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Image Processing in Agriculture and Forestry

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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
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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).

Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers

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ISBN: 9783039212057 9783039212064 Year: Pages: 132 DOI: 10.3390/books978-3-03921-206-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Environmental Sciences
Added to DOAB on : 2019-12-09 16:10:12
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In recent decades, there has been an increase in the development of strategies for water ecosystem mapping and monitoring. Overall, this is primarily due to legislative efforts to improve the quality of water bodies and oceans. Remote sensing has played a key role in the development of such approaches—from the use of drones for vegetation mapping to autonomous vessels for water quality monitoring. Within the specific context of vegetation characterization, the wide range of available observations—from satellite imagery to high-resolution drone aerial imagery—has enabled the development of monitoring and mapping strategies at multiple scales (e.g., micro- and mesoscales). This Special Issue, entitled “Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers”, collates recent advances in remote sensing-based methods applied to ocean, river, and lake vegetation characterization, including seaweed, kelp, submerged and emergent vegetation, and floating-leaf and free-floating plants. A total of six manuscripts have been compiled in this Special Issue, ranging from area mapping substrates in riverine environments to the identification of macroalgae in marine environments. The work presented leverages current state-of-the-art methods for aquatic vegetation monitoring and will spark further research within this field.

Carbon Capture and Storage

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ISBN: 9783039213993 9783039214006 Year: Pages: 178 DOI: 10.3390/books978-3-03921-400-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Climate change is one of the main threats to modern society. This phenomenon is associated with an increase in greenhouse gas (GHGs, mainly carbon dioxide—CO2) emissions due to anthropogenic activities. The main causes are the burning of fossil fuels and land use change (deforestation). Climate change impacts are associated with risks to basic needs (health, food security, and clean water), as well as risks to development (jobs, economic growth, and the cost of living). The processes involving CO2 capture and storage are gaining attention in the scientific community as an alternative for decreasing CO2 emissions, reducing its concentration in ambient air. The carbon capture and storage (CCS) methodologies comprise three steps: CO2 capture, CO2 transportation, and CO2 storage. Despite the high research activity within this topic, several technological, economic, and environmental issues as well as safety problems remain to be solved, such as the following needs: increase of CO2 capture efficiency, reduction of process costs, and verification of the environmental sustainability of CO2 storage.

Remote Sensing of Above Ground Biomass

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ISBN: 9783039212095 9783039212101 Year: Pages: 264 DOI: 10.3390/books978-3-03921-210-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

Keywords

multi-angle remote sensing --- forest structure information --- vegetation indices --- forest biomass --- Bidirectional Reflectance Distribution Factor --- biomass --- yield --- AquaCrop model --- spectral index --- particle swarm optimization --- winter wheat --- TerraSAR-X --- Landsat --- pasture biomass --- Wambiana grazing trial --- foliage projective cover --- fractional vegetation cover --- ALOS2 --- mixed forest --- biomass --- lidar --- NDVI --- grass biomass --- SPLSR --- vegetation indices --- estimation accuracy --- pasture biomass --- ground-based remote sensing --- ultrasonic sensor --- field spectrometry --- sensor fusion --- short grass --- alpine grassland conservation --- anthropogenic disturbance --- ecological policies --- climate change --- grazing exclusion --- grazing management --- regional sustainability --- rice --- biomass --- dry matter index --- chlorophyll index --- CIRed-edge --- NDLMA --- forest above ground biomass (AGB) --- random forest --- mapping --- alpine meadow grassland --- above-ground biomass --- inversion model --- error analysis --- applicability evaluation --- Land Surface Phenology --- wetlands --- above ground biomass --- NDVI --- MODIS time series --- food security --- Sahel --- Niger --- rangeland productivity --- livestock --- MODIS --- NDVI --- aboveground biomass --- Atriplex nummularia --- carbon mitigation --- carbon inventory --- forage crops --- remote sensing --- vegetation index --- stem volume --- dry biomass --- conifer --- broadleaves --- light detection and ranging (LiDAR) --- regression analysis --- correlation coefficient --- n/a

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

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ISBN: 9783038978206 9783038978213 Year: Pages: 224 DOI: 10.3390/books978-3-03897-821-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Geography --- Science (General)
Added to DOAB on : 2019-12-09 11:49:16
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Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices.

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

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ISBN: 9783039217823 9783039217830 Year: Pages: 188 DOI: 10.3390/books978-3-03921-783-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Ecology
Added to DOAB on : 2019-12-09 11:49:16
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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.

Google Earth Engine Applications

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ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: English
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

Viticulture and Winemaking under Climate Change

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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
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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

Remote Sensing Applications for Agriculture and Crop Modelling

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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
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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

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