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1
Comerford, Kathleen M. "The European Jesuit Libraries Provenance Project." Journal of Jesuit Studies 7, no. 2 (January 29, 2020): 299–310. http://dx.doi.org/10.1163/22141332-00702009.
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The European Jesuit Libraries Provenance Project is a census of books once owned by a Jesuit college or house in Europe between the 1550s and 1773. The suppression of the Society of Jesus led to the dispersal of its books, and the ejlpp uses both manuscript inventories and searches in modern libraries to locate the volumes once associated with the Society of Jesus. It is multimedia, digital humanities endeavor, supervised by Kathleen M. Comerford and employing student interns at Georgia Southern University.
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Fraser, Benjamin T., and Russell G. Congalton. "Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories." Remote Sensing 13, no. 15 (July 28, 2021): 2971. http://dx.doi.org/10.3390/rs13152971.
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The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.
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Windrim, Lloyd, and Mitch Bryson. "Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning." Remote Sensing 12, no. 9 (May 6, 2020): 1469. http://dx.doi.org/10.3390/rs12091469.
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Accurate measurements of the structural characteristics of trees such as height, diameter, sweep and taper are an important part of forest inventories in managed forests and commercial plantations. Both terrestrial and aerial LiDAR are currently employed to produce pointcloud data from which inventory metrics can be determined. Terrestrial/ground-based scanning typically provides pointclouds resolutions of many thousands of points per m 2 from which tree stems can be observed and inventory measurements made directly, whereas typical resolutions from aerial scanning (tens of points per m 2 ) require inventory metrics to be regressed from LiDAR variables using inventory reference data collected from the ground. Recent developments in miniaturised LiDAR sensors are enabling aerial capture of pointclouds from low-flying aircraft at high-resolutions (hundreds of points per m 2 ) from which tree stem information starts to become directly visible, enabling the possibility for plot-scale inventories that do not require access to the ground. In this paper, we develop new approaches to automated tree detection, segmentation and stem reconstruction using algorithms based on deep supervised machine learning which are designed for use with aerially acquired high-resolution LiDAR pointclouds. Our approach is able to isolate individual trees, determine tree stem points and further build a segmented model of the main tree stem that encompasses tree height, diameter, taper, and sweep. Through the use of deep learning models, our approach is able to adapt to variations in pointcloud densities and partial occlusions that are particularly prevalent when data is captured from the air. We present results of our algorithms using high-resolution LiDAR pointclouds captured from a helicopter over two Radiata pine forests in NSW, Australia.
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Martín-Alcón, Santiago, Lluís Coll, Miquel De Cáceres, Lídia Guitart, Mariló Cabré, Ariadna Just, and José Ramón González-Olabarría. "Combining aerial LiDAR and multispectral imagery to assess postfire regeneration types in a Mediterranean forest." Canadian Journal of Forest Research 45, no. 7 (July 2015): 856–66. http://dx.doi.org/10.1139/cjfr-2014-0430.
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Wildfires play a major role in driving vegetation changes and can cause important environmental and economic losses in Mediterranean forests, especially where the dominant species lacks efficient postfire regeneration mechanisms. In these areas, postdisturbance vegetation management strategies need to be based on detailed, spatially continuous inventories of the burned area. Here, we present a methodology in which we combine airborne LiDAR and multispectral imagery to assess postfire regeneration types in a spatially continuous way, using a Mediterranean black pine (Pinus nigra Arn ssp. salzmannii) forest that burned in 1998 as a case study. Five postfire regeneration types were obtained by clustering field-plot data using Ward’s method. Two of the five regeneration types presented high tree cover (one clearly dominated by hardwoods and the other dominated by pines), a third type presented low to moderate tree cover, being dominated by hardwoods, and the remaining two types matched to areas dominated by soil–herbaceous or shrub layers with very low or no tree cover (i.e., very low to no tree species regeneration). These five types of regeneration were used to conduct a supervised classification of remote sensing data using a nonparametric supervised classification technique. Compared with independent field validation points, the remote sensing based assessment method resulted in a global classification accuracy of 82.7%. Proportions of regeneration types in the study area indicated a general shift from the former pine-dominated forest toward hardwood dominance and showed no serious problems of regeneration failure. Our methodological approach appears to be appropriate for informing postdisturbance vegetation management strategies over large areas.
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Close, Odile, Beaumont Benjamin, Sophie Petit, Xavier Fripiat, and Eric Hallot. "Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium." Land 7, no. 4 (December 8, 2018): 154. http://dx.doi.org/10.3390/land7040154.
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Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and Forestry (LULUCF) sector are still mostly relying on annual census and periodic surveys for such inventories. This study proposes a new approach based on per-pixel supervised classification using Sentinel-2 imagery from 2016 for mapping greenhouse gas emissions and removals associated with the LULUCF sector in Wallonia, Belgium. The Land Use/Cover Area frame statistical Survey (LUCAS) of 2015 was used as training data and reference data to validate the map produced. Then, we investigated the performance of four widely used classifiers (maximum likelihood, random forest, k-nearest neighbor, and minimum distance) on different training sample sizes. We also studied the use of the rich spectral information of Sentinel-2 data as well as single-date and multitemporal classification. Our study illustrates how open source data can be effectively used for land use and land cover classification. This classification, based on Sentinel-2 and LUCAS, offers new opportunities for LULUCF inventory of greenhouse gas on a European scale.
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Anugrah, Fajrian Noor. "Shifting National Holiday Times In the Context of the Labor Law System." ARRUS Journal of Social Sciences and Humanities 3, no. 2 (April 20, 2023): 104–14. http://dx.doi.org/10.35877/soshum1681.
