Academic literature on the topic 'Forest species mapping'
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Journal articles on the topic "Forest species mapping"
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Welle, Torsten, Lukas Aschenbrenner, Kevin Kuonath, Stefan Kirmaier, and Jonas Franke. "Mapping Dominant Tree Species of German Forests." Remote Sensing 14, no. 14 (July 11, 2022): 3330. http://dx.doi.org/10.3390/rs14143330.
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The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making for site-adapted tree species selection. An accurate assignment of tree species in relation to their location allows conclusions about potential resilience or vulnerability to biotic and abiotic factors. Identifying areas at risk helps the long-term strategy of forest conversion towards a natural, diverse, and climate-resilient forest. In the framework of the national forest inventory (NFI) in Germany, data on forest tree species are collected in sample plots, but there is a lack of a full coverage map of the tree species distribution. The NFI data were used to train and test a machine-learning approach that classifies a dense Sentinel-2 time series with the result of a dominant tree species map of German forests with seven main tree species classes. The test of the model’s accuracy for the forest type classification showed a weighted average F1-score for deciduous tree species (Beech, Oak, Larch, and Other Broadleaf) between 0.77 and 0.91 and for non-deciduous tree species (Spruce, Pine, and Douglas fir) between 0.85 and 0.94. Two additional plausibility checks with independent forest stand inventories and statistics from the NFI show conclusive agreement. The results are provided to the public via a web-based interactive map, in order to initiate a broad discussion about the potential and limitations of satellite-supported forest management.
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Gansner, David A., Susan L. King, Stanford L. Arner, and David A. Drake. "Mapping Shifts in the Relative Stocking of Tree Species." Northern Journal of Applied Forestry 13, no. 2 (June 1, 1996): 92–95. http://dx.doi.org/10.1093/njaf/13.2.92.
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Abstract Baseline standards for measuring the "health" of our forests do not exist. But, one factor that can be considered when making judgments about the health of a particular forest tree species is change in the relative stocking of that species, that is, the extent to which the species isgaining or losing ground in its ecosystem. The forest survey unit at the Northeastern Forest Experiment Station used remeasured forest inventory plot data to estimate current average annual change in the relative stocking of common forest tree species in Pennsylvania. Spatial shifts in therelative stocking of individual species were mapped. The procedure can be readily extended to other species in other regions. Information on shifts in relative stocking can provide a symptomatic guide to recognizing problems of forest health, and it gives us a better understanding of the complexworkings of a dynamic ecosystem. North. J. Appl. For. 13(2):92-95.
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Bjerreskov, Kristian Skau, Thomas Nord-Larsen, and Rasmus Fensholt. "Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data." Remote Sensing 13, no. 5 (March 3, 2021): 950. http://dx.doi.org/10.3390/rs13050950.
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Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-sensor data will have a specific strength in the further classification of nemoral forest landscapes owing to the distinct seasonal patterns in the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and a random forest classifier trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90%, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34–74%. Species groups with coniferous species were the least confused, whereas the broadleaf groups, especially Quercus species, had higher error rates. The results are applied in Danish national accounting of greenhouse gas emissions from forests, resource assessment, and assessment of forest biodiversity potentials.
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Grabska, Ewa, Patrick Hostert, Dirk Pflugmacher, and Katarzyna Ostapowicz. "Forest Stand Species Mapping Using the Sentinel-2 Time Series." Remote Sensing 11, no. 10 (May 20, 2019): 1197. http://dx.doi.org/10.3390/rs11101197.
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Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.
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Maděra, Petr, Radomír Řepka, Jan Šebesta, Tomáš Koutecký, and Martin Klimánek. "Vascular plant biodiversity of floodplain forest geobiocoenosis in Lower Morava river Basin (forest district Tvrdonice), Czech Republic." Journal of Landscape Ecology 6, no. 2 (December 1, 2013): 34–64. http://dx.doi.org/10.2478/v10285-012-0067-3.
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ABSTRACT This paper presents an evaluation of full-area floristic mapping of floodplain forest in Tvrdonice forest district (Židlochovice Forest Enterprise) based on a single forest stand inventory. The study area encompasses 2,200 ha of forests, where 769 segments were inventoried, and 46,886 single records about presence of vascular plant species were catalogued. We found 612 species (incl. subspecies and hybrids), out of which 514 were herbs, 98 were woody plants, 113 were endangered species and 170 were adventive species. The average area of a segment is 2.86 ha. The mean number of species per segment is 60.97 in a range of 4-151.
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YI, Hai-Yan, Yuan ZENG, Yu-Jin ZHAO, Zhao-Ju ZHENG, Jie XIONG, and Dan ZHAO. "Forest species diversity mapping based on clustering algorithm." Chinese Journal of Plant Ecology 44, no. 6 (2020): 598–615. http://dx.doi.org/10.17521/cjpe.2019.0347.
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Tagliabue, Giulia, Cinzia Panigada, Roberto Colombo, Francesco Fava, Chiara Cilia, Frédéric Baret, Kristin Vreys, Koen Meuleman, and Micol Rossini. "Forest species mapping using airborne hyperspectral APEX data." Miscellanea Geographica 20, no. 1 (March 1, 2016): 28–33. http://dx.doi.org/10.1515/mgrsd-2016-0002.
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Abstract The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.
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Murgaš, Vlastimil, Ivan Sačkov, Maroš Sedliak, Daniel Tunák, and František Chudý. "Assessing horizontal accuracy of inventory plots in forests with different mix of tree species composition and development stage." Journal of Forest Science 64, No. 11 (December 3, 2018): 478–85. http://dx.doi.org/10.17221/92/2018-jfs.
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Global navigation satellite systems (GNSS) have a wide range of applications in forest industry, including forest inventory. In this study, the horizontal accuracy of 45 inventory plots in different forest environments and 5 inventory plots under open sky conditions were examined. The inventory plots were located using a mapping-grade GNSS receiver during leaf-on season in 2017. True coordinates of the plot centres were acquired using a survey-grade GNSS receiver during leaf-off season in 2018. A study was conducted across a range of forest conditions in the forest unit Vígľaš, which is located in Slovakia (Central Europe). Root mean square error of horizontal accuracies was 8.45 m in the plots under forest canopy and 6.61 m under open sky conditions. We note decreased positional errors in coniferous forests as well as in younger forests. However, results showed that there is no statistically significant effect of tree species composition and stand age on horizontal accuracy.
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Shikhov, A. N., R. K. Abdullin, and A. V. Semakina. "Mapping forest areas threatened by fires and windthrows (on the example of the Ural territory)." Geodesy and Cartography 958, no. 4 (May 20, 2020): 19–30. http://dx.doi.org/10.22389/0016-7126-2020-958-4-19-30.
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The authors discuss the methods and results of mapping the forest susceptibility to wildfires and windthrows on the example of the Ural region. We used the previously published database of fire-and wind-related forest damages in the Ural region for 2000–2016 as input data. The method of mapping is based on the analysis of the relationships of fire- and wind-damaged area with forest species composition, landscape and climatic variables, and with some indicators of anthropogenic development of the territory. The predominant forest species make the main factor determining the exposure to wildfires and windthrows. So, the calculations were performed separately for forests with various predominant species. As a result, the maps of forest susceptibility to wildfires and windthrows were created for the entire territory of the Ural, Perm region and separately for the Krasnovishersk district of the mentioned region. The obtained estimates can be used both in forestry planning and improving the monitoring of wildfires and windthrows.
