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Artykuły w czasopismach na temat "Habitat classification"
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Marcinkowska-Ochtyra, Adriana, Krzysztof Gryguc, Adrian Ochtyra, Dominik Kopeć, Anna Jarocińska i Łukasz Sławik. "Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats". Remote Sensing 11, nr 19 (28.09.2019): 2264. http://dx.doi.org/10.3390/rs11192264.
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Accurately identifying Natura 2000 habitat areas with the support of remote sensing techniques is becoming increasingly feasible. Various data types and methods are used for this purpose, and the fusion of data from various sensors and temporal periods (terms) within the phenological cycle allows natural habitats to be precisely identified. This research was aimed at selecting optimal datasets to classify three grassland Natura 2000 habitats (codes 6210, 6410 and 6510) in the Ostoja Nidziańska Natura 2000 site in Poland based on hyperspectral imagery and botanical on-ground reference data acquired in three terms during one vegetative period in 2017 (May, July and September), as well as a digital terrain model (DTM) obtained by airborne laser scanning (ALS). The classifications were carried out using a random forest (RF) algorithm on minimum noise fraction (MNF) transform output bands obtained for single terms, as well as data fusion combining the topographic indices (TOPO) calculated from the DTM, multitemporal hyperspectral data, or a combination of the two. The classification accuracy statistics were analysed in various combinations based on the datasets and their terms of acquisition. Topographic indices improved the classification accuracy of habitats 6210 and 6410, with the greatest impact noted in increased classification accuracy of xerothermic grasslands. The best terms for identifying specific habitats were autumn for 6510 and summer for 6210 and 6410, while the best results overall were obtained by combining data from all terms. The highest obtained values of the F1 coefficient were 84.5% for habitat 6210, 83.2% for habitat 6410, and 69.9% for habitat 6510. Comparing the data fusion results for habitats 6210 and 6410, greater accuracy was obtained by adding topographic indices to multitemporal hyperspectral data, while for habitat 6510, greater accuracy was obtained by fusing only multitemporal hyperspectral data.
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Lavrinenko, Igor A. "Habitat classification of East-European tundra". Transaction Kola Science Centre 12, nr 6-2021 (31.12.2021): 13–18. http://dx.doi.org/10.37614/2307-5252.2021.6.12.9.001.
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The small contour and mosaic of tundra landscapes leads to the predominance of complex territorial units of vegetation (complexes, serial and ecological series, combinations). For accurate diagnostics and mapping of habitat categories in the tundra zone, we have developed a typological scheme based on the types of combinations of territorial units of vegetation. It takes into account not only the syntaxonomic composition, but also the peculiarities of the spatial organization of habitats.
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Foglini, Federica, Valentina Grande, Fabio Marchese, Valentina A. Bracchi, Mariacristina Prampolini, Lorenzo Angeletti, Giorgio Castellan i in. "Application of Hyperspectral Imaging to Underwater Habitat Mapping, Southern Adriatic Sea". Sensors 19, nr 10 (16.05.2019): 2261. http://dx.doi.org/10.3390/s19102261.
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Hyperspectral imagers enable the collection of high-resolution spectral images exploitable for the supervised classification of habitats and objects of interest (OOI). Although this is a well-established technology for the study of subaerial environments, Ecotone AS has developed an underwater hyperspectral imager (UHI) system to explore the properties of the seafloor. The aim of the project is to evaluate the potential of this instrument for mapping and monitoring benthic habitats in shallow and deep-water environments. For the first time, we tested this system at two sites in the Southern Adriatic Sea (Mediterranean Sea): the cold-water coral (CWC) habitat in the Bari Canyon and the Coralligenous habitat off Brindisi. We created a spectral library for each site, considering the different substrates and the main OOI reaching, where possible, the lower taxonomic rank. We applied the spectral angle mapper (SAM) supervised classification to map the areal extent of the Coralligenous and to recognize the major CWC habitat-formers. Despite some technical problems, the first results demonstrate the suitability of the UHI camera for habitat mapping and seabed monitoring, through the achievement of quantifiable and repeatable classifications.
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Hafizt, Muhammad, Marindah Yulia Iswari i Bayu Prayudha. "Kajian Metode Klasifikasi Citra Landsat-8 untuk Pemetaan Habitat Bentik di Kepulauan Padaido, Papua". Oseanologi dan Limnologi di Indonesia 2, nr 1 (5.05.2017): 1. http://dx.doi.org/10.14203/oldi.2017.v2i1.69.
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<strong>Assessment of Landsat-8 Classification Method for Benthic Habitat Mapping in Padaido Islands, Papua.</strong> Indonesia is the biggest archipelagic country in the world with an area of coral reefs of 39,583 km.This area has to be managed effectively and efficiently utilizing satellite remote sensing technique capable of mapping of benthic habitat coverage, such as coral reefs, seagrasses, macroalgae, and bare substrates. The technique is supported by the availability of Landsat-8 OLI satellite images that have been recording the regions of Indonesia continuously every 16 days. This research was carried out in June 2015 in parts of Padaido Islands, Papua. This area was selected due to high coral reef damages. This study utilized Landsat-8 OLI to compare two classification methods, namely pixel based and object based methods using ‘maximum 2 likelihood’ (ML) and ‘example based feature extraction’ classifications, respectively, after water column correction (Lyzenga method). The results showed that both methods produced benthic habitat maps with 7 class covers. The pixel-based classification resulted in a better overall accuracy (47.57%) in the mapping of benthic habitats than object-based classification approach (36.17%). Thus, the ML classification is applicable for benthic habitat mapping in Padaido Islands. However, the consistency of this method must be analyzed in many diffrent locations of Indonesian waters.
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Lustyk, Pavel, i Petr Vahalík. "Threat Degree Classification According to Habitat Quality: A Case Study from the Czech Republic". Forests 12, nr 1 (14.01.2021): 85. http://dx.doi.org/10.3390/f12010085.
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Important sources of information in the field of nature protection are red lists, which define the degree of threat to individual species. In practice, an assessment of the quality of the habitats in which a species occurs is used to a very limited extent in the preparation of red lists of vascular plants. At the same time, this parameter is usually essential to determine their degree of threat. At present, habitat quality data are available for the territory of the Czech Republic; these were obtained during Natura 2000 habitat mapping in the years 2000–2019. In this paper we propose the use of habitat quality data to determine the degree of threat to selected species of vascular plants and to compile a national red list. Nine plant species from three habitat types were selected for this study: meadows and wetland habitats in the alluvium of large rivers (Cardamine matthioli Moretti, Gratiola officinalis L., Teucrium scordium L.), fen habitats (Carex appropinquata Schumach., C. cespitosa L., C. lepidocarpa Tausch) and ecotone shrub habitats (Rosa agrestis Savi, R. micrantha Borrer ex Sm., R. spinosissima L.). For these species, the quality of the habitats in which they occur was analysed and grid maps were created, which present (1) the level of knowledge of habitat quality and (2) the average habitat quality. The results were compared with the degree of threat in the current national red list. Habitat quality analysis should also be used in the future to detect threatened species, which today are outside the red list and this assessment may be useful in compiling another updated red list of vascular plants of the Czech Republic.
