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

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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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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

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

Google Earth Engine Applications

Authors: ---
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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Abstract

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

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eng (3)


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2019 (3)