Statistical sampling to characterize recent United States land-cover change

The U.S. Geological Survey, in conjunction with the U.S. Environmental Protection Agency, is conducting a study focused on developing methods for estimating changes in land-cover and landscape pattern for the conterminous United States from 1973 to 2000. Eleven land-cover and land-use classes are interpreted from Landsat imagery for five sampling dates. Because of the high cost and potential effect of classification error associated with developing change estimates from wall-to-wall land-cover maps, a probability sampling approach is employed. The basic sampling unit is a 20×20 km area, and land cover is obtained for each 60×60 m pixel within the sampling unit. The sampling design is stratified based on ecoregions, and land-cover change estimates are constructed for each stratum. The sampling design and analyses are documented, and estimates of change accompanied by standard errors are presented to demonstrate the methodology. Analyses of the completed strata suggest that the sampling unit should be reduced to a 10×10 km block, and poststratified estimation and regression estimation are viable options to improve precision of estimated change.     

Mapping sub-pixel proportional land cover with AVHRR imagery

A problem with NOAA AVHRR imagery is that the intrinsic scale of spatial variation in land cover in the U.K. is usually finer than the scale of sampling imposed by the image pixels. The result is that most NOAA AVHRR pixels contain a mixture of land cover types (sub-pixel mixing). Three techniques for mapping the sub-pixel proportions of land cover classes in the New Forest, U.K. were compared: (i) artificial neural networks (ANN); (ii) mixture modelling; and (iii) fuzzy c -means classification. NOAA AVHRR imagery and SPOT HRV imagery, both for 28 June 1994, were obtained. The SPOT HRV images were classified using the maximum likelihood method, and used to derive the 'known' sub-pixel proportions of each land cover class for each NOAA AVHRR pixel. These data were then used to evaluate the predictions made (using the three techniques and the NOAA AVHRR imagery) in terms of the amount of information provided, the accuracy with which that information is provided, and the ease of implementation. The ANN was the most accurate technique, but its successful implementation depended on accurate co-registration and the availability of a training data set. Supervised fuzzy c -means classification was slightly more accurate than mixture modelling.     

Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.     

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.     

The IGBP-DIS global 1km land cover data set,DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.     

The surface temperature-vegetation index space for land cover and land-cover change analysis

Most previous applications of coarse scale remote sensing data for land-cover mapping and land-cover change analysis were based on multi-temporal Normalized Difference Vegetation Index (NDVI) data. Recent empirical studies have documented that the combination of measurements of thermal infrared radiation (e.g., land brightness temperature, Ts) and vegetation indices (VI) improves the mapping and monitoring of land cover at broad scales. We investigate the biophysical justification for such a combination, using 10 years of Advanced Very High Resolution Radiometer (AVHRR) global area coverage ( GAC) data over the African continent. First, we review recent findings on the biophysical interpretation of the TS-VI space. Second, we analyse the seasonal time trajectories of different biomes in the TS-NDVI space. Third, we measure the relative role of multi-temporal NDVI and Ts data in the discrimination of land cover classes for land-cover mapping. Fourth, we analyse trajectories of land-cover change in the TS-NDVI space for study sites in three different environments. We illustrate the usefulness of the ratio between T_s and VI as an index to perform measurements in the Tj-NDVI space.     

A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images

A hybrid method that incorporates the advantages of supervised and unsupervised approaches as well as hard and soft classifications was proposed for mapping the land use/cover of the Atlanta metropolitan area using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The unsupervised ISODATA clustering method was initially used to segment the image into a large number of clusters of pixels. With reference to ground data based on 1?:?40?000 colour infrared aerial photographs in the form of Digital Orthophoto Quarter Quadrangle (DOQQ), homogeneous clusters were labelled. Clusters that could not be labelled because of mixed pixels were clipped out and subjected to a supervised fuzzy classification. A final land use/cover map was obtained by a union overlay of the two partial land use/cover maps. This map was evaluated by comparing with maps produced using unsupervised ISODATA clustering, supervised fuzzy and supervised maximum likelihood classification methods. It was found that the hybrid approach was slightly better than the unsupervised ISODATA clustering in land use/cover classification accuracy, most probably because of the supervised fuzzy classification, which effectively dealt with the mixed pixel problem in the low-density urban use category of land use/cover. It was suggested that this hybrid approach can be economically implemented in a standard image processing software package to produce land use/cover maps with higher accuracy from satellite images of moderate spatial resolution in a complex urban environment, where both discrete and continuous land cover elements occur side by side.     

