4,136 research outputs found

    Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

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    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

    Koneoppimiskehys OPC UA datalle (Industry 4.0)

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    Machine learning has rapidly gained popularity in all industries with the increase of computational power and data gathering capabilities. Process industry is a good candidate for machine learning based modeling due to the large amounts of data gathered and need for accurate process state predictions. In this work the viability of combining the OPC UA protocol with existing open source machine learning libraries to create data driven models and generate real time predictions was studied. Scikit-learn was used to generate soft sensor style models for the butane content of a debutanizer column output. The data for offline model training was dynamically fetched from an OCP UA server and with a trained model predictions could be generated in real time. The accuracy of the generated models needs to be further researched with better methodology and larger datasets.Koneoppiminen on kasvattanut suosiotaan nopeasti kaikilla toimialoilla laskentatehon ja datankeruun kasvaessa. Prosessiteollisuus on hyvä kandidaatti koneoppimispohjaiselle mallinnukselle suurien datamäärien sekä vaadittujen tarkkojen prosessimallien takia. Tässä työssä tutkittiin mahdollisuutta OPC UA protokollan yhdistämistä olemassaolevien avoimen lähdekoodin koneoppimiskirjastojen kanssa mittausdataan perustuvien mallien opettamiseksi ja reaaliaikaisten ennusteiden luomiseksi. Scikit-learn kirjastoa käytettiin luomaan malleja butaaninpoistokolonnin ulostulon butaanipitoisuuden ennustamiseen. Data mallien offline opetukseen ladattiin dynaamisesti OPC UA palvelimelta ja valmiiksi opetetulla mallilla ennusteita voitiin generoida reaaliaikaisesti. Luotujen mallien tarkkuutta täytyy tutkia tarkemmin paremmalla metodologialla ja suuremmilla datamäärillä

    Mapping and monitoring the Akagera wetland in Rwanda

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    Wetland maps are a prerequisite for wetland development planning, protection, and restoration. The present study aimed at mapping and monitoring Rwanda's Akagera Complex Wetland by means of remote sensing and geographic information systems (GIS). Landsat data, spanning from 1987 to 2015, were acquired from different sensor instruments, considering a 5-year interval during the dry season and the shuttle radar topographic mission (SRTM) digital elevation model (30-m resolution) was used to delineate the wetland. The mapping and delineation results showed that the wetland narrowly extends along the Rwanda-Tanzania border from north to south, following the course of Akagera River and the total area can be estimated at 100,229.76 ha. After waterbodies that occupy 30% of the wetland's surface area, hippo grass and Cyperus papyrus are also predominant, representing 29.8% and 29%, respectively. Floodplain and swamp forest have also been inventoried in smaller proportions. While the wetland extent has apparently remained stable, the inhabiting waterbodies have been subject to enormous instability due to invasive species. Lakes, such as Mihindi, Ihema, Hago and Kivumba have been shrinking in extent, while Lake Rwanyakizinga has experienced a certain degree of expansion. This study represents a consistent decision support tool for Akagera wetland management in Rwanda

    Ways of Seeing in Environmental Law: How Deforestation Became an Object of Climate Governance

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    Few areas of law are as deeply implicated with science and technology as environmental law, yet we have only a cursory understanding of how science and technology shape the field. Environmental law, it seems, has lost sight of the constitutive role that science and technology play in fashioning the problems that it targets for regulation. Too often, the study and practice of environmental law and governance take the object of governance--be it climate change, water pollution, biodiversity, or deforestation--as self-evident, natural, and fully-formed without recognizing the significant scientific and technological investments that go into making such objects and the manner in which such investments shape the possibilities for response. This Article seeks to broaden environmental law\u27s field of vision, replacing the tendency to naturalize environmental problems with an exploration of how particular scientific and technological knowledge practices make environmental problems into coherent objects of governance. Such knowledge practices, or ways of seeing, are instrumental in shaping regulatory possibilities and must be interrogated directly as key constituents of particular forms of governance. The argument is developed through a case study of how tropical deforestation, which accounts for some 15 percent of global carbon dioxide emissions but which was expressly excluded from the Kyoto Protocol, has recently become a viable object of climate governance, demonstrating the fundamental importance of conceptual advances in carbon cycle research, the synoptic view of global land cover change made possible by remote sensing, and new carbon accounting techniques in rendering the problem comprehensible for climate policy. Building on the case study, this Article identifies and elaborates on three general ways of seeing--kind-making, calculability, and equivalence--that operate through particular scientific and technical practices to shape and inform the substance of environmental law, with specific attention to the implications of the overall approach for a comprehensive theory of the field

    Earth Observation Open Science and Innovation

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    geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc
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