Pub Date : 2022-01-01Epub Date: 2022-09-19DOI: 10.1007/s41064-022-00217-9
Ralf Bill, Jörg Blankenbach, Martin Breunig, Jan-Henrik Haunert, Christian Heipke, Stefan Herle, Hans-Gerd Maas, Helmut Mayer, Liqui Meng, Franz Rottensteiner, Jochen Schiewe, Monika Sester, Uwe Sörgel, Martin Werner
Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors - members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany - have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future.
{"title":"Geospatial Information Research: State of the Art, Case Studies and Future Perspectives.","authors":"Ralf Bill, Jörg Blankenbach, Martin Breunig, Jan-Henrik Haunert, Christian Heipke, Stefan Herle, Hans-Gerd Maas, Helmut Mayer, Liqui Meng, Franz Rottensteiner, Jochen Schiewe, Monika Sester, Uwe Sörgel, Martin Werner","doi":"10.1007/s41064-022-00217-9","DOIUrl":"https://doi.org/10.1007/s41064-022-00217-9","url":null,"abstract":"<p><p>Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors - members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany - have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future.</p>","PeriodicalId":91030,"journal":{"name":"Journal of photogrammetry, remote sensing and geoinformation science","volume":"90 4","pages":"349-389"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33483416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1007/s41064-019-00076-x
Nima Ahmadian, Tobias Ullmann, Jochem Verrelst, Erik Borg, Reinhard Zölitz, Christopher Conrad
The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.
{"title":"Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data.","authors":"Nima Ahmadian, Tobias Ullmann, Jochem Verrelst, Erik Borg, Reinhard Zölitz, Christopher Conrad","doi":"10.1007/s41064-019-00076-x","DOIUrl":"https://doi.org/10.1007/s41064-019-00076-x","url":null,"abstract":"<p><p>The biomass of three agricultural crops, winter wheat <i>(Triticum aestivum</i> L.), barley <i>(Hordeum vulgare</i> L.), and canola <i>(Brassica napus</i> L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with <i>R<sup>2</sup></i> > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the <i>R</i> <sup>2</sup> > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show <i>R</i> <sup>2</sup> < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.</p>","PeriodicalId":91030,"journal":{"name":"Journal of photogrammetry, remote sensing and geoinformation science","volume":"87 ","pages":"159-175"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41064-019-00076-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40353857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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