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Sustainable Territorial Management

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ISBN: 9783038972129 9783038972136 Year: Pages: 224 DOI: 10.3390/books978-3-03897-213-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Environmental Sciences
Added to DOAB on : 2018-09-21 09:54:53
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Human development has made remarkable social and economic progress possible for most of us but has also entailed a range of serious impacts on natural resources, local communities and the economy at multiple scales. Thus, achieving sustainable territorial management that combines healthy and prosperous societies with the long-term maintenance of biodiversity and productive ecosystem services remains the biggest challenge of our modern world. This Special Issue seeks to collect a coherent set of studies on techniques and experiences (case studies) aimed at increasing the environmental, social, economic &/or institutional sustainability of landscapes and seascapes from a range of geographic and socioeconomic contexts. Ten case studies representing urban areas, rural areas (chiefly protected areas) and coastal areas from four countries in Europe and Asia by internationally renowned authors are shown.

Urban Land Systems: An Ecosystems Perspective

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ISBN: 9783038429173 9783038429180 Year: Pages: VIII, 192 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Environmental Sciences
Added to DOAB on : 2018-07-02 12:55:50
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Global urbanization creates challenges towards sustainability and human well-being. Urban areas are dependent on the ecosystems beyond the city limits but also benefit from the internal urban green places. An understanding of the importance of urban ecosystem services means that urban greenery can be designedly maintained or even expanded. As cities are expected to grow at a rapid rate in the coming decades, it is important that the ecosystem perspective is understood and valued by city planners and political decision-makers. This special issue highlights some aspects related to urban sprawl dynamics and urban ecosystem management. Observations and studies presented in ten papers show that urbanization affects essential ecological, economic, and social landscape functions, whose importance is often undervalued in cities worldwide.

The Long-Term Perspective of Human Impact on Landscape for Environmental Change and Sustainability

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ISBN: 9783039217960 9783039217977 Year: Pages: 258 DOI: 10.3390/books978-3-03921-797-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Environmental Sciences
Added to DOAB on : 2019-12-09 11:49:16
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The research studies included in this Special Issue highlight the fundamental contribution of the knowledge of environmental history to conscious and efficient environment conservation and management. The long-term perspective of the dynamics that govern the human–climate ecosystem is becoming one of the main focuses of interest in biological and earth system sciences. Multidisciplinary bio-geo-archaeo investigations into the underlying processes of human impact on the landscape are crucial to envisage possible future scenarios of biosphere responses to global warming and biodiversity losses. This Special Issue seeks to engage an interdisciplinary dialog on the dynamic interactions between nature and society, focusing on long-term environmental data as an essential tool for better-informed landscape management decisions to achieve an equilibrium between conservation and sustainable resource exploitation.

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

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ISBN: 9783038976981 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Managing Forests and Water for People under a Changing Environment

Authors: --- --- ---
ISBN: 9783039288236 / 9783039288243 Year: Pages: 198 DOI: 10.3390/books978-3-03928-824-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Forestry
Added to DOAB on : 2020-06-09 16:38:57
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Forests cover 30% of the Earth’s land area, or nearly four billion hectares. Enhancing the benefits and ecosystem services of forests has been increasingly recognized as an essential part of nature-based solutions for solving many emerging global environmental problems today. A core science supporting forest management is understanding the interactions of forests, water, and people. These interactions have become increasingly complex under climate change and its associated impacts, such as the increases in the intensity and frequency of drought and floods, increasing population and deforestation, and a rise in global demands for multiple ecosystem services including clean water supply and carbon sequestration. Forest watershed managers have recognized that water management is an essential component of forest management. Global environmental change is posing more challenges for managing forests and water toward sustainable development. New science on forest and water is critically needed across the globe. The International Forests and Water Conference 2018, Valdivia, Chile (http://forestsandwater2018.cl/), a joint effort of the 5th IUFRO International Conference on Forests and Water in a Changing Environment and the Second Latin American Conference on Forests and Water provided a unique forum to examine forest and water issues in Latin America under a global context. This book represents a collection of some of the peer-reviewed papers presented at the conference that were published in a Special Issue of Forests.

