Global land cover classi cation at 1 km spatial resolution using a classi cation tree approach
نویسنده
چکیده
This paper on reports the production of a 1 km spatial resolution land cover classi cation using data for 1992–1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar e ort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 Ö 1km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red re ectance, peak annual Normalized Di erence Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the nal product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.
منابع مشابه
Global land cover classi ® cations at 8 km spatial resolution : the use of training data derived from Landsat imagery in decision tree classi ® ers
This paper reports a study which aims to (i) develop methodologies for global land cover classi® cations that are objective, reproducible and feasible to implement as new satellite data become available in the future and (ii) provide a global land cover classi® cation product based on the National Aeronautics and Space Administration/National Oceanic and Atmospheric Administration Path® nder La...
متن کاملA comparison of the IGBP DISCover and University of Maryland 1 km global land cover products
Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere–Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DIS...
متن کاملBOSTON UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES Thesis BIOME LEVEL CLASSIFICATION OF LAND COVER AT CONTINENTAL SCALES USING DECISION TREES by ALEXANDER LOTSCH
Land cover plays a key role in terrestrial biogeochemical processes. Therefore many problems require accurate information on the distribution and properties of land cover. A decision tree classi cation algorithm is used to generate a land cover map of North America from remotely sensed data with 1 km resolution in a 6-biome classi cation scheme. To do this, the normalized di erence vegetation i...
متن کاملDevelopment of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data
Researchers from the U.S. Geological Survey, University of Nebraska– Lincoln and the European Commission’s Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continentalto global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity...
متن کاملClassi ® cation by progressive generalization : a new automated methodology for remote sensing multichannel data
A new procedure for digital image classi® cation is described. The procedure, labelled Classi ® cation by Progressive Generalization (CPG), was developed to avoid drawbacks associated with most supervised and unsupervised classi® cations. Using lessons from visual image interpretation and map making, non-recursive CPG aims to identify all signi® cant spectral clusters within the scene to be cla...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004