Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

Ren, Hsuan; Wang, Yung Ling; Huang, Min; Chang, Yang Lang; Kao, Hung Ming

doi:10.3390/rs6032069

Cited by 25 publications

(10 citation statements)

References 17 publications

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“…Therefore, EEMD, as an innovative statistical tool, has been widely used to determine the time series trend in variables across various fields, such as signal and image processing, economics, health, environment, and climate determinations [28][29][30][31]. Recently, EEMD has been applied to the remote sensing field [32,33].…”

Section: Introductionmentioning

confidence: 99%

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Xue

Zhang

et al. 2019

Remote Sensing

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Vegetation is the major component of the terrestrial ecosystem. Understanding both climate change and anthropogenically induced vegetation variation is essential for ecosystem management. In this study, we used an ensemble empirical mode decomposition (EEMD) method and a linear regression model to investigate spatiotemporal variations in the normalized difference vegetation index (NDVI) over the agropastoral ecotone of northern China (APENC) during the 1982–2015 period. A quantitative approach was proposed based on the residual trend (RESTREND) method to distinguish the effects of climatic (i.e., temperature (TEM), precipitation (PRE), total downward solar radiation (RAD), and near surface wind speed (SWS)) and anthropogenic effects on vegetation variations. The results showed that the NDVI exhibited a significant greening trend of 0.002 year−1 over the entire study period of 1982–2015 and that areas with monotonous greening dominated the entire APENC, occupying 40.97% of the region. A browning trend was also found in the central and northern parts of the APENC. PRE presented the highest spatial correlation with the NDVI and climate factors, suggesting that PRE was the most important factor affecting NDVI changes in the study area. In addition, the RESTREND results indicated that anthropogenic contributions dominated the vegetation variations in the APENC. Therefore, reusing farmland for grass and tree planting made a positive contribution to vegetation restoration, while deforestation, overgrazing, and the reclamation of grasslands were the opposite. In addition, with the continuous implementation of national ecological engineering programs such as the Grain to Green Program, positive human activity contributions to vegetation greening significantly increased. These results will support decision- and policy-making in the assessment and rehabilitation of ecosystems in the study region.

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Section: Introductionmentioning

confidence: 99%

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Xue

Zhang

et al. 2019

Remote Sensing

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“…2a and Table 1). When compared with those reported in other urban atmospheres, the long-term average value of 2.6 ppb ranked at a moderately low concentration level (Whitehead et al, 2007;Saylor et al, 2010;Alebic-Juretic, 2008;Meng et al, 2011). Interannual variations were evident at Site 1 with the coefficient of variation (CV) of 0.11, defined as the ratio of the standard deviation to the average.…”

Section: Temporal Variations Of Atmospheric Nh 3 At the Six Canadian Sitesmentioning

confidence: 80%

Trends in atmospheric ammonia at urban, rural and remote sites across North America

Yao¹,

Zhang²

2016

Preprint

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Abstract. Interannual variabilities in atmospheric ammonia (NH3) during the most recent seven to eleven years were investigated at fourteen sites across North America using the monitored data obtained from NAPS, CAPMoN and AMoN networks. The long-term average of atmospheric NH3 ranged from 0.8 to 2.6 ppb, depending on location, at four urban and two rural/agriculture sites in Canada. The annual average at these sites did not show any deceasing trend with largely decreasing anthropogenic NH3 emission. An increasing trend was actually identified from 2003 to 2014 at the downtown Toronto site using either the Mann-Kendall or the Ensemble Empirical Mode Decomposition method, but &#8220;no&#8221; or &#8220;stable&#8221; trends were identified at other sites. The ~20 % increase during the 11-year period at the site was likely caused by changes in NH4+/NH3 partitioning and/or air-surface exchange process as a result of the decreased sulfur emission and increased ambient temperature. The long-term average from 2008 to 2015 was 1.6&#8211;4.9 ppb and 0.3&#8211;0.5 ppb at four rural/agriculture and at four remote U.S. sites, respectively. A stable trend in NH3 mixing ratio was identified at one rural/agricultural site while increasing trends were identified at three rural/agricultural (0.6&#8211;2.6 ppb, 20&#8211;50 % increase from 2008&#8211;2015) and four remote sites (0.3&#8211;0.5 ppb, 100&#8211;200% increase from 2008&#8211;2015). Increased ambient temperature was identified to be a cause for the increasing trends in NH3 mixing ratio at four out of the seven U.S. sites, but what caused the increasing trends at other U.S. sites needs further investigation.

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“…Efficient implementation is obtained by using GPU. In [ 10 ], the similarity between spectra is measured to discriminate materials and evaluate the performance of parameter-selection procedures. The Ensemble Empirical Model Decomposition is used in this case and spectral features are effectively extracted.…”

Section: Related Workmentioning

confidence: 99%

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

Giordano

Guccione

2017

Sensors

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In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA’s CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target’s related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area.

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Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

Cited by 25 publications

References 17 publications

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Trends in atmospheric ammonia at urban, rural and remote sites across North America

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

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