SemantiX: a cross-sensor semantic EO data cube to open and leverage AVHRR time-series and essential climate variables with scientists and the public 

2021 ◽  
Author(s):  
Hannah Augustin ◽  
Martin Sudmanns ◽  
Helga Weber ◽  
Andrea Baraldi ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Research Group ◽  
Data Cube ◽  
Climate Variables ◽  
The Public ◽  
Spatio Temporal ◽  
University Of Bern ◽  
The University ◽  
Avhrr Imagery

<p>Long time series of essential climate variables (ECVs) derived from satellite data are key to climate research. SemantiX is a research project to establish, complement and expand Advanced Very High Resolution Radiometer (AVHRR) time series using Copernicus Sentinel-3 A/B imagery, making them and derived ECVs accessible using a semantic Earth observation (EO) data cube. The Remote Sensing Research Group at the University of Bern has one of the longest European times series of AVHRR imagery (1981-now). Data cube technologies are a game changer for how EO imagery are stored, accessed, and processed. They also establish reproducible analytical environments for queries and information production and are able to better represent multi-dimensional systems. A semantic EO data cube is a newly coined concept by researchers at the University of Salzburg referring to 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 (Augustin et al. 2019). Offering analysis ready data (i.e., calibrated and orthorectified AVHRR Level 1c data) in a data cube along with semantic enrichment reduces barriers to conducting spatial analysis through time based on user-defined AOIs.</p><p>This contribution presents a semantic EO data cube containing selected ECV time series (i.e., snow cover extent, lake surface water temperature, vegetation dynamics) derived from AVHRR imagery (1981-2019), a temporal and spatial subset of AVHRR Level 1c imagery (updated after Hüsler et al. 2011) from 2016 until 2019, and, for the later, semantic enrichment derived using the Satellite Image Automatic Mapper (SIAM). SIAM applies a fully automated, spectral rule-based routine based on a physical-model to assign spectral profiles to colour names with known semantic associations; no user parameters are required, and the result is application-independent (Baraldi et al. 2010). Existing probabilistic cloud masks (Musial et al. 2014) generated by the Remote Sensing Research Group at the University of Bern are also included as additional data-derived information to support spatio-temporal semantic queries. This implementation is a foundational step towards the overall objective of combining climate-relevant AVHRR time series with Sentinel-3 imagery for the Austrian-Swiss alpine region, a European region that is currently experiencing serious changes due to climate change that will continue to create challenges well into the future.</p><p>Going forward, this semantic EO data cube will be linked to a mobile citizen science smartphone application. For the first time, scientists in disciplines unrelated to remote sensing, students, as well as interested members of the public will have direct and location-based access to these long EO data time series and derived information. SemantiX runs from August 2020-2022 funded by the Austrian Research Promotion Agency (FFG) under the Austrian Space Applications Programme (ASAP 16) (project #878939) in collaboration with the Swiss Space Office (SSO).</p>

scholarly journals Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

2020 ◽  
Vol 12 (21) ◽  
pp. 3513
Author(s):  
Jonas Koehler ◽  
Claudia Kuenzer
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Land Surface ◽  
Input Data ◽  
Earth Observation ◽  
Series Data ◽  
Climate Projections ◽  
Auto Regressive ◽  
Spatio Temporal

Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

Retrieving freeze/thaw-cycles using Sentinel 1 data in Eastern Nunavik (Québec, Canada)

2021 ◽  
Author(s):  
Yueli Chen ◽  
Lingxiao Wang ◽  
Monique Bernier ◽  
Ralf Ludwig
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Retrieval Algorithm ◽  
State Transitions ◽  
Freeze Thaw ◽  
High Quality Information ◽  
Different Depths ◽  
Spatio Temporal

<p>In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Sentinel-1 continues to deliver high-resolution microwave remote sensing than ever before and has therefore a large potential of usage for monitoring. In light of this, the capability and responses of its radar backscatter to landscape FT processes in different surface soil depths should be examined to provide a thorough grounding for a robust application of the F/T retrieval algorithm.</p><p>This study presents a seasonal threshold approach, which examines the time series progression of remote sensing measurements relative to signatures acquired during seasonal reference frozen and thawed states. It is developed to estimate the FT-state from the Sentinel 1 database and applied and evaluated for the region of Eastern Nunavik (Québec, Canada). In this course, the FT state transitions derived from Sentinel 1 data are compared to temporally overlapping situ measurements of soil moisture from different depths within the top 20cm soil. This work allows to explore differences in the sensitivity of the Sentinel 1 at different surface soil depths in higher detail; this information is used to examine the penetration performance of the Sentinel 1 under different conditions of permafrost and permafrost-dominated landscapes.</p><p>This work is dedicated to providing more accurate data to capture the spatio-temporal heterogeneity of freeze/thaw transitions. As Sentinel-1 continues to deliver high-quality information, the provided threshold algorithm delivers an extended time series to analyze FT-states and improve our understanding of related processes in permafrost landscapes.</p>

