Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical climates. This issue has become more severe and increasingly occurred in the Mekong River Delta of Vietnam. The main objective of this work is to map soil salinity intrusion in Ben Tre province located on the Mekong River Delta of Vietnam using the Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined with five state-of-the-art machine learning models, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Gaussian Processes (GP), Support Vector Regression (SVR), and Random Forests (RF). For this purpose, 63 soil samples were collected during the field survey conducted from 4–6 April 2018 corresponding to the Sentinel-1 SAR imagery. The performance of the five models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r). The results revealed that the GP model yielded the highest prediction performance (RMSE = 2.885, MAE = 1.897, and r = 0.808) and outperformed the other machine learning models. We conclude that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change.

No abstract

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

No abstract

Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against insitu measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R 2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m 2 .m −2 , NRMSE = 9.57%, MAE = 0.1 m 2 .m −2 ). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.

The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

Nghiên cứu này được thực hiện, nhằm đánh giá các nhân tố tác động đến ý định sử dụng dịch vụ ngân hàng điện tử (NHĐT) của khách hàng tại Ngân hàng Thương mại Cổ phần Đầu tư và Phát triển Việt Nam (BIDV) - CN. Tây Sài Gòn, bằng các phương pháp: Đánh giá độ tin cậy của thang đo; Phân tích nhân tố khám phá (EFA); Phân tích hồi quy bội, với dữ liệu 421 khách hàng tại BIDV - CN. Tây Sài Gòn. Kết quả nghiên cứu cho thấy, ý định sử dụng dịch vụ NHĐT của khách hàng bị ảnh hưởng bởi các nhân tố, bao gồm: ảnh hưởng xã hội; nhân tố thương hiệu; nhận thức hữu ích; nhận thức dễ sử dụng; công nghệ và bảo mật. Trên cơ sở kết quả nghiên cứu, tác giả đề xuất các hàm ý quản trị, nhằm nâng cao ý định sử dụng dịch vụ NHĐT của khách hàng tại BIDV - CN. Tây Sài Gòn.

Accurate detection of landslide spatial patterns is vital in susceptibility, hazard, and risk disaster mapping. Geographic Information System (GIS)-based quantitative approaches provide a rigorous procedure for gaining deep insight into natural and anthropogenic landslides from different scales. This study aims to implement a comprehensive solution for retrieving the landslide susceptibility index. For that purpose, a landslide inventory was performed in a tropical monsoon climate region, with a magnitude of elevation spanning from −65 m to 1,900 m above the sea, considering 15 fundamental causative factors belonging to the groups of topography, hydrology, geology, land cover conditions and anthropogenic activities, and weather. The frequency ratio (FR) was implemented to rank subclasses in each causative factor. For factor weight estimation, different approaches were applied, including the subjective-based analytic hierarchy process (AHP), objective-based Shannon entropy (SE), and a synergy of both methods (AHP–SE), built on these two approaches. Out of the 271 identified landslide locations, 70% (196 points) were used for training and the remaining 30% (71 points) were applied for validation. The results showed that the integrated AHP–SE outperformed the two individual approaches, with the area under the receiver operating characteristic curve (AUC) reaching 0.876, following SE (AUC = 0.848) and AHP (AUC = 0.818). In the synergy approach, the climate pattern under tropical monsoons was confirmed as the most crucial landslide-predisposing factor. The research contributes to a novel discussion by integrating knowledge-based consultation and statistical data analysis of accurate geospatial data, incorporating significant explanatory factors toward a reliable landslide-prone zonation over space and time dimensions.