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: 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
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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

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

Sustainable Built Environment and Urban Growth Management

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ISBN: 9783039281862 9783039281879 Year: Pages: 242 DOI: 10.3390/books978-3-03928-187-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:08
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Nowadays, the sustainable built environment planning in most cities has come to a turning point as the growth in traffic and population has become a serious concern and put tremendous pressure on both the environment and people in these cities. It is therefore important to find new ways or lifestyles—such as compact city, transit-oriented development (TOD) formulations—that are more flexible, inclusive, and sustainable. Furthermore, for the sustainable built environment and urban growth management, not only should the growth management principles—which include smart growth, sustainable growth, and inclusive growth—be taken into account but innovative/smart planning strategies—such as mixed use design, green transport, and new urbanism—are also utilized in planning sustainable built environments in order to prevent the urban sprawl development that has occurred.

Keywords

construction materials --- green supply chain --- integrated carbon policy --- interactive strategy --- low carbon --- CO2 emissions --- transport --- urban block --- urban design --- ecological well-being changes --- rural-urban land conversion --- transformation factors --- urban residents --- rural residents --- China --- renovation extent --- energy retrofitting --- rent affordability --- tenure --- energy performance certificate --- decision support --- embodied environmental impact --- apartment building --- major building material --- life-cycle assessment --- place attachment --- commercial types --- commercial activities --- social bonding --- physical activities --- resilience quantification --- resilience engineering --- multiple threat assessment --- urban form --- Maximum Likelihood Classification --- Support Vector Machines --- Artificial Neural Networks --- significant transitions --- urban growth --- Nayarit (Mexico) --- behavior --- built environment --- green tourism --- intention --- sustainability --- environmentally responsible interior design --- sustainable interior design --- environmental activation of interior elements --- indoor environment quality --- resource use --- energy use --- interior space utilization --- buildings --- sharing --- digitalization --- social performance --- social performance evaluation --- fuzzy analytical hierarchy process --- empirical study --- driving factors --- farmland price-value distortion --- GIS --- price:value ratio --- quantile regression --- spatial spectrum --- green belt --- urban growth --- land cover --- urban living environment --- climate change --- surface temperature --- air quality --- atmospheric concentration --- conservation --- sustainable use --- urban growth management --- sustainable built environment --- quality of life (QoL) --- smart city &amp --- big data

Earth Observation Data Cubes

Authors: --- --- ---
ISBN: 9783039280926 9783039280933 Year: Pages: 302 DOI: 10.3390/books978-3-03928-093-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2020-04-07 23:07:09
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Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10

Keywords

topology based map algebra --- data cubes --- big data --- map algebra --- earth oberservation --- GRASS GIS --- earth observations --- satellite imagery --- R --- data cubes --- Sentinel-2 --- Sentinel-1 --- SAR --- analysis ready data --- ARD --- interoperability --- data cube --- Earth observation --- pyroSAR --- data cube --- image cube --- image data cube --- imagery --- Landsat --- Sentinel --- earth observation --- GIS --- web services --- web application --- analysis --- GIS --- Open Data Cube --- Earth Observations --- interoperability --- visualization --- Sentinel --- Analysis Ready Data --- Sentinel-1 --- Synthetic Aperture Radar --- Data Cube --- dual-polarimetric decomposition --- interferometric coherence --- Digital Earth Australia --- remote sensing --- big Earth data --- big EO data --- information extraction --- semantic enrichment --- time-series --- Open Data Cube --- remote sensing --- geospatial standards --- landsat --- sentinel --- analysis ready data --- dynamic data citation --- subset --- data curation --- persistent identifier --- data provenance --- metadata --- versioning --- query store --- data sharing --- FAIR principles --- big earth data --- sustainable development goals --- swiss DC --- Armenian DC --- Landsat --- sentinel --- analysis ready data --- data discovery --- metadata --- knowledge base --- graph data --- intelligent semantic agents --- data cube --- optical remote sensing --- snow cover --- Gran Paradiso National Park --- climate change --- land cover classification --- change --- Digital Earth Australia --- open data cube --- Landsat --- Australia --- Open Data Cube --- UN 2030 Agenda for Sustainable Development --- UN System of Environmental Economic Accounting --- Earth observation data --- open science --- reproducibility --- earth observations --- data cube --- analysis ready data --- remote sensing --- satellite imagery

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