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.     

Land-use coverage as an indicator of riparian quality

Sustaining or restoring riparian quality is essential to achieve and maintain good stream health, as well as to guarantee the ecological functions that natural riparian areas provide. Therefore, quantifying riparian quality is a fundamental step to identify river reaches for conservation and/or restoration purposes. Most of the existing methods assessing riparian quality concentrate on field surveys of a few hundreds of metres, which become very laborious when trying to evaluate whole catchments or long river corridors. Riparian quality assessment obtains higher scores when riparian vegetation consists of forested areas, while land-uses lacking woody vegetation typically represent physical and functional discontinuities along river corridors that undermine riparian quality. Thus, this study aimed to analyse the ability of riparian land-cover data for modelling riparian quality over large areas. Multiple linear regression and Random Forest techniques were performed using land-use datasets at three different spatial scales: 1:5000 (Cantabrian Riparian Cover map), 1:25,000 (Spanish Land Cover Information System) and 1:100,000 (Corine Land Cover). Riparian quality field data was obtained using the Riparian Quality Index. Hydromorphological pressures affecting riparian vegetation were also included in the analysis to determine their relative weight in controlling riparian quality. Linear regression showed better predictive ability than Random Forest, although this may be due to our relatively small dataset (approx. 150 cases). Forest coverage highly determined riparian quality, while hydromorphological pressures and land-use coverage related to human activities played a smaller role in the models. While acceptable results were obtained when using high-resolution datasets, the use of Corine Land Cover led to a poor predictive ability.     

Modeling the potential natural vegetation of Minnesota,USA

Assessing the effects of human land use and management decisions requires an understanding of how temporal changes in biodiversity influence the rate of ecosystem functions and subsequent delivery of ecosystem services. In highly modified anthromes, the spatial distribution of natural vegetation types is often unknown or coarsely represented challenging comparative analyses seeking to assess changes in biodiversity and potential downstream effects on ecosystem processes and functions. In this context, the objectives of this study were to construct a multi-resolution representation of potential natural vegetation at four hierarchical classification levels of increasing floristic and physiognomic detail for the state of Minnesota, USA. Using a collection of natural/near-natural vegetation relevés, a series of Random Forest classification models were used to project the potential distribution of natural vegetation types based on their association with a variety of environmental variables.Model performance varied within and between classification levels with overall accuracy ranging between 64–99% (kappa 0.44–0.99). Model performance tended to decrease and become more variable with increasing floristic complexity at finer classification levels. Classwise performance metrics including precision and sensitivity were also reported. A method for exploring potential class confusion resulting from niche overlap using Random Forest proximities and Nonmetric Multidimensional Scaling is demonstrated.Collectively, the results presented here provide an analytically supported baseline representation of potential natural vegetation for the state of Minnesota, USA. These data can provide a backdrop to further analyses surrounding the influence of human activity on ecosystem processes and services as well as inform future conservation and restoration efforts.     

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.     

全色型及彩色红外型航空像片解译方法在植被制图中的应用

在编制大、中比例尺植被图的过程中,热红外的,多光谱的、彩色红外的和全色型的航空像片都可以利用。我们在云南省腾冲进行了航空遥感试验。根据直接解译标志和间接解译标志的不同组合可以进行植被类型的解译。这种综合的方法用于遥感图像的解译可以获得良好的效果。文中提出以下的直接解译标志,即植物群落影像的颜色、色调及其影纹结构,植物群落、群落片断或单株的形状、大小和高度以及它们的投影形状。间接解译标志包括植物生长地所处的海拔高度、纬度位置和地貌部位以及成土母岩的性质。间接标志还有人类活动对植被分布影响的程度。为了便于进行自动的植被分类,可以用检索表将各植被类型的详细解译标志表示出来。本文列出了位于常绿阔叶林区的云南省腾冲植被类型解译检索表。所用全色型航空像片的比例尺是1:35000,而彩色红外型航空像片的比例尺是1:34000。     

INFLUENCE OF VEGETATION,TOPOGRAPHY, AND ROADS ON COUGAR MOVEMENT IN SOUTHERN CALIFORNIA

Abstract: Models of individual movement can help conserve wide-ranging carnivores on increasingly human-altered landscapes, and cannot be constructed solely by analyzing the daytime resting locations typically collected in carnivore studies. We examined the movements of 10 female and 7 male cougars (Puma concolor) at 15-min intervals during 44 nocturnal or diel periods of hunting or traveling in the Santa Ana Mountain Range of southern California, USA, between 1988 and 1992. Cougars tended to move in a meandering path (mean turning angle ∼54°), and distance moved (mean and mode ∼300 m) was not correlated with turning angle. Cougars used a broader range of habitats for nocturnal or diel movements than for previously described daybed locations for this same population. Riparian vegetation ranked highest in a compositional analysis of vegetation types selected during movement; grassland, woodland and urbanized sites were least selected. During periods of stasis (we presume many of these were stalking locations), patterns of selection were less marked. Cougars spent a disproportionate amount of time in highly ranked vegetation types, and traveled slowest through riparian habitats and fastest through human-dominated areas. Our results suggest that travel speed may provide an efficient index of habitat selection in concert with other types of analysis. Hunting or traveling individuals consistently used travel paths that were less rugged than their general surroundings. Traveling cougars avoided 2-lane paved roads, but dirt roads may have facilitated movement. Maintenance and restoration of corridors between large wildlands is essential to conserving cougars in southern California. Our results indicate that riparian vegetation, and other vegetation types that provide horizontal cover, are desirable features in such corridors, that dirt roads should not impede cougar use of corridors, and that corridors should lie along routes with relatively gentle topography. Our results suggest that cougars do not key in on highway-crossing structures in a way that creates a prey trap. Our empirical frequency distributions of distances and turning angles, along with cougar responses to vegetation, topography, and roads can help parameterize an individually-based movement model for cougars in human-altered landscapes.     

Mapping landscape corridors

Corridors are important geographic features for biological conservation and biodiversity assessment. The identification and mapping of corridors is usually based on visual interpretations of movement patterns (functional corridors) or habitat maps (structural corridors). We present a method for automated corridor mapping with morphological image processing, and demonstrate the approach with a forest map derived from satellite imagery of northern Slovakia. We show how the approach can be used to differentiate between relatively narrow (‘line’) and wide (‘strip’) structural corridors by mapping corridors at multiple scales of observation, and indicate how to map functional corridors with maps of observed or simulated organism movement. An application to environmental reporting is demonstrated by assessing structural forest corridors in relation to forest types in northern Slovakia.     

Classifying the Neotropical savannas of Belize using remote sensing and ground survey

Aim This paper evaluates a method of combining data from GPS ground survey with classifications of medium spatial resolution LANDSAT imagery to distinguish variations within Neotropical savannas and to characterize the boundaries between savanna areas and the associated gallery forests, seasonally dry forests and wetland communities. Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central America. Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001 covering a conservation area of 240,000 acres (97,459 ha), were rectified to sub‐pixel accuracy using ground control points positioned by GPS ground survey. The 1993 image was used to assess the accuracy with which the boundaries between the savanna matrix and gallery forests, high forests, wetlands and water bodies could be discriminated. The image was classified by a maximum likelihood (ML) classifier and the shapes and areas of forest and wetland classes were compared with an interpretation of these land cover types from 1 : 24,000 aerial photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to assess whether different subtypes of savanna could be distinguished from LANDSAT data. This required the creation of a reference (‘ground truth’) data set for testing classifications of the image. One hundred and sixty sample patches (650 ha, distributed over an area of 7000 ha) of ten sub‐types of savanna vegetation and associates identified using a physiognomic classification scheme, were delineated on the ground by GPS and divided into two subsets for training and testing. Continuous classifications of LANDSAT data covering the savannas were developed that estimated potential contributions from up to five sub‐types of land cover (grassland, wetland, pine woodland, gallery forest and palmetto). The accuracy of each classification was assessed by comparison against ground data. An ML classification was also produced for the 2001 image using the same areas for training. This allowed a comparison of the relative accuracy of both continuous and Boolean ML methods for classifying savanna areas. Results The boundary between savannas and evergreen forests, gallery forests and open water in the study region could be delineated by the ML classifier to within 2 pixels (60 m) using LANDSAT imagery. However, the constituent sub‐types within the savanna were poorly discriminated. Whilst the shape and extent of closed canopy forest, gallery forest, wetlands and water bodies agreed closely with the distributions interpreted from aerial photography, classes such as ‘open pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against ground data. A continuous classification, estimating the proportions of three savanna vegetation subtypes (grassland, marshland and woodland) present in each pixel, correctly classified more of the ground data for these cover types than the comparable ML result. Proportional mixtures of the land cover estimated by the continuous classifier also compared realistically with the vegetation formations observed along ground transects. Main conclusions By using GPS, a ground survey of vegetation cover was accurately matched to remotely sensed imagery and the accuracy of delineating boundaries and classifying areas of savanna was assessed directly. This showed that ML classification techniques can reliably delineate the boundaries of savannas, but continuous classifiers more accurately and realistically represent the distribution of the subtypes comprising savanna land cover. By combining these ground survey and image classification methods, medium spatial resolution satellite sensor data can provide an affordable means for land managers to assess the nature, extent and distribution of savanna formations. Over time, using the archives of LANDSAT (and SPOT) data together with marker sites surveyed in the field, quantitative changes in the extents and boundaries of savannas in response to both natural (e.g. fire, hurricane and drought) and anthropogenic (e.g. cutting and disturbance) factors can be assessed.     

红皮云杉林的植被分类及其环境解释

红皮云杉林(Picea koraiensis Forest Alliance)是以红皮云杉为群落共优势种的森林植被类型,具有结构复杂及物种多样性高等特点。在遵从《中国植被志》研编规范的基础上,提出了红皮云杉林新的分类系统,包括5个群丛组8个群丛,分别归属于常绿针叶林、落叶与常绿针叶混交林和针叶与阔叶混交林3个植被型。群丛组分类主要基于群落层片分化,以及乔木层的共优势种和特征种的差异,并以常绿针叶树层片重要值的66%为阈值划分常绿针叶林和针叶与阔叶混交林。在群丛分类中,通过双向指示种分析筛选特征种,综合考虑群落生境和群落演替阶段等因素确定分类方案。该分类方案是《中国植被志》研编规范的一个应用示例,对植被分类工作的借鉴意义表现在4个方面。(1)作为植被分类的重要凭证,样方数据质量控制是植被分类工作的重要环节;其中,物种的准确鉴定对确保植被分类方案的合理性至关重要。(2)对于乔木层物种组成丰富、优势种不明显的群落,群落层片分化和特征种是植被类型划分的重要依据。(3)以特定物种组合为共建种所组成的多个植被类型可归属为不同的植被型。(4)森林采伐等人类活动可能对植被与环境的关系形成干扰。因此,在...     

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.     

应用间接排序识别盐沼植被的光谱特征:以崇明东滩为例

遥感技术已成为大尺度植被分类的重要手段,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。该研究选择上海崇明东滩自然保护区的盐沼植物群落为对象,应用ASD地物光谱仪测定其植物群落的光谱反射率,并采用10个小型机载成像光谱仪(CASI)默认植被波段组,应用主分量分析法和相关分析分析了不同群落光谱特征与生态环境因子之间的关系。分析结果表明,间接排序法PCA能够识别盐沼植被中光滩、海三棱 草(Scirpus mariqueter)群落、芦苇(Phragmites australis)群落和互花米草(Spartina alterniflora)等群落的光谱特征,绝大多数盐沼湿地植物群落组成与光谱特征之间有显著的相关,识别效果最好的波段组是736~744 nm、746~753 nm、775~784 nm、815~824 nm和860~870 nm;对光谱反射率影响最大的生态环境因子分别是植物群落的高度和盖度,高程和其它环境因子的影响次之。研究成果可为遥感监测崇明东滩自然保护区内入侵种互花米草的空间分布和扩散规律提供技术支撑,为高光谱遥感影像的影像判读和解译分类以及盐沼湿地植被制图提供科学依据。     

Vegetation types of the upper Madeira River in Rondônia,Brazil

We characterize the vegetation types of the upper basin of the Madeira River in the Brazilan state of Rondônia, a biodiverse region with elevated rates of habitat loss. Vegetation and environmental parameters were recorded from 37 observation points distributed along and near a 160 km stretch of the Madeira River and representing the range of regional environments. Analysis of structure and floristic variables, as well as associated edaphic attributes and water table fluctuation, permitted recognition of five main vegetation types and seven subtypes. Open Ombrophilous Forest was the most frequently encountered vegetation type and occurred on well-drained, nutrient-poor soils, whereas Dense Ombrophilous Forest was seldom recorded. Alluvial Ombrophilous Forests (várzea) were found along a narrow strip of land along the banks of the Madeira River on the most fertile soils in the study area. Semideciduous Forests were found on small areas of rocky outcrops with shallow soils and reduced water availability during the dry season. Campinaranas, which range from open savanna physiognomies to closed canopy forests, were found to be a key environment in the lowlands south of the Madeira River on silty hydromorphic soils, where they harbor a peculiar flora tolerant of flooding during the rainy season. Our classification of the main vegetation types in the upper Madeira River illuminates a high degree of floristic and environmental heterogeneity in a highly threatened region. Our results will be useful for designing conservation strategies aimed at protecting the full range of floristic diversity present in the region.     

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.     

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.     

Computerized classification of Mediterranean vegetation using panchromatic aerial photographs

Abstract. Historical aerial photographs are an important source for data on medium- to long-term (10 - 50 yr) vegetation changes. Older photographs are panchromatic, and manual interpretation has traditionally been used to derive vegetation data from such photographs. We present a method for computerized analysis of panchromatic aerial photographs, which enables one to create high resolution, accurate vegetation maps. Our approach is exemplified using two aerial photographs (from 1964 and 1992) of a test area on Mt. Meron, Israel. Spatial resolution (pixel size) of the geo-rectified photos was 0.30 m and spatial accuracy (RMS error) ca. 1 m. An illumination adjustment prior to classification was found to be essential in reducing misclassification error rates. Two classification approaches were employed: a standard maximum-likelihood supervised classifier, and a modification of a supervised classification, which takes into account spectral properties of individual pixels as well as their neighbourhood characteristics. Accuracy of the maximum likelihood classification was 81 % in the 1992 image and 54 % in the 1964 image. The neighbour classifier increased accuracy to 89 % and 82 % respectively. The overall results suggest that computerized analysis of sequences of panchromatic aerial photographs may serve as a valuable tool for the quantification of medium-term vegetation changes.     

Change in Vegetation Patterns Over a Large Forested Landscape Based on Historical and Contemporary Aerial Photography

Changes to vegetation structure and composition in forests adapted to frequent fire have been well documented. However, little is known about changes to the spatial characteristics of vegetation in these forests. Specifically, patch sizes and detailed information linking vegetation type to specific locations and growing conditions on the landscape are lacking. We used historical and recent aerial imagery to characterize historical vegetation patterns and assess contemporary change from those patterns. We created an orthorectified mosaic of aerial photographs from 1941 covering approximately 100,000 ha in the northern Sierra Nevada. The historical imagery, along with contemporary aerial imagery from 2005, was segmented into homogenous vegetation patches and classified into four relative cover classes using random forests analysis. A generalized linear mixed model was used to compare topographic associations of dense forest cover on the historical and contemporary landscapes. The amount of dense forest cover increased from 30 to 43% from 1941 to 2005, replacing moderate forest cover as the most dominant class. Concurrent with the increase in extent, the area-weighted mean patch size of dense forest cover increased tenfold, indicating greater continuity of dense forest cover and more homogenous vegetation patterns across the contemporary landscape. Historically, dense forest cover was rare on southwesterly aspects, but in the contemporary forest, it was common across a broad range of aspects. Despite the challenges of processing historical air photographs, the unique information they provide on landscape vegetation patterns makes them a valuable source of reference information for forests impacted by past management practices.     

Animal-habitat relationships in the Knysna Forest,South Africa: discrimination between forest types by birds and invertebrates

Summary Effects of forest plant species composition and physiognomy on bird and invertebrate communities were investigated in three discrete, relatively undisturbed forest types along a dry-wet soil moisture gradient. Using discriminant function analysis, a 100% floristic and a 78% vegetation structural discrimination were obtained between the three forest types. However, the bird communities of these different forest types were very similar in species composition, and had much lower densities than those normally encountered in other, superficially similar forests. Although an 81% discrimination between forest types was attained through analysis of ground surface invertebrates, measures of litter and aerial invertebrate abundance were also of limited use as discriminators. Historical and biogeographic factors, as well as the low nutritional levels in the soil and vegetation may be the causes of low bird and invertebrate density and diversity. It is concluded that floristics and vegetation structure have, at best, a minor influence on bird community structure, and possibly also on invertebrate community structure in the Knysna Forest.     

Classification of vegetation in the Western Australian wheatbelt using Landsat MSS data

We examined the use of Landsat multispectral scanner (MSS) data to provide preliminary information on broad vegetation types present within nature reserves in the wheatbelt region of Western Australia. We analysed Landsat data for an area of natural vegetation for which ground survey and aerial photographic data are available. We used canonical variate analysis to examine the degree of spectral separation between training sites selected in the main structural vegetation types. The training classes were then grouped into spectral classes and an allocation procedure used to map the pixels in the reserve into these classes. The analysis provided a good correspondence between spectral classes and broad vegetation types recognised from aerial photography, but did not discriminate between differences in dominant species (e.g. between different types of Eucalypt woodland). The classification derived from the study reserve was then applied successfully to two nearby reserves, indicating that the data can be used to provide initial information on the broad vegetation types present in wheatbelt reserves, although it is not suitable for finer resolution studies.Abbreviation MSS = Multi-spectral scanner     

Soil millipede diversity in tropical forest patches and its relation to landscape structure in northeastern Puerto Rico

Uncontrolled urban development is threatening the survival of many terrestrial species worldwide, especially those with limited dispersal capabilities. Soil invertebrates account for a high proportion of the global biodiversity but there are few studies on how soil biodiversity varies with different parameters of landscape structure, especially in fragmented tropical habitats. Millipedes are among the most abundant detritivore invertebrates in the soil-litter macrofauna. We examined the relationships between soil millipede diversity and landscape structure in 12 forest patches of approximately 30 years of age, in northeastern Puerto Rico. Spatial characteristics of the landscape were determined from aerial color photographs and were digitized into a GIS package for analysis. Millipede species diversity and composition in these forest patches showed correlations with their surroundings (e.g. amount of forest in the matrix) and with the presence of vegetation corridors that connected to other forest patches, rather than forest patch attributes such as patch area and shape. Millipede species richness correlated negatively with the degree of isolation of forest patch (within 600 m radius), while species evenness correlated positively to the amount of forest within a 50 m buffer. Millipede species composition was related with the presence of vegetation corridors and the distance to the Luquillo Experimental Forest reserve. These findings show that a low degree of patch isolation, forested buffers, and presence of vegetation corridors need to be considered for the conservation and management of forest patches surrounded by urban developments, especially to protect terrestrial invertebrate species that require forested habitats for their dispersal.     

道路影响下野生动物廊道的选址——以大熊猫保护廊道为例

沿道路设置供野生动物迁徙、扩散和连接栖息地的廊道是应对道路干扰最有效的措施,科学选址则是野生动物廊道建设的前提,也是廊道研究的薄弱领域。以大熊猫廊道为例对野生动物廊道选址指标体系、方法和程序进行了探索,将栖息地特征、地形因素、植被可转化性、工程成本作为大熊猫廊道选址指标,基于Arcgis和栖息地格局、海拔、坡度、植被数据,为四川306省道椅子垭口段确定了两处大熊猫廊道位置,并用监测数据证明了所选位置具有较大的可行性和准确性。研究表明栖息地格局是廊道选址的重要基础,应侧重对地形因素的研究。研究为廊道选址方法和流程进行了示范,还对选址指标体系优化、提高选址的科学性进行了探讨,有助于推动野生动物廊道研究从理论探索走向实际应用。