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Dussault, Christian, Réhaume Courtois, Jean Huot i Jean-Pierre Ouellet. "The use of forest maps for the description of wildlife habitats: limits and recommendations". Canadian Journal of Forest Research 31, nr 7 (1.07.2001): 1227–34. http://dx.doi.org/10.1139/x01-038.
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We evaluated the reliability of forest maps for describing wildlife habitats. During the summer of 1997, we sampled 186 boreal forest stands located in Jacques-Cartier Park, Quebec. In each stand, we measured slope, crown closure, basal area, as well as tree height and age. We determined if map classifications, with regard to dominant species composition, density, tree height, tree age, and slope, correlated with field observations. We also measured lateral cover and deciduous browse availability, variables that are considered useful for the characterization of wildlife habitats, to examine how these habitat features were related to map classification. Age (57% of the sites correctly classified) and density (34%) were the variables for which map classification had the best and worst correspondence with field measurements, respectively. Dominant species on maps were correctly identified in <74, <55, and <40% of the sites in coniferous, mixed, and deciduous stands, respectively. The use of a simple classification method based on cover type alone resulted in improved correlations, since 94, 60, and 29% of the coniferous, mixed, and deciduous stands, respectively, were properly identified on maps. We related lateral cover and food availability to stand categories using the most reliable map variables. We conclude that forest maps are useful for describing major habitats at the stand level. When forest resource maps are to be used for studying habitat suitability, we recommend sampling a subset of stands to assess if important wildlife habitat features, which reflect species requirements, can be related to habitat characteristics as determined by the maps.
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Alifatri, La Ode, Bayu Prayudha i Kasih Anggraini. "Klasifikasi Habitat Bentik Berdasarkan Citra Sentinel-2 di Kepulauan Kei, Maluku Tenggara". Jurnal Ilmu Pertanian Indonesia 27, nr 3 (1.07.2022): 372–84. http://dx.doi.org/10.18343/jipi.27.3.372.
Pełny tekst źródłaStreszczenie:
Imagery classification has long been used in analyzing remote sensing data. The use of the classification algorithm model can affect the results in interpreting benthic habitats in shallow water. This study aimed to determine the best classification algorithm model for mapping benthic habitat cover through Sentinel-2 satellite imagery. Three algorithm models were employed: Maximum Likelihood Classification (MLC), Minimum Distance Classification (MDC), and Mahalanobis Distance Classification (MaDC). The benthic habitat types were extracted using Lyzenga correction, giving three categories: coral, seagrass, and sand. The results showed that the application algorithm models of the MLC, MDC, and MaDC on the benthic habitat mapping resulted in an accuracy value that was not significantly different at the 95% confidence interval. However, of the three algorithms used, the MaDC algorithm provides the best results in overall accuracy (78.35%) than the MDC (74.45%) and the MLC (74.33%). It shows that the MaDC algorithm can be referred to as the mapped benthic habitat cover in the Kei Islands. However, this algorithm model needs to be continuously studied and compared to other models in other locations.
Keywords: benthic, habitat classification, Kei Islands, sentinel
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Fomin, Valery V., Natalya S. Ivanova, Sergey V. Zalesov i Anna P. Mikhailovich. "Pan-European Approaches to the Classification of Habitats, Vegetation and Forest Types". Lesnoy Zhurnal (Forestry Journal), nr 4 (5.07.2022): 9–24. http://dx.doi.org/10.37482/0536-1036-2022-4-9-24.
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The article describes the approaches and features of classification of forests, habitats and vegetation at the Pan-European level on the example of the classification of European forest types (EFT), the EUNIS habitat classification and the Europe vegetation classification created by the phytosociologists of the European Vegetation Survey (EVS). The forest type in the EFT classification is a large forest vegetation unit distinguished within biogeographic regions by the similarity of forest site conditions, structure and productivity of the plantation, and the degree of anthropogenic transformation of forests. Accounting for the successional dynamics of forest biogeocoenosis is worked out at the theoretical level, in practice, the accounting is possible due to the information obtained from the EUNIS habitat classification, which is linked to the EVS classification by cross-references. The EUNIS classification is a Pan-European reference set of habitat units. It was created using the results of previous large-scale studies in Europe, which resulted in the creation of a number of classifications of biotopes, soil cover and marine habitats. The EVS classification is a comprehensive hierarchical syntaxonomic system of unions, orders and classes of Brown-Blanquet syntaxonomy for vascular plants, mosses, lichens and algae native to Europe. The great advantage of the EFT classification is the inclusion of anthropogenic impacts among the key diagnostic features of a forest type, which are defined by assessing the degree of naturalness of forests, the number of forest species, the type and intensity of anthropogenic impacts. The strength of the EFT classification is to establish cross-links with other forest type classification systems used both within national forest inventory systems and at the EU level. The use of the Braun-Blanquet ecological and floristic approach implemented in the classification of phytosociological alliances makes it possible to conduct a detailed ecological analysis and taking into account not only the stand productivity, but also the level of stand biodiversity, which makes the classification more useful for scientific research and nature preservation.
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Nababan, Bisman, La Ode Khairum Mastu, Nurul Hazrina Idris i James P. Panjaitan. "Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses". Remote Sensing 13, nr 21 (5.11.2021): 4452. http://dx.doi.org/10.3390/rs13214452.
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Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.
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Wicaksono, Pramaditya, Prama Ardha Aryaguna i Wahyu Lazuardi. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms". Remote Sensing 11, nr 11 (29.05.2019): 1279. http://dx.doi.org/10.3390/rs11111279.
Pełny tekst źródłaStreszczenie:
This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.
Rozprawy doktorskie na temat "Habitat classification"
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Torres, Torres Mercedes. "Automatic image annotation applied to habitat classification". Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/28419/.
