The PlanetScope (PS) satellite constellation, developed by Planet Labs Inc., represents a significant advancement in Earth observation, offering high spatial resolution and daily revisit capabilities. This study provides a comprehensive bibliometric analysis of PS satellite imagery, exploring its utilization in scientific research from 2017 to 2023. Using data extracted from the Scopus database, 582 documents were analyzed to uncover the publication trends, key research disciplines, collaboration networks, and research themes related to PS imagery. The results highlight the increasing use of PS data in Earth and Planetary Sciences, Environmental Science, and Computer Science, with a notable concentration of research outputs from the United States, China, and Brazil. Furthermore, our findings indicate that PS data is applied in diverse fields, including land use/land cover classification, agriculture, environmental monitoring, and disaster assessment. Notably, machine learning techniques are increasingly applied to PS data, enhancing analysis capabilities. Despite the growing adoption of PS imagery, challenges related to data access, particularly in low-income regions, were identified, and PS data often plays a secondary or supplementary role in many studies. Recommendations for enhancing interdisciplinary collaboration, expanding open-access data programs, and integrating advanced processing techniques are proposed to maximize the impact of PS data in addressing global environmental challenges. This study provides valuable insights into the evolving landscape of PS-based research, emphasizing the potential of PS data and identifying areas for future exploration.

Water resources play a crucial role in the global water cycle and are affected by human activities and climate change. However, the impacts of hydropower infrastructures on the surface water extent and volume cycle are not well known. We used a multi-satellite approach to quantify the surface water storage variations over the 2000-2020 period and relate these variations to climate-induced and anthropogenic factors over the whole basin. Our results highlight that dam operations have strongly modified the water regime of the Mekong River, exhibiting a 55 % decrease in the seasonal cycle amplitude of inundation extent (from 3178 km2 to 1414 km2) and a 70 % decrease in surface water volume (from 1109 km3 to 327 km3) over 2000-2020. In the floodplains of the Lower Mekong Basin, where rice is cultivated, there has been a decline in water residence time by 30 to 50 days. The recent commissioning of big dams (2010 and 2014) has allowed us to choose 2015 as a turning point year. Results show a trend inversion in rice production, from a rise of 40 % between 2000 and 2014 to a decline of 10 % between 2015 and 2020, and a strong reduction in aquaculture growth, from +730 % between 2000 and 2014, to +53 % between 2015 and 2020. All these results show the negative impact of dams on the Mekong basin, causing a 70 % decline in surface water volumes, with major repercussions for agriculture and fisheries over the period 2000-2020. Therefore, new future projects such as the Funan Techo canal in Cambodia, scheduled to start construction at the end of 2024, will particularly affect 1300 km2 of floodplains in the lower Mekong basin, with a reduction in the amount of water received, and other areas will be subjected to flooding. The human, material and economic damage could be catastrophic.

This work investigates the efficacy of L-band and C-band Synthetic Aperture Radar (SAR) sensors onboard ALOS-2 and Sentinel-1 satellites, as compared to optical sensors onboard Sentinel-2 satellite, for mapping open water of the Tri An reservoir, one of the largest artificial reservoirs in South Vietnam, during the 2016-2023 period. The Google Earth Engine (GEE) was the primary computing platform to pre-process all satellite observations. The Otsu threshold algorithm was employed to generate water/non-water maps derived from the VH- and HH-polarized backscatter coefficient data acquired by Sentinel-1 and ALOS-2 satellites and from the Modified Normalized Difference Water Index (MNDWI) data acquired by Sentinel-2 satellite, respectively. The findings reveal the stability of Tri An reservoir’s surface water extent from 2017 to 2022, followed by a significant decline of nearly 70% during the dry season of 2023 to approximately 100 km2. This substantial decrease can be explained by the impact of a robust El Niño phase occurring in the region simultaneously. Overall, there is a high consistency between results derived from SAR and optical sensors, but the correlation between Sentinel-1 and Sentinel-2 (R = 0.9774) was higher than that between ALOS-2 and Sentinel-2 (R = 0.9145). During the drought period, both C-band and L-band SAR sensors overestimate the reservoir’s surface water extent due to the similarity in their backscatter coefficient between water and dry flat soil surfaces. This misclassification is more pronounced in ALOS-2 data than Sentinel-1 data, suggesting that the C-band sensor is more suitable than the L-band sensor for mapping the lake’s open water areas.