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Dornelles, Mariane Paludette, Gustavo Heiden, Eimear Nic Lughadha, and João Iganci. "Quantifying and mapping angiosperm endemism in the Araucaria Forest." Botanical Journal of the Linnean Society 199, no. 1 (December 23, 2021): 449–69. http://dx.doi.org/10.1093/botlinnean/boab092.
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Abstract Neotropical forests are home to exceptional biodiversity, especially along the eastern coast of tropical and subtropical South America. In the Atlantic Forest, the subtropical Araucaria Forest harbours both tropical and temperate plant lineages. Is the presence of Araucaria angustifolia the only attribute characterizing the south-eastern South American Araucaria Forest, or is this formation also defined by the co-occurrence of other endemic species? To answer this question, we revisited the history of this vegetation from published lists and from the current distribution data of angiosperm species. We aimed to identify species endemic to the Araucaria Forest, and to analyse areas of endemism, species richness and occurrence records across the study area. The taxa reported were classified as endemic, near-endemic or non-endemic. A list of 52 endemic taxa and 28 near-endemics was built from public databases and refined. Our results indicate that the eastern region of the southern plateau between the states of Rio Grande do Sul and Santa Catarina has the highest endemism diversity. We suggest that complex interactions are involved in the origin of endemism and that lineages represented in the list of endemic species may contain key taxa for future understanding these drivers in space and time.
Dissertations / Theses on the topic "Forest species mapping"
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Galidaki, Georgia. "Mediterranean Forest Species Mapping Using Hyperspectral Imagery." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8553.
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2011/2012Advances in hyperspectral technology provides scientists the opportunity to investigate problems that were difficult if not impossible to approach using multispectral data; among those, species composition which is a very important and dynamic forest parameter, linked with many environmental qualities that we want to map and monitor. This study addresses the problem of Mediterranean forest species mapping using satellite EO-1 Hyperion imagery (30m, 196 bands). Two pixel based techniques were evaluated, namely Spectral Angle Mapper (SAM) and Support Vector Machines (SVM), as well as an object oriented approach (GEOBIA). These techniques were applied in two study areas with different species composition and pattern complexity, namely Thasos and Taksiarchis. Extensive field work provided reference data for the accuracy assessment of the produced maps. Image preprocessing included several steps of data corrections and the Minimum Noise Fraction transformation, as means for data dimensionality reduction. In the case of Thasos, where two conifer species are present, SAM technique resulted in an overall accuracy (OA) of 3.9%, SVM technique yielded OA of 89.0% and GEOBIA achieved an OA of 85.3%. In the case of Taksiarchis, where more species are present – both conifers and broadleaved- the respective OA was 80.0%, 82.6% and 74.1%. All three methodologies implemented to investigate the value of hyperspectral imagery in Mediterranean forest species mapping, achieved very accurate results; in some cases equivalent to forest inventory maps. SAM was the straightest forward to implement, only depending on the training samples. Implementation SVM involved the specification of several parameters as well as the use of custom software and was more successful in the challenging landscape of Taksiarchis. GEOBIA adapted to scale through segmentation and extended the exercise of classification, allowing for knowledge based refinement. Lower accuracies could be attributed to the assessment method, as research on alternative assessment methods better adapted to the nature of object space is ongoing. Two typical Mediterranean forests were studied. In Thasos, two conifer species of the same genus, namely Pinus brutia and Pinus nigra, dominate a big part of the island. Both of them were accurately mapped by all methodologies. In Taksiarchis primarily stands of Quercus frainetto mix with stands of Fagus sylvatica and the aforementioned pines. The two pines were again mapped with high accuracy. However, there was a notable confusion between the two broadleaved species, indicating the need for further research, possibly taking advantage of species phenology. The outcome of the proposed methodologies could confidently meet the current needs for vegetation geographical data in regional to national scale, and demonstrate the value of hyperspectral imagery in Mediterranean forest species mapping.
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1981
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Johnson, Derek. "Predictive vegetation mapping of forest overstorey species using terrain /." St. Lucia, Qld, 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18018.pdf.
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Weisgerber, Elizabeth Kay. "Classifying and mapping diversity in a species-poor system: the mangrove meta-community of Laguna Chacahua National Park, Oaxaca, Mexico." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/622.
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ABSTRACT
Classifying and Mapping Diversity in a Species-Poor System: the mangrove meta-community of Laguna Chacahua National Park, Oaxaca, Mexico
by
Elizabeth Kay Weisgerber
Both field transects and imagery grid plots were analyzed with the goal of creating a community classification map for the mangrove forest of Parque Nacional Lagunas de Chacahua. In total, data was collected in 49 sites throughout the park, recording measures such as DBH, basal area, estimated dominance, frequency, cover and relative dominance. Field locations were marked and georeferenced with a GPS and grid plots overlaid on satellite imagery of the park were generated via a random number table. Species' attributes delineating the identifiable features for each dominant species within Chacahua were noted and used to recognize patterns in species assemblages.
Fourteen distinct ‘sub-communities’ within the mangrove meta-community were recognized using this data and verified with 165 field photos. Relative dominance values were compared between field and grid data. These data revealed a similar pattern with Rhizophora mangle being most dominant under each method. Avicennia germinans was second in relative dominance, Laguncularia racemosa a close third while Conocarpus erectus was rarely found, most likely due to over-harvesting.
Rapid degradation of mangrove forests is occurring on a global scale. Understanding the complex dynamics that occur within the mangrove meta-community is essential to its conservation. Vegetation maps are essential tool in monitoring changes throughout the mangrove but are rarely of sufficient detail for everyday use. Generating highly detailed vegetation maps in a cost-effective and timely manner is an important step for their conservation, particularly in developing countries. This study demonstrates methods and techniques for producing a vegetation map that portrays the level of complexity that exists within the species-poor mangrove environment. This map will be donated to the management team of Parque Nacional Lagunas de Chacahua to better aid in management.
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Krahwinkler, Petra Maria [Verfasser]. "Machine learning based classification for semantic world modeling : support vector machine based decision tree for single tree level forest species mapping / Petra Maria Krahwinkler." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2013. http://d-nb.info/1038510147/34.
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Sunarto, Sunarto. "Ecology and restoration of Sumatran tigers in forest and plantation landscapes." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/37392.
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Tigers (Panthera tigris Linnaeus, 1758) are in danger of extinction. Their populations have declined from ~100,000 to only ~3,000 individuals in a century and their habitat has shrunk to less than 7% of the historic range. Of the five extant tiger subspecies, the Sumatran tiger (Panthera tigris sumatrae Pocock, 1929) is the most seriously threatened. Currently determined as Critically Endangered under IUCN criteria, the Sumatran tiger is likely to become extinct unless effective conservation measures are enacted. Threats to the tiger include habitat destruction, killing due to conflict with humans and livestock, and poaching for illegal wildlife trade.
Long-term survival of Sumatran tigers depends largely on the effectiveness of current conservation efforts in every tiger landscape. Successful conservation and management require accurate information on ecology of the species upon which decisions can be based. This study investigated basic ecological aspects of tigers and developed strategies for management and restoration to improve tiger viability in the Central Sumatra landscape. This landscape is comprised of natural forests and plantations managed for timber and agricultural commodities.
