In order to improve the cartography efficiency and the intelligent level of forestry map,we put forward the symbolization scheme of forestry map based on MapGIS K9 platform.Firstly,the symbol library of forestry map w...In order to improve the cartography efficiency and the intelligent level of forestry map,we put forward the symbolization scheme of forestry map based on MapGIS K9 platform.Firstly,the symbol library of forestry map was constructed according to The Graphic Representation of Forestry Map which was issued by the National Forestry Bureau in October,2010.Then,the symbol configuration rule database was established to realize the expression of symbol configuration rule in the digital environment.Finally,the symbolization module was realized with the support of MapGIS Objects component development technology,and the thematic data of forestry were visualized according to the symbol configuration rule.The practice proved that the scheme could basically satisfy the cartography requirements of common forestry maps which included the forest management inventory map.展开更多
The global tree cover percentage is an important parameter used to understand the global environment. However, the available percent tree cover products on global or continental-scale are few, and efforts to quantitat...The global tree cover percentage is an important parameter used to understand the global environment. However, the available percent tree cover products on global or continental-scale are few, and efforts to quantitatively validate these maps have been limited. We produced a new percent tree cover dataset at 500 m resolution in 2008 for Eurasia using reference data interpreted from Google Earth. It is a part of percent tree cover (PTC) data in Global Mapping project. In this study, the dataset was compared with existing global percent tree cover dataset, MODIS Vegetation Continuous Fields, MOD44B. We assessed the agreement of these datasets with two existing global categorical land cover datasets and statistic data in Eurasia. The result showed that estimates of tree cover in our new map and MOD44B were relatively similar at randomly sampled sites. Our map and MOD44B agreed with either or both of land cover maps at 93% of sites and 91% of sites, respectively, for pixel blocks. However, we found that MOD44B disagreed with our map and categorical land cover datasets at about half of the sampled sites where the difference of tree cover percentage between our map and MOD44B was large, especially in the areas with significant differences (more than 50%). Disagreed areas were concentrated in forests of Russia and Indonesia, and in herbaceous dominated vegetation of UK and Ireland. We also found that both our map and MOD44B were somewhat different from the data reported by FRA 2010.展开更多
基金Supported by"Fine Recognition Technology of Types of Land CoversFaced the Land Parcel and Its Application"of National High Technology Research Development Plan(863 Plan)Item(2007AA12Z181)~~
文摘In order to improve the cartography efficiency and the intelligent level of forestry map,we put forward the symbolization scheme of forestry map based on MapGIS K9 platform.Firstly,the symbol library of forestry map was constructed according to The Graphic Representation of Forestry Map which was issued by the National Forestry Bureau in October,2010.Then,the symbol configuration rule database was established to realize the expression of symbol configuration rule in the digital environment.Finally,the symbolization module was realized with the support of MapGIS Objects component development technology,and the thematic data of forestry were visualized according to the symbol configuration rule.The practice proved that the scheme could basically satisfy the cartography requirements of common forestry maps which included the forest management inventory map.
文摘The global tree cover percentage is an important parameter used to understand the global environment. However, the available percent tree cover products on global or continental-scale are few, and efforts to quantitatively validate these maps have been limited. We produced a new percent tree cover dataset at 500 m resolution in 2008 for Eurasia using reference data interpreted from Google Earth. It is a part of percent tree cover (PTC) data in Global Mapping project. In this study, the dataset was compared with existing global percent tree cover dataset, MODIS Vegetation Continuous Fields, MOD44B. We assessed the agreement of these datasets with two existing global categorical land cover datasets and statistic data in Eurasia. The result showed that estimates of tree cover in our new map and MOD44B were relatively similar at randomly sampled sites. Our map and MOD44B agreed with either or both of land cover maps at 93% of sites and 91% of sites, respectively, for pixel blocks. However, we found that MOD44B disagreed with our map and categorical land cover datasets at about half of the sampled sites where the difference of tree cover percentage between our map and MOD44B was large, especially in the areas with significant differences (more than 50%). Disagreed areas were concentrated in forests of Russia and Indonesia, and in herbaceous dominated vegetation of UK and Ireland. We also found that both our map and MOD44B were somewhat different from the data reported by FRA 2010.