Spatio-Temporal Segmentation Applied to Optical Remote Sensing Image Time Series

2018 ◽  
Vol 15 (8) ◽  
pp. 1299-1303 ◽  
Author(s):  
Wanderson Santos Costa ◽  
Leila Maria Garcia Fonseca ◽  
Thales Sehn Korting ◽  
Hugo do Nascimento Bendini ◽  
Ricardo Cartaxo Modesto de Souza
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Temporal Segmentation ◽  
Optical Remote Sensing Image ◽  
Spatio Temporal

scholarly journals A Portal Offering Standard Visualization and Analysis on top of an Open Data Cube for Sub-National Regions: The Catalan Data Cube Example

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 96 ◽  
Author(s):  
Joan Maso ◽  
Alaitz Zabala ◽  
Ivette Serral ◽  
Xavier Pons
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Big Data ◽  
Spatial Distribution ◽  
Geographical Area ◽  
Open Data ◽  
Data Cube ◽  
Web Browser ◽  
Web Map ◽  
Sentinel 2

The amount of data that Sentinel fleet is generating over a territory such as Catalonia makes it virtually impossible to manually download and organize as files. The Open Data Cube (ODC) offers a solution for storing big data products in an efficient way with a modest hardware and avoiding cloud expenses. The approach will still be useful up to the next decade. Yet, ODC requires a level of expertise that most people who could benefit from the information do not have. This paper presents a web map browser that gives access to the data and goes beyond a simple visualization by combining the OGC WMS standard with modern web browser capabilities to incorporate time series analytics. This paper shows how we have applied this tool to analyze the spatial distribution of the availability of Sentinel 2 data over Catalonia and revealing differences in the number of useful scenes depending on the geographical area that ranges from one or two images per month to more than one image per week. The paper also demonstrates the usefulness of the same approach in giving access to remote sensing information to a set of protected areas around Europe participating in the H2020 ECOPotential project.

scholarly journals Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna

2019 ◽  
Vol 11 (5) ◽  
pp. 496 ◽  
Author(s):  
Shupeng Gao ◽  
Xiaolong Liu ◽  
Yanchen Bo ◽  
Zhengtao Shi ◽  
Hongmin Zhou
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Temporal Resolution ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rubber Plantation ◽  
Optical Remote Sensing ◽  
Rubber Plantations ◽  
Sensing Data ◽  
Spatio Temporal

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R2 (R2 greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

scholarly journals Satellite and VIZ–Radiosonde Intercomparisons for Diagnosis of Nonclimatic Influences

2006 ◽  
Vol 23 (9) ◽  
pp. 1181-1194 ◽  
Author(s):  
John R. Christy ◽  
William B. Norris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Lower Troposphere ◽  
University Of Alabama ◽  
High Significance ◽  
Sensing Systems ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Remote Sensing Systems ◽  
The University

Abstract Radiosonde datasets of temperature often suffer from discontinuities due to changes in instrumentation, location, observing practices, and algorithms. To identify temporal discontinuities that affect the VIZ/Sippican family of radiosondes, the 1979–2004 time series of a composite of 31 VIZ stations are compared to composites of collocated values of layer temperatures from two microwave sounding unit datasets—the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Discontinuities in the radiosonde time series relative to the two satellite datasets were detected with high significance and with similar magnitudes; however, some instances occurred where only one satellite dataset differed from the radiosondes. For the products known as lower troposphere (LT; surface–300 hPa) and midtroposphere (MT; surface–75-hPa layer), significant discontinuities relative to both satellite datasets were found—two cases for LT and four for MT. These are likely associated with changes in the radiosonde system. Three apparent radiosonde discontinuities were also determined for the lower-stratospheric product (LS; 150–15 hPa). Because they cannot be definitely traced to changes in the radiosonde system, they could be the result of common errors in the satellite products. When adjustments are applied to the radiosondes based independently on each satellite dataset, 26-yr trends of UAH (RSS) are consistent with the radiosondes for LT, MT, and LS at the level of ±0.06, ±0.04, and ±0.07 (±0.12, ±0.10, and ±0.10) K decade−1. Also, simple statistical retrievals based on radiosonde-derived relationships of LT, MT, and LS indicate a higher level of consistency with UAH products than with those of RSS.

