scholarly journals Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series

Geomatics ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 369-382
Author(s):  
Ionuț Iosifescu Enescu ◽  
Lucia de Espona ◽  
Dominik Haas-Artho ◽  
Rebecca Kurup Buchholz ◽  
David Hanimann ◽  
...  
Keyword(s):  
Time Series ◽  
Data Cube ◽  
Video Encoding ◽  
Environmental Data ◽  
User Requirements ◽  
Geospatial Information ◽  
Web Based ◽  
Efficient Storage ◽  
Data Portal ◽  
Environmental Time Series

The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals. The new Cloud Optimized Raster Encoding (CORE) format enables an efficient storage and management of gridded data by applying video encoding algorithms. Inspired by the cloud optimized GeoTIFF (COG) format, the design of CORE is based on the same principles that enable efficient workflows on the cloud, addressing web-EGIS visualization challenges for large environmental time series in geosciences. CORE is a web-native streamable format that can compactly contain raster imagery as a data hypercube. It enables simultaneous exchange, preservation, and fast visualization of time series raster data in environmental repositories. The CORE format specifications are open source and can be used by other platforms to manage and visualize large environmental time series.

scholarly journals A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements

2019 ◽  
Vol 11 (18) ◽  
pp. 2133 ◽  
Author(s):  
Sabatino Buonanno ◽  
Giovanni Zeni ◽  
Adele Fusco ◽  
Michele Manunta ◽  
Maria Marsella ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Large Data ◽  
Interferometric Synthetic Aperture Radar ◽  
Geospatial Information ◽  
Web Based ◽  
Data Infrastructures ◽  
Open Geospatial Consortium ◽  
Spatial Data Infrastructures ◽  
Spatio Temporal

This work presents the development of an efficient tool for managing, visualizing, analysing, and integrating with other data sources, the deformation time-series obtained by applying the advanced differential interferometric synthetic aperture radar (DInSAR) techniques. To implement such a tool we extend the functionalities of GeoNode, which is a web-based platform providing an open source framework based on the Open Geospatial Consortium (OGC) standards, that allows development of Geospatial Information Systems (GIS) and Spatial Data Infrastructures (SDI). In particular, our efforts have been dedicated to enable the GeoNode platform to effectively analyze and visualize the spatio/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. The examples we present, based on Sentinel-1 DInSAR results relevant to Italy, demonstrate the effectiveness of the extended version of the GeoNode platform.

Developing Food Sensory Analysis Information System using Waterfall Software Engineering Model

2017 ◽  
Vol 4 (2) ◽  
pp. 63-81
Author(s):  
Stefanus Oliver ◽  
Abdullah Muzi Marpaung ◽  
Maulahikmah Galinium
Keyword(s):  
Software Engineering ◽  
Sensory Analysis ◽  
Acceptance Testing ◽  
Design Activity ◽  
User Requirements ◽  
Human Errors ◽  
Web Based ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Analysis System

Food sensory analysis is the terms from the field of Food Technology that has a meaning which means sensory evaluation of food that is conducted by the food sensory evaluators. Currently, food sensory analysis is conductedmanually. It can caus e human errors and consume much ti me. The objective of this research is to build a web based application that is specific for food sensory analysis using PHP programming language. This research followsfour first steps of waterfall software engineering mod el which are user requirements ana lysis (user software and requirements analysis), system design (activity, use cases, architecture, and entity relationship diagram),implementation (software development), and testing (software unit, functionality, validit y, and user acceptance testing). T he software result is well built. It is also acceptable for users and all functionality features can run well after going through those four software testing. The existence of the software brings easiness to deal with the manual food sensory analysis exper iment. It is considered also for the future it has business value by having open source and premium features.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Feature-preserving interpolation and filtering of environmental time series

2015 ◽  
Vol 72 ◽  
pp. 71-76 ◽  
Author(s):  
Gregoire Mariethoz ◽  
Niklas Linde ◽  
Damien Jougnot ◽  
Hassan Rezaee
Keyword(s):  
Time Series ◽  
Feature Preserving ◽  
Environmental Time Series

scholarly journals An Empirical Mode-Spatial Model for Environmental Data Imputation

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 63 ◽  
Author(s):  
Benjamin Nelsen ◽  
D. Williams ◽  
Gustavious Williams ◽  
Candace Berrett
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Missing Values ◽  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Data Imputation ◽  
Accurate Data ◽  
Target Station ◽  
Periodic Components

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes.

scholarly journals FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT

Telematika ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 11
Author(s):  
Rifki Indra Perwira ◽  
Danang Yudhiantoro ◽  
Endah Wahyurini
Keyword(s):  
Time Series ◽  
Seed Quality ◽  
Crop Yields ◽  
Genetic Data ◽  
Time Series Model ◽  
Environmental Data ◽  
Fuzzy Time Series ◽  
Market Demand ◽  
Cultivation Technique ◽  
Processed Products

Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.

scholarly journals Trend detection of atmospheric time series

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai-Lan Chang ◽  
Martin G. Schultz ◽  
Xin Lan ◽  
Audra McClure-Begley ◽  
Irina Petropavlovskikh ◽  
...  
Keyword(s):  
Time Series ◽  
Atmospheric Chemistry ◽  
Surface Ozone ◽  
High Surface ◽  
Effective Means ◽  
Environmental Data ◽  
Model Specification ◽  
Trend Detection ◽  
Detection Methods ◽  
Sources Of Uncertainty

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to (1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry, (2) describe a range of trend-detection methods, and (3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short and long term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to (1) review trend detection methods for addressing different levels of data complexity in different chemical species, (2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability, (3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates, and (4) present an advanced method of quantifying regional trends based on the inter-site correlations of multisite data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

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