The third-generation meteorological satellites equipped with highly-improved imagers provide a huge amount of Earth observation data. Himawari-8 is the first unit of the Japan Meteorological Agency’s third generation of geostationary satellites. After its starting operation in 2015, there are several websites that provide remotely sensed images in real time. However, it is hard to develop a real-time

The accurate acquisition of information concerning farmland in coal mining areas with high groundwater levels can provide a basis for land dynamic monitoring and protection. In this study, visible light images from an unmanned aerial vehicle (UAV) were used as the data source, from which farmland located in the coal mining areas with high groundwater levels were extracted. Based on the optimal scale

A methodology to select the maximum level of wavelet decomposition to forecast seven days of daily inflows by a hybrid model wavelet-based artificial neural network (WANN) is proposed. The wavelet decomposition was employed to decompose an input time series into approximation and detail components, and the approximations were used as inputs to artificial neural networks (ANN) for WANN hybrid models

High quality, accessible Digital Elevation Model (DEM) datasets play a major role in monitoring the changes in the Earth’s surface. This study proposes a novel method to increase the vertical precision of CARTOSAT 10 m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods. Machine learning methods such as Genetic Programming (GP) and Artificial

Dynamic monitoring data of the groundwater level form an important basis to understand the current state of groundwater development and utilization, which is required to plan its sustainable exploitation. The dynamic data of groundwater level are typical space-time series data with the characteristics of nonlinear strong space-time correlation. However, because of the influences of human or natural

Physical height changes play an important role in the modernization of national/international levelling networks in the discipline of geodesy. This paper represents a software package, named PHCSOFT (Physical Height Changes SOFTware) which allows to compute and plot physical height changes from Global Geopotential Models (GGMs) based on a monthly interval GRACE (Gravity Recovery and Climate Experiment)

Forests play a vital role in the regulation of climate, absorption of carbon dioxide, hydrological cycle, conservation of water, soil and biodiversity and help mitigate natural disasters. With the help of various remote sensors, high-resolution satellite images are being collected nowadays, which helps in tackling the global challenges of forest mapping in remote areas. Each landscape will grow different

Gullies characterized by frequent material exchange have profoundly affected the loess landforms evolution in the Loess Plateau of China. Accompanying gully development, various types of gullies have shaped the distinctive loess landforms. Thus, the spatial variations of gully morphology map the regional differences of gully development and loess landforms. Based on 6 key test areas with 5 m resolution

Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral

Land use classification is fundamental both for monitoring and predicting regional development patterns and for planning and regulating land use. This research proposed a joint probability-based classifier for land use classification of multispectral remote sensing data and applied it to the Lake Baiyangdian region of North China. This classifier, based on the vine copula method, was suitable for dealing

The huge amount of active research has been focused on developing the remote sensing based applications for providing the object classification procedure focusing on the energy reflected on the earth surface, the remote sensor collects data and its management with analysis of all kinds of spatial information with enhanced accuracy. This paper proposes the Real-time Image processing method to implement

Nomenclature in any science is of fundamental importance. From performance comparison of isometric and modified or hybrid log-ratio transformations of major elements, we present a complete multidimensional classification scheme for altered igneous rocks for High-Mg rock varieties and magma and rock types. This scheme is efficiently applicable through a computer package called Multidimensional Igneous

Due to the recent climate changes and their consequences such as flash floods and droughts, there is a need for Land Use Land Cover mapping to monitor environmental changes which have effects on ecology, policy management, health, and disaster management. It should be noticed that recent droughts caused by climate change, and on the other hand, population growth has increased the rate of urbanization

With the availability of multi-sensor, multi-temporal, multi-resolution and multi-spectral images from operational Earth observation satellites, remote sensing image fusion has become a valuable tool. The goal of remote sensing image fusion is to integrate complementary information from multi-source data such that the new images are more suitable for human visual perception and computer-processing

This paper proposes an eigenvector spatial filtering-based (ESF-based) regression model for land cover pattern simulation in China’s Hubei province. The significance and influence of biophysical, climatic, and socio-economic factors have been detected and analyzed in the study region. The ESF-based multinomial logistic regression (spatial model) is constructed for discrete choices to take spatial autocorrelation

