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Google Earth Engine Applications

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ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

New Approaches for the Discovery of Pharmacologically-Active Natural Compounds

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ISBN: 9783039211043 / 9783039211050 Year: Pages: 158 DOI: 10.3390/books978-3-03921-105-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Therapeutics
Added to DOAB on : 2019-08-28 11:21:27
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A major source of active compounds, natural products from different sources supply a large variety of molecules that have been approved for clinical use or used as the starting points of optimization programs. This book features nine papers (eight full articles and one review paper) written by more than 45 scientists from around the world. These papers illustrate the development and application of a broad range of computational and experimental techniques applied to natural product research. On behalf of the contributors to the book, our hope is that the research presented here contributes to advancements in the field, and encourages multidisciplinary teams, young scientists, and students to further advance in the discovery of pharmacologically-active natural compounds

Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions

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ISBN: 9783038979920 / 9783038979937 Year: Pages: 212 DOI: 10.3390/books978-3-03897-993-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Therapeutics
Added to DOAB on : 2019-06-26 08:44:06
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Oral health is general health. If the oral cavity is kept healthy, the whole body is always healthy. Bacteria in the oral cavity do not stay in the oral cavity, but rather they travel throughout the body and can induce various diseases. Periodontal pathogens are involved in tooth loss. The number of remaining teeth decreases with age. People with more residual teeth can bite food well and live longer with lower incidence of dementia. There are many viruses in the oral cavity that also cause various diseases. Bacteria and viruses induce and aggravate inflammation, and therefore should be removed from the oral cavity. In the natural world, there are are many as yet undiscovered antiviral, antibacterial and anti-inflammatory substances. These natural substances, as well as chemically modified derivatives, help our oral health and lead us to more fulfilling, high quality lives. This Special Issue, entitled “Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions”, was written by specialists from a diverse variety of fields. It serves to provide readers with up-to-date information on incidence rates in each age group, etiology and treatment of stomatitis, and to investigate the application of such treatments as oral care and cosmetic materials.

Keywords

metabolomics --- oral cell --- benzaldehyde --- eugenol --- inflammation --- cytotoxicity --- stomatitis --- recurrent aphthous stomatitis --- oral lichen planus --- CCN2 --- glucocorticoids --- alkaloids --- anti-human immunodeficiency virus (HIV) --- antiviral --- natural product --- human virus --- inflammatory disease --- stomatitis --- periodontitis --- anti-osteoclast activity --- cepharanthin --- herbal medicine --- natural product --- arachidonic acid cascade --- allergic rhinitis --- mice --- quercetin --- thioredoxin --- nasal epithelial cell --- production --- increase --- in vitro --- in vivo --- nutritionally variant streptococci --- antimicrobial susceptibilities --- oral microbiota --- infective endocarditis --- kampo formula --- traditional Japanese herbal medicine --- stomatitis --- mucositis --- Hangeshashinto --- polyphenol --- chromone --- lignin-carbohydrate complex --- alkaline extract --- Kampo medicine --- glucosyltransferase --- angiotensin II blocker --- QSAR analysis --- oral diseases --- dental application --- Chinese herbal remedies --- stomatitis --- periodontitis --- Kampo --- traditional medicine --- Jixueteng --- Juzentaihoto --- technical terms --- gargle --- tongue diagnosis --- mastic --- pathogenic factors --- quantitative structure-activity relationship --- machine learning --- random forest --- natural products --- tumour-specificity --- Kampo medicine --- constituent plant extract --- stomatitis --- oral inflammation --- quantitative structure-activity relationship (QSAR) analysis --- metabolomics

Empirical Finance

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ISBN: 9783038977063 Year: Pages: 276 DOI: 10.3390/books978-3-03897-707-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2019-04-05 10:34:31
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There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.

Keywords

text similarity --- text mining --- machine learning --- SVM --- neural network --- LSTM --- credit risk --- ensemble learning --- deep learning --- bagging --- random forest --- boosting --- deep neural network --- causality-in-variance --- cross-correlation function --- housing and stock markets --- algorithmic trading --- take profit --- stop loss --- MACD --- ATR --- city banks --- dependence structure --- copula --- n/a --- market microstructure --- price discovery --- latency --- currency crisis --- random forests --- wavelet transform --- predictive accuracy --- housing price --- bank credit --- housing loans --- real estate development loans --- TVP-VAR model --- exchange rate --- volatility --- exports --- ARDL --- Vietnam --- crude oil futures prices forecasting --- convolutional neural networks --- short-term forecasting --- utility of international currency --- inertia --- liquidity risk premium --- US dollar --- Japanese yen --- cointegration --- statistical arbitrage --- natural gas --- wholesale electricity --- futures market --- spark spread --- earnings management --- earnings manipulation --- earnings quality --- initial public offering --- IPO --- asset pricing model --- data mining --- bankruptcy prediction --- financial and non-financial variables --- institutional investors’ shareholdings --- panel data model --- piecewise regression model --- global financial crisis --- gold return --- asymmetric dependence --- financial market stress --- robust regression --- quantile regression --- structural break --- flight to quality