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Habitat classification, the process of mapping a site with its habitats, is a crucial activity for monitoring environmental biodiversity. Phase 1 classification, a 10-class four-tier hierarchical scheme, is the most widely used scheme in the UK. Currently, no automatic approaches have been developed and its classification is carried out exclusively by ecologists. This manual approach using surveyors is laborious, expensive and subjective. To this date, no automatic approach has been developed. This thesis presents the first automatic system for Phase 1 classification. Our main contribution is an Automatic Image Annotation (AIA) framework for the automatic classification of Phase 1 habitats. This framework combines five elements to annotate unseen photographs: ground-taken geo-referenced photography, low-level visual features, medium-level semantic information, random projections forests and location-based weighted predictions. Our second contribution are two fully-annotated ground-taken photograph datasets, the first publicly available databases specifically designed for the development of multimedia analysis techniques for ecological applications. Habitat 1K has over 1,000 photographs and 4,000 annotated habitats and Habitat 3K has over 3,000 images and 11,000 annotated habitats. This is the first time ground-taken photographs have been used with such ecological purposes. Our third contribution is a novel Random Forest-based classifier: Random Projection Forests (RPF). RPFs use Random Projections as a dimensionality reduction mechanism in their split nodes. This new design makes their training and testing phase more efficient than those of the traditional implementation of Random Forests. Our fourth contribution arises from the limitations that low-level features have when classifying similarly visual classes. Low-level features have been proven to be inadequate for discriminating high-level semantic concepts, such as habitat classes. Currently, only humans posses such high-level knowledge. In order to obtain this knowledge, we create a new type of feature, called medium-level features, which use a Human-In-The-Loop approach to extract crucial semantic information. Our final contribution is a location-based voting system for RPFs. We benefit from the geographical properties of habitats to weight the predictions from the RPFs according to the geographical distance between unseen test photographs and photographs in the training set. Results will show that ground-taken photographs are a promising source of information that can be successfully applied to Phase 1 classification. Experiments will demonstrate that our AIA approach outperforms traditional Random Forests in terms of recall and precision. Moreover, both our modifications, the inclusion of medium-level knowledge and a location-based voting system, greatly improve the recall and precision of even the most complex habitats. This makes our complete image-annotation system, to the best of our knowledge, the most accurate automatic alternative to manual habitat classification for the complete categorization of Phase 1 habitats.
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Stevens, Tim, i n/a. "Mapping Benthic Habitats for Representation in Marine Protected Areas". Griffith University. School of Environmental and Applied Science, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20040303.124815.
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Virtually all marine conservation planning and management models in place or proposed have in common the need for improved scientific rigour in identifying and characterising the marine habitats encompassed. An emerging central theme in the last few years has been the concept of representativeness, or representative systems of Marine Protected Areas (MPAs). The habitat classification and mapping needed to incorporate considerations of representativeness into MPA planning must logically be carried out at the same scale at which management occurs. Management of highly protected areas occurs almost exclusively at local scales or finer, independent of the reservation model or philosophy employed. Moreton Bay, on Australias east coast, was selected for studies at the local scale to map and classify macrobenthic habitats. In a site scale (1 km) trial for the major habitat classification study, remote underwater videography was used to map and characterise an unusual assemblage of epibenthic invertebrates on soft sediments. The assemblage included congregations of the comatulid crinoid Zygometra cf. Z. microdiscus (Bell) at densities up to 0.88 individuals.m-2, comparable to those found in coral reef habitats. There was no correlation between the distribution of this species and commonly used abiotic surrogates depth (6 18 m), sediment composition and residual current. This site scale trial is the first quantitative assessment of crinoid density and distribution in shallow water soft-sediment environments. The high densities found are significant in terms of the generally accepted picture of shallow-water crinoids as essentially reefal fauna. The findings highlight the conservation benefits of an inclusive approach to marine habitat survey and mapping. Assemblages such as the one described, although they may be of scientific and ecological significance, would have been overlooked by common approaches to marine conservation planning which emphasise highly productive or aesthetically appealing habitats. Most habitat mapping studies rely solely or in part on abiotic surrogates for patterns of biodiversity. The utility of abiotic variables in predicting biological distributions at the local scale (10 km) was tested. Habitat classifications of the same set of 41 sites based on 6 abiotic variables and abundances of 89 taxa and bioturbation indicators were compared using correlation, regression and ordination analyses. The concepts of false homogeneity and false heterogeneity were defined to describe types of errors associated with using abiotic surrogates to construct habitat maps. The best prediction by abiotic surrogates explained less than 30% of the pattern of biological similarity. Errors of false homogeneity were between 20 and 62%, depending on the methods of estimation. Predictive capability of abiotic surrogates at the taxon level was poor, with only 6% of taxon / surrogate correlations significant. These results have implications for the widespread use of abiotic surrogates in marine habitat mapping to plan for, or assess, representation in Marine Protected Areas. Abiotic factors did not discriminate sufficiently between different soft bottom communities to be a reliable basis for mapping. Habitat mapping for the design of Marine Protected Areas is critically affected by the scale of the source information. The relationship between biological similarity of macrobenthos and the distance between sites was investigated at both site and local scales, and for separate biotic groups. There was a significant negative correlation between similarity and distance, in that sites further apart were less similar than sites close together. The relationship, although significant, was quite weak at the site scale. Rank correlograms showed that similarity was high at scales of 10 km or less, and declined markedly with increasing distance. There was evidence of patchiness in the distributions of some biotic groups, especially seagrass and anthozoans, at scales less than 16 km. In other biotic groups there was an essentially monotonic decline in similarity with distance. The spatial agglomeration approach to habitat mapping was valid in the study area. Site spacing of less than 10 km was necessary to capture important components of biological similarity. Site spacing of less than 2.5 km did not appear to be warranted. Macrobenthic habitat types were classified and mapped at 78 sites spaced 5 km apart. The area mapped was about 2,400 km2 and extended from estuarine shallow subtidal waters to offshore areas to the 50 m isobath. Nine habitat types were recognised, with only one on hard substrate. The habitat mapping characterised several habitat types not previously described in the area and located deepwater algal and soft coral reefs not previously reported. Seagrass beds were encountered in several locations where their occurrence was either unknown or had not previously been quantified. The representation of the derived habitat types within an existing marine protected area was assessed. Only two habitat types were represented in highly protected zones, with less than 3% of each included The study represents the most spatially comprehensive survey of epibenthos undertaken in Moreton Bay, with over 40,000 m2 surveyed. Derived habitat maps provide a robust basis for inclusion of representative examples of all habitat types in marine protected area planning in and adjacent to Moreton Bay. The utility of video data to conduct a low-cost habitat survey over a comparatively large area was also demonstrated. The method used has potentially wide application for the survey and design of marine protected areas.
3
Stevens, Tim. "Mapping Benthic Habitats for Representation in Marine Protected Areas". Thesis, Griffith University, 2004. http://hdl.handle.net/10072/367557.