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In the world of labor today, companies often shift national holiday time for power efficiency, but the rule that working on official holidays must be counted as overtime creates two different perceptions of the shift. The company applies national holidays to weekdays so that the company considers it normal work, but workers see it as a shift. Normative research analyzes laws and regulations. This research inventories and analyzes legislation on adjusting national holiday time in Indonesia's labor law system to determine workers' rights and obligations on government-set holidays and the legal basis for doing so. The study found that employers can order workers to work on government-set public holidays if they meet certain conditions and obligations based on statutory provisions, employment agreements, or collective labor agreements, which are supervised by the government, in this case the Ministry of Manpower, by imposing administrative and criminal penalties. provided that it meets the law and its implementing rules and work agreements or collective bargaining agreements pertaining to the nature and type of labor and the responsibility for Employers to pay overtime compensation for work on public holidays to Laborers.
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Krůček, Martin, Kamil Král, KC Cushman, Azim Missarov, and James R. Kellner. "Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees." Remote Sensing 12, no. 19 (October 7, 2020): 3260. http://dx.doi.org/10.3390/rs12193260.
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We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems >15 cm DBH, and 87% of stems >50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions.
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Rick, Brianna, Daniel McGrath, William Armstrong, and Scott W. McCoy. "Dam type and lake location characterize ice-marginal lake area change in Alaska and NW Canada between 1984 and 2019." Cryosphere 16, no. 1 (January 25, 2022): 297–314. http://dx.doi.org/10.5194/tc-16-297-2022.
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Abstract. Ice-marginal lakes impact glacier mass balance, water resources, and ecosystem dynamics and can produce catastrophic glacial lake outburst floods (GLOFs) via sudden drainage. Multitemporal inventories of ice-marginal lakes are a critical first step in understanding the drivers of historic change, predicting future lake evolution, and assessing GLOF hazards. Here, we use Landsat-era satellite imagery and supervised classification to semi-automatically delineate lake outlines for four ∼5-year time periods between 1984 and 2019 in Alaska and northwest Canada. Overall, ice-marginal lakes in the region have grown in total number (+183 lakes, 38 % increase) and area (+483 km2, 59 % increase) between the time periods of 1984–1988 and 2016–2019. However, changes in lake numbers and area were notably unsteady and nonuniform. We demonstrate that lake area changes are connected to dam type (moraine, bedrock, ice, or supraglacial) and topological position (proglacial, detached, unconnected, ice, or supraglacial), with important differences in lake behavior between the sub-groups. In strong contrast to all other dam types, ice-dammed lakes decreased in number (six fewer, 9 % decrease) and area (−51 km2, 40 % decrease), while moraine-dammed lakes increased (56 more, 26 % and +479 km2, 87 % increase for number and area, respectively) at a faster rate than the average when considering all dam types together. Proglacial lakes experienced the largest area changes and rate of change out of any lake position throughout the period of study and moraine-dammed lakes which experienced the largest increases are associated with clean-ice glaciers (<19 % debris cover). By tracking individual lakes through time and categorizing lakes by dam type, subregion, and topological position, we are able to parse trends that would otherwise be aliased if these characteristics were not considered. This work highlights the importance of such lake characterization when performing ice-marginal lake inventories and provides insight into the physical processes driving recent ice-marginal lake evolution.
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Zhang, He, Marijn Bauters, Pascal Boeckx, and Kristof Van Oost. "Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches." Remote Sensing 13, no. 18 (September 20, 2021): 3777. http://dx.doi.org/10.3390/rs13183777.
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Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.
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Knevels, Raphael, Helene Petschko, Philip Leopold, and Alexander Brenning. "Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software." ISPRS International Journal of Geo-Information 8, no. 12 (December 2, 2019): 551. http://dx.doi.org/10.3390/ijgi8120551.
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With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.
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Pérez-Martín, Enrique, Serafín López-Cuervo Medina, Tomás Herrero-Tejedor, Miguel Angel Pérez-Souza, Julian Aguirre de Mata, and Alejandra Ezquerra-Canalejo. "Assessment of Tree Diameter Estimation Methods from Mobile Laser Scanning in a Historic Garden." Forests 12, no. 8 (July 30, 2021): 1013. http://dx.doi.org/10.3390/f12081013.
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Geo-referenced 3D models are currently in demand as an initial knowledge base for cultural heritage projects and forest inventories. The mobile laser scanning (MLS) used for geo-referenced 3D models offers ever greater efficiency in the acquisition of 3D data and their subsequent application in the fields of forestry. In this study, we have analysed the performance of an MLS with simultaneous localisation and mapping technology (SLAM) for compiling a tree inventory in a historic garden, and we assessed the accuracy of the estimates of diameter at breast height (DBH, a height of 1.30 m) calculated from three fitting algorithms: RANSAC, Monte Carlo, and Optimal Circle. The reference sample used was 378 trees from the Island Garden, a historic garden and UNESCO World Heritage site in Aranjuez, Spain. The time taken to acquire the data by MLS was 27 min 37 s, in an area of 2.38 ha. The best results were obtained with the Monte Carlo fitting algorithm, which was able to estimate the DBH of 77% of the 378 trees in the study, with a root mean squared error (RMSE) of 5.31 cm and a bias of 1.23 cm. The proposed methodology enabled a supervised detection of the trees and automatically estimated the DBH of most trees in the study, making this a useful tool for the management and conservation of a historic garden.
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Mirmazloumi, S. Mohammad, Armin Moghimi, Babak Ranjgar, Farzane Mohseni, Arsalan Ghorbanian, Seyed Ali Ahmadi, Meisam Amani, and Brian Brisco. "Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis." Remote Sensing 13, no. 20 (October 9, 2021): 4025. http://dx.doi.org/10.3390/rs13204025.
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A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.
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Singh, P., V. Maurya, and R. Dwivedi. "PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 721–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-721-2021.