Marine Geomorphometry

Authors: --- ---
ISBN: 9783038979548 / 9783038979555 Year: Pages: 400 DOI: 10.3390/books978-3-03897-955-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:07
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Geomorphometry is the science of quantitative terrain characterization and analysis, and has traditionally focused on the investigation of terrestrial and planetary landscapes. However, applications of marine geomorphometry have now moved beyond the simple adoption of techniques developed for terrestrial studies, driven by the rise in the acquisition of high-resolution seafloor data and by the availability of user-friendly spatial analytical tools. Considering that the seafloor represents 71% of the surface of our planet, this is an important step towards understanding the Earth in its entirety.This volume is the first one dedicated to marine applications of geomorphometry. It showcases studies addressing the five steps of geomorphometry: sampling a surface (e.g., the seafloor), generating a Digital Terrain Model (DTM) from samples, preprocessing the DTM for subsequent analyses (e.g., correcting for errors and artifacts), deriving terrain attributes and/or extracting terrain features from the DTM, and using and explaining those terrain attributes and features in a given context. Throughout these studies, authors address a range of challenges and issues associated with applying geomorphometric techniques to the complex marine environment, including issues related to spatial scale, data quality, and linking seafloor topography with physical, geological, biological, and ecological processes. As marine geomorphometry becomes increasingly recognized as a sub-discipline of geomorphometry, this volume brings together a collection of research articles that reflect the types of studies that are helping to chart the course for the future of marine geomorphometry.

Keywords

bedforms --- forage fish --- Pacific sand lance --- sediment habitats --- bathymetry --- currents --- seabed mapping --- marine geology --- submarine topography --- marine geomorphology --- terrain analysis --- multibeam echosounder --- bathymetry --- DEM --- satellite imagery --- multi beam echosounder --- filter --- geomorphology --- coral reefs --- Acoustic applications --- object segmentation --- seafloor --- underwater acoustics --- Cretaceous --- Cenomanian–Turonian --- paleobathymetry --- paleoclimate --- paleoceanography --- reconstruction --- simulation --- shelf-slope-rise --- geomorphometry --- GIS --- spatial scale --- spatial analysis --- terrain analysis --- seafloor geomorphometry --- domes --- volcanoes --- digital elevation models (DEMs) --- Canary Basin --- Atlantic Ocean --- cold-water coral --- carbonate mound --- habitat mapping --- spatial prediction --- image segmentation --- geographic object-based image analysis --- random forest --- accuracy --- confidence --- global bathymetry --- Seabed 2030 --- Nippon Foundation/GEBCO --- seafloor mapping technologies --- seafloor mapping standards and protocols --- benthic habitats --- shelf morphology --- eastern Brazilian shelf --- geomorphometry --- terrain analysis --- bathymetry --- surface roughness --- benthic habitat mapping --- python --- geomorphology --- submerged glacial bedforms --- deglaciation --- sedimentation --- multibeam --- acoustic-seismic profiling --- swath geometry --- multibeam spatial resolution --- integration artefacts --- Multibeam bathymetry --- benthic habitat mapping --- multiscale --- Random Forests --- pockmarks --- automated-mapping --- ArcGIS --- Glaciated Margin --- North Sea --- Malin Basin --- Barents Sea --- bathymetry --- thalwegs --- canyons --- Alaska --- Bering Sea --- multibeam sonar --- carbonate banks --- semi-automated mapping --- polychaete --- Northwestern Australia --- Oceanic Shoals Australian Marine Park --- Bonaparte Basin --- Timor Sea --- bathymetry --- digital terrain analysis --- geomorphometry --- geomorphology --- habitat mapping --- marine remote sensing

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-03-08 11:42:05
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This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water

