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Drones for Biodiversity Conservation and Ecological Monitoring

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ISBN: 9783039219803 / 9783039219810 Year: Pages: 176 DOI: 10.3390/books978-3-03921-981-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2020-01-07 09:08:26
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Unmanned aerial vehicles (UAV) have already become an affordable and cost-efficient tool to quickly map a targeted area for many emerging applications in the arena of ecological monitoring and biodiversity conservation. Managers, owners, companies, and scientists are using professional drones equipped with high-resolution visible, multispectral, or thermal cameras to assess the state of ecosystems, the effect of disturbances, or the dynamics and changes within biological communities inter alia. We are now at a tipping point on the use of drones for these type of applications over natural areas. UAV missions are increasing but most of them are testing applicability. It is time now to move to frequent revisiting missions, aiding in the retrieval of important biophysical parameters in ecosystems or mapping species distributions. This Special Issue shows UAV applications contributing to a better understanding of biodiversity and ecosystem status, threats, changes, and trends. It documents the enhancement of knowledge in ecological integrity parameters mapping, long-term ecological monitoring based on drones, mapping of alien species spread and distribution, upscaling ecological variables from drone to satellite images: methods and approaches, rapid risk and disturbance assessment using drones, mapping albedo with UAVs, wildlife tracking, bird colony and chimpanzee nest mapping, habitat mapping and monitoring, and a review on drones for conservation in protected areas.

Google Earth Engine Applications

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

Keywords

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

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: eng
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.

Earth Observation, Remote Sensing and Geoscientific Ground Investigations for Archaeological and Heritage Research

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ISBN: 9783039211937 / 9783039211944 Year: Pages: 304 DOI: 10.3390/books978-3-03921-194-4 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-08-28 11:21:27
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This book collects 15 papers written by renowned scholars from across the globe that showcase the forefront research in Earth observation (EO), remote sensing (RS), and geoscientific ground investigations to study archaeological records and cultural heritage.Archaeologists, anthropologists, geographers, remote sensing, and archaeometry experts share their methodologies relying on a wealth of techniques and data including, but not limited to: very high resolution satellite images from optical and radar space-borne sensors, air-borne surveys, geographic information systems (GIS), archaeological fieldwork, and historical maps.A couple of the contributions highlight the value of noninvasive and nondestructive laboratory analyses (e.g., neutron diffraction) to reconstruct ancient manufacturing technologies, and of geological ground investigations to corroborate hypotheses of historical events that shaped cultural landscapes.Case studies encompass famous UNESCO World Heritage Sites (e.g., the Nasca Lines in Peru), remote and yet-to-discover archaeological areas in tropical forests in central America, European countries, south Asian changing landscapes, and environments which are arid nowadays but were probably full of woody vegetation in the past.Finally, the reader can learn about the state-of-the-art of education initiatives to train site managers in the use of space technologies in support of their activities, and can understand the legal aspects involved in the application of EO and RS to address current challenges of African heritage preservation.

Keywords

analytic hierarchy process (AHP) --- archaeology --- predictive model --- tumuli --- remote sensing --- multi-criteria --- Saharan Morocco --- airborne laser scanning --- orthophotographs --- archaeological survey --- field reconnaissance --- Arran --- national archaeological mapping programme --- synthetic aperture radar --- subsurface imaging --- microwave penetration --- archaeology --- arid environments --- remote sensing --- Oman --- e-learning --- Earth observation --- education --- capacity development --- cultural and natural heritage --- UNESCO --- photogrammetry --- RPAS --- UAV --- Peru --- geoglyph Pista --- mapping --- drones --- remote sensing --- free satellite imagery --- GoogleEarth --- Bing Maps --- archaeological fieldwork --- arid environments --- basalt desert --- landscape accessibility --- Harra --- Jordan --- archaeological landscapes --- settlements --- historical maps --- Survey of India --- Archaeological Survey of India --- heritage --- colonial studies --- remote sensing --- historical landscapes --- landscape archaeology --- settlements --- colonial studies --- river morphology --- Indus --- floods --- remote sensing --- satellite --- Sentinel-2 --- surface survey --- Roman archaeology --- Sumerian pottery --- neutron techniques --- neutron diffraction --- chemometric analysis --- Mega El Niño --- pampa of Nazca --- Cuenca Pisco --- Rio Grande de Nazca --- grain-size --- volcaniclastic layer --- stratigraphy --- petrography --- Lidar --- GIS --- Mesoamerica --- Archaeology --- Caves --- Landscape --- Ritual --- Visualization --- Maya --- Belize --- Sacred --- automated detection --- OBIA --- LiDAR --- Difference Map --- field monument --- Burial Mound --- Motte-and-Bailey castle --- Ridge and Furrow --- space law --- disaster and conservation management --- Geographic Information System (GIS) --- international boundaries --- Africa --- Cameroon-Nigeria Mixed Commission --- satellite imagery --- Boundary Demarcation --- international law --- relict boundaries --- Earth Observation --- remote sensing --- optical --- SAR --- drone --- airborne LiDAR --- GIS --- OBIA --- neutron diffraction --- archaeological prospection --- pattern recognition --- archaeometry --- geological mapping

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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ISBN: 9783039212156 / 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: eng
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
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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.

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

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