Journal articles on the topic 'Habitat classification'
To see the other types of publications on this topic, follow the link: Habitat classification.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Habitat classification.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Marcinkowska-Ochtyra, Adriana, Krzysztof Gryguc, Adrian Ochtyra, Dominik Kopeć, Anna Jarocińska, and Łukasz Sławik. "Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats." Remote Sensing 11, no. 19 (September 28, 2019): 2264. http://dx.doi.org/10.3390/rs11192264.
Full textAbstract:
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.
2
Lavrinenko, Igor A. "Habitat classification of East-European tundra." Transaction Kola Science Centre 12, no. 6-2021 (December 31, 2021): 13–18. http://dx.doi.org/10.37614/2307-5252.2021.6.12.9.001.
Full textAbstract:
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.
3
Foglini, Federica, Valentina Grande, Fabio Marchese, Valentina A. Bracchi, Mariacristina Prampolini, Lorenzo Angeletti, Giorgio Castellan, et al. "Application of Hyperspectral Imaging to Underwater Habitat Mapping, Southern Adriatic Sea." Sensors 19, no. 10 (May 16, 2019): 2261. http://dx.doi.org/10.3390/s19102261.
Full textAbstract:
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.
4
Hafizt, Muhammad, Marindah Yulia Iswari, and Bayu Prayudha. "Kajian Metode Klasifikasi Citra Landsat-8 untuk Pemetaan Habitat Bentik di Kepulauan Padaido, Papua." Oseanologi dan Limnologi di Indonesia 2, no. 1 (May 5, 2017): 1. http://dx.doi.org/10.14203/oldi.2017.v2i1.69.
Full textAbstract:
<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.
5
Lustyk, Pavel, and Petr Vahalík. "Threat Degree Classification According to Habitat Quality: A Case Study from the Czech Republic." Forests 12, no. 1 (January 14, 2021): 85. http://dx.doi.org/10.3390/f12010085.
Full textAbstract:
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.
6
Dussault, Christian, Réhaume Courtois, Jean Huot, and Jean-Pierre Ouellet. "The use of forest maps for the description of wildlife habitats: limits and recommendations." Canadian Journal of Forest Research 31, no. 7 (July 1, 2001): 1227–34. http://dx.doi.org/10.1139/x01-038.
Full textAbstract:
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.
7
Alifatri, La Ode, Bayu Prayudha, and Kasih Anggraini. "Klasifikasi Habitat Bentik Berdasarkan Citra Sentinel-2 di Kepulauan Kei, Maluku Tenggara." Jurnal Ilmu Pertanian Indonesia 27, no. 3 (July 1, 2022): 372–84. http://dx.doi.org/10.18343/jipi.27.3.372.
Full textAbstract:
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
8
Fomin, Valery V., Natalya S. Ivanova, Sergey V. Zalesov, and Anna P. Mikhailovich. "Pan-European Approaches to the Classification of Habitats, Vegetation and Forest Types." Lesnoy Zhurnal (Forestry Journal), no. 4 (July 5, 2022): 9–24. http://dx.doi.org/10.37482/0536-1036-2022-4-9-24.
Full textAbstract:
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.
9
Nababan, Bisman, La Ode Khairum Mastu, Nurul Hazrina Idris, and James P. Panjaitan. "Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses." Remote Sensing 13, no. 21 (November 5, 2021): 4452. http://dx.doi.org/10.3390/rs13214452.
Full textAbstract:
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.
10
Wicaksono, Pramaditya, Prama Ardha Aryaguna, and Wahyu Lazuardi. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms." Remote Sensing 11, no. 11 (May 29, 2019): 1279. http://dx.doi.org/10.3390/rs11111279.
Full textAbstract:
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.
11
Subarno, Tarlan, Vincentius Paulus Siregar, and Syamsul Bahri Agus. "OBIA AND BTM INTEGRATION FOR MAPPING HABITAT COMPLEXITY OF CORAL REEFS ON HARAPAN-KELAPA ISLANDS, KEPULAUAN SERIBU." Coastal and Ocean Journal (COJ) 2, no. 1 (May 10, 2018): 11–22. http://dx.doi.org/10.29244/coj.2.1.11-22.
Full textAbstract:
The habitat complexity is indirectly closely related to reef fish abundance. This study aims to map reefs habitat complexity by integrating object-based image analysis (OBIA) and habitat complexity analysis using benthic terrain modeler (BTM). The datasets used were SPOT-7 imagery and water depth derived from satellite imagery. The ground check was conducted to collect field data used as reference for classification and accuracy assessment of classification results. Classification of SPOT-7 imagery was performed using support vector machines (SVM) algorithm, by grouping shallow waters habitats into 4 classes on level 2 and 3 classes on level 3. Accuracy assessment was done by confusion matrix and resulting overall accuracy (OA) 83.55% for level 2 and 79.66% for level 3. The habitat complexity was analyzed using rugosity analysis method (Arc-Chord Ratio) from benthic terrain modeler (BTM) to obtain rugosity index in reefs area. The substrate covers were obtained from OBIA and complexity of habitats were obtained from BTM, then the overlay result shows varying rugosity index on the reef area in Harapan-Kelapa Islands.
Keywords:
coral reefs, OBIA, habitat complexity, rugosity
12
Anggoro, Ari, Zamdial Zamdial, Dede Hartono, Deddy Bakhtiar, Nurlaila Ervina Herliany, and Maya Angraini Fajar Utami. "PEMETAAN HABITAT PERAIRAN DANGKAL MENGGUNAKAN CITRA RESOLUSI MENENGAH DENGAN METODE KLASIFIKASI BERBASIS PIKSEL (STUDI KASUS PULAU TIKUS)." JURNAL ENGGANO 5, no. 1 (April 30, 2020): 78–90. http://dx.doi.org/10.31186/jenggano.5.1.78-90.