Pełny tekst źródłaStreszczenie:
Virtually all marine conservation planning and management models in place or proposed have in common the need for improved scientific rigour in identifying and characterising the marine habitats encompassed. An emerging central theme in the last few years has been the concept of representativeness, or representative systems of Marine Protected Areas (MPAs). The habitat classification and mapping needed to incorporate considerations of representativeness into MPA planning must logically be carried out at the same scale at which management occurs. Management of highly protected areas occurs almost exclusively at local scales or finer, independent of the reservation model or philosophy employed. Moreton Bay, on Australia’s east coast, was selected for studies at the local scale to map and classify macrobenthic habitats. In a site scale (1 km) trial for the major habitat classification study, remote underwater videography was used to map and characterise an unusual assemblage of epibenthic invertebrates on soft sediments. The assemblage included congregations of the comatulid crinoid Zygometra cf. Z. microdiscus (Bell) at densities up to 0.88 individuals.m-2, comparable to those found in coral reef habitats. There was no correlation between the distribution of this species and commonly used abiotic surrogates depth (6 – 18 m), sediment composition and residual current. This site scale trial is the first quantitative assessment of crinoid density and distribution in shallow water soft-sediment environments. The high densities found are significant in terms of the generally accepted picture of shallow-water crinoids as essentially reefal fauna. The findings highlight the conservation benefits of an inclusive approach to marine habitat survey and mapping. Assemblages such as the one described, although they may be of scientific and ecological significance, would have been overlooked by common approaches to marine conservation planning which emphasise highly productive or aesthetically appealing habitats. Most habitat mapping studies rely solely or in part on abiotic surrogates for patterns of biodiversity. The utility of abiotic variables in predicting biological distributions at the local scale (10 km) was tested. Habitat classifications of the same set of 41 sites based on 6 abiotic variables and abundances of 89 taxa and bioturbation indicators were compared using correlation, regression and ordination analyses. The concepts of false homogeneity and false heterogeneity were defined to describe types of errors associated with using abiotic surrogates to construct habitat maps. The best prediction by abiotic surrogates explained less than 30% of the pattern of biological similarity. Errors of false homogeneity were between 20 and 62%, depending on the methods of estimation. Predictive capability of abiotic surrogates at the taxon level was poor, with only 6% of taxon / surrogate correlations significant. These results have implications for the widespread use of abiotic surrogates in marine habitat mapping to plan for, or assess, representation in Marine Protected Areas. Abiotic factors did not discriminate sufficiently between different soft bottom communities to be a reliable basis for mapping. Habitat mapping for the design of Marine Protected Areas is critically affected by the scale of the source information. The relationship between biological similarity of macrobenthos and the distance between sites was investigated at both site and local scales, and for separate biotic groups. There was a significant negative correlation between similarity and distance, in that sites further apart were less similar than sites close together. The relationship, although significant, was quite weak at the site scale. Rank correlograms showed that similarity was high at scales of 10 km or less, and declined markedly with increasing distance. There was evidence of patchiness in the distributions of some biotic groups, especially seagrass and anthozoans, at scales less than 16 km. In other biotic groups there was an essentially monotonic decline in similarity with distance. The spatial agglomeration approach to habitat mapping was valid in the study area. Site spacing of less than 10 km was necessary to capture important components of biological similarity. Site spacing of less than 2.5 km did not appear to be warranted. Macrobenthic habitat types were classified and mapped at 78 sites spaced 5 km apart. The area mapped was about 2,400 km2 and extended from estuarine shallow subtidal waters to offshore areas to the 50 m isobath. Nine habitat types were recognised, with only one on hard substrate. The habitat mapping characterised several habitat types not previously described in the area and located deepwater algal and soft coral reefs not previously reported. Seagrass beds were encountered in several locations where their occurrence was either unknown or had not previously been quantified. The representation of the derived habitat types within an existing marine protected area was assessed. Only two habitat types were represented in highly protected zones, with less than 3% of each included The study represents the most spatially comprehensive survey of epibenthos undertaken in Moreton Bay, with over 40,000 m2 surveyed. Derived habitat maps provide a robust basis for inclusion of representative examples of all habitat types in marine protected area planning in and adjacent to Moreton Bay. The utility of video data to conduct a low-cost habitat survey over a comparatively large area was also demonstrated. The method used has potentially wide application for the survey and design of marine protected areas.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Environmental and Applied Science
Full Text
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au, M. Wildsmith@murdoch edu, i Michelle Wildsmith. "Relationships between benthic macroinvertebrate assemblages and habitat types in nearshore marine and estuarine waters along the lower west coast of Australia". Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20081029.93910.
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The following four broad aims were addressed in this study. (1) To ascertain whether the characteristics of the benthic macroinvertebrate assemblages within the different nearshore marine habitat types identified by Valesini et al. (2003) on the lower west coast of Australia differ significantly, and whether the pattern of those spatial differences matches those among the environmental characteristics that were used to distinguish those habitat types; (2) To develop a quantitative approach for classifying nearshore habitats in estuarine waters that employs readily-available data for a range of enduring environmental characteristics, and to use that approach to classify the various habitat types present in nearshore waters of the Swan-Canning Estuary on the lower west coast of Australia; (3) To test the hypothesis that the characteristics of the benthic macroinvertebrate assemblages in the in the Swan-Canning Estuary differ significantly among nearshore habitat types, and that the pattern of those differences matches that among the environmental characteristics used to distinguish those habitat types and (4) To test the hypothesis that, as a result of environmental changes in the Swan-Canning Estuary, the characteristics of the benthic macroinvertebrate assemblages at various habitats in this estuary in 1986/7 differ from those in 2003/4.
To address the first aim, benthic macroinvertebrates were sampled seasonally for one year in the subtidal waters and intertidal zone (upper and lower swash zones) at the six nearshore habitat types that were identified by Valesini et al. (2003) on the lower west coast of Australia. The habitat types, which differed mainly in the extent of their exposure to wave activity and whether seagrass and/or nearshore reefs were present, had been distinguished quantitatively using values for a suite of seven statistically-selected enduring environmental characteristics. The faunal samples yielded a total of 121 species representing eight phyla, among which the Polychaeta, Malacostraca and Bivalvia were the most speciose classes and contributed ~ 38, 23 and 10%, respectively, to the total number of individuals. The total number of species and mean density of macroinvertebrates was far greater at the most protected habitat type (1), which also contained dense beds of seagrass, than at any other habitat type, i.e. 70 species and 209.2 individuals 0.1 m-2, compared to 32 species and 36.9 individuals 0.1 m-2 at the most exposed habitat type (6), which had a substrate comprised only of sand. Differences among habitat type influenced the benthic macroinvertebrate species composition to a greater extent than differences among either zones or seasons. Significantly different faunal compositions were detected among those latter two factors only at the most protected habitat type. The faunal assemblage at habitat type 1 was clearly the most distinct from those at the other five habitat types, particularly in the subtidal zone (R-statistics=0.642-0.831, p=0.1%), and was typified by five abundant polychaete species that were adapted to deposit-feeding. In contrast, the fauna at habitat type 6 was typified by four crustacean species and a species of bivalve and polychaete, whose mobility and tough external surface facilitated their survival and feeding in those turbulent waters. The extents of the differences in species composition among the six habitat types was significantly matched with that among the suite of enduring environmental characteristics that distinguished those habitat types, particularly in the case of the subtidal zone (Rho=0.676). Such results indicated that the environmental variables used to distinguish the nearshore habitat types could be used to reliably predict the types of benthic macroinvertebrate species likely to occur at any site along the lower west coast of Australia.
