Earth Observation Sensor Web: An Overview

Di, Liping; Moe, Karen; Zyl, Terence L. van

doi:10.1109/jstars.2010.2089575

Cited by 41 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the unified satellite metadata standard refers to the ISO 19115-2:2009-based geographic information metadata standard [23]. All of the remote sensing metadata in the distributed sub-centers should be transformed into the ISO 19115-based metadata format before integration to enable uniform data retrieval and management.…”

Section: Distributed Integration Of Multi-source Remote Sensing Datamentioning

confidence: 99%

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Fan

Yan

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Since Landsat-1 first started to deliver volumes of pixels in 1972, the volumes of archived data in remote sensing data centers have increased continuously. Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and revisit periods of these archived data are vastly different. In addition, the remote sensing data received continuously by each data center arrives at a faster code rate; it is best to ingest and archive the newly received data to ensure users have access to the latest data retrieval and distribution services. Hence, an excellent data integration, organization, and management program is urgently needed. However, the multi-source, massive, heterogeneous, and distributed storage features of remote sensing data have not only caused difficulties for integration across distributed data center spatial infrastructures, but have also resulted in the current modes of data organization and management being unable meet the rapid retrieval and access requirements of users. Hence, this paper proposes an object-oriented data technology (OODT) and SolrCloud-based remote sensing data integration and management framework across a distributed data center spatial infrastructure. In this framework, all of the remote sensing metadata in the distributed sub-centers are transformed into the International Standardization Organization (ISO) 19115-based unified format, and then ingested and transferred to the main center by OODT components, continuously or at regular intervals. In the main data center, in order to improve the efficiency of massive data retrieval, we proposed a logical segmentation indexing (LSI) model-based data organization approach, and took SolrCloud to realize the distributed index and retrieval of massive metadata. Finally, a series of distributed data integration, retrieval, and comparative experiments showed that our proposed distributed data integration and management program is effective and promises superior results. Specifically, the LSI model-based data organization and the SolrCloud-based distributed indexing schema was able to effectively improve the efficiency of massive data retrieval.

show abstract

Section: Distributed Integration Of Multi-source Remote Sensing Datamentioning

confidence: 99%

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Fan

Yan

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…If annual precipitation over 1500mm in an area, and evenly distribution, generally little or no forest fires , such as tropical rain forests , hot and humid all year round [2]. If annual precipitation lot, but the distribution is uneven, with a clear dry and wet season of points, a fire occurred in the dry season, such as seasonal forest; if precipitation is less than 1000mm, prone to forest fires.…”

Section: Regional Overview Researchmentioning

confidence: 99%

Research of Regional Forest Fire Prediction Method based on Multivariate Linear Regression

Liu¹,

Zhang²

2015

IJSH

View full text Add to dashboard Cite

show abstract

“…The ground based measurement method based on wireless sensor networks (WSN) technology is a suitable method for validation and provides a good opportunity to evaluate the temporal consistency of remote sensing products [36][37][38]. The WSN technology allows spatially distributed ground instruments, and can perform automatic temporal monitoring, greatly reducing labor and time costs.…”

Section: Introductionmentioning

confidence: 99%

An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface

et al. 2015

View full text Add to dashboard Cite

Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the wireless sensor network (WSN) observation system. Since heterogeneity within a site usually arises from the mixture of vegetation and non-vegetation surfaces, the spatial heterogeneity of vegetation and land cover types were separately considered. Representative LAI time series of vegetation surfaces were obtained by upscaling in situ measurements using an optimal weighted combination method, incorporating the expectation maximum (EM) algorithm to derive the weights. The ground truth of LAI over the whole site could then be determined using area weighted combination of representative LAIs of different land cover types. The algorithm was evaluated using a dataset collected in Heihe Watershed Allied Telemetry Experimental Research (HiWater) experiment. The proposed algorithm can effectively obtain the representative ground truth of LAI time series in heterogeneous cropland areas. OPEN ACCESSRemote Sens. 2015, 7 12888Using the normal method of an average LAI measurement to represent the heterogeneous surface produced a root mean square error (RMSE) of 0.69, whereas the proposed algorithm provided RMSE = 0.032 using 23 sampling points. The proposed ground truth derived method was implemented to validate four major LAI products.

show abstract

Earth Observation Sensor Web: An Overview

Cited by 41 publications

References 2 publications

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

Research of Regional Forest Fire Prediction Method based on Multivariate Linear Regression

An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface

Contact Info

Product

Resources

About