Full textAbstract:
Pulau Tikus adalah pulau kecil yang terletak di Kota Bengkulu yang memiliki potensi terumbu karang disekitar perairan dangkal. Tujuan penelitian ini untuk memetakan kawasan habitat perairan dangkal ekosistem terumbu karang Pulau Tikus menggunakan citra satelit Landsat 8 OLI dan menguji akurasi klasifikasi peta habitat perairan dangkal di Pulau Tikus. Metode klasifikasi yang digunakan adalah klasifikasi terbimbing maximum likelihood classification. Hasil klasifikasi citra Landsat 8 OLI berdasarkan skema klasifikasi yang digunakan dari lima kelas habitat di Pulau Tikus tersebut yaitu karang hidup seluas 71,46 ha, karang campur pasir 106,9425 ha, karang mati 67,365 ha, makro alga 31,815 ha, dan pasir 40,05 ha. Uji akurasi dari perbandingan hasil klasifikasi citra dan data lapangan mendapatkan total akurasi keseluruhan yaitu sebesar 77%.SHALLOW WATER HABITATS MAPPING USING A MEDIUM RESOLUTION IMAGE WITH CLASSIFICATION METHOD PIKSEL-BASED (CASE STUDY OF THE TIKUS ISLAND). Tikus Island is a small island which located in Bengkulu City has the potential of coral reefs around the shallow water. The aims of this research were to map the area of benthic habitat in Tikus Island Bengkulu using Landsat 8 OLI satellite imagery and to test the accuracy on the benthic habitat map in Tikus Island. The method used supervised classification using maximum likelihood classification. The result of Landsat 8 OLI classification base on the five class habitats classification scheme used obtained in Tikus island showed coral reef (71,46 ha), coral mix sand (106,9425 ha), dead coral (67,365 ha), macroalgae (31,815 ha), and sand (40,05 ha). Accuracy test from the comparison of classification results and ground truth data get a total overall accuracy of 77%.
13
Duffey, Eric. "Spider Habitat Classification and the Development of Habitat Profiles." Arachnology 15, no. 1 (March 2010): 1–20. http://dx.doi.org/10.13156/arac.2010.15.1.1.
Full text
14
Kwong, Ivan H. Y., Frankie K. K. Wong, Tung Fung, Eric K. Y. Liu, Roger H. Lee, and Terence P. T. Ng. "A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong." Remote Sensing 14, no. 1 (December 24, 2021): 67. http://dx.doi.org/10.3390/rs14010067.
Full textAbstract:
Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.
15
Regos, Adrián, and Jesús Domínguez. "The contribution of Earth observation technologies to the reporting obligations of the Habitats Directive and Natura 2000 network in a protected wetland." PeerJ 6 (March 21, 2018): e4540. http://dx.doi.org/10.7717/peerj.4540.
Full textAbstract:
Background Wetlands are highly productive systems that supply a host of ecosystem services and benefits. Nonetheless, wetlands have been drained and filled to provide sites for building houses and roads and for establishing farmland, with an estimated worldwide loss of 64–71% of wetland systems since 1900. In Europe, the Natura 2000 network is the cornerstone of current conservation strategies. Every six years, Member States must report on implementation of the European Habitats Directive. The present study aims to illustrate how Earth observation (EO) technologies can contribute to the reporting obligations of the Habitats Directive and Natura 2000 network in relation to wetland ecosystems. Methods We analysed the habitat changes that occurred in a protected wetland (in NW Spain), 13 years after its designation as Natura 2000 site (i.e., between 2003 and 2016). For this purpose, we analysed optical multispectral bands and water-related and vegetation indices derived from data acquired by Landsat 7 TM, ETM+ and Landsat 8 OLI sensors. To quantify the uncertainty arising from the algorithm used in the classification procedure and its impact on the change analysis, we compared the habitat change estimates obtained using 10 different classification algorithms and two ensemble classification approaches (majority and weighted vote). Results The habitat maps derived from the ensemble approaches showed an overall accuracy of 94% for the 2003 data (Kappa index of 0.93) and of 95% for the 2016 data (Kappa index of 0.94). The change analysis revealed important temporal dynamics between 2003 and 2016 for the habitat classes identified in the study area. However, these changes depended on the classification algorithm used. The habitat maps obtained from the two ensemble classification approaches showed a reduction in habitat classes dominated by salt marshes and meadows (24.6–26.5%), natural and semi-natural grasslands (25.9–26.5%) or sand dunes (20.7–20.9%) and an increase in forest (31–34%) and reed bed (60.7–67.2%) in the study area. Discussion This study illustrates how EO–based approaches might be particularly useful to help (1) managers to reach decisions in relation to conservation, (2) Member States to comply with the requirements of the European Habitats Directive (92/43/EEC), and (3) the European Commission to monitor the conservation status of the natural habitat types of community interest listed in Annex I of the Directive. Nonetheless, the uncertainty arising from the large variety of classification methods used may prevent local managers from basing their decisions on EO data. Our results shed light on how different classification algorithms may provide very different quantitative estimates, especially for water-dependent habitats. Our findings confirm the need to account for this uncertainty by applying ensemble classification approaches, which improve the accuracy and stability of remote sensing image classification.
16
Rolkier, Gatluak, and Kumelachew Yeshitela. "Vegetation Classification and Habitat Types of Gambella National Park." International Journal of Forestry Research 2020 (June 16, 2020): 1–12. http://dx.doi.org/10.1155/2020/8612593.
Full textAbstract:
Gambella National Park has a diverse set of habitat types which Ethiopia shares with its neighbor, South Sudan, and the park is considered as one of the top wildlife areas of Ethiopia. The objectives of this research were to determine vegetation types and identify habitat types on recent satellite imageries. The method used for vegetation data collection was transects lines. PC-ORD software was used for analyzed vegetation data while Rapid Eye image 5 m resolution 2012 was analyzed by ArcGIS version 10.1 to classify the habitats map of Gambella National Park. The cluster analysis classified the Gambella National Park into 6 vegetation communities, and the relative abundance and relative frequency were used for naming vegetation community types. However, the satellite image had classified the Gambella National Park into 5 major habitat types.
17
Ivanova, Natalya, Valery Fomin, and Antonín Kusbach. "Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics." Sustainability 14, no. 6 (March 14, 2022): 3384. http://dx.doi.org/10.3390/su14063384.
