Digital Elevation Models (DEMs) can be generated from remote sensing data using techniques like stereoscopic interpretation of satellite or aerial imagery, radar interferometry, and LiDAR (Light Detection and Ranging) technology. Water | Free Full-Text | A Review on Applications of Remote Sensing and Current remote-sensing methodology used in mapping soil salinity could be significantly improved if decision-tree analysis (DTA) is incorporated in such efforts. Applications of Remote Sensing in Geoscience | IntechOpen In such cases, spatial regression models had often been parameterized using data variables derived from remotely sensed products. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. However, the application of remote sensing in SA faces many challenges, e.g., persistent high cloud covers during monsoon seasons, very limited availability of ground truth samples, and excessively small and irregularly shaped agriculture fields, etc. Machine learning approaches focuses on computer programs that can assess data and use them to learn and improve from experience of them without being explicitly programmed [27]. The Malaria Atlas Project founded in 2006 took over from previous mapping efforts and demonstrated the application of geography based variables to map and disseminate accurate information on malaria endemicity. Int J Remote Sens 21(17):31973208, Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. From the early usage of aerial photographs taken onboard aircraft to the launch of satellite-based sensors, remotely sensed data products have been in the forefront of scientific research on the land use and land cover changes of the earth. From mapping land cover and land use to supporting disaster management and response, remote sensing enhances various aspects of cartography and contributes to fields such as environmental monitoring, urban planning, natural resource management, and more. Save my name, email, and website in this browser for the next time I comment. PubMedGoogle Scholar, Principal Scientist, Division of Remote Sensing Applications, ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur, India, Director, ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur, India, 2018 Springer International Publishing AG, part of Springer Nature, Srivastava, R. (2018). The results of which had been obtainable within reasonable time frames compared to earlier computation efforts of similar data. A special thanks to the reviewers for their valuable inputs during the peer-reviewing process to enhance the overall quality of the manuscripts. The measurement and recording of the electromagnetic radiation are made by sensors mounted on a platform (namely, satellite, aerial, unmanned airborne systems) above the earths surface. Remote sensing data collection methods can be passive or active. In addition, the number of spectral bands used by the sensors to capture images had also increased, aiding in an appealing appearance to the human eye. Other GIS software programs such as ESRI ArcGIS had over the years added more customized mapping tools and extensions meant to support disease mapping and epidemiology efforts. Provides valuable data for flood modeling and management, helping to assess flood risks and plan effective mitigation strategies. This association of diseases with their environment was first noted in some of the early ecological studies that demonstrated the capability of remote sensing products in disease mapping, including authors such as Beck et al. Also, true color visualization web-based software such as Google Earth which had even given mapping novices some level of confidence due to the fact that it had been made without any associated sophistication had contributed to the hype about disease mapping, rapid risk assessment, and prediction among epidemiologists. Google Scholar, Bartholomeus H, Epema G, Schaepman ME (2007) Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy. Most of the studies discussed above were primarily focused on the application of remote sensing to identify and map potential vector habitats and breeding sites based on vegetation, water, and soil. https://doi.org/10.5772/61974, Chapter Furthermore, environmental proxies had been incorporated as covariates in statistical models aimed at mapping, analyzing, and predicting spatial phenomenon relating to disease epidemiology. Geotechnologies and the Environment, vol 21. Remote Sensing, Submitted: August 2nd, 2020 Reviewed: August 19th, 2020 Published: December 16th, 2020, Edited by Andrew Hammond and Patrick Keleher, Total Chapter Downloads on intechopen.com. Remote sensing uses satellites, primarily based on mapping and integration in a GIS platform. J Indian Soc Remote Sens 45(2):307315, Stoner ER, Baumgardner MF (1981) Characteristic variations in reflectance of surface soils. In addition recent studies have focused on assessing the issue of predictive modeling and disease transmission risk based on the application of remote sensing and GIS technologies. As a result, most of the diseases mapping efforts currently applied have either practically or theoretically ended up either utilizing remotely sensed data or its associated geographic data products into spatial analysis models as often the resultant modeling outputs would be displayed in a mapping environment. Mapping and Monitoring Land Use/Land Cover Changes. Notify me of follow-up comments by email. Epidemiology is concerned with investigating the cause of disease, and often, these causes vary in frequency and in space. The groundtruthed field measurement data, that were observable using GPS, would then be used to evaluate the accuracy of the remote sensing based predictive models. Planimetry includes identification Remote sensing is a method that involves obtaining data, detection, analysis, observation of the physical characteristics of a region by recording. Contact our London head office or media team