Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia
The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and mo...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/234 |
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| author | Carl Bethuel Damien Arvor Thomas Corpetti Julia Hélie Adrià Descals David Gaveau Cécile Chéron-Bessou Jérémie Gignoux Samuel Corgne |
| author_facet | Carl Bethuel Damien Arvor Thomas Corpetti Julia Hélie Adrià Descals David Gaveau Cécile Chéron-Bessou Jérémie Gignoux Samuel Corgne |
| author_sort | Carl Bethuel |
| collection | DOAJ |
| description | The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. |
| format | Article |
| id | doaj-art-6be7853c28bb4046907f724af6b02499 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6be7853c28bb4046907f724af6b024992025-01-24T13:47:49ZengMDPI AGRemote Sensing2072-42922025-01-0117223410.3390/rs17020234Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, IndonesiaCarl Bethuel0Damien Arvor1Thomas Corpetti2Julia Hélie3Adrià Descals4David Gaveau5Cécile Chéron-Bessou6Jérémie Gignoux7Samuel Corgne8CNRS (French National Center for Scientific Research), Université Rennes 2, LETG, UMR 6554, 35000 Rennes, FranceCNRS (French National Center for Scientific Research), Université Rennes 2, LETG, UMR 6554, 35000 Rennes, FranceCNRS (French National Center for Scientific Research), Université Rennes 2, LETG, UMR 6554, 35000 Rennes, FrancePSE (Paris School of Economics), EHESS (School of Advanced Studies in the Social Sciences), UMR 8545, 48 boulevard Jourdan, 75014 Paris, FranceCREAF (Center for Ecological Research and Forest Applications), Cerdanyola del Vallès, 08193 Barcelona, SpainTheTreeMap, 46600 Martel, FranceCIRAD (French Agricultural Research Center for International Development), UMR ABSys, Elsa Group, Av. Agropolis, 34398 Montpellier, FrancePSE (Paris School of Economics), INRAE (French National Research Institute for Agriculture, Food and Environment), UMR 8545, 48 boulevard Jourdan, 75014 Paris, FranceCNRS (French National Center for Scientific Research), Université Rennes 2, LETG, UMR 6554, 35000 Rennes, FranceThe remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis.https://www.mdpi.com/2072-4292/17/2/234data fusionDempster–Shafer theoryoil palm mappingIndonesia |
| spellingShingle | Carl Bethuel Damien Arvor Thomas Corpetti Julia Hélie Adrià Descals David Gaveau Cécile Chéron-Bessou Jérémie Gignoux Samuel Corgne Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia Remote Sensing data fusion Dempster–Shafer theory oil palm mapping Indonesia |
| title | Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia |
| title_full | Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia |
| title_fullStr | Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia |
| title_full_unstemmed | Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia |
| title_short | Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia |
| title_sort | applying the dempster shafer fusion theory to combine independent land use maps a case study on the mapping of oil palm plantations in sumatra indonesia |
| topic | data fusion Dempster–Shafer theory oil palm mapping Indonesia |
| url | https://www.mdpi.com/2072-4292/17/2/234 |
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