Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere-Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.     

A platform for crowdsourcing the creation of representative,accurate landcover maps

Accurate landcover maps are fundamental to understanding socio-economic and environmental patterns and processes, but existing datasets contain substantial errors. Crowdsourcing map creation may substantially improve accuracy, particularly for discrete cover types, but the quality and representativeness of crowdsourced data is hard to verify. We present an open-sourced platform, DIYlandcover, that serves representative samples of high resolution imagery to an online job market, where workers delineate individual landcover features of interest. Worker mapping skill is frequently assessed, providing estimates of overall map accuracy and a basis for performance-based payments. A trial of DIYlandcover showed that novice workers delineated South African cropland with 91% accuracy, exceeding the accuracy of current generation global landcover products, while capturing important geometric data. A scaling-up assessment suggests the possibility of developing an Africa-wide vector-based dataset of croplands for $2–3 million within 1.2–3.8 years. DIYlandcover can be readily adapted to map other discrete cover types.     

Characterizing spatial patterns of land ownership at the parcel level in south-central Indiana, 1928–1997

Parcelization of land ownership is a process that has dramatic implications for how landscapes are managed and how socio-economic changes ultimately affect the pattern, composition and characteristics of the landscape. For a study area in south-central Indiana, a digital spatial dataset of ownership parcels was created from historical plat maps for the period of 1928–1997. The patterns of ownership parcelization were categorized into a typology based on characteristics of the parent and child parcels using a cluster analysis. These types were then used to describe the trajectory of parcelization, or life history, of a parcel using a transition matrix approach. Results show that the most common type of parcelization is one where a parent parcel splits into two equally sized child parcels. The results from the transition matrix analysis suggest that the history of a parcel is of importance in that it does enable and constrain future parcelization types. Possible relationships between the results from the quantitative analysis and underlying household social processes are also discussed and evaluated.     

土地覆盖遥感专题信息的分层提取方法及其应用

重点讨论了土地覆盖遥感专题信息的分层提取方法,即根据各类目标的不同特征,采取相应的信息提取方法,分别建立专题信息层,最后把各专题层合并得到整体分类图;在此方案实施过程中,对各类目标开展了野外遥感调查和特征系统分析,并对样本区进行了ＧＰＳ定位,以获取空间辅助信息,提高提取精度;分层提取方法充分考虑到各类目标的不同特点,从而避免了通常分类方法中,单纯利用光谱特征所造成的地物混分现象