Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia

AbstractThe spatio-temporal characteristics of remote sensing are considered to be the primary advantage in environmental studies. With long-term and frequent satellite observations, it is possible to monitor changes in key biophysical attributes such as phenological characteristics, and relate them to climate change by examining their correlations. Although a number of remote sensing methods have been developed to quantify vegetation seasonal cycles using time-series of vegetation indices, there is limited effort to explore and monitor changes and trends of vegetation phenology in the Monsoon Southeast Asia, which is adversely affected by changes in the Asian monsoon climate. In this study, MODIS EVI and TRMM time series data, along with field survey data, were analyzed to quantify phenological patterns and trends in the Monsoon Southeast Asia during 2001–2010 period and assess their relationship with climate change in the region. The results revealed a great regional variability and inter-annual fluctuation in vegetation phenology. The phenological patterns varied spatially across the region and they were strongly correlated with climate variations and land use patterns. The overall phenological trends appeared to shift towards a later and slightly longer growing season up to 14 days from 2001 to 2010. Interestingly, the corresponding rainy season seemed to have started earlier and ended later, resulting in a slightly longer wet season extending up to 7 days, while the total amount of rainfall in the region decreased during the same time period. The phenological shifts and changes in vegetation growth appeared to be associated with climate events such as EL Niño in 2005. Furthermore, rainfall seemed to be the dominant force driving the phenological changes in naturally vegetated areas and rainfed croplands, whereas land use management was the key factor in irrigated agricultural areas

Laboratory Spectroscopy Assessments of Rainfed Paddy Soil Samples on Visible and Near-Infrared Spectroscopy Reflectance for Estimating Soil Organic Carbon

Visible and near-infrared spectroscopy is a rapid, less expensive, and nondestructive alternative to conventional methods of soil analysis. This study aimed to investigate appropriate soil sample preparations and particle sizes for estimating soil organic carbon (SOC) through the use of laboratory spectroscopy. Rainfed paddy soils were sampled from 240 sampling sites to record their spectral reflectance and to measure their SOC contents in the laboratory. Partial least squares regression was applied to select the best model to estimate SOC using soil spectra. The results showed that the highest accuracy of SOC estimation was gained from soil samples prepared by 2 mm sieving. A short-wave infrared region was the most appropriate spectral wavelength for SOC estimation of rainfed paddy soil. Although the model showed potential in SOC prediction, the accuracy of partial least squares regression prediction in each spectral region varied between sampling times. Therefore, these models and methods should be further tested in soils sampled from different seasons and other regions to prove consistent validity. However, these results are useful for wavelength selection and soil sample preparation in future laboratory spectroscopy

Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images

Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well.During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI predictionbased on satellite images. Our hybrid model consists of two parts as follows: • The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation. • The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI.From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them.Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication

Semantic Labeling in Remote Sensing Corpora Using Feature Fusion-Based Enhanced Global Convolutional Network with High-Resolution Representations and Depthwise Atrous Convolution

One of the fundamental tasks in remote sensing is the semantic segmentation on the aerial and satellite images. It plays a vital role in applications, such as agriculture planning, map updates, route optimization, and navigation. The state-of-the-art model is the Enhanced Global Convolutional Network (GCN152-TL-A) from our previous work. It composes two main components: (i) the backbone network to extract features and ( i i ) the segmentation network to annotate labels. However, the accuracy can be further improved, since the deep learning network is not designed for recovering low-level features (e.g., river, low vegetation). In this paper, we aim to improve the semantic segmentation network in three aspects, designed explicitly for the remotely sensed domain. First, we propose to employ a modern backbone network called “High-Resolution Representation (HR)” to extract features with higher quality. It repeatedly fuses the representations generated by the high-to-low subnetworks with the restoration of the low-resolution representations to the same depth and level. Second, “Feature Fusion (FF)” is added to our network to capture low-level features (e.g., lines, dots, or gradient orientation). It fuses between the features from the backbone and the segmentation models, which helps to prevent the loss of these low-level features. Finally, “Depthwise Atrous Convolution (DA)” is introduced to refine the extracted features by using four multi-resolution layers in collaboration with a dilated convolution strategy. The experiment was conducted on three data sets: two private corpora from Landsat-8 satellite and one public benchmark from the “ISPRS Vaihingen” challenge. There are two baseline models: the Deep Encoder-Decoder Network (DCED) and our previous model. The results show that the proposed model significantly outperforms all baselines. It is the winner in all data sets and exceeds more than 90% of F 1 : 0.9114, 0.9362, and 0.9111 in two Landsat-8 and ISPRS Vaihingen data sets, respectively. Furthermore, it achieves an accuracy beyond 90% on almost all classes

Cross-platform mobile app development for disseminating public health information to travelers in Thailand: development and usability

