Estuarine Ecosystem and Climate Stressors Evaluation: Spatiotemporal NDVI Investigation on Intertidal Sedimentary Landforms | Chapter 2 | Research Advances in Environment, Geography and Earth Science Vol. 3

 

The ratio between the red (R) and near-infrared (NIR) pixel values of satellite imagery is determined by the normalized difference vegetation index or NDVI. At a landscape level, NDVI analyses the spectral wave reflections to investigate the ecosystem health through the greenish canopy dynamism as a main ecosystem's body mass on an intertidal landform. The vegetation cover pixel values (0-255) are transformed into a -1 to +1 scale via the NDVI index. NDVIs showed vegetation canopy was clearly influenced by the coastal environments. Thus, the main environmental and climate change factors can be investigated, including temperature, precipitation, and sea level rise, that are affecting the health and distribution of coastal vegetation. Meanwhile, the runoff and sedimentation rates that can impact coastal areas are investigated as well. This study investigates vegetation canopy dynamics on three various coastal ecosystems in southeastern Australia over the past 40 years (1975-2015), as a comparison case study using NDVI index as an indicator. Landsat 1-8 satellite imagery is used to drive temporal-datasets into NDVI values. These values were then regressed to the climatic and geomorphic variables. At the study sites, results show a clear increase in NDVI at Towamba and Wandandian estuaries, compared to a slight decline at Comerong Island (southeastern Australia). The most significant positive impact on NDVI was the sedimentation rate since it has the potential to provide additional space for vegetation. Except on Comerong Island, the temperature and sea level rise have positive effects as well, but rainfall has no significant effect on the NDVI at any site. Different NDVI trends have been recorded at these three coastal sites reflecting different correlations between the vegetation, climatic and geomorphic (as independent) variables. The largest impact on vegetation cover and, hence, on NDVI was the geomorphological characteristics of such highly dynamic intertidal estuarine landforms, which are subject to active erosion and deposition processes. Using NDVI as an evaluation tool to assess the vegetation cover has produced temporal-dynamic datasets that are correlated with the primary environmental factors. Using this approach, resource managers will be able to assess the possible effects of any environmental changes and then make more informed decisions for conservation sustainability plans.

 

Author(s)details:-

 

Ali K. M. Al-Nasrawi
Environmental Futures Research Centre, School of Earth, Atmosphere and Life Sciences (SEALS), University of Wollongong, NSW 2522, Australia and Department of Geography, University of Babylon, Hillah, Babylon, Iraq.

 

Ameen A. Kadhim
Apply Geography Department, Karbala University, Karbala, Iraq and Department of Geography, Environment and Spatial Sciences, Michigan State University, Michigan, USA.

Sarah M. Hamylton
Environmental Futures Research Centre, School of Earth, Atmosphere and Life Sciences (SEALS), University of Wollongong, NSW 2522, Australia.

 

Brian G. Jones
Environmental Futures Research Centre, School of Earth, Atmosphere and Life Sciences (SEALS), University of Wollongong, NSW 2522, Australia.

 

Please See the book here :-  https://doi.org/10.9734/bpi/raeges/v3/3625G

Robust Data Depth Procedures Based Weighted Estimator with Application in Multivariate Discriminant Analysis | Chapter 9 | Research Updates in Mathematics and Computer Science Vol. 4

Data depth concept used to measure the deepness of a given point in the entire multivariate data cloud. It leads to center-outward ordering of sample points used rather than usual smallest to largest rank. The ordering starts from middle and moves in all directions. Multivariate location and scatter can be computed by using the depth value of each data point. Various depth procedures have been established by many authors. In this paper, a new depth procedure is proposed, namely Modified Mahalanobis Depth (MMD), which calculates depth based on robust distance with Minimum Covariance Determinant (MCD) approach and a weight function is established to determine the location and scale. The superiority of the proposed depth based procedure over existing depth procedures has been studied in simulated environment using R software with respect to application in discriminant analysis. In order to study the superiority of the proposed data depth procedure (MMD) it has been applied in discriminant analysis by comparing the Apparent Error Rate (AER) in the context of classification problems. From the experiment, through real and simulation studies, it reveals that, the AER of proposed data depth procedure is almost similar to existing depth procedures in case of less contamination level. But, when the contamination level, sample size and the number of dimension increases, the AER of the proposed data depth procedure (MMD) is less compared with other existing depth procedures. The proposed depth procedure performs well when compared with the existing procedures even with higher contamination levels and larger sample sizes. Further, it is concluded that the proposed procedure gives more accuracy in the context of classifying the objects when compared with the existing procedures. The proposed procedure is most suitable to the research communities who are performing statistical data analysis techniques by computing the measure of location and scatter. The data depth procedure introduced in this thesis can be beneficial to researchers, who work on machine learning techniques by considering the factors such as noise, computational time, ease algorithm approach and high dimensionality.

Author(s) Details:

R. Muthukrishnan,
Department of Statistics, Bharathiar University, Coimbatore 641 046, Tamil Nadu, India.

G. Poonkuzhali,
Department of Statistics, Bharathiar University, Coimbatore 641 046, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/RUMCS-V4/article/view/14141