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Abstract. Landslide is one of the most common natural disasters triggered mainly due to heavy rainfall, cloud burst, earthquake, volcanic eruptions, unorganized constructions of roads, and deforestation. In India, field surveying is the most common method used to identify potential landslide regions and update the landslide inventories maintained by the Geological Survey of India, but it is very time-consuming, costly, and inefficient. Alternatively, advanced remote sensing technologies in landslide analysis allow rapid and easy data acquisitions and help to improve the traditional method of landslide detection capabilities. Supervised Machine learning algorithms, for example, Support Vector Machine (SVM), are challenging to conventional techniques by predicting disasters with astounding accuracy. In this research work, we have utilized open-source datasets (Landsat 8 multi-band images and JAXA ALOS DSM) and Google Earth Engine (GEE) to identify landslides in Rudraprayag using machine learning techniques. Rudraprayag is a district of Uttarakhand state in India, which has always been the center of attention of geological studies due to its higher density of landslide-prone zones. For the training and validation purpose, labeled landslide locations obtained from landslide inventory (prepared by the Geological Survey of India) and layers such as NDVI, NDWI, and slope (generated from JAXA ALOS DSM and Landsat 8 satellite multi-band imagery) were used. The landslide identification has been performed using SVM, Classification and Regression Trees (CART), Minimum Distance, Random forest (RF), and Naïve Bayes techniques, in which SVM and RF outperformed all other techniques by achieving an 87.5% true positive rate (TPR).
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Terentieva, I. E., M. V. Glagolev, E. D. Lapshina, A. F. Sabrekov, and S. S. Maksyutov. "High resolution wetland mapping in West Siberian taiga zone for methane emission inventory." Biogeosciences Discussions 12, no. 24 (December 16, 2015): 20149–78. http://dx.doi.org/10.5194/bgd-12-20149-2015.
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Abstract. High latitude wetlands are important for understanding climate change risks because these environments sink carbon and emit methane. Fine scale heterogeneity of wetland landscapes pose challenges for producing the greenhouse gas flux inventories based on point observations. To reduce uncertainties at the regional scale, we mapped wetlands and water bodies in the taiga zone of West Siberia on a scene-by-scene basis using a supervised classification of Landsat imagery. The training dataset was based on high-resolution images and field data that were collected at 28 test areas. Classification scheme was aimed at methane inventory applications and included 7 wetland ecosystem types composing 9 wetland complexes in different proportions. Accuracy assessment based on 1082 validation polygons of 10 × 10 pixels indicated an overall map accuracy of 79 %. The total area of the wetlands and water bodies was estimated to be 52.4 Mha or 4–12 % of the global wetland area. Ridge-hollow complexes prevail in WS's taiga, occupying 33 % of the domain, followed by forested bogs or "ryams" (23 %), ridge-hollow-lake complexes (16 %), open fens (8 %), palsa complexes (7 %), open bogs (5 %), patterned fens (4 %), and swamps (4 %). Various oligotrophic environments are dominant among the wetland ecosystems, while fens cover only 14 % of the area. Because of the significant update in the wetland ecosystem coverage, a considerable revaluation of the total CH4 emissions from the entire region is expected. A new Landsat-based map of WS's taiga wetlands provides a benchmark for validation of coarse-resolution global land cover products and wetland datasets in high latitudes.
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Heinisch, Philipp, and Philipp Cimiano. "A multi-task approach to argument frame classification at variable granularity levels." it - Information Technology 63, no. 1 (February 1, 2021): 59–72. http://dx.doi.org/10.1515/itit-2020-0054.
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Abstract Within the field of argument mining, an important task consists in predicting the frame of an argument, that is, making explicit the aspects of a controversial discussion that the argument emphasizes and which narrative it constructs. Many approaches so far have adopted the framing classification proposed by Boydstun et al. [3], consisting of 15 categories that have been mainly designed to capture frames in media coverage of political articles. In addition to being quite coarse-grained, these categories are limited in terms of their coverage of the breadth of discussion topics that people debate. Other approaches have proposed to rely on issue-specific and subjective (argumentation) frames indicated by users via labels in debating portals. These labels are overly specific and do often not generalize across topics. We present an approach to bridge between coarse-grained and issue-specific inventories for classifying argumentation frames and propose a supervised approach to classifying frames of arguments at a variable level of granularity by clustering issue-specific, user-provided labels into frame clusters and predicting the frame cluster that an argument evokes. We demonstrate how the approach supports the prediction of frames for varying numbers of clusters. We combine the two tasks, frame prediction with respect to media frames categories as well as prediction of clusters of user-provided labels, in a multi-task setting, learning a classifier that performs the two tasks. As main result, we show that this multi-task setting improves the classification on the single tasks, the media frames classification by up to +9.9 % accuracy and the cluster prediction by up to +8 % accuracy.
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Ge, Shaojia, Weimin Su, Hong Gu, Yrjö Rauste, Jaan Praks, and Oleg Antropov. "Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series." Remote Sensing 14, no. 21 (November 4, 2022): 5560. http://dx.doi.org/10.3390/rs14215560.
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Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.
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Kombate, Arifou, Fousseni Folega, Wouyo Atakpama, Marra Dourma, Kperkouma Wala, and Kalifa Goïta. "Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine." Land 11, no. 11 (October 25, 2022): 1889. http://dx.doi.org/10.3390/land11111889.
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Carbon stocks in forest ecosystems, when released as a result of forest degradation, contribute to greenhouse gas (GHG) emissions. To quantify and assess the rates of these changes, the Intergovernmental Panel on Climate Change (IPCC) recommends that the REDD+ mechanism use a combination of Earth observational data and field inventories. To this end, our study characterized land-cover changes and forest-cover dynamics in Togo between 1985 and 2020, using the supervised classification of Landsat 5, 7, and 8 images on the Google Earth Engine platform with the Random Forest (RF) algorithm. Overall image classification accuracies for all target years ranged from 0.91 to 0.98, with Kappa coefficients ranging between 0.86 and 0.96. Analysis indicated that all land cover classes, which were identified at the beginning of the study period, have undergone changes at several levels, with a reduction in forest area from 49.9% of the national territory in 1985, to 23.8% in 2020. These losses of forest cover have mainly been to agriculture, savannahs, and urbanization. The annual change in forest cover was estimated at −2.11% per year, with annual deforestation at 422.15 km2 per year, which corresponds to a contraction in forest cover of 0.74% per year over the 35-year period being considered. Ecological Zone IV (mountainous, with dense semi-deciduous forests) is the one region (of five) that has best conserved its forest area over this period. This study contributes to the mission of forestry and territorial administration in Togo by providing methods and historical data regarding land cover that would help to control the factors involved in forest area reductions, reinforcing the system of measurement, notification, and verification within the REDD+ framework, and ensuring better, long-lasting management of forest ecosystems.