In this study, high spatial resolution (3 m) PlanetScope (PS) imagery was utilized to map burned areas caused by a wildfire occurring on January 10, 2024, on Co Tien Mountain in Nha Trang city, Khanh Hoa province, South Central Coast of Vietnam. A pre-fire image, acquired ten days earlier, on December 31, 2023, and a post-fire one, acquired nearly one month after, on February 04, 2024, were used to create pre- and post-fire Normalized Difference Vegetation Index (NDVI) maps of the study area, then the difference of NDVI (dNDVI). A threshold (T = 0.20), proposed by the author, was applied to the histogram of the dNDVI product to classify the study area into two clusters: burned pixels (dNDVI > T) and unburned pixels (dNDVI <= T). Classification results estimate that a total of 16.11 ha of grass, reeds, small shrubs and vegetation have been burned out during the wildfire. A field trip is required to map the burned areas using unmanned aerial vehicles (UAVs) for an accurate validation of results derived purely from PS satellite observations. Although lacking a ground truth dataset for validation is a significant limitation, the proposed approach remains beneficial for local managers and decision-makers. It enables the rapid assessment of damages caused by small wildfires and provides essential data for effective disaster management and recovery planning, particularly in remote areas.

The lake Chad region faces a humanitarian crisis with simultaneous risks coming from conflict and fragility. Climate change is compounding the many political, environmental, economic, and security challenges facing the region, exacerbating the already complex security challenges. Climate change is causing higher temperatures and greater fluctuations in rainfall patterns, making it harder for communities around Lake Chad to sustain their livelihoods. At the same time, the conflict between armed opposition groups and state security forces is increasing people’s vulnerability to climate change risks and undermining traditional coping mechanisms. This article presents the findings of the climate-security nexus assessment of the Lake Chad region to inform response options for the region. We conducted locally grounded, participatory conflict analysis. The conflict analysis is based on 229 in-depth one-to-one interviews, which have been conducted by a locally-led research team with affected communities around Lake Chad in all four countries of the region (Cameroon, Chad, Niger, Nigeria). It contributes to an evidence base to assess the specific ways climate change interacts with the risk landscape, systematically analysing how climate change shapes risks and determining appropriate responses for the Lake Chad region. The article sets out four climate-fragility risks: (i) Climate and ecological change increase livelihood insecurity; (ii) Vulnerability heightens as conflict and fragility strain coping capacities.; (iii) Resource conflicts rise due to scarcity.; and (iv) Livelihood insecurity fuel recruitment into armed groups. We argue that the success of stabilisation efforts to end violence in the region hangs on their ability to account for climate risks affecting Lake Chad.

Central Sahel is affected by a reinforcement of rainfall since the beginning of 1990s. This increase in rainfall is affected by high inter-annual variability and is characterized by extreme rain events causing floods of unprecedented magnitude. However, few studies have been carried out on these extreme events. Moreover, with current climate change expected to strengthen the hydrological cycle, we don’t know if these events could become more frequent. Here, we report the hydrological changes that currently occur in the Lake Chad basin. Based on ground observations and satellite data, we focused on the 2022 flood event, demonstrating that it was the most important event from the last 60 years, comparable to what occurred during the last wet period between the 1950s and the 1960s. We showed that under this precipitation regime and if warming is not regulated at a global scale, the return period of the 2022 major riverine flood is expected to be between 2 and 5 years. By using modelling experiments, our study also suggested that in the next decade, future flow rates of the main rivers draining the Lake Chad basin could reach the values observed in the 1950s. These results strongly suggest anticipating water management in a context of poor infrastructural development.

This study compares the capability of Sentinel-1, Sentinel-2, and PlanetScope (PS) satellites in monitoring the variations of surface water of Dai Lai Lake, located in North Vietnam, for the 2018-2023 period. The analysis involves the utilization of Google Earth Engine to partially process Sentinel-1 and Sentinel-2 observations, while PS observations are processed using local computers, to generate VH-polarized backscatter coefficient, Normalized Difference Water Index (NDWI), and Modified of Normalized Difference Water Index (MNDWI) maps. The method for making binary water/non-water maps primarily employs the Otsu algorithm on each single map derived from the previous step. Findings reveal that the lake's water extent remains relatively stable over the 6-year period, and is not strongly affected by the seasonal cycle. Although the spatial distribution patterns of the lake exhibit significant similarity, average water extent of the lake derived from 3-m resolution PS imagery is about 2.17 and 5.60% more than that obtained from 10-m resolution Sentinel-2 and Sentinel-1 imagery, respectively. PS observations are effective for monitoring small lakes, but it is advised to check the quality of its NIR band. Sentinel-2 observations prove great effectiveness for lake monitoring, using both NDWI and MNDWI. For Sentinel-1 observations, potential misclassifications could arise due to similarities in VH-polarized backscatter coefficients between water surfaces and other flat surfaces.