Full textAbstract:
Due to global climate change and increased forest transformation by humans, accounting for the dynamics of forest ecosystems is becoming a central problem in forestry. We reviewed the success of considering vegetation dynamics in the most influential ecological forest classifications in Russia, the European Union, and North America. Out of the variety of approaches to forest classification, only those that are widely used in forestry and forest inventory were selected. It was found that the system of diagnostic signs developed by genetic forest typology based on the time-stable characteristics of habitats as well as the developed concept of dynamic series of cenosis formation allows us to successfully take into account the dynamics of vegetation. While forest dynamics in European classifications is assessed at a theoretical level, it is also possible to assess forest dynamics in practice due to information obtained from EUNIS habitat classification. In ecological classifications in North America, the problem of vegetation dynamics is most fully solved with ecological site description (ESD), which includes potential vegetation and disturbance factors in the classification features. In habitat type classification (HTC) and biogeoclimatic ecosystem classification (BEC), vegetation dynamics is accounted based on testing the diagnostic species and other signs of potential vegetation for resistance to natural and anthropogenic disturbances. Understanding of vegetation–environment associations is fundamental in forming proper forest management methods and improving existing classification structures. We believe that this topic is relevant as part of the ongoing search for new solutions within all significant forest ecological classifications.
18
Nandika, Muhammad Rizki, Azura Ulfa, Andi Ibrahim, and Anang Dwi Purwanto. "Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms." Geomatics and Environmental Engineering 17, no. 2 (January 30, 2023): 69–87. http://dx.doi.org/10.7494/geom.2023.17.2.69.
Full textAbstract:
Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.
19
Agrillo, Emiliano, Federico Filipponi, Alice Pezzarossa, Laura Casella, Daniela Smiraglia, Arianna Orasi, Fabio Attorre, and Andrea Taramelli. "Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping." Remote Sensing 13, no. 7 (March 24, 2021): 1231. http://dx.doi.org/10.3390/rs13071231.
Full textAbstract:
In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.
20
Hamidah, M., R. A. Pasaribu, and F. A. Aditama. "Benthic habitat mapping using Object-Based Image Analysis (OBIA) on Tidung Island, Kepulauan Seribu, DKI Jakarta." IOP Conference Series: Earth and Environmental Science 944, no. 1 (December 1, 2021): 012035. http://dx.doi.org/10.1088/1755-1315/944/1/012035.
Full textAbstract:
Abstract Tidung Island is one of the islands in Kepulauan Seribu, DKI Jakarta, Indonesia. This island has various benthic that live on the coastal areas, and benthic habitat has various functions both ecologically and economically. Nowadays, remote sensing technology is one way to detect benthic habitats in coastal areas. Mapping benthic habitat is essential for sustainable coastal resource management and to predict the distribution of benthic organisms. This study aims to map the benthic habitats using the object-based image analysis (OBIA) and calculate the accuracy of benthic habitat classification results in Tidung Island, Kepulauan Seribu, DKI Jakarta. The field data were collected on June 2021, and the image data used is satellite Sentinel-2 imagery acquired in June 2021. The result shows that the benthic habitat classification was produced in 4 classes: seagrass, rubble, sand, and live coral. The accuracy test result obtained an overall accuracy (OA) of 74.29% at the optimum value of the MRS segmentation scale 15;0,1;0.7 with the SVM algorithm. The results of benthic habitat classification show that the Seagrass class dominates the shallow water area at the research site with an area of 118.77 ha followed by Life Coral 104.809 ha, Sand 43.352 ha, and the smallest area is the Rubble class of 42.28 Ha.
21
Astaman, I. Dewa Made Krisna Putra, I. Wayan Gede Astawa Karang, I. Gede Hendrawan, and Kuncoro Teguh Setiawan. "Pemetaan Habitat Dasar Perairan Dangkal Menggunakan Citra Satelit SPOT-7 di Pulau Nusa Lembongan, Bali." Journal of Marine and Aquatic Sciences 7, no. 2 (December 1, 2021): 184. http://dx.doi.org/10.24843/jmas.2021.v07.i02.p07.
Full textAbstract:
Shallow water habitat is one of the regions that has high dynamics and has an important role are ecologically and economically. The high dynamics of the ideal shallow water habitat is always followed by updating information so that an overview of the area is obtained in accordance with reality. Remote sensing technology is one of the technologies that can be used for mapping natural resource studies such as mapping of shallow water habitats with the satellite imagery. This study aims to map the distribution of shallow water habitats using SPOT-7 satellite imagery on Nusa Lembongan Island, Bali and test the level of accuracy. The method used true color composite and DII (Depth Invariant Index) transformation and uses the maximum likelihood classification. The classification scheme used 6 classes, namely sand, seagrass, macro algae, rubble, live coral, and dead coral. The results of this study indicate the distribution of shallow water habitat on Nusa Lembongan Island, Bali spread equally based on the level of water depth with a total area of shallow water habitat of 453.41 ha. The results of mapping accuracy test showed the overall accuracy of the DII transformation classification results is better than the composite image classification results with an overall accuracy of 75.43% and a kappa coefficient is 0.71. So from these results can be said that used of a water column correction with the DII method can improve image accuracy in mapping shallow water habitats.
22
Price, Bronwyn, Nica Huber, Anita Nussbaumer, and Christian Ginzler. "The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product." Remote Sensing 15, no. 3 (January 21, 2023): 643. http://dx.doi.org/10.3390/rs15030643.
Full textAbstract:
Habitat maps at high thematic and spatial resolution and broad extents are fundamental tools for biodiversity conservation, the planning of ecological networks and the management of ecosystem services. To derive a habitat map for Switzerland, we used a composite methodology bringing together the best available spatial data and distribution models. The approach relies on the segmentation and classification of high spatial resolution (1 m) aerial imagery. Land cover data, as well as habitat and species distribution models built on Earth observation data from Sentinel 1 and 2, Landsat, Planetscope and LiDAR, inform the rule-based classification to habitats defined by the hierarchical Swiss Habitat Typology (TypoCH). A total of 84 habitats in 32 groups and 9 overarching classes are mapped in a spatially explicit manner across Switzerland. Validation and plausibility analysis with four independent datasets show that the mapping is broadly plausible, with good accuracy for most habitats, although with lower performance for fine-scale and linear habitats, habitats with restricted geographical distributions and those predominantly characterised by understorey species, especially forest habitats. The resulting map is a vector dataset available for interactive viewing and download from open EnviDat data sharing platform. The methodology is semi-automated to allow for updates over time.
23
Richards, Laura J., Robert Kieser, Timothy J. Mulligan, and John R. Candy. "Classification of Fish Assemblages based on Echo Integration Surveys." Canadian Journal of Fisheries and Aquatic Sciences 48, no. 7 (July 1, 1991): 1264–72. http://dx.doi.org/10.1139/f91-152.