Digital elevation model (DEM) provides information of geographic landscapes at multiple scales. However, the study on the scales of DEM is not sufficient and there still remain many issues in DEM scaling. Generating DEMs of different scales has to consider terrain skeletons and terrain structural details for preserving terrain feature hierarchies. Existing literatures (e.g., maximum Z-tolerance approach)

Generally, traditional convolutional neural networks (CNN) models require a long training time and output high-dimensional features for content-based remote sensing image retrieval (CBRSIR). This paper aims to examine the retrieval performance of the MobileNets model and fine-tune it by changing the dimensions of the final fully connected layer to learn low dimensional representations for CBRSIR. Experimental

Modern Underwater Wireless Sensor Networks (UWSN) would provide big administrations with numerous underwater surveying and technical applications, working in the unstable submerged deep-water conditions. A huge obstacle in these networks is the lifetime limit. The submerged correspondence frameworks mostly employ acoustic communication today. Acoustic interchange communication offers longer ranges

PNN is a feed-forward neural network in which there is an important parameter called smoothing parameter. This work implemented a combination of PNN with PSO optimization in order to estimate unique smoothing parameters for each SWIR bands of ASTER image and classified the ASTER image to different hydrothermal alteration zones (argillic, phyllic, propylitic and vegetation covering). The stydy area

With the development of remote sensing and large-scale environmental modelling, large amount of environmental data are continuously becoming available. An intuitive and comprehensive visualization of these data could facilitate data exploration, communication and collaboration between the stakeholders for informed decisions making. In Poyang lake basin regions, we demonstrate how to develop a software

Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better

In this paper, we present a calculation for settling times of grains falling under the action of the gravitational field inside a Jupiter mass protoplanet formed via disk instability, where the grains are assumed to be grown by the process of cold welding. As the energy equation, we have considered the conductive-radiative case of heat transport. In our calculation, we have not used any density model

Due to the extremely complex regional geological tectonic movements in the Alpide Himalayan seismic belt with the dense population and rapid economic development, the frequent seismic activity has caused huge concern. In geology, the research on the spatiotemporal characteristics of seismic activity in different areas at different scales has always been valuable to explore the mechanism of seismic

Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized

There are substantial problems in the photogrammetric image matching especially in the images taken by UAVs in the regions where grassland, waterfront, forest, buildings, bridges, high-voltage lines etc. in the urban and rural areas. The main reason of these problems are color, tone, texture, contrast and scale changes cannot be successfully detected in between sequential images. To solve these problems

An efficient and effective spatiotemporal prediction algorithm for PM2.5 (i.e. particulate matter with a diameter of less than 2.5 micrometers) is urgently needed to study the distribution of PM2.5 over a continuous spatiotemporal domain, which not only helps to make scientific decisions on the prevention and control of PM2.5 pollution but also promotes meaningful assessment of the quantitative relationship

An integrated approach of morphometric and litho-structural studies for flood risk assessment of Ala River Basin (ARB) in Akure city, southwestern Nigeria aided by geospatial techniques was carried out. The Ala River Basin comprises of four sub-basins covering a total area of 427km2 with fourth stream order of dendritic and sub-trellis drainage patterns. The values obtained for the morphometric features

When 3D modeling of the geological body is carried out based on the section, problems such as unable to close the structural plane, slow rendering speed of the model and uneven and long and narrow section triangle network will occur due to the situation of super near point or too dense fold point. To solve the appealing problem, this paper proposes a section thinning-densification method based on the

The stability of rock slopes is a difficult problem in the field of geotechnical and geological engineering. Less than 20% of all landslides are predictable each year, so a simple, fast, reliable and low-cost method to predict the stability of slopes is urgently needed. This study investigates a new regularized online sequential extreme learning machine, incorporated with the variable forgetting factor

Spartina alterniflora is the main invasive vegetation in wetland ecosystems, and information on its chlorophyll content is important data for quantitative research on the key ecological functions of wetland ecosystems. The Dongtan wetland of the Yangtze River estuary was used as the experimental research area, and the artificial cultivation field of Eupatorium was taken as the research object. To prevent

Predictive mapping of environment is an important means for environment assessment and management. The selection of predictor variables (or environmental covariates) is the first and key step in predictive mapping. A number of machine learning and statistical models have been developed to select what and how many environmental covariates in a wide range of predictive mapping. Nevertheless, those models