The above biological validation of the nearshore marine habitat classification scheme developed by Valesini et al. (2003) provided the justification for the approach to the second broad aim of this study, namely to develop a quantitative scheme for classifying habitat types in the Swan-Canning Estuary. This approach was similar to that employed by Valesini et al. (2003) in that it considers that differences among habitat types are well reflected by differences in a suite of enduring environmental variables. However, it improves on that earlier method by employing a completely objective and quantitative approach.
Thus, a large number of environmentally-diverse nearshore sites (102) were initially selected throughout the Swan-Canning Estuary and a suite of 13 enduring environmental variables quantified at each using remotely-sensed images of the estuary in a Geographic Information System. Such variables were chosen to reflect either (i) the type of substrate and submerged vegetation present, (ii) the extent of exposure to wave action or (iii) the location of the site within the estuary with respect to its vicinity to marine and fresh water sources. These data were then subjected to the CLUSTER routine and associated SIMPROF procedure in the PRIMER v6 multivariate statistical package to quantitatively identify those groups of sites that did not differ significantly in their environmental characteristics, and thus represented habitat types. Eighteen habitat types were identified, which were shown to well reflect spatial differences in a suite of non-enduring water quality and sediment characteristics that were measured in situ at a range of estuarine sites during both summer and winter in 2005 (Rho=0.683 and 0.740, respectively, p=0.1%). However, those latter environmental characteristics required far more time in the field and laboratory to quantify than the enduring variables used to identify the habitat types.
Benthic macroinvertebrates were sampled during summer and winter in 2005 in the shallow subtidal regions (~1 m depth) at sites representing eight of the habitat types identified in the Swan-Canning Estuary. These samples contained a total of 51 and 36 species during summer and winter, respectively, and, in both seasons, represented nine phyla, namely Annelida, Crustacea, Mollusca, Sipuncula, Nematoda, Platyhelminthes, Cnidaria, Uniramia and Nemertea. The compositions of the benthic macroinvertebrate assemblages differed significantly among habitat types and, to a similar extent, between seasons (Global R-statistic=0.408 and 0.409, respectively, p=0.1%). However, the spatial differences were considerable greater in winter than in summer (Global R-statistic=0.536 vs 0.280, p=0.1%), presumably due to the greater spatial variation in particular non-enduring in situ environmental characteristics, such as redox depth and salinity. While the number of species, overall density and taxonomic distinctness of benthic macroinvertebrates also differed significantly among habitats, those variables differed to a greater extent between seasons, being greater in winter than in summer. While the measures of taxonomic distinctness tended to be greater at habitat types located in the lower to middle reaches, i.e. habitat types 6, 7, 9, 10, 13 and 18, than the upper reaches i.e. habitat types 1 and 3, the number of species and overall density reflected this trend only during winter. During summer, the mean numbers of species at habitat types 1, 3, 6 and 10 (3.4-6.0) were significantly lower than those at habitat types 7, 13, and 18 (8.8-10.9), whereas the overall density of benthic macroinvertebrates was far greater at habitat type 7 (32260 individuals 0.1 m-2)than at any other habitat type in this season (3135-18552 individuals 0.1 m-2).
Overall, the greatest differences in assemblage composition occurred between those at habitat types 1 and 18 (R-statistic=0.669, p=0.1%), which were located in the uppermost region of the estuary and the lower reaches of the basin, respectively, and differed to the greatest extent in their enduring environmental characteristics. The assemblage at habitat type 1, and also that at habitat type 3, located just downstream, were relatively distinct from those at all other habitat types, particularly during winter (R-statistics=0.666-0.993, p=0.1%). The fauna at the first of these habitat types was relatively depauperate, containing low numbers of species and densities, and was characterised by the polychaetes Leitoscoloplos normalis and Ceratonereis aequisetis and the bivalve Arthritica semen. The assemblage at habitat type 3 was also characterised by those three species and the amphipod Paracorophium minor and the polychaete Boccardiella limnicola. In contrast, the assemblage at habitat type 18 was characterised by a more diverse assemblage, i.e. the polychaetes Capitella capitata, C. aequisetis, L. normalis and Pseudopolydora kempi, the amphipods, Grandidierella propodentata and Corophium minor and the bivalve Sanguinolaria biradiata. The number of species was among the highest at this habitat type during both seasons, which was also reflected in the high taxonomic diversity, and the overall density was the highest in winter and second highest in summer. Despite the above faunal differences, those between assemblages at habitat types 7 and 9, which were both located in the basin of the Swan-Canning Estuary, were similar in magnitude to those that occurred between pairs of habitat types located in two different regions of the estuary. Although both habitat types 7 and 9 were characterised by a similar suite of species, i.e. Oligochaete spp., C. aequisetis, C. capitata, C. minor, G. propodentata, L. normalis, and S. biradiata, the substantial differences in assemblage composition between these habitat types in both summer and winter (R-statistics=0.570 and 0.725, respectively) was due to marked differences in the relative contributions of each of these species.
Significant and strong correlations were shown to exist in both summer and winter between the pattern of differences in the benthic macroinvertebrate assemblages among habitat types and that among the enduring environmental characteristics used to identify those habitat types (Rho=0.625 and 0.825, respectively, p=0.1%). Furthermore, these correlations were greater than those obtained between the benthic macroinvertebrate fauna and any combination of the non-enduring environmental characteristics (i.e. water quality and sediment parameters) recorded in situ at each habitat type (Rho=0.508 and 0.824, in summer and winter, respectively, p=o.1%). This demonstrates the greater capacity of surrogate enduring environmental characteristics to account for differences in the range of variables that may influence the distribution of benthic invertebrate fauna. Thus, the lists of characteristic benthic macroinvertebrate taxa produced for each of the eight habitat types studied in the Swan-Canning Estuary provide a reliable benchmark by which to gauge any future changes in those fauna. Moreover, these results indicate that the above habitat classification scheme can be used to reliably predict the types of benthic macroinvertebrate fauna that are likely to occur at any nearshore site of interest in this estuarine system.
