Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

Monitoring vegetation recovery in the early stages of the Dirk Hartog Island Restoration Programme using high temporal frequency Landsat imagery

Dirk Hartog Island is the largest island off the West Australian coast. From the 1860s to 2008, the island was managed as a pastoral lease. In 2009, the island was gazetted as a National Park and the process of removing introduced animals, to allow for the reintroduction of a suite of 12 native mammal species, began. With the removal of high numbers of goats and sheep concerns were raised regarding the proliferation of weed species (which would no longer be controlled through grazing) and the ability of the island’s native vegetation to recover. A vegetation monitoring programme was developed which integrated detailed floristic surveys, repeated site photography and Landsat time series data to provide a comprehensive picture of how the island’s vegetation cover had changed since destocking. The integration of these elements has allowed present‐day observations to be put into context of longer term landscape dynamics. Through statistical analysis of temporal sequences of Landsat satellite imagery, the timing of changes to phenology and cover at monitoring sites was related directly to the management action of stock removal. With the use of field observations, the species responsible for the increase in cover were identified. These data sources, when analysed together, allowed management to have confidence that, following destocking, native vegetation cover is increasing.     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

Rangeland Condition Monitoring: A New Approach Using Cross-Fence Comparisons of Remotely Sensed Vegetation

A need exists in arid rangelands for effective monitoring of the impacts of grazing management on vegetation cover. Monitoring methods which utilize remotely-sensed imagery may have comprehensive spatial and temporal sampling, but do not necessarily control for spatial variation of natural variables, such as landsystem, vegetation type, soil type and rainfall. We use the inverse of the red band from Landsat TM satellite imagery to determine levels of vegetation cover in a 22,672km² area of arid rangeland in central South Australia. We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management. The cross-fence comparison methodology generates and solves simultaneous equations of the relationship between each paddock and all other paddocks, derived from pairs of cross-fence sample points. We compare this ranking from two image dates separated by six years, during which management changes are known to have taken place. Changes in paddock rank resulting from the cross-fence comparison method show strong correspondence to those predicted by grazing management in this region, with a significant difference between the two common management types; a change from full stocking rate to light 20% stocking regime (Major Stocking Reduction) and maintenance of full 100% stocking regime (Full Stocking Maintained) (P = 0.00000132). While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities. These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged. For the first time, this method allows us to rank non-adjacent paddocks in a rangeland region relative to each other, while controlling for natural spatio-temporal variables such as rainfall, soil type, and vegetation community distributions, due to the nature of the cross-fence experimental design, and the spatially comprehensive data available in satellite imagery. This method provides a potential tool to aid land managers in decision making processes, particularly with regard to stocking rates.     

A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery

Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (α < 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R² values for beta regression models of vegetation cover from RapidEye image information varied from 0.1525 to 0.7732 in White River and 0.2455 to 0.6085 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, normalized RMSE was lower for any-hit cover (indicating better model performance) or minimally higher than top-hit cover for all indicators in each study area. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator in the ecosystem of interest, and second on how well the indicator performs in modeling applications.     

Summit-to-sea mapping and change detection using satellite imagery: tools for conservation and management of coral reefs

Continuous summit-to-sea maps showing both land features and shallow-water coral reefs have been completed in Puerto Rico and the U.S. Virgin Islands, using circa 2000 Landsat 7 Enhanced Thematic Mapper (ETM+) Imagery. Continuous land/sea terrain was mapped by merging Digital Elevation Models (DEM) with satellite-derived bathymetry. Benthic habitat characterizations were created by unsupervised classifications of Landsat imagery clustered using field data, and produced maps with an estimated overall accuracy of>75% (Tau coefficient >0.65). These were merged with Geocover-LC (land use/land cover) data to create continuous land/ sea cover maps. Image pairs from different dates were analyzed using Principle Components Analysis (PCA) in order to detect areas of change in the marine environment over two different time intervals: 2000 to 2001, and 1991 to 2003. This activity demonstrates the capabilities of Landsat imagery to produce continuous summit-to-sea maps, as well as detect certain changes in the shallow-water marine environment, providing a valuable tool for efficient coastal zone monitoring and effective management and conservation.     

