Temporal Mixture Analysis of Arctic Sea Ice Imagery: A New Approach for Monitoring Environmental Change

Piwowar, Joseph M.; Peddle, Derek R.; LeDrew, E.

doi:10.1016/s0034-4257(97)00105-3

Cited by 55 publications

(41 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The TMA, which is algebraically identical to the SMA but uses temporal spectra instead of electromagnetic spectra, was first proposed by Piwowar et al (1998), and has been mainly applied in the agriculture field of remote sensing (Defries et al, 2000;Lobell and Asner, 2004). As has been suggested previously, the TMA has good potential for use in reducing endmember variability and thus could provide an improved ISA estimation (Somers et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan

Yang

Matsushita

Fukushima

et al. 2012

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

a b s t r a c tAs a proxy measure of the human ecological footprint, impervious surface area (ISA) has recently become a key concept in the field of urban remote sensing, with a focus on estimation of the ISA at a city-scale by using Landsat-style satellite images. However, ISA estimation is also in demand in disciplines such as the environmental assessment and policy making at a national scale. This paper proposes a new method for estimating the ISA fraction in Japan based on a temporal mixture analysis (TMA) technique. The required inputs for the proposed method are rearranged MODIS NDVI time-series datasets at the temporal stable zone (i.e., the first to the sixth largest NDVI values in a year). Three ISA distribution maps obtained from Landsat-5 TM data were used as reference maps to evaluate the performance of the proposed method. The results showed that the proposed TMA-based method achieved a large reduction in the effects of endmember variability compared with the previous methods (e.g., SMA and NSMA), and thus the new method has promising accuracy for estimating ISA in Japan. The overall root mean square error (RMSE) of the proposed method was 8.7%, with a coefficient of determination of 0.86, and there was no obvious underestimation or overestimation for the whole ISA range. Ó

show abstract

Section: Introductionmentioning

confidence: 99%

Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan

Yang

Matsushita

Fukushima

et al. 2012

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The advantage of multitemporal or hypertemporal procedures is that they have the potential to quantify variability characteristics of the phenomenon under investigation, not just change results. A variety of these procedures have been developed, including ARMA Time Series Modelling (Piwowar and LeDrew, 2002), Temporal Mixture Analysis (Piwowar et al, 1997), and Principal Components Analysis (Eastman and Fulk, 1993;Piwowar and LeDrew, 1996;Millward et al, 2006), with each method extracting different statistical properties of the data. Principal Components Analysis (PCA), for example, is a procedure that efficiently identifies unique spatial and temporal patterns; however, it does not produce statistically significance forecasts.…”

Section: Environmental Change Analysis By Remote Sensingmentioning

confidence: 99%

Spatial-Temporal Variability of Northern Hemisphere Sea Ice Concentrations and Concurrent Atmospheric Teleconnections

Piwowar

2008

J ENV INFORM

View full text Add to dashboard Cite

ABSTRACT. Because the Arctic is so integrally connected with the Earth's climate system, there is concern that the recent era of global warming has irreversibly altered the polar sea ice cover. This can have global consequences. However, we need to improve our knowledge of sea ice-atmosphere teleconnections if we are to increase our confidence in such climate change scenarios. In this paper, we apply the method of principal components analysis to twenty years of pentad-averaged Arctic sea ice concentration anomaly data derived from satellite observations to retrieve the most significant spatial and temporal patterns. We then linked these patterns to coincident 500 mb geopotential height (500Z) data. The strongest spatial-temporal ice concentration anomaly pattern over the 1980-1999 period was the phase-shifted anomalies between the Greenland and Barents Seas and the Labrador Sea and in Davis Strait. We found this pattern to be associated with strong low pressure anomalies over the North Atlantic and is significantly correlated with the large-scale atmospheric patterns of the North Atlantic Oscillation and El Niño. Other regions where spatially and temporally consistent patterns of anomalous ice concentrations were identified include the Beaufort Sea, on the Siberian side of the Arctic Basin, and the Sea of Okhotsk. We identify the atmospheric patterns coincident with these anomalies. We conclude that our approach of linking PCA results of ice anomaly data with highly loading 500Z patterns is an effective analysis method. We recommend that this study be extended to link full atmospheric PCA results with the sea ice pentads used here.

show abstract

“…TMA provides seasonal characteristics of univariate information such as SIC as well as a unique summary of long-term time series (Piwowar et al, 1998, Chi et al, 2016. In addition to sea ice studies, TMA has been applied to various applications in agriculture and urban studies A recent study conducted by Chi et al (2016) analyzed long-term Antarctic daily SICs using machine learning-based TMA techniques.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Antarctic Sea Ice Concentrations Using Results of Temporal Mixture Analysis

Chi

Kim

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Sea ice concentration (SIC) data acquired by passive microwave sensors at daily temporal frequencies over extended areas provide seasonal characteristics of sea ice dynamics and play a key role as an indicator of global climate trends; however, it is typically challenging to study long-term time series. Of the various advanced remote sensing techniques that address this issue, temporal mixture analysis (TMA) methods are often used to investigate the temporal characteristics of environmental factors, including SICs in the case of the present study. This study aims to forecast daily SICs for one year using a combination of TMA and time series modeling in two stages. First, we identify temporally meaningful sea ice signatures, referred to as temporal endmembers, using machine learning algorithms, and then we decompose each pixel into a linear combination of temporal endmembers. Using these corresponding fractional abundances of endmembers, we apply a autoregressive model that generally fits all Antarctic SIC data for 1979 to 2013 to forecast SIC values for 2014. We compare our results using the proposed approach based on daily SIC data reconstructed from real fractional abundances derived from a pixel unmixing method and temporal endmember signatures. The proposed method successfully forecasts new fractional abundance values, and the resulting images are qualitatively and quantitatively similar to the reference data.

show abstract

Temporal Mixture Analysis of Arctic Sea Ice Imagery: A New Approach for Monitoring Environmental Change

Cited by 55 publications

References 10 publications

Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan

Temporal mixture analysis for estimating impervious surface area from multi-temporal MODIS NDVI data in Japan

Spatial-Temporal Variability of Northern Hemisphere Sea Ice Concentrations and Concurrent Atmospheric Teleconnections

Forecasting Antarctic Sea Ice Concentrations Using Results of Temporal Mixture Analysis

Contact Info

Product

Resources

About