Remote Sensing  
This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat  ...  The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM) algorithm using neural network regression model, from 60 m to 30 m resolution.  ...  [38] integrated Landsat TM and MERIS data, however, the results were highly dependent on the quality of the land cover map generated from Landsat images.  ... 
doi:10.3390/rs70404424 fatcat:pbmvgqzyq5epllbkbtdqt767ou

Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation

Zhichao Li, Helen Gurgel, Nadine Dessay, Luojia Hu, Lei Xu, Peng Gong
2020 International Journal of Environmental Research and Public Health  
Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated.  ...  In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU),  ...  , and entomological/epidemiological dengue risk mapping benefits from the use of satellite EO data [5] .  ... 
doi:10.3390/ijerph17124509 pmid:32585932 fatcat:s7wr6oupbrcylkwrmuyw47fbdm

A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain

Rochelle Schneider, Ana M Vicedo-Cabrera, Francesco Sera, Pierre Masselot, Massimo Stafoggia, Kees de Hoogh, Itai Kloog, Stefan Reis, Massimo Vieno, Antonio Gasparrini
2020 Remote Sensing  
Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions.  ...  Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5.  ...  Acknowledgments: The authors are grateful for the technical support received from the European Centre for Medium-Range Weather Forecasts (ECMWF).  ... 
doi:10.3390/rs12223803 pmid:33408882 pmcid:PMC7116547 fatcat:6t346aty2rcdzhsdcxxfpjcgq4

Monitoring spatio-temporal variability of the Adour River turbid plume (Bay of Biscay, France) with MODIS 250-m imagery

Caroline Petus, Vincent Marieu, Stefani Novoa, Guillem Chust, Nicolas Bruneau, Jean-Marie Froidefond
2014 Continental Shelf Research  
allows quantifying and mapping suspended matter in coastal waters.  ...  The results presented in this study show the potential of 250-m MODIS images to monitor small river plumes systems and support management and assessment of the water quality in the south-eastern Bay of  ...  Acknowledgements The authors would like to acknowledge the sponsors of this project: the Technical Littoral Center of the Lyonnaise des eaux of Biarritz, the Basque Water Agency (URA) and the Funds for  ... 
doi:10.1016/j.csr.2013.11.011 fatcat:4wivgrlnmfffrhaufm4m5zcsly

A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain [article]

Rochelle Schneider dos Santos, Ana Vicedo-Cabrera, Francesco Sera, Massimo Stafoggia, Kees de Hoogh, Itai Kloog, Stefan Reis, Massimo Vieno, Antonio Gasparrini
2020 medRxiv   pre-print
Data from satellites, reanalysis and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolution.  ...  Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5.  ...  Acknowledgments: The authors are grateful for the technical support received from the European Centre for Medium-Range Weather Forecasts (ECMWF).  ... 
doi:10.1101/2020.07.19.20157396 fatcat:qrzs4wvlkrddpit5ryjdwhwtsy

Land cover classification from remote sensing images based on multi-scale fully convolutional network

Rui Li, Shunyi Zheng, Chenxi Duan, Libo Wang, Ce Zhang
2022 Geo-spatial Information Science  
time series interaction from the reshaped spatio-temporal remote sensing images.  ...  to exploit discriminative representations from two-dimensional (2D) satellite images.  ...  The batch size is set as 16 for WHDLD and GID, and 4 for GF2 spatio-temporal satellite images.  ... 
doi:10.1080/10095020.2021.2017237 fatcat:aips7z2n2bfutjdckjdiob5n4q

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data

Li-wen Zhang, Jing-feng Huang, Rui-fang Guo, Xin-xing Li, Wen-bo Sun, Xiu-zhen Wang
2013 Journal of Zhejiang University SCIENCE B  
, a novel spatio-temporal algorithm for T(a) estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed.  ...  However, the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only  ...  To solve the problems above, we first adopt the classical multi-regression model to estimate the maximum and minimum T a from LST images, and then implement a novel spatio-temporal algorithm that combines  ... 
doi:10.1631/jzus.b1200169 pmid:23365013 pmcid:PMC3566407 fatcat:cw7dckn7b5a65hq5j7llqtkz7i

Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis using Satellite Data over North Bihar during the August 2017 Flood Event

Tripathi, Parida, Pandey
2019 Hydrology  
The specific objective of this study was to analyze the satellite-derived Near Real Time (NRT) MODIS flood product for spatiotemporal mapping of flood progression and regression over the North Bihar.  ...  So in this study, we employed high temporal resolution data to capture inundation extent and further, the flood extent has been validated with high spatial resolution data.  ...  The spatio temporal variability of rainfall and consequent flood progression and regression were well captured by the MODIS-based NRT flood data.  ... 
doi:10.3390/hydrology6020038 fatcat:qsqektz6gvc6zcktffytb6jpnq
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