Keywords

data scarce basins --- runoff series --- data forward prediction --- ensemble empirical mode decomposition (EEMD) --- stopping criteria --- method of tracking energy differences (MTED) --- deep learning --- convolutional neural networks --- superpixel --- urban water bodies --- high-resolution remote-sensing images --- monthly streamflow forecasting --- artificial neural network --- ensemble technique --- phase space reconstruction --- empirical wavelet transform --- hybrid neural network --- flood forecasting --- self-organizing map --- bat algorithm --- particle swarm optimization --- flood routing --- Muskingum model --- machine learning methods --- St. Venant equations --- rating curve method --- nonlinear Muskingum model --- hydrograph predictions --- flood routing --- Muskingum model --- hydrologic models --- improved bat algorithm --- Wilson flood --- Karahan flood --- flood susceptibility modeling --- ANFIS --- cultural algorithm --- bees algorithm --- invasive weed optimization --- Haraz watershed --- ANN-based models --- flood inundation map --- self-organizing map (SOM) --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- ensemble technique --- artificial neural networks --- uncertainty --- streamflow predictions --- sensitivity --- flood forecasting --- extreme learning machine (ELM) --- backtracking search optimization algorithm (BSA) --- the upper Yangtze River --- deep learning --- LSTM network --- water level forecast --- the Three Gorges Dam --- Dongting Lake --- Muskingum model --- wolf pack algorithm --- parameters --- optimization --- flood routing --- flash-flood --- precipitation-runoff --- forecasting --- lag analysis --- random forest --- machine learning --- flood prediction --- flood forecasting --- hydrologic model --- rainfall–runoff, hybrid & --- ensemble machine learning --- artificial neural network --- support vector machine --- natural hazards & --- disasters --- adaptive neuro-fuzzy inference system (ANFIS) --- decision tree --- survey --- classification and regression trees (CART), data science --- big data --- artificial intelligence --- soft computing --- extreme event management --- time series prediction --- LSTM --- rainfall-runoff --- flood events --- flood forecasting --- data assimilation --- particle filter algorithm --- micro-model --- Lower Yellow River --- ANN --- hydrometeorology --- flood forecasting --- real-time --- postprocessing --- machine learning --- early flood warning systems --- hydroinformatics --- database --- flood forecast --- Google Maps

Entropy in Image Analysis

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ISBN: 9783039210923 / 9783039210930 Year: Pages: 456 DOI: 10.3390/books978-3-03921-093-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.

Keywords

image retrieval --- multi-feature fusion --- entropy --- relevance feedback --- chaotic system --- image encryption --- permutation-diffusion --- SHA-256 hash value --- dynamic index --- entropy --- keyframes --- Shannon’s entropy --- sign languages --- video summarization --- video skimming --- image encryption --- multiple-image encryption --- two-dimensional chaotic economic map --- security analysis --- image encryption --- chaotic cryptography --- cryptanalysis --- chosen-plaintext attack --- image information entropy --- blind image quality assessment (BIQA) --- information entropy, natural scene statistics (NSS) --- Weibull statistics --- discrete cosine transform (DCT) --- ultrasound --- hepatic steatosis --- Shannon entropy --- fatty liver --- metabolic syndrome --- multi-exposure image fusion --- texture information entropy --- adaptive selection --- patch structure decomposition --- image encryption --- time-delay --- random insertion --- information entropy --- chaotic map --- uncertainty assessment --- deep neural network --- random forest --- Shannon entropy --- positron emission tomography --- reconstruction --- field of experts --- additive manufacturing --- 3D prints --- 3D scanning --- image entropy --- depth maps --- surface quality assessment --- machine vision --- image analysis --- Arimoto entropy --- free-form deformations --- normalized divergence measure --- gradient distributions --- nonextensive entropy --- non-rigid registration --- pavement --- macrotexture --- 3-D digital imaging --- entropy --- decay trend --- discrete entropy --- infrared images --- low contrast --- multiscale top-hat transform --- image encryption --- DNA encoding --- chaotic cryptography --- cryptanalysis --- image privacy --- computer aided diagnostics --- colonoscopy --- Rényi entropies --- structural entropy --- spatial filling factor --- binary image --- Cantor set --- Hénon map --- Minkowski island --- prime-indexed primes --- Ramanujan primes --- Kapur’s entropy --- color image segmentation --- whale optimization algorithm --- differential evolution --- hybrid algorithm --- Otsu method --- image encryption --- dynamic filtering --- DNA computing --- 3D Latin cube --- permutation --- diffusion --- fuzzy entropy --- electromagnetic field optimization --- chaotic strategy --- color image segmentation --- multilevel thresholding --- contrast enhancement --- sigmoid --- Tsallis statistics --- q-exponential --- q-sigmoid --- q-Gaussian --- ultra-sound images --- person re-identification --- image analysis --- hash layer --- quantization loss --- Hamming distance --- cross-entropy loss --- image entropy --- Shannon entropy --- generalized entropies --- image processing --- image segmentation --- medical imaging --- remote sensing --- security

Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li

Authors: --- ---
ISBN: 9783038972709 Year: Volume: 1 Pages: 404 DOI: 10.3390/books978-3-03897-271-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-03-08 11:42:05
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Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing.