Spatio-temporal analysis of the relationship between climate variables and waterlogging using satellite remote sensing

2021 ◽  
Vol 14 (14) ◽  
Author(s):  
Harkanwal Singh Sekhon ◽  
Raj Setia ◽  
Som Pal Singh ◽  
Pavneet Kaur Kingra ◽  
Junaid Ansari
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Temporal Analysis ◽  
Climate Variables ◽  
Spatio Temporal ◽  
The Relationship

scholarly journals A Spatio-temporal disaggregation method to derive time series of Normalized Difference Vegetation Index and Land Surface Temperature at fine spatial resolution

2014 ◽  
Vol XL-8 ◽  
pp. 1397-1401
Author(s):  
V. M. Bindhu ◽  
B. Narasimhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Energy Balance ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fine Spatial Resolution ◽  
Temporal Disaggregation ◽  
Energy Balance Models ◽  
Spatio Temporal ◽  
Fine Resolution

Estimation of evapotranspiration (ET) from remote sensing based energy balance models have evolved as a promising tool in the field of water resources management. Performance of energy balance models and reliability of ET estimates is decided by the availability of remote sensing data at high spatial and temporal resolutions. However huge tradeoff in the spatial and temporal resolution of satellite images act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. Hence a need exists to derive finer resolution data from the available coarse resolution imagery, which could be applied to deliver ET estimates at scales to the range of individual fields. The current study employed a spatio-temporal disaggregation method to derive fine spatial resolution (60 m) images of NDVI by integrating the information in terms of crop phenology derived from time series of MODIS NDVI composites with fine resolution NDVI derived from a single AWiFS data acquired during the season. The disaggregated images of NDVI at fine resolution were used to disaggregate MODIS LST data at 960 m resolution to the scale of Landsat LST data at 60 m resolution. The robustness of the algorithm was verified by comparison of the disaggregated NDVI and LST with concurrent NDVI and LST images derived from Landsat ETM+. The results showed that disaggregated NDVI and LST images compared well with the concurrent NDVI and LST derived from ETM+ at fine resolution with a high Nash Sutcliffe Efficiency and low Root Mean Square Error. The proposed disaggregation method proves promising in generating time series of ET at fine resolution for effective water management.

Influence of Data Representations and Deep Architectures in Image Time Series Classification

2021 ◽  
pp. 2160001
Author(s):  
Mohamed Chelali ◽  
Camille Kurtz ◽  
Anne Puissant ◽  
Nicole Vincent
Keyword(s):  
Time Series ◽  
Spatial Information ◽  
Time Series Data ◽  
Satellite Image ◽  
Classification Problem ◽  
Data Cube ◽  
Series Data ◽  
Temporal Features ◽  
Spatio Temporal ◽  
The Impact

Image time series, such as Satellite Image Time Series (SITS) or MRI functional sequences in the medical domain, carry both spatial and temporal information. In many pattern recognition applications such as image classification, taking into account such rich information may be crucial and discriminative during the decision making stage. However, the extraction of spatio-temporal features from image time series is difficult to handle due to the complex representation of the data cube. In this paper, we present a strategy based on Random Walk to build a novel segment-based representation of the data, passing from a 2D[Formula: see text] dimension to a 2D one, more easily manipulable and without losing too much spatial information. Such new representation is then used to feed a classical Convolutional Neural Network (CNN) in order to learn spatio-temporal features with only 2D convolutions and to classify image time series data for a particular classification problem. The influence of the way the 2D[Formula: see text] data are represented, as well as the impact of the network architectures on the results, are carefully studied. The interest of this approach is highlighted on a remote sensing application for the classification of complex agricultural crops.

scholarly journals Geovisual Analytics of Spatio-Temporal Earthquake Data in Indonesia

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Febrian Fitryanik Susanta ◽  
Cecep Pratama ◽  
Trias Aditya ◽  
Alian Fathira Khomaini ◽  
Hadi Wijaya Kusuma Abdillah
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Visual Analytics ◽  
The Public ◽  
Coordinate Position ◽  
Geovisual Analytics ◽  
History Of ◽  
Spatio Temporal ◽  
High Level ◽  
Earthquake Data

Indonesia is one of the nations located in the Ring of Fire. Indonesia has a high level of geodynamic activities so that it's often earthquake tectonics. The earthquakes are caused by Indonesia position located in the confluence of four main plates. At present, the history of earthquake data in Indonesia has been accessible by the public. However, general visualization which can present history earthquake in the form maps and summary statistics have not been available. Therefore, this research aims to visualize the history of earthquake data interactively combining spatial data and temporal data. The data used for this research was obtained from BMKG website. The data variables used in this research include CORS stations and history of earthquake phenomenons between 2004 and 2019. The earthquake phenomenon consists of occurrence time, coordinate position, depth and magnitude. The data are processed using Ms Excel and ArcGIS Online Map then are visualized by Web AppBuilder for ArcGIS. The results of the data processing are maps presented in a dashboard with time-series animation and widgets features. We performed maps, graphics and time-series animation as interactive visual interfaces and matched the tasks to visual analytics techniques that are capable to support them. In this paper, we introduce the relationship between variables and present the visual analytics techniques using several example scenarios of Spatio-temporal earthquake data.

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