Full textAbstract:
Echo integration data typically collected during fish biomass surveys have additional utility in species recognition. Our analysis is based on a rockfish (family Scorpaenidae) hydroacoustic survey off the northwest coast of Vancouver Island, British Columbia, Canada. Survey data from 105 echogram sections were divided into two categories on the basis of ocean bottom habitat. One category corresponded to an area of bedrock outcrops and the other to the continental slope adjacent to the shelf break. Hydroacoustic characteristics of the bottom-oriented fish assemblages associated with these habitats could be described by time of day, mean volume density, dispersion, and mean off-bottom distance of the biomass. Nearest-neighbour analysis of these features classified assemblages into habitat categories with up to 97% success. Between-habitat differences in the assemblages were also evident from concurrent experimental fishing. Different rockfish species dominated the assemblages in the two areas. Our results suggest general methods for distinguishing among species groups in hydroacoustic survey data.
24
Zarnetske, Phoebe L., Thomas C. Edwards, and Gretchen G. Moisen. "HABITAT CLASSIFICATION MODELING WITH INCOMPLETE DATA: PUSHING THE HABITAT ENVELOPE." Ecological Applications 17, no. 6 (September 2007): 1714–26. http://dx.doi.org/10.1890/06-1312.1.
Full text
25
Jackson, Susan E., and Carolyn J. Lundquist. "Limitations of biophysical habitats as biodiversity surrogates in the Hauraki Gulf Marine Park." Pacific Conservation Biology 22, no. 2 (2016): 159. http://dx.doi.org/10.1071/pc15050.
Full textAbstract:
The Hauraki Gulf Marine Park (HGMP) is recognised for its diverse natural environment and associated biodiversity, as well as the extensive social, cultural and economic values derived therein. Here, we evaluate the current level of biodiversity protection provided by existing Marine Protected Areas (MPAs) within the HGMP. We use abiotic datasets to develop a habitat classification system to identify the suite of biophysical habitats found in the Marine Park, and their relative protection within existing MPAs (both no-take marine reserves and Cable Protection Zones). Our analysis demonstrated that half of the biophysical habitats identified in the HGMP are not currently afforded protection within MPAs, and that biophysical classifications poorly differentiate across subtidal, soft-sediment habitats using available data layers. We then evaluated representation of these environmental surrogates within a biodiversity prioritisation analysis based on distribution models for demersal fish species. Biophysical habitat surrogates showed poor representation across habitats within highest-priority areas based on prioritisations of demersal fish biodiversity. This suggests the need for further development of biophysical habitat surrogates that are more strongly correlated with biodiversity, if they are to be used to inform biodiversity protection in the HGMP.
26
Br Ginting, Devica Natalia, and Anang Dwi Purwanto. "Semi-Automatic Classification Model on Benthic Habitat Using Spot-7 Imagery in Penerusan Bay, Bali." Jurnal Segara 17, no. 3 (December 21, 2021): 185. http://dx.doi.org/10.15578/segara.v17i3.9771.
Full textAbstract:
Benthic habitats are one of the interesting marine resources and its existence must be preserved. Provision of up-to-date benthic habitat information requires a relatively large amount of time and money. The use of remote sensing technology is one of the best solution. This study aims to develop a semi-automatic processing model that is fast, accurate, and with broad spatial coverage. The satellite image data used is the SPOT-7 image acquired on April 11th, 2018. The method used is a supervised classification with a decision tree algorithm. The analysis was carried out using a script developed in the open-source R application. The results showed that the model used was able to accelerate the processing of benthic habitat extracted from the initial process to the classification. The model developed is able to classify habitat classes based on the training sample data provided so that it does not affect the user’s ability to determine the habitat class. The resulting model accuracy is 93.6%. The validation of the resulting classification showed an overall accuracy of 59% and a kappa accuracy of 0.46. It is necessary to carry out further research by increasing quality and quantity of training samples from each object of benthic habitats and developing scripts in order to produce better mapping accuracy.
27
Siebert, S. J., A. E. Van Wyk, G. J. Bredenkamp, and F. Siebert. "Vegetation of the rock habitats of the Sekhukhuneland Centre of Plan Endemism, South Africa." Bothalia 33, no. 2 (September 9, 2003): 207–28. http://dx.doi.org/10.4102/abc.v33i2.454.
Full textAbstract:
A hierarchical classification, description, and ecological and floristic interpretations are presented on the vegetation types of the ultramafic rock habitats of the Sekhukhuneland Centre of Plant Endemism. Relevés were compiled in 100 stratified random plots. A TWINSPAN classification, refined by Braun-Blanquet procedures, revealed 17 plant communities, which are classified into 13 associations belonging to four proposed alliances. Many new syntaxa are ecologically interpreted and described. For each syntaxon, the species richness, endemism and conservation status was determined. Much of the plant community distribution can be ascribed to specific habitat preference. The floristic and habitat information, proposed classification, general description and vegetation key are provided to aid future identification of conservation areas, land use planning and research. An ordination (DECORANA) based on floristic data confirmed potential relationships that could exist between the plant communities and associated habitats and environmental gradients.
28
Shields, F. Douglas. "Aquatic Habitat Bottom Classification Using ADCP." Journal of Hydraulic Engineering 136, no. 5 (May 2010): 336–42. http://dx.doi.org/10.1061/(asce)hy.1943-7900.0000181.
Full text
29
Guarino, Riccardo, Salvatore Pasta, Giuseppe Bazan, Alessandro Crisafulli, Orazio Caldarella, Gian Pietro Giusso del Galdo, Alessandro Silvestre Gristina, et al. "Relevant habitats neglected by the Directive 92/43 EEC: the contribution of Vegetation Science for their reappraisal in Sicily." Plant Sociology 58, no. 2 (December 31, 2021): 49–63. http://dx.doi.org/10.3897/pls2021582/05.
