Search results: Found 13

Listing 1 - 10 of 13 << page
of 2
>>
Sort by
Image Processing in Agriculture and Forestry

Authors: ---
ISBN: 9783038970972 9783038970989 Year: Pages: 222 DOI: 10.3390/books978-3-03897-098-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Mechanical Engineering
Added to DOAB on : 2018-09-27 09:15:10
License:

Loading...
Export citation

Choose an application

Abstract

Image processing in agriculture and forestry represents a challenge towards the automation of tasks for better performances. Agronomists, computer and robotics engineers, and agricultural machinery industry manufacturers now have at their disposal a book containing a collection of methods, procedures, designs, and descriptions at the technological forefront, which serves as an important support and aid for the implementation and development of their own ideas.The book describes: (1) Applications (canopy on trees, aboveground biomass, phenotyping, chlorophyll, leaf area index, water and nutrient content, land cover change, soil properties, and secure autonomous navigation); (2) Imaging devices onboard robots, unmanned aerial vehicles (UAVs), and satellites operating at different spectral ranges (visible, infrared, hyper-multispectral bands, and radar), as well as guidelines for selecting machine vision systems in outdoor environments; and (3) (Specific computer vision methods (generic and convolutional neural networks, machine learning, specific segmentation approaches, vegetation indices, and three-dimensional (3D) reconstruction).

Recent Advances in Remote Sensing for Crop Growth Monitoring

ISBN: 9783038422266 9783038422273 Year: Pages: XX, 386 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2016-08-19 08:32:15
License:

Loading...
Export citation

Choose an application

Abstract

This special issue book gathers sixteen papers focusing on the application of remote sensing techniques to crop growth monitoring. The studies feature multi-scale and multi-source remotely sensed data, a combination of empirical and physical approaches, and a range of topics on crop growth parameters estimation and crop mapping. It is recommended to graduate students, professors, scientists and engineers who have broad interests in the agricultural applications of remote sensing.

Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

Authors: --- ---
ISBN: 9783039212392 9783039212408 Year: Pages: 334 DOI: 10.3390/books978-3-03921-240-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:15
License:

Loading...
Export citation

Choose an application

Abstract

Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

Keywords

conifer forest --- leaf area index --- smartphone-based method --- canopy gap fraction --- terrestrial laser scanning --- forest inventory --- density-based clustering --- forest aboveground biomass --- root biomass --- tree heights --- GLAS --- artificial neural network --- allometric scaling and resource limitation --- structure from motion (SfM) --- 3D point cloud --- remote sensing --- local maxima --- fixed tree window size --- managed temperate coniferous forests --- point cloud --- spectral information --- structure from motion (SfM) --- unmanned aerial vehicle (UAV) --- chlorophyll fluorescence (ChlF) --- drought --- Mediterranean --- photochemical reflectance index (PRI) --- photosynthesis --- R690/R630 --- recovery --- BAAPA --- remote sensing --- household survey --- forest --- farm types --- automated classification --- sampling design --- adaptive threshold --- over and understory cover --- LAI --- leaf area index --- EPIC --- simulation --- satellite --- MODIS --- biomass --- evaluation --- southern U.S. forests --- VIIRS --- leaf area index (LAI) --- Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) --- MODIS --- consistency --- uncertainty --- evaluation --- downscaling --- Pléiades imagery --- unmanned aerial vehicle --- stem volume estimation --- remote sensing --- clumping index --- leaf area index --- trunk --- terrestrial LiDAR --- HemiView --- forest above ground biomass (AGB) --- polarization coherence tomography (PCT) --- P-band PolInSAR --- tomographic profiles --- canopy closure --- global positioning system --- hemispherical sky-oriented photo --- signal attenuation --- geographic information system --- digital aerial photograph --- aboveground biomass --- leaf area index --- photogrammetric point cloud --- recursive feature elimination --- machine-learning --- forest degradation --- multisource remote sensing --- modelling aboveground biomass --- random forest --- Brazilian Amazon --- validation --- phenology --- NDVI --- LAI --- spectral analyses --- European beech --- altitude --- forests biomass --- remote sensing --- REDD+ --- random forest --- Tanzania --- RapidEye

Short Rotation Woody Crop Production Systems for Ecosystem Services and Phytotechnologies

Authors: --- --- ---
ISBN: 9783039215096 9783039215102 Year: Pages: 316 DOI: 10.3390/books978-3-03921-510-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Forestry
Added to DOAB on : 2020-01-07 09:08:26
License:

