Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research.While the global land cover maps attempt to present a consistent and h...Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research.While the global land cover maps attempt to present a consistent and homogenous data in terms of the production process,the existing datasets offer coarse resolution data,e.g.1000 m for IGBP DISCover and 300 m for GlobeCover 2009 that is oftentimes challenging.Recently,GlobeLand30 data based on Landsat archive for two timestamps of 2000 and 2010 has been released.It presents a finer spatial resolution of 30 m,which provides numerous opportunities for a wide range of studies.The main objective of this study is to use this dataset for characterizing global land cover patterns,monitoring,and identifying extreme land change cases with their types and magnitude.The findings reveal massive land change patterns including deforestation,desertification,shrinkage of water bodies,and urbanization across the globe.The results and discussions of this research can help policy-makers,environmental planners,ecosystem services providers and climate change researchers to gain finer insights about the forms of global land change.Future research calls for further investigation of the underlying causes of the massive changes and their consequences on our ecosystems and human populations.展开更多
Identification and monitoring of species composition and richness isneeded to formulate effective mangrove management and conservationpriorities. Prior studies have used commercial satellite images which arecost prohi...Identification and monitoring of species composition and richness isneeded to formulate effective mangrove management and conservationpriorities. Prior studies have used commercial satellite images which arecost prohibitive for national and global applications. Here, we usedfreely available Landsat satellite data and new indices to discriminatemangrove species in Maros Regency, South Sulawesi, Indonesia andSegara Anakan, West Java, Indonesia. We use sensitive algorithm of theprincipal polar spectral (PPS) indices to discriminate mangroves species.PPS Indices were produced from a set of 3-dimensional Landsat 8Operational Land Imager (OLI) spectral indices (PPS Brightness, PPSGreenness, and PPS Wetness) determined by a polar change of theprincipal component axes of a spectral image of reference scene. Wequalitatively compare this set of PPS indices with the set of conventionalRGB multi-bands image composition and conventional NormalizedDifference Vegetation Indices (NDVI) for mangroves speciesdiscrimination. The comparisons indicate that the set of PPS indiceshave the potential for regional and possibly global applications inmangroves species mapping and monitoring.展开更多
文摘Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research.While the global land cover maps attempt to present a consistent and homogenous data in terms of the production process,the existing datasets offer coarse resolution data,e.g.1000 m for IGBP DISCover and 300 m for GlobeCover 2009 that is oftentimes challenging.Recently,GlobeLand30 data based on Landsat archive for two timestamps of 2000 and 2010 has been released.It presents a finer spatial resolution of 30 m,which provides numerous opportunities for a wide range of studies.The main objective of this study is to use this dataset for characterizing global land cover patterns,monitoring,and identifying extreme land change cases with their types and magnitude.The findings reveal massive land change patterns including deforestation,desertification,shrinkage of water bodies,and urbanization across the globe.The results and discussions of this research can help policy-makers,environmental planners,ecosystem services providers and climate change researchers to gain finer insights about the forms of global land change.Future research calls for further investigation of the underlying causes of the massive changes and their consequences on our ecosystems and human populations.
文摘Identification and monitoring of species composition and richness isneeded to formulate effective mangrove management and conservationpriorities. Prior studies have used commercial satellite images which arecost prohibitive for national and global applications. Here, we usedfreely available Landsat satellite data and new indices to discriminatemangrove species in Maros Regency, South Sulawesi, Indonesia andSegara Anakan, West Java, Indonesia. We use sensitive algorithm of theprincipal polar spectral (PPS) indices to discriminate mangroves species.PPS Indices were produced from a set of 3-dimensional Landsat 8Operational Land Imager (OLI) spectral indices (PPS Brightness, PPSGreenness, and PPS Wetness) determined by a polar change of theprincipal component axes of a spectral image of reference scene. Wequalitatively compare this set of PPS indices with the set of conventionalRGB multi-bands image composition and conventional NormalizedDifference Vegetation Indices (NDVI) for mangroves speciesdiscrimination. The comparisons indicate that the set of PPS indiceshave the potential for regional and possibly global applications inmangroves species mapping and monitoring.
