An Inductive Approach to Simulating Multispectral MODIS Surface Reflectance Time Series

Grobler, T. L.; Ackermann, Etienne; Zyl, Gusti van; Olivier, J.C.; Kleynhans, W.; Salmon, B.P.

doi:10.1109/lgrs.2012.2208446

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Most often, partial information is known, e.g., changed and unchanged pixels are known, but the exact time points at which the changes occurred in the respective time series are unknown, hence making the performance evaluation difficult and compromised. Many studies have used and highlighted the importance of simulated data, in which, changes are introduced at desired time points [10]- [12], [14], [16], [49]. Such data can be helpful in evaluating performance, sensitivity, and robustness of the method to different magnitudes of noise.…”

Section: A Simulated Datamentioning

confidence: 99%

A Relative Density Ratio-Based Framework for Detection of Land Cover Changes in MODIS NDVI Time Series

Anees

Aryal

O’Reilly

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To improve statistical approaches for near real-time land cover change detection in nonGaussian time-series data, we propose a supervised land cover change detection framework in which a MODIS NDVI time series is modeled as a triply modulated cosine function using the extended Kalman filter and the trend parameter of the triply modulated cosine function is used to derive repeated sequential probability ratio test (RSPRT) statistics. The statistics are based on relative density ratios estimated directly from the training set by a relative unconstrained least squares importance Fitting (RULSIF) algorithm, unlike traditional likelihood ratio-based test statistics. We test the framework on simulated, synthetic, and real-world beetle infestation datasets, and show that using estimated relative density ratios, instead of assuming the individual density functions to be Gaussian or approximating them with Gaussian Kernels, in the RSPRT statistics achieves better performance in terms of accuracy and detection delay. We verify the efficiency of the proposed approach by comparing its performance with three existing methods on all the three datasets under consideration in this study. We also propose a simple heuristic technique that tunes the threshold efficiently in difficult cases of near real-time change detection, when we need to take three performance indices, namely, false positives, false negatives, and mean detection delay, into account simultaneously. Index Terms-Change detection, extended Kalman filter (EKF), model fitting, MODIS, relative density ratio, time series. I. INTRODUCTIONL AND COVER change detection research has seen significant recent contributions [1]- [19]. However, every proposed framework has its limitations on global scale due to particularity of the task at hand and the circumstances under which it is developed. Hence, no single framework is optimal in a wide range of scenarios simultaneously. Therefore, an efficient change detection framework is always in demand for a particular task and circumstances under consideration. In this Manuscript

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Section: A Simulated Datamentioning

confidence: 99%

A Relative Density Ratio-Based Framework for Detection of Land Cover Changes in MODIS NDVI Time Series

Anees

Aryal

O’Reilly

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Many technologies can be developed to monitor and detect the presence of chlorophyll-a in the sea (Liu et al, 2017;Nneti Onyia et al, and Sala et al, 2018), one of which is a remote sensing technology that uses sensor technology and with multispectral capabilities (Cole et al, 2018;Grobler et al, 2013;van der Meer et al, 2012), multitemporal and high spatial resolution (Miura et al, 2008;Petitcolin & Vermote, 2002), the selection of satellite imagery as a vehicle for detecting water quality is indispensable.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Variance and Correlation Study of Chlorophyll-a in North and South Coast of Lombok Island Using Aqua MODIS Image Data

Wibisana

Zainab

Kamandang

et al. 2020

Aceh Int. J. Sci. Technol

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Chlorophyll-a mapping is widely used in the coastal environment. The mapping results can be utilized as guidance for fishing activities. The presence of chlorophyll-a in phytoplankton is much investigated concerning the existence of fish as a source of livelihood for fishermen around the globe. One method extensively developed is the use of satellite imagery to map the chlorophyll-content in the coastal region from a small area to a global scale, such as Aqua MODIS image data. This study aims to monitor the distribution of chlorophyll-a on the north coast and south coast of Lombok island. Analysis of variance and parametric statistical tests with t-distribution was utilized to examine the correlation between the two types of chlorophyll-a distribution of the coast area, Lombok Island. The result shows that the distribution of chlorophyll-a concentrations on the north coast and the south of the coast is not the same abundance; the south coast of Lombok island has smaller variances, with the concentration distribution is relatively the same. It differs from the north part; the distribution shows less diversity. This result indicates that a reasonably wide interval wherein some coordinates has a relatively diffuse chlorophyll-a concentration with a sufficiently high distribution level. Analysis of variance result also shows that both the north and south coasts have different fertility levels for their coastal waters, which are characterized by the growth rate of chlorophyll-a on the surface water along the coastal region examined

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“…Since 1995, a diverse range of applications have been developed which involved extracting meaningful information from hypertemporal data. These include landcover mapping [10,13], landuse mapping [14], change detection [15][16][17][18], ecosystem structure and species modelling [19][20][21][22][23], phenology mapping [24][25][26], gradient analysis [27][28][29], and data quality assessment [30]. To date, a limited range of ocean-focused hypertemporal studies have been conducted (Figure 3), hampered by validation challenges and limited knowledge on the partitioning of temporal patterns over the ocean's surface.…”

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From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

Scarrott

Cawkwell

Jessopp

et al. 2019

Water

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Increases in the temporal frequency of satellite-derived imagery mean a greater diversity of ocean surface features can be studied, modelled, and understood. The ongoing temporal data “explosion” is a valuable resource, having prompted the development of adapted and new methodologies to extract information from hypertemporal datasets. Current suitable methodologies for use in hypertemporal ocean surface studies include using pixel-centred measurement analyses (PMA), classification analyses (CLS), and principal components analyses (PCA). These require limited prior knowledge of the system being measured. Time-series analyses (TSA) are also promising, though they require more expert knowledge which may be unavailable. Full use of this resource by ocean and fisheries researchers is restrained by limitations in knowledge on the regional to sub-regional spatiotemporal characteristics of the ocean surface. To lay the foundations for more expert, knowledge-driven research, temporal signatures and temporal baselines need to be identified and quantified in large datasets. There is an opportunity for data-driven hypertemporal methodologies. This review examines nearly 25 years of advances in exploratory hypertemporal research, and how methodologies developed for terrestrial research should be adapted when tasked towards ocean applications. It highlights research gaps which impede methodology transfer, and suggests achievable research areas to be addressed as short-term priorities.

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An Inductive Approach to Simulating Multispectral MODIS Surface Reflectance Time Series

Cited by 3 publications

References 15 publications

A Relative Density Ratio-Based Framework for Detection of Land Cover Changes in MODIS NDVI Time Series

A Relative Density Ratio-Based Framework for Detection of Land Cover Changes in MODIS NDVI Time Series

Analysis of Variance and Correlation Study of Chlorophyll-a in North and South Coast of Lombok Island Using Aqua MODIS Image Data

From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

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