The final component of this study showed that the benthic macroinvertebrate assemblages at four sites in the middle reaches of the Swan-Canning Estuary in 2003/4 differed significantly from those recorded at the same sites in 1986/7. Such differences were reflected in (1) changes in the relative densities of a suite of ten species that were responsible for distinguishing the faunas in these two periods, (2) the absence of 22 rare species in 2003/4 (i.e. 42% of the number of species recorded in 1986/7), (3) the presence of 17 new species in 2003/4, including an abundant polychaete that is likely to have been introduced and (4) a far greater extent of seasonal variation in the number of species and densities of benthic macroinvertebrates in 2003/4. Such changes are likely to be related to lower sediment oxygen levels in certain seasons in 2003/4, as well as an altered hydrological regime due to increased temperatures and decreased rainfall in that more recent period. The fact that these changes have occurred within the Swan-Canning Estuary highlights the need for effective management tools, such as the habitat classification scheme and associated faunal survey undertaken in this study. Such data will provide a sound basis by which to examine the ways in which fauna vary spatially within the system, and allow for the establishment of comprehensive benchmarks for detecting future changes.
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Willis, Susan Denise Margaret. "The classification and management of limestone pavements : an endangered habitat". Thesis, University of Chester, 2011. http://hdl.handle.net/10034/200750.
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This thesis describes an in-depth study of limestone pavements across North West England and North Wales. The aim was to combine elements of geodiversity and biodiversity in order to create a holistic limestone pavement classification to inform future management. A field-based research protocol was used to assess a stratified random sample (46 pavements), accounting for approximately 10% of the limestone pavements in the geographical area. Detailed analyses of key elements are presented, along with important issues that continue to pose threats to this Annex One Priority Habitat. This research resulted in a comprehensive classification, using TWINSPAN analysis and Nonmetric Multidimensional Scaling, identifying six distinct holistic functional groups. The prime factors driving limestone pavement morphology, and hence the classification, were established to be lithology, proximity to structural fault, altitude and human intervention, particularly in terms of grazing intensity. Three upland, open limestone pavement classes were formed. Of these, the richest in terms of geodiversity and biodiversity was the group with the thickest bedding planes and hence the deepest grikes, typically greater than 1m. The class that was most species-poor was "at the highest altitude (above 450m), formed on the thin limestones of the Yoredales. These were characterised by shallow, wide grikes. The third upland limestone pavement group had mid-range grikes, generally 0.5-1m in depth, and small clints. Two wooded classes were identified. One was a lowland 'classic' wooded limestone pavement group with deep, narrow grikes and shallow soils. Indicator species included Juniperus communis and Taxus baccata. The second wooded group was situated proximal to a major structural fault. In this group the pavement dip ranged between 10°-40° with well-runnelled clints that were heavily moss-covered. The sixth group was low altitude, proximal to the coast, characterised by low moss growth, un-vegetated clints and the presence of Ulex europaeus. Conservation management was identified as key to the quality of the limestone pavement habitat and this thesis identifies best management practises and links these to the holistic limestone pavement classification. Finally, as a sample case study, this thesis presents mollusc species and diversity from eleven of the Yorkshire limestone pavements. Analysis establishes significant links between geodiversity and mollusc populations, with key drivers for mollusc communities echoing those of plant species on limestone pavement.
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Sun, Ye. "Studies on Spring Conservation: Biological Indicators, Habitat Classification and its Assessment". Kyoto University, 2020. http://hdl.handle.net/2433/253446.
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付記する学位プログラム名: 京都大学大学院思修館Kyoto University (京都大学)
0048
新制・課程博士
博士(総合学術)
甲第22610号
総総博第10号
新制||総総||2(附属図書館)
京都大学大学院総合生存学館総合生存学専攻
(主査)教授 山敷 庸亮, 准教授 趙 亮, 准教授 竹門 康弘
学位規則第4条第1項該当
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Hamilton, Anthony Neil. "Classification of coastal grizzly bear habitat for forestry interpretations and the role of food in habitat use by coastal grizzly bears". Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/27933.
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A grizzly bear habitat classification was developed by modifying and expanding the climax-based Biogeoclimatic Ecosystem Classification (BEC) to accommodate serai vegetation. Locations of radio-collared bears were assigned to a large number (N=110) of structurally and floristically unique habitats. An interpretive classification of 14 Bear Habitat Units (BHUs) was derived from the taxa of the BEC system; units were amalgamated on the basis of grizzly bear habitat value and similarity of response to forest management practices. Fifteen climax forest, three subalpine, three wetland, and three avalanche chute units were identified and described in the lower Kimsquit River. Two adult female grizzly bears (numbers 08 and 25) were monitored for 1238 and 1196 days, respectively, from April 1982 to October 1985 and had multi-annual minimum convex polygon (MCP) home ranges of 85 km² (N=23 6), and 60 km² (N=241). River floodplain BHUs were used most heavily by bears 08 and 25 during their active seasons (65% of locations and 51% of time for bear 08; 75% of locations and 63% of time for bear 25) followed by avalanche chutes and sidehill climax and old-growth forests. Rank testing between quality/quantity indices (food plant nutrient content, biomass, berry abundance) and grizzly bear use indicated that movements were generally correlated with food availability at the higher, or BHU, level of the classification (rs=0.61 and 0.83, p<.05 for bears 08 and 25, respectively). Salmon (Oncorhynchus spp.) and insects were the only common non-plant items in a diet of over 3 0 species, although food habits differed between bears. It is concluded that, although food plays a critical role in habitat selection of coastal grizzly bears, a relatively rich environment precludes the need for individuals to forage optimally at a micro-habitat level. Except for the early spring and late fall, food can be found in a number of units that collectively meet life requisites. These analyses were used in combination with other use and habitat quality information to develop seasonal habitat values. Assigned values allowed predictions about the effects of forest management practices on habitat capability.Forestry, Faculty of
Graduate
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Breyer, Johanna. "Habitat classification using airborne and spaceborne remote sensing for biodiversity assessment in Wales". Thesis, Aberystwyth University, 2009. http://hdl.handle.net/2160/111b985f-106c-4940-9e56-9e1b3b4ed399.