Keywords

evapotranspiration --- Northeast China --- MS–PT algorithm --- spatial-temporal variations --- controlling factors --- potential evapotranspiration --- vegetation remote sensing --- reflectance model --- spectra --- leaf --- copper --- PROSPECT --- leaf area density --- terrestrial LiDAR --- tree canopy --- vertical structure --- voxel --- spatial representativeness --- heterogeneity --- validation --- land-surface temperature products (LSTs) --- observations --- HiWATER --- remote sensing --- spatiotemporal representative --- cost-efficient, sampling design --- heterogeneity --- validation --- FY-3C/MERSI --- GLASS --- Land surface temperature --- Land surface emissivity --- GPP --- SIF --- MuSyQ-GPP algorithm --- BEPS --- vegetation phenology --- Tibetan Plateau --- MODIS --- NDVI --- start of growing season (SOS) --- end of growing season (EOS) --- GLASS LAI time series --- forest disturbance --- disturbance index --- latent heat --- machine learning algorithms --- plant functional type --- high-resolution freeze/thaw --- AMSR2 --- MODIS --- LAI --- ZY-3 MUX --- GF-1 WFV --- HJ-1 CCD --- maize --- PROSPECT-5B+SAILH (PROSAIL) model --- spatial heterogeneity --- variability --- evapotranspiration --- land surface variables --- probability density function --- HiWATER --- spectral --- albedometer --- interference filter --- photoelectric detector --- validation --- land surface albedo --- multi-scale validation --- rugged terrain --- MRT-based model --- MCD43A3 C6 --- precipitation --- statistics methods --- China --- Tibetan Plateau --- South China’s --- drought --- SPI --- TMI data --- crop-growing regions --- downward shortwave radiation --- machine learning --- gradient boosting regression tree --- AVHRR --- CMA --- BRDF --- aerosol --- MODIS --- sunphotometer --- arid/semiarid --- solar-induced chlorophyll fluorescence --- fluorescence quantum efficiency in dark-adapted conditions (FQE) --- SCOPE --- Fraunhofer Line Discrimination (FLD) --- gross primary productivity (GPP) --- longwave upwelling radiation (LWUP) --- Visible Infrared Imaging Radiometer Suite (VIIRS) --- surface radiation budget --- hybrid method --- remote sensing --- leaf age --- leaf spectral properties --- leaf area index --- Cunninghamia --- Chinese fir --- canopy reflectance --- NIR --- EVI2 --- geometric optical radiative transfer (GORT) model --- land surface albedo --- snow-free albedo --- rugged terrain --- topographic effects --- black-sky albedo (BSA) --- GPP --- NPP --- MODIS --- validation --- phenology --- RADARSAT-2 --- rice --- Synthetic Aperture Radar (SAR) --- decision tree --- forest canopy height --- aboveground biomass --- ICESat GLAS --- Landsat --- random forest model --- anisotropic reflectance --- BRDF --- rugged terrain --- solo slope --- composite slope --- surface solar irradiance --- geostationary satellite --- polar orbiting satellite --- LUT method --- SURFRAD --- downward shortwave radiation --- daily average value --- Antarctica --- sinusoidal method --- cloud fraction --- interpolation --- boreal forest --- GPP --- spatiotemporal distribution and variation --- meteorological factors --- phenological parameters --- multisource data fusion --- aerosol retrieval --- urban scale --- vegetation dust-retention --- multiple ecological factors --- geographical detector model --- snow cover --- passive microwave --- FY-3C/MWRI --- algorithmic assessment --- China --- land surface temperature --- satellite observations --- flux measurements --- latitudinal pattern --- land cover change --- fractional vegetation cover (FVC) --- multi-data set --- northern China --- spatio-temporal --- inter-annual variation --- uncertainty --- standard error of the mean --- downscaling --- GPP --- spatial heterogeneity --- remote sensing --- subpixel information --- LiDAR --- point cloud --- leaf --- gap fraction --- 3D reconstruction --- biodiversity --- remote sensing --- species richness --- metric comparison --- metric integration --- leaf area index --- MODIS products --- Landsat --- high resolution --- homogeneous and pure pixel filter --- pixel unmixing --- vertical vegetation stratification --- gross primary production (GPP) --- light use efficiency --- dense forest --- MODIS --- VPM --- temperature profiles --- humidity profiles --- n/a --- geometric-optical model --- thermal radiation directionality --- quantitative remote sensing inversion --- scale effects --- comprehensive field experiment

Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li

Authors: --- ---
ISBN: 9783038972761 Year: Volume: 2 Pages: 404 DOI: 10.3390/books978-3-03897-277-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-03-08 11:42:05
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Abstract

Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing.