here. Consequently, even nonexpert remote sensing users had been drawn into remote sensing by the beautiful pictures and availability of some of the end products of remote sensing. This would often result in missing data as observation could not be completed on those days when the weather station failed. Ballantine JAC, Okin GS, Prentiss DE, Roberts DA (2005) Mapping North African landforms using continental scale unmixing of MODIS imagery. Application of Remote Sensing and Google Earth Engine for Agricultural Geoderma 117(12):352, McKenzie NJ, Ryan PJ (1999) Spatial prediction of soil properties using environmental correlation. Geoderma 142(12):6979, Chang CW, Laird DA (2002) Near-infrared reflectance spectroscopic analysis of soil C and N. Soil Sci 167(2):110116, Chattaraj S, Srivastava R, Barthwal AK, Giri JD, Mohekar DS, Reddy GPO, Daripa A, Chatterji S, Singh SK (2017) Semi-automated object-based landform classification modelling in a part of the Deccan Plateau of Central India. Mapping Land Cover and Land Use One of the primary applications of remote sensing in cartography is mapping land cover and land use. On the other hand, land mapping means mapping an area with certain characteristics. Provides essential information for assessing the impact of land cover changes on the environment and ecosystem services. Epidemiology is concerned with investigating the causes of diseases, and often, these causes vary in frequency and in space. The capability of Geostatistics to incorporate technical algorithms that could be used to forecast disease burden in space and time had also contributed to the wide adoption of such approaches as it meant that control programs could a priori be informed about disease risk and thus be better prepared to deal with disease outbreaks. Planimetry includes identification and geolocation of basic land cover, drainage and evolution options, urban infrastructure, and transport networks within the x,y plane. In statistical models, disease analysis had been pursued by adjusting models with spatial covariates derived from remote sensing and regressed with any outcome of interest. By Eduardo Landulfo, Alexandre Cacheffo, Alexandre Calzavara Yoshida, Antonio Arleques Gomes, Fbio Juliano da Silva Lopes, Gregori de Arruda Moreira, Jonatan Joo da Silva, Vania Andrioli, Alexandre Pimenta, Chi Wang, Jiyao Xu, Maria Paulete Pereira Martins, Paulo Batista, Henrique de Melo Jorge Barbosa, Diego Alves Gouveia, Boris Barja Gonzlez, Felix Zamorano, Eduardo Quel, Clodomyra Pereira, Elian Wolfram, Facundo Ismael Casasola, Facundo Orte, Jacobo Omar Salvador, Juan Vicente Pallotta, Lidia Ana Otero, Maria Prieto, Pablo Roberto Ristori, Silvina Brusca, John Henry Reina Estupian, Estiven Sanchez Barrera, Juan Carlos Antua-Marrero, Ricardo Forno, Marcos Andrade, Judith Johanna Hoelzemann, Anderson Guimares Guedes, Cristina Tobler Sousa, Daniel Camilo Fortunato dos Santos Oliveira, Edicl de Souza Fernandes Duarte, Marcos Paulo Arajo da Silva and Renata Sammara da Silva Santos. Remotes sensing is an important tool for topographical mapping in the past, present, and most importantly, future. . In: Marghany M (ed) Environmental applications of remote sensing. These studies also use remote sensing to predict which populations or villages are at risk of transmission. Remote Sens Environ 55(2):95107, Rossel RAV, McBratney AB (1998) Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content. IEEE Trans Geosci Remote Sens 29(2):331339, Manchanda ML, Kudrat M, Tiwari AK (2002) Soil survey and mapping using remote sensing. Open Access Published: 11 January 2021 Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, northwestern China Yu-qing. The preprocessing steps involved before such data could be added as covariates into models also determine their uptake and usage by nonexperts. Wiley, New York, pp 358, Dalal RC, Henry RJ (1986) Simultaneous determination of moisture, organic carbon, & total nitrogen by near infrared reflectance spectrophotometry. That offers a cost-effective way in environmental and ground change detection and monitoring. (PDF) Remote Sensing and Its Applications - ResearchGate The recent advances in remote-sensing imaging acquisition and availability of images can help geoscientists to explore and . Geoderma 112(34):179196, Srivastava R, Saxena RK (2004) Technique of large-scale soil mapping in basaltic terrain using satellite remote sensing. Wiley, Hoboken, Sabins FF Jr, Ellis JM (2020) Remote sensing: principles, interpretation, and applications, 4th edn. In: Bhattacharyya, T. Sarkar, D. & Pal, D.K. Using remote sensing to model tree species distribution in Peruvian lowland Amazonia. : A proposed framework Practicalities of the framework in EIAs . How? During the processing and analysis of such data, the missing values in the data would often require sophisticated methods of data imputation which would not escape criticism from the scientific peer review community. Satellites are launched frequently to monitor the Earth's dynamic surface processes. Remote Sensing | Special Issue : Application of Artificial - MDPI Figure 1 is an example of a high-resolution satellite imagery that had been used to develop a land cover classification map for part of Eswatini as shown in Figure 2. The studies had also been used to demonstrate the potential offered by remote sensing application to disease mapping and epidemiology and to support surveillance and control efforts. Furthermore, remote sensing and GIS technologies have significantly promoted the progress of identification, mapping, monitoring, early warning, and risk evaluation of earthquake-triggered landslides. Remote sensing and GIS applications are well established in soil erosion mapping due to the availability of up-to-date data at various . These models combine a Poisson process in the first level with a Gaussian Process at the second level and are used to analyze point patterns. J Indian