Composition and structure of spruce forests of the southwestern part of Moscow region

Coenotic features of boreal, nemoral, and subnemoral spruce stands of the southwestern part of Moscow region have been studied using ground-based and remote sensing data. Despite significant modifications of the vegetation cover in the region due to human impacts, the species composition of the spruce communities still retains typical zonal features of the regional vegetation and is associated with certain landscape elements. Cartographic modeling has allowed us to identify the spatial distribution patterns for various spruce forest types and produce a series of geobotanical maps (scale 1: 100000). The ecophytocoenotic approach was used for classifying the forest vegetation. An analysis of the spatial differentiation of the forest cover—using spruce forests with different species composition as an example—has confirmed the ecotonal structure of the study area demonstrated through a characteristic latitudinal distribution of geoecolological spectra of species.     

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Zagros forests in western Iran have widely been destroyed because of various reasons. This study was performed to provide the land cover and forest density maps in Zagros forests of Khuzestan province using Sentinel-2, Google Earth and field data. The forest boundary in Khuzestan province was digitized in Google Earth. Sentinel-2 satellite images were provided for the study area. One 1:25000 index sheet of Iranian Mapping Organization (IMO) was selected as pilot area in the province. Sentinel-2 image of the pilot area was classified using different supervised classification algorithms to select the best algorithm for land cover mapping in Khuzestan province. In addition, to evaluate the accuracy of Google Earth data, field sampling was performed using random plots in different land covers. Field data of forest plots were applied to investigate tree canopy cover percent (forest density), as well. Classification of Sentinel-2 image in Zagros area of Khuzestan province was done using the best algorithm and the land cover was obtained. The forest density map was also obtained using a linear regression model between tree canopy cover percent (obtained from field plots) and normalized difference vegetation index (NDVI) (obtained from NDVI map). Finally, the accuracy of land cover map was assessed by some square plots on Google Earth. Results demonstrated that support vector machine (SVM) algorithm had the highest accuracy for land cover mapping. Results also showed that Google Earth images had a good accuracy in the Zagros forests of Khuzestan province. Results demonstrated that NDVI has been a good predicator to estimate tree canopy cover in the study area. Based on results, an area of 443,091.22 ha is covered by Zagros forests in Khuzestan province. Results of accuracy assessment of the land cover map showed the good accuracy of this map in Khuzestan province (overall accuracy: 91% and kappa index: 0.83). For optimum management of Zagros forests, it is suggested that the land cover and forest density mapping will be performed using SVM algorithm, NDVI, and Sentinel-2 satellite images in Zagros forests of Khuzestan province in the certain periods.     

Forest cover classification and geoinformation modeling of forest regeneration dynamics (Based on the example of the southern part of near-Yenisei Siberia)

An automated conjugate classification of forest growing conditions and forest vegetation has been developed based on the example of the southern part of Yenisei Siberia; maps of potential forest growing conditions and forest-cover regeneration dynamics have been constructed on the basis of the automated recognition of remote sensing data and a system of conjugate analysis of dissimilar data in GIS. The proposed approach makes it possible to promptly create and update forest cover maps, which is particularly important for the vast taiga regions of Siberia.     

Changes in the forest cover after intense logging in southern taiga of the russian federation

Specific features of the forest cover formation after industrial timber cutting during a period of 50 years in southern taiga spruce forests (Kostroma oblast) are described. The statistical analysis of the forest use intensity, the forested area, and the dynamics of the age and species structure of forests was performed. Plans of forest stands obtained for five inspections and taxation databases of 1954 and 1997 were also used. The potentialities of analyzing the structure of forest mapping units using means of overlay in the geoinformation system are considered. Parameters for distinguishing forest compartments were calculated. The dynamics of these parameters reflect the natural development of forest, effects of adverse environmental factors, economic history of the region, and specific features of individual interpretation of the survey materials in forest management.     