Abstract Background The risk of disease is a key factor that travelers have identified when planning to travel abroad, as many people are concerned about getting sick. Mobile devices can be an effective means for travelers to access information regarding disease prevalence in their planned destinations, potentially reducing the risk of exposure. Methods We developed a mobile app, ThaiEpidemics, using cross-platform technology to provide information about disease prevalence and status for travelers to Thailand. We aimed to assess the app’s usability in terms of engagement, search logs, and effectiveness among target users. The app was developed using the principle of mobile application development life cycle, for both iOS and Android. As its data source, the app used weekly data from national disease-surveillance reports. We conduced our study among visitors to the Travel Clinic in the Hospital for Tropical Diseases, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. The participants were informed that the app would collect usage and search logs related to their queries. After the second log-in, the app prompted participants to complete an e-survey regarding their opinions and preferences related to their awareness of disease prevalence and status. Results We based our prototype of ThaiEpidemics on a conceptualized framework for visualizing the distribution of 14 major diseases of concern to tourists in Southeast Asia. The app provided users with functions and features to search for and visualize disease prevalence and status in Thailand. The participants could access information for their current location and elsewhere in the country. In all, 83 people installed the app, and 52 responded to the e-survey. Regardless of age, education, and continent of origin, almost all e-survey respondents believed the app had raised their awareness of disease prevalence and status when travelling. Most participants searched for information for all 14 diseases; some searched for information specifically about dengue and malaria. Conclusions ThaiEpidemics is evidently potentially useful for travelers. Should the app be adopted for use by travelers to Thailand, it could have an impact on wider knowledge distribution, which might result in decreased exposure, increased prophylaxis, and therefore a potential decreased burden on the healthcare system. For app developers who are developing/implementing this kind of app, it is important to address standardization of the data source and users’ concerns about the confidentiality and safety of their mobile devices

Bayesian spatiotemporal analysis of malaria infection along an international border: Hlaingbwe Township in Myanmar and Tha-Song-Yang District in Thailand

Abstract Background One challenge in moving towards malaria elimination is cross-border malaria infection. The implemented measures to prevent and control malaria re-introduction across the demarcation line between two countries require intensive analyses and interpretation of data from both sides, particularly in border areas, to make correct and timely decisions. Reliable maps of projected malaria distribution can help to direct intervention strategies. In this study, a Bayesian spatiotemporal analytic model was proposed for analysing and generating aggregated malaria risk maps based on the exceedance probability of malaria infection in the township-district adjacent to the border between Myanmar and Thailand. Data of individual malaria cases in Hlaingbwe Township and Tha-Song-Yang District during 2016 were extracted from routine malaria surveillance databases. Bayesian zero-inflated Poisson model was developed to identify spatial and temporal distributions and associations between malaria infections and risk factors. Maps of the descriptive statistics and posterior distribution of predicted malaria infections were also developed. Results A similar seasonal pattern of malaria was observed in both Hlaingbwe Township and Tha-Song-Yang District during the rainy season. The analytic model indicated more cases of malaria among males and individuals aged ≥ 15 years. Mapping of aggregated risk revealed consistently high or low probabilities of malaria infection in certain village tracts or villages in interior parts of each country, with higher probability in village tracts/villages adjacent to the border in places where it could easily be crossed; some border locations with high mountains or dense forests appeared to have fewer malaria cases. The probability of becoming a hotspot cluster varied among village tracts/villages over the year, and some had close to no cases all year. Conclusions The analytic model developed in this study could be used for assessing the probability of hotspot cluster, which would be beneficial for setting priorities and timely preventive actions in such hotspot cluster areas. This approach might help to accelerate reaching the common goal of malaria elimination in the two countries

SPATIO-TEMPORAL ANALYSIS OF MALARIA INCIDENCE ALONG HLAINGBWE TOWNSHIP IN MYANMAR AND THA SONG YANG DISTRICT IN THAILAND

Background: Malaria stays a serious public health problem in many countries of the world. The border regions are difficult to control for the malaria elimination due to the importation or reintroduction of malaria. A key to address such problem is reinforcing of surveillance activities with rapid identification.  The objective of the study was to describe the malaria incidence rate and analyze the space and time distribution of malaria incidence rate in the high endemic border areas between Myanmar andThailand, the Hlaingbwe Township and Tha Song Yang District.  Methodology: Daily malaria data were collected, using a passive surveillance system, from patients visiting local health facilities in both Tha Song Yang and Hlaingbwe regions. ArcMap software version 10.4.1 was used to describe the disease mapping of malaria incidence rate in both regions. Results: Compared to their counterparts, male gender had higher malaria incidence rates in both Tha Song Yang and Hlaingbwe regions. Non-Thai people had higher incidence rate than Thai in Tha Song Yang district. The higher incidence rates had seasonal pattern and the pattern was similar in both regions. The areas with a higher incidence rate could be seen in both inner side and along Thai- Myanmar border (upper and lower parts) in Tha Song Yang area. But in Hlaingbwe Township, the higher incidence rate occurred only in the inner and upper parts except for Me La Yaw and Tar Le areas which are situated along the Thai-Myanmar border. Along the border, the higher incidence rates were connected to the adjacent area in upper and lower parts between these two regions. Conclusion: The descriptive statistics and presented map in this study gave the health policy makers an important overview of malaria situation in this regions in order to intervene high risk areas more effectively, and distribute the resources in a useful manner