The first chapter assesses the variation in tiger abundance across forest types in Southern Riau, and over time in Tesso Nilo National Park, all in Central Sumatra. Using camera traps, my team and I systematically sampled five blocks representing three major forest types in the region: peat land, flat lowland, and hilly lowland. I found that tiger abundance varied by forest type and through time. Excluding two sampling blocks where no tigers were photographed, the lowest tiger density was in peat land forest of Kerumutan, and the highest density was in the flat lowland forest of Tesso Nilo. Repeated sampling in the newly established Tesso Nilo National Park documented a trend of increasing tiger density (SE) from 0.90 (0.38) individuals/100 km2 in 2005 to 1.70 (0.66) individuals/100 km2 in 2008. Overall, tiger densities from this study were lower than most previous estimates from other parts of Sumatra. The trend of increasing tiger density in Tesso Nilo, however, suggests that the tiger population could be augmented by protection of habitats that were previously logged and severely disturbed.
The second chapter examines the occupancy and habitat-use of the tiger across the major landcover types (natural forest, acacia plantation, oilpalm plantation, rubber plantation, and mixed agriculture). I found that tigers used some plantation areas, although they significantly preferred forests over plantations. In all landcover types, sites with tiger detections had thicker understory cover than sites without tiger detection. Modeling tiger occupancy while recognizing that probability of detection is not always perfect, I found that tiger occupancy covaried positively and significantly with altitude and negatively, but not significantly, with distance-to-forest-cores. Probability of habitat use by tigers covaried positively and significantly with understory cover and altitude, and negatively and significantly with human settlement and landcover rank. The results suggested that with adjustments in plantation management, tigers could use or roam through plantations within the habitat mosaic provided that the plantations had adequate understory cover and low level of human activity. They also could use riparian forests (as corridors) and smaller forest patches (as stepping stones) to travel between the main habitat patches across the forest and plantation landscape.
The third chapter investigates the ecological characteristics and possible inter-specific interactions among wild felids, including tigers and smaller cats, based on data collected using systematic camera trapping in combination with information on their natural history. I found that despite overlap in resource needs of the five felid species, each appears adapted to specific environmental conditions allowing coexistence with other felids. The five felid species used statistically different elevations, with the golden cat found to inhabit the highest elevation. Two-species occupancy models showed that only leopard cats were found to co-occur with other felid species more frequently than expected by chance under independence. Species of similar size or eating similar-sized prey generally tended to have low coefficients of temporal activity overlap, suggesting avoidance. Temporal avoidance is likely occurring in three pairs of felids, namely clouded leopards and golden cats, clouded leopards and marbled cats, and marbled cats and leopard cats. Based on the differences in morphological and ecological characteristics, and on patterns of spatial and temporal occurrence, I identified six possible mechanisms by which felids in Central Sumatra maintain coexistence. I discussed the implications of this study for management, focusing on how to balance diversity and abundance of felids.
The fourth chapter presents the tiger distribution models as a case study to illustrate the importance of accounting for uncertainty in species distribution mapping. I applied four modeling approaches, differing in how the response variable (tiger presence) is constructed and used in the models. I compared the performance and output of different models based on the relative importance of variables, descriptive statistics of the predictions, cross comparison between models using an error matrix, and validation using tiger presence data collected from independent surveys. All models consistently identified forest area within the grid as one of the most important variables explaining tiger probability of occurrence. Three models identified altitude as another important factor. While the four models were consistent in predicting relatively high probability of tiger occurrence for high elevation forest areas such as Rimbang Baling and Bukit Tigapuluh, they generally had a lower level of agreement in predictions for low elevation areas, particularly the peat land in the northeastern part of the study area. Based on the results of cross evaluation of the predictions among models and validation with the independent data, I considered the occupancy model to be superior to the others. If data collection format permits, I advocate the use of occupancy instead of the other modeling techniques to develop predictive species distribution maps.
The last chapter constructs a strategy to restore the tiger population across the ecosystem of Central Sumatra through integration of knowledge on tiger ecology from previous chapters with consideration of the ecological conditions of the landscape in the region. The strategy combines existing knowledge of tiger conservation and regional ecosystem restoration. It recognizes the limitations and challenges of traditional nature protection and considers existing and new opportunities. Emerging opportunities and new mechanisms, such as direct and indirect economic incentives for nature conservation and restoration, are taken into account. These, coupled with increased awareness of the stakeholders, better policies and implementation of good governance, and the willingness and know-how to maintain coexistence with wildlife among the local people, are expected to support and accelerate the recovery of tigers and their ecosystem.Ph. D.
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Dogan, Hakan Mete. "Understanding And Modeling Plant Biodiversity Of Nallihan (a3-ankara) Forest Ecosystem By Means Of Geographic Information Systems And Remote Sensing." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/4/1172436/index.pdf.
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In this study, geographic information systems (GIS) and remote sensing (RS)
tools were integrated and used to investigate the plant species diversity of the Nallihan
forest ecosystem. Two distinct indices, Shannon Wiener and Simpson, were employed in
order to express species diversity. The relationships between the indices and pertinent
independent variables (topography, geology, soil, climate, supervised classes, and
Normalized Difference Vegetation Index (NDVI) classes) were investigated to develop
two distinct models for each index. After detecting important components with factor
analysis, two models were developed by using multiple regression statistics. Running the
models, two plant species diversity maps in grid format were produced. The validity of the
models were tested by (1) mapping residuals to predict the locations where the models
work perfectly, and (2) logical interpretations in ecological point of view. Elevation and
climatic factors formed the most important component that are effective on plant species
diversity. Geological formations, soil, land cover and land-use characteristics were also
found influential for both models. Considering the disturbance and potential
evapotranspiration (PET), the model developed for Shannon Wiener index was found out
more suitable comparing the model for Simpson index.
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Hill, Louise. "Ecological impacts of ash dieback in Great Britain." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:941ee684-798b-47b6-ae51-64baeb5f821e.
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Ash dieback is a severe disease of ash trees (Fraxinus spp.), caused by the invasive fungus Hymenoscyphus fraxineus. In its native East Asia, H. fraxineus is a harmless endophyte, but since its accidental import into Europe in the early 1990s it has infected over 90% of ash trees in some areas, with long-term mortality sometimes exceeding 90%. The disease was discovered in Great Britain in 2012, and has since spread rapidly. This thesis investigates some of the possible impacts on biodiversity, ecosystem functioning, and society, and in doing so identifies ways to alleviate some impacts. Britain has only 13% tree cover (among the lowest in Europe), so may be particularly vulnerable to ash loss. Better understanding of the effects and how to minimise them is critical to deliver an evidence-based response. First, we investigated impacts in woodlands by experimentally killing woodland ash trees by ring-barking. We found no short-term effect of ash loss on ground flora or earthworm communities, or on the regeneration or growth of other woody species. Observational evidence suggested that remaining canopy trees rapidly filled gaps left by ash, perhaps contributing to stability. Our woodlands appeared to be remarkably resilient to ash loss, although there may be long-term effects or impacts on other species that this experiment failed to observe. To investigate broader-scale impacts, we required high-quality abundance maps for ash and other trees across Britain. Using species distribution modelling and random forest regression, we developed a protocol to produce abundance maps from readily available data. We tested the predictive power of the resulting maps using cross validation. Our maps are the best available for abundance of British tree species, and will be useful across a wide range of disciplines. We then used them to model ecosystem vulnerability to ash loss, based on the abundance of ash and other tree species, and their ecological trait similarity. We identified areas at risk of the largest impacts, and produced guidance for positive management actions to minimise ecological change. Lastly, we investigated the financial impacts of ash dieback, estimating the total cost to Britain at £9.2 billion. This figure is many times larger than the value of lost trade if biosecurity were improved to prevent future invasions, questioning the validity of financial arguments against biosecurity. We also found that loss of ecosystem services accounted for less than a third of the total cost, suggesting that ecosystem service assessments may miss a large proportion of the true cost of biodiversity loss. Overall, we found that some impacts may be less than expected, such as local effects on woodland ground flora, and others, such as the economic cost, may be much larger than expected. However, the resilience of ecosystems to a major shock such as loss of a common species, and actions to mitigate the impacts, depend on having a diversity of other trees present. The ash dieback outbreak highlights the importance of preventing other severe pests and diseases of trees from being introduced; something that has been increasing exponentially, largely due to international trade in trees. This thesis provides further firm evidence that there is an ecological and social imperative to halt this trend.