Loading...
Export citation

Choose an application

Abstract

While international efforts in the development of short rotation woody crops (SRWCs) have historically focused on the production of biomass for bioenergy, biofuels, and bioproducts, research and deployment over the past decade has expanded to include broader objectives of achieving multiple ecosystem services. In particular, silvicultural prescriptions developed for SRWCs have been refined to include woody crop production systems for environmental benefits such as carbon sequestration, water quality and quantity, and soil health. In addition, current systems have been expanded beyond traditional fiber production to other environmental technologies that incorporate SRWCs as vital components for phytotechnologies, urban afforestation, ecological restoration, and mine reclamation. In this Special Issue of the journal Forests, we explore the broad range of current research dedicated to our topic: International Short Rotation Woody Crop Production Systems for Ecosystem Services and Phytotechnologies

Remote Sensing Technology Applications in Forestry and REDD+

Authors: --- --- ---
ISBN: 9783039284702 9783039284719 Year: Pages: 244 DOI: 10.3390/books978-3-03928-471-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2020-04-07 23:07:09
License:

Loading...
Export citation

Choose an application

Abstract

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion.

Keywords

sentinel imagery --- above-ground biomass --- predictive mapping --- machine learning --- geographically weighted regression --- canopy cover (CC) --- spectral --- texture --- digital hemispherical photograph (DHP) --- random forest (RF) --- gray level co-occurrence matrix (GLCM) --- forest inventory --- LiDAR --- tall trees --- overstory trees --- tree mapping --- crown delineation --- aboveground biomass --- Landsat --- random forest --- topography --- human activity --- aboveground biomass estimation --- remote sensing --- crown density --- low-accuracy estimation --- model comparison --- old-growth forest --- multispectral satellite imagery --- random forest --- forest classification --- remote sensing --- forestry --- phenology --- silviculture --- forest growing stock volume (GSV) --- full polarimetric SAR --- subtropical forest --- topographic effects --- environment effects --- geographic information system --- support vector machine --- random forest --- ensemble model --- hazard mapping --- 3D tree modelling --- aboveground biomass estimation --- destructive sampling --- Guyana --- LiDAR --- local tree allometry --- model evaluation --- quantitative structural model --- Pinus massoniana --- specific leaf area --- leaf area --- terrestrial laser scanning --- voxelization --- forest canopy --- REDD+ --- Cameroon --- reference level --- deforestation --- agriculture --- forest baseline --- airborne laser scanning --- terrestrial laser scanning --- remote sensing --- REDD+ --- forestry

Remote Sensing Applications for Agriculture and Crop Modelling

Author:
ISBN: 9783039282265 9783039282272 Year: Pages: 308 DOI: 10.3390/books978-3-03928-227-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2020-04-07 23:07:08
License:

Loading...
Export citation

Choose an application

Abstract

Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling,

Keywords

crop residue management --- remote sensing --- satellite images --- hyperspectral sensor --- vegetation index --- yield monitoring --- remote sensing --- proximal sensing --- crop modeling --- soil --- plant --- management zone --- spatial variability --- temporal variability --- precision agriculture --- Á Trous algorithm --- conservation agriculture --- crop inventory --- remote sensing --- spectral-weight variations in fused images --- soil stoichiometry --- land use change --- soil organic carbon --- nitrogen --- Tarim Basin --- SPAD --- leaf nitrogen concentration --- nitrogen nutrition index --- grain yield --- dynamic model --- wheat --- disease --- yield --- septoria tritici blotch --- leaf area index --- crop modelling --- decision support system for agrotechnology transfer (DSSAT) --- Cropsim-CERES Wheat --- sorghum biomass --- prediction modeling --- machine learning --- fAPAR --- Sentinel-2 satellite imagery --- big data technology --- remote sensing --- UAV --- vegetation indices --- relative frequencies --- yield --- precision agriculture --- cultivars --- crop growth model --- data assimilation --- Leaf Area Index --- Sentinel-2 --- EPIC model --- yield estimation --- NDVI --- remote sensing --- GIS --- precision farming --- variable rate technology --- yield mapping --- protein content --- wheat --- canopy temperature depression --- NDVI --- RGB images --- grain yield --- ?13C --- UAV chemical application --- droplet drift --- flat-fan atomizer --- simulation analysis --- control variables --- agricultural land-cover --- multi-spectral --- generalized model --- machine learning --- crop type mapping --- Integrated Administration and Control System --- remote sensing --- hydroponic --- vegetable monitoring --- crop production --- spectral simulation --- hyperspectral data --- n/a --- fractional cover --- irrigation --- satellite --- crop simulation model --- AquaCrop --- yield mapping --- remote sensing --- durum wheat --- precision agriculture --- large cardamom --- remote sensing --- species modelling --- habitat assessment --- climate change

