Vegetation mapping of the Great Fish River basin,South Africa: Integrating spatial and multi‐spectral remote sensing techniques

Abstract. We present a remote sensing based vegetation mapping technique well suited to a heterogeneous, semi‐arid environment. 10 structural vegetation classes were identified and described on the ground. Using Landsat‐TM from two different seasons and a combination of three conventional classification techniques (including a multi‐temporal classification) we were unsuccessful in delineating all of the desired vegetation classes. We then employed a simple tex‐tural classification index, known as the Moving Standard Deviation Index (MSDI), that has been used to map degradation status. MSDI measures spatial variations in the landscape and is calculated by passing a 3 × 3 standard deviation filter across the Landsat‐TM red band. High MSDI values are associated with degraded or disturbed rangelands whilst low MSDI values are associated with undisturbed rangeland. A combination of two conventional multi‐spectral techniques and MSDI were used to produce a final vegetation classification at an accuracy of 84 %. MSDI successfully discriminated between two contrasting vegetation types of identical spectral properties and significantly strengthened the accuracy of the classification. We recommend the use of a tex‐tural index such as MSDI to supplement conventional vegetation classification techniques in heterogeneous, semi‐arid or arid environments.     

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.     

Mapping mountain vegetation using species distribution modeling,image‐based texture analysis,and object‐based classification

Objective: The objective of this study was to map vegetation composition across a 24 000 ha watershed. Location: The study was conducted on the western slope of the Sierra Nevada mountain range of California, USA. Methods: Automated image segmentation was used to delineate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classified using a decision tree and data sources extracted from individual species distribution models, Landsat spectral data, and life form cover estimates derived from aerial image‐based texture variables. Results: A total of 12 plant communities were mapped with an overall accuracy of 75% and a χ‐value of 0.69. Species distribution model inputs improved map accuracy by approximately 15% over maps derived solely from image data. Automated mapping of existing vegetation distributions, based solely on predictive distribution model results, proved to be more accurate than mapping based on Landsat data, and equivalent in accuracy to mapping based on all image data sources. Conclusions: Results highlight the importance of terrain, edaphic, and bioclimatic variables when mapping vegetation communities in complex terrain. Mapping errors stemmed from the lack of spectral discernability between vegetation classes, and the inability to account for the confounding effects of land use history and disturbance within a static distribution modeling framework.     

Satellite remote sensing of wetlands

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

Spatial analysis of vegetation patterns in southern Western Australia: implications for reserve design

Spatial analysis of vegetation and soil data collected from 64 sites in southern Western Australia suggests that both soil characteristics and geographic distance between sites are important predictors of the floristic resemblance of sites on a regional scale. These two factors are largely independent, a finding that may reflect recent, rapid speciation in the study area postulated in other studies. Spatial patterns of plant guilds suggest geographic replacement, and contingent exclusion may be an important mechanism maintaining species-richness. Existing soil and vegetation maps used to delineate reserve boundaries are found to be accurate, although the soil maps include information on vegetation patterns, independent of information on soil patterns. Within broad vegetation formations, there is some correlation of floristics in mallee stands with soil characteristics. Ordination analysis indicates a soil moisture/nutrient axis. In contrast, there are few important correlations of floristics with soil characters in kwongan (sand heath) or halophytic vegetation. Geographic distance between sites is a much more important factor. The absence of edaphic correlations implies that the observed geographic replacement of species between sites is a historical legacy, the result of recent, rapid speciation in the spatially patchy environment. It is concluded that if reserves in the region are to conserve the flora, especially rare species, the reserve system should include replicates of stands within the same broad formations and soil types at intervals less than 15 km, the minimum scale of resolution of this study.     

A quantitative analysis of photometric data from aerial photographs for vegetation survey

This paper describes the application of quantitative density analysis to black and white aerial photographs for vegetation survey using a Quantimet 720 image analyser. The photometric data are analysed using both a supervised and an unsupervised classification strategy. Floristic data collected from an independent ground survey, are used to categorise those vegetation classes of interest against which the photometric data classifications are assessed. The preliminary results obtained suggest that broad classes of vegetation types may be distinguished automatically from their grey scale distribution patterns.     

Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin

Aim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km²). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

基于LSMM和改进的FCM提取城市植被覆盖度——以北京市海淀区为例

植被是城市生态系统的重要组成部分,及时获取植被覆盖信息对城市生态环境监测具有重要意义。利用中分辨率Landsat TM遥感数据,采用线性光谱分解模型(LSMM)开展城市植被覆盖度提取;同时,通过改进训练样本选择方式,在最小噪声变换(MNF)、像元纯净指数分析(PPI)、N维可视化分析基础上得到端元样本,再运用模糊C-均值(FCM)获取植被覆盖度;最后以高分辨率SPOT5遥感数据对两种方式的提取结果进行精度检验。结果显示,基于LSMM和改进的FCM提取的城市植被覆盖度与检验数据相关系数分别为0.8252和0.9381,后者可以较好地处理其他要素的非线性影响,因而具有较高精度。     

Application of landsat satellite data for mapping aquatic areas in north-eastern Finland

The application of the aquatic area research by computer-assisted Landsat MSS remote sensing is presented. Two lakes in northern Finland were mapped into spectrally identified classes.The open water classes depend mainly on the water quality and depth. The aquatic vegetation classes display the main life-forms (helophyte, nymphaeid, isoetid) and thus the amount of the chlorophyll above, on, or just below the water surface.     