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Biodiversity and its conservation are an important subject as human pressure on natural resources increases continuously. Without accurate means of measuring biodiversity, however, monitoring is very di cult and conservation e orts might not be targeted e ectively. There is a great demand for biodiversity assessment on a regional scale in order to support national conservation aims as stated, for example, in the UK Biodiversity Action Plan. Remote sensing lends itself to interpretation at the landscape scale and this study aims to assess a variety of optical and laser remote sensing data with regard to their usefulness for biodiversity assessment in Wales. The study was divided into four distinct areas to evaluate di erent remote sensing data with regard to their utility for facilitating the measurement and assessment of distinct elements of biodiversity. These components are vegetation composition and condition, land cover on a regional scale, three-dimensional woodland structure and the interaction of ora and fauna within the landscape structure. Methodological advances include a novel land cover mapping approach from multispectral remote sensing data comparable to traditional manual habitat surveys as well as an analysis of forest vertical pro le under consideration of bird habitat preferences. Remote sensing data investigated included airborne hyperspectral data, multispectral satellite imagery and airborne LiDAR. The potential of hyperspectral data for the di erentiation of grasslands of varying levels of improvement was tested at two experimental grassland study sites and the results suggest a strong correlation between biomass and the red-edge region of the electromagnetic spectrum. A relationship between the presence of nonphotosynthetic vegetation and the level of agricultural improvement was further established and utilized in the formulation of rules for the classi cation of grassland habitats. The outcomes of this study were used to support the landscape-scale land cover mapping of the extent of 38 classes from a multi-temporal combination of two spaceborne multispectral sensors (SPOT 5 HRG and IRS LISS IV). The derived maps achieved a moderate accuracy of 64%, though individual classes, especially woodlands and bogs, exceeded this value. The ability of Light Detection and Ranging (LiDAR) and terrestrial laser scanner data to capture the three-dimensional structure of forests was investigated. It was found that both sensor types were limited in their ability to accurately represent forest vertical pro le due to respective downward and upward signal attenuation through the canopy. However, both provided an accurate digital terrain model and correlated well in their estimation of canopy height. Despite the limitations of vertical forest structure assessment from airborne Li- DAR, observation of bird species could be linked to distinct forest vertical pro les. Specialist woodland species were found to have the strongest habitat preferences with regard to the vertical forest structure. This project has achieved advancements in the mapping of agricultural land and habitats in Wales, using remote sensing data, speci cally in the di erentiation of grassland improvement levels and tree species discrimination from multispectral satellite imagery. Furthermore, a strong correspondence between airborne and terrestrial laser scanner outputs has been established and LiDAR forest pro les have been shown to relate well to known woodland bird habitat preferences. The added value derived from examining these four research areas as part of a single study, consists of the knowledge gained in how best to harness the respective remote sensing methods for the evaluation of very di erent aspects of biodiversity. It has further been shown that it is possible to use optical remote sensing data at a high spatial and spectral resolution, but low availability to inform and improve the utilization of more widely accessible, but less detailed images. Furthermore, a method has been developed which allows the interpolation of avian diversity from the assessment vertical forest structure. As biodiversity consists of many di erent elements at a wide variety of scales it is crucial to be able to perform such integrated analyses of its various components. However, only a combined approach towards the utilization of remote sensing, as demonstrated in this study, is likely to gain the necessary data. The outcomes of this research support Wales-wide assessment of biodiversity and facilitate the production of regional or national vegetation maps as well as structural attributes for input into models. Components of the study can be used to support, for example, climate change research, assessments of biodiversity and policy decisions. Optical and laser remote sensing data can be successfully utilized for Wales-wide biodiversity components analysis.
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Connolly, Véronique. "Characterization and classification of Bicknell's thrush (Catharus bicknelli) habitat in the Estrie region, Québec". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ64335.pdf.
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Petrou, Zisis. "Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification". Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/26589.
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Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster-Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.
Książki na temat "Habitat classification"
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Liñán-Cabello, Marco Agustín. Corals: Classification, habitat, and ecological significance. Hauppauge, N.Y: Nova Science Publisher's, Inc., 2011.
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McLea, Murray. Using river habitat classification in regional plans. Wellington, N.Z: Ministry for the Environment, 2000.
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McCain, Michael E. Stream habitat classification and inventory procedures for northern California. Eureka, CA?]: U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Region, 1990.
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Ferguson, Dennis E. Classification of grand fir mosaic habitats. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1996.
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David, Roy, i European Topic Centre on Nature Conservation., red. Towards a European habitat classification: Background review 1989-1995. Luxembourg: Office for Official Publications of the European Communities, 1998.
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Busch, W. D. N. Development of an aquatic habitat classificationsystem for lakes. Boca Raton: CRC Press, 1992.
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E, Wells R. Piping plover habitat classification and inventory for selected Parkland Region lakes. [Edmonton]: Alberta Environment, Land and Forest Service, Resource Data Division, Ecosystem Data Section, 1999.
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Noble, Elizabeth B. Classification Pamlico Sound nursery areas: Recommendations for critical habitat criteria. Morehead City, NC (P.O. Box 769, Morehead City 28557): North Carolina Dept. of Environment, Health, and Natural Resources, Division of Marine Fisheries, 1991.
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Parenzan, Pietro. Animalia Speluncarum Italiae: Et omnis alii subterranei habitat terrae marisque. Galatina: Congedo, 2002.
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Muldavin, Esteban. A classification of forest habitat types, southern Arizona and portions of the Colorado Plateau. Fort Collins, Colo. (240 W. Prospect Rd., Fort Collins 80526): United States Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1996.
Znajdź pełny tekst źródłaCzęści książek na temat "Habitat classification"
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Groombridge, Brian. "Global Habitat Classification". W Global Biodiversity, 248–53. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2282-5_18.
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Upadhyay, Anand, Prajna Tantry i Aarohi Varade. "Classification of Seagrass Habitat Using Probabilistic Neural Network". W Advances in Intelligent Systems and Computing, 250–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49339-4_25.
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Ahmad, Khursheed, i Majid Farooq. "Vegetation Classification and Habitat Mapping of Dachigam National Park, Kashmir, India". W Vegetation of Central Asia and Environs, 119–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99728-5_5.
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Carbajal Hernández, José Juan, Luis Pastor Sánchez Fernández i Marco Antonio Moreno Ibarra. "Assessment of the Artificial Habitat in Shrimp Aquaculture Using Environmental Pattern Classification". W Lecture Notes in Computer Science, 113–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13681-8_14.
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McKelvey, Kevin S., i Barry R. Noon. "Incorporating Uncertainties in Animal Location and Map Classification into Habitat Relationships Modeling". W Spatial Uncertainty in Ecology, 72–90. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0209-4_4.
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Lindenmayer, D. B., i R. B. Cunningham. "A Habitat-Based Microscale Forest Classification System for Zoning Wood Production Areas to Conserve a Rare Species Threatened by Logging Operations in South-Eastern Australia". W Global to Local: Ecological Land Classification, 543–57. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1653-1_38.
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Violante, Crescenzo. "Computer-Aided Geomorphic Seabed Classification and Habitat Mapping at Punta Licosa MPA, Southern Italy". W Computational Science and Its Applications – ICCSA 2020, 681–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58802-1_49.
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Valk, Arnold G. van der. "Wetlands: Classification". W Wetlands and Habitats, 143–52. Second edition. | Boca Raton: CRC Press, [2020] | Revised: CRC Press, 2020. http://dx.doi.org/10.1201/9780429445507-20.
