Development 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 continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

A comparison of different optimization algorithms for retrieving aerosol optical depths from satellite data: an example of using a dual-angle algorithm

Optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were evaluated using both simulated data and actual AATSR (advanced along track scanning radiometer) data. Several typical land cover types and aerosol opacity levels were also considered in the simulations to make the tests more representative. It was observed that the absolute error in retrieval would rise after a certain number of iterations due to the round-off error, and the algorithms showed different performances in the inversions without any a priori knowledge. When combined with reasonable a priori knowledge, the selection of various algorithms only slightly affected the retrieval accuracy. Given a summary of all the comparison tests, a special class named ‘trust-region methods’ (TR) was demonstrated to be the optimal choice in general cases. In contrast, some widely used optimization methods in aerosol research, for example, the Levenberg–Marquardt (LM) algorithm, seemed not to display a persuasive performance.     

Regional land cover mapping in the humid tropics using combined optical and SAR satellite data—a case study from Central Sulawesi,Indonesia

The frequent mapping of the spatial extent of land cover and its change from satellite data at the regional level provides essential input to spatially explicit land use analysis and scenario modelling. The accuracy of a land cover map is the key factor describing the quality of a map, and hence affecting the results of land use modelling. In tropical regions, land cover mapping from optical satellites is hampered by cloud coverage and thus alternative data sources have to be evaluated. In the present study, data from Landsat‐ETM+ and Envisat‐ASAR satellite sensors were tested for their ability to assess small scaled landscape patterns in a tropical environment. A focus was on the detection of intensively managed perennial and intra‐annual cropping systems (cocoa, rice). The results confirm previous knowledge about the general potential and advantages of multi‐temporal SAR data compared to mono‐temporal SAR‐based mapping but also show the limitations of different polarization modes in SAR analysis for land cover mapping. In the present case study, cross‐polarized data from Envisat‐ASAR did not yield notable profit for tropical land cover mapping compared to common, co‐polarized time series of ASAR data. However, the general outcome of the study underlines the synergy of optical and radar satellite data for land cover mapping in tropical regions.     

函数依赖集的有向图表示及在教学中的应用

以有向图表示函数依赖集,将求属性集的闭包转换成有向图的遍历问题,在此基础上,给出了求解候选码、最小覆盖等问题的解决方案。教学实践表明,该方法为相关内容的教学提供了图形化的手段,有助于学生利用数据结构知识来理解新的教学内容以及有关算法的编程和验证。     

Updating land cover classification using a rule‐based decision system

This paper proposes a land cover classification methodology in agricultural contexts that provides satisfactory results with a single satellite image per year acquired during the growing period. Our approach incorporates ancillary data such as cropping history (inter‐annual crop rotations), context (altitude, soil type) and structure (parcels size and shape) to compensate for the lack of radiometric data resulting from the use of a single image. The originality of the proposed method resides in the three successive steps used: S1: per‐pixel classification of a single SPOT XS image with a restricted number of land cover classes (RL) chosen to ensure good accuracy; S2: conversion of RLs into a per‐parcel classification system using ancillary parcel boundaries; and S3: parcel allocation using exhaustive land cover classes (EL) and its refinement through the application of decision rules. The method was tested on a 120?km² area (Sousson river basin, Gers, France) where exhaustive knowledge of land cover for two successive years allowed complete validation of our method. It allocated 87% of the parcels with a 75% accuracy rate according to the exhaustive list (EL). This is a satisfactory result obtained with one SPOT XS image in a small agricultural parcel context.     

