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.     

Validation of the Greater Hunter Native Vegetation Mapping as it pertains to the Upper Hunter region of New South Wales

Reliable vegetation maps are an important component of any long‐term landscape planning initiatives. A number of approaches are available but one, in particular, pattern recognition (segmentation) combined with modelling from floristic site data, is currently being used to map vegetation across NSW. An independent assessment of this approach based on a review of the Greater Hunter Native Vegetation Mapping (GHM_v4) was undertaken in order to assess its ability to cater for regional, local, strategic and landscape planning. The validation process tested 2151 locations across the Upper Hunter Valley region of New South Wales (NSW), Australia. The results suggest that mapping at the coarsest level of NSW vegetation classification, the Formation, is generally poor, with only Dry Sclerophyll Forest and Woodland modelled with some level of reliability. The modelled mapping of individual plant community types (PCTs) was found to be highly inaccurate with only 17% of validation points attributed as ‘correct’ and a further 13% ‘essentially correct’. Therefore, a majority of PCTs were mapped with an accuracy of less than 30%. The results of this validation suggest that the GHM_v4 is of such a low level of accuracy within the upper Hunter as to be inherently unusable for broad‐scale regional and local landscape planning or environmental assessment, including locating compensatory offsets for the loss of native vegetation due to developments. The GHM_v4 methods of pattern recognition of mainly SPOT5 satellite imagery combined with modelling from plot data have not produced reliable vegetation maps of plant community types. Yet this mapping programme is extending across NSW and could be misused for environmental decisions or as a regulation.     

Using landscape context to map invasive species with medium‐resolution satellite imagery

The spread of invasive species is a global problem of major ecological and economic concern. Landscape level assessment of invasive spread is critical, but remote sensing (RS) analyses are often complicated by the spectral similarity of species and the need to balance spatial resolution with data storage and analysis complexity. One example is the ridge and slough landscape (RSL) of the Florida Everglades, where inflowing nutrients have facilitated large‐scale cattail invasions. Hand delineation of aerial imagery has been successful in mapping cattail spread, but this technique requires considerable time and effort. Computerized classification of medium‐resolution imagery would increase the ability of scientists to provide up‐to‐date data for water management decisions. Advances in RS technologies have created opportunities that were not previously available in landscapes such as the RSL—to automatically classify sawgrass and cattail communities with medium‐resolution satellite imagery using knowledge of the invasion ecology of cattail and landscape context. We developed a computer‐classification technique that provided measure of cattail expansion that matched ground‐truthed data and show an increase in cattail area (similar to previous estimates), but a reduction in the rate of expansion over time. Although this technique can miss small patches of plants that might indicated new invasions, its rapid mapping can improve tracking of invasion fronts in the Everglades and other landscapes.     

A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

Structural and functional mapping of geosigmeta in Atlantic coastal marshes (France) using a satellite time series

Geosynphytosociology deals with the study of combinations of vegetation series – or geosigmeta – within landscape. Its main advantage is to assess conservation status based on vegetation dynamics. However, this field-based approach has not been widely applied, because local surveys are not representative of spatio-temporal landscape complexity, which leads to uncertainties and errors for geosigmeta structural and functional mapping. In this context, satellite time series appear as relevant data for monitoring vegetation dynamics. This article aims to assess the contribution of an annual satellite time series for geosigmeta structural and functional mapping. The study area, which focuses on the French Atlantic coast (4630 km²), includes salt, brackish, sub-brackish and fresh marshes. A structural vegetation map was derived from the classification of an annual time series of 38 MODIS images validated with field surveys. The functional vegetation map was derived from the annual Integral of Normalized Difference Vegetation Index (NDVI-I), as an indicator of above-ground net primary production. Results show that geosigmeta were successfully mapped at a scale of 1:250,000 with an overall accuracy of 82.9%. The geosigmeta functional map highlights a strong gradient from the lowest NDVI-I values in salt marshes to the highest values in fresh marshes.     

