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Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed.

normalized difference vegetation index (NDVI) --- SRTMGL1 --- SPOT-6 --- urban ecology --- terrestrial laser scanner --- Lantana camara --- terrestrial laser scanning --- harvester --- product recovery --- imputation --- optimization --- multi-spectral --- function --- ZiYuan-3 stereo images --- spatial noise --- 3D remote sensing --- tree measurement --- diameter at breast height (DBH) --- DSM --- metabolic scale theory --- municipal forestry --- digital photogrammetry --- Norway spruce --- missing observations --- interrater agreement --- measurement error --- stump height --- Fractional cover analysis --- google earth engine --- high-voltage power transmission lines --- habitat fragmentation --- codispersion coefficient --- forest fire --- tree height --- nu SVR --- RapidEye --- uneven-aged mountainous --- random Hough transform --- kriging --- street trees --- ground validation --- Google Street View --- laser --- species identification --- composition --- maximum forest heights --- mountainous areas --- landscape fragmentation --- Landsat 8 --- forest canopy height --- allometric scaling and resource limitation model --- urban forestry --- point cloud --- GSV --- stump diameter --- structure --- 3D --- codispersion map --- forest ecology --- polarimetery --- crowdsourced data

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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

artificial neural network --- n/a --- model switching --- sensitivity analysis --- neural networks --- logit boost --- Qaidam Basin --- land subsidence --- land use/land cover (LULC) --- naïve Bayes --- multilayer perceptron --- convolutional neural networks --- single-class data descriptors --- logistic regression --- feature selection --- mapping --- particulate matter 10 (PM10) --- Bayes net --- gray-level co-occurrence matrix --- multi-scale --- Logistic Model Trees --- classification --- Panax notoginseng --- large scene --- coarse particle --- grayscale aerial image --- Gaofen-2 --- environmental variables --- variable selection --- spatial predictive models --- weights of evidence --- landslide prediction --- random forest --- boosted regression tree --- convolutional network --- Vietnam --- model validation --- colorization --- data mining techniques --- spatial predictions --- SCAI --- unmanned aerial vehicle --- high-resolution --- texture --- spatial sparse recovery --- landslide susceptibility map --- machine learning --- reproducible research --- constrained spatial smoothing --- support vector machine --- random forest regression --- model assessment --- information gain --- ALS point cloud --- bagging ensemble --- one-class classifiers --- leaf area index (LAI) --- landslide susceptibility --- landsat image --- ionospheric delay constraints --- spatial spline regression --- remote sensing image segmentation --- panchromatic --- Sentinel-2 --- remote sensing --- optical remote sensing --- materia medica resource --- GIS --- precise weighting --- change detection --- TRMM --- traffic CO --- crop --- training sample size --- convergence time --- object detection --- gully erosion --- deep learning --- classification-based learning --- transfer learning --- landslide --- traffic CO prediction --- hybrid model --- winter wheat spatial distribution --- logistic --- alternating direction method of multipliers --- hybrid structure convolutional neural networks --- geoherb --- predictive accuracy --- real-time precise point positioning --- spectral bands --- naïve Bayes

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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.

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

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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.

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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

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