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

Spatial variation and biogeography of sand forest avian assemblages in South Africa

Aim To examine biogeographical affiliations, habitat‐associated heterogeneity and endemism of avian assemblages in sand forest patches and the savanna‐like mixed woodland matrix. Location Two reserves in the Maputaland Centre of Endemism (MC) on the southern Mozambique Coastal Plain of northern KwaZulu‐Natal, South Africa. Methods Replicated surveys were undertaken in each of the two habitat types in each reserve, providing species abundance data over a full year. Vegetation structure at each of the survey sites was also quantified. Differences between the bird assemblages and the extent to which vegetation structure explained these differences were assessed using multi‐variate techniques. Biogeographical comparisons were based on species presence/absence data and clustering techniques. Results Bird assemblages differed significantly between habitats both within a given reserve and between reserves, and also between reserves for a given habitat. Differences in vegetation structure contributed substantially to differences between the avian assemblages. Of the four species endemic to the MC, three (Neergaard’s sunbird, Rudd’s apalis, and Woodward’s batis) were consistently present in sand forest. The fourth (pink‐throated twinspot) preferred mixed woodland. None of these endemic species was classed as rare. In the biogeographical analysis, both the sand forest and the mixed woodland bird assemblages were most similar to bird assemblages found in the forest biome or the Afromontane forest biome, depending on the biome classification used. Main conclusions The close affinities of sand forest and mixed woodland assemblages to those of the forest biome are most likely due to similarities in vegetation structure of these forests. Bird assemblages differ between the sand forest and mixed woodland habitats both within a given reserve and between reserves, and also between reserves for a given habitat. These differences extend to species endemic to the MC. Thus, conservation of sand forest habitat in a variety of areas is necessary to ensure the long‐term persistence of the biota.     

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.     

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.     

Factor analysis and classification of remotely sensed data for monitoring tidal flats

Interest in using remote sensing techniques, principally those involving satellite, in Wadden Sea research has centred on attempting a classification of the various sediment surface types present. Unlike most recent studies which have used mainly Landsat Multispectral Scanner data, we have assessed the feasibility of using Landsat Thematic Mapper data, which in conjunction with time series aerial photography, forms the basis of a strategy for remotely sensing the Wadden Sea. This paper focusses on an approach for extracting potentially “hidden” within-pixel information from multispectral data sets. A hierarchical (unsupervised) classification of a Thematic Mapper image successfully classified five different classes, including land, saltmarsh, water, cloud and tidal flat areas. This procedure thus enabled a “masking-out” of all classes other than those classified as tidal flat, following which a factor analysis was used to determine the minimum number of independent factors necessary to explain the observed variation in the signal received by the satellite. Three factors accounted for a total of 82% of the variation in all seven TM channels. Preliminary studies of the primary factor (score) image shows a good correlation with existing latterday cartographic data. Considering the proximate relationship between topography and other important biotic and abiotic sedimentary characteristics, this approach may prove valuable for future applications of satellite data for monitoring long-term change in physical and thus biological Wadden Sea characteristics. Ongoing research efforts are focussing on a classification and quantification of sub-pixel patchiness using aerial photography and ground surveys. The approaches taken and results obtained to date are discussed. Presented at the VI International Wadden Sea Symposium (Biologische Anstalt Helgoland, Wattenmeerstation Sylt, D-2282 List, FRG, 1–4 November 1988)     

Assessing native desert vegetation recovery in a war‐affected area using multispectral and hyperspectral imagery: a case study of the Sabah Al‐Ahmad Nature Reserve,Kuwait

The primary aim of this project was to assess vegetation changes in the Sabah Al‐Ahmad Nature Reserve, Kuwait, which is a war‐affected area following the Iraqi invasion in 1990. After the liberation in 1991, several portions of the reserve were under a restoration program. Remote sensing has been used as a tool to assess vegetation and land cover changes. We studied the feasibility of three common methods—the Mahalanobis distance (MD), maximum likelihood (ML), and support vector machine (SVM)—for classification of the multispectral imagery (Landsat) and hyperspectral (Hyperion). The reserve was also compared to the demilitarized zone (DMZ) located at Umm Nigga at the northern border of Kuwait, as it had recovered naturally, to distinguish between an autogenic recovery and a restored area. We discovered that the location was damaged during the military occupation, but a rapid recovery of the vegetation was then recorded in the reserve after the war from less than 1% measured in 1991 to 42% in 1998. Then, the vegetation cover significantly decreased in 2002 (26%) and slightly increased in 2013 (28%). We found that similar rapid increase in vegetation cover occurred in most parts of the reserve that was under the restoration program, and in the DMZ, which was naturally recovered. We concluded that remote sensing technologies are helpful tools in understanding the process of vegetation recovery as it provides information on location and timing of recovery, particularly where optimal condition exists.     