基于3S技术的土地利用/土地覆盖变化野外采样框架设计 ——以东北地区黑龙江省为例

土地利用/土地覆盖变化是全球环境变化的重要组成部分,随着3S技术的不断成熟和发展,运用RS、GPS和GIS技术进行土地利用/土地覆盖变化研究已成为一种越来越成熟的方式和手段。从空间抽样模型理论出发,以我国黑龙江省为例,运用RS、GPS和GIS技术,通过对黑龙江省道路网、土地利用区划、土地利用/土地覆盖类型、土地利用/土地覆盖1 km×1 km格网数据等空间信息分布的综合考虑、分析,设计了土地利用/土地覆盖变化的综合野外采样框架。框架主要包括采样区的布设、采样路线和采样点的选择等。由于以多层空间信息为采样依据,经实践检验,该采样框架具有经济实用等优点。     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

Mapping land-cover over large areas using multispectral data derived from the NOAA-AVHRR: A case study of Nigeria

Land-cover information for Nigeria was obtained from a countrywide, low-level aerial survey conducted in 1990. A range of spectral vegetation indices (SVIs) and ground surface temperature estimates were calculated for Nigeria using daily data throughout 1990 from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. A supervised classification of the land-cover classes was then performed using a modified discriminant analysis in which predictor variables were selected from the mean, maximum, minimum and standard deviation of the raw waveband AVHRR data, AVHRR derived products and a digital elevation model (DEM). With a 60 per cent threshold coverage by any one of eight major vegetation types the analysis correctly predicted land-cover type with producer accuracies (excluding 'bare ground' with only a few points) of between 48 per cent (cultivation) and 100 per cent (mangrove) (average 74.5 per cent).     

Satellite-derived land surface climate 'signal' for the Midwest U.S.A.

As a potential strategy for developing regional Land surface climatologies, a statistical method to estimate the land-cover signal from the Normalized Difference Vegetation Index (NDVl) is developed and applied to the Midwest U.S.A. summer growing season. The method evaluates the temporal correlation of NDVl for non-consecutive scenes of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) at Local Area Coverage (LAC) resolutions. Conventional mapped data help separate the low frequency variations related to phenology from shorter-term fluctuations involving surface moisture. The land surface signal is more stable temporally when pixel data are aggregated to spatial resolutions commensurate with the Global Area Coverage (GAC) data.     

Broad scale land-cover classification and interannual climatic variability

Previous attempts to map land cover at broad scales were based on single year time series and usually on the Normalized Difference Vegetation Index ( NDVI) Single years of data lack statistical representativity and the NDVI is partially driven by short-term climatic characteristics. We investigate two approaches to produce land-cover classifications that are not excessively influenced by short-term climatic variability: (i) averaging a climate-driven variable over several years, and (ii) measuring a more climate-independent variable. We test, compare and combine these two approaches for the African continent using 8 years of AVHRR Global Area Coverage (GAC) data. Our results demonstrate that times series of the ratio between surface temperature and NDVI are less influenced by interannual variations in climatic conditions than NDVI time series and thus produce more stable land-cover classifications. This finding is consistent with the biophysical interpretations of these two variables. When data are averaged over longer periods, NDVI- or T_s/ NDVI-based land-cover classifications display few differences.     

Land-cover classification methods for multi-year AVHRR data

Advanced Very High Resolution Radiometer (AVHRR) data have been extensively used for global land-cover classification, but few studies have taken direct and full advantage of the multi-year properties of AVHRR data. This study focused on generating effective classification features from multi-year AVHRR data to improve classification accuracy. Three types of features were derived from 12-year monthly composite normalized difference vegetation index (NDVI) and channel 4 brightness temperature from the NOAA/NASA Pathfinder AVHRR Land data for land-cover classification. The first is based on the shape of the annual average NDVI or brightness-temperature profile, which was then approximated by a Fourier series. The coefficients estimated by the weighted least-squares method were used for classification. The second and third features were based on the raw periodogram of the time series and the auto-regressive modelling. A global land-cover training database created from Landsat Thematic Mapper and Multi-spectral Scanner imagery was used for training and validation. Both quadrature discriminate analysis (QDA) and linear discriminate analysis (LDA) were explored for classification, and results indicate that QDA performs much better than LDA. The first feature, based on the mean annual shape, produced much better results than the other two features. It was also found that NDVI signals worked better than brightness-temperature signals. That is probably because topof-atmosphere signals were used, and atmospheric contaminations significantly disturb the thermal signals and correlation structures of different cover types. Further validations are needed.     