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Wang, Yuan, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang. "Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method." Earth System Science Data 15, no. 8 (August 10, 2023): 3597–622. http://dx.doi.org/10.5194/essd-15-3597-2023.
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Abstract. Precise and continuous monitoring of long-term carbon dioxide (CO2) and methane (CH4) over the globe is of great importance, which can help study global warming and achieve the goal of carbon neutrality. Nevertheless, the available observations of CO2 and CH4 from satellites are generally sparse, and current fusion methods to reconstruct their long-term values on a global scale are few. To address this problem, we propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe on grids of 0.25∘. A total of three datasets are applied in our study, including the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4). Attributed to the significant sparsity of data from GOSAT and OCO-2, the spatiotemporal discrete cosine transform is considered for our fusion task. Validation results show that the proposed method achieves a satisfactory accuracy, with standard deviations of bias (σ) of ∼1.18 ppm for XCO2 and 11.3 ppb for XCH4 against Total Carbon Column Observing Network (TCCON) measurements from 2010 to 2020. Meanwhile, the determination coefficients (R2) of XCO2 and XCH4 reach 0.91 or 0.95 (2010–2014 or 2015–2020) and 0.9 (2010–2020), respectively, after fusion. Overall, the performance of fused results distinctly exceeds that of CAMS-EGG4, which is also superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission inventories for COVID-19 lockdowns in 2020. Moreover, the fused results present coincident spatial patterns with GOSAT and OCO-2, which accurately display the long-term and seasonal changes in globally distributed XCO2 and XCH4. The daily global seamless gridded (0.25∘) XCO2 and XCH4 from 2010 to 2020 can be freely accessed at https://doi.org/10.5281/zenodo.7388893 (Wang et al., 2022a).
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Comer, Patrick J., Jon C. Hak, Daryn Dockter, and Jim Smith. "Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas." Vegetation Classification and Survey 3 (February 15, 2022): 29–43. http://dx.doi.org/10.3897/vcs.67537.
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Aims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area: Our two case studies include national (USA) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods: Both case studies used a supervised modeling approach with the International Vegetation Classification (IVC) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results: The USALANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions: Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe. Abbreviations: CART = Classification and Regression Tree; CONUS = Conterminous United States; DSWE = Dynamic Surface Water Extent; EPA = United States Environmental Protection Agency; FGDC = Federal Geographic Data Committee; IVC = International Vegetation Classification; LANDFIRE = Landscape Fire and Resource Management Planning Tools Project; LFRDB = LANDFIRE Reference Database; LiDAR = Light Detection and Ranging; NDVI = Normalized Difference Vegetation Index; NLCD = National Land Cover Database; USNVC = United States National Vegetation Classification; USA = United States of America; WWF = World Wildlife Fund or Worldwide Fund for Nature.
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Nuryamin, Yamin, Eko Setia Budi, and Abdul Rahman Kadafi. "Sistem Manajemen Inventori Gadget dengan Metode Waterfall." Journal of Information System Research (JOSH) 4, no. 2 (January 28, 2023): 501–7. http://dx.doi.org/10.47065/josh.v4i2.2901.
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Stock management system online can do supervision for the better.Lack of supervision be negative effects for the acceptance or expenditure so management reporting and oversight of the use of goods delayed.The purpose of this research has persediaan management information system in use of terjun mhk gadgets that can be accessed and supervised online.Design system using the unified modelling language, programming language and mysql php database.Research it produces persediaan management information system that provides information goods in and freight out and facilitate report receipts and disbursements goods, so that the be stock the information can be conducted easily.
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Vertehel, Serhii, Viacheslav Vyshniakov, Vitalii Hurelia, Serhii Slastin, Oleh Piskun, Serhii Kharchenko, and Viacheslav Moroz. "Development of a method for creating and updating the cartographic base using space images from «SUPER VIEW-1» satellites." Environmental safety and natural resources 41, no. 1 (April 15, 2022): 89–101. http://dx.doi.org/10.32347/2411-4049.2022.1.89-101.
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Data obtained by remote sensing of land (remote sensing) from space, at this time in the world are widely used to create orthophotos in solving the following tasks: creating inventories and land management; creating and updating maps; planning and management of municipal territories; general monitoring of territories; in architecture and construction; in geological works; in design and survey works; when creating a basic cartographic substrate for various applications. The article presents the authors' views on the possibility of using Earth Remote Sensing data from the «SuperView-1» satellites to create and update cartographic bases based on the capabilities of the NSFCTC. The advantages of using digital orthorectification technology based on space images are presented. The technical characteristics of the SuperView-1 satellites and UNSPI-8.2 receiving station, which makes it possible to receive data from satellites are provided. The algorithm and results of practical experiment for orthophotos creation on a scale of 1:10,000 using space images from SuperView-1 satellites are presented. In general, the data from SuperView-1 satellites have been shown to be suitable for creating orthophotos on a scale of 1:10,000. The application of this technology to create digital cartographic support of territories on the basis of space survey materials will significantly reduce the cost of obtaining planning and cartographic materials, which in turn will reduce the time and cost of designing spatial data infrastructure, preparation of relevant documents for spatial planning. At the same time, it is possible to update planning and cartographic materials by monitoring and adjusting their changes.
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Nusyirwan, Deny, Lukman Hamdani, Hardyansah Hardyansah, Muhammad Idris Syahputra, Marzuki Marzuki, Ilham Fikri, and Tauriq Fuji Nur Akbar. "Pelatihan Tech for Kids untuk Meningkatkan Kesadaran dan Peminatan Siswa tentang Rekayasa sebagai Jalur Karir." PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat 5, no. 2 (March 30, 2020): 173–84. http://dx.doi.org/10.33084/pengabdianmu.v5i2.916.