Reconstructed natural versus potential natural vegetation in vegetation mapping - a discussion of concepts

Reconstruction mapping of the natural (primary) vegetation of intensively cultivated land is based on: (1) classification of actually existing remains of natural or near-natural plant communities as mapping units; (2) delimitation of their habitat types; (3) detection of correlations between vegetation units and habitat types. Natural plant communities thus serve as indicators of abiotic habitat conditions. Reconstruction mapping is based on the extrapolation of the potential distribution of individual vegetation units to sites of similar habitat types where the natural vegetation does not exist any more. The same procedure is used for mapping the potential natural vegetation. Both types of natural vegetation maps are identical on sites where the abiotic natural habitat conditions (relief, geological substratum, climate, water regime, soils) remain practically unchanged. On sites where the natural habitat conditions have been considerably changed by man, e.g. in areas with superficial coal mining (complete destruction of the landscape, removal of soil cover, creation of large slag heaps) or in towns, no natural (primary) vegetation exists. This causes difficulties in the hypothetical concept of the potential natural vegetation and its definition. In contrast, in such sites reconstruction vegetation mapping uses the extrapolation of mapping units of the primary vegetation to the original natural habitat conditions.     

融入植被连续性因子的生态单元制图法在城市生物多样性维护中的应用

基于一个以植被结构为构建框架的生态单元分类系统,构建了融入了植被覆盖连续性因子的改良城市生态单元制图模型,并将其应用于瑞典赫尔辛堡市的绿色空间研究.使用原生林地指示种或林地连续性指示种（AWIS）鉴定长、短连续性林地的分布,对比其含有维管束植物的物种丰富度,对植被覆盖的连续性因子进行评估检验.结果表明： 长连续性林地中含有较多的AWIS;在建群种均龄大于30年的林地中,长连续性林地相对于结构相似的短连续性林地通常含有较高的生物多样性.融入植被连续性因子的生态单元制图模型是调查城市生物多样性的重要工具,通过图谱中各生态单元所含有的生物多样性信息,可对今后城市生物多样性的维护提出相应策略.     

Identification of characteristic plant co-occurrences in neotropical secondary montane forests

Aims Inferring environmental conditions from characteristic patterns of plant co-occurrences can be crucial for the development of conservation strategies concerning secondary neotropical forests. However, no methodological agreement has been achieved so far regarding the identification and classification of characteristic groups of vascular plant species in the tropics. This study examines botanical and, in particular, statistical aspects to be considered in such analyses. Based on these, we propose a novel data-driven approach for the identification of characteristic plant co-occurrences in neotropical secondary mountain forests.Methods Floristic inventory data were gathered in secondary tropical mountain forests in Ecuador. Vegetation classification was performed by coupling locally adaptive isometric feature mapping, a non-linear ordination method and fuzzy- c -means clustering. This approach was designed for dealing with underlying non-linearities and uncertainties in the inventory data.Important findings The results indicate that the applied non-linear mapping in combination with fuzzy classification of species occurrence allows an effective identification of characteristic groups of co-occurring species as fuzzy-defined clusters. The selected species indicated groups representing characteristic life-form distributions, as they correspond to various stages of forest regeneration. Combining the identified 'characteristic species groups' with meta-information derived from accompanying studies indicated that the clusters can also be related to habitat conditions. In conclusion, we identified species groups either characteristic of different stages of forest succession after clear-cutting or of impact by fire or a landslide. We expect that the proposed data-mining method will be useful for vegetation classification where no a priori knowledge is available.     

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.     

