Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

Global warming is becoming more serious and causing changes in rainfall pattern and runoff regime in most river basins. Exploration of the changes will help develop appropriate management and adaptation strategies. This study presents an assessment of changes in rainfall and runoff in the upper Thu Bon River basin in central Vietnam in the near-term (2026-2035) climate using direct Coupled Model Intercomparison Project Phase 5 (CMIP5) high-resolution model outputs. A nearly calibration-free parameter rainfall-runoff model was employed to explore the runoff response in the study basin. Most model simulations have detected greater decreases in the near-term runoff in the dry season compared with those of any preceding decades in the baseline (1979-2008) climate, though the rainfall in this period is expected to increase slightly. Meanwhile, monsoonal season flooding has the potential to become more severe, and Japanese models project further increase in the intensity of such extreme weather events. The results also indicate that the treatment of the model physical parameterization schemes tends to contribute more sensitivity to the future projections.

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

A land use/land cover map is an important input for different applications. However, the accuracy of land cover maps remains a great uncertainty and mapping accuracy assessment is not well-documented. The objective of this paper is to examine the relationship between overall accuracy and the number of classification classes by conducting a literature review of land cover/ land use studies. The results revealed a weak negative correlation between the map's accuracy and the number of classes. The paper suggests a decrease of 0.77% map's overall accuracy with respect to the increase of 1 land cover class. The average overall accuracy produced by 05 sensor types does not show the big difference. In addition, high spatial resolution sensor such as Airborne might not be always advantageous for producing high overall accuracy map since its accuracy depends on several factors including the number of land cover classes.

No abstract

Exploring potential floods is both essential and critical to making informed decisions for adaptation options at a river basin scale. The present study investigates changes in flood extremes in the future using downscaled CMIP5 (Coupled Model Intercomparison Project—Phase 5) high-resolution ensemble projections of near-term climate for the Upper Thu Bon catchment in Vietnam. Model bias correction techniques are utilized to improve the daily rainfall simulated by the multi-model climate experiments. The corrected rainfall is then used to drive a calibrated supper-tank model for runoff simulations. The flood extremes are analyzed based on the Gumbel extreme value distribution and simulation of design hydrograph methods. Results show that the former method indicates almost no changes in the flood extremes in the future compared to the baseline climate. However, the later method explores increases (approximately 20%) in the peaks of very extreme events in the future climate, especially, the flood peak of a 50-year return period tends to exceed the flood peak of a 100-year return period of the baseline climate. Meanwhile, the peaks of shorter return period floods (e.g., 10-year) are projected with a very slight change. Model physical parameterization schemes and spatial resolution seem to cause larger uncertainties; while different model runs show less sensitivity to the future projections.

KeywordsGlobal warming has caused dramatic changes in regional climate variability, particularly regarding fluctuations in temperature and rainfall. Thus, it is predicted that river flow regimes will be altered accordingly. The purpose of this paper is to present the results of modeling such changes by simulating discharge using the HEC-HMS model. The precipitation was projected using superhigh resolution multiple climate models (20 km resolution) with newly updated emission scenarios as the input for the HEC-HMS model for flow analysis at the Red River Basin in the northern area of Vietnam. The findings showed that climate change impact on the river flow regimes tend towards a decrease in the dry season and a longer duration of flood flow. A slight runoff reduction is simulated for November while a considerable runoff increase is modeled for July and August amounting to 30% and 25%, respectively. The discharge scenarios serve as a basis for water managers to develop suitable adaptation methods and responses on the river basin scale. 82Journal of Natural Resources and Development 2016; 06: 81 -91 DOI number: 10.5027/jnrd.v6i0.09 Climate change is believed to be one of the predominant challenges for mankind in the 21 st century. It has resulted in immense adverse effects on human and natural systems around the world. Many fields are also being impacted by climate change. For example, a decline of agriculture production and heightening risk of animal and plant extinction are created by rising temperatures; severe flood events are leading to the destruction of infrastructure and loss of lives; and severe droughts occurring in dry seasons will likely lead to water conflict. A regional assessment of climate change on mankind was to some extent addressed in the Fifth Assessment Report by the Intergovernmental Panel on Climate Change [1].The key factors of climate change are the increases in temperature and variability of precipitation. According to observed data, the last decade has been recorded as the warmest in the last hundred years. . Increases in temperature are likely to lead to change in hydrological cycles, particularly the growth of spatiotemporal variation in rainfall. It is expected that river flow regimes will fluctuate. Flow in most tropical areas is predicted to rise because of the higher frequency of extreme precipitation. At the same time, more serious drought events during dry seasons may lead to water shortage and further inland salinity intrusion.The assessment of climate change impacts on hydrology has been addressed for several years. It has been constantly revised thanks to the improvement of climate model outputs regarding spatiotemporal resolution and projection capability. Most estimations are based chiefly upon the coupling method between global atmospheric general circulation models (GCMs), which are set up to simulate the past and current climate and then used to project the future state of the global climate with specific greenhouse gas emission scenarios and hydrological mod...