A Computer Model to Forecast Wetland Vegetation Changes Resulting from Restoration and Protection in Coastal Louisiana

2013 ◽  
Vol 67 ◽  
pp. 51-59 ◽  
Author(s):  
Jenneke M. Visser ◽  
Scott M. Duke-Sylvester ◽  
Jacoby Carter ◽  
Whitney P. Broussard
Keyword(s):  
Computer Model ◽  
Wetland Vegetation ◽  
Vegetation Changes ◽  
Coastal Louisiana ◽  
Restoration And Protection

scholarly journals Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana

2019 ◽  
Vol 11 (21) ◽  
pp. 2533 ◽  
Author(s):  
Daniel Jensen ◽  
Kyle C. Cavanaugh ◽  
Marc Simard ◽  
Gregory S. Okin ◽  
Edward Castañeda-Moya ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Aboveground Biomass ◽  
Wetland Vegetation ◽  
Synthetic Aperture ◽  
Forested Wetland ◽  
Imaging Spectrometer ◽  
Synthetic Aperture Radar Data ◽  
Coastal Louisiana ◽  
Spectrometer Data ◽  
Aperture Radar

Aboveground biomass (AGB) plays a critical functional role in coastal wetland ecosystem stability, with high biomass vegetation contributing to organic matter production, sediment accretion potential, and the surface elevation’s ability to keep pace with relative sea level rise. Many remote sensing studies have employed either imaging spectrometer or synthetic aperture radar (SAR) for AGB estimation in various environments for assessing ecosystem health and carbon storage. This study leverages airborne data from NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to assess their unique capabilities in combination to estimate AGB in coastal deltaic wetlands. Here we develop AGB models for emergent herbaceous and forested wetland vegetation in coastal Louisiana. In addition to horizontally emitted, vertically received (HV) backscatter, SAR parameters are expressed by the Freeman–Durden polarimetric decomposition components representing volume and double-bounce scattering. The imaging spectrometer parameters include normalized difference vegetation index (NDVI), reflectance from 290 visible-shortwave infrared (VSWIR) bands, the first derivatives from those bands, or partial least squares (PLS) x-scores derived from those data. Model metrics and cross-validation indicate that the integrated models using the Freeman-Durden components and PLS x-scores improve AGB estimates for both wetland vegetation types. In our study domain over Louisiana’s Wax Lake Delta (WLD), we estimated a mean herbaceous wetland AGB of 3.58 Megagrams/hectare (Mg/ha) and a total of 3551.31 Mg over 9.92 km2, and a mean forested wetland AGB of 294.78 Mg/ha and a total of 27,499.14 Mg over 0.93 km2. While the addition of SAR-derived values to imaging spectrometer data provides a nominal error decrease for herbaceous wetland AGB, this combination significantly improves forested wetland AGB prediction. This integrative approach is particularly effective in forested wetlands as canopy-level biochemical characteristics are captured by the imaging spectrometer in addition to the variable structural information measured by the SAR.

scholarly journals Monitoring the Vegetation Dynamics in the Dongting Lake Wetland from 2000 to 2019 Using the BEAST Algorithm Based on Dense Landsat Time Series

2020 ◽  
Vol 10 (12) ◽  
pp. 4209
Author(s):  
Yaotong Cai ◽  
Shutong Liu ◽  
Hui Lin
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Vegetation Dynamics ◽  
Dongting Lake ◽  
Wetland Vegetation ◽  
Dynamic Monitoring ◽  
Interval Length ◽  
Absolute Difference ◽  
Vegetation Changes ◽  
Temporal Validation

The dynamic monitoring and analysis of wetland vegetation play important roles in revealing the change, restoration and reconstruction of the ecosystem environment. The increasing availability of high spatial-temporal resolution remote sensing data provides an unprecedented opportunity for wetland dynamic monitoring and change detection. Using the reconstructed dense monthly Landsat time series, this study focuses on the continuous monitoring of vegetation dynamics in Dongting Lake wetland, south China, in the last two decades (2000–2019) by using the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method. Firstly, the flexible spatiotemporal data fusion (FSDAF) model is applied to blend Landsat and moderate-resolution imaging spectroradiometer (MODIS) images on the basis of the input image pair selection strategy named “cross-fusion” to generate the monthly time-series normalized difference vegetation index (NDVI) with the spatial resolution of 30 m. Then, the abrupt changes, trend, and seasonality of the vegetation in the study area as well as the uncertainties of change detection are estimated by the BEAST method. Results show that there is a close relationship between the ground true data and the estimated changepoints. A high overall accuracy (OA) of 87.37% and Kappa coefficient of 0.85 were achieved by the proposed framework. Additionally, the temporal validation got the interval intersection of 86.57% and the absolute difference of mean interval length of 6.8 days. All of the results demonstrate that the vegetation changes in the Dongting Lake wetland varied spatially and temporally in the last two decades, because of extreme weathers and anthropogenic factors. The presented approach can accurately identify the vegetation changes and time of disturbance in both the spatial and temporal domains, and also can retrieve the evolution process of wetland vegetation under the influence of climate changes and human activities. Therefore, it can be used to reveal potential causes of the degradation and recovery of wetland vegetation in subtropical areas.

1593: Computer Model to Predict Patency Outcome after Vasoepididymostomy

2004 ◽  
Vol 171 (4S) ◽  
pp. 420-420
Author(s):  
Sijo J. Parekattil ◽  
Paul Shin ◽  
Anthony J. Thomas ◽  
Ashok Agarwal
Keyword(s):  
Computer Model

Interpreting Open- and Closed-Loop Transfer Relations Between Cardiorespiratory Parameters: Lessons Learned from a Computer Model of Beat-to-Beat Cardiovascular Regulation

1997 ◽  
Vol 36 (04/05) ◽  
pp. 237-240
Author(s):  
P. Hammer ◽  
D. Litvack ◽  
J. P. Saul
Keyword(s):  
Computer Model ◽  
Closed Loop ◽  
Computer Models ◽  
Cardiovascular Regulation ◽  
Cardiovascular Control ◽  
Lessons Learned ◽  
Multiple Systems ◽  
Response Characteristics ◽  
Similarities And Differences

Abstract:A computer model of cardiovascular control has been developed based on the response characteristics of cardiovascular control components derived from experiments in animals and humans. Results from the model were compared to those obtained experimentally in humans, and the similarities and differences were used to identify both the strengths and inadequacies of the concepts used to form the model. Findings were confirmatory of some concepts but contrary to some which are firmly held in the literature, indicating that understanding the complexity of cardiovascular control probably requires a combination of experiments and computer models which integrate multiple systems and allow for determination of sufficiency and necessity.

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