Long-term river extent dynamics and transition detection using remote sensing: Case studies of Mekong and Ganga River.

River dynamics are currently comprehensively studied at either a bankline or reach-scale level. Monitoring large-scale and long-term river extent dynamics provides fundamental insights relevant to the impact of climatic factors and anthropogenic activities on fluvial geomorphology. This study analyzed the two most populous rivers, Ganga and Mekong, to understand the river extent dynamics using 32 years of Landsat satellite data (1990-2022) in a cloud computing platform. This study categorizes river dynamics and transitions using the combination of pixel-wise water frequency and temporal trends. This approach can demarcate the river channel stability, areas affected by erosion and sedimentation, and the seasonal transitions in the river. The results illustrate that the Ganga river channel is found to be relatively unstable and very prone to meandering and migration as almost 40 % of the river channel has been altered in the past 32 years. The seasonal transitions, such as lost seasonal and seasonal to permanent changes are more prominent in the Ganga river, and the dominance of meandering and sedimentation in the lower course is also illustrated. In contrast, the Mekong river has a more stable course with erosion and sedimentation observed at sparse locations in the lower course. However, the lost seasonal and seasonal to permanent changes are also dominant in the Mekong river. Since 1990, Ganga and Mekong rivers have lost approximately 13.3 % and 4.7 % of their seasonal water respectively, as compared to the other transitions and categories. Factors such as climate change, floods, and man-made reservoirs could all be critical in triggering these morphological changes.

Spatial calibration and PM2.5 mapping of low-cost air quality sensors.

The data quality of low-cost sensors has received considerable attention and has also led to PM2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.

Health inequality: a longitudinal study on geographic variations in lung cancer incidence and mortality in Taiwan.

BACKGROUND: This study is aimed toward an analysis of the variations in lung cancer incidence and mortality, adjusted by population factors (age, gender, and year), between administrative areas. METHODS: This is a retrospective study, using 2005-2014 data in each administrative area from the Taiwan Cancer Registry database organized by the Health Promotion Administration. The yearly age-standardized (overall) and crude (stratified by gender and age) incidence/mortality (and their growth rates) for each administrative area were collected and calculated. We used a mixed model to analyze the repeated measurements of yearly incidence and mortality rates and used general linear regression to analyze their growth rates. RESULTS: It was found that male and elderly populations had significantly higher lung cancer incidence and mortality in Taiwan. After adjusting for gender, age, and calendar year, there were no significant variations in incidence among the administrative areas, while the mortality in Yilan County was significantly higher than that in Taipei City (the capital city of Taiwan). On the other hand, the incidence in the female and younger population and mortality growth rates were higher. The incidence growth rate in Keelung City was significantly lower than that in Taipei City, while there were no significant variations in mortality growth rate among administrative areas. CONCLUSIONS: This study found an inequality in the lung cancer burden among cities in Taiwan, which can serve as the basis for future resource allocations for lung cancer prevention and treatment in Taiwan.

Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption.

Land subsidence caused by groundwater overexploitation is a serious global problem. The acquisition of spatiotemporal pumping rates and volumes is a first step for water managers to develop a strategic plan for mitigating land subsidence. This study investigates an empirical formulation to estimate the monthly maximum pumped volume over a 10-year period based on electric power consumption data. A spatiotemporal variability analysis of monthly pumped volume is developed to provide an improved understanding of seasonal pumping patterns and the role of irrigation type. The analysis of regional pumped volume provides an approximation of the spatiotemporal patterns of the variations in pumped volume. Results show the effects of climate, seasonal changes in pumping from irrigation, and the local differences in pumping caused to crop types. A seasonal pumped volume peak occurs annually, with the highest and least pumped volumes occurring in March (highest peak) and September (lowest peak), respectively. However, the majority of the historical maximum pumped volumes have occurred during the last few years. Extracted volumes continue to increase in some locations. The analysis reveals increasing trends in pumping, thereby possibly providing the locations where increased effective stresses may lead to land subsidence.

Spectral and spatial kernel water quality mapping.

An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya-Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water.Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.

Establishment of Regional Concentration-Duration-Frequency Relationships of Air Pollution: A Case Study for PM2.5.

Poor air quality usually leads to PM2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration-duration-frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control.

Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland.

Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can visualize the topographic slope and terrain concavities and convexities in the hue, saturation, and value (HSV) color system. Various combinations of the topographic parameters can be used in the relief map, for instance, using wetness index for upstream representation. In particular, 3P relief maps are integrated from three critical topographic parameters including wetness or aspect, slope, and openness data. This study offers an effective way to explore the combination of topographic parameters in visualizing terrain features using multi-parameter relief maps in badlands and in showing the effects of smoothing and parameter selection. The multi-parameter relief images of high-resolution DEMs clearly show micro-topographic features, e.g., popcorn-like morphology and rill.

Spectral Feature Selection Optimization for Water Quality Estimation.

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m - 3 to 6.37 mg · m - 3 , and the Pearson's correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.

PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models.

