Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.     

Caractérisation de formations végétales mediterranéennes à partir de données ‘Thematic Mapper’ Une étude de cas en Andalousie (Espagne)

Abstract

The study is focused on the characterization of vegetation formations in a Mediterranean area (943 km²) located in southern Spain: herbaceous canopies (rangelands), shrubby vegetation (‘matorral’) and complex woody/herbaceous formations (‘dehesa’). Vegetation formations (physiognomical units) have been characterized by their spectral responses in the six reflective TM channels and by vegetation indices. From the ratio index TM4/TM3 there has been derived a map displaying seven classes (water, bare soil and five biomass levels reflecting the hierarchy of vegetation formations). Channels TM3, TM4 and TM5 have been considered for a supervised classification into nine land-cover categories (seven vegetation formations, bare soil and water). The proportion of correct classification of vegetation formations is about 78 per cent when considering test areas. Classification made from three principal components gives similar results.     

Testing the Woodcock-Harward image segmentation algorithm in an area of southern California chaparral and woodland vegetation

Vegetation maps were produced by applying a region-growing segmentation algorithm to Landsat Thematic Mapper (TM) data, and labelling the resulting segments or map polygons by overlay of a per-pixel classification and applying a plurality rule. Thus, each segment was assigned a vegetation class label based on the most frequently occurring pixels in the segment. The segmentation improved overall map accuracies by an average of 10 per cent relative to the underlying per-pixel classification for three subimages within a southern California montane watershed based on a comparison with photointerpreted maps. While it was hypothesized that including transformed slope aspect and image texture as input to the segmentation would improve map accuracy by creating segments corresponding more closely to vegetation stands, our results did not support these hypotheses. Further, performing the segmentation on principal components bands, or a vegetation index, did not improve results over the segmentation based on TM bands 2, 3, and 4.     

Regional scale hierarchical classification of temporal series of AVHRR vegetation index

In this paper a hierarchical approach is taken to classify temporal sequences of images of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), using Iberia as an example. Iberia is a convenient area of study because it has a high environmental diversity and very strong environmental gradients, and yet a reduced size at the spatial resolution of current global data-sets. An Iberian subset of a global temporal series of AVHRR-NDVI images facilitates test and validation of different approaches while producing results that are likely to be valid over much larger areas. Our hierarchical clustering approach yields maps with nested legends. We compare these maps to a digitized map of potential natural vegetation, which reveals a clear bioclimatic control. The highest level of the hierarchical classification separates vegetation with a Summer peak of NDVI from vegetation with a Spring peak of NDVI. Such a discontinuity corresponds to the discontinuity between Atlantic and Submediterranean vegetation in the vegetation map. Lower levels in the hierarchical classification produce maps of increasing complexity but that keep a high degree of spatial continuity. A correspondence analysis between a 16-classes NDVI map and the digitized map of potential vegetation produces an ordination that is bioclimatically coherent. According to the known characteristics of the potential vegetation units, the two first correspondence axes can be interpreted, respectively, as water availability and temperature. These results are a consequence of the temporal NDVI series being an accurate signal of vegetative phenology, which in turn is a fundamental vegetation property. A comparison of our results with several global land cover digital maps by means of the Wilk's ratio indicates that the global maps do not produce an appropriate partition of the region in terms of the NDVI temporal course. We conclude that the analysis of temporal series of NDVI yield relevant ecological information at finer scales and with more detailed legends that had not been attempted until now, and, therefore, are suitable for regional scale applications. Our results also indicate the interest of a bioclimatic analysis and modeling of the NDVI signatures for their correct ecological understanding. Maps at a global scale can be produced based on such an understanding.     

参考1维光谱差异的区域生长种子点选取方法

目的 区域生长法是遥感影像分割中常用的算法,该算法首先选取适当的像元作为生长的起始点（种子点）。现有的种子点选取方法存在种子点数目较多、效率低以及地物细节种子点不足等问题。针对种子点选取存在的问题,提出一种基于1维光谱差异的区域生长种子点的选取方法。方法 首先计算像元间1维（水平、竖直）方向上的光谱差异,然后选取光谱差异的局部极小值作为种子点,最后对种子点进行优选,得到区域生长的起点。结果 应用本文方法选取种子点,对高分辨率的IKONOS遥感影像进行了区域生长。将实验结果与分形网络演化方法及Kernel Graph Cuts方法的分割结果进行了目视对比,并且分别计算了3种方法所得分割结果的基元内部同质性和基元间相关性的评价指数。目视比较的结果表明,本文的种子点选取方法能够为区域生长提供具有代表性的种子点,得到了精细的分割结果。在定量评价上,本文方法也表现出了数值优势,各波段分割质量指数均提高15%以上。结论 提出的种子点选取方法能够为高分辨率遥感图像的区域生长分割提供具有代表性的种子点,产生精细的分割图像,对于地物细节有良好的分割效果,具有较高的实用价值。     