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Yadav, BKV. "Mapping species composition and structure in wet eucalypt forest using multi-source remote sensing data." Thesis, 2019. https://eprints.utas.edu.au/34651/1/Yadav_whole_thesis.pdf.
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Tasmanian wet eucalypt forests are internationally important for wood and paper production, carbon storage and biodiversity conservation. These forests contain tall eucalypts over dense understories of rainforest and wet sclerophyll species. This research was motivated by a need for tools to replace costly aerial photo interpretation (PI-type) mapping for describing forest species composition and stand structure. Overall, I aimed to develop approaches for assessing and mapping tree species distribution and forest structure of wet eucalypt forest in a 5 km by 5 km area of the Warra Supersite, Tasmania, using multi-source remote sensing data.
My first study used an airborne LiDAR-derived canopy height model (CHM) and hyperspectral imagery to classify up to five dominant tree species of the forest. I used random forest classifiers on objects generated using data segmentation under a range of scenarios. Fused CHM and Minimum Noise Fraction (MNF) datasets yielded the highest segmentation accuracy (88.71%). The fusion of hyperspectral imagery, CHM and vegetation indices produced the best classifiers (overall accuracy (OA) of 66.7%) followed by the fused dataset of MNF and CHM (OA = 66.0%). Hyperspectral imagery alone provided the lowest classification accuracy (OA = 59.0%). Accuracy for the dominant canopy species (Eucalyptus obliqua) was 90.86% for four vegetation classes and 86.11% for five classes. Classification accuracies for the important understory species, Dicksonia antarctica, were also high under the best models (~84%). Accuracies for other species were low. Thus, fused hyperspectral and LiDAR data were robust and capable of spatially discriminating several important forest species.
My second study utilised LiDAR-derived topographic attributes and mapped geological strata to develop a model for predicting three understory layers of the forest (≥2 to ≤10 m, >10 to ≤30 m and >30 to ≤50 m as proxies for the lower, middle and upper layers, respectively) using five different spatial resolutions using random forest regression. Overall, the 30 m resolution provided the best model for predicting understory layers compared to 1 m, 5 m, 10 m and 20 m resolutions. The predictive power for the upper layer was greatest (R\(^2\) = 0.82), followed by the lower layer and the middle layer. Geology had the highest variable importance score for 5 m, 10 m, 20 m and 30 m resolutions, whereas terrain position index had the highest variable importance score for 1 m resolution. This research demonstrated that LiDAR-derived topographic attributes and geology data could be used to predict the understory vegetation structure.
My third study developed robust and cost-effective approaches for predicting the densities of vertical structural layers of the forest based on multispectral satellite data and simulated operational LiDAR datasets. I assessed the robustness of forest structure models based on thirteen schemes of derivatives (vegetation indices, texture features, and topographic attributes) from three different data sources (Airborne LiDAR downscaled to operational density, WorldView-3 and Landsat-8 (OLI)) at spatial resolutions (1.60 m, 7.5 m and 30 m). Models for the upper and middle layers were better than those for the lower layer. The 30 m Landsat-8 data provided the best results for all three-pixel sizes (R2 values ranged 0.15 to 0.65). Fused data from Landsat-8 and the simulated low-density LiDAR showed modest accuracy for predicting the density of three vertical layers and could be adopted by forest managers and planners. The WorldView-3 data of 1.6 m pixel size did not produce useful models.
In conclusion, the fusion of remote sensing datasets may help assess and map woody plant species composition and structure of wet eucalypt forests with opportunities to replace the traditional, subjective and time-consuming mapping technique of aerial photo interpretation. My results highlight the potential of freely available Landsat-8 (OLI) and operational LiDAR data, and random forest machine learning techniques for predicting and mapping forest species and vertical structural layers of wet eucalypt forests. This thesis addressed data complexities, including multidimensionality and nonlinearity in multi-source data, and provided a robust approach for the assessment of wet eucalypt forest composition and structure.
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Cruz, Pablo Henrique Alves. "Mapping urban tree species in a tropical environment using airborne multispectral and LiDAR data." Master's thesis, 2021. http://hdl.handle.net/10362/113904.
Full textAbstract:
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAccurate and up-to-date urban tree inventory is an essential resource for the development of strategies towards sustainable urban planning, as well as for effective management and preservation of biodiversity. Trees contribute to thermal comfort within urban centers by lessening heat island effect and have a direct impact in the reduction of air pollution. However, mapping individual trees species normally involves time-consuming field work over large areas or image interpretation performed by specialists. The integration of airborne LiDAR data with high-spatial resolution and multispectral aerial image is an alternative and effective approach to differentiate tree species at the individual crown level. This thesis aims to investigate the potential of such remotely sensed data to discriminate 5 common urban tree species using traditional Machine Learning classifiers (Random Forest, Support Vector Machine, and k-Nearest Neighbors) in the tropical environment of Salvador, Brazil. Vegetation indices and texture information were extracted from multispectral imagery, and LiDAR-derived variables for tree crowns, were tested separately and combined to perform tree species classification applying three different classifiers. Random Forest outperformed the other two classifiers, reaching overall accuracy of 82.5% when using combined multispectral and LiDAR data. The results indicate that (1) given the similarity in spectral signature, multispectral data alone is not sufficient to distinguish tropical tree species (only k-NN classifier could detect all species); (2) height values and intensity of crown returns points were the most relevant LiDAR features, combination of both datasets improved accuracy up to 20%; (3) generation of canopy height model derived from LiDAR point cloud is an effective method to delineate individual tree crowns in a semi-automatic approach.
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Dinger, Eric J. "Characterizing early-seral competitive mechanisms influencing Douglas-fir seedling growth, vegetation community development, and physiology of selected weedy plant species." Thesis, 2012. http://hdl.handle.net/1957/29479.
Full textAbstract:
Three studies were conducted to characterize and present early-seral
competition between Douglas-fir seedlings and the surrounding vegetation
communities during Pacific Northwest forest establishment. The first experiment
served as the foundation for this dissertation and was designed to quantify tradeoffs
associated with delaying forest establishment activities by introducing a fallow year
in order to provide longer-term management of competing vegetation. A range of six
operationally relevant treatments were applied over two growing seasons that
included in the first (1) a no-action control, (2) a spring release only, (3) a fall site
preparation without sulfometuron methyl followed by a spring release, as well as (4) a
fall site preparation with sulfometuron methyl and a spring release. In the second
year, there was (5) a fall site preparation without sulfometuron methyl followed by a
spring release and also in the second year (6) a fall site preparation with sulfometuron
methyl and a spring release. Treatments 5 and 6 were left fallow without planting
during the first year. These treatments were applied in two replicated experiments
within the Oregon Coast Range.
After adjusting for initial seedling size, year-3 results indicated that plantation
establishment and competition control immediately after harvest (i.e. no fallow
period) enabled seedlings to be physically larger than those planted after a one year
delay. At the Boot study site, limiting vegetation below 20% for the first growing
season improved year-3 Douglas-fir seedling stem volume over 273 cm³. Delaying
establishment activities one year and reducing competing vegetation below 11%
enabled seedling volume after two years to be statistically the same as three year old
seedlings in the no-action control, a volume range of between 148 to 166 cm³.