Earth observation data have revolutionized Earth science and significantly enhanced the ability to forecast weather, climate and natural hazards. The storage format of the majority of Earth observation data can be classified into swath, grid or point structures. Earth science studies frequently involve resampling between swath, grid and point data when combining measurements from multiple instruments

This study was performed along the shorelines of Lake Salda in Turkey during the elapsed period from 1975 to 2019 in order to detect shoreline changes. Within this framework, geographic information system, digital shoreline analysis system, Modified Normalized Difference Water Index, and multi-temporal satellite images were utilized. The measurement of shoreline displacement was mainly divided into

Possible sunshine duration (PSD) determines the solar energy received by the surface of Earth. The objective of this research is to explore the spatial structure of PSD which is closely related to slope and PSD. We chose 40 sample areas representing diversified landforms in China and then computed the PSD by using the digital elevation models (DEMs) with a resolution of 30 m for the sample areas. We

Change Detection from remote sensing images is one of the most challenging and controversial topics among researchers in this field. Given the variety of types of surveyed areas and the data used, it cannot be claimed that there is a comprehensive way to identify different types of remote sensing data.This research aims to represent three robust and novel cost functions based on improving conventional

Soil moisture (SM) is a key variable in hydrological processes, bio-ecological processes, and biogeochemical processes. Long-term observations of soil moisture over large areas are critical to research on flooding and drought monitoring, water resource management, and crop yield forecasts. In this paper, Fengyun (FY3B and FY3C) SM products, Japan Aerospace Exploration Agency (JAXA) SM products from

In Earth science, original data elaboration is often very complex and the results are generally provided in the form of (computer-made) maps, a qualitative representation. The aim of this paper is to illustrate a graphic method of analysis that allows the reverse: tracing back, on scientific bases, quantitative data from computer-mapped information, by using widely-available graphic tools, including

Earth Observation (EO) data play an important role in our society today, but there is still tremendous opportunity to improve how these data are used to affect change. In this paper, we provide guidance to help data providers and intermediaries within the EO value chain (from data to applications) increase the societal value of the EO data, information, and data products that they work with. We first

In the big data era, high-resolution raster-based geocomputation has been widely employed in geospatial studies. The algorithms used in local map algebra operations are data-intensive and require a large memory space and massive computing power. Simply employing distributed computing framework such as Hadoop to serve such applications incurs storage and performance issues. In this paper, we present

The earthquake magnitude prediction is a task of utmost difficulty that has been addressed by using many different strategies, with no further transformation thus far. This work evaluates the Haskell based deterministic dendritic cell algorithm (hDCA)’s accuracy when used to predict earthquake magnitude in Sichuan and surroundings. First, eight seismicity indicators have been retrieved from the literature

Deterministic interpolation can be applied automatically by tuning only limited number of parameters. Although it is an easy way to use mapping, potential spatial relationships in a data set are widely omitted. To represent spatial relationships, a topological-index based interpolation (TIBI) has been introduced. The weighting-based smoothing uses graph theory and it numerically characterizes spatial

With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the

Terraces are important objects of soil and water conservation and land use surveys in the Loess Plateau and considerably impact soil erosion and agricultural production. Obtaining the spatial distribution of terraces has great significance for soil and water conservation research and government policy-making. A new method of terrace extraction was proposed in this study based on remote sensing images

The ability to access and search metadata for marine science data is both a key requirement for answering fundamental principles of data management (making data Findable, Accessible, Interoperable and Reusable) and also in meeting domain-specific, community defined standards and legislative requirements placed on data publishers. This paper describes a modular data model to answer the functional requirements

Partial unmixing categorizes each pixel based on a particular mineral while a pixel may contain several minerals. Hence, it seems that the full unmixing procedure is essential to make real results. In this study, at first, the hydrothermal alteration minerals have been mapped by directed matched filtering (DMF) algorithm as a partial unmixing method on the most important porphyry copper deposit in

In this study, a new strategy based on integrating geostatistical seismic inversion and optimized support vector regression (OSVR) will be utilized to transform multi seismic attributes to sand fraction log. In first step, owing to compatibility relation between acoustic impedance (AI) and sand fraction, a high resolution value of this important attribute was extracted through a geostatistical seismic