Full textAbstract:
Field investigation carried out by the Sicilian botanists in the last 20 years enabled them to identify eight habitat types of high biogeographic and conservation interest, neglected by the Directive 92/43, which deserve ad hoc conservation measures. For each of these habitats, a syntaxonomic interpretation of the corresponding plant communities, their main ecological, physiognomic and syndynamic traits and a list of diagnostic species are provided. Their classification into the macrotypes listed in the Annex I of the Directive 92/43 and the respective correspondence in EUNIS habitat classification are proposed. The habitats here described integrate those already proposed by the Italian Botanical Society, with the hope of an adequate recognition at national at supranational level.
30
Amani, Meisam, Candace Macdonald, Abbas Salehi, Sahel Mahdavi, and Mardi Gullage. "Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland." Water 14, no. 23 (November 23, 2022): 3809. http://dx.doi.org/10.3390/w14233809.
Full textAbstract:
Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.
31
ZHANG, Yue, Zongming WANG, Chunying REN, Hao YU, Zhangyu DONG, Chunyan LU, and Dehua MAO. "CHANGES IN HABITAT SUITABILITY FOR WATERBIRDS OF THE MOMOGE NATURE RESERVE OF CHINA DURING 1990–2014." Journal of Environmental Engineering and Landscape Management 25, no. 4 (December 21, 2017): 367–78. http://dx.doi.org/10.3846/16486897.2017.1316982.
Full textAbstract:
There is increasing empirical evidence that changes in habitat quality play an important role in determining species distributions and biodiversity. However, most research has focused on habitat quality, and we still lack approaches for tracking habitat quality dynamics. In this paper, by establishing qualitative and quantitative relationships between waterbird populations and key habitat indicators such as water abundance, food, shelter conditions and disturbance, we developed an object-oriented classification method, in conjunction with a geographic information systems (GIS) based centroid moving method, to assess habitat suitability dynamics for waterbirds at the Momoge Nature Reserve, China. Our results showed that habitat suitability improved during 1990–2000 and declined during 2000– 2014. Habitats with very good and good grades increased by 71.47 km2 (4.88%) during 1990–2000 and decreased by 200.66 km2 (13.78%) during 2000–2014. The habitat area with a good grade moved to the north, while the habitat area with a poor grade moved to the south during 1990–2014. This was mainly because the surrounding cropland area increased and shifted as oil and gas projects developed. These findings suggest that our object-oriented classification and centroid moving methods have great potential for use in biodiversity conservation and ecosystem management.
32
Sittaro, Fabian, Christopher Hutengs, Sebastian Semella, and Michael Vohland. "A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data." Remote Sensing 14, no. 4 (February 10, 2022): 823. http://dx.doi.org/10.3390/rs14040823.
Full textAbstract:
Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types.
33
Fomin, Valery, and Anna Mikhailovich. "Russian Approaches to the Forest Type Classification." IOP Conference Series: Earth and Environmental Science 906, no. 1 (November 1, 2021): 012023. http://dx.doi.org/10.1088/1755-1315/906/1/012023.
Full textAbstract:
Abstract The results of researches characterizing the geographical distribution of forest-ecological, phytocoenotic, and genetic classifications of forest types in the Russian Federation nowadays are presented in the thesis. A comparative analysis was carried out for the following items: the inclusive concept of a classification unit (a type of habitat conditions; a type of forest); features of distinguishing the border of the classification units; classification features used to determine the type of habitat conditions; features of the classification of phytocoenoses used to determine the forest type; the degree to which the successional dynamics of forest stands are taken into consideration; the degree to which the influence of anthropogenic factors are taken into consideration; the level of implementation in forest management and forestry practice; regions of implementation. In the process of development of forest typologies, the concept of a forest type changed from understanding it as a forest area homogeneous by composition, structure, and appearance (homogeneity in space) in natural classifications to the concepts of a forest type, in which priority is given to homogeneity in origin (genesis), as well as developmental processes and dynamics (homogeneity in time) in genetic and dynamic typologies. Currently, there is the following forest type classification in the Russian Federation: forest-ecological, phytocoenotic, genetic, and dynamic. When classifying forest areas within the forest-ecological direction provided by E.V. Alekseev – P.S. Pogrebnyak, the priority is given to the characteristics of the habitat conditions. Within the phytocoenotic direction provided by V.N. Sukachev, the priority is given to the phytocoenosis characteristics. Within the genetic approach provided by B.A. Ivashkevich – B.P. Kolesnikov, a forest type is considered as a series of alternating phases – types of phytocoenosis within the same type of habitat conditions. In this case, phytocoenotic classifications can be a part of the genetic classifications for the climax forest phytocoenosis. And the dynamic approach provided by I.S. Melekhov is very close to the genetic one and is a superstructure over the classical phytocoenotic forest typology provided by V.N. Sukachev. The current use of forest typological classifications by forest inventory management enterprises in the Russian Federation was studied. A map of the geographical distribution of forest typologies of the above-listed directions of forest typology researches was created. Forest-ecological classifications are used mainly in the southern regions of the European part of Russia and the North Caucasus. Forest typologies created based on a genetic approach to the forest type classification are used in Western Siberia, in the south of the Far East and Eastern Siberia, and in some regions of the Urals. Phytocoenotic classifications of forest types are used in other regions of the Russian Federation.
34
Zhang, Lianjun, Chuangmin Liu, and Craig J. Davis. "A mixture model-based approach to the classification of ecological habitats using Forest Inventory and Analysis data." Canadian Journal of Forest Research 34, no. 5 (May 1, 2004): 1150–56. http://dx.doi.org/10.1139/x04-005.
Full textAbstract:
A Gaussian mixture model (GMM) is used to classify Forest Inventory and Analysis (FIA) plots into six ecological habitats in the northeastern USA. The GMM approach captures intra-class variation by modeling each habitat class as a mixture of subclasses of Gaussian distributions. The classification is achieved based on the appropriate posterior probability. The GMM classifier outperforms a traditional statistical method (i.e., linear discriminant analysis or LDA), and produces similar overall accuracy rates to a commonly used neural network model (i.e., multi-layer perceptrons or MLP). For the classifications of individual ecological habitats, however, MLP produces better (or same) producers' classification accuracies for five of the six ecological habitats than does GMM. But the GMM's accuracy rates are more consistent (92%97%) across the six ecological habitats than those of the MLP model (82%99%). This study shows that GMM offers an attractive alternative for modeling the complex stand structure and relationships between variables in mixed-species forest stands.