Delaying forest establishment at Jackson Mast improved seedling survivorship over
88% when a spring heat event reduced survivorship of trees planted a year earlier to
less than 69%. The combined effect of applying a fall site preparation and spring
release was necessary to reduce competitive cover below 10% in the year following
treatment and provided longer-lasting control of woody/semi-woody plants. Less
intense control measures (i.e. no-action control and treatment 2) were not able to
restrain woody/semi-woody plant cover which grew to nearly 40% at Boot and over
24% at Jackson Mast in three years. No treatment regime provided multi-year control
of herbaceous species. Including sulfometuron methyl in the fall site preparation
tank-mix did not have a negative effect on seedling growth or provide significant
reductions in plant community abundance in the year following application when
compared to similar regimes that did not include the chemical. Delaying
establishment lengthened the amount of time associated with forest regeneration
except on a site that accentuated a spring heat event.
In the second study, horizontal distance and azimuth readings provided by a
ground-based laser were used to stem map seedling locations and experimental unit
features at Boot. These data were used to create a relative Cartesian coordinate
system that defined spatially explicit polygons enabling, for the first time, the ability
to collect positional data on competing forest vegetation within an entire experimental
unit. Deemed "vixels" or vegetation pixels, these polygons were assessed for
measures of total cover and cover of the top three most abundance species during the
initial three years of establishment. An alternate validity check of research protocols
was provided when total cover resulting from this vixel technique was compared to a
more traditional survey of four randomly located subplots. The resulting linear
regression equation had an adjusted R² of 0.90 between these two techniques of
assessing total cover. When compared within a treatment and year, total cover
differed by less than 12 percentage points between the two techniques. Analysis of
year-3 woody/semi-woody plant cover produced by the techniques led to identical
treatment differences. Two treatments resulted in woody/semi-woody cover of
approximately 1500 ft² by the vixel method and nearly 40% cover by the subplot
method while the remaining four treatments were grouped below 600 ft² or 20%
cover, respectively. With continued refinement, these techniques could visually
present forest development through all phases and provide long-term information
used to bolster growth and yield models, measures of site productivity, as well as
community ecology research.
The third study evaluated the season-long gas exchange and biomass
partitioning of four weedy plant species capable of rapidly colonizing Pacific
Northwest regenerating forests. Cirsium arvense, Cirsium vulgare, Rubus ursinus
and Senecio sylvaticus were studied at two sites. A greenhouse was used to introduce
two levels of irrigation (well-watered and droughty). These species were also studied
while growing among a larger vegetation community at a field site. Irrigation
treatments had little impact on gas exchange rates. Species achieved maximum
photosynthetic rates of 30, 20, 15 and 25 μmol CO₂ m⁻² s⁻¹ (respectively) prior to
mid-July coinciding with an active phase of vegetative growth. As the season
progressed, photosynthetic rates declined in spite of well-watered conditions while
transpiration rates remained relatively consistent even when soil water decreased
below 0.25 m³ H₂O/m³ soil. Water use efficiency was high until late-July for all study
species, after which time it decreased below 5 μmol CO₂ · mmol H₂O⁻¹. Multi-leaf
gas exchange measurements as well as biomass data provided a holistic view of plantlevel
mechanisms used to shunt activity toward developing tissues. Herbaceous
species had assimilation rates that differed vertically (within each species) by as much
as 10 to 20 μmol CO₂ m⁻² s⁻¹ from July to September as lower leaves senesced in
favor of those higher on study plants. Specific leaf area was greatest in June for all
species then declined indicating species placed little effort into sacrificial early season
leaves when compared to those higher on the plant that could continue to support
flowering or vegetative growth. The study of seasonal gas exchange in the presence
of declining water availability has helped to describe competitive mechanisms at
work during forest regeneration as well as provide physiologic support for the
application of vegetation management regimes.Graduation date: 2013
Books on the topic "Forest species mapping"
1
Morin, R. S. Mapping host-species abundance of three major exotic forest pests. Newtown Square, PA (11 Campus Blvd., Newton Square 19073-3294): U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 2005.
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2
Hershey, Rachel Riemann. Creating a "first-cut" species distribution map for large areas from forest inventory data. Radnor, PA: U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 1999.
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Hershey, Rachel Riemann. Creating a "first-cut" species distribution map for large areas from forest inventory data. Radnor, PA (1 Radnor Corp Ctr, Suite 200, Radnor 19087-4585): U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 1999.
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4
Hershey, Rachel Riemann. Creating a "first-cut" species distribution map for large areas from forest inventory data. Radnor, PA: U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 1999.
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5
Hershey, Rachel Riemann. Creating a "first-cut" species distribution map for large areas from forest inventory data. Radnor, PA (1 Radnor Corp Ctr, Suite 200, Radnor 19087-4585): U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 1999.
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6
Hershey, Rachel Riemann. Creating a "first-cut" species distribution map for large areas from forest inventory data. Radnor, PA (1 Radnor Corp Ctr, Suite 200, Radnor 19087-4585): U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 1999.
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7
Hershey, Rachel Riemann. Creating a "first-cut" species distribution map for large areas from forest inventory data. Radnor, PA: U.S. Dept. of Agriculture, Forest Service, Northeastern Research Station, 1999.
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8
Newton, Adrian C. Towards a global tree conservation atlas: Mapping the status and distribution of the world's threatened tree species. Cambridge, United Kingdom: UNEP World Conservation Monitoring Centre, 2003.
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9
United States. Science and Education. Plant genome research: A report of a Conference on Crop and Forest Species Genome Mapping. 1988.
Find full textBook chapters on the topic "Forest species mapping"
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Kala, Abhishek K., Suchismita Mukhopadhyay, and Akshay Paygude. "SmarteR Approach for the Mapping of Invasive Plant Species." In Forest Dynamics and Conservation, 363–84. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0071-6_17.
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Kumar, Rohit, Akhilesh Singh, Uttara Pandey, Parul Srivastava, and Swapan Mehra. "Mapping the Extent of Invasive Species: An Assessment Based on High-Resolution Data for Selected Species in Parts of Eastern Himalaya in Sikkim." In Forest Dynamics and Conservation, 249–59. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0071-6_12.
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Löfstrand, R., S. Folving, P. Kennedy, J. Puumalainen, T. Coch, B. Kenter, M. Köhl, et al. "Habitat Characterization and Mapping for Umbrella Species — An Integrated Approach Using Satellite and Field Data." In Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring, 191–204. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0649-0_15.
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Huntley, Brian John. "Biomes: Concepts, Characteristics and Terminology." In Ecology of Angola, 15–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18923-4_2.
Full textAbstract:
AbstractA cornerstone of ecology is the mapping and classification of biogeographical and ecological diversity, defining patterns in species distribution and abundance. This Chapter presents the concepts of biomes, biogeographical realms and centres of endemism, and the relationship of Angolan and southern African biogeographic units to the rest of the continent. Key ecological terms are defined and characterised using Angolan examples, within the context of the biomes and transitional mosaics found across Africa. The 16 ecoregions of Angola are mapped and their surface area, altitudinal range, and mean annual rainfall quantified. The ecological characterisation of rain forest, mesic savanna and arid savanna biomes are explained in quantitative and graphic detail.
