scholarly journals Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat

2021 ◽  
Vol 13 (8) ◽  
pp. 1437
Author(s):  
Claire E. Krause ◽  
Vanessa Newey ◽  
Matthew J. Alger ◽  
Leo Lymburner
Keyword(s):  
Water Resource Management ◽  
Temporal Dynamics ◽  
Observation Data ◽  
Detection Algorithms ◽  
Water Detection ◽  
History Of ◽  
Spatio Temporal ◽  
Earth Observation Data ◽  
Spatial Locations ◽  
Pixel Scale

Water detection algorithms are now being routinely applied to continental and global archives of satellite imagery. However, water resource management decisions typically take place at the waterbody rather than pixel scale. Here, we present a workflow for generating polygons of persistent waterbodies from Landsat observations, enabling improved monitoring and management of water assets across Australia. We use Digital Earth Australia’s (DEA) Water Observations from Space (WOfS) product, which provides a water classified output for every available Landsat scene, to determine the spatial locations and extents of waterbodies across Australia. We generated a polygon set of waterbodies that identified 295,906 waterbodies ranging in size from 3125 m2 to 4820 km2. Each polygon was used to generate a time series of WOfS, providing a history of the change in surface area of each waterbody every ~16 days since 1987. We demonstrate the applications of this new dataset, DEA Waterbodies, to understanding local through to national-scale surface water spatio-temporal dynamics. DEA Waterbodies provides new insights into Australia’s water availability and enables the monitoring of important landscape features such as lakes and dams, improving our ability to use earth observation data to make meaningful decisions.

Snowmelt runoff models

1999 ◽  
Vol 23 (2) ◽  
pp. 205-227 ◽  
Author(s):  
R. I. Ferguson
Keyword(s):  
Snow Cover ◽  
Water Resource Management ◽  
Flood Hazard ◽  
Observation Data ◽  
Internal Validation ◽  
Distributed Models ◽  
Current Trends ◽  
Meltwater Runoff ◽  
Basin Scale ◽  
Earth Observation Data

Models that predict meltwater runoff at a daily timescale are important in water resource management, flood hazard assessment and climate-change impact studies. This article identifies four basic components of such models: meteorological extrapolation, snowmelt estimation at a point, snow-cover depletion and runoff routing. Alternative ways of handling these are discussed, with emphasis on the contrasting treatments in two widely used models: HBV and SRM. Many of the issues in meltwater modelling reflect wider debates in hydrological and environmental modelling, including problems of complexity vs. simplicity, the appropriate level of spatial disaggregation, parameter identification and calibration, and internal validation. In reviewing current trends emphasis is placed on the potential and limitations of fully distributed models, problems in using energy-balance rather than temperature-index melt models at basin scale, ways to deal with spatial variability in snow cover, and the value and limitations of earth observation data.

FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES

2009 ◽  
Vol 05 (01) ◽  
pp. 265-286
Author(s):  
MUSTAFA C. OZTURK ◽  
JOSE C. PRINCIPE
Keyword(s):  
Computational Models ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Reservoir Computing ◽  
Short Term ◽  
System Parameters ◽  
History Of ◽  
Spatio Temporal ◽  
Universal Approximators ◽  
Theoretical Results

Walter Freeman in his classic 1975 book "Mass Activation of the Nervous System" presented a hierarchy of dynamical computational models based on studies and measurements done in real brains, which has been known as the Freeman's K model (FKM). Much more recently, liquid state machine (LSM) and echo state network (ESN) have been proposed as universal approximators in the class of functionals with exponential decaying memory. In this paper, we briefly review these models and show that the restricted K set architecture of KI and KII networks share the same properties of LSM/ESNs and is therefore one more member of the reservoir computing family. In the reservoir computing perspective, the states of the FKM are a representation space that stores in its spatio-temporal dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out made up of a KI, information related to the input history can be accessed and read out. This work provides two important contributions. First, it emphasizes the need for optimal readouts, and shows how to adaptively design them. Second, it shows that the Freeman model is able to process continuous signals with temporal structure. We will provide theoretical results for the conditions on the system parameters of FKM satisfying the echo state property. Experimental results are presented to illustrate the validity of the proposed approach.

scholarly journals National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 144 ◽  
Author(s):  
Trevor Dhu ◽  
Gregory Giuliani ◽  
Jimena Juárez ◽  
Argyro Kavvada ◽  
Brian Killough ◽  
...  
Keyword(s):  
International Development ◽  
Water Resource Management ◽  
Open Data ◽  
Data Cube ◽  
Observation Data ◽  
Country Level ◽  
Global Trend ◽  
Development Policies ◽  
Earth Observation Data ◽  
Development Agendas

The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting.

scholarly journals Semantic Earth Observation Data Cubes

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 102 ◽  
Author(s):  
Hannah Augustin ◽  
Martin Sudmanns ◽  
Dirk Tiede ◽  
Stefan Lang ◽  
Andrea Baraldi
Keyword(s):  
Data Retrieval ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Cubes ◽  
Sensory Data ◽  
Main Challenge ◽  
Data Content ◽  
Spatio Temporal ◽  
Earth Observation Data

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.

Macrostratigraphy and Its Promise for Paleobiology

2008 ◽  
Vol 14 ◽  
pp. 205-231 ◽  
Author(s):  
Shanan E. Peters
Keyword(s):  
Temporal Resolution ◽  
Temporal Dynamics ◽  
The United States ◽  
Sediment Supply ◽  
Marine Animals ◽  
Base Level ◽  
Genus Richness ◽  
History Of ◽  
Spatio Temporal ◽  
Shelf Sedimentation

Macrostratigraphy is the study and statistical analysis of sediment packages that formed continuously at a specified scale of temporal resolution and that are bound by gaps recognizable at that same scale. The temporal ranges of gap-bound packages, compiled separately for different geographic locations, permit area-weighted, survivorship-based measures of rock quantity and spatio-temporal environmental continuity to be measured. Analytical basin fill models suggest that the parameters controlling sedimentation and sequence stratigraphic architecture, such as base level and sediment supply, can be detected quantitatively by macrostratigraphy.Macrostratigraphic analysis of the marine sedimentary rock record in the United States at a temporal resolution of ~106 years reproduces most of the well-known Sloss sequences, but it also identifies two prominent megasequences, the Paleozoic and Modern megasequences, which are separated by a Permian-Triassic discontinuity and Phanerozoic minimum in rock quantity. Many short- and long-term features of the macroevolutionary history of marine animals are reproduced by macrostratigraphy, including 1) many patterns in genus richness, 2) patterns in rates of genus extinction and, to a lesser degree, rates of origination, and 3) patterns of extinction selectivity and the shifting relative richness of Sepkoski's Paleozoic and Modern evolutionary faunas. The extent to which macrostratigraphy reproduces the macroevolutionary history of marine animals transcends what is expected by geologically-controlled sampling biases. Instead, the processes which control the spatio-temporal dynamics of shelf sedimentation, including expansions and contractions of shallow epicontinental seas, have probably exerted a consistent influence on the macroevolutionary history of marine animals. Exploring the common cause hypothesis by putting fossils back into rocks and rocks into a new quantitative framework for physical environmental change holds considerable promise for paleobiology.

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