35
Davis, Tom R., David Harasti, and Stephen D. A. Smith. "Developing a habitat classification typology for subtidal habitats in a temperate estuary in New South Wales, Australia." Marine and Freshwater Research 67, no. 8 (2016): 1186. http://dx.doi.org/10.1071/mf15123.
Full textAbstract:
Effective estuarine management depends on adequate data about the ecology, extent and biodiversity of component habitats. However, these data are often scant, as exemplified by the Port Stephens estuary, part of the Port Stephens–Great Lakes Marine Park (NSW, Australia), for which even basic descriptions of habitat types and extent are lacking. Herein we present the results of the first quantitative assessment of subtidal benthic communities within the estuary, involving 130km of towed video transects over an area exceeding 50km2. We identified previously undocumented macroalgae-dominated habitat types and found strong correlations between habitat types and depth. The soft coral Dendronephthya australis habitat is of particular interest because this was found to occur exclusively outside current sanctuary (no take) zones. The habitat map of Port Stephens generated during the study provides the basis for more objective representative planning in future iterations of zoning in the estuarine section of the marine park. The study also suggests that depth may be a useful proxy for estuarine habitat types where specific data are lacking. The classification methodology developed during the study was cost-effective, generated robust data and consequently has potential for wider application in other large estuarine bays.
36
Espriella, Michael C., and Vincent Lecours. "Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis." Drones 6, no. 6 (June 7, 2022): 140. http://dx.doi.org/10.3390/drones6060140.
Full textAbstract:
Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications.
37
Haryoko, Tri, Anik Budhi Dharmayanthi, Mark O’Hara, Berenika Mioduszewska, Dewi Malia Prawiradilaga, Hari Sutrisno, Lilik Budi Prasetyo, and Ani Mardiastuti. "Land Cover Analysis and Habitat Identification of Tanimbar Corella (Cacatua Goffiniana) on Tanimbar Islands, Maluku." Journal of Hunan University Natural Sciences 49, no. 3 (March 28, 2022): 257–64. http://dx.doi.org/10.55463/issn.1674-2974.49.3.29.
Full textAbstract:
The Tanimbar Corella (Cacatua goffiniana) is a protected bird species endemic to the Tanimbar Islands (Yamdena and its satellite islands) in the Maluku province of Indonesia. The major threats identified to this species are hunting, illegal trade, and habitat loss. Therefore, this study aims to classify the land cover, distribution, and habitat types occupied by C. goffiniana on the Tanimbar Islands. Classification of land cover was analyzed using a supervised classification method of the Erdas Imagine 2014 software. Direct field observations were conducted to identify the distribution of Tanimbar Corella and its habitat use. The land cover was classified into six classes: forest (54.26 %), open area (20.76%), plantation (14.81%), mangrove (6.89%), settlement (1.85%), and wetland (1.43%). C. goffiniana is distributed on five islands, Yamdena, Selaru, Larat, Sera, and Molu. Furthermore, this species was observed in forest, plantation, and open land habitats. The results identified seventeen nest trees of six species: Canarium Indicum, Pometia pinnata, Instia bijuga, Sterculita foetida, Maranther corymbosa, and Alstonia scholaris, with a range diameter of 40.00-136.71 cm and nest cavity heights of 9.00-34.22 m (n = 17). This research brings new data in three areas: 1) a reassessment of the distribution of C. goffiniana on the Tanimbar Islands; 2) a detailed classification of the land cover concerning habitat types of the Tanimbar Islands; 3) an identification of the tree species utilized by nesting Tanimbar Corellas. Information on preferred habitats and nesting trees is crucial for selecting release sites of confiscated birds to ensure post-release survival.
38
Ilich, Alexander R., Jennifer L. Brizzolara, Sarah E. Grasty, John W. Gray, Matthew Hommeyer, Chad Lembke, Stanley D. Locker, et al. "Integrating Towed Underwater Video and Multibeam Acoustics for Marine Benthic Habitat Mapping and Fish Population Estimation." Geosciences 11, no. 4 (April 13, 2021): 176. http://dx.doi.org/10.3390/geosciences11040176.
Full textAbstract:
The west Florida shelf (WFS; Gulf of Mexico, USA) is an important area for commercial and recreational fishing, yet much of it remains unmapped and unexplored, hindering effective monitoring of fish stocks. The goals of this study were to map the habitat at an intensively fished area on the WFS known as “The Elbow”, assess the differences in fish communities among different habitat types, and estimate the abundance of each fish taxa within the study area. High-resolution multibeam bathymetric and backscatter data were combined with high-definition (HD) video data collected from a near-bottom towed vehicle to characterize benthic habitat as well as identify and enumerate fishes. Two semi-automated statistical classifiers were implemented for obtaining substrate maps. The supervised classification (random forest) performed significantly better (p = 0.001; α = 0.05) than the unsupervised classification (k-means clustering). Additionally, we found it was important to include predictors at a range of spatial scales. Significant differences were found in the fish community composition among the different habitat types, with both substrate and vertical relief found to be important with rock substrate and higher relief areas generally associated with greater fish density. Our results are consistent with the idea that offshore hard-bottom habitats, particularly those of higher vertical relief, serve as “essential fish habitat”, as these rocky habitats account for just 4% of the study area but 65% of the estimated total fish abundance. However, sand contributes 35% to total fish abundance despite comparably low densities due to its large area, indicating the importance of including these habitats in estimates of abundance as well. This work demonstrates the utility of combining towed underwater video sampling and multibeam echosounder maps for habitat mapping and estimation of fish abundance.
39
Khapugin, Anatoliy A. "Environment Status Estimation of the Forest Communities Based on Floristic Surveys in the Mordovia State Nature Reserve, Russia." Forests 12, no. 11 (October 28, 2021): 1475. http://dx.doi.org/10.3390/f12111475.