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Bouchard, Andre, Stuart Hay, Yves Bergeron i Alain Leduc. "The Vascular Flora of Gros Morne National Park, Newfoundland: A Habitat Classification Approach Based on Floristic, Biogeographical and Life-Form Data". W Quantitative approaches to phytogeography, 123–57. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-2063-7_4.
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Silva, Sara, i Yao-Ting Tseng. "Classification of Seafloor Habitats Using Genetic Programming". W Lecture Notes in Computer Science, 315–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78761-7_32.
Pełny tekst źródłaStreszczenia konferencji na temat "Habitat classification"
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Pizarro, Oscar, Paul Rigby, Matthew Johnson-Roberson, Stefan B. Williams i Jamie Colquhoun. "Towards image-based marine habitat classification". W OCEANS 2008. IEEE, 2008. http://dx.doi.org/10.1109/oceans.2008.5152075.
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Pizarro, Oscar, Stefan B. Williams i Jamie Colquhoun. "Topic-based habitat classification using visual data". W OCEANS 2009-EUROPE (OCEANS). IEEE, 2009. http://dx.doi.org/10.1109/oceanse.2009.5278260.
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Spampinato, Concetto. "Session details: Environment monitoring and habitat classification". W MM '12: ACM Multimedia Conference. New York, NY, USA: ACM, 2012. http://dx.doi.org/10.1145/3245371.
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Muslim, Aidy M., T. Komatsu i D. Dianachia. "Evaluation of classification techniques for benthic habitat mapping". W SPIE Asia-Pacific Remote Sensing, redaktorzy Robert J. Frouin, Naoto Ebuchi, Delu Pan i Toshiro Saino. SPIE, 2012. http://dx.doi.org/10.1117/12.999305.
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"Comparing Habitat Classification Schemes for Assessing Landscape Diversity". W GI_Forum 2013 - Creating the GISociety. Vienna: Austrian Academy of Sciences Press, 2013. http://dx.doi.org/10.1553/giscience2013s512.
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Torres, Mercedes, i Guoping Qiu. "Habitat classification using random forest based image annotation". W 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738306.
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Steinberg, Daniel M., Oscar Pizarro, Stefan B. Williams i Michael V. Jakuba. "Dirichlet process mixture models for autonomous habitat classification". W OCEANS 2010 IEEE - Sydney. IEEE, 2010. http://dx.doi.org/10.1109/oceanssyd.2010.5603617.
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Steinberg, Daniel M., Stefan B. Williams, Oscar Pizarro i Michael V. Jakuba. "Towards autonomous habitat classification using Gaussian Mixture Models". W 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5652480.
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Torres Torres, Mercedes, i Guoping Qiu. "Crowd-sourcing Applied to Photograph-Based Automatic Habitat Classification". W the 3rd ACM International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2661821.2661824.
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Delalieux, S., B. Somers, B. Haest, L. Kooistra, C. A. Mucher i J. Vanden Borre. "Monitoring heathland habitat status using hyperspectral image classification and unmixing". W 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2010. http://dx.doi.org/10.1109/whispers.2010.5594895.
Pełny tekst źródłaRaporty organizacyjne na temat "Habitat classification"
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Bryant, M. D., B. E. Wright i B. J. Davies. Application of a hierarchical habitat unit classification system: stream habitat and salmonid distribution in Ward Creek, southeast Alaska. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1992. http://dx.doi.org/10.2737/pnw-rn-508.
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Hoffman, George R., i Robert R. Alexander. Forest vegetation of the Black Hills National Forest of South Dakota and Wyoming: A habitat type classification. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1987. http://dx.doi.org/10.2737/rm-rp-276.
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Hansen, Paul L., i George R. Hoffman. The vegetation of the Grand River/Cedar River, Sioux, and Ashland Districts of the Custer National Forest: a habitat type classification. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1988. http://dx.doi.org/10.2737/rm-gtr-157.
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Taverna, Kristin. Vegetation classification and mapping of land additions at Richmond National Battlefield Park, Virginia: Addendum to technical report NPS/NER/NRTR 2008/128. National Park Service, wrzesień 2022. http://dx.doi.org/10.36967/2294278.
Pełny tekst źródłaStreszczenie:
In 2008 and 2015, the Virginia Department of Conservation and Recreation, Division of Natural Heritage produced vegetation maps for Richmond National Battlefield Park, following the protocols of the United States Geological Survey (USGS) – National Park Service (NPS) Vegetation Mapping Program. The original 2008 report was part of a regional project to map and classify the vegetation in seven national parks in Virginia. The 2015 report was an addendum to the original report and mapped the vegetation in newly acquired parcels. Since 2015, the park has acquired an additional 820 acres of land within 12 individual parcels, including the 650 acre North Anna unit. This report is an addendum to the 2008 and 2015 reports and documents the mapping of vegetation and other land-use classes for the 12 new land parcels at Richmond National Battlefield Park, with an updated vegetation map for the entire park. The updated map and associated data provide information on the sensitivity and ecological integrity of habitats and can help prioritize areas for protection. The vegetation map of the new land parcels includes eighteen map classes, representing 14 associations from the United States National Vegetation Classification, one nonstandard, park-specific class, and three Anderson Level II land-use categories. The vegetation classification and map classes are consistent with the original 2008 report. Vegetation-map classes for the new land parcels were identified through field reconnaissance, data collection, and aerial photo interpretation. Aerial photography from 2017 served as the base map for mapping the 12 new parcels, and field sampling was conducted in the summer of 2020. Three new map classes for the Park were encountered and described during the study, all within the North Anna park unit. These map classes are Coastal Plain / Outer Piedmont Basic Mesic Forest, Northern Coastal Plain / Piedmont Oak – Beech / Heath Forest, and Southern Piedmont / Inner Coastal Plain Floodplain Terrace Forest. The examples of Coastal Plain / Outer Piedmont Basic Mesic Forest and Southern Piedmont / Inner Coastal Plain Floodplain Terrace Forest at North Anna meet the criteria of size, condition, and landscape context to be considered a Natural Heritage exemplary natural community occurrence and should be targeted for protection and management as needed. New local and global descriptions for the three map classes are included as part of this report. Refinements were made to the vegetation field key to include the new map classes. The updated field key is part of this report. An updated table listing the number of polygons and total hectares for each of the 28 vegetation- map classes over the entire park is also included in the report. A GIS coverage containing a vegetation map for the entire park with updated Federal Geographic Data Committee (FGDC) compliant metadata was completed for this project. The attribute table field names are the same as the 2008 and 2015 products, with the exception of an additional field indicating the year each polygon was last edited.