An integrated vegetation mapping approach for northern Alaska (1:4 M scale)

A six-step integrated vegetation mapping approach is described for making a small-scale (1:4 million) map of northern Alaska. The method uses two primary maps: (1) a Phytogeographic subzones and Floristic subprovinces Map (PFM) adjusted to Advanced Very High Resolution Radiometer false colour infrared (AVHRR CIR) imagery, and (2) an Integrated Vegetation-Complex Map (IVCM). The IVCM map-polygon boundaries are guided by information from a variety of remote-sensing data (AVHRR imagery, maximum greenness maps and classified images) and hard-copy source maps (surficial geology, bedrock geology, soils, percentage water cover). The map-polygon boundaries are integrated so that they conform to terrain features that are interpretable on the AVHRR CIR. The PFM and IVCM are overlaid in a geographic information system (GIS), and a series of derived maps is created through the use of look-up tables. Northern Alaska is a prototype area for the Circumpolar Arctic Vegetation Mapping (CAVM) project, which has a goal of producing a new vegetation map of the region north of the arctic tree line by the year 2001. The method could be modified and adapted to any region of the Arctic based on locally available information.     

Environmental factors and the detection of open surface water areas with X-band radar imagery

The ability to map open surface water is integral to many hydrologic and agricultural models, wildlife management programmes, and recreational and natural resource studies. Open surface water is generally regarded as easily detected on radar imagery. However, this view is an oversimplification. This study used X-band HH polarized airborne Synthetic Aperture Radar ( SAR) imagery to examine the potential of SAR data to map open fresh water areas extant on 1:100000 USGS topographic maps. Seven study sites in the U.S.A. with a combined area of over 68000km²were analysed. Detection accuracies and minimum size for detection varied among the seven locations. Size and shape of water bodies and radar shadow all affected detection. However, environmental modulation factors including vegetation and forest cover, moisture, and landscape composition and morphology had the greatest influence and exhibited the most complex role in explaining variability     

Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model

Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.     

Development of an IKONOS image classification rule-set for multi-scale mapping of Mediterranean rural landscapes

In this research, a rule-set of object-based classification of IKONOS imagery for fine-scale mapping of Mediterranean rural landscapes was developed. This study was conducted on the Mediterranean island of Crete (Greece). A three-level classification hierarchy was designed in a bottom-up approach containing a total number of 22 classes. The first level was associated with vegetation physiognomy (6 classes), the second level with linear features (6 classes) and the third level with land uses existing in the area (10 classes). Image objects were created with multiresolution segmentation, an algorithm supplied by eCognition software. The segmentation parameters were selected through a trial-and-error approach after visual evaluation of the resulting image objects. The rule-set comprised 100 classification rules described with the ‘Membership Function’ classifier. The classification stability was found to lie between 0.59 and 0.77, inversely proportional to the complexity of each level's classification. For an accuracy assessment, the error matrix method was used in a set of 250 randomly selected points. The overall classification accuracy achieved at the first level was 74%, at the second level 50% and at the third level 64%. The geometric accuracy of the classification was beyond the scope of this research; and moreover, consistent reference data sets were not available. The conclusion is that the use of rules in an object-based image analysis (OBIA) process has the potential to produce accurate landscape maps even in the case of complex environments, in which ancillary data are not available. Future work should focus on testing the transferability of the rule-set in different Mediterranean study sites, in order to draw a conclusion in relation to its potential operational use.     