Full textAbstract:
Environmental scales include species responsive to changes in environmental conditions. The present paper aims to apply floristic survey data to reveal environmental conditions in habitats studied in the Mordovia State Nature Reserve, European Russia. In total, 161 square plots were established within a selected forest area. In each plot, all species were registered to conduct a further analysis. Then, average values of six environmental factors were calculated based on the Tsyganov environmental scale. Contour maps were created for four factors to demonstrate spatial changes through the study area. All study pots were assigned to seven habitat types during the field surveys. To test the correctness of the determined classification, a principal component analysis was performed based on Tsyganov’s environmental factors. Additionally, PERMANOVA was used to test the correctness of the habitat distinguishing. The results demonstrate that differences in environmental conditions among the majority (mires, coniferous forests, broadleaved forests, mixed forests) of the distinguished habitats are statistically significant, except for water bodies, forest gaps and roads, which have no significant differences in environmental factors compared with other habitats. We assume that this is caused by the very small sampling size for these habitat types. To obtain correct results, each habitat group should be represented by at least 3–4% samples of the whole sampling set. The main conclusion represents a simple way to assess the habitat environmental status based on floristic data. Based on Tsyganov’s environmental factors, the spatial distribution of only plant specialists can be recognised well. The allocation of plant generalists is impossible based on the proposed approach. Finally, the correctness of habitat classification based on dominated plants is well-testable using environmental conditions found on these sites. We also recommend the use of the here applied approach in plant ecology studies in the subzone of coniferous–deciduous forests of Eastern Europe.
40
Howard, Rebecca J., and Joseph S. Larson. "A Stream Habitat Classification System for Beaver." Journal of Wildlife Management 49, no. 1 (January 1985): 19. http://dx.doi.org/10.2307/3801833.
Full text
41
Upadhyay, Anand, Ratan Singh, and Omkar Dhonde. "Random forest based classification of seagrass habitat." Journal of Information and Optimization Sciences 41, no. 2 (February 17, 2020): 613–20. http://dx.doi.org/10.1080/02522667.2020.1753303.
Full text
42
Davenport, Angela J., Angela M. Gurnell, and Patrick D. Armitage. "Habitat survey and classification of urban rivers." River Research and Applications 20, no. 6 (October 14, 2004): 687–704. http://dx.doi.org/10.1002/rra.785.
Full text
43
Kolada, Agnieszka, Ryszard Piotrowicz, Elżbieta Wilk-Woźniak, Piotr Dynowski, and Piotr Klimaszyk. "Conservation status of the Natura 2000 habitat 3110 in Poland: Monitoring, classification and trends." Limnological Review 17, no. 4 (December 1, 2017): 215–22. http://dx.doi.org/10.1515/limre-2017-0020.
Full textAbstract:
Abstract Soft water lakes, or so-called lobelia lakes, which are inhabited by a specific vegetation composed of isoetids, have been subjected to intense research aimed at evaluating their condition and conservation status for many years in Poland. At the time of Poland’s accession to the European Union and the implementation of the EU Habitats Directive, these lakes were classified as natural habitat 3110. In accordance with the provision of the Habitat Directive a comprehensive methodology for monitoring and classification of the state of this habitat has been developed. Using this methodology, two monitoring trials (in 2009–2010 and again in 2016–2017) were carried out at 45 and 43 sites of the 3110 natural habitat, respectively. These studies confirm the high sensitivity of these poorly buffered aquatic ecosystems to all external influences, both natural and anthropogenic. The overall conservation status of the 3110 habitat in Poland showed a relatively high stability, with similar proportions of sites classified as favourable (FV), unfavourable inadequate (U1) and unfavourable bad (U2) between 2009–2010 (35%, 49% and 16%, respectively) and 2016–2017 (33%, 56% and 11%, respectively). Out of 43 sites examined in 2016–2017, 29 remained unchanged compared with the results of the previous survey concerning their overall status. Results of the monitoring research also allow for the observation and evaluation of mechanisms and directions of changes in the functioning of these ecosystems. Based on the experiences from two series of monitoring conducted so far, the methodology has been assessed as appropriate for the assessment of the conservation status of the 3110 natural habitat, however, some modifications and additions have been suggested.
44
Tait, Leigh, Jochen Bind, Hannah Charan-Dixon, Ian Hawes, John Pirker, and David Schiel. "Unmanned Aerial Vehicles (UAVs) for Monitoring Macroalgal Biodiversity: Comparison of RGB and Multispectral Imaging Sensors for Biodiversity Assessments." Remote Sensing 11, no. 19 (October 8, 2019): 2332. http://dx.doi.org/10.3390/rs11192332.
Full textAbstract:
Developments in the capabilities and affordability of unmanned aerial vehicles (UAVs) have led to an explosion in their use for a range of ecological and agricultural remote sensing applications. However, the ubiquity of visible light cameras aboard readily available UAVs may be limiting the application of these devices for fine-scale, high taxonomic resolution monitoring. Here we compare the use of RGB and multispectral cameras deployed aboard UAVs for assessing intertidal and shallow subtidal marine macroalgae to a high taxonomic resolution. Our results show that the diverse spectral profiles of marine macroalgae naturally lend themselves to remote sensing and habitat classification. Furthermore, we show that biodiversity assessments, particularly in shallow subtidal habitats, are enhanced using six-band discrete wavelength multispectral sensors (81% accuracy, Cohen’s Kappa) compared to three-band broad channel RGB sensors (79% accuracy, Cohen’s Kappa) for 10 habitat classes. Combining broad band RGB signals and narrow band multispectral sensing further improved the accuracy of classification with a combined accuracy of 90% (Cohen’s Kappa). Despite notable improvements in accuracy with multispectral imaging, RGB sensors were highly capable of broad habitat classification and rivaled multispectral sensors for classifying intertidal habitats. High spatial scale monitoring of turbid exposed rocky reefs presents a unique set of challenges, but the limitations of more traditional methods can be overcome by targeting ideal conditions with UAVs.
45
Tyusov, G. A., and K. V. Ivanova. "Diagnostics and mapping of southern tundra habitats (on the example of Shapkina River key site)." Geobotanical mapping, no. 2021 (2021): 54–61. http://dx.doi.org/10.31111/geobotmap/2021.54.
