scholarly journals Spatiotemporal Characterization of Mangrove Phenology and Disturbance Response: The Bangladesh Sundarban

2019 ◽  
Vol 11 (17) ◽  
pp. 2063 ◽  
Author(s):  
Christopher Small ◽  
Daniel Sousa
Keyword(s):  
Time Series ◽  
Mangrove Forest ◽  
Landsat Imagery ◽  
Methodological Approach ◽  
Principal Component ◽  
Spatiotemporal Analysis ◽  
Low Rank ◽  
Eof Analysis ◽  
Vegetation Fraction ◽  
Disturbance Response

This work presents a spatiotemporal analysis of the phenology and disturbance response in the Sundarban mangrove forest on the Ganges-Brahmaputra Delta in Bangladesh. The methodological approach is based on an Empirical Orthogonal Function (EOF) analysis of the new Harmonized Landsat Sentinel-2 (HLS) BRDF and atmospherically corrected reflectance time series, preceded by a Robust Principal Component Analysis (RPCA) separation of Low Rank and Sparse components of the image time series. Low Rank components are spatially and temporally pervasive while Sparse components are transient and localized. The RPCA clearly separates subtle spatial variations in the annual cycle of monsoon-modulated greening and senescence of the mangrove forest from the spatiotemporally complex agricultural phenology surrounding the Sundarban. A 3 endmember temporal mixture model maps spatially coherent differences in the 2018 greening-senescence cycle of the mangrove which are both concordant and discordant with existing species composition maps. The discordant patterns suggest a phenological response to environmental factors like surface hydrology. On decadal time scales, a standard EOF analysis of vegetation fraction maps from annual post-monsoon Landsat imagery is sufficient to isolate locations of shoreline advance and retreat related to changes in sedimentation and erosion, as well as cyclone-induced defoliation and recovery.

scholarly journals Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

2020 ◽  
Vol 12 (19) ◽  
pp. 3120
Author(s):  
Luojia Hu ◽  
Nan Xu ◽  
Jian Liang ◽  
Zhichao Li ◽  
Luzhen Chen ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Google Earth Engine ◽  
Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

Temporal patterns in species zonation in a mangrove forest in the Mekong Delta, Vietnam, using a time series of Landsat imagery

2017 ◽  
Vol 147 ◽  
pp. 144-154 ◽  
Author(s):  
Eric L. Bullock ◽  
Sergio Fagherazzi ◽  
William Nardin ◽  
Phuoc Vo-Luong ◽  
Phong Nguyen ◽  
...  
Keyword(s):  
Time Series ◽  
Mangrove Forest ◽  
Landsat Imagery ◽  
Mekong Delta ◽  
Temporal Patterns ◽  
Species Zonation

scholarly journals <sup>10</sup>Be in late deglacial climate simulated by ECHAM5-HAM – Part 2: Isolating the solar signal from <sup>10</sup>Be deposition

2014 ◽  
Vol 10 (2) ◽  
pp. 687-696 ◽  
Author(s):  
U. Heikkilä ◽  
X. Shi ◽  
S. J. Phipps ◽  
A. M. Smith
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Climate Model ◽  
Principal Component ◽  
Deposition Flux ◽  
Data Sets ◽  
Eof Analysis ◽  
Climate System Model ◽  
Frequency Components ◽  
Solar Signal

Abstract. This study investigates the effect of deglacial climate on the deposition of the solar proxy 10Be globally, and at two specific locations, the GRIP site at Summit, Central Greenland, and the Law Dome site in coastal Antarctica. The deglacial climate is represented by three 30 year time slice simulations of 10 000 BP (years before present = 1950 CE), 11 000 and 12 000 BP, compared with a preindustrial control simulation. The model used is the ECHAM5-HAM atmospheric aerosol–climate model, driven with sea-surface temperatures and sea ice cover simulated using the CSIRO Mk3L coupled climate system model. The focus is on isolating the 10Be production signal, driven by solar variability, from the weather- or climate-driven noise in the 10Be deposition flux during different stages of climate. The production signal varies at lower frequencies, dominated by the 11 year solar cycle within the 30 year timescale of these experiments. The climatic noise is of higher frequencies than 11 years during the 30 year period studied. We first apply empirical orthogonal function (EOF) analysis to global 10Be deposition on the annual scale and find that the first principal component, consisting of the spatial pattern of mean 10Be deposition and the temporally varying solar signal, explains 64% of the variability. The following principal components are closely related to those of precipitation. Then, we apply ensemble empirical decomposition (EEMD) analysis to the time series of 10Be deposition at GRIP and at Law Dome, which is an effective method for adaptively decomposing the time series into different frequency components. The low-frequency components and the long-term trend represent production and have reduced noise compared to the entire frequency spectrum of the deposition. The high-frequency components represent climate-driven noise related to the seasonal cycle of e.g. precipitation and are closely connected to high frequencies of precipitation. These results firstly show that the 10Be atmospheric production signal is preserved in the deposition flux to surface even during climates very different from today's both in global data and at two specific locations. Secondly, noise can be effectively reduced from 10Be deposition data by simply applying the EOF analysis in the case of a reasonably large number of available data sets, or by decomposing the individual data sets to filter out high-frequency fluctuations.

scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery

2015 ◽  
Vol 187 (9) ◽  
Author(s):  
Jing Li ◽  
Carl E. Zipper ◽  
Patricia F. Donovan ◽  
Randolph H. Wynne ◽  
Adam J. Oliphant
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Landsat Imagery ◽  
Disturbance History ◽  
Series Analysis

Assessment of ecological disturbance in the mangrove forest of Sundarbans caused by cyclones using MODIS time-series data (2001–2011)

2015 ◽  
Vol 79 (2) ◽  
pp. 775-790 ◽  
Author(s):  
Dibyendu Dutta ◽  
Prabir Kumar Das ◽  
Soubhik Paul ◽  
Jaswant Raj Sharma ◽  
Vinay Kumar Dadhwal
Keyword(s):  
Time Series ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Series Data ◽  
Ecological Disturbance

An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

2021 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Yan Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Mangrove Area ◽  
Google Earth Engine ◽  
Forest Maps ◽  
Sentinel 2

&lt;p&gt;A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (&lt; 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area &lt; 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (&lt; 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.&lt;/p&gt;

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