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Academic literature on the topic 'INVENTORIES ARE SUPERVISED'

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Published: 11 September 2023

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Journal articles on the topic "INVENTORIES ARE SUPERVISED"

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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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Dissertations / Theses on the topic "INVENTORIES ARE SUPERVISED"

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SRIVASTAVA, SWATI. "INVENTORY MANAGEMENT AND CONTROL AT HERSHEY COMPANY." Thesis, Delhi Technological University, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18330.

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The objective of the project is to understand the importance of Inventoy control and management within an organization. Productive stock organization ought to achieve the development of the owners' wealth. Therefore, a firm should neither hold over the top inventories nor hold lacking inventories, i.e., it should hold the ideal level of stock. The ideal level ofstock endeavor lies between the reason for pointless and lacking levels. The chocolate business in India the way things are, overpowered by two associations, both multinationals. The market head is Cadbury with a great deal of 70%. The association's brands (Five Star, Jewels, Eclairs, Advantage, Dairy Milk) are pioneers their sections. Hershey Company has a part of stock organization where finished things inventories are directed and controlled. Inventories are supervised by promoting of things over all stock keeping. The division analyzes the sudden change in available stock in a fitting way. The foundations of changes are found and appropriate moves are made. The workplace perceives the things to proceed with the flood framework and gives its contribution to all accomplices As there was no primary research carried out, for secondary information on operations and supply chain management of coca cola, research was taken from various sources. The overall findings from this project shows the organization has finished and exact information on the stock over the units and stock administration at all units extraordinarily improved after execution of SAP. Coordinating desire with result is a significant element for progress, particularly for organizations spread across region of India
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Book chapters on the topic "INVENTORIES ARE SUPERVISED"

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"Comparison of the 1947 Plan with the 1942 Plan and the CNOF Plan To facilitate the adoption of the plan, the Committee for the Normalization of Accounting did not want to upset accounting traditions unduly. Since the 1942 Plan had already been intro­ duced in some companies, it seemed natural that the committee base its work on that plan, and try to improve upon it. The com­ mittee benefited from companies’ experience with the 1942 Plan, and took into account the criticisms that had been expressed of the earlier plan. The 1947 Plan was a major advance over the 1942 Plan. First, to number the first class, the zero was dropped and replaced by the number one to facilitate the use of accounting machines. The zero was used thereafter for statistical accounts. Second, separate classes were created for fixed assets and third-party accounts (short-term receivables and payables). Third, class number 2 of the 1942 Plan, which contained the regularization and engage­ ment accounts, was abolished, and the accounts reallocated to other classes. Fourth, purchases now appeared in class 6 instead of class 3, which was reserved for inventories, and the cumber­ some accounts for purchases added to inventory were eliminated. Lastly, accounts were classified in the same order on the balance sheet and in the chart of accounts. The separation of cost accounting from financial accounting, a feature of the CNOF Plan, was retained, together with the impu­ tation of both expenses and revenues in the cost accounts. As in the CNOF Plan, contra-accounts were placed in the same catego­ ries as the accounts they corrected, and accounts that had the same function in the firm were designated by the same number of digits. The rational classification used in the CNOF Plan was adopted for the balance sheet. However, the committee did not retain the classification into ordres and categories found in the CNOF Plan, since the flexibility of decimal coding was preferred. This meant that, as in the 1942 Plan, the balances of the 1947 chart's classes were meaningless. Applicability of the 1947 Plan The Superior Council for Accounting, created by a January 1947 decree, was to supervise the application of the 1947 Plan. The conditions under which it was to operate were to be specified 295." In Accounting in France (RLE Accounting), 351. Routledge, 2014. http://dx.doi.org/10.4324/9781315871042-52.

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