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ur Rahman, Amjad, Esra Gürbüz, Semih Ekercin, and Shujaul Mulk Khan. "Spatial Dynamics of Forest Cover and Land Use Changes in the Western Himalayas of Pakistan." In Vegetation Index and Dynamics. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.98401.
Full textAbstract:
The current study deals with the mapping and evaluation of forest and land use cover changes in the western Himalayas, Pakistan. These forest types include i) Moist temperate forests ii) Mixed coniferous forests and iii) Sub-tropical broad leaved forests. Moist temperate forest mostly consists of evergreen conifers with some of oaks and deciduous trees. Subtropical pine forest are mostly dominated by Pinus roxburghii. These forest type are mostly mixed by Pinus roxburghii and other coniferous species like Pinus wallichiana at the upper ranges in Dewal, Angoori, Nambal, Aucha and Khanitak etc. The broad-leaved subtropical forests are recorded on the hills and in the lower slopes of Himalaya near Islamabad and Rawalpindi. The high quantity of vegetation index were observed in winter season as compared to summer. The Landsat satellite images of years 1988, 1998, 2008 and 2018 were classified into land-cover units. Vegetation land decreased in the total area whereas the bare land class increased in the total. Water class further reduced and the built- up class increased up in the Murree area, Pakistan.
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Wickramasinghe, Amila, Nazmul Khan, Alexander Filkov, and Khalid Moinuddin. "Physics-Based Modelling for Mapping Firebrand Flux and Heat Load on Structures in the Wildland-Urban Interface’." In Advances in Forest Fire Research 2022, 746–50. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_114.
Full textAbstract:
The mechanisms of structure ignition by wildfires are classified into direct flame contact, radiant heat, firebrand attack and a combination of two or all of them. Arguably, airborne firebrands play a vital role as the main cause for structure ignition and fire propagation by forming spot fires far from the fire front. Firebrand flux and the heat load are important parameters to calculate the wildfire risk on structures. Australian Building Standard AS3959 is developed based on radiation heat flux and it does not quantify the effects of firebrand landing flux on structures to assess the wildfire risk completely. To improve the assessment of the Bushfire Attack Level (BAL) in AS3959, there is a need for firebrand flux quantification at different scales of wildfires. Lacking information about firebrand generation from various vegetation species at different environmental conditions creates a gap to estimate the firebrand flux accurately. In this study, we aim to use a physics-based model to quantify the firebrand generation rate of Eucalyptus dominant forest vegetation at different severities of wildfires expressed by the Fire danger indices (FDI) of 100, 80, 50. The wind speed is adjusted while keeping the temperature, relative humidity, and drought factor as constants to obtain the focused FDIs. A 40 m height Eucalyptus forest is modelled with 25 t/ha understorey and 10 t/ha canopy fuel loads as per AS3959 forest vegetation classification. The forest fires are prescribed with the intensities of 53.4, 43.1, and 27 MW/m with 100 m length to replicate the fire events explained by FDIs. The depth of the fireline is approximated according to the fire residence time and the spread rate. The firebrand size, shape, and quantity are taken from our previous firebrand generation study (Wickramasinghe et al. 2022) and the particles are injected randomly through the forest volume which is engulfed by the fire. The distances between the modelled structure that follows an Australian standard house design and the vegetation are maintained according to the BALs. We obtained the radiative heat flux on the houses close to the algorithm provided in AS3959 for each BAL. In this study, both firebrand and heat flux are quantified at strategic locations of the house. We find a logarithmic relationship exists between firebrand flux and radiative heat flux in the range of R2 0.96 to 0.99. Hence, for a certain BAL, the firebrand flux increases with the FDI similar to radiative heat flux. Results from this study can be used to quantify the firebrand flux on houses from different vegetation fires, which may improve the design standards and construction requirements of buildings to mitigate the vulnerability of wildfires at the wildland-urban interface (WUI).
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Horning, Ned, Julie A. Robinson, Eleanor J. Sterling, Woody Turner, and Sacha Spector. "Human interfaces and urban change." In Remote Sensing for Ecology and Conservation. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780199219940.003.0019.
Full textAbstract:
For the first time in human history, more people live in urban areas than in rural areas, and the patterns of suburbanization and urban sprawl once characteristic of North America are now present globally (Obaid 2007). As conservation biologists seek to prioritize conservation efforts worldwide, urbanization and agricultural development emerge as two of the most extensive processes that threaten biodiversity. Suburban and rural sprawl are significant drivers of forest fragmentation and biodiversity loss (e.g., Murphy 1988; Radeloff et al. 2005). Data on human impacts is often averaged across political boundaries rather than biogeographic boundaries, making it challenging to use existing data sets on human demography in ecological studies and relate human population change to the changes in populations of other species. Remotely sensed data can make major contributions to mapping human impacts in ecologically relevant ways. For example, Ricketts and Imhoff (2003) assigned conservation priorities (based on species richness and endemism) for the United States and Canada using several different types of remotely sensed data. For mapping urban cover, they used the map of “city lights at night” from the Defense Meteorological Satellite Program (Imhoff et al. 1997) to classify land as urbanized or not urbanized. For mapping agricultural cover, they used the USGS North America Seasonal Land Cover map (Loveland et al. 2000), derived from the Advanced Very High Resolution Radiometer (AVHRR), lumping five categories to create an agricultural land class. For ecological data, they used a compilation of ecoregion boundaries combined with range maps for over 20,000 species in eight taxa (birds, mammals, butterflies, amphibians, reptiles, land snails, tiger beetles, and vascular plants; Ricketts et al. 1999). Analyzing these data, Ricketts and Imhoff (2003) identified a strong correlation between species richness and urbanization. Of the 110 ecoregions studied, 18 ranked in the top third for both urbanization and biodiversity (species richness, endemism, or both); some of the ecoregions identified as priorities were not identified by a previous biodiversity assessment that did not include the remotely sensed mapping of urbanization (Ricketts et al. 1999).
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Lorenț, Adrian, Marius Petrila, Vladimir Gancz, Florin Capalb, Bogdan Apostol, and Nicolae-Ovidiu Badea. "Quick assessment of burn area and burn severity on black locust stands using Sentinel 2 imagery in South-West Romania." In Advances in Forest Fire Research 2022, 1282–91. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_194.
Full textAbstract:
In Romania forest fires have a strong annual and seasonal variability, having a distribution pattern correlated with human activities like stubble burning practices. The most fire affected region is located in the southwest of Romania, in the counties of Mehedinți and Gorj with mosaic landscapes of forests with agricultural land and human settlements. Although the year 2021 was generally a mild fire season at national level, almost 60% of the forests burned area occurred in Mehedinți, county whose surface covers 2.06% of the national territory. The affected area is determined by measurements with GPS receivers or by an approximate estimate of the percentage of affected area in a forest parcel. The accuracy of those determinations is not being verified through other procedures like remote sensing-based methods, thus there is a need for independent evaluation of the reported fire impact. The aim of this research is to perform a quick robust enough assessment of the areas affected by forest fires in August 2021 and their severity in a forested area covered mostly by black locust (Robinia pseudoacacia), located in the southwestern part of Mehedinți county, using Sentinel 2 imagery and in field validation. In order to assess the degree of damage of the forest stands, Sentinel 2 satellite images were used, before, during and after the fire events. Additionally, official RGB and CIR aerial orthophoto images with a spatial resolution of 0.5 m from 2015 were used together with ultra-high resolution orthoimages obtained by processing RGB aerial photographs taken post-fire event with an UAV. Several remote sensing-based indices mostly used in forest fire-science literature were tested in order to extract the affected burn area and burn severity, among which the Difference Normalized Burn Ratio (dNBR) proved to give the best estimates. A field inspection was carried out in order to verify whether the burn severity classes are corresponding to the ground truth impact of the fires. By comparing the dNBR mapping with terrain observations, we found that burn severity is corelated with canopy density and height of the trees, at least in black locust stands, who naturally have lower canopy density than other deciduous tree species. The correlation between dNBR and the density of the trees is negative, that means low density shows a false high severity on dNBR map and vice versa because the burnt grass and understory layer is more visible from above and denser in sparse forest stands. Also, low height of the trees induces high severity on the dNBR, the flames and heat produced by the burnt understory reaching easily to the canopy level. These observations were afterwards used to adjust the burn severity map.