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Riau Islands Province requires the readiness of Human Resources (HR) who are able to compete in the global market, with its position next to neighboring Malaysia and Singapore can be an advantage and also a weakness if HR is unable to compete globally. Therefore, the electrical engineering department with the tech for kids program seeks to provide personality and technical skills training to elementary and secondary school children in the coastal areas of Tanjung Pinang. The tech for kids program focuses on understanding the engineering design process to be able to produce innovations and products that are able to compete globally and on target. Activities that are carried out routinely weekly are accompanied by students as facilitators who will assist the supervisor during the training. The pattern of training and learning that integrates the technical ability of design using Autodesk inventors with non-technical forms of the ability to think innovatively, critically, and be able to work together with friends in a group will create a superior personality in the era of the industrial revolution 4.0. The training results obtained satisfactory results where students can attend the training enthusiastically and still remain enthusiastic until the end of the activity. The limited use of computer equipment is not an obstacle in the implementation in the field because students are able to manage training schedules well.
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Costa, Wenyka Preston Leite Batista da, Jandeson Dantas da Silva, Rodrigo José Guerra Leone, Maria Naiula Monteiro Pessoa, and Sergio Luiz Pedrosa Silva. "FATORES INFLUENCIADORES DA ADOÇÃO DE UM MÉTODO DE COSTING NA PERSPECTIVA DE PROFISSIONAIS EM CONTABILIDADE COM ATUAÇÃO NO SETOR INDUSTRIAL." RACE - Revista de Administração, Contabilidade e Economia 15, no. 3 (October 4, 2016): 1169. http://dx.doi.org/10.18593/race.v15i3.7285.
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<p>Os métodos de custeio são responsáveis por definir a forma pela qual os custos são apropriados aos seus portadores finais e possuem forte relevância na obtenção das informações gerenciais necessárias para os aspectos decisórios, na mensuração de estoques e na evidenciação dos resultados. Dessa forma, o período de adoção de um método de custeio é uma fase à qual uma entidade deve realizar análise detalhada dos objetivos pertinentes, buscando atender às necessidades dos diversos setores de forma eficiente e eficaz. Nesse sentido, o objetivo com esta pesquisa foi identificar os fatores que influenciam a adoção de um método de custeio nas empresas do setor industrial. A pesquisa possui natureza descritiva e quantitativa; a coleta de dados ocorreu por meio de um questionário eletrônico aplicado a 175 profissionais de contabilidade atuantes no setor industrial. Os resultados mostram que os fatores influenciadores da adoção de um método de custeio, em ordem de influência; são competitividade, gerenciamento, controle, legalidade, planejamento, apropriação, supervisão, comparabilidade, confiabilidade e precisão.</p><p>Palavras-chave: Método de custeio. Contabilidade de custos. Adoção de um método.</p><p> </p><p align="center"><strong><em>Factors influencing the adoption of a cost method in professional perspective in accounting with operations in the industrial sector</em></strong></p><p align="center"><em>Abstract</em></p><p> <strong></strong></p><p><em>The costing methods are responsible for defining the way in which the costs are appropriate to their final carriers and have strong relevance in obtaining the management information necessary for decision-making aspects, in the measurement of inventories and in the disclosure of results. In this way, the period of adoption of a costing method is a stage at which an entity should perform detailed analysis of the relevant objectives, seeking to meet the needs of the many sectors efficiently and effectively. Accordingly, the objective with this research was to identify the factors influencing the adoption of a costing method in industrial companies. The research has descriptive and quantitative nature, the data was collected through an electronic questionnaire applied to 175 accounting professionals working in the industrial sector. The results show that the factors influencing the adoption of a costing method, in order of influence, are competitiveness, management, governance, legality, planning, ownership, supervision, comparability, reliability and accuracy.</em></p><p><em>Keywords: Costing method. Costing accounting. </em><em>Adoption of a method.</em></p>
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DALMASSO, Elsa Inés. "Las Conferencias sobre el Cambio Climático – COP: Compromiso de Estados, Empresas y Comunidad." Revista Em Tempo 17, no. 01 (November 30, 2018): 448. http://dx.doi.org/10.26729/et.v17i01.2630.
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Resumen:
La Convención Marco de las Naciones Unidas para el Cambio Climático (CMNUCC) establecida en mayo de 1992, en la «Cumbre de la Tierra de Río de Janeiro», entró en vigor en marzo de 1994 con la premisa de reforzar la conciencia pública a escala mundial sobre los problemas relativos al Cambio Climático. Entre sus objetivos principales se destaca la estabilización de las concentraciones de Gases de Efecto Invernadero (GEI) en la atmósfera, para impedir riesgos en el sistema climático.
La Conferencia de las Partes (COP) se establece como el órgano supremo de la Convención y la asociación de todos los países que forman parte de ella. Como asimismo lo es en calidad de Reunión de las Partes del Protocolo de Kioto (PK),
En las reuniones anuales de la COP participan expertos en medio ambiente, ministros, jefes de estado y organizaciones no gubernamentales con la función de supervisar y examinar la aplicación de la Convención y del Protocolo. El objetivo es preparar inventarios de las emisiones de gases de efecto invernadero por las fuentes y su absorción por los sumideros, promoviendo y facilitando el intercambio de información sobre las medidas adoptadas y el desarrollo del proceso de negociación entre las Partes de la Convención. Concluyendo con la elaboración de un Compromiso de Estados, Empresas y Comunidad respecto a la regulación de los efectos sobre cambio climático.
Palabras clave: Cambio Climático; Naciones Unidas; Conferencia de las Partes.
Abstract:
The United Nations Framework Convention on Climate Change (UNFCCC) established in May 1992, at the "Earth Summit of Rio de Janeiro"; It entered into force in March 1994 with the premise of strengthening public awareness on a global scale about the problems related to Climate Change. Among its main objectives is the stabilization of concentrations of greenhouse gases (GHG) in the atmosphere, to prevent risks in the climate system.