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: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-03-08 11:42:05
License:

Loading...
Export citation

Choose an application

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: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-03-08 11:42:05
License:

Loading...
Export citation

Choose an application

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

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Authors: ---
ISBN: 9783039212156 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
License:

Loading...
Export citation

Choose an application

Abstract

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.

Keywords

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

Viticulture and Winemaking under Climate Change

Author:
ISBN: 9783039219742 9783039219759 Year: Pages: 294 DOI: 10.3390/books978-3-03921-975-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Meteorology and Climatology
Added to DOAB on : 2020-01-07 09:08:26
License:

Loading...
Export citation

Choose an application

Abstract

The importance of viticulture and the winemaking socio-economic sector is acknowledged worldwide. The most renowned winemaking regions show very specific environmental characteristics, where climate usually plays a central role. Considering the strong influence of weather and climatic factors on grapevine yields and berry quality attributes, climate change may indeed significantly impact this crop. Recent trends already point to a pronounced increase in growing season mean temperatures, as well as changes in precipitation regimes, which have been influencing wine typicity across some of the most renowned winemaking regions worldwide. Moreover, several climate scenarios give evidence of enhanced stress conditions for grapevine growth until the end of the century. Although grapevines have high resilience, the clear evidence for significant climate change in the upcoming decades urges adaptation and mitigation measures to be taken by sector stakeholders. To provide hints on the abovementioned issues, we have edited a Special Issue entitled “Viticulture and Winemaking under Climate Change”. Contributions from different fields were considered, including crop and climate modeling, and potential adaptation measures against these threats. The current Special Issue allows for the expansion of scientific knowledge in these particular fields of research, as well as providing a path for future research.

Keywords

viticulture --- crop model --- phenology --- physiological processes --- climate --- micrometeorology --- microclimate --- climate change --- water limitation --- dry mass partitioning --- assimilation --- intercellular CO2 --- stomatal conductance --- leaf water potential --- Vitis vinifera L. --- production system --- S-ABA --- rate of anthocyanin accumulation --- CIRG --- bioactive compounds --- Botrytis cinerea --- low-input --- mechanical thinning --- viticultural training system --- yield formation --- leaf area --- table grapes --- photosynthesis --- berry composition --- phenolics --- natural hail --- grapevine --- phenology --- phenology modelling platform --- Touriga Franca --- Touriga Nacional --- climate change --- RCP4.5 --- EURO-CORDEX --- Douro wine region --- Portugal --- global warming --- technological and phenolic ripeness --- grape --- wine --- sensory analysis --- climate change --- elevated CO2 --- grapevine pest --- mealybug --- parasitoid --- FACE --- predawn water potential --- PRI --- remote sensing --- vineyards --- water status --- WI --- climate change --- Vitis vinifera L. --- general circulation model --- EURO-CORDEX --- phenological model --- grapevine --- Virtual Riesling --- climate change --- temperature --- plant architecture --- crop management --- modelling --- climate change --- viticulture --- adaptation --- temperature --- drought --- plant material --- rootstock --- training system --- phenology --- modeling --- Vitis vinifera --- autochthonous cultivar --- ’Uva Rey’ --- unmanned aerial vehicles --- vigour maps --- spatial variability --- normalized difference vegetation index --- crop water stress index --- crop surface model --- precision viticulture --- climate change --- multi-temporal analysis --- Vitis vinifera (L.) --- SO2 pads --- B. cinerea mold --- grape quality --- light micro-climates --- mitigation strategies --- kaolin --- irrigation --- Vitis vinifera L. --- grape berry tissues --- pulse amplitude modulated (PAM) fluorometry --- photosynthesis --- photosynthetic pigments --- viticulture --- winemaking --- climatic influence --- climate change --- adaptation measures

Listing 1 - 10 of 13 << page
of 2
>>
Sort by
Narrow your search

Publisher

MDPI - Multidisciplinary Digital Publishing Institute (13)


License

CC by-nc-nd (13)


Language

english (13)


Year
From To Submit

2020 (2)

2019 (9)

2018 (1)

2016 (1)