基于MODIS时序数据的澳大利亚土地利用/覆被分类与验证

选取对气候变化敏感的澳大利亚作为研究区,基于MOD13Q1数据,对澳大利亚2000年土地利用/覆被进行分类。通过Savizky-Golay滤波方法构建高质量NDVI时序数据,为分类奠定数据基础。采用了以决策树为主的混合分类方法对研究区土地利用/覆被进行分类,该方法综合利用了ISODATA分类结果、NDVI阈值及其时间序列主成分分析特征量等数据。通过面积对比和空间位置匹配等多角度验证的方法,综合比较MOD12Q1,GLC_2000与本研究的结果,发现本研究的总体分类精度为63.65%,Kappa系数为0.56,较以上两种已有的土地覆盖产品具有一定优势。     

Development and validation of AVHRR-derived regional forest cover data for the north-eastern U.S.

As part of developing the geographic information system (GIS) to support a north-eastern U.S. regional forest change modelling effort, we investigated the utility of several sources of AVHRR data in regional forest cover mapping. Single-date classified Advanced Very High Resolution Radiometer (AVHRR) imagery in combination with existing USGS Land Use/Land Cover data was used to create a forest cover database that encompassed eastern New York state and all of New England. The USGS EROS Data Center Conterminous U.S. Land Cover Characteristics database was also evaluated for comparison. Statistical analysis showed that the AVHRR-derived regional land cover datasets provided estimates of total forest area that were comparable to U.S. Forest Service county level estimates. The AVHRR imagery recorded after leaf fall appeared to enhance the discrimination of coniferous vs. deciduous forests.     

Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape

From its inception, land-use and land-cover mapping have been major themes in remote-sensing research and applications. Although frequently considered together, land use and land cover (LULC) are defined differently, with land use referring to the economic function of the Earth’s surface and land cover to its natural or engineered biophysical cover. Land cover can be observed directly using remote sensing, but land use must be inferred from the cover type. In this study, we test whether object-based image analysis (OBIA) can improve the land-cover and land-use classification in a complex agricultural landscape located along the border between Poland and Ukraine. We quantitatively compared the results of OBIA-based versus per-pixel classifications for both land cover and land use, respectively. Our results show that land-cover classification was not significantly improved when OBIA-based methods were used. Although overall classification accuracy was modest, land-use classification was significantly improved when OBIA-based methods were applied using both spectral and spatial/geometric features of image objects, but not when spectral or spatial/geometric features were used independently. Our results suggest that in anthropogenically altered landscapes where the geometry and arrangement of surface spatial structure may convey land-use information, use of OBIA-based techniques may provide a powerful tool for improving classification.     

美国高分辨率土地覆盖信息提取技术研究进展

土地覆盖数据是进行全球变化研究的基础。美国地质调查局组织的土地覆盖特征研究项目成功的开展了两次工作, 建设了土地覆盖数据库, 得到了多方面的认可, 代表了美国相关领域的前沿技术。项目中涉及一系列技术, 包括土地覆盖分类分区、遥感影像选取、数据预处理过程、图像转换变化分析、土地覆盖分类技术、数据产品验证、土地覆盖数据库建设等。另外还有关键技术研究, 包括: 土地覆盖多边形分析、纹理特征分析、树冠密度信息提取、城市不透性表面估算等。就美国地质调查局项目中土地覆盖遥感影像数据处理方法、技术流程、数据库建设, 以及有关土地覆盖度等相关关键技术进行了介绍, 期望能够反映其进展情况, 对国内的相关工作起到启示作用。     

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.     

A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data

We present results from analyses conducted to evaluate the performance of advanced supervised classification algorithms (decision trees and neural nets) applied to AVHRR data to map regional land cover in Central America. Our results indicate that the sampling procedure used to stratify ground data into train and test sub-populations can substantially bias accuracy assessment results. In particular, we found spatial autocorrelation in test data to inflate estimates of classification accuracy by up to 50 points. Results from evaluations performed using independent train and test data suggest that the feature space provided by AVHRR NDVI data is poorly suited for most land cover mapping problems, with the exception of those involving highly generalized classes.