Landscape-scale detection and mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G. Don Ciferri) in SW Sydney,Australia using satellite remote sensing

Question: Is satellite imagery an effective tool for mapping and examining the distribution of the invasive species Olea europaea L. ssp. cuspidata at a regional landscape scale? Location: Southwest Sydney, Australia. Methods: Remote sensing software was used to classify pixels of Olea europaea L. ssp. cuspidata (African Olive) and major vegetation types from satellite imagery, using a “supervised classification” technique across a 721 km² study area in the Cumberland Plain region of western Sydney. A map of African Olive distribution was produced from the image analysis and checked for accuracy at 337 random locations using ground observation and comparison with existing vegetation maps. The African Olive distribution data were then used in a GIS analysis with additional spatial datasets to investigate the relationship between the distribution of African Olive and environmental factors, and to quantify the conservation threat to endangered native vegetation. Results: A total area of 1907 ha of dense African Olive infestation was identified, with an omission error of 7.5% and a commission error of 5.4%. African Olive was found to occur on the steepest slopes (mean slope 14.3°) of the vegetation classes examined, with aspect analysis identifying a high prevalence on south- and southwest-facing slopes. The analysis also quantified the level of African Olive infestation in endangered ecological communities, with Western Sydney Dry Rainforest (25% affected) and Moist Shale Woodland (28% affected) identified as most vulnerable to African Olive invasion. Conclusion: The distribution of African Olive can be efficiently mapped at a landscape scale. This technique, used in association with additional spatial datasets, identified African Olive as a significant environmental weed in SW Sydney, occupying a greater area than previously recognised and threatening several endangered native vegetation communities.     

A low-cost procedure for automatic seafloor mapping, with particular reference to coral reef conservation in developing nations

Loss of marine biodiversity through benthic habitat destruction has created urgent needs for low-cost, high-performance seafloor survey methods. However, accurate seafloor mapping and classification is usually an expensive undertaking requiring sophisticated equipment, which excludes important low-budget user groups in developing nations. In this paper, we introduce a low-cost procedure for seafloor mapping based on free-of-charge data acquisition software that can be downloaded from the Internet. Using a Malaysian coral-reef case-study, we show how comprehensive bathymetric mapping can be implemented with such software using inexpensive eccosounder and GPS, and describe principles for integration of environmental data, either by connecting additional instruments to the computer (up to 32 instruments with up to eight channels each can be handled simultaneously), or by using simple synchronisation techniques with equipment that records to separate media, such as video cameras. We mapped a Malaysian coral-reef area of 114 hectares in 6 hours of video mapping, achieving reef mapping rates that matches rates currently achieved only with air-borne imaging devices. However, the present methodology results in completely ground-truthed data that can be classified according to many schemes by direct observation of the habitat, and provides detailed bathymetric data that satellite imagery or air-borne spectrographic sensors do not provide. The method can be used as a stand-alone reef mapping and classification tool on the scale of tens to hundreds of square kilometres. On larger scales (thousands of square kilometres), airborne survey methods are likely to remain more cost-effective than boat-based methods, yet also in such settings this simple method offer unprecedented capacity for ground truthing and thereby increased capacity for habitat classification.     

高分辨率影像支持的群落尺度沼泽湿地分类制图

湿地作为众多野生动物和植物的栖息地,具有稳定环境及物种基因保护等重要功能.但是,湿地复杂的水陆交界生境特征及难以进入等客观条件限制给湿地研究造成了很大的困难.因此,遥感技术作为地表生态环境过程参量获取的重要工具,在当今湿地科学领域发挥着重要作用,特别是,当前高空间分辨率影像的性能与应用水平不断得到提高.以自然状态下的黑龙江三江平原洪河国家级自然保护区为研究对象,应用飞艇搭载的空间高分辨率摄像系统获取影像地面分辨率为0.13m的影像数据,主要结合面向对象分类方法,开展了基于湿地植物群落尺度的分类制图研究.结果表明:①因飞艇影像对植物形态、纹理等细致特征的刻画非常充分,沼泽植被型、草甸植被型和各种乔木、灌木植被型,都可以在合适的遥感分类方法下提取出来,总体分类精度能达到91.77％;②通过采用针对高分辨率影像面向对象的分类方法与传统的最大似然比遥感分类方法对比,前者达到很高的精度,而后者效果不理想,说明遥感分类方法的选择对于群落尺度湿地植物分类制图结果非常重要;③遥感分类制图的结果显示出研究区湿地植物群落分布格局受到水分环境梯度和微地貌的共同控制,呈现交替环带状分布规律.     

High‐resolution riparian vegetation mapping to prioritize conservation and restoration in an impaired desert river

In highly impaired watersheds, it is critical to identify both areas with desirable habitat as conservation zones and impaired areas with the highest likelihood of improvement as restoration zones. We present how detailed riparian vegetation mapping can be used to prioritize conservation and restoration sites within a riparian and instream habitat restoration program targeting 3 native fish species on the San Rafael River, a desert river in southeastern Utah, United States. We classified vegetation using a combination of object‐based image analysis (OBIA) on high‐resolution (0.5 m), multispectral, satellite imagery with oblique aerial photography and field‐based data collection. The OBIA approach is objective, repeatable, and applicable to large areas. The overall accuracy of the classification was 80% (Cohen's κ = 0.77). We used this high‐resolution vegetation classification alongside existing data on habitat condition and aquatic species' distributions to identify reaches' conservation value and restoration potential to guide management actions. Specifically, cottonwood (Populus fremontii) and tamarisk (Tamarix ramosissima) density layers helped to establish broad restoration and conservation reach classes. The high‐resolution vegetation mapping precisely identified individual cottonwood trees and tamarisk thickets, which were used to determine specific locations for restoration activities such as beaver dam analogue structures in cottonwood restoration areas, or strategic tamarisk removal in high‐density tamarisk sites. The site prioritization method presented here is effective for planning large‐scale river restoration and is transferable to other desert river systems elsewhere in the world.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities,Taiwan

The purpose of this study is to apply different remote sensing techniques to monitor shifting mangrove vegetation in the Danshui River estuary in Taipei, Taiwan, in order to evaluate a long-term wetland conservation strategy compromising between comprehensive wetland ecosystem management and urban development. In the Danshui estuary, mangrove dominated by Kandelia candel is the major vegetation, and a large area of marsh with freshwater grasses has been protected in three reserves along the river shore. This study applied satellite imagery from different remote sensors of various resolutions for spectral analysis in order to compare shifting wetland vegetation communities at different times. A two-stage analytical process was used for extracting vegetation area and types. In the first-stage, a normalized difference vegetation index (NDVI) was adopted to analyze SPOT, Landsat, and QuickBird imagery to obtain the spatial distribution of vegetation covers. In the second stage, a maximum likelihood classification (MLC) program was used to classify mangrove and non-mangrove areas. The results indicated that the spatial distribution of mangroves expanded 15.18 and 40 ha in two monitoring sites in 10 years, demonstrating the success of establishing reserves for protecting mangrove habitats. The analytical results also indicated that satellite imagery can easily discern the difference in characteristics between imagery of mangrove and other vegetation types, and that the logistical disadvantages of monitoring long-term vegetation community changes as well as evaluating an inaccessible area may be overcome by applying remote sensing techniques.     

Random Forest characterization of upland vegetation and management burning from aerial imagery

Aim The upland moorlands of Great Britain form distinctive landscapes of international conservation importance, comprising mosaics of heathland, acid grassland, blanket bog and bracken. Much of this landscape is managed by rotational burning to create gamebird habitat and there is concern over whether this is driving long‐term changes in upland vegetation communities. However, the inaccessibility and scale of uplands means that monitoring changes in vegetation and burning practices is difficult. We aim to overcome this problem by developing methods to classify aerial imagery into high‐resolution maps of dominant vegetation cover, including the distribution of burns on managed grouse moors. Location  Peak District National Park, England, UK. Methods Colour and infrared aerial photographs were classified into seven dominant land‐cover classes using the Random Forest ensemble machine learning algorithm. In addition, heather (Calluna vulgaris) was further differentiated into growth phases, including sites that were newly burnt. We then analysed the distributions of the vegetation classes and managed burning using detrended correspondence analysis. Results Classification accuracy was c. 95% and produced a 5‐m resolution map for 514 km² of moorland. Cover classes were highly aggregated and strong nonlinear effects of elevation and slope and weaker effects of aspect and bedrock type were evident in structuring moorland vegetation communities. The classification revealed the spatial distribution of managed burning and suggested that relatively steep areas may be disproportionately burnt. Main conclusions Random Forest classification of aerial imagery is an efficient method for producing high‐resolution maps of upland vegetation. These may be used to monitor long‐term changes in vegetation and management burning and infer species–environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.     

基于对象特征的山东省丘陵地区多时相遥感土地覆被自动分类

对于基于像元的土地覆被分类来说,植被的分类是难点。使用多时相面向对象分类方法可以较好的解决这个问题。以山东省烟台市丘陵地区为研究区,采用Landsat TM(Landsat Thematic Mapper remotely sensed imagery)、DEM(Digital Elevation Model)、坡度、坡位、坡向等多种数据,利用基于对象特征的多时相分类方法对研究区进行土地覆盖自动分类。首先对影像进行多尺度分割并检验分割结果选取合适的分割尺度,然后分析对象的光谱、纹理、形状特征。根据各类地物的光谱特征、地理相关性、形状、空间分布等特征,明确类别之间的差异。建立决策树使用隶属度函数进行模糊分类,借助支持向量机提高分类精度。研究结果表明,通过使用多时相影像采用面向对象分类方法,相对于传统的基于像素的分类可以明显提高分类精度,尤其是解决了乔灌草的区分问题。     

Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments.     

Spectral feature differences between shrub and grass communities and shrub coverage retrieval in shrub-encroached grassland in Xianghuang Banner,Nei Mongol,China北大核心CSCD

Aims: Shrub-encroached grassland has become an important vegetation type in China's arid and semi-arid region. Our study objective is to explore the spectral features of shrub and grass communities, as well as their empirical relationships with shrub coverage. The quantitative estimation of shrub cover based on medium-resolution Landsat satellite imagery provides the practical basis for long term retrieval of large areas of shrub expansion in the grassland region. Methods: Linear models and Multiple Endmember Spectral Analysis Model (MESMA) based on medium resolution Landsat satellite imagery were developed to quantify the shrub coverage in a shrub-encroached grassland region in Xianghuang Banner, Nei Mongol using the spectral features and their seasonal differences between the shrub and grass communities. Important findings: Compared to Leymus chinensis and Stipa krylovii dominated grass communities, Caragana microphylla community had a higher normalized difference vegetation index (NDVI), modified red edge normalized difference vegetation index (mNDVI705), and red edge slope. The red edge position of C. microphylla community shifted to longer wavelengths. The average and the maximum shrub coverage was 13% and 25%, respectively, in the shrub-encroached grassland based on both models. The correlation coefficient of determination (R2) and root mean square error (RMSE) of the linear model was 0.31 and 0.05, respectively. We found that the linear model based on seasonal differences of shrub and grass community was more suitable for retrieving shrub coverage in the study area from medium resolution imagery than the MESMA model that is based on mid-summer images.     