Soc Remote Sens 43(4):751759, Srivastava R, Sethi M, Yadav RK, Bundela DS, Singh M, Chattaraj S, Singh SK, Nasre RA, Bishnoi SR, Dhale S, Mohekar DS, Barthwal AK (2017) Visible-near infrared reflectance spectroscopy for rapid characterization of salt-affected soil in the indo-Gangetic Plains of Haryana, India. The use of remote sensing in soil and terrain mapping A review This article explores the diverse applications of remote sensing in cartography, showcasing how it enhances mapping and contributes to a wide range of fields, including environmental monitoring, urban planning, disaster management, and more. Figure 3 is an example of the spatial distribution of malaria vector breeding sites and their distance to subsistence farming in Eswatini. Chamin HI, Teixeira J, Freitas L, Pires A, Silva RS, Pinho T, Monteiro R, Costa AL, Abreu T, Trigo JF, Afonso MJ, Carvalho JM (2016) From engineering geosciences mapping towards sustainable urban planning. Advances made in mapping software, particularly geographic information system (GIS) software, had seen interest being stimulated among disease researchers and epidemiologist. The growth of cities demands constant monitoring and planning. Int J Remote Sens 35:37643781, Nanni MR, Dematt JAM (2006) Spectral reflectance methodology in comparison to traditional soil analysis. Home > Remote sensing could be described as the science of scanning the earth using sensors onboard a satellite platform launched into space or high flying aircraft to obtain information and also monitor land use and land cover changes on the earth surface [1]. Challenges in using remote sensing for cartography include data inaccuracy or inconsistencies due to cloud cover, shadows, or atmospheric interference. It is often incorporated into modeling through the use of coordinates attached to the data that are being analyzed. Remote sensing helps in identifying and mapping the ocean floor. We used some examples from a case study conducted in Eswatini in Southern Africa. For example, statistical software programs such as STATA, R, WINBUGS, and others have been used to process and analyze climatic data derived from remote sensing. As the resolutions (both spatial and temporal) of remote sensing sensors had improved from the first generation of this technology, so has the interest and confidence in the use of their data products increased among the scientific community. Recently, the capability of big data, machine learning and other location intelligence methods to handle a large array of data sets have contributed to the awareness about the application of geographic data as often models using these methods would be performed on geographic software. It is done by capturing the mirrored radiation. Articles here address several approaches: (i) a set of papers addressing studies in remote sensing-based mapping on biodiversity, forestry and land cover issues; (ii) several papers are related mainly to GIS mapping and remote sensing techniques for delineating potential groundwater recharge zones, remote sensing and GIS-based analysis for urban sprawl, sustainable groundwater resources management for the evaluation of potential recharge zones using geospatial and Multiple-Criteria Decision-Making (MCDA) techniques, and GIS-based modelling for irrigation water suitability; (iii) a valuable set of papers highlighting a novel technique for developing flood hazard map by using AHP (Analytical Hierarchy Process), case studies underlining depletion of surface water bodies and floodplains using geospatial analysis, and land use/cover mapping change derivated from flood issues; (iv) a set of articles stressing the importance and application of spatial variability analysis and GIS-mapping on soil studies; (v) several case studies related to assessment of public open spaces and landscape quality, integrated remote sensing and field-based mapping to delineate glacial landform features, and remote sensing-based evaluation on river morphology evolution; (vi) other papers highlight several applications of remote sensing techniques, such as: coupling a hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification; discussing a geospatial analysis of electricity in an integrated hybrid renewable energy system model; presenting a numerical approach for ionospheric delay estimation of single-frequency NavIC satellite receiver, and showing a study related to the integration of C band SAR (Synthetic-Aperture Radar) and optical temporal data for identification of paddy fields. Geostatistics is a branch of statistics that is used to analyze and predict the values associated with spatial and temporal phenomena [17]. Remote Sensing | Free Full-Text | NASA ICESat-2: Space-Borne - MDPI Your email address will not be published. One of the primary applications of remote sensing in geography is land use mapping. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Other landscape features such as coniferous forest, deciduous forest, mixed forest, water bodies, glades, and housing developments had also been identified via remote sensing. Application of hyperspectral remote sensing for supplementary - Nature Soil Sci Soc Am J 50:120123, Dobos E, Micheli E, Baumgardner MF, Biehl L, Helt T (2000) Use of combined digital elevation model and satellite radiometric data for regional soil mapping. Remote sensing plays a vital role in digital elevation modeling, which involves creating accurate representations of the Earths surface topography. This application was demonstrated in rice mapping for Chitwan district in Nepal. Applications of Remote Sensing and GIS in Wasteland mapping In addition, archived remotely sensed data products had often been offered to researchers free of charge and this have enabled spatial analysts to perform various analysis techniques such as time-series analysis, data mining and other data learning techniques.
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