Impacts of spatial heterogeneity patterns on long-term trends of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature time series

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE). Land surface temperature (LST) is a crucial parameter for global climate change studies. LST changes are also directly associated with the large-scale changes in land cover. Previous studies carried out a comparative analysis of satellite-derived LST response between periods before and after homogenous land cover changes. We present an alternative approach that quantifies long-term LST variability in response to various land use/land cover change (LULCC) patterns over Phuket Island, Thailand, from 2003 to 2017. First, four Moderate Resolution Imaging Spectroradiometer (MODIS) overpass times of LST time series were adjusted for seasonal effects using a cubic spline function to preserve the number of original data and enable estimates of LST dynamics and trends using the generalized least squared models. Second, LULCC patterns were classified according to land cover type conversion and spatial pattern transformations between the years 2000 and 2016. Spatial homogeneity and heterogeneity were quantified by the coverage percentage for each land use and land cover (LULC) type within a given location. Finally, the influence of LULCC patterns on the long-term spatiotemporal behavior of LST was assessed using the generalized estimating equation model. Results showed that different land cover transitions influence the dynamics of daytime LST but not the nighttime LST. The proportion of different land cover types within an LST pixel and transition amounts contributed to the quantity of increasing surface temperature, especially over impervious surface types. Diverse LULCC patterns with considerations of spatial heterogeneity improved our insight about a relatively strong effect of combined LULC types on LST responses. The climatic effect through the gradual conversion of heterogeneous land cover is necessary to be considered in climate research studies

An Ensemble Method: Case-Based Reasoning and the Inverse Problems in Investigating Financial Bubbles

This paper presents an ensemble approach and model; IPCBR, that leverages the capabilities of Case based Reasoning (CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) in time series datasets from historical stock market prices. The framework proposes to use a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem; Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns. The technique brings a novel perspective to the problem of asset bubbles predictability. Conventional research practice uses traditional forward approaches to predict abnormal fluctuations in financial time series; conversely, this work proposes a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points. This suggests a deviation from the existing research trend

Annual seasonality extraction using the cubic spline function and decadal trend in temporal daytime MODIS LST data

© 2017 by the author. Examining climate-related satellite data that strongly relate to seasonal phenomena requires appropriate methods for detecting the seasonality to accommodate different temporal resolutions, high signal variability and consecutive missing values in the data series. Detection of satellite-based Land Surface Temperature (LST) seasonality is essential and challenging due to missing data and noise in time series data, particularly in tropical regions with heavy cloud cover and rainy seasons. We used a semi-parametric approach, involving the cubic spline function with the annual periodic boundary condition and weighted least square (WLS) regression, to extract annual LST seasonal pattern without attempting to estimate the missing values. The time series from daytime Aqua eight-day MODIS LST located on Phuket Island, southern Thailand, was selected for seasonal extraction modelling across three different land cover types. The spline-based technique with appropriate number and placement of knots produces an acceptable seasonal pattern of surface temperature time series that reflects the actual local season and weather. Finally, the approach was applied to the morning and afternoon MODIS LST datasets (MOD11A2 and MYD11A2) to demonstrate its application on seasonally-adjusted long-term LST time series. The surface temperature trend in both space and time was examined to reveal the overall 10-year period trend of LST in the study area. The result of decadal trend analysis shows that various Land Use and Land Cover (LULC) types have increasing, but variable surface temperature trends

Statistical model for land surface temperature change over mainland southeast Asia

© Geoinformatics International. This study presents an alternative statistical methodology for estimating changes in land surface temperatures over mainland Southeast Asia (SEA). The method comprises of seasonal adjusting and autocorrelation filtering of MODIS LST time series obtained from 2000 to 2019 at systematic 45 sample locations. Furthermore, the filtered seasonal-adjusted LST time series were estimated to quantify the decadal change of LST using linear regression model. The long-term dynamic of temperature change was revealed by curve fitting using a spline model with different knots. The overall LST changes in sub-regional and regional scale were estimated using multivariate regression model which adjusted for spatial correlation and aggregated information of LST change from all individual sample locations irrespective of their strength of statistical evidence (p-value). The final result showed that the surface temperature change in the SEA region increases by 0.126 °C/decade. 95% confident interval for increasing ranges between 0.04 to 0.21 °C/decade, which shows evidence of substantial warming surface in this region

Understanding enviromental factors associated with cyanobacterial bloom

Occurrences of cyanobacterial abundance and their consequence in toxin produced by the bloom-forming cyanobacterium Microcystis aeruginosa were studied based on the data collected from 2003 to 2005 in the Wingecarribee reservoir, New South Wales, Australia. Environmental conditions associated with cyanobacterial abundance were high pH, low dissolved oxygen, low light penetration, high total nitrogen, and low nitrate/nitrite-nitrogen and ammonium-nitrogen (p-values < 0.05, r² = 0.80). Total nitrogen was found to be a nutrient limiting for cyanobacterial abundance in this reservoir rather than total phosphorous. The toxic bloom-forming cyanobacterium M. aeruginosa was most dominant in summer 2004 and autumn 2005 when a ratio of total nitrogen to total phosphorous was high (95%CI: 19.81--23.96) due to low total phosphorous concentration (95%CI: 18--24 μg per litre). However, microcystin concentration was detected only in summer 2004 despite high toxic bloom-forming cyanobacterium M. aeruginosa at the time. Our study suggested that the formation of toxic or non-toxic cyanobacterial blooms in the reservoir was associated more with nitrogen than with phosphorous and toxic cyanobacterial bloom was influenced by high ratio of total nitrogen to total phosphorous and ammonium-nitrogen. Microcystin concentration may not be a good indicator for M. aeruginosa dominance.8 page(s