Ground water exploration in the Saurashtra peninsula

The fact that groundwater in hard-rock formations is generally confined to fissures, fractures, joints and weathered zones makes space imagery extremely useful when prospecting for groundwater in hard-rock areas. Keeping this in mind, multitemporal LANDSAT imagery of the Saurashtra region has been studied by employing visual/manual-interpretation techniques. Various hydrogeomorphological features, such as abandoned channels, buried channels, lineaments, water bodies, vegetation and floodplains, were mapped at a scale of 1:250 000. Using these maps, areas with groundwater potential were identified. Resistivity surveys were conducted in selected areas. Using these results, sites for exploratory drilling were chosen. The pumping-test results at most of the sites were quite encouraging. The present study therefore demonstrates the usefulness of remotely sensed data in groundwater exploration.     

Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data

Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4 m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes.     

A drainage system analysis evaluation of,and comparison between,Landsat-3 RBV,Landsat-5 TM and SPOT PA imageries covering the Central Macedonia district,Greece

Abstract

Landsat-3 RBV, Landsat-5 TM imageries and SPOT PA stereopair diapositives were visually interpreted for the purpose of finding the accuracy of certain morphometric variables of three drainage basin sample areas in Central Macedonia, North Greece, drawn separately from each of the above three types of satellite imageries and comparisons were made between the efficiency of drainage systems drawn from each of the above imageries and the drainage systems extracted from the available topographic maps of 1:50000 scale.

The main findings were the following: (1) SPOT PA stereopair diapositives of 1:200000 scale can be used to map drainage systems to an order of magnitude slightly more than TM imagery of 1:125000 scale, but significantly more than RBV imagery of 1:125000 scale. This slight superiority of SPOT imagery over TM imagery implies that the greater spectral range of TM, compared with the shorter range of SPOT imageries, vastly outweighs the advantage of SPOT'S superior resolution, but not the superiority of stereoscopic view; (2) TM imagery can be used to map drainage systems to an order of magnitude significantly more than RBV imagery; and (3) RBV imagery can be used to map drainage systems to an order of magnitude less than topographic maps of 1:50000 scale but better than topographic maps of 1:100000 scale.     

Distinguishing wetland vegetation and channel features with object-based image segmentation

Mapping landscape features within wetlands using remote-sensing imagery is a persistent challenge due to the fine scale of wetland pattern variation and the low spectral contrast among plant species. Object-based image analysis (OBIA) is a promising approach for distinguishing wetland features, but systematic guidance for this use of OBIA is not presently available. A sensitivity analysis was tested using OBIA to distinguish vegetation zones, vegetation patches, and surface water channels in two intertidal salt marshes in southern San Francisco Bay. Optimal imagery sources and OBIA segmentation settings were determined from 348 sensitivity tests using the eCognition multiresolution segmentation algorithm. The optimal high-resolution (≤1 m) imagery choices were colour infrared (CIR) imagery to distinguish vegetation zones, CIR or red, green, blue (RGB) imagery to distinguish vegetation patches depending on species and season, and RGB imagery to distinguish surface water channels. High-resolution (1 m) lidar data did not help distinguish small surface water channels or other features. Optimal segmentation varied according to segmentation setting choices. Small vegetation patches and narrow channels were more recognizable using small scale parameter settings and coarse vegetation zones using larger scale parameter settings. The scale parameter served as a de facto lower bound to median segmented object size. Object smoothness/compactness weight settings had little effect. Wetland features were more recognizable using high colour/low shape weight settings. However, an experiment on a synthetic non-wetland image demonstrated that, colour information notwithstanding, segmentation results are still strongly affected by the selected image resolution, OBIA settings, and shape of the analysis region. Future wetland OBIA studies may benefit from strategically making imagery and segmentation setting choices based on these results; such systemization of future wetland OBIA approaches may also enhance study comparability.     

斑块状植被遥感检测研究进展

斑块状植被是世界上干旱—半干旱区常见的景观类型,对于它们的形成、结构和演替研究能够提高人们对干旱—半干旱地区生态系统动态及其重要的生态水文过程的理解,具有重要的理论研究意义和应用价值.传统的基于地面调查和长期定位观测的方法观测范围有限,已无法满足目前区域斑块状植被分布及其空间格局特征研究的需要.利用遥感技术快速重复获取...     

