| dc.contributor.author | Xiao, X. |
| dc.contributor.author | Biradar, Chandrashekhar M. |
| dc.contributor.author | Czarnecki, C. |
| dc.contributor.author | Alabi, T. |
| dc.contributor.author | Keller, M. |
| dc.date.accessioned | 2019-12-04T11:11:39Z |
| dc.date.available | 2019-12-04T11:11:39Z |
| dc.date.issued | 2009-08 |
| dc.identifier.citation | Xiao, X., Biradar, C.M., Czarnecki, C., Alabi, T. & Keller, M. (2009). A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia. Remote Sensing, 1(3), 355-374. |
| dc.identifier.issn | 2072-4292 |
| dc.identifier.uri | https://hdl.handle.net/20.500.12478/2473 |
| dc.description | Open Access Journal |
| dc.description.abstract | The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution. |
| dc.description.sponsorship | United States National Aeronautics and Space Administration |
| dc.description.sponsorship | National Institutes of Health, United States |
| dc.description.sponsorship | Wildlife Conservation Society, United States |
| dc.format.extent | 355-374 |
| dc.language.iso | en |
| dc.subject | Modis Image |
| dc.subject | Land Surface Water Index |
| dc.subject | Temporal Profile Analysis |
| dc.subject | Evergreen Forests |
| dc.title | A simple algorithm for largescale mapping of evergreen forests in tropical America, Africa and Asia |
| dc.type | Journal Article |
| dc.description.version | Peer Review |
| cg.contributor.affiliation | University of Oklahoma |
| cg.contributor.affiliation | University of New Hampshire |
| cg.contributor.affiliation | International Institute of Tropical Agriculture |
| cg.contributor.affiliation | National Ecological Observatory Network, United States |
| cg.coverage.region | Acp |
| cg.coverage.region | Africa |
| cg.coverage.region | Asia |
| cg.coverage.region | South America |
| cg.coverage.region | Central Africa |
| cg.coverage.region | Southeast Asia |
| cg.coverage.country | Brazil |
| cg.coverage.country | Peru |
| cg.coverage.country | Colombia |
| cg.coverage.country | Venezuela |
| cg.coverage.country | Congo, Dr |
| cg.coverage.country | Congo |
| cg.coverage.country | Cameroon |
| cg.coverage.country | Gabon |
| cg.coverage.country | Angola |
| cg.coverage.country | Malaysia |
| cg.coverage.country | Philippines |
| cg.isijournal | ISI Journal |
| cg.authorship.types | CGIAR and advanced research institute |
| cg.iitasubject | Land Use |
| cg.iitasubject | Forestry |
| cg.journal | Remote Sensing |
| cg.howpublished | Formally Published |
| cg.accessibilitystatus | Open Access |
| local.dspaceid | 93224 |
