2bf52a0374c5bd11c4572268bacb8429 remotesensing-17-00234.pdf 23311f763c69d382af1cee6df8fcd8079d25c361 remotesensing-17-00234.pdf db87055c9a3b75b7d0a86c4d2352ec3e9c1346cf206caa2c04cbe313fe566ee8 remotesensing-17-00234.pdf 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 Subject: 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. Keywords: data fusion; Dempster–Shafer theory; oil palm mapping; Indonesia Author: Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Fri Jan 10 10:31:58 2025 CET ModDate: Fri Jan 10 10:39:55 2025 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 25 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 29532993 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- LWSUZS+URWPalladioL-Roma Type 1 Custom yes yes yes 10 0 QTMVVL+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 UOZJZA+URWPalladioL-Ital Type 1 Custom yes yes yes 21 0 VQGNZR+CMSY10 Type 1 Builtin yes yes yes 47 0 SYFPBV+CMMI10 Type 1 Builtin yes yes yes 64 0 NIZJUZ+PazoMath Type 1 Builtin yes yes yes 88 0 YKONGJ+PazoMath-Italic Type 1 Builtin yes yes yes 93 0 SOSTRQ+CMR10 Type 1 Builtin yes yes yes 98 0 EGMRNE+CMEX10 Type 1 Builtin yes yes yes 117 0 CDWKRP+PazoMath-BoldItalic Type 1 Builtin yes yes yes 128 0 VZUVJT+URWPalladioL-BoldItal Type 1 Custom yes yes yes 133 0 OMAAQR+CMBSY10 Type 1 Builtin yes yes yes 138 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-02-17 16:52:18 CET RepresentationInformation: remotesensing-17-00234.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-02-12 14:55:43 CET Size: 29532993 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 470 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Study Case Destination: section.2 Item: Title: Data Destination: section.3 Children: Item: Title: GAVEAU Map (Reference) Destination: subsection.3.1 Item: Title: DESCALS Map (Input Source 1) Destination: subsection.3.2 Item: Title: IIASA Map (Input Source 2) Destination: subsection.3.3 Item: Title: XU Map (Input Source 3) Destination: subsection.3.4 Item: Title: MAPBIOMAS Map (Input Source 4) Destination: subsection.3.5 Item: Title: Methods Destination: section.4 Children: Item: Title: Introduction to the Dempster-Shafer Theory Destination: subsection.4.1 Children: Item: Title: Definition of the Discernment Framework Destination: subsubsection.4.1.1 Item: Title: The Mass Functions Assignment (MFA) Destination: subsubsection.4.1.2 Item: Title: Dempster Shafer Fusion Rule Destination: subsubsection.4.1.3 Item: Title: Dempster Shafer Decision Rule Destination: subsubsection.4.1.4 Item: Title: Implementation of the Dempster Shafer Theory Destination: subsection.4.2 Item: Title: Validation Framework Destination: subsection.4.3 Item: Title: Results Destination: section.5 Children: Item: Title: Mass Functions Assignment Destination: subsection.5.1 Item: Title: Statistical Validation of the DST Decision Map Destination: subsection.5.2 Item: Title: Spatial Variability Assessment Destination: subsection.5.3 Item: Title: Discussion Destination: section.6 Item: Title: Conclusions Destination: section.7 Item: Title: Appendix A Destination: appendix.A. Item: Title: References Destination: appendix.B. Info: 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 Author: Subject: 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. Keywords: data fusion; Dempster Shafer theory; oil palm mapping; Indonesia Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Fri Jan 10 10:31:58 CET 2025 ModDate: Fri Jan 10 10:39:55 CET 2025 ID: 0x30775754061ee74d4a12c5a1a25f84a3, 0x30775754061ee74d4a12c5a1a25f84a3 Filters: FilterPipeline: FlateDecode Images: Image: NisoImageMetadata: FormatName: image/png ImageWidth: 2770 ImageHeight: 1917 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Image: NisoImageMetadata: FormatName: image/png ImageWidth: 9049 ImageHeight: 15518 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Image: NisoImageMetadata: FormatName: image/png ImageWidth: 699 ImageHeight: 727 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Image: NisoImageMetadata: FormatName: image/png ImageWidth: 794 ImageHeight: 463 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Image: 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