浙江省森林信息提取及其变化的空间分布

如何利用遥感技术提取森林信息是遥感应用的重要领域之一。以不同时相的Landsat TM/ETM+为数据源,采用面向对象和基于多级决策树的分类方法得到浙江省2000年、2005年以及2010年的森林植被覆被图。经实地采样点验证,2010年分类精度达到92.76%,精度满足要求。介绍了浙江森林信息的快速提取方法,即统计不同森林类型的Landsat TM影像原始波段和LBV变换值以及各种植被指数在各时相上的差异,经过C5决策树训练,选取合适的规则和阈值实现森林信息的提取。结果表明,面向对象分割与决策树算法结合可以作为森林信息提取的有效方法。最后,通过对3期森林专题图进行空间叠加分析,得到了森林资源动态变化的空间分布,并以此为基础对林地变化的类型及原因进行分析,结果显示浙江省森林资源变化主要分布在浙西北山区、浙中南山区以及沿海地带,这一结果可以为有关部门的决策提供依据。     

Changes in a Wetland Ecosystem: A Vegetation Reconstruction Study Based on Historical Panchromatic Aerial Photographs and Succession Patterns

This paper presents the methodology and results of a vegetation reconstruction method based on botanical sampling, the knowledge of succession pattern, digital photograph-interpretation and automatic delineation via image segmentation. The aim is to provide a methodology for interpretation of archived black-and-white aerial photographs, which can be applied at other study sites. Our study area was the Nyíres-tó mire in the Bereg Plain (NE Hungary). Initially, botanical sampling was carried out, and this was followed by separation and identification of current vegetation types. In our study we selected automatic delineation using multi-resolution image segmentation as the method for vegetation mapping. Based on the present-day vegetation map produced and the known successional pathway of the mire, archive aerial photographs were analyzed separately in reverse chronological order to derive plant associations present at the different photograph acquisition dates. With this method we were able to make a chronological sequence of digital vegetation maps over a period of almost fifty years (1956–2002). The analysis of vegetation maps showed that forest cover increased steadily until 1988. After an artificial water supply was introduced (in 1986), the spread of tree-dominated associations became slower, and the relative cover of the different vegetation types reached a stable state.     

Forest classification in an Amazonian rainforest landscape using pteridophytes as indicator species

Habitat classification systems are poorly developed for tropical rainforests, where extremely high plant species richness causes numerous methodological difficulties. We used an indicator species approach to classify primary rainforest vegetation for purposes of comparative wildlife habitat studies. We documented species composition of pteridophytes (ferns and fern allies) in 635 plots (2×100 m) along 8 transects within a continuous rainforest landscape in northeastern Peruvian Amazonia. Considerable floristic variation was found when the data were analyzed using multivariate methods. The obtained forest classification was interpreted with the help of indicator value analysis and known soil preferences of the pteridophyte species. The final classification included four forest types: 1) inundated forests, 2) terrace forests, 3) intermediate tierra firme forests and 4) Pebas Formation forests. This rapid and relatively simple vegetation classification technique offers a practical, quantitative method for large-scale vegetation inventory in complex rainforest landscapes.     

A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.     

Application of the Ancient Forest Concept to Potential Natural Vegetation Mapping in Flanders, A Strongly Altered Landscape in Northern Belgium

Construction of potential natural vegetation (PNV) poses particular challenges in landscapes heavily altered by human activity and must be based on transparent, repeatable methods. We integrated the concept of ancient forest (AF) and ancient forest species (AFS) into a four-step procedure of PNV mapping: 1) classification of forest vegetation relevés; 2) selection of those vegetation types that can serve as PNV units, based on AF and AFS; 3) merging of selected vegetation types into five PNV units that can be predicted from a digital morphogenetic soil map; 4) mapping of three additional PNV units based on additional environmental data. The second step, concerning the selection of reference forest vegetation, is of particular interest for PNV construction in Flanders (northern Belgium), where forest cover has been subject to temporal disruption and spatial fragmentation. Among the variety of extant forest recovery states, we chose as PNV units those vegetation types for which a high proportion of relevés had been located in AF and that contained many AFS. As the frequency of AFS depends on site conditions, we only compared and selected vegetation types that are found on similar sites according to average Ellenberg indicator values. While succession is irrelevant for the definition of PNV, colonization rates of AFS can be used to estimate the time required for PNV to be restored in a site.     