The Conference of the Parties (COP) is established as the supreme organ of the Convention and the association of all the countries that are part of it. As it is also in the capacity of the Meeting of the Parties to the Kyoto Protocol (KP),
Environmental experts, ministers, heads of state and non-governmental organizations participate in the annual meetings of the COP, with the function of supervising and examining the application of the Convention and the Protocol, in order to prepare inventories of greenhouse gas emissions. greenhouse effect by the sources and their absorption by the sinks, promoting and facilitating the exchange of information on the measures adopted and the development of the negotiation process between the Parties to the Convention. Concluding with the elaboration of a Commitment of States, Companies and Community regarding the regulation of the effects on climate change
Keywords: Climate Change; United Nations; Conference of the Parties
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Callery, Oisín, and Anthony Grehan. "Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies." Frontiers in Marine Science 10 (April 5, 2023). http://dx.doi.org/10.3389/fmars.2023.1139425.
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The patchy nature and overall scarcity of available scientific data poses a challenge to holistic ecosystem-based management that considers the whole range of ecological, social, and economic aspects that affect ecosystem health and productivity in the deep sea. In particular, the evaluation of, for instance, the impact of human activities/climate change, the adequacy and representativity of MPA networks, and the valuation of ecosystem goods and services is hampered by the lack of detailed seafloor habitat maps and a univocal classification system. To maximize the use of current evidence-based management decision tools, this paper investigates the potential application of a supervised machine learning methodology to expand a well-established habitat classification system throughout an entire ocean basin. A multi-class Random Forest habitat classification model was built using the predicted distributions of 6 deep-sea fish and 6 cold-water corals as predictor variables (proxies). This model, found to correctly classify the area covered by an existing European seabed habitat classification system with ~90% accuracy, was used to provide a univocal deep-sea habitat classification for the North Atlantic. Until such time as global seabed mapping projects are complete, supervised machine learning approaches, as described here, can provide the full coverage classified maps and preliminary habitat inventories needed to underpin marine management decision making.
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Prakash, Nikhil, Andrea Manconi, and Simon Loew. "A new strategy to map landslides with a generalized convolutional neural network." Scientific Reports 11, no. 1 (May 6, 2021). http://dx.doi.org/10.1038/s41598-021-89015-8.
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AbstractRapid mapping of event landslides is crucial to identify the areas affected by damages as well as for effective disaster response. Traditionally, such maps are generated with visual interpretation of remote sensing imagery (manned/unmanned airborne systems or spaceborne sensors) and/or using pixel-based and object-based methods exploiting data-intensive machine learning algorithms. Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data. These methods follow a standard supervised learning workflow that involves training a model using a landslide inventory covering a relatively small area. The trained model is then used to predict landslides in the surrounding regions. Here, we propose a new strategy, i.e., a progressive CNN training relying on combined inventories to build a generalized model that can be applied directly to a new, unexplored area. We first prove the effectiveness of CNNs by training and validating on event landslides inventories in four regions after earthquakes and/or extreme meteorological events. Next, we use the trained CNNs to map landslides triggered by new events spread across different geographic regions. We found that CNNs trained on a combination of inventories have a better generalization performance, with a bias towards high precision and low recall scores. In our tests, the combined training model achieved the highest (Matthews correlation coefficient) MCC score of 0.69 when mapping landslides in new unseen regions. The mapping was done on images from different optical sensors, resampled to a spatial resolution of 6 m, 10 m, and 30 m. Despite a slightly reduced performance, the main advantage of combined training is to overcome the requirement of a local inventory for training a new deep learning model. This implementation can facilitate automated pipelines providing fast response for the generation of landslide maps in the post-disaster phase. In this study, the study areas were selected from seismically active zones with a high hydrological hazard distribution and vegetation coverage. Hence, future works should also include regions from less vegetated geographic locations.
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Bhullar, Amanjot, Khurram Nadeem, and R. Ayesha Ali. "Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning." Scientific Reports 13, no. 1 (April 26, 2023). http://dx.doi.org/10.1038/s41598-023-33840-6.
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AbstractLand suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013–2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. Through k-fold cross-validation, we show that compared to the single crop models, our multi-crop model could produce up to a 2.82 fold reduction in mean absolute error for any particular crop. We found that barley, oats, and mixed grains were more tolerant to soil-climate-landscape variations and could be grown in many regions of Canada, while non-grain crops were more sensitive to environmental factors. Predicted crop suitability was associated with a region’s growing season length, which supports climate change projections that regions of northern Canada will become more suitable for agricultural use. The proposed multi-crop model could facilitate assessment of the suitability of northern lands for crop cultivation and be incorporated into cost-benefit analyses.
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Chandel, Abhinav, Wajida Sarwat, Abdul Najah, Sujay Dhanagare, and Meghna Agarwala. "Evaluating methods to map burned area at 30-meter resolution in forests and agricultural areas of Central India." Frontiers in Forests and Global Change 5 (December 20, 2022). http://dx.doi.org/10.3389/ffgc.2022.933807.
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Biomass burning is a major phenomenon that plays an important role in small-scale ecological processes such as vegetation dynamics and soil erosion, and global processes such as hydrological cycles and climate change. However, global fire databases have low accuracies for burned area detection in areas with small fires, low biomass and in woodlands and open forests that characterize Central India. The present study uses higher resolution (30 meter) Landsat imagery to test accuracies for burned area detection using spectral indices (SI), machine learning (ML) algorithms and supervised classification. We find that detection of burned area by global fire product Fire Information for Resource Management System (FIRMS) is very low (<20%). Accuracies are higher for Landsat-based classification of burned area using supervised classification, random forest (RF) and Support Vector Machines (SVM). Accuracies are higher in April–May than in February–March and vary by azimuth angle on the day of image acquisition. RF produced the most consistently high classification accuracies for April (>80%), but had a tendency to misclassify less frequently available land covers; SVM had similar classification accuracies but had a tendency to overfit the model. Both lead to the potential for increasing commission errors and need to be used carefully when predicting burned area. Inclusion of SI had high relative importance in predicting burned area and reduced commission errors. Given these caveats, we recommend using ML algorithms for mapping burned area in the future, as it requires less time investment than classification and can yield consistent results. Accurate mapping of high-resolution fires is important for more accurate inputs into carbon inventories and ecological understanding of land-use dynamics and drivers.
