Mapping the Musamus University Campus Using the Bm Tie Point at Mopah Merauke Airport
DOI:
https://doi.org/10.54783/influencejournal.v4i2.51Keywords:
Survey, Mapping, Musamus.Abstract
Musamus University is one of the state universities located in the easternmost region of the archipelago, which is currently under construction. In the current era of development, the availability of maps is something that cannot be left behind, especially for the physical development of facilities and infrastructure, as progress in the field of science and technology is so rapid. Measurement and mapping work is an integral part of civil engineering work planning or building design. This writing needs to be done in order to provide correct information, the presence of information will be used to plan something. Likewise, mapping vehicles can not only be carried out terrestrially, but also photogrammetrically, and even propagate in space with satellite technology with various advantages. Each vehicle has advantages and disadvantages so it really depends on the mapping, the level of detail of the objects that must be presented, and the coverage of the area to be mapped. And the methodology that the author uses in this study is the Literature methodology and the Observation Methodology. Surveys and mapping were carried out on the Musamus University campus and its supporting infrastructure, the results of the calculation are as follows: top thread = 1550m, middle thread = 1300m, bottom thread = 1050m, optical distance = 50m, flat distance = 49.98m, height difference = 1.51m and angle correlation = 0 1’26’’
References
Arrouays, D., McBratney, A., Bouma, J., Libohova, Z., Richer-de-Forges, A. C., Morgan, C. L., ... & Mulder, V. L. (2020). Impressions of Digital Soil Maps: The Good, the Not So Good, and Making Them Ever Better. Geoderma Regional, 20, e00255.
Ballabio, C., Lugato, E., Fernández-Ugalde, O., Orgiazzi, A., Jones, A., Borrelli, P., ... & Panagos, P. (2019). Mapping LUCAS Topsoil Chemical Properties at European Scale using Gaussian Process Regression. Geoderma, 355, 113912.
Ballabio, C., Panagos, P., Lugato, E., Huang, J. H., Orgiazzi, A., Jones, A., ... & Montanarella, L. (2018). Copper Distribution in European Topsoils: An Assessment Based on LUCAS Soil Survey. Science of The Total Environment, 636, 282-298.
Batjes, N. H., Ribeiro, E., & Van Oostrum, A. (2020). Standardised Soil Profile Data to Support Global Mapping and Modelling (WoSIS Snapshot 2019). Earth System Science Data, 12(1), 299-320.
Behrens, T., Schmidt, K., MacMillan, R. A., & Viscarra Rossel, R. A. (2018). Multi-Scale Digital Soil Mapping with Deep Learning. Scientific Reports, 8(1), 1-9.
Brogi, C., Huisman, J. A., Pätzold, S., Von Hebel, C., Weihermüller, L., Kaufmann, M. S., ... & Vereecken, H. (2019). Large-Scale Soil Mapping using Multi-Configuration EMI and Supervised Image Classification. Geoderma, 335, 133-148.
Brus, D. J. (2019). Sampling for Digital Soil Mapping: A Tutorial Supported by R Scripts. Geoderma, 338, 464-480.
Chabrillat, S., Ben-Dor, E., Cierniewski, J., Gomez, C., Schmid, T., & van Wesemael, B. (2019). Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics, 40(3), 361-399.
Chaney, N. W., Minasny, B., Herman, J. D., Nauman, T. W., Brungard, C. W., Morgan, C. L., ... & Yimam, Y. (2019). POLARIS Soil Properties: 30‐m Probabilistic Maps of Soil Properties Over the Contiguous United States. Water Resources Research, 55(4), 2916-2938.
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., ... & Yan, F. (2019). A Review of the Global Soil Property Maps for Earth System Models. Soil, 5(2), 137-158.
Demattê, J. A., Dotto, A. C., Paiva, A. F., Sato, M. V., Dalmolin, R. S., Maria do Socorro, B., ... & do Couto, H. T. Z. (2019). The Brazilian Soil Spectral Library (BSSL): A General View, Application and Challenges. Geoderma, 354, 113793.
Gallo, B. C., Demattê, J. A., Rizzo, R., Safanelli, J. L., Mendes, W. D. S., Lepsch, I. F., ... & Lacerda, M. P. (2018). Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology. Remote Sensing, 10(10), 1571.
Keskin, H., Grunwald, S., & Harris, W. G. (2019). Digital Mapping of Soil Carbon Fractions with Machine Learning. Geoderma, 339, 40-58.
Lamichhane, S., Kumar, L., & Wilson, B. (2019). Digital Soil Mapping Algorithms and Covariates for Soil Organic Carbon Mapping and Their Implications: A Review. Geoderma, 352, 395-413.
Liang, Z., Chen, S., Yang, Y., Zhou, Y., & Shi, Z. (2019). High-Resolution Three-Dimensional Mapping of Soil Organic Carbon in China: Effects of SoilGrids Products on National Modeling. Science of The Total Environment, 685, 480-489.
Ma, Y., Minasny, B., Malone, B. P., & Mcbratney, A. B. (2019). Pedology and Digital Soil Mapping (DSM). European Journal of Soil Science, 70(2), 216-235.
Malone, B., Stockmann, U., Glover, M., McLachlan, G., Engelhardt, S., & Tuomi, S. (2022). Digital Soil Survey and Mapping Underpinning Inherent and Dynamic Soil Attribute Condition Assessments. Soil Security, 6, 100048.
Nguyen, K. A., Liou, Y. A., Tran, H. P., Hoang, P. P., & Nguyen, T. H. (2020). Soil Salinity Assessment by Using Near-Infrared Channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science, 7(1), 1-16.
Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using Deep Learning for Digital Soil Mapping. Soil, 5(1), 79-89.
Shahid, S. A., Zaman, M., & Heng, L. (2018). Soil Salinity: Historical Perspectives and a World Overview of the Problem. In Guideline for Salinity Assessment, Mitigation and Adaptation using Nuclear and Related Techniques (pp. 43-53). Springer, Cham.
Teng, H. F., Jie, H. U., Yue, Z. H. O. U., Zhou, L. Q., & Zhou, S. H. I. (2019). Modelling and Mapping Soil Erosion Potential in China. Journal of Integrative Agriculture, 18(2), 251-264.
Teng, H., Rossel, R. A. V., Shi, Z., & Behrens, T. (2018). Updating a National Soil Classification with Spectroscopic Predictions and Digital Soil Mapping. Catena, 164, 125-134.
Tümsavaş, Z., Tekin, Y., Ulusoy, Y., & Mouazen, A. M. (2019). Prediction and Mapping of Soil Clay and Sand Contents using Visible and Near-Infrared Spectroscopy. Biosystems Engineering, 177, 90-100.
Wadoux, A. M. C., Padarian, J., & Minasny, B. (2019). Multi-Source Data Integration for Soil Mapping using Deep Learning. Soil, 5(1), 107-119.
Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P. (2019). Digital Mapping of Soil Properties using Multiple Machine Learning in a Semi-Arid Region, Central Iran. Geoderma, 338, 445-452.
Zeraatpisheh, M., Bakhshandeh, E., Hosseini, M., & Alavi, S. M. (2020). Assessing the Effects of Deforestation and Intensive Agriculture on the Soil Quality through Digital Soil Mapping. Geoderma, 363, 114139.
Zeraatpisheh, M., Jafari, A., Bodaghabadi, M. B., Ayoubi, S., Taghizadeh-Mehrjardi, R., Toomanian, N., ... & Xu, M. (2020). Conventional and Digital Soil Mapping in Iran: Past, Present, and Future. Catena, 188, 104424.
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