Full textAbstract:
A classification of habitats is given for the area studied on the right bank of the Shapkina River, (East European tundra, southern subzone). The classification is based on the topographic position of biotopes and their plant communities. The mosaic of tundra vegetation makes it difficult for mapping. Usually in nature the combinations of plant communities are confined to specific types of habitats. To display the regularities of a fine-contour vegetation cover on maps, we used the habitat approach diagnosed by combinations of plant communities. The field survey was done in the summer of 2020; total area of about 150 km2 was surveyed. Ultra-high resolution (3–5 cm/px) aerial photography was carried out for key areas, using a DJI Mavic Pro Platinum quadrocopter (shooting height from 80 to 200 m). 25 geobotanical relevés were completed; in addition 180 short descriptions were made for map verification. All types of habitats in the proposed scheme are correlated with EUNIS units, lists of syntaxa are given. A large-scale map (1 : 50 000) of habitats was prepared. All tundra habitats are divided into two groups. First level of habitat classification confined to the main landscape types: watersheds and river valleys of watercourses with a floodplain regime. Watershed habitats are subdivided into 5 categories (second level of habitat classification), determined by their runoff-geochemical position on the generalized geomorphological profile (from the highest relief elements to the lowest ones), including underlying rocks, moisture regime and migration of elements, exposure features. Due to the small amount of data mapping of river valley habitats was performed only for units of the second level. At the third level watershed habitats are well diagnosed by vegetation at the level of associations, combinations of commuities, soil cover, and microrelief. As a result, most units of the second and third levels are clearly distinguished on aerial photography obtained using a quadrocopter, and also correlate with specific syntaxa. As a result of field data and aerial photographs analyses, 12 categories of habitats, represented by 17 syntaxa, were identified for watersheds at the 3rd level.
46
Mohamed, Hassan, Kazuo Nadaoka, and Takashi Nakamura. "Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry." Remote Sensing 12, no. 1 (January 1, 2020): 127. http://dx.doi.org/10.3390/rs12010127.
Full textAbstract:
The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.
47
Zielke, Luisa, Nicole Wrage-Mönnig, Jürgen Müller, and and Carsten Neumann. "Implications of Spatial Habitat Diversity on Diet Selection of European Bison and Przewalski´s Horses in a Rewilding Area." Diversity 11, no. 4 (April 18, 2019): 63. http://dx.doi.org/10.3390/d11040063.
Full textAbstract:
In Europe, the interest in introducing megaherbivores to achieve ambitious habitat restoration goals is increasing. In this study, we present the results of a one-year monitoring program in a rewilding project in Germany (Doeberitzer Heide), where European bison (Bison bonasus) and Przewalski´s horses (Equus ferus przewalskii) were introduced for ecological restoration purposes. Our objectives were to investigate diet and habitat preferences of Przewalski´s horses and European bison under free-choice conditions without fodder supplementation. In a random forest classification approach, we used multitemporal RapidEye time series imagery to map the diversity of available habitats within the study area. This spatially explicit habitat distribution from satellite imagery was combined with direct field observations of seasonal diet preferences of both species. In line with the availability of preferred forage plants, European bison and Przewalski´s horses both showed seasonal habitat preferences. Because of their different preferences for forage plants, they did not overlap in habitat use except for a short time in the colder season. European bison used open habitats and especially wet open habitats more than expected based on available habitats in the study area. Comparative foraging and feeding niches should be considered in the establishment of multispecies projects to maximize the outcome of restoration processes.
48
Espriella, Michael C., Vincent Lecours, Peter C. Frederick, Edward V. Camp, and Benjamin Wilkinson. "Quantifying Intertidal Habitat Relative Coverage in a Florida Estuary Using UAS Imagery and GEOBIA." Remote Sensing 12, no. 4 (February 19, 2020): 677. http://dx.doi.org/10.3390/rs12040677.
Full textAbstract:
Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services including shoreline erosion control, habitat provision, and water filtration. However, these systems face significant global change as a result of a combination of anthropogenic stressors like coastal development and environmental stressors such as sea-level rise and disease. Traditional intertidal habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are mapped in a way that is often insufficient to make informed management decisions. Unoccupied aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface model from the UAS imagery. These products were used in a geographic object-based image analysis (GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a more informed classification than traditional techniques by providing textural and geometric context to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA can differentiate intertidal habitats. This method allows for effective monitoring that can inform management and restoration efforts.
49
Ostojić, Dragana, Biljana Krsteski, Zoran Stojković, Ana Petković, Bogosav Stojiljković, Ivana Jovanović, and Tamara Bosić. "Composing a vegetation-stand map for the protected area of 'Radan' Nature Park." Zastita prirode 70, no. 1-2 (2020): 13–36. http://dx.doi.org/10.5937/zaspri2001013o.
Full textAbstract:
Protected areas are one of the priorities for mapping habitats, especially forest habitats, which are dominant in most protected areas of central Serbia, such as "Radan" Nature Park. This paper presents the forest habitat mapping in the protected area of "Radan" NP and the development of vegetation-stand map of the protected area in an effort to examine the methodology of forest habitats mapping in Serbia, which presumes a long term systematic data collection. Although much has been done on the classification of habitats in Serbia, considering both botanical and the forestry approach, the practical application of this knowledge in habitat mapping is still in its infancy, with the exception of longstanding practical work on data collection for Forest Management Plans for the state-owned forests. Data on forest stands in Nature Park "Radan" collected in this manner were essential to developing the vegetation-stand map of "Radan" NP. The results of data processing and harmonization of typology and classification of state-owned forests have been presented in this paper, as well as the analysis of forest habitat types in this protected area. The paper presents the vegetation-stand maps of the state-owned forests in the protected area and of the pilot area of privately owned forests, for which detailed field data collection was necessary. These maps are intended for the management of protected areas and systematic and efficient implementation of protection measures and activities. Habitat mapping in protected areas is a prerequisite for an adequate biodiversity monitoring, as well as for management and sustainable use of natural resources of the protected area.
50
Schneider-Binder, Erika, and Felizia Kuhlke. "Habitats with Sea Grape (Ephedra distachya) on the Dunes of Letea (Danube Delta, Romania)." Transylvanian Review of Systematical and Ecological Research 17, no. 2 (December 1, 2015): 45–56. http://dx.doi.org/10.1515/trser-2015-0062.
Full textAbstract:
Abstract The Danube Delta is known for its unique, biogeographical-important ecosystem complex that includes a large range of habitats from the permanently water-covered to the extremely dry area. These ecosystems are well represented in the area nearest to the Black Sea in the dune area of Letea, Caraorman and Sărăturile. Sea grape (Ephedra distachya) communities taking part of the habitat type 2130* fixed coastal dunes with herbaceous vegetation (grey dunes), Subtype 16.22 B Pontic fixed dunes and their classification in the European habitat system are discussed and a proposal is made for their appropriate integration in a corresponding category of habitat types in the frame of the Pontic bioregion.