An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter (PM2.5) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and PM2.5 data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed PM2.5 and AOD data, were used for mapping of PM2.5 over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution (DT3K) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) "Optical_Depth_Land_And_Ocean". AOD observations were also obtained from the merged DT and DB (deep blue) product (DTB3K) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-PM2.5 with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of PM2.5 from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after PM2.5 mapping. The hotspot and spatial variability of PM2.5 maps can give us a summary of the spatiotemporal patterns of PM2.5 variations.

Minimizing Spatial Variability of Healthcare Spatial Accessibility-The Case of a Dengue Fever Outbreak.

Outbreaks of infectious diseases or multi-casualty incidents have the potential to generate a large number of patients. It is a challenge for the healthcare system when demand for care suddenly surges. Traditionally, valuation of heath care spatial accessibility was based on static supply and demand information. In this study, we proposed an optimal model with the three-step floating catchment area (3SFCA) to account for the supply to minimize variability in spatial accessibility. We used empirical dengue fever outbreak data in Tainan City, Taiwan in 2015 to demonstrate the dynamic change in spatial accessibility based on the epidemic trend. The x and y coordinates of dengue-infected patients with precision loss were provided publicly by the Tainan City government, and were used as our model's demand. The spatial accessibility of heath care during the dengue outbreak from August to October 2015 was analyzed spatially and temporally by producing accessibility maps, and conducting capacity change analysis. This study also utilized the particle swarm optimization (PSO) model to decrease the spatial variation in accessibility and shortage areas of healthcare resources as the epidemic went on. The proposed method in this study can help decision makers reallocate healthcare resources spatially when the ratios of demand and supply surge too quickly and form clusters in some locations.

GIS-aided planning of insecticide spraying to control dengue transmission.

BACKGROUND: The purpose of this paper is to integrate a multi-objective integer programming formulation and geographic information system (GIS) into dynamically planning the insecticide spraying area for preventing the transmission of dengue fever. METHODS: The optimal spraying area to combat dengue infections is calculated by the multi-objective integer programming model using the dengue epidemic in 2007 in Tainan City of southern Taiwan and is compared with the areas actually sprayed by the local health department. The dynamic epidemic indicators (i.e. frequency, intensity and duration) that identify major temporal characteristics of the dynamic process of an epidemic are all incorporated into the model. RESULTS: The results indicate that the model can design the spraying area effectively when the trade-off between the coverage of dengue epidemics risk and area compactness is considered. CONCLUSIONS: The model provides an alternative way to obtain a cost-effective spraying area in controlling future dengue epidemics. The proposed model in this study will be beneficial for strategically allocating dengue control resources.

GPS/MEMS INS data fusion and map matching in urban areas.

This paper presents an evaluation of the map-matching scheme of an integrated GPS/INS system in urban areas. Data fusion using a Kalman filter and map matching are effective approaches to improve the performance of navigation system applications based on GPS/MEMS IMUs. The study considers the curve-to-curve matching algorithm after Kalman filtering to correct mismatch and eliminate redundancy. By applying data fusion and map matching, the study easily accomplished mapping of a GPS/INS trajectory onto the road network. The results demonstrate the effectiveness of the algorithms in controlling the INS drift error and indicate the potential of low-cost MEMS IMUs in navigation applications.

Identifying the relationships between water quality and land cover changes in the Tseng-Wen reservoir watershed of Taiwan.

The effects on water quality of land use and land cover changes, which are associated with human activities and natural factors, are poorly identified. Fine resolution satellite imagery provides opportunities for land cover monitoring and assessment. The multiple satellite images after typhoon events collected from 2001 to 2010 covering land areas and land cover conditions are evaluated by the Normalized Difference Vegetation Index (NDVI). The relationship between land cover and observed water quality, such as suspended solids (SS) and nitrate-nitrogens (NO(3)-N), are explored in the study area. Results show that the long-term variations in water quality are explained by NDVI data in the reservoir buffer zones. Suspended solid and nitrate concentrations are related to average NDVI values on multiple spatial scales. Annual NO(3)-N concentrations are positively correlated with an average NDVI with a 1 km reservoir buffer area, and the SS after typhoon events associated with landslides are negatively correlated with the average NDVI in the entire watershed. This study provides an approach for assessing the influences of land cover on variations in water quality.

Adaptation of land-use demands to the impact of climate change on the hydrological processes of an urbanized watershed.

The adaptation of land-use patterns is an essential aspect of minimizing the inevitable impact of climate change at regional and local scales; for example, adapting watershed land-use patterns to mitigate the impact of climate change on a region's hydrology. The objective of this study is to simulate and assess a region's ability to adapt to hydrological changes by modifying land-use patterns in the Wu-Du watershed in northern Taiwan. A hydrological GWLF (Generalized Watershed Loading Functions) model is used to simulate three hydrological components, namely, runoff, groundwater and streamflow, based on various land-use scenarios under six global climate models. The land-use allocations are simulated by the CLUE-s model for the various development scenarios. The simulation results show that runoff and streamflow are strongly related to the precipitation levels predicted by different global climate models for the wet and dry seasons, but groundwater cycles are more related to land-use. The effects of climate change on groundwater and runoff can be mitigated by modifying current land-use patterns; and slowing the rate of urbanization would also reduce the impact of climate change on hydrological components. Thus, land-use adaptation on a local/regional scale provides an alternative way to reduce the impacts of global climate change on local hydrology.