This paper introduces an optimized approach for the integration of digital surface models (DSM), orthophotos, and point clouds constructed by unmanned aerial vehicle (UAV) photogrammetry for mapping topography and canopy height of mangrove forests. Object-oriented classification has been applied to orthophoto to accurately determine open mudflats. Elevation points of the point cloud that fall within open mudflats are used to linearly interpolate a digital terrain model (DTM) of mangroves, which is generally unavailable due to the great challenge for both remote sensing and field measurement methods. A canopy height model (CHM) of mangroves is generated by subtracting the DTM from the DSM. Consequently, the constructed CHM has higher accuracy compared to previous studies using only one fixed ground elevation or the water mean level to represent the irregular topography of mangrove forests. The optimized approach is applied to UAV images acquired in 2014 and 2020 to investigate topographical changes and vertical growth of mangroves in a 4 km 2 estuarine area of Xuan Thuy national park in Vietnam. Results show that the study area is vertically eroded with the rate of -0.015 m year −1 due to the groundwater flows, while the vertical growths of the two dominant mangrove species Kandelia obovata and the Sonneratia caseolaris are 0.242 m year −1 and 0.565 m year −1, respectively. A very weak correlation between topographical changes and vertical growth (R = -0.286) is the empirical evidence for the insignificant influence of erosion and/or deposition on the mangrove growth. This study used sole UAV-SfM method to provide comprehensive information on both the topography and canopy structure of fragmented mangrove forests. A combination of LiDAR data and UAV photogrammetry is recommended for dense mangrove inventory.

Purpose This paper aims to analyze the development of global human resource development (HRD) articles published in journals indexed in the Scopus database since 1960s until present time. Design/methodology/approach A publication collection of 1,905 articles collected from the Scopus database was downloaded and analyzed by using bibliometric techniques available in the VOSviewer and Biblioshiny software. Findings Three different development stages of HRD research have been identified: a seeding stage between 1962 and 1989, a growth stage between 1990 and 2007 and a development stage from 2008 onward. The USA and the UK were the biggest contributors who participated to 30.02% and 12.55% of articles in the collection and received 43.82% and 19.54% of the total number of citations, respectively. Scholars with the most publications and citations are mostly from the USA and the UK, and nine over ten most cited articles having first author’s affiliation located there. Emerald Group is the most popular publishing house, as five over ten most popular journals belong to this publishing house. Originality/value After six decades of development, it is necessary to examine the evolution of HRD research, its characteristics and its intellectual framework as this type of analysis is not yet available in the literature. This study helps scholars better understand this research field, as well as better prepare for future work in HRD.

Since its official establishment in 2010, Google Earth Engine (GEE) has developed rapidly and has played a significant role in the global remote sensing community. A bibliometric analysis was conducted on 1995 peer-reviewed articles related to GEE, indexed in the Scopus database up to December 2022 to investigate its trends and main applications. Our main findings are as follows: (1) The number of GEE-related articles has increased rapidly, with nearly 85% of them published in the last three years; (2) The top three domains where GEE has been extensively applied are earth and planetary sciences, environmental science, and agricultural and biological sciences. The majority of GEE-related articles were authored by scholars from China and the US, accounting for 58% of the total, with US scholars having the largest impact on the community, contributing to over 50% of the total citations; (3) Remote Sensing published the highest number of articles (26.82%), whereas Remote Sensing of Environment received the highest number of citations (30.40%); (4) The applications of GEE covered a broad range of topics, with a focus on land applications, water resource applications, climate change, and crop mapping; (5) Landsat imagery were the most popular and widely used dataset; and (6) Random forest, decision trees, support vector machines were the most commonly used machine learning algorithms in GEE. Although having a few limitations, this type of analysis should be conducted regularly to observe the development of this field on a regular basis, as the number of publications related to GEE is expected to continue to increase strongly in the coming years.

This work estimates the surface water volume variation of the Cambodian Tonle Sap Lake at a monthly scale from 2015-2022. To achieve this, radar Sentinel-1 imagery was processed using the Google Earth Engine platform to generate backscatter coefficient maps. The Otsu method was utilized to identify the optimal threshold to classify each backscatter coefficient map into water or non-water clusters. Additionally, altimetry data from three satellites (i.e., Sentinel-3, Jason-3, and Jason-CS/Sentinel-6) was processed to estimate Tonle Sap Lake’s water level variation using the AlTiS software. Surface water maps of the lake, derived from MODIS and clear-sky Sentinel-2 imagery, were used to validate the lake’s surface water extent time series, while in situ water level data collected at Prek Kdam station was used to validate the variation of the lake’s water height. Our results estimated that the lake’s open water area varies from 2200 to 6000 km2, while its water level ranges from 3.1 to 10.9 m. Combining the two time series, we estimated that Tonle Sap Lake’s water volume varies between approximately -7.2 and 9.4 km3 month-1, which shows high correlation with the variation of the water volume flowing through Chau Doc and Tan Chau stations (R = 0.9528 after removing the time lag). This study highlights the ability of satellite data for lake monitoring, which is very useful in remote areas where gauge stations are limited or unavailable. Future work aims to test the accuracy of the proposed methodology in other types of environments, particularly in mountainous regions of North Vietnam, where the terrain is very steep.

