This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects.
Considerable attention has been paid to the potentially confounding effects of geological and seasonal variation on outputs from bioassessments in temperate streams, but our understanding about these influences is limited for many tropical systems. We explored variation in macroinvertebrate assemblage composition and the environmental characteristics of 3 rd -to 5 th -order streams in a geologically heterogeneous tropical landscape in the wet and dry seasons. Study streams drained catchments with land cover ranging from predominantly forested to agricultural land, but data indicated that distinct water-chemistry and substratum conditions associated with predominantly calcareous and silicate geologies were key determinants of macroinvertebrate assemblage composition. Most notably, calcareous streams were characterized by a relatively abundant noninsect fauna, particularly a pachychilid gastropod snail. The association between geological variation and assemblage composition was apparent during both seasons, but significant temporal variation in compositional characteristics was detected only in calcareous streams, possibly because of limited statistical power to detect change at silicate sites, or the limited extent of our temporal data. We discuss the implications of our findings for tropical bioassessment programs. Our key findings suggest that geology can be an important determinant of macroinvertebrate assemblages in tropical streams and that geological heterogeneity may influence the scale of temporal response in characteristic macroinvertebrate assemblages.
Flood monitoring systems are crucial for flood management and consequence mitigation in flood prone regions. Different remote sensing techniques are increasingly used for this purpose. However, the different approaches suffer various limitations, including cloud and weather effects (optical data), and low spatial resolution and poor colour presentation (synthetic aperture radar data). This study fuses two data types (Landsat and Sentinel-1) to overcome these limitations and produce better quality images for a prototype flood application in the Vietnam Open Data Cube (VODC). Visual and quantitative evaluation of fused image quality revealed improvement in the images compared with the original scenes. Ground-truth data was used to develop the study flood extraction algorithm and we found a good agreement between our results and SERVIR Mekong (a joint initiative by the US agency for International Development (USAID), National Aeronautics and Space Administration (NASA), Myanmar, Thailand, Cambodia, Laos and Vietnam) maps. While the algorithm is run on a personal computer (PC), it has a clear potential to be developed for application on a big data system.
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