scholarly journals Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh

10.5772/57443 ◽  
2014 ◽  
Author(s):  
Andrew Nelson ◽  
Mirco Boschetti ◽  
Giacinto Manfron ◽  
Francesco Holecz ◽  
Francesco Collivignarelli ◽  
...  
Keyword(s):  
Time Series ◽  
Rice Crop ◽  
Resolution Time ◽  
Moderate Resolution

scholarly journals A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products

2013 ◽  
Vol 10 (6) ◽  
pp. 4055-4071 ◽  
Author(s):  
S. Kandasamy ◽  
F. Baret ◽  
A. Verger ◽  
P. Neveux ◽  
M. Weiss
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Course ◽  
Singular Spectrum Analysis ◽  
Gaussian Function ◽  
Gap Filling ◽  
Area Index ◽  
Filling Time ◽  
Moderate Resolution ◽  
Temporal Profiles

Abstract. Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.

scholarly journals System Performance Corresponding to Bacterial Community Succession after a Disturbance in an Autotrophic Nitrogen Removal Bioreactor

mSystems ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Hsiao-Pei Lu ◽  
Yung-Hsien Shao ◽  
Jer-Horng Wu ◽  
Chih-hao Hsieh
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
System Performance ◽  
Time Series Data ◽  
Microbial Consortia ◽  
Series Data ◽  
Resolution Time ◽  
Physicochemical Variables ◽  
System Functioning ◽  
Fine Resolution

ABSTRACT Performance of a bioreactor is affected by complex microbial consortia that regulate system functional processes. Studies so far, however, have mainly emphasized the selective pressures imposed by operational conditions (i.e., deterministic external physicochemical variables) on the microbial community as well as system performance, but have overlooked direct effects of the microbial community on system functioning. Here, using a bioreactor with ammonium as the sole substrate under controlled operational settings as a model system, we investigated succession of the bacterial community after a disturbance and its impact on nitrification and anammox (anaerobic ammonium oxidation) processes with fine-resolution time series data. System performance was quantified as the ratio of the fed ammonium converted to anammox-derived nitrogen gas (N2) versus nitrification-derived nitrate (npNO3−). After the disturbance, the N2/npNO3− ratio first decreased, then recovered, and finally stabilized until the end. Importantly, the dynamics of N2/npNO3− could not be fully explained by physicochemical variables of the system. In comparison, the proportion of variation that could be explained substantially increased (tripled) when the changes in bacterial composition were taken into account. Specifically, distinct bacterial taxa tended to dominate at different successional stages, and their relative abundances could explain up to 46% of the variation in nitrogen removal efficiency. These findings add baseline knowledge of microbial succession and emphasize the importance of monitoring the dynamics of microbial consortia for understanding the variability of system performance. IMPORTANCE Dynamics of microbial communities are believed to be associated with system functional processes in bioreactors. However, few studies have provided quantitative evidence. The difficulty of evaluating direct microbe-system relationships arises from the fact that system performance is affected by convolved effects of microbiota and bioreactor operational parameters (i.e., deterministic external physicochemical forcing). Here, using fine-resolution time series data (daily sampling for 2 months) under controlled operational settings, we performed an in-depth analysis of system performance as a function of the microbial community in the context of bioreactor physicochemical conditions. We obtained statistically evaluated results supporting the idea that monitoring microbial community dynamics could improve the ability to predict system functioning, beyond what could be explained by operational physicochemical variables. Moreover, our results suggested that considering the succession of multiple bacterial taxa would account for more system variation than focusing on any particular taxon, highlighting the need to integrate microbial community ecology for understanding system functioning.

scholarly journals Spatiotemporal Mapping and Monitoring of Whiting in the Semi-Enclosed Gulf Using Moderate Resolution Imaging Spectroradiometer (MODIS) Time Series Images and a Generic Ensemble Tree-Based Model