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Leavesley, Adam, Marta Yebra, Petter Nyman, and Tony Scherl. "Manipulating fire regimes in sensitive ecosystems to adapt to climate change." In Advances in Forest Fire Research 2022, 914–20. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_138.
Full textAbstract:
Fire regimes in Australian temperate forests have changed with the area burnt by bushfires having increased due to changing climate extremes every decade for the past 40 years. The Australian Capital Territory (ACT) has been heavily impacted with year-since-last-fire in forests being the shortest of all Australian jurisdictions due to large, intense bushfires during the droughts of 2003 and 2020. The forested landscapes of the ACT are predominantly mountainous and scattered throughout are species and communities which are likely to be disadvantaged by an increase in the frequency of high intensity fire. Examples include alpine bogs, Alpine Ash (Eucalyptus delegatensis) and Mountain Plum Pine (Podocarpus laurencei). Many of these systems were severely burnt in 2003 and 2020. Protection of these species and communities from bushfires is a high priority for conservation, but suppression operations are difficult due to inaccessibility and the danger to firefighters. A potential solution is to use prescribed burning to manipulate fire regimes to reduce risk. In this paper, we describe an approach designed to reduce bushfire risk while optimising land management workloads and total area burnt. The approach has two key components: 1) development of a method for managing landscape bushfire risk in time; and 2) utilisation of landscape flammability mapping to design burn infrastructure to meet ecological objectives. Bushfire risk planning is focused on space, but risk also changes in time with the effects of drought on fuel moisture accumulating and drying over multiple years. This time-scale offers an opportunity to intervene to reduce bushfire risk in fire sensitive ecosystems and influence fire regimes in favour of those ecosystems using prescribed burning. To do this, bushfire planners need to identify ecosystems at risk and develop burns which are to be implemented contingent on agreed climatic triggers. Landscape flammability in mountainous landscapes in southeastern Australia during the autumn prescribed burning season is driven by solar radiation with north faces being drier and much more likely to burn than south faces. This imposes a critical constraint on prescribed burn planning. We analysed conducted an assessment of the feasibility of designing burns to protect alpine bogs, Mountain Plum Pine and Alpine Ash. There appears to be some potential for reducing fuels around alpine bogs and good potential for emhancing protection of Mountain Plum Pine. On the other hand Alpine Ash stands largely occur on southern slopes are do not appear to be easily amenable to fire regime manipulation.
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Ganteaume, Anne, Bruno Guillaume, and Bertrand Girardin. "Impact of the WUI vegetation management on damage to building: comparing post-fire damage assessment and CFD modelling results." In Advances in Forest Fire Research 2022, 626–37. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_95.
Full textAbstract:
In most Wildland-Urban Interface (WUI) fires, damage to building can result from poor vegetation management, comprising a lack of mandatory brush-clearing around building or an unwise location of trees (e.g. too close to building or overhanging the roof). It was interesting to check if post fire damage can be predicted by CFD fire modelling in different scenarios of vegetation management (treated vs untreated) according to different past-fire events. Ultimately, this work will help to assess if the fuel treatment regulation in the framework of fire prevention is efficient and to pinpoint possible limitations. Different scenarios of vegetation treatments were tested on four study cases of dwellings surrounded by gardens juxtaposed to wildland vegetation (therefore submitted to the regulation on fuel management in WUI) that were affected by the Vidauban fire in 2003 or the Rognac fire in 2016 (SE France). In each case, comparisons of modeled and observed fire behaviour and impacts on buildings were performed using the Fire Dynamic Simulator model (FDS) and very accurate georeferenced ornamental vegetation mapping, especially around buildings. Despite problems to adapt FDS modelling to the high fuel moisture content (FMC) of some species, results showed that overall brush-clearing mitigated fire intensity and propagation and therefore damage to building and ornamental vegetation, sometimes highlighting that this fuel management measure could be softened (decreasing the radius treated) or, on the contrary, strengthened according to the topography and wind. The fuel biomass treatment involving vegetation residues left on site was also found as deleterious as the lack of treatment. Overall, the modelling using FDS, at the WUI scale and taking into account a very refined vegetation distribution, was mostly successfully validated by post-fire damage recorded during the past-fire events, which was rarely attempted in the complex environment of forest fires in the French Mediterranean area. The current scientific limitations of the model did not allowed obtaining realistic results in terms of heat flux received by the building, even if they were coherent in global intensity comparing treated and untreated vegetation, and have to be addressed in future improvements of the model.
Conference papers on the topic "Forest species mapping"
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Cord, Anna, Rene R. Colditz, Michael Schmidt, and Stefan Dech. "Species distribution and forest type mapping in Mexico." In 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009). IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5417681.
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Chehata, Nesrine, Hedia Chakroun, Rania Youssfi, Mohamed Amine Maaoui, Anis Manai, Rami Werhani, Kamel Aloui, Nizar Kouki, Wafa Talhaoui, and Thouraya Sahli. "Improving Forest Species Mapping Using Sentinel-2 Time Series." In 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). IEEE, 2020. http://dx.doi.org/10.1109/m2garss47143.2020.9105159.
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Yoshii, Tatsuki, Chinsu Lin, Satoshi Tatsuhara, Satoshi Suzuki, and Takuya Hiroshima. "Tree Species Mapping of a Hemiboreal Mixed Forest Using Mask R-CNN." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884826.
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Xiaohui Yang, Nadia Rochdi, Jinkai Zhang, James Banting, David Rolfson, Chelsea King, Karl Staenz, Shane Patterson, and Brett Purdy. "Mapping tree species in a boreal forest area using RapidEye and LiDAR data." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946357.
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Stavrakoudis, Dimitris, Ioannis Gitas, Christos Karydas, Polychronis Kolokoussis, and Vassilia Karathanassi. "Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2015. http://dx.doi.org/10.1117/12.2194663.
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Rochdi, N., X. Yang, K. Staenz, S. Patterson, and B. Purdy. "Mapping tree species in a boreal forest area using RapidEye and Lidar data." In SPIE Remote Sensing, edited by Ulrich Michel and Karsten Schulz. SPIE, 2014. http://dx.doi.org/10.1117/12.2067506.
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Martín-Luis, Antonio, Manuel Arbelo, Pedro Hernández-Leal, and Manuel Arbelo-Bayó. "Mapping species distribution of Canarian Monteverde forest by field spectroradiometry and satellite imagery." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241993.
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Beh, B. C., M. Z. MatJafri, and H. S. Lim. "Mangrove species mapping in Kuala Sepetang Mangrove Forest, Perak using high resolution airborne data." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2015. http://dx.doi.org/10.1117/12.2195435.
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Wijana, Nyoman, and I. Gusti Agung Nyoman Setiawan. "Mapping and Distribution of Useful Plant Species in Bukit Kangin Forest, Pegringsingan Village, Karangasem, Bali." In Proceedings of the 3rd International Conference on Innovative Research Across Disciplines (ICIRAD 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200115.015.