An object‐oriented approach for analysing and characterizing urban landscape at the parcel level

This paper presents an object‐oriented approach for analysing and characterizing the urban landscape structure at the parcel level using high‐resolution digital aerial imagery and LIght Detection and Ranging (LIDAR) data. Additional spatial datasets including property parcel boundaries and building footprints were used to both facilitate object segmentation and obtain greater classification accuracy. The study area is the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, MD. A three‐level hierarchical network of image objects was generated, and objects were classified. At the two lower levels, objects were classified into five classes, building, pavement, bare soil, fine textured vegetation and coarse textured vegetation, respectively. The object‐oriented classification approach proved to be effective for urban land cover classification. The overall accuracy of the classification was 92.3%, and the overall Kappa statistic was 0.899. Land cover proportions as well as vegetation characteristics were then summarized by property parcel. This exercise resulted in a knowledge base of rules for urban land cover classification, which could potentially be applied to other urban areas.     

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.     

Land cover mapping of large areas from satellites: Status and research priorities

Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and 'fine' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies - including pixel or scene compositing, radiometric corrections, classification and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processing and classification in relation to forthcoming improvements in data sources as well as new requirements for land cover information.     

Remote sensing of savanna vegetation changes in Eastern Zambia 1972-1989

The use of the historical Landsat Multi-Spectral Scanner (MSS) archive to monitor changes in savanna vegetation between 1972 and 1989 in the South Luangwa National Park region, Eastern Zambia, was investigated. Land-cover types in the region were mapped and major changes in land cover from 1972 to 1989 were detected from MSS data. Woody canopy cover, which provides a quantitative measure of woodland structure, was estimated for woodland vegetation from MSS data using a linear relationship between woody canopy cover and red reflectance. The canopy cover changes estimated from MSS data agreed with those measured from multitemporal aerial photographs (r=0.94). Woody canopy cover changed significantly in the region from 1972 to 1989 and revealed strong spatial patterns of deforestation in Colophospermum mopane woodland on alluvial soils and vegetation regrowth of valley miombo vegetation and riverine woodland. This information on the spatial patterns of canopy cover change from 1972 to 1989 suggests certain criteria that any causative process must satisfy, and it provides a baseline for the National Park and Wildlife Services to manage the natural resources in the region. The canopy cover estimated from MSS data also provides an important input to biophysical and climatic process models for estimating the impact of vegetation structure on vegetation and climate processes.     

Incorporating domain knowledge and spatial relationships into land cover classifications: a rule‐based approach

For some tropical regions, remote sensing of land cover yields unacceptable results, particularly as the number of land cover classes increases. This research explores the utility of incorporating domain knowledge and multiple algorithms into land cover classifications via a rule‐based algorithm for a series of satellite images. The proposed technique integrates the fundamental, knowledge‐based interpretation elements of remote sensing without sacrificing the ease and consistency of automated, algorithm‐based processing. Compared with results from a traditional maximum likelihood algorithm, classification accuracy was improved substantially for each of the six land cover classes and all three years in the image series. Use of domain knowledge proved effective in accurately classifying problematic tropical land covers, such as tropical deciduous forest and seasonal wetlands. Results also suggest that ancillary data may be most useful in the classification of historic images, where the greatest improvement was observed relative to results from maximum likelihood. The cost of incorporating contextual knowledge and extensive spatial data sets may be justified, since results from the proposed technique suggest a considerable improvement in accuracy may be achieved.     

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.     

Land cover in East Asia classified using Terra MODIS and DMSP OLS products

Land cover is classified over East Asia using 250‐m Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance, MODIS snow cover and Operational Linescan System (OLS) human settlement data. The classification method includes a decision tree classification scheme that considers 11 kinds of land surface features derived from the OLS product and the time series of two MODIS products in 2000. The decision tree was defined manually based on the experiment because of insufficient training data, ease of tuning by visual interpretation, and extensibility to further research. The resulting classification is compared to three kinds of reference data, i.e. MODIS land cover product, Chinese digital land cover map, and Chinese census. The land cover classification can be input into a hydrological model applied to the Yellow River in China.     

Application of knowledge for automated land cover change monitoring

This paper outlines an approach for updating baseline land cover datasets. Knowledge about land cover, as used during manual mapping, is combined with simple remote sensing analyses to determine land cover change direction. The philosophy is to treat reflectance data as one source of information about land cover features. Applying expert knowledge with reflectance and biogeographical data allows generic solutions to the problem. The approach is demonstrated in areas of semi-natural vegetation and shown to differentiate ecologically subtle but spectrally similar land cover classes. Further, the advantages of manual mapping techniques and of high resolution remotely sensed imagery are combined. This approach is suitable for incorporation into automated approaches: it makes no assumption about the distribution of land cover features, can be applied to different remotely sensed data and is not classification specific. It has been incorporated into SYMOLAC, an expert system for monitoring land cover change.     

Exploring point-of-interest data from social media for artificial surface validation with decision trees

Artificial surfaces represent one of the key land cover types, and validation is an indispensable component of land cover mapping that ensures data quality. Traditionally, validation has been carried out by confronting the produced land cover map with reference data, which is collected through field surveys or image interpretation. However, this approach has limitations, including high costs in terms of money and time. Recently, geo-tagged photos from social media have been used as reference data. This procedure has lower costs, but the process of interpreting geo-tagged photos is still time-consuming. In fact, social media point of interest (POI) data, including geo-tagged photos, may contain useful textual information for land cover validation. However, this kind of special textual data has seldom been analysed or used to support land cover validation. This paper examines the potential of textual information from social media POIs as a new reference source to assist in artificial surface validation without photo recognition and proposes a validation framework using modified decision trees. First, POI datasets are classified semantically to divide POIs into the standard taxonomy of land cover maps. Then, a decision tree model is built and trained to classify POIs automatically. To eliminate the effects of spatial heterogeneity on POI classification, the shortest distances between each POI and both roads and villages serve as two factors in the modified decision tree model. Finally, a data transformation based on a majority vote algorithm is then performed to convert the classified points into raster form for the purposes of applying confusion matrix methods to the land cover map. Using Beijing as a study area, social media POIs from Sina Weibo were collected to validate artificial surfaces in GlobeLand30 in 2010. A classification accuracy of 80.68% was achieved through our modified decision tree method. Compared with a classification method without spatial heterogeneity, the accuracy is 10% greater. This result indicates that our modified decision tree method displays considerable skill in classifying POIs with high spatial heterogeneity. In addition, a high validation accuracy of 92.76% was achieved, which is relatively close to the official result of 86.7%. These preliminary results indicate that social media POI datasets are valuable ancillary data for land cover validation, and our proposed validation framework provides opportunities for land cover validation with low costs in terms of money and time.     

Validation of a large area land cover product using purpose-acquired airborne video

Large area land cover products generated from remotely sensed data are difficult to validate in a timely and cost effective manner. As a result, pre-existing data are often used for validation. Temporal, spatial, and attribute differences between the land cover product and pre-existing validation data can result in inconclusive depictions of map accuracy. This approach may therefore misrepresent the true accuracy of the land cover product, as well as the accuracy of the validation data, which is not assumed to be without error. Hence, purpose-acquired validation data is preferred; however, logistical constraints often preclude its use — especially for large area land cover products. Airborne digital video provides a cost-effective tool for collecting purpose-acquired validation data over large areas. An operational trial was conducted, involving the collection of airborne video for the validation of a 31,000 km² sub-sample of the Canadian large area Earth Observation for Sustainable Development of Forests (EOSD) land cover map (Vancouver Island, British Columbia, Canada). In this trial, one form of agreement between the EOSD product and the airborne video data was defined as a match between the mode land cover class of a 3 by 3 pixel neighbourhood surrounding the sample pixel and the primary or secondary choice of land cover for the interpreted video. This scenario produced the highest level of overall accuracy at 77% for level 4 of classification hierarchy (13 classes). The coniferous treed class, which represented 71% of Vancouver Island, had an estimated user's accuracy of 86%. Purpose acquired video was found to be a useful and cost-effective data source for validation of the EOSD land cover product. The impact of using multiple interpreters was also tested and documented. Improvements to the sampling and response designs that emerged from this trial will benefit a full-scale accuracy assessment of the EOSD product and also provides insights for other regional and global land cover mapping programs.     