Tree model based eco-climatic vegetation classification and fuzzy mapping in diverse tropical deciduous ecosystems using multi-season NDVI

Many vegetation classification strategies in tropical ecosystems involving conventional image processing of original satellite imagery bands require considerable prior site knowledge, statistical assumptions, and are difficult, expensive and inconsistent. In this paper we show that the intra-annual variation and rates of change in NDVI for different parts of a large forest area in combination with rules derived from a tree model can be used for detailed vegetation mapping. We used three-date NDVI data for the Biligiri Rangaswamy Temple Wildlife Sanctuary in Karnataka, southern India comprising mean NDVI, coefficient of variation (CV) and two NDVI change vectors corresponding to intraseasonal NDVI differences. A rule-based classification using a tree model was developed at two levels. The overall kappa statistic is 0.61 at level 1 classification, indicating a strong correspondence with the raster version of the available vector reference map based on ground data, even though the two maps are not strictly comparable. Several limitations of the available reference map have been highlighted by the new technique, especially the absence of ecotones. At level two the tree model map has provided detailed classification of dry deciduous forests and new classes not available in the reference map. The method in combination with reference data also provides a framework for fuzzy classification. This technique offers a relatively simple cost-effective alternative to existing classification strategies, especially for areas with diverse evergreen and deciduous vegetation elements.     

A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery

Based on very high resolution satellite images, object-based classification methods can be used to produce large scale maps for forest management. These new products require a method to derive quantitative information about the accuracy and precision of delineated boundaries. This assessment would complement any measure of thematic accuracy derived from the confusion matrix. This study aims to assess the positional quality of the boundaries between different landscape units produced by automated segmentation of IKONOS and SPOT-5 satellite images over temperate forests. A robust method was developed to assess the accuracy and the precision of the forest boundaries, respectively measured by the bias and the standard deviation. The two main sources of positional error, namely residual parallax and automatic segmentation, were independently assessed. Positional errors caused by the residual parallax were quantified using a 3D model. Forest stand boundaries generated by automatic segmentation were compared to corresponding visual delineations. The results showed that residual parallax was the major source of positive bias (area overestimation) along forest/non-forest boundaries and depended on the interactions between forest stand patterns and sensor viewing angles. Due mainly to tree shade, the automatic segmentation also produced a positive bias on forest areas, which remained under 1 m for both IKONOS-2 and SPOT-5 images. Standard deviation did not increase linearly with pixel size and was influenced by the nature of the boundary. Production of 1:20,000 scale forest maps from very high resolution satellite data clearly requires acquisition of near nadir imagery or knowledge of landscape object height for true orthorectification. In these cases, IKONOS-2 segmentation outputs were found to correspond with 1:20,000 scale map specification, and SPOT-5 imagery with 1:30,000 scale.     

Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level.

The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation.

First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover.

A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale.     

Mapping of Siberian forest landscapes along the Yenisey transect with AVHRR

In this paper NOAA AVHRR data acquired at the Sukachev Institute of Forest in Siberia, Russia is evaluated for forest management mapping applications. First a classification of the entire 1000km 2 3000km transect was performed, but was found to be too general to be of value. More useful interpretation procedures require a landscape-ecological approach. This means that computer classification should be made separately for segments of territory based ecologically distinct regions. This segmentation of the transect into ecological regions was found to improve the level of detail available in the classification. Using this approach AVHRR data were found to be adequate for small scale mapping at the level of vegetation types or plant formations. A limited study using AVHRR data for classification of mountainous regions showed that AVHRR-derived maps were more detailed than existing landscape maps. AVHRR derived classifications also compared favourably to larger scale forest management maps of softwood and hardwood forests. Current forest management in Siberia relies on very small-scale inventory maps. Thus, there is a potential role for AVHRR (or Terra) data for northern Siberian forest monitoring. The southern forests of the Yenisey meridian (below the 57th parallel ) are less uniform due to considerable human activity, and NOAA/AVHRR data will play a subordinate role in its monitoring.     

Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model

Flood extent maps derived from remotely sensed data can provide distributed validation data for hydraulic models of fluvial flow, and can be used for flood relief management and to develop spatially accurate hazard maps. A statistical active contour model is used to delineate a flood from the first European Remote Sensing satellite Synthetic Aperture Radar (ERS-1 SAR) imagery as a region of homogeneous speckle statistics. The segmentation uses both local tone and texture measures and is capable of accurate feature boundary representation. The results are assessed by comparison with simultaneous aerial photography, the SAR segmentation scheme classifying 75% by area of the shoreline region correctly. Seventy per cent of the shoreline coincides with the ground data to within 20 m. The main error is due to unflooded vegetation giving similar radar returns to open water.     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.