Recharge signal identification based on groundwater level observations.

This study applied a method of the rotated empirical orthogonal functions to directly decompose the space-time groundwater level variations and determine the potential recharge zones by investigating the correlation between the identified groundwater signals and the observed local rainfall records. The approach is used to analyze the spatiotemporal process of piezometric heads estimated by Bayesian maximum entropy method from monthly observations of 45 wells in 1999-2007 located in the Pingtung Plain of Taiwan. From the results, the primary potential recharge area is located at the proximal fan areas where the recharge process accounts for 88% of the spatiotemporal variations of piezometric heads in the study area. The decomposition of groundwater levels associated with rainfall can provide information on the recharge process since rainfall is an important contributor to groundwater recharge in semi-arid regions. Correlation analysis shows that the identified recharge closely associates with the temporal variation of the local precipitation with a delay of 1-2 months in the study area.

Evaluating and mapping of spatial air ion quality patterns in a residential garden using a geostatistic method.

Negative air ions (NAI) produce biochemical reactions that increase the levels of the mood chemical serotonin in the environment. Moreover, they benefit both the psychological well being and the human body's physiological condition. The aim of this research was to estimate and measure the spatial distributions of negative and positive air ions in a residential garden in central Taiwan. Negative and positive air ions were measured at thirty monitoring locations in the study garden from July 2009 to June 2010. Moreover, Kriging was applied to estimate the spatial distribution of negative and positive air ions, as well as the air ion index in the study area. The measurement results showed that the numbers of NAI and PAI differed greatly during the four seasons, the highest and the lowest negative and positive air ion concentrations were found in the summer and winter, respectively. Moreover, temperature was positively affected negative air ions concentration. No matter what temperature is, the ranges of variogram in NAI/PAI were similar during four seasons. It indicated that spatial patterns of NAI/PAI were independent of the seasons and depended on garden elements and configuration, thus the NAP/PAI was a good estimate of the air quality regarding air ions. Kriging maps depicted that the highest negative and positive air ion concentration was next to the waterfall, whereas the lowest air ions areas were next to the exits of the garden. The results reveal that waterscapes are a source of negative and positive air ions, and that plants and green space are a minor source of negative air ions in the study garden. Moreover, temperature and humidity are positively and negatively affected negative air ions concentration, respectively. The proposed monitoring and mapping approach provides a way to effectively assess the patterns of negative and positive air ions in future landscape design projects.

Hotspot analysis of spatial environmental pollutants using kernel density estimation and geostatistical techniques.

Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary investigation, kernel density estimation (KDE) was a technique used for hotspot analysis of soil pollution from a set of observed occurrences of hazards. In addition, the study estimates the hazardous probability of each heavy metal using geostatistical techniques such as the sequential indicator simulation (SIS) and indicator kriging (IK). Results show that there are multiple hotspots for these four heavy metals and they are strongly correlated to the locations of industrial plants and irrigation systems in the study area. Moreover, the pollution hotspots detected using the KDE are the almost same to those estimated using IK or SIS. Soil pollution hotspots and polluted sampling densities are clearly defined using the KDE approach based on contaminated point data. Furthermore, the risk of hazards is explored by these techniques such as KDE and geostatistical approaches and the hotspot areas are captured without requiring exhaustive sampling anywhere.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.

Effects of land cover changes induced by large physical disturbances on hydrological responses in Central Taiwan.

This study analyzes the significant impacts of typhoons and earthquakes on land cover change and hydrological response. The occurrence of landslides following typhoons and earthquakes is a major indicator of natural disturbance. The hydrological response of the Chenyulan watershed to land use change was assessed from 1996 to 2005. Land use changes revealed by seven remote images corresponded to typhoons and a catastrophic earthquake in central Taiwan. Hydrological response is discussed as the change in quantities and statistical distributions of hydrological components. The land cover change results indicate that the proportion of landslide relative to total area increased to 6.1% after the Chi-Chi earthquake, representing the largest increase during the study period. The study watershed is dominated by forest land cover. Comparisons of hydrological components reveal that the disturbance significantly affects base flow and direct runoff. The hydrological modeling results demonstrate that the change in forest area correlates with the variation of base flow and direct runoff. Base flow and direct runoff are sensitive to land use in discussions of distinction. The proposed approach quantifies the effect of typhoons and earthquakes on land cover changes.

Using a hybrid approach to optimize experimental network design for aquifer parameter identification.

This research develops an optimum design model of groundwater network using genetic algorithm (GA) and modified Newton approach, based on the experimental design conception. The goal of experiment design is to minimize parameter uncertainty, represented by the covariance matrix determinant of estimated parameters. The design problem is constrained by a specified cost and solved by GA and a parameter identification model. The latter estimates optimum parameter value and its associated sensitivity matrices. The general problem is simplified into two classes of network design problems: an observation network design problem and a pumping network design problem. Results explore the relationship between the experimental design and the physical processes. The proposed model provides an alternative to solve optimization problems for groundwater experimental design.