Soil erosion, rapid geomorphological change and vegetation degradation are major threats to the human and natural environment. Unmanned Aerial Systems (UAS) can be used as tools to provide detailed and accurate estimations of landscape change. The effect of flight strategy on the accuracy of UAS image data products, typically a digital surface model (DSM) and orthophoto, is unknown. Herein different

It is a good method to utilize the grain size distribution curves and cumulative frequency curves of marine or river sediments to estimate the hydrodynamic conditions, transportation processes and sedimentary environment. However, researchers can only rely on Excel or Grapher to plot the curves one by one at the present day. The manual plotting procedures are complicated, and calculating the truncation

Data are the foundation of urban storm and flood disaster management and research; therefore, the question of how to improve the efficiency of data management and service systems has become the central issue in the field of urban flood disasters. In this study, a data management framework for urban flood disasters was established on the basis of ontology engineering in order to analyze heterogeneous

State-of-the-art machine learning methods, such as (deep) neural networks and support vector classifiers, have been successfully applied to problems related to the prediction of natural hazard events. However, the effectiveness of those methods is, in general, obtained at the cost of a complex algorithmic architecture that requires fine-tuning of several parameters. Moreover, their current popularity

In this study a new method for predicting soybean yield over large spatial scales, overcoming the difficulties of scalability, is proposed. The method is based on the so-called “simplified triangle” remote sensing technique which is coupled with a crop prediction model of Doorenbos and Kassam 1979 (DK) and the climatological water balance model of Thornthwaite and Mather 1955 (ThM). In the method,

The coastline can be used as the demarcation line between the ocean and the continent, which plays a crucial role in the development of marine economy. The purpose of this paper is to achieve stitching of coastline images from different areas automatically and create panoramic coast images. To be specific, we proposed a multi-resolution image fusion method which is based on the optimal seam-line method

The Ganges deltaic coast of Bangladesh experiences an incessant movement over the time. Understanding the shoreline movement of this alluvial delta and a suitable method to calculate the rate of change poses a challenge for this highly dynamic coast having erosion and accretion. Using GIS and multi temporal LANDSAT images, the study investigated the positional change of the Ganges deltaic shoreline

Identification of groundwater recharge zones in an area is important to properly utilize and safeguard the groundwater resources. The objective of this study is to delineate the groundwater potential zones in the Chinnar River basin of Perambalur district, southern India, using remote sensing and GIS methods. Toposheets and satellite imageries were used to prepare various thematic maps such as geology

Lineaments whose dominant azimuth reflects the basic framework of regional structure are mainly related to faults, but their spatial morphology and characteristics are different at different scales. The changing characteristics of multi-scale lineaments extracted by wavelet edge detection are quantitatively analyzed through image characteristics, azimuth characteristics, histogram, box-plot and paired-samples

This paper contributes an artificial intelligence classifier for hyperspectral images by simultaneously employing spectral and spatial information in kernel functions and using the stochastic relaxation of spectral and spatial energy. The spectral and spatial features are integrated with the Consecutive Spectral-Spatial/Spatial-Spectral (CS2) kernel which exhibits great flexibility in the combination

The mechanism of landslide occurrence is complicated due to the dependency and nonlinear relationship of various physical factors. A promising prediction model, which can be used to locate the high-risk regions and a corresponding prevention strategy can be prepared to reduce the slide occurrence and its consequence, is therefore desired. To perform the landslide assessment for a large-scale slope

By experimental measurements and theoretical analyses, this paper investigates the variation of the Sun’s visual shape and figures out the reasons for the variation of its shape. First, the method of image processing, the method of moments and the least-square method are combined to perform experimental measurements and calculations, and the features of the Sun’s visual shape are extracted from the

Road blockage information extraction from a single-phase postdisaster image is difficult because roads are narrow and easily covered by vegetation. The traditional object-oriented image analysis method is restrictive, and its detection is slow. A deep learning algorithm, i.e., the convolution neural network (CNN), is applied to rapidly extract road blockage information. An algorithm for sample generation

We present a method for determining surface flows from solar images based upon optical flow techniques. We apply the method to sets of images obtained by a variety of solar imagers to assess its performance. The opflow3d procedure is shown to extract accurate velocity estimates when provided perfect test data and quickly generates results consistent with completely distinct methods when applied on