Remote Sensing Big Data: Theory, Methods and Applications

Liu, Peng; Di, Liping; Du, Qian; Wang, Lizhe

doi:10.3390/rs10050711

Cited by 61 publications

(39 citation statements)

References 8 publications

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“…There is currently a growing interest in the potential of big data (Liu et al. ), which in a remote sensing context refers to the recent increase in the volume and variety of remote sensing data available, as well as the increase in processing velocity (Chi et al. ).…”

Section: Resultsmentioning

confidence: 99%

“…However, this has changed thanks to the availability of services such as the cloud-based platform Google Earth Engine (Gorelick et al 2017) and the free virtual machines provided by the European Space Agency's Research and User Support Service (RUS 2018). There is currently a growing interest in the potential of big data (Liu et al 2018), which in a remote sensing context refers to the recent increase in the volume and variety of remote sensing data available, as well as the increase in processing velocity (Chi et al 2016). These developments in online platforms and virtual machines should help those working in conservation to capitalize on the potential of big data to monitor large areas.…”

Section: Resultsmentioning

confidence: 99%

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A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Crowson

Warren‐Thomas

Hill

et al. 2018

Remote Sens Ecol Conserv

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The loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up‐to‐date information on peat swamp forest loss across large areas, and support spatial explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can ‘see through’ cloud, but experience so far has shown that it doesn't discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel‐1 and Sentinel‐2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel‐based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object‐based classification or pixel‐based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalize on the potential of big data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Crowson

Warren‐Thomas

Hill

et al. 2018

Remote Sens Ecol Conserv

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“…For example, researchers interested in the estimation of LAI, level of defoliation, or biomass can propagate the uncertainties in the remotely sensed data due to calibration, sensor and environmental factors, and the compensation algorithms to their models and thereby can evaluate the error budget associated with their estimates. Although very few studies have used such uncertainty metrics in their application, with the focus and use of big data in remote sensing, many algorithms, models, and applications have to consider the intrinsic and extrinsic characteristics of data, such as our canopy spectral BRDF models, defoliation characterization models, and uncertainty estimates, to provide more useful and accurate information to the scientific community [11,12]. Furthermore, the paradigm shift to the "big data in remote sensing" using Graphic Processing Unit (GPU) programming, cloud computing, and cloud storage supports new applications that rely on simulation environments to model complex phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Rengarajan

Schott

2018

Remote Sensing

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Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

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“…Remote-sensing data are big (Guo, Wang, Chen, & Liang, 2014), with great variety as well as volume: from what is collected at sensors through to how data are provided to users, with differences in pixel size, spectral regions sampled, revisit rate, and so on. New analytical approaches for big remote-sensing data have been recommended (e.g., Ball, Anderson, & Chan, 2018;Liu, Di, Du, & Wang, 2018), particularly to support the ubiquitous challenge of and demand for real-time processing (Ma et al, 2015). One such development is the provision of analysis-ready data (ARD) suitable for analysis in large purpose-developed data cubes (e.g., Gorelick et al, 2017;Lewis et al, 2017).…”

Section: Lclu Data and Conte X Tmentioning

confidence: 99%

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

Comber

Wulder

2019

Transactions in GIS

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Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community.

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Remote Sensing Big Data: Theory, Methods and Applications

Abstract: Nowadays, our ability to acquire remote sensing data has been improved to an unprecedented level.[...]

Cited by 61 publications

References 8 publications

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

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