基于图像融合与混合像元分解的城市植被盖度提取

城市植被盖度提取对于开展城市绿色空间保护和城市规划具有重要意义。随着遥感技术的发展,混合像元分解模型被广泛用于从中等分辨率的多光谱影像提取城市植被盖度,但较低的影像空间分辨率限制了该模型的应用领域。为此,以杭州市为例,首先引入Gram-Schmidt(GS)方法对Landsat ETM+的多光谱波段和全色波段进行融合,再通过混合像元分解模型从ETM+融合影像上提取城市植被盖度,最后利用SPOT影像进行精度检验。结果发现,采用GS方法对影像进行融合后,标准差、信息熵、平均梯度提高,相对偏差小于0.07,说明在保留多光谱信息的基础上提高了其空间分辨率。与SPOT影像相比,在融合影像上75%以上样本的植被盖度值相似,误差较大的区域是市区植被特别稀疏或茂盛的像元。与源影像相比,从融合影像上提取的植被盖度的均方根误差和系统误差降低了0.01。该方法在降低城市植被监测成本、提高监测精度方面具有潜力。     

Land surface phenology as an indicator of biodiversity patterns

With the rapid decline in biodiversity worldwide it is imperative to develop procedures for assessing changes in biodiversity across space. The synoptic view provided by imaging remote sensors constitutes a suitable approach for analyzing biodiversity from local to regional scales. A procedure based on the close relationship between floristic similarity and the similarity in land surface phenology was recently developed and successfully applied to assess diversity patterns using time series imagery acquired by the Moderate Resolution Imaging Spectro-radiometer (MODIS). However, as it depends on high temporal resolution remotely sensed data (e.g., MODIS), the procedure is constrained by the coarse spatial resolution characterizing these high temporal resolution data. Using an optimized technique for image fusion, we combined high temporal resolution data acquired by the MODIS sensor system with moderate spatial resolution data acquired by the Landsat TM/ETM+ sensor systems. Our results show that the MODIS/Landsat data fusion allows the characterization of land surface phenology at higher spatial resolutions, which better corresponded with information acquired within vegetation survey plots established in temperate montane forests located in Wolong Nature Reserve, Sichuan Province, China. As such, the procedure is useful for capturing changes in biodiversity induced by disturbances operating at large spatial scales and constitutes a suitable tool for monitoring and managing biodiversity.     

Leakiness: A new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data

The health of arid and semiarid lands needs to be monitored, particularly if they are used to produce food and fiber, and are prone to loss of vegetation cover and soil. Indicators of landscape health based on remotely sensed data could cost-effectively integrate structural and functional attributes of land surfaces across a range of scales. In this paper, we describe a new index for remotely monitoring changes in the health of land. The new index takes important aspects of landscape structure and function into account by focusing on the potential for landscapes to lose or ‘leak’ (not retain) soil sediments. We combined remotely sensed vegetation patchiness data with digital elevation model (DEM) data to derive a quantitative metric, the landscape leakiness index, LI. This index is strongly linked to landscape function by algorithms that reflect the way in which spatial configuration of vegetation cover and terrain affect soil loss. Linking LI to landscape function is an improvement on existing indicators that are based on qualitatively assessing remotely sensed changes in vegetation cover. Using archived Landsat imagery and Shuttle Radar Topography Mission DEMs, we found for example that LI indicated improvements in the condition or health of a rangeland paddock that was monitored from 1980 to 2002. This paddock is located in central Australia and its improved health is documented by photographs and field data. Although the full applicability of LI remains to be explored, we have demonstrated that it has the potential to serve as a useful ecological indicator for monitoring the health of arid and semiarid landscapes.     

Satellite remote sensing for ecological analysis of forested landscape

The spatial characterisation and vertical analysis are together considered important to evaluate structure of forested landscapes. In recent years, increased human impacts have resulted in changes in landscape and structure of the forest ecosystem. The present study is aimed to analyse impacts of disturbance on landscape structure using satellite remote sensing and a geographic information system (GIS) in Madhav National Park of India. The Landsat TM data have been used to identify vegetation types. The patch characteristics of the vegetation like size, shape, porosity and patch density have been studied. The physical and humanmade features have divided the national park in three zones. These zones are also utilised as management zones by the State Forest Department. The study indicates that the central zone is distinctly different from the south and north zones. The patch size and porosity have been found to be most important parameters to discriminate differences in the ecological status of three different zones of the park. The patchiness and shape provide supportive information and characterise the patches of the zones. The structural analysis of the vegetation revealed effect on species diversity and biomass distribution in the different disturbance regimes.     

A new land-cover map of Africa for the year 2000

Aim In the framework of the Global Land Cover 2000 (GLC 2000), a land‐cover map of Africa has been produced at a spatial resolution of 1 km using data from four sensors on‐board four different Earth observing satellites. Location The map documents the location and distribution of major vegetation types and non‐vegetated land surface formations for the entire African continent plus Madagascar and the other surrounding islands. Methods The bulk of these data were acquired on a daily basis throughout the year 2000 by the VEGETATION sensor on‐board the SPOT‐4 satellite. The map of vegetation cover has been produced based upon the spectral response and the temporal profile of the vegetation cover. Digital image processing and geographical information systems techniques were employed, together with local knowledge, high resolution imagery and expert consultation, to compile a cartographic map product. Radar data and thermal sensors were also used for specific land‐cover classes. Results A total of 27 land cover categories are documented, which has more thematic classes than previously published land cover maps of Africa contain. Systematic comparison with existing land cover data and 30‐m resolution imagery from Landsat are presented, and the map is also compared with other pan‐continental land cover maps. The map and digital data base are freely available for non‐commercial uses from http://www.gvm.jrc.it/tem/africa/products.htm Main conclusions The map improves our state of knowledge of the land‐cover of Africa and presents the most spatially detailed view yet published at this scale. This first version of the map should provide an important input for regional stratification and planning purposes for natural resources, biodiversity and climate studies.     

Monitoring coastal wetland communities in north-eastern NSW using ASTER and Landsat satellite data

The coastal wetland communities of north-eastern New South Wales (NSW) Australia exist in a subtropical climate with high biodiversity and are affected by anthropogenic and natural stressors such as urbanization and climate change. The aim of the research is to map and monitor the coastal wetland communities in north eastern NSW using satellite data. Advanced Spaceborne Thermal Emission and Reflectance Radiometer, Landsat ETM+ and Landsat TM satellite imagery of November 2003, June 2001 and September 1989 respectively were used to identify and monitor the wetland communities. Supervised classification was performed using the maximum likelihood standard algorithm. Normalized Difference Vegetation Index was produced and the health of the wetland vegetation was evaluated. The wetland maps present significant changes in the coastal wetland communities in the months of September 1989, June 2001 and November 2003. This information could be used by coastal wetland managers in order to enhance the management of these ecosystems.     

Natural and anthropogenic changes to mangrove distributions in the Pioneer River Estuary (QLD,Australia)

We analyzed a time series of aerial photographs and Landsat satellite imagery of the Pioneer River Estuary (near Mackay, Queensland, Australia) to document both natural and anthropogenic changes in the area of mangroves available to filter river runoff between 1948 and 2002. Over 54 years, there was a net loss of 137 ha (22%) of tidal mangroves during four successive periods that were characterized by different driving mechanisms: (1) little net change (1948–1962); (2) net gain from rapid mangrove expansion (1962–1972); (3) net loss from clearing and tidal isolation (1972–1991); and (4) net loss from a severe species-specific dieback affecting over 50% of remaining mangrove cover (1991–2002). Manual digitization of aerial photographs was accurate for mapping changes in the boundaries of mangrove distributions, but this technique underestimated the total loss due to dieback. Regions of mangrove dieback were identified and mapped more accurately and efficiently after applying the Normalized Difference Vegetation Index (NDVI) to Landsat Thematic Mapper satellite imagery, and then monitoring changes to the index over time. These remote sensing techniques to map and monitor mangrove changes are important for identifying habitat degradation, both spatially and temporally, in order to prioritize restoration for management of estuarine and adjacent marine ecosystems.     

Monitoring Wetland Habitat Restoration in Southern California Using Airborne Multi spectral Video Data

Remote sensing provides a complementary approach to field sampling to assess whether restored wetland areas provide suitable habitat for the Light-footed Clapper Rail (Rallus longirostris levipes). Habitat requirements for the clapper rail are specified by the composition of vegetation species and their spatial extent in its nesting home range. A major salt marsh construction project has been completed at the Sweetwater Marsh National Wildlife Refuge (“the refuge”), San Diego County. In this paper we describe the application of image classification techniques to high-spatial-resolution digital video imagery (0.8-m pixels) to delimit patches of different marsh vegetation at the refuge. Using maps of vegetation types derived from multi spectral imagery, we estimated the area occupied by each vegetation type in potential clapper rail home ranges. Preliminary field-checking results indicate that this approach is an accurate, noninvasive and cost-efficient means of providing ecological information for restoration monitoring in southern California's remnant wetlands.     

Application of remote sensing to site characterisation and environmental change analysis of North African coastal lagoons

This article describes the use of satellite imagery for identifying key environmental characteristics within three North African coastal lagoons (Merja Zerga, Morocco; Ghar El Melh, Tunisia and Lake Manzala, Egypt) and for detecting the major environmental changes within these environments. A combination of Landsat MSS, Landsat TM, Landsat ETM+ and ASTER imagery was acquired for the three sites for a period covering the last three decades (1972–2004). Following geometric correction and enhancement, the interpretation of the most recent image acquired for each of the three lagoons provides important insights into their current conditions. For Merja Zerga, these include the distribution of the largest channels which drain extensive inter-tidal mudflats and the two major depositional features associated with sources of freshwater. The distribution of marginal aquatic vegetation is highlighted as is the intensive use of the surrounding landscape for agriculture. Intensive agriculture around Ghar El Melh is also indicated. The influence of the Mejerda River, which was diverted away from the lagoon over 100 years ago, is shown to persist as a residual area of deltaic deposits in shallow water that has been eroded over time. Coastal processes including the direction of the alongshore sediment transport and the influence of engineering work associated with port construction can also be recognised. Within Lake Manzala, vegetated islands divide the lake into a series of sub-basins which can be clearly distinguished. The large influence of human activities within this lake can be identified and include reclamation for agriculture and the conversion of parts of the lake bed for fish farms. The historical images available for the three lagoons provide important insights into decadal scale changes, which have been greatest at Lake Manzala. Since the early 1970s large parts of the lake, in particular in the southwest where the shoreline has migrated northwards, have been reclaimed. Major engineering works, such as the El Salam Canal and road embankments, are shown to have resulted in significant lake change. The distribution of emergent vegetation within the lake has also changed. Classification of images for this lake into open water, vegetation and land enables the quantification of these changes. Between 1973 and 2003, the lake declined in area by approximately 50%. Changes at Merja Zerga over the last three decades include reconfiguration of the marine outlet and the expansion of the internal delta at the end of the Nador Canal. The images of this site clearly demonstrate the intensification of agriculture around the lagoon. The most marked changes evident within the images of Ghar El Melh concern the sand bars that separate the lagoon from the sea. Geomorphological processes operating within the coastal zone have resulted in the straightening of the bars with central sections migrating out towards the sea. Remote sensing is established as a promising application for detecting the quantitative surface cover changes in coastal lagoons and their near landscapes. Guest editors: J. R. Thompson & R. J. Flower Hydro-ecological Monitoring and Modelling of North African Coastal Lagoons     

Satellite remote sensing of wetlands

To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.     

城市公园春季声景观与植被结构的关系

城市公园是城市生态系统的重要组成部分和城市生物多样性热点地区,具有丰富的声景观资源。由于声景观及声学方法具有信息量大,成本低,低侵入的特点,因此其研究和应用对生态系统健康及监测具有较高价值。声景观研究通过总结生物声的活动或多样性来衡量生物多样性。记录了北京20个城市公园的春季声景观,使用定量方法描述了声景观特征与变化;测试三种了已被证明与生物多样性相关并被广泛使用的声学指数（BIO、ADI、NDSI）与植被群落关系,完成了城市环境中声景观与环境关系的初步探究。研究结果表明：（1）声学指数能够有效表征城市公园声景观信息,具有显著的时间动态特性,能准确反映鸟类黎明合唱等重要生物生态活动;（2）声学强度指数也具有显著的时间动态变化和沿频率梯度的变化,不同的频率区间反映了不同声学群落的活动信息;（3）植被结构尤其是垂直结构对声景观起着重要作用,垂直异质性越大,声学多样性越高。发现支持声景观作为公园植被状况的度量,强调了其作为生物多样性和生态系统健康状况监测方法,用于城市管理和可持续发展的巨大潜力。     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

1988-2006年辽河三角洲植被结构的变化

在人类干扰和气候波动的影响下,辽河三角洲的植被结构发生了显著变化.该文研究了辽河三角洲植被结构的变化,对于保护该地区生态系统、促使其健康发展具有重要意义.研究区域包括了整个辽河三角洲.资料来源主要是1988年和2006年的TM(Landsat Thematic Mapper)遥感图像.采用数字化、制作植被图和叠加分析等方法研究探讨了辽河三角洲植被结构的特点.以及植被的空间分布转移和面积变化.结果显示,以双台子河口为中心,4大植被类型(自然湿地植被、人工湿地植被、人工旱地植被和自然旱地植被)大体上构成半环状分布格局.从整体上看,从1988年至2006年,植被结构仍然保持半环状的空间格局.但是,主要植被类型的空间分布趋于集中,形成了比较大的斑块,而不是离散、破碎的.从植被类型间相互转化的情况看,几乎所有植被类型的空间分布和面积都有明显改变.在绝对面积上,水稻(Oryza saliva)田、玉米(Zea mays)地和滨海芦苇(Phragmites australis)湿地是辽河三角洲3个面积最大的植被类型.水稻田是面积增加最多的植被类型,增加977.1 km~2;而玉米地是面积减少最多的植被类型,减少622.2 km~2.在面积变化幅度上,水稻田、玉米地和滨海芦苇湿地的变化幅度分别为33.2%、-16.1%、-23.2%.面积减少幅度最大的植被类型是草地,-77.9%;面积增加幅度最大的植被类型是翅碱蓬(Suaedaheteroptera)盐化草甸,212.1%.