2019 ◽  
Vol 11 (10) ◽  
pp. 1193 ◽  
Author(s):  
Abdallah Shanableh ◽  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Cristina Flesia ◽  
Saeed AL-Mansoori
Keyword(s):  
Time Series ◽  
Spectral Band ◽  
Classification Model ◽  
Series Data ◽  
Natural Phenomenon ◽  
Adaptive Boosting ◽  
Rule Based ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Whiting events in seas and lakes are a natural phenomenon caused by suspended calcium carbonate (CaCO3) particles. The Arabian Gulf, which is a semi-enclosed sea, is prone to extensive whiting that covers tens of thousands of square kilometres. Despite the extent and frequency of whiting events in the Gulf, studies documenting the whiting phenomenon are lacking. Therefore, the primary objective of this study was to detect, map and document the spatial and temporal distributions of whiting events in the Gulf using daily images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites from 2002 to 2018. A method integrating a geographic object-based image analysis, the correlation-based feature selection technique (CFS), the adaptive boosting decision tree (AdaBoost DT) and the rule-based classification were used in the study to detect, quantify and assess whiting events in the Gulf from the MODIS data. Firstly, a multiresolution segmentation was optimised using unsupervised quality measures. Secondly, a set of spectral bands and indices were investigated using the CFS to select the most relevant feature(s). Thirdly, a generic AdaBoost DT model and a rule-based classification were adopted to classify the MODIS time series data. Finally, the developed classification model was compared with various tree-based classifiers such as random forest, a single DT and gradient boosted DT. Results showed that both the combination of the mean of the green spectral band and the normalised difference index between the green and blue bands (NDGB), or the combination of the NDGB and the colour index for estimating the concentrations of calcium carbonates (CI) of the image objects, were the most significant features for detecting whiting. Moreover, the generic AdaBoost DT classification model outperformed the other tested tree-based classifiers with an overall accuracy of 97.86% and a kappa coefficient of 0.97. The whiting events during the study period (2002–2018) occurred exclusively during the winter season (November to March) and mostly in February. Geographically, the whiting events covered areas ranging from 12,000 km2 to 60,000 km2 and were mainly located along the southwest coast of the Gulf. The duration of most whiting events was 2 to 6 days, with some events extending as long as 8 to 11 days. The study documented the spatiotemporal distribution of whiting events in the Gulf from 2002 to 2018 and presented an effective tool for detecting and motoring whiting events.

scholarly journals SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea

2020 ◽  
Vol 12 (14) ◽  
pp. 2241
Author(s):  
María José López García
Keyword(s):  
Time Series ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
Western Mediterranean ◽  
Western Mediterranean Sea ◽  
Temporal And Spatial Variability ◽  
Moderate Resolution ◽  
Long Term Trends ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Development And Validation

Sea Surface Temperature (SST) is a key parameter for understanding atmospheric and oceanic processes. Since the late 1980s, infrared satellite images have been used to complement in situ records for studying the temporal and spatial variability of SST. The Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)’s satellite was the first sensor successfully used to compute SST following the development and validation of the atmospheric correction algorithm known as “split-window”. More recently, the MODerate-resolution Imaging Spectroradiometer (MODIS) on board the National Aeronautics and Space Administration (NASA)’s Terra and Aqua satellites, launched in 1999 and 2002, respectively, also provides SST products which can be combined with AVHRR series to complete the analysis of time series. This paper presents a comparison of the monthly SST data derived from both sensors, AVHRR and MODIS, in a series of ten years (2000–2009) in the Western Mediterranean basins. The results showed a high correlation (R2 = 0.99) between the sensors when averaged values at the regional scale were compared. SST obtained from AVHRR were slightly higher (+0.18 °C ± 0.2 °C, on average) than SST from MODIS. The series were most similar during winter and spring (+0.09 °C ± 0.1 °C for January to May) with a greater difference from June to December (+0.24 °C ± 0.2 °C). The comparative analysis showed that the two sensors can be used jointly to estimate long-term trends at the regional scale.

Wetlands classification and assessment of Ramsar sites in China based on time series Moderate Resolution Imaging Spectroradiometer (MODIS) imagery

2018 ◽  
Vol 69 (5) ◽  
pp. 658 ◽  
Author(s):  
Liwei Xing ◽  
Zhenguo Niu ◽  
Panpan Xu ◽  
Dachong Li
Keyword(s):  
Time Series ◽  
Landscape Metrics ◽  
Time Series Data ◽  
Wetland Loss ◽  
Series Data ◽  
Landscape Disturbance ◽  
Landscape Integrity ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Globally, wetland loss and degradation have become serious environmental and ecological issues. Wetland monitoring of Ramsar sites in China is important for developing reasonable strategies to protect wetlands. Satellite image time series may be used for the long-term monitoring of wetland ecosystems. The present study used moderate-resolution imaging spectroradiometer (MODIS) time series data collected in 2001 and 2013 for 20 Ramsar sites in China and assessed the environmental status of these reserves using landscape metrics. The results showed that specific seasonal wetland classes, such as flooded mud, permanent water and seasonal marshes, can be identified using MODIS time series data with acceptable accuracy. In addition to wetland area, we suggest using other landscape metrics, including landscape integrity and landscape disturbance or degradation indices, to assess wetland environmental quality. The slight wetland loss (0.8%) noted in the 20 reserves evaluated herein could indicate the effectiveness of efforts of the Chinese government and local government agencies to protect Ramsar sites. The existing unfavourable environmental conditions, which were manifested by low landscape integrity and high landscape disturbance or degradation for some reserves, were caused primarily by increasing water requirements outside the reserves and by agricultural development within reserves. Therefore, determining how to balance relationships between economic development and ecological protection of the reserves will be important in the future.

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