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Keramitsoglou, Iphigenia, Charalambos Kontoes, Konstantinos Koutroumbas, Olga Sykioti, and Nicolaos Sifakis. "Mapping of forest species and tree density using new Earth observation sensors for wildfire applications." In Remote Sensing, edited by Manfred Owe and Guido D'Urso. SPIE, 2005. http://dx.doi.org/10.1117/12.626600.
Full textReports on the topic "Forest species mapping"
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Morin, Randall S., Andrew M. Liebhold, Eugene R. Luzader, Andrew J. Lister, Kurt W. Gottschalk, and Daniel B. Twardus. Mapping host-species abundance of three major exotic forest pests. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station, 2005. http://dx.doi.org/10.2737/ne-rp-726.
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Ruiz, Pablo, Craig Perry, Alejando Garcia, Magali Guichardot, Michael Foguer, Joseph Ingram, Michelle Prats, Carlos Pulido, Robert Shamblin, and Kevin Whelan. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, November 2020. http://dx.doi.org/10.36967/nrr-2279586.
Full textAbstract:
The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within these two federally-managed units as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in park management, resource management, research, and monitoring. This mapping project covers an area of approximately 7,400 square kilometers (1.84 million acres [ac]) and consists of seven mapping regions: four regions in Everglades National Park, Regions 1–4, and three in Big Cypress National Preserve, Regions 5–7. The report focuses on the mapping effort associated with the Northwest Coastal Everglades (NWCE), Region 4 , in Everglades National Park. The NWCE encompasses a total area of 1,278 square kilometers (493.7 square miles [sq mi], or 315,955 ac) and is geographically located to the south of Big Cypress National Preserve, west of Shark River Slough (Region 1), and north of the Southwest Coastal Everglades (Region 3). Photo-interpretation was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed specifically for this project. Additionally, photo-interpreters categorized the absolute cover of cattail (Typha sp.) and any invasive species detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%). A total of 178 thematic classes were used to map the NWCE. The most common vegetation classes are Mixed Mangrove Forest-Mixed and Transitional Bayhead Shrubland. These two communities accounted for about 10%, each, of the mapping area. Other notable classes include Short Sawgrass Marsh-Dense (8.1% of the map area), Mixed Graminoid Freshwater Marsh (4.7% of the map area), and Black Mangrove Forest (4.5% of the map area). The NWCE vegetation map has a thematic class accuracy of 88.4% with a lower 90th Percentile Confidence Interval of 84.5%.
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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner, and Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, February 2021. http://dx.doi.org/10.36967/nrr-2284511.
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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.
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Galvin, Jeff, and Sarah Strudd. Vegetation inventory, mapping, and characterization report, Saguaro National Park: Volume II, association summaries. Edited by Alice Wondrak Biel. National Park Service, March 2021. http://dx.doi.org/10.36967/nrr-2284793.
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The Sonoran Desert Network (SODN) conducted a vegetation mapping and characterization effort at the two districts of Saguaro National Park from 2010 to 2018. This project was completed under the National Park Service (NPS) Vegetation Mapping Inventory, which aims to complete baseline mapping and classification inventories at more than 270 NPS units. The vegetation map data were collected to provide park managers with a digital map product that meets national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and national context. A total of 97 distinct vegetation communities were described: 83 exclusively at the Rincon Mountain District, 9 exclusively at the Tucson Mountain District, and 5 occurring in both districts. These communities ranged from low-elevation creosote (Larrea tridentata) shrub-lands spanning broad alluvial fans to mountaintop Douglas fir (Pseudotsuga menziesii) forests on the slopes of Rincon Peak. All 97 communities were described at the association level, each with detailed narratives including lists of species found in each association, their abundance, landscape features, and overall community structural characteristics. Only 15 of the 97 vegetation types were existing “accepted” types within the National Vegetation Classification (NVC). The others are newly described and specific to Saguaro National Park (and will be proposed for formal status within the NVC). This document is Volume II of three volumes comprising the Saguaro National Park Vegetation Mapping Inventory. This volume provides two-page summaries of the 97 associations identified and mapped during the project, and detailed in Volume I. Summaries are presented by district, starting with the Tucson Mountain District. These summaries are abridged versions of the full association descriptions found in Volume III.
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Galvin, Jeff, and Sarah Studd. Vegetation inventory, mapping, and characterization report, Saguaro National Park: Volume III, type descriptions. Edited by Alice Wondrak Biel. National Park Service, March 2021. http://dx.doi.org/10.36967/nrr-2284802.
Full textAbstract:
The Sonoran Desert Network (SODN) conducted a vegetation mapping and characterization effort at the two districts of Saguaro National Park from 2010 to 2018. This project was completed under the National Park Service (NPS) Vegetation Mapping Inventory, which aims to complete baseline mapping and classification inventories at more than 270 NPS units. The vegetation map data were collected to provide park managers with a digital map product that meets national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and national context. A total of 97 distinct vegetation communities were described: 83 exclusively at the Rincon Mountain District, 9 exclusively at the Tucson Mountain District, and 5 occurring in both districts. These communities ranged from low-elevation creosote (Larrea tridentata) shrub-lands spanning broad alluvial fans to mountaintop Douglas fir (Pseudotsuga menziesii) forests on the slopes of Rincon Peak. All 97 communities were described at the association level, each with detailed narratives including lists of species found in each association, their abundance, landscape features, and overall community structural characteristics. Only 15 of the 97 vegetation types were existing “accepted” types within the NVC. The others are newly de-scribed and specific to Saguaro National Park (and will be proposed for formal status within the NVC). This document is Volume III of three volumes comprising the Saguaro National Park Vegetation Mapping Inventory. This volume provides full type descriptions of the 97 associations identified and mapped during the project, and detailed in Volume I. Volume II provides abridged versions of these full descriptions, briefly describing the floristic and structural characteristics of the vegetation and showing representative photos of associations, their distribution, and an example of the satellite imagery for one polygon.
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Mevarech, Moshe, Jeremy Bruenn, and Yigal Koltin. Virus Encoded Toxin of the Corn Smut Ustilago Maydis - Isolation of Receptors and Mapping Functional Domains. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7613022.bard.
Full textAbstract:
Ustilago maydis is a fungal pathogen of maize. Some strains of U. maydis encode secreted polypeptide toxins capable of killing other susceptible strains of U. maydis. Resistance to the toxins is conferred by recessive nuclear genes. The toxins are encoded by genomic segments of resident double-strande RNA viruses. The best characterized toxin, KP6, is composed of two polypeptides, a and b, which are not covalently linked. It is encoded by P6M2 dsRNA, which has been cloned, sequenced and expressed in a variety of systems. In this study we have shown that the toxin acts on the membranes of sensitive cells and that both polypeptides are required for toxin activity. The toxin has been shown to function by creating new pores in the cell membrane and disrupting ion fluxes. The experiments performed on artificial phospholipid bilayers indicated that KP6 forms large voltage-independent, cation-selective channels. Experiments leading to the resolution of structure-function relationship of the toxin by in vitro analysis have been initiated. During the course of this research the collaboration also yielded X-ray diffracion data of the crystallized a polypeptide. The effect of the toxin on the pathogen has been shown to be receptor-mediated. A potential receptor protein, identified in membrane fractions of sensitive cells, was subjected to tryptic hydrolysis followed by amino-acid analysis. The peptides obtained were used to isolate a cDNA fragment by reverse PCR, which showed 30% sequence homology to the human HLA protein. