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.     

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.     

Assessment of forest spatial differentiation in Murmansk Province using field surveys and remote sensing data

The results of study of the spatial differentiation of forest using field data, remote sensing, and map data are presented. Different classification approaches are used while analyzing the diversity of forest communities, i.e., ecological-dominant, ecological-topological, and dynamical. The interpolation of local chatacteristics of plant associations and syntaxonomic units at the upper levels using the data of spectral satellite imagery and quantitative methods of processing allow to use in mapping important information on the structure and properties of vegetation. The results of our studies include thematic maps of the specific parameters of forest and a 1: 100000-scale vegetation map of the central part of Murmansk Province. The role of natural and anthropogenic factors is reflected in the legend to the map.     

Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest

Vegetation type and its biomass are considered important components affecting biosphere-atmosphere interactions. The measurements of biomass per unit area and productivity have been set as one of the goals for International Geosphere-Biosphere Programme (IGBP). Ground assessment of biomass, however, has been found insufficient to present spatial extent of the biomass. The present study suggests approaches for using satellite remote sensing data for regional biomass mapping in Madhav National Park (MP). The stratified random sampling in the homogeneous vegetation strata mapped using satellite remote sensing has been effectively utilized to extrapolate the sample point biomass observations in the first approach. In the second approach attempt has been to develop empirical models with satellite measured spectral response and biomass. The results indicate that there is significant relationships with spectral responses. These relationships have seasonal dependency in varying phonological conditions. The relationships are strongest in visible bands and middle infrared bands. However, spectral biomass models developed using middle infrared bands would be more reliable as compared to the visible bands as the later spectral regions are less sensitive to atmospheric changes It was observed that brightness and wetness parameters show very strong relationship with the biomass values. Multiple regression equations using brightness and wetness isolates have been used to predict biomass values. The model used has correlation coefficient of 0.77. Per cent error between observed and predicted biomass was 10.5%. The biomass estimated for the entire national park using stratified and spectral response modelling approaches were compared and showed similarity with the difference of only 4.69%. The results indicate that satellite remote sensing data provide capability of biomass estimation     

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.     

Gut bacterial assemblages of freshwater macroinvertebrate functional feeding groups

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high-resolution image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus peltatus, Callitriche obtusangula, Potamogeton natans L., Sparganium emersum R. and Potamogeton crispus L., were classified from the data using object-based image analysis and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image resulted in 53% overall accuracy. These consistent results not only show promise for species-level mapping in such biodiverse environments but also prompt a discussion on assessment of classification accuracy.     

Space remote sensing for spatial vegetation characterization

The study area, Madhav National Park (MP) represents northern tropical dry deciduous forest. The national park, due to its unique location (nearest to township), is under tremendous biotic pressure. In order to understand vegetation structure and dynamics, vegetation mapping at community level was considered important. Prolonged leafless period and background reflection due to open canopy poses challenge in interpretation of satellite data. The vegetation of Madhav National Park was mapped using Landsat TM data. The ground data collected from sample points were subjected to TWINSPAN analysis to cluster sample point data into six communities. The vegetation classification obtained by interpretation (visual and digital) of remote sensing data and TWINSPAN were compared to validate the vegetation classification at community level. The phytosociological data collected from sample points were analysed to characterize communities. The results indicate that structural variations in the communities modulate spectral signatures of vegetation and form basis to describe community structure subjectively and at spatial level.     

Vegetation abundance on the Barton Peninsula,Antarctica: estimation from high-resolution satellite images

Polar biodiversity should be monitored as an indicator of climate change. Biodiversity is mainly observed by field survey although this is very limited in broad inaccessible polar regions. Satellite imagery may provide valuable data with less bias, although spatial, spectral, and temporal resolutions are limited for analyzing biodiversity. The present study has two objectives. The first is constructing a first-ever vegetation map of the entire Barton Peninsula, Antarctica. The second is developing a monitoring method for long-term variation of vegetation, based on satellite images. Dominant mosses and lichens are distributed in small and sparse patches, which are limited to analysis using high-resolution satellite images. A sub-pixel classification method, spectral mixture analysis, is applied to overcome limited spatial resolution. As a result, vegetation shows high abundance along the southeastern shore and low-to-medium abundance in the nearly snow-free inland area. Even though spatial patterns of vegetation were almost invariant over 6 years, there was interannual variation in abundance aspects because of meteorological conditions. Therefore, extensive and long-term monitoring is needed for aspects of distribution and abundance. The present results can be used to design field surveys and monitor long-term variation as elementary data.     

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.     

太湖湖滨带生态系统健康评价

根据湖滨带生态系统的特点,运用综合健康指数法建立了湖滨带生态系统健康评价体系,由目标层、准则层、指标层构成,其中准则层由湖滨带水质状况、底泥状况、植被状况、其它生物状况(浮游动物、浮游植物、底栖动物)、岸带物理状况5项组成,指标层由总氮、总磷、溶解氧、挺水植物覆盖率等15项指标构成。采用专家打分法、熵值法分别确定了准则层、指标层的权重系数。对太湖湖滨带33个点位进行了采样分析,并进行无量纲化处理后应用到所建立的评价体系中。评价结果显示33个点位中为"很健康"、"健康"、"亚健康"、"疾病"、"严重疾病"的分别占0%、24.2%、21.2%、51.5%及3.0%,也即超过一半的点位处于"疾病"状态。只有东太湖刚刚超过"健康"分数的下限,东部沿岸、贡湖、南部沿岸均处于"亚健康"状态,而梅梁湾、竺山湾、西部沿岸属于"疾病"状态,且竺山湾的生态健康状态最差。该评价结果与太湖湖滨带各分区的实际调查情况相符合,评价方法可靠性、可行性较强,可为其它湖泊湖滨带的生态系统健康评价提供一定的参照。     

普者黑岩溶湖泊湿地湖滨带景观格局演变对水质的影响

湖滨带作为湖泊与陆地之间的过渡带,是健康湖泊生态系统的重要组成部分。湖滨带景观格局的演变会对湿地水质产生重要影响,因此探究影响岩溶湿地水质变化的湖滨带关键景观因子,对深入了解景观格局对岩溶湿地水质的影响过程与机制具有重要意义。选择普者黑岩溶湖泊湿地为研究对象,以2005、2007、2009、2011年共4年的Landsat遥感影像及水质监测数据为基础,通过划定湖泊湿地湖滨带缓冲区域,运用秩相关分析和冗余分析研究湖滨带景观格局对普者黑岩溶湖泊湿地水质的影响。结果表明,湖滨带不同缓冲区内景观结构类型比例差异较大;枯水期水质与土地利用类型和景观格局指数的影响大于丰水期;景观格局在不同缓冲区尺度对岩溶湿地的水质具有不同的效应;随着监测点缓冲距离的增加,个别景观指数可较好的揭示湖滨带景观格局演变对岩溶湿地水质的影响,其中,蔓延度指数(CONTAG)、斑块结合度指数(COHSION)、均匀度指数(SHEI)对水质参数的影响较大,边界密度(ED)、聚集度(AI)对水质参数的影响随缓冲距离的增加逐渐减弱,其他景观指数对水质影响差异并不明显,最大斑块指数(LPI)在缓冲距离≤300m的区域内与水质的关系较密切,面积加权平均斑块分维数(AWMPFD)与水质参数有显著负相关性,多样性指数(SHDI)对水质的影响具有不确定性;另外,大部分水质参数与土地利用面积比例有较好的相关性,且湿地面积比例是表征岩溶湖泊湿地水环境质量的主要指标。     

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of  Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution.     

Assessing the potential for recovery of lakeshore vegetation: species richness of sediment propagule banks

To assess the potential and develop appropriate techniques for the recolonization of lakeshore vegetation at sites where it had been completely lost, a pilot project was launched at Lake Kasumigaura, Japan. We investigated the species composition and density of the soil seed banks (propagule banks) of lake sediments at nine sites (total area, 65,200 m²) where lake sediments were spread thinly (∼10 cm) on the surfaces of artificial littoral zones. These zones were constructed in front of concrete levees and had microtopographic variations. In total, 180 species, including six endangered or vulnerable species and 12 native submerged plants that had disappeared from the aboveground vegetation of the lake, were recorded during the first year of restoration. The distribution of each restored species at the sites suggested the importance of microtopographic variation for recolonizing species-rich lakeshore vegetation. Furthermore, the origin of the source sediment affected the species composition of the established vegetation.     

Vegetation Mapping of the Mond Protected Area of Bushehr Province (South-west Iran)

Add regions of the wodd occupy up to 35% of the earth's surface, the basis of various definitions of climatic conditions,vegetation types or potential for food production. Due to their high ecological value, monitoring of add regions is necessary and modem vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps.Sixteen map units covering t2 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata,Salicornia europaaa-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.     

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.     

遥感用于森林生物多样性监测的进展

随着物种和栖息地的丧失,全球范围的生物多样性保护已经成为迫切的需要。航空航天技术的迅猛发展使遥感成为能提供跨越不同时空尺度监测陆地生态系统生物多样性的重要工具,这方面的研究在欧美等国已经有了小范围的开展,在国内刚刚起步。国外关于生物多样性遥感探测的方法基本有3种:1.利用遥感数据直接对物种或生境制图,进而估算生物多样性;2 .建立遥感数据的光谱反射率与地面观测物种多样性的关系模型;3.与野外调查数据结合直接在遥感数据上进行生物多样性指数制图。研究表明,物种直接制图法只能应用于较小的范围;生境制图的方法,应用广泛,技术相对成熟,研究范围局限于几百公里的范畴,但不能获取生境内部的多样性信息。光谱模型技术目前正处于探索阶段,对于植被复杂、生物多样性高的地域,具有较大的应用潜力。在遥感数据上直接进行生物多样性制图在加拿大已经得到了应用。     

上海滩涂植被资源遥感分析

利用2003年8月2日L andsat5-TM多光谱遥感影像,运用遥感处理软件ERDA S Im ag ine 8.6,经几何校正分幅裁剪等图像预处理后,采用监督分类和目视解译相结合对上海市滩涂植被进行解译分析。结合全球定位系统(GPS)样点定位,对分类结果进行全面的野外核实和修正,同时利用地理信息系统(G IS)对解译结果进行数据合成,统计出滩涂各类植被的分布区域及面积等数据。实际调查及其分析统计显示,上海滩涂植物群落总面积为21302.1hm2,主要植被组成为芦苇、海三棱草及互花米草三大群落,滩涂植物群落具有明显的高程梯度分布规律。大尺度的上海市滩涂植被的空间分布现状及其数量调查为上海市滩涂资源的合理规划、生物多样性保护和可持续开发利用提供科学依据。     

Transferability of object-based rule sets for mapping coastal high marsh habitat among different regions in Georgian Bay,Canada

Coastal wetlands of eastern and northern Georgian Bay, Canada provide critical habitat for a variety of biota yet few have been delineated and mapped because of their widespread distribution and remoteness. This is an impediment to conservation efforts aimed at identifying significant habitat in the Laurentian Great Lakes. We propose to address this deficiency by developing an approach that relies on use of high-resolution remote sensing imagery to map wetland habitat. In this study, we use IKONOS satellite imagery to classify coastal high marsh vegetation (seasonally inundated) and assess the transferability of object-based rule sets among different regions in eastern Georgian Bay. We classified 24 wetlands in three separate satellite scenes and developed an object-based approach to map four habitat classes: emergent, meadow/shrub, senescent vegetation and rock. Independent rule sets were created for each scene and applied to the other images to empirically examine transferability at broad spatial scales. For a given habitat feature, the internally derived rule sets based on field data collected from the same scene provided significantly greater accuracy than those derived from a different scene (80.0 and 74.3%, respectively). Although we present a significant effect of ruleset origin on accuracy, the difference in accuracy is minimal at 5.7%. We argue that this should not detract from its transferability on a regional scale. We conclude that locally derived and object-based rule sets developed from IKONOS imagery can successfully classify complex vegetation classes and be applied to different regions without much loss of accuracy. This indicates that large–scale mapping automation may be feasible with images with similar spectral, spatial, contextual, and textural properties.     

Vegetation survey in the former brown coal mining area of eastern Germany by integrating remote sensing and ground-based methods

Abstract. In the former brown coal mining area of eastern Germany, now scheduled as a nature conservation area, an analysis of the spatial distribution of vegetation was considered as an important tool in landscape planning. Therefore a comprehensive vegetation survey by means of satellite imagery (Landsat-TM), airborne imagery (CASI), and ground-based methods, notably habitat mapping and vegetation sampling was carried out. With respect to the scales of resolution the classification results of the four methods are, to a certain degree, comparable. Differences in the outcome can be ascribed to the fact that methods of low resolution result in a discrete array of polygons whereas methods of high resolution depict a mosaic structure with an underlying, continuously changing gradient. Provided that the biological meaning of the remote sensing classification is known, a shift from single vegetation patterns to the landscape scale will be possible. Neither satellite nor airborne imagery is restricted to the purpose of mapping but may also serve for vegetation classification itself.     

Remote sensing for lake research and monitoring – Recent advances

Lakes are important ecosystems providing various ecosystem services. Stressors such as eutrophication or climate change, however, threaten their ecological functions. National and international legislations address these threats and claim consistent, long-term monitoring schemes. Remote sensing data and products provide synoptic, spatio-temporal views and their integration can lead to a better understanding of lake ecology and water quality. Remote sensing therefore gains increasing awareness for analysing water bodies. Various empirical and semi-analytical algorithms exist to derive remote sensing indicators as proxies for climate change or ecological response variables. Nevertheless, most monitoring networks lack an integration of remote sensing data. This review article therefore provides a comprehensive overview how remote sensing can support lake research and monitoring. We focus on remote sensing indicators of lake properties, i.e. water transparency (suspended particulate matter, coloured dissolved organic matter, Secchi disc depth, diffuse attenuation coefficient, turbidity), biota (phytoplankton, cyanobacteria, submerged and emerged aquatic vegetation), bathymetry, water temperature (surface temperature) and ice phenology (ice cover, ice-on, ice-out). After a brief background introducing principles of lake remote sensing we give a review on available sensors and methods. We categorise case studies on remote sensing indicators with respect to lake properties and processes. We discuss existing challenges and benefits of integrating remote sensing into lake monitoring and ecological research including data availability, ready-to-use tools and accuracies.