Keywords

evapotranspiration --- Northeast China --- MS–PT algorithm --- spatial-temporal variations --- controlling factors --- potential evapotranspiration --- vegetation remote sensing --- reflectance model --- spectra --- leaf --- copper --- PROSPECT --- leaf area density --- terrestrial LiDAR --- tree canopy --- vertical structure --- voxel --- spatial representativeness --- heterogeneity --- validation --- land-surface temperature products (LSTs) --- observations --- HiWATER --- remote sensing --- spatiotemporal representative --- cost-efficient, sampling design --- heterogeneity --- validation --- FY-3C/MERSI --- GLASS --- Land surface temperature --- Land surface emissivity --- GPP --- SIF --- MuSyQ-GPP algorithm --- BEPS --- vegetation phenology --- Tibetan Plateau --- MODIS --- NDVI --- start of growing season (SOS) --- end of growing season (EOS) --- GLASS LAI time series --- forest disturbance --- disturbance index --- latent heat --- machine learning algorithms --- plant functional type --- high-resolution freeze/thaw --- AMSR2 --- MODIS --- LAI --- ZY-3 MUX --- GF-1 WFV --- HJ-1 CCD --- maize --- PROSPECT-5B+SAILH (PROSAIL) model --- spatial heterogeneity --- variability --- evapotranspiration --- land surface variables --- probability density function --- HiWATER --- spectral --- albedometer --- interference filter --- photoelectric detector --- validation --- land surface albedo --- multi-scale validation --- rugged terrain --- MRT-based model --- MCD43A3 C6 --- precipitation --- statistics methods --- China --- Tibetan Plateau --- South China’s --- drought --- SPI --- TMI data --- crop-growing regions --- downward shortwave radiation --- machine learning --- gradient boosting regression tree --- AVHRR --- CMA --- BRDF --- aerosol --- MODIS --- sunphotometer --- arid/semiarid --- solar-induced chlorophyll fluorescence --- fluorescence quantum efficiency in dark-adapted conditions (FQE) --- SCOPE --- Fraunhofer Line Discrimination (FLD) --- gross primary productivity (GPP) --- longwave upwelling radiation (LWUP) --- Visible Infrared Imaging Radiometer Suite (VIIRS) --- surface radiation budget --- hybrid method --- remote sensing --- leaf age --- leaf spectral properties --- leaf area index --- Cunninghamia --- Chinese fir --- canopy reflectance --- NIR --- EVI2 --- geometric optical radiative transfer (GORT) model --- land surface albedo --- snow-free albedo --- rugged terrain --- topographic effects --- black-sky albedo (BSA) --- GPP --- NPP --- MODIS --- validation --- phenology --- RADARSAT-2 --- rice --- Synthetic Aperture Radar (SAR) --- decision tree --- forest canopy height --- aboveground biomass --- ICESat GLAS --- Landsat --- random forest model --- anisotropic reflectance --- BRDF --- rugged terrain --- solo slope --- composite slope --- surface solar irradiance --- geostationary satellite --- polar orbiting satellite --- LUT method --- SURFRAD --- downward shortwave radiation --- daily average value --- Antarctica --- sinusoidal method --- cloud fraction --- interpolation --- boreal forest --- GPP --- spatiotemporal distribution and variation --- meteorological factors --- phenological parameters --- multisource data fusion --- aerosol retrieval --- urban scale --- vegetation dust-retention --- multiple ecological factors --- geographical detector model --- snow cover --- passive microwave --- FY-3C/MWRI --- algorithmic assessment --- China --- land surface temperature --- satellite observations --- flux measurements --- latitudinal pattern --- land cover change --- fractional vegetation cover (FVC) --- multi-data set --- northern China --- spatio-temporal --- inter-annual variation --- uncertainty --- standard error of the mean --- downscaling --- GPP --- spatial heterogeneity --- remote sensing --- subpixel information --- LiDAR --- point cloud --- leaf --- gap fraction --- 3D reconstruction --- biodiversity --- remote sensing --- species richness --- metric comparison --- metric integration --- leaf area index --- MODIS products --- Landsat --- high resolution --- homogeneous and pure pixel filter --- pixel unmixing --- vertical vegetation stratification --- gross primary production (GPP) --- light use efficiency --- dense forest --- MODIS --- VPM --- temperature profiles --- humidity profiles --- n/a --- geometric-optical model --- thermal radiation directionality --- quantitative remote sensing inversion --- scale effects --- comprehensive field experiment

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