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Meena, Sansar Raj, Omid Ghorbanzadeh, Cees J. van Westen, Thimmaiah Gudiyangada Nachappa, Thomas Blaschke, Ramesh P. Singh, and Raju Sarkar. "Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach." Landslides, January 5, 2021. http://dx.doi.org/10.1007/s10346-020-01602-4.
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AbstractRainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions.
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Frühe, Larissa, Xavier Pochon, Olivier Laroche, Nigel Keeley, Verena Dully, Tristan Cordier, Jan Pawlowski, and Thorsten Stoeck. "Biogeographic structuring of benthic microbial communities influence the establishment of global bioindicators for coastal aquaculture impact monitoring." ARPHA Conference Abstracts 4 (March 4, 2021). http://dx.doi.org/10.3897/aca.4.e64933.
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With an increasing demand for finfish from aquaculture, the importance of monitoring the impact of aquaculture installations on coastal ecosystems becomes more and more important. The traditional approach employs macroinvertebrates as bioindicators to assess the ecological quality (EQ) of near-field benthic ecosystem, which is costly and time consuming. Modern approaches using environmental DNA metabarcodes of microbial organisms (eukaryotes and prokaryotes) have shown to be a powerful alternative. It is crucial that the ecological quality determined by molecular bioindicator signatures mirrors the results of standard macroinvertebrate inventories. Previous studies looking for bioindicators of organic enrichment have so far only analyzed samples within a specific geographic location (e.g. one farm, farms from one country). To answer the question if there are global bioindicators (or core communities) across different biogeographic regions, we analyzed 328 samples from three salmon producing countries (Norway, New Zealand, Scotland) to find commonalities in bacterial and ciliate DNA metabarcodes communities within the same ecological quality groups. We used supervised machine learning (SML) to predict both country origin and ecological status for both bacterial and ciliate eDNA markers. We then investigated if prediction accuracy for ecological quality increases if the dataset is reduced to single-country or two-country subsets to eliminate country/region specific effects. Further, core communities including only cosmopolitan ASVs were tested to detect possible global bioindicators for each individual EQ class. Based on the obtained results, we discuss the potential of a globally applicable database and a global training of an SML algorithm to predict ecological quality in aquafarm sediments using eDNA metabarcodes from benthic bacterial and ciliate communities.
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de Almeida, Laura R., S. Valery Ávila-Mosqueda, Rodolfo Silva, Edgar Mendoza, and Brigitta I. van Tussenbroek. "Mapping the structure of mixed seagrass meadows in the Mexican Caribbean." Frontiers in Marine Science 9 (December 9, 2022). http://dx.doi.org/10.3389/fmars.2022.1063007.
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The physical and ecological importance of seagrass meadows in coastal processes is widely recognized, and the development of tools facilitating characterization of their structure and distribution is important for improving our understanding of these processes. Mixed (multi-specific) meadows in a Mexican Caribbean reef lagoon were mapped employing a multiparameter approach, using PlanetScope remote sensing images, and supervised classification based on parameters related to the structure of the seagrasses meadows, including the cover percentages of seagrass/algae/sediment, algae thalli and seagrass shoot densities, canopy heights and estimated leaf area index (LAI). The cover, seagrass and algae densities, and seagrass canopy heights were obtained using ground truth sampling, while the LAI was estimated using data obtained from long-term monitoring programs. The maps do not show the differentiation of seagrass species, but ground truthing contemplated characterization of the density of Thalassia testudinum, Syringodium filiforme and Halodule wrightii and their respective LAIs. S. filiforme was the dominant species in terms of shoot density, and T. testudinum was dominant in terms of LAI. In the multiparameter-based map four classes were defined, based on the cover and structural characteristics, and its overall accuracy was very high (~90%). Maps based on sediment cover and LAI alone also had 4 classes, but they were less accurate than the multiparameter-based map (~70% and ~80%, respectively). The multiparameter-based seagrass map provided spatially-explicit data on the abundance and structure of seagrasses, useful for future monitoring of the changes in the meadows, and also for studies of that require data of large-scale meadow structure, such as inventories of associated biota, blue carbon storage, or modelling of the local hydrodynamics.
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Potić, Ivan, Ljiljana M. Mihajlović, Vanja Šimunić, Nina B. Ćurčić, and Miroljub Milinčić. "Deforestation as a Cause of Increased Surface Runoff in the Catchment: Remote Sensing and SWAT Approach—A Case Study of Southern Serbia." Frontiers in Environmental Science 10 (June 1, 2022). http://dx.doi.org/10.3389/fenvs.2022.896404.
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In the past two decades, the South part of Serbia has been affected by exploitive and illegal logging. As this trend is not decreasing to this day, there is a need to determine the area where this logging occurred precisely. The consequences of these actions are tremendous, causing the forest owners’ financial loss (regardless of whether it is private or state property) and a negative impact on the environment. Significant environmental and forest management problems deriving from these actions are erosion increase and more frequent torrential floods occurrence in the catchment. Since it is difficult to update the national forest inventories in remote areas, remote sensing techniques using different satellite imagery types can provide up-to-date data. The initial analysis that employed Normalized Difference Vegetation Index (created using Landsat 7 and Landsat 8 imagery) indicates massive deforestation in the research area between 1999 and 2021. Headwaters of the Štavska river catchment is selected as the research area to determine the amount of erosion in two periods—before and after deforestation occurred. Change in land cover (LC) is presented with two LC maps created applying supervised classification to Landsat 7 imagery from 1999 as a pre-deforestation LC state and Landsat 8 imagery acquired in 2021 as the current LC state. The erosion in the catchment for both periods is determined using the Soil and Water Assessment Tool (SWAT). The analysis results show the erosion change incurred as a deforestation effect in the river catchment. With the data obtained by remote sensing and SWAT analysis, it is possible to track changes in the area and acquire essential data, making the right and fast decisions to protect the natural resources economy and make sustainable development possible in this impoverished region.
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Niggli, Matthias. "‘Moving On’—investigating inventors’ ethnic origins using supervised learning." Journal of Economic Geography, January 30, 2023. http://dx.doi.org/10.1093/jeg/lbad001.
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Abstract Patent data provides rich information about technical inventions, but does not disclose the ethnic origin of inventors. In this article, I use supervised learning techniques to infer this information. To do so, I construct a dataset of 96′777 labeled names and train an artificial recurrent neural network with long short-term memory (LSTM) to predict ethnic origins based on names. The trained network achieves an overall performance of 91.4% across 18 ethnic origins. I use this model to predict and investigate the ethnic origins of 2.68 million inventors and provide novel descriptive evidence regarding their ethnic origin composition over time and across countries and technological fields. The global ethnic origin composition has become more diverse over the last decades, which was mostly due to a relative increase of Asian origin inventors. Furthermore, the prevalence of foreign-origin inventors is especially high in the USA, but has also increased in other high-income economies. This increase was mainly driven by an inflow of non-Western inventors into emerging high-technology fields for the USA, but not for other high-income countries.
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"Correction to: ‘Moving On’—investigating inventors’ ethnic origins using supervised learning." Journal of Economic Geography, February 17, 2023. http://dx.doi.org/10.1093/jeg/lbad003.
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AZEVEDO, Jânie Alves de, Melina Martins GOULARTE, Michelle Ferreira de ALMEIDA, and Larissa COMARELLA. "INVENTORY CONTROL SYSTEM: COMPARATIVE OF INVENTORIES FROM THREE HOSPITAL PHARMACIES." Visão Acadêmica 16, no. 2 (June 30, 2015). http://dx.doi.org/10.5380/acd.v16i2.40088.
Full textAbstract:
A gestão do estoque das farmácias hospitalares mediante sistemas de controle tem grande relevância na garantia dos serviços farmacêuticos e na administração dos custos dos serviços de saúde. Esse estudo avaliou o sistema de controle de estoque de três farmácias hospitalares de diferentes unidades de saúde, mediante análises dos inventários realizados antes e após a implantação de melhorias nos sistemas adotados. A acuracidade dos estoques e os valores financeiros foram utilizados como parâmetros de medidas. Verificou-se que as medidas de melhorias adotadas impactaram positivamente no controle de estoque de todas as unidades estudas. O sistema de controle de estoque necessita de supervisão adequada e de acompanhamento constante dos resultados obtidos, medidas para melhorias da gestão dos insumos e alimentação correta das informações são fundamentais para a garantia da acuracidade dos estoques.
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Delgado, Mercedes, and Fiona E. Murray. "Faculty as catalysts for training new inventors: Differential outcomes for male and female PhD students." Proceedings of the National Academy of Sciences 120, no. 36 (August 28, 2023). http://dx.doi.org/10.1073/pnas.2200684120.
Full textAbstract:
STEM PhDs are a critical source of human capital in the economy, contributing to commercial as well as academic science. We examine whether STEM PhD students become new inventors (file their first patent) during their doctoral training at the top 25 U.S. universities (by patenting). We find that 4% of PhDs become new inventors. However, among PhDs of faculty who are themselves top (prolific) inventors, this figure rises to 23%. These faculty train 44% of all the new inventor PhDs by copatenting with their advisees. We also explore whether new inventor PhDs are equally distributed by gender. In our university sample, the female share of new inventors is 9% points (pp) lower than the female share of PhDs. Several channels contribute to this: First, female PhDs are less likely to be trained by top inventor advisors (TIs) than male PhDs. Second, they are less likely to be trained by (the larger number of) male top inventors: The estimated gap in the female % of PhDs between female and male TIs is 7 to 9 pp. Third, female PhDs (supervised by top inventors and especially by other faculty) have a lower probability of becoming new inventors relative to their male counterparts. Notably, we find that male and female top inventors have similar rates of transforming their female advisees into new inventors at 4 to 8 pp lower (17 to 26% lower rate) than for male advisees. The gap remains at 4 pp comparing students of the same advisor and controlling for thesis topic.
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Holt, Susan L., Mary Farrell, and Richard H. Corrigan. "Developing the SVN CLEI: A Novel Psychometric Instrument for Evaluating the Clinical Learning Environment of Student Veterinary Nurses in the UK." Journal of Veterinary Medical Education, January 25, 2022. http://dx.doi.org/10.3138/jvme-2021-0136.
Full textAbstract:
Student veterinary nurses (SVNs) in the United Kingdom can spend over half their training time within the clinical learning environment (CLE) of a training veterinary practice before achieving clinical competency. Sociocultural complexities and poor management within the CLE may have a significant impact on the learning experiences of SVNs, as has been found in studies involving student human nurses. The aim of this research was to develop and validate the SVN CLE Inventory (CLEI) using principal component analysis (PCA), via a cross-sectional design, based on inventories already established in human nursing CLEs. The SVN CLEI was distributed to SVNs via an online survey over a 3-month period, generating 271 responses. PCA resulted in a valid and reliable SVN CLEI with 25 items across three factors with a total variance explained of 61.004% and an overall Cronbach’s alpha (α) of .953 (factor 1: clinical supervisor support of learning [α = .935]; factor 2: pedagogical atmosphere of the practice [α = .924]; factor 3: opportunities for engagement [α = .698]). Gaining student feedback is a requirement set out by the Royal College of Veterinary Surgeons Standards Framework for Student Veterinary Nurse Education and Training, and the SVN CLEI can be used to complement the current evaluation of the training veterinary practice CLE. This will facilitate development of a more comparable, consistent, and positive experience for SVNs during clinical training in the UK.