Mapping functional connectivity

An objective and reliable assessment of wildlife movement is important in theoretical and applied ecology. The identification and mapping of landscape elements that may enhance functional connectivity is usually a subjective process based on visual interpretations of species movement patterns. New methods based on mathematical morphology provide a generic, flexible, and automated approach for the definition of indicators based on the classification and mapping of spatial patterns of connectivity from observed or simulated movement and dispersal events. The approach is illustrated with data derived from simulated movement on a map produced from satellite imagery of a structurally complex, multi-habitat landscape. The analysis reveals critical areas that facilitate the movement of dispersers among habitat patches. Mathematical morphology can be applied to any movement map providing new insights into pattern-process linkages in multi-habitat landscapes.     

Spatial and temporal modelling for parasite transmission studies and risk assessment

Spatial and temporal modelling of parasite transmission and risk assessment require relevant spatial information at appropriate spatial and temporal scales. There is now a large literature that demonstrates the utility of satellite remote sensing and spatial modelling within geographical information systems (GIS) and firmly establishes these technologies as the key tools for spatial epidemiology. This review outlines the strength of satellite remotely sensed data for spatial mapping of landscape characteristics in relation to disease reservoirs, host distributions and human disease. It is suggested that current satellite technology can fulfill the spatial mapping needs of disease transmission and risk modelling, but that temporal resolution, which is a function of the satellite data acquisition characteristics, may be a limitating factor for applications requiring information about landscape or ecosystem dynamics. The potential of the Modis sensor for spatial epidemiology is illustrated with reference to mapping spatial and temporal vegetation dynamics and small mammal parasite hosts on the Tibetan plateau. Future research directions and priorities for landscape epidemiology are considered.     

Mapping three invasive weeds using airborne hyperspectral imagery

Invasive plant species present a serious problem to the natural environment and have adverse ecological and economic impacts on both terrestrial and aquatic ecosystems they invade. This article presents three case studies on the use of hyperspectral remote sensing for mapping invasive plant species in both terrestrial and aquatic environments. Methods and procedures for acquisition, processing and classification of airborne hyperspectral imagery as well as accuracy assessment are presented. Examples are excerpted and adapted from published work to illustrate how airborne hyperspectral imagery has been used to map two terrestrial weeds, Ashe juniper (Juniperus ashei Buchholz) and Broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby], and one aquatic weed, waterhyacinth [Eichhornia crassipes (Mart.) Solms], in Texas. In addition to the standard classification methods used in the previous studies, a spectral unmixing technique, mixture tuned matched filtering (MTMF), was applied to the three study cases and the classification results are reported in this paper. A brief discussion is provided on the considerations of different types of remote sensing imagery for mapping invasive weeds.     

基于高分辨率卫星图的川中丘陵区村级景观格局特征研究

以Erle C.Ellis 建立的村级景观分类和景观制图方法,利用IKONOS高分辨率（1 m）卫星遥感图,进行典型抽样和地形→土地利用→土地覆被→生态立地的景观分类和分层制图,研究了四川盆地中部丘陵地区村级景观的构成和格局特征,并就有关方法作了讨论.结果表明,川中盆地丘陵村级景观类型多样,其多样性指数从地形、土地覆被、土地利用到生态立地变化在1.08～2.26之间,丰富度除土地覆被较高,达到85%以外,其余都较低,在42.22%～58.62%之间.分布普遍的生态立地类型为12.5%,其余88.5%的类型都以较大差异分布在各村级景观内.景观破碎化指数较高,不同村级景观之间为2.93～4.27,地形到生态立地的破碎度为2.86～5.63.村级景观构成中,人口密度、道路面积和农家院落面积与景观格局指数存在较大程度的线性相关关系,但以农家院落面积与景观分形指数、景观破碎度的线性相关显著,分别为0.957*和0.991**.运用景观4级分类和制图方法研究村级景观,多数景观格局指标都表现出显著的统计学差异,表明运用高分辨率卫星遥感图研究村级景观,以多级景观单元分类研究比单一分类的更能够反映相关信息,增加对村级景观格局的认识和理解.