Mapping Land Cover from Remotely Sensed Data with a Softened Feedforward Neural Network Classification

Remote sensing has considerable potential as a source of data for land cover mapping. This potential remains to be fully realised due, in part, to the methods used to extract land cover information from the remotely sensed data. Widely used statistical classifiers provide a poor representation of land cover, make untenable assumptions about the data and convey no information on the quality of individual class allocations. This paper shows that a softened classification, providing information on the strength of membership to all classes for each image pixel, may be derived from a neural network. This information may be used to indicate classification quality on a per-pixel basis. Moreover, a soft or fuzzy classification may be derived to more appropriately represent land cover than the conventional hard classification.     

Cover Airborne MSS data processing for forest classification

Abstract

Four SPOT HRV images of the same area of East Anglia, acquired between February and September 1986, have been evaluated at the National Remote Sensing Centre for their potential use in agricultural land cover mapping. Spectral coincidence plots were used in feature selection. Information from single images contained a high level of spectral confusion between cover types. Vegetation index images and original data were used in supervised maximum likelihood classification. Higher classification accuracies were achieved using the original data than the vegetation indices. An overall classification accuracy of 71 per cent for 10 land cover types was improved to 88 per cent by reducing the number of classes. Although the imagery acquired for the study did not correspond well to key dates in the crop calendar, the broad land cover categories, cereal crops, field crops (sugar beet and vegetables), grass land and broadleaved woodlands could be mapped from SPOT. Using vegetation indices from the whole scene, a map of land cover has been produced for an administrative district within the scene. Comparison with simulated Thematic Mapper data indicates greater crop discrimination is provided in the mid-infrared part of the spectrum.     

Monitoring land use and cover around parks: A conceptual approach

Many of the habitats and resources which influence ecological functioning within National Parks, and protected areas in general, are located outside of their borders in unprotected areas. Hence, land use and land cover changes in surrounding areas may substantially influence the natural resources within parks. The US National Park Service has recognized these threats and incorporated land use and land cover monitoring into its Inventory and Monitoring Program. The purpose of this paper is to provide a framework based on a conceptual approach for planning and implementing monitoring within this Program. We present a conceptual model, based on ecological theory, which illustrates how land use and land cover change impact park resources, and helps to identify monitoring indicators that will measure relevant attributes of land use and land cover change. We also discuss potential sources of data for quantifying indicators of land use and land cover change over time, including remote sensing data and ancillary spatial datasets. Finally, we describe steps for analyzing monitoring data so that the intensity and direction of changes in land use and land cover over time are quantified, as well as trends in the status of important park resources impacted by these changes. Integration of land use and land cover monitoring data and park resource data will allow for analysis of change from past to present, and can be used to project trends into the future to provide knowledge about potential land use and land cover change scenarios and ecological impacts. We illustrate our monitoring approach with an example from the Inventory and Monitoring Program's Greater Yellowstone Network.     

Sharing Knowledge and Expertise: The CSCW View of Knowledge Management

Knowledge Management (KM) is a diffuse and controversial term, which has been used by a large number of research disciplines. CSCW, over the last 20 years, has taken a critical stance towards most of these approaches, and instead, CSCW shifted the focus towards a practice-based perspective. This paper surveys CSCW researchers’ viewpoints on what has become called ‘knowledge sharing’ and ‘expertise sharing’. These are based in an understanding of the social contexts of knowledge work and practices, as well as in an emphasis on communication among knowledgeable humans. The paper provides a summary and overview of the two strands of knowledge and expertise sharing in CSCW, which, from an analytical standpoint, roughly represent ‘generations’ of research: an ‘object-centric’ and a ‘people-centric’ view. We also survey the challenges and opportunities ahead.