不同端元模型下湿地植被覆盖度的提取方法——以北京市野鸭湖湿地自然保护区为例

植被覆盖度作为反映湿地植物生长状况的重要生态学参数,在评估和检测湿地生态环境方面起着关键的作用.以华北内陆典型的淡水湿地——北京市野鸭湖湿地自然保护区为研究对象,中等分辨率的Landsat TM影像为数据源,基于线性光谱混合模型(LSMM)对研究区的植被覆盖度进行了估算.针对湿地植被类型丰富、土地利用类型多样化的特点,利用归一化植被指数(NDVI)在反映植物生长状况、覆盖程度以及区分地表覆盖类型方面的优势,通过对原始Landsat TM影像增加NDVI数据维对影像进行维度扩展,克服了传统研究中通常从Landsat TM影像上提取3-4种端元的局限,经最小噪声分离变换(MNF变换)、纯像元指数(PPI)计算以及人机交互端元选取等一系列运算,构建以陆生植物、水生植物、高反射率地物、低反射率地物、裸露土壤为组分的五端元模型来反映研究区的地物组成;同时,以原始Landsat TM影像为基础,构建植物、高反射率地物、低反射率地物、裸露土壤为组分的四端元模型.针对两种端元模型,采用全约束下的LSMM算法进行混合像元分解以获取研究区的植被覆盖度,其次辅以研究区的纯水体信息对其进行优化.精度检验采用相同时期的高分辨率WorldView-2多光谱影像来进行.研究表明:虽然四端元模型与五端元模型对植被覆盖度的估算结果在空间上具有基本一致的分布趋势,但是前者的估算结果在数值上要普遍低于后者,在研究区的水体及其附近,四端元模型难以体现水生植物的植被覆盖信息;另外,五端元模型的估算结果与检验数据的相关系数R达到0.9023,均方根误差(RMSE)为0.0939,明显优于四端元模型的R=0.8671和RMSE=0.1711.这反映了通过对影像进行维度扩展的方法来改进端元提取的数量是可行的,而由此构建的五端元模型可以更充分的反映研究区地物之间的光谱差异,从而获得更好的估算精度.     

湿地自然保护区保护价值评价方法

提出了一套侧重水鸟保护的湿地自然保护区保护价值评价方法.该方法建立的指标体系经过专家咨询和会议讨论确定指标,采用层次分析法(AHP)建立了递阶层次结构模型.指标体系共分为目标层1项、系统层5项、准则层11项和指标层26项.将获取资料的湿地自然保护区按国家有关分类标准与原则归为3个类型(海洋与海岸生态系统类型、内陆湿地与水域生态系统类型和野生动物类型),每个类型内的自然保护区再结合自身湿地主体进一步划分为4个小类型(近海与海岸湿地、河流湿地、湖泊湿地、沼泽湿地).实例分析了海洋与海岸生态系统类型中的以近海及海岸湿地为主体的自然保护区,内陆湿地与水域生态系统类型中的以沼泽湿地为主体的自然保护区和野生动物类型中的以河流湿地为主体的自然保护区,并依照保护价值指数进行了等级划分.为湿地自然保护区的保护价值和发展地位,总体规划和改建变更提供了依据.     

Predictive map of Ixodes ricinus high-incidence habitats and a tick-borne encephalitis risk assessment using satellite data

The main objective of this project was to predict Ixodes ricinus abundant habitats reliably as a means of tick-borne encephalitis (TBE) risk assessment for the prevention of this disease. The vegetation types were used as the indicators of an ecosystem suitable for tick occurrence, for TBE virus circulation and, accordingly, for the existence of natural foci of this infection. Remote sensing methods were used to determine the indicative plant cover. Satellite data covering an experimental area of 70 × 70 km in Central Bohemia, the Czech Republic, was acquired by the Landsat 5 TM scanner. Nine forest classes were recognized in the experimental area by successive supervised and unsupervised classifications and identified in a field-checking botanical survey. An epidemiological TBE map based on human cases contracted in the territory under study was exploited for the evaluation of risk in particular forest classes. Predictive maps are expressed both in digital and in printed forms at a scale of 1 : 300 000 for an overall risk evaluation and at a scale of 1 : 25 000 for a detailed local orientation.