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Building a hybrid land cover map with crowdsourcing and geographically weighted regression.

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Title: Building a hybrid land cover map with crowdsourcing and geographically weighted regression.
Authors: See, Linda1 see@iiasa.ac.at, Schepaschenko, Dmitry1, Lesiv, Myroslava2,3, McCallum, Ian1, Fritz, Steffen1, Comber, Alexis4, Perger, Christoph1, Schill, Christian5, Zhao, Yuanyuan6, Maus, Victor7, Siraj, Muhammad Athar8, Albrecht, Franziska9, Cipriani, Anna10,11, Vakolyuk, Mar’yana1,12, Garcia, Alfredo13, Rabia, Ahmed H.14, Singha, Kuleswar15, Marcarini, Abel Alan16, Kattenborn, Teja17, Hazarika, Rubul18
Source: ISPRS Journal of Photogrammetry & Remote Sensing. May2015, Vol. 103, p48-56. 9p.
Subjects: Land cover, Environmental mapping, Crowdsourcing, Regression analysis, MODIS (Spectroradiometer)
Abstract: Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products. [ABSTRACT FROM AUTHOR]
Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Building a hybrid land cover map with crowdsourcing and geographically weighted regression.
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  Data: <searchLink fieldCode="AR" term="%22See%2C+Linda%22">See, Linda</searchLink><relatesTo>1</relatesTo><i> see@iiasa.ac.at</i><br /><searchLink fieldCode="AR" term="%22Schepaschenko%2C+Dmitry%22">Schepaschenko, Dmitry</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lesiv%2C+Myroslava%22">Lesiv, Myroslava</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22McCallum%2C+Ian%22">McCallum, Ian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Fritz%2C+Steffen%22">Fritz, Steffen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Comber%2C+Alexis%22">Comber, Alexis</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Perger%2C+Christoph%22">Perger, Christoph</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Schill%2C+Christian%22">Schill, Christian</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yuanyuan%22">Zhao, Yuanyuan</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Maus%2C+Victor%22">Maus, Victor</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Siraj%2C+Muhammad+Athar%22">Siraj, Muhammad Athar</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Albrecht%2C+Franziska%22">Albrecht, Franziska</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Cipriani%2C+Anna%22">Cipriani, Anna</searchLink><relatesTo>10,11</relatesTo><br /><searchLink fieldCode="AR" term="%22Vakolyuk%2C+Mar’yana%22">Vakolyuk, Mar’yana</searchLink><relatesTo>1,12</relatesTo><br /><searchLink fieldCode="AR" term="%22Garcia%2C+Alfredo%22">Garcia, Alfredo</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Rabia%2C+Ahmed+H%2E%22">Rabia, Ahmed H.</searchLink><relatesTo>14</relatesTo><br /><searchLink fieldCode="AR" term="%22Singha%2C+Kuleswar%22">Singha, Kuleswar</searchLink><relatesTo>15</relatesTo><br /><searchLink fieldCode="AR" term="%22Marcarini%2C+Abel+Alan%22">Marcarini, Abel Alan</searchLink><relatesTo>16</relatesTo><br /><searchLink fieldCode="AR" term="%22Kattenborn%2C+Teja%22">Kattenborn, Teja</searchLink><relatesTo>17</relatesTo><br /><searchLink fieldCode="AR" term="%22Hazarika%2C+Rubul%22">Hazarika, Rubul</searchLink><relatesTo>18</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22ISPRS+Journal+of+Photogrammetry+%26+Remote+Sensing%22">ISPRS Journal of Photogrammetry & Remote Sensing</searchLink>. May2015, Vol. 103, p48-56. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Land+cover%22">Land cover</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+mapping%22">Environmental mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Crowdsourcing%22">Crowdsourcing</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22MODIS+%28Spectroradiometer%29%22">MODIS (Spectroradiometer)</searchLink>
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  Data: Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=102099220
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        Value: 10.1016/j.isprsjprs.2014.06.016
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        Text: English
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        StartPage: 48
    Subjects:
      – SubjectFull: Land cover
        Type: general
      – SubjectFull: Environmental mapping
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      – SubjectFull: Crowdsourcing
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      – SubjectFull: Regression analysis
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      – SubjectFull: MODIS (Spectroradiometer)
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