Coastal landscape classification using convolutional neural network and remote sensing data in Vietnam

Hierarchical classification for improving parcel-scale crop mapping using time-series Sentinel-1 data

Parcel-scale crop classification utilizing time-series satellite observations is of significant importance in precision agriculture. The prior knowledge that crop types can be organized in a hierarchical tree structure is beneficial for improving crop classification. Moreover, the crop hierarchy aligns with the coarse-to-fine cognitive process of geographic scenes. Based on the crop hierarchy, this study developed a general hierarchical classification framework for enhancing crop mapping using time-series Sentinel-1 data. Central to this method is a deep-learning-based hierarchical classification model that explores and makes use of crop hierarchical knowledge. First, preprocessed Sentinel-1 data were geometrically overlaid onto farmland parcel maps to derive parcel-scale time-series features. Second, we constructed a hierarchical crop type system for study areas based on the crop phenology of labeled crop-type samples. Third, we developed a deep-learning-based hierarchical classification model to identify crop types for each parcel, to generate final crop-type classification maps. The proposed approach was further discussed and verified through the implementation of parcel-scale time-series crop hierarchical classifications in a study area in France with farmland parcel maps and time-series Sentinel-1 data. The classification results, indicating significant improvements greater than 4.0% in overall accuracy and 5.0% in F1 score over comparative methods, demonstrated the effectiveness of the proposed method in learning multi-scale time-series features for hierarchical crop classification utilizing Sentinel-1 data sequences.

Determining the factors impacting the quality of life among the general population in coastal communities in central Vietnam

People living in coastal areas are frequently affected by natural disasters, such as floods and storms. This study aimed to assess the quality of life (QoL) of people living in disadvantaged coastal communes (subdivision of Vietnam) and identify their associated factors by using the World Health Organization's quality of life instrument (WHOQOL-BREF). To achieve this, a cross-sectional descriptive study was conducted on 595 individuals aged 18 years and above living in the coastal communes in Thua Thien Hue province, Vietnam, from October 2022 to February 2023. The results showed that the mean overall QoL (mean ± SD) was 61.1 ± 10.8. Among the four domains of QoL, the physical health (57.2 ± 12.3) domain had a lower score than the psychological health (61.9 ± 13.0), social relations (63.4 ± 13.4), and environment (61.9 ± 13.3) domains. The QoL score of the domains for participants affected by flooding was significantly lower than that of those not affected, except for social relations. Multivariable logistic regression showed that subjects with not good QoL had the educational background with no formal education (Odds ratio (OR) = 2.63, 95% CI 1.19-5.83), fairly poor/poor households (OR = 2.75, 95% CI 1.48-5.12), suffered Musculoskeletal diseases (OR = 1.61, 95% CI 1.02-2.56), unsatisfaction with health status (OR = 5.27, 95% CI 2.44-11.37), family conflicts (OR = 4.51, 95%CI 2.10-9.69), and low levels of social support (OR = 2.62; 95% CI 1.14-6.02). The analysis also revealed that workers (OR = 0.17, 95% CI 0.04-0.66) had a better QoL than farmer-fisherman. QoL in disadvantaged coastal communes was low, with the lowest scores in the physical health domain. Based on the socioeconomic factors associated with not good QoL identified here, it is recommended that local authorities take more appropriate and practical measures to increase support, including measures for all aspects of physical health, psychological health, social relations, and the living environment, especially for people affected by floods.

Deep learning models for monitoring landscape changes in a UNESCO Global Geopark

By identifying Earth heritage sites, UNESCO Global Geoparks (UGGps) have promoted geo-tourism and regional economic prosperity. However, commercial and tourism development has altered the natural contexts of these geoparks, diminishing their initial value. Before implementing land use policies, spatial landscape parameters should be monitored in multiple dimensions and in real time. This study aims to develop Bilateral Segmentation Network (BiSeNet) models employing an upgraded U-structured neural network in order to monitor land use/cover changes and landscape indicators in a Vietnamese UGGp. This network has proven effective at preserving input image data and restricting the loss of spatial information in decoding data. To demonstrate the utility of deep learning, eight trained BiSeNet models were evaluated against Maximum Likelihood, Support Vector Machine, and Random Forest. The trained BSN-Nadam model (128x128), with a precision of 94% and an information loss of 0.1, can become a valuable instrument for analyzing and monitoring monthly changes in land uses/covers once tourism activities have been rapidly expanded. Three tourist routes and 41 locations in the Dak Nong UGGp were monitored for 30 years using three landscape indices: Disjunct Core Area Density (DCAD), Total Edge Contrast Index (TECI), Shannon's Diversity Index (SHDI), based on the results of the model. As a result, 18 identified geo-sites in the Daknong Geopark have been influenced significantly by agricultural and tourist activities since 2010, making these sites less uniform and unsustainable management. It promptly alerts UNESCO management to the deterioration of geological sites caused by urbanization and tourist development.

Comparison between U-shaped structural deep learning models to detect landslide traces

Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.