Are CORINE land cover classes reliable proxies of plant species assemblages? A test in Mediterranean forest landscapes

In land cover mapping, the complexity of landscapes is fitted into classes that may limit the recognition of natural variability. In this study, we tested the power of land cover classes (defined on the CORINE land cover classification scheme, a standardized legend set by EU for land cover inventory) to separate different vascular plant assemblages in forest ecosystems. In order to separately identify the role of different sources of inconsistency between land cover classes and species composition, we compared three different inventory processes, based on (i) dominant tree species as observed in the field, (ii) visual interpretation of remotely sensed images and (iii) semi-automatic supervised classification of satellite images. Our results underline that classifying forest ecosystems on the basis of their canopy species produces an over-simplification of habitat variability. Consequently, land cover maps based on non-specialized classification schemes should not be regarded as good proxies for plant biodiversity. If land cover maps are intended to describe and manage landscapes and their associated biodiversity, it is necessary to improve their capacity to represent the complexity of ecosystems.     

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.     

Management oriented forest map of South India: thematic derivations

The principles and methods of the vegetation mapping undertaken at the French Institute, Pondicherry, are dealt with herein. Particularly, the characterisation of the different types of vegetation and especially the originality of the method: the dynamic interpretation of the vegetation and the depiction of the bioclimatic conditions. The programme of the forest map of South India at scale 1:250 000, undertaken in collaboration with the forest departments of the concerned states, is then described with special attention given to the source and the collection of data. This map has been conceived to serve as a basic document for the sustainable management of the forests. Three examples of its application are given. They concern the detection of anomalies between the existing forest cover and the prevalent climatic environment; the detection of areas for which protection is urgently needed; the selection of regions showing a particular interest in the field of nature conservation or as gene pool reserve. Finally, an example of a thematic map of sensibility of the forests is given, using the vegetation map as a basis.     

Vegetation classification,mapping, and monitoring at Voyageurs National Park,Minnesota: An application of the U.S. National Vegetation Classification

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with ‘alliance’and ‘association’as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification.     

Dealing with vagueness in complex forest landscapes: A soft classification approach through a niche-based distribution model

The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classification of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classification in a complex Mediterranean area, has been investigated. Maxent, a niche-based model of species/habitat distribution, allowed researchers to estimate the potential distribution of four forest types: Holm oak, Mixed oak, Mixed broadleaved and Riparian forests. The Maxent model's internal tests have proved a powerful tool for estimating the model's accuracy and analyzing the effects of the most important variables in the produced models. Moreover the comparison with a spectral response-based fuzzy classification, showed a higher accuracy in the Maxent outputs, demonstrating how the use of environmental variables, combined with spectral information in the classification of natural or semi-natural land cover classes, improves map accuracies. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, can represent a useful approach to making more efficient and effective field inventories and to developing effective conservation policies.     

Environmental conditions in relation to variability of deciduous forest vegetation — a study from volcanic mountain in central Slovakia

The deciduous forests represent dominant natural vegetation of Central European landscape and an important functional component for maintenance of biological diversity. However, their syntaxonomy and ecological gradients still remain unclear. The numerical classification was conducted to determine the main units of forest vegetation, while ordination techniques were used to explain the structure of vegetation-environmental data matrix consisting of 110 forest stands in the Štiavnické vrchy Mts (central Slovakia). Ten vegetation types within the phytosociological classes of deciduous forests Quercetea robori-petraeae and Querco-Fagetea were distinguished. The major environmental driver responsible for variation in forest species composition was interpreted as a response to soil moisture which also accounted for a large part of species variability (3.74%). Soil nutrient/acidity complex expressed by pH, Ca and Al concentration was also an important source of vegetation variability. Relevance of soil conditions in relation to plant survival and community distribution was discussed. Along the soil moisture gradient, vegetation types were arranged from the subxerophilous oak forests through the mesophilous beech and ravine forests to the hygrophilous alder ash vegetation.