Seven years after the launch of the first Sentinel-1 satellite, its data have been widely used in the scientific community. This study provides the first quantitative analysis of the visibility of the Sentinel-1 mission to the scientific literature through a bibliometric analysis of 1628 articles published in scientific journals during the 2014–2020 period. The main findings show that the number of Sentinel-1 mission-related papers increased significantly over the years, with an annual growth rate of 83%. Remote sensing is the most popular journal where 31.75% of the publication collection has been published. China and the USA are the two most productive countries with a share of 22.30% and 16.22% in the collection. Research based on the Sentinel-1 data covered a wide range of topics in geoscience disciplines. The use of SAR interferometry, focusing on the studies of landslide, earthquake, ground deformation, and subsidence, is the most important research direction using Sentinel-1 data. Image fusion of Sentinel-1 and Sentinel-2 observations for mapping and monitoring applications is the second most important research direction. Other popular research areas are glaciology, soil moisture, agriculture, rice monitoring, and ship detection. This study uses bibliographic data derived only from the Scopus database; therefore, it might not cover all Sentinel-1 related documents. However, this paper is a good reference for researchers who want to use Sentinel-1 data in their studies. The two Sentinel-1 satellites will provide scientific data for years to come, meaning that this type of analysis should be done on a regular basis.

Bibliometric analysis of 3105 publications retrieved from the Scopus database was conducted to evaluate bibliographic content of scientific output on social sciences in Vietnam, for the 2000–2019 period. Our main findings show that the number of publications on social sciences from Vietnam has increased significantly over the last two decades, and there was a spike in the scientific output for the recent three years when the number of publications accounted for 53.76% of the collection. The most productive authors came from a few public research institutes with strong resources as the top 10 institutions participated in 44.22% of the collection. Vietnamese scholars tend not to submit their works to high-ranking journals since five Q1 journals in the top 10 publishing journals published only 6.17% of the collection. For international collaboration, Australia and the United States ranked first and second based on the number of publications and citations. Other countries in top 10 mostly located in Europe and Asia. Research topics were diverse focusing on gender, poverty, HIV, higher education and sustainable development. We suggest that supporting policies and funding need to be provided to help Vietnamese scholars improve their works, and to boost their scientific production in the future.

This study estimates monthly variation of surface water volume of Thac Mo hydroelectric reservoir (located in South Vietnam), during the 2016–2021 period. Variation of surface water volume is estimated based on variation of surface water extent, derived from Sentinel-1 observations, and variation of surface water level, derived from Jason-3 altimetry data. Except for drought years in 2019 and 2020, surface water extent of Thac Mo reservoir varies in the range 50–100 km2, while its water level varies in the range 202–217 m. Correlation between these two components is high (R = 0.948), as well as correlation between surface water maps derived from Sentinel-1 and free-cloud Sentinel-2 observations (R = 0.98), and correlation between surface water level derived from Jason-3 altimetry data and from in situ measurement (R = 0.99; RMSE = 0.86 m). We showed that water volume of Thac Mo reservoir varies between −0.3 and 0.4 km3 month−1, and it is in a very good agreement with in situ measurement (R = 0.95; RMSE = 0.0682 km3 month−1). This study highlights the advantages in using different types of satellite observations and data for monitoring variation of lakes’ water storage, which is very important for regional hydrological models. Similar research can be applied to monitor lakes in remote areas where in situ measurements are not available, or cannot be accessed freely.

Surface water storage is an essential component of the hydrological cycle. Remote sensing offers valuable tools for monitoring both surface water extent from satellite images and water levels from radar altimetry. Combining both information, we were able to estimate the variations of surface water extent and storage in the Lower Mekong Basin from 2000 to 2020. Signatures of the extreme climatic events - floods from 2000 to 2002, of 2011, drought of 2015 clearly appear on both extent and storage. The mean amplitude of these variables shows a strong decrease when comparing the periods of 2000–2010 and 2011–2020. Between these two periods, a large reduction of the annual average number of days with the presence of floods can be observed in most of the Lower Mekong Basin, except around the Tonle Sap (Cambodia) and in some parts of the delta.

For the first time, this study estimates the variation of surface water extent of Nui Coc Lake located in Thai Nguyen province in North Vietnam at high spatial (20 m) and temporal resolution (bi-weekly). The classification methodology was developed based on the use of the Otsu threshold algorithm on the histogram of the backscatter coefficient of the SAR Sentinel-1 signal. Totally, more than 150 SAR Sentinel-1 images have been processed for the 2016-2020 period. Except for extreme drought and flood conditions, the average minimum and maximum of the lake’s surface water extent are 17 km2 (in May) and 24 km2 (in September/October), respectively, and Nui Coc Lake’s surface water was stable during the last five years. Classification results are in good agreement with the corresponding surface water extent maps derived from free-cloud Sentinel-2 images, with the occurrence map derived from the Landsat-derived Global Surface Water (GSW) product, and with in situ precipitation data. Compared to Sentinel-2, the lake’s surface water extent detected from Sentinel-1 is 4-4.5% less. The water occurrence is similar between our results and that derived from the GSW product, but Sentinel-1 data provide more details as its spatial resolution is higher than Landsat. This study clearly shows the great potential of SAR Sentinel-1 data for monitoring small lake’s water surface at low costs, especially over tropical regions.

This study estimates monthly variation of surface water volume of Thac Mo water reservoir, located in Binh Phuoc province, in South Vietnam, for the 2016–2021 period, using SAR Sentinel-1 observations and Jason-3 altimetry data. SAR Sentinel-1 observations have been pre-processed mainly using the Google Earth Engine cloud computing platform to estimate variation of surface water extent, while Jason-3 radar altimetry data has been processed using the AlTiS software to estimate variation of surface water level of the reservoir. Results show a very good agreement between variations of the reservoir's surface water volume derived from satellite data and that derived from in situ measurement, with a correlation of 95.4%.

Bibliometric analysis was performed to study the development of publications related to Industry 4.0 and its key technologies in Vietnam. Comparisons with data from other ASEAN countries, and with global data have been done to identify distinctive characteristics of Industry 4.0 literature from Vietnam. The collection of 1,470 retrieved papers was analysed to answer seven research questions. Our results highlighted some valuable insights of Industry 4.0 literature in Vietnam. The number of papers in Industry 4.0 in Vietnam increased rapidly in recent years, mostly focused on Computer Science, Engineering, and Mathematics. Iran, China, and South Korea were the most productive partner countries with Vietnam in Industry 4.0. Machine learning, artificial intelligence, big data, deep learning, Internet of things, neural networks, and data mining were among the most popular research themes in Industry 4.0 in Vietnam. Vietnam ranked third among 10 Southeast Asian countries, based on the number of published papers in Industry 4.0, but the gap with the two top countries was large. Compared to the global data, the annual growth rate of Industry 4.0 papers in Vietnam, and other Southeast Asian countries was lower. Findings from this work can be helpful for other scholars in establishing potential future research lines related to Industry 4.0 in Vietnam.

Studying the spatial and temporal distribution of surface water resources is critical, especially in highly populated areas and in regions under climate change pressure. There is an increasing number of satellite Earth observations that can provide information to monitor surface water at global scale. However, mapping surface waters at local and regional scales is still a challenge for numerous reasons (insufficient spatial resolution, vegetation or cloud opacity, limited time-frequency or time-record, information content of the instrument, lack in global retrieval method, interpretability of results, etc.). In this paper, we use 17 years of the MODIS (MODerate-resolution Imaging Spectro-radiometer) observations at a 8-day resolution. This satellite dataset is combined with ground expertise to analyse the evolution of surface waters at the Cambodia/Vietnam border in the Upper Mekong Delta. The trends and evolution of surface waters are very significant and contrasted, illustrating the impact of agriculture practices and dykes construction. In most of the study area in Cambodia. surface water areas show a decreasing trend but with a strong inter-annual variability. In specific areas, an increase of the wet surfaces is even observed. Ground expertise and historical knowledge of the development of the territory enable to link the decrease to ongoing excavation of drainage canals and the increase of deforestation and land reclamation, exposing flooded surfaces previously hidden by vegetation cover. By contrast, in Vietnam, the decreasing trend in wet surfaces is very clear and can be explained by the development of dykes dating back to the 1990s with an acceleration in the late 2000s as part of a national strategy of agriculture intensification. This study shows that coupling satellite data with ground-expertise allows to monitor surface waters at mesoscale (<100 × 100 km2), demonstrating the potential of interdisciplinary approaches for water ressource management and planning.