Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery

Syariz, Muhammad Aldila; Lin, Bor-Luh; Denaro, Lino Garda; Jaelani, Lalu Muhamad; Nguyen, Mạnh Van; Lin, Ching Hsuan

doi:10.1016/j.isprsjprs.2018.11.007

Cited by 32 publications

(17 citation statements)

References 27 publications

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“…Many variations of PIF are used in algorithms for investigating time series (e.g. MAD/IR-MAD by Canty & Nielsen, 2008;Schroeder, Cohen, Song, Canty, & Yang, 2006;Syariz et al, 2019). The PIF-Based Linear Regression method is based on linear alignment using linear regression.…”

Section: Data Processingmentioning

confidence: 99%

Evaluation of the influence of disturbances on forest vegetation using Landsat time series; a case study of the Low Tatras National Park

Hladky

Laštovička

Holman

et al. 2020

European Journal of Remote Sensing

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This study is focused on the evaluation of forest vegetation changes that took place between 1992 and 2015 in the Low Tatras National Park in Slovakia, using time series on Landsat 4, 5, 7, and 8 data. Time-series analysis was performed by evaluating the development of six vegetation indices in nine different localities selected based on the type of damage. The CDR (Climate Data Records) of the Landsat data was first normalized using the PIF method, and the trajectories of the used vegetation indices were compared with in-situ data. The area was damaged by both wind and bark beetles that significantly affected the forest vegetation in the Low Tatras National Park at the beginning of the 21st century. The results confirmed the excellent predictive abilities of vegetation indices based on SWIR bands (e.g. NDMI) for the purpose of evaluating the individual stages of a disaster. The use of the Landsat data CDR in the research of long-term forest vegetation changes is of high relevance and perspective owing to the free availability and distribution of the corrected data. Finally, several applications of remote sensing data are proposed for the management and the protection of national parks.

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Section: Data Processingmentioning

confidence: 99%

Evaluation of the influence of disturbances on forest vegetation using Landsat time series; a case study of the Low Tatras National Park

Hladky

Laštovička

Holman

et al. 2020

European Journal of Remote Sensing

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“…Additionally, this method normalizes each band separately. Though this is a common technique in other ecological systems and has been used in the Arctic (e.g., Edwards and Treitz 2017;Bonney et al 2018), some suggest that normalizing each band individually may result in unexpected discrepancies (Syariz et al 2019). This study used a spectral index (i.e., NDVI -a ratio between the red and NIR bands) to examine terrestrial ecosystem change.…”

Section: Benefits and Challenges Of Using High Spatial Resolution Data From Multiple Sensorsmentioning

confidence: 99%

A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004–2018)

Freemantle

Atkinson

et al. 2020

Canadian Journal of Remote Sensing

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Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 C; GDD 5 ) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. There was no significant change in NDVI nor in GDD 5 over time. However, there were statistically significant (p < 0.05) relationships between the GDD 5 and NDVI for all vegetation types. Using field measurements with high spatial resolution remote sensing data helps link changes in NDVI with changes to vegetation and earth surface processes. The challenges of integrating high spatial resolution satellite data from different sensors in a time series analysis are also discussed. RÉSUMÉ

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“…The absolute correction predicts and removes the scattering due to gases and aerosols in the atmosphere [15,16], and several models have been proposed for water bodies [17][18][19]. By contrast, the relative correction minimizes numerical differences among images using image processing techniques [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

et al. 2020

Self Cite

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The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

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Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery

Cited by 32 publications

References 27 publications

Evaluation of the influence of disturbances on forest vegetation using Landsat time series; a case study of the Low Tatras National Park

Evaluation of the influence of disturbances on forest vegetation using Landsat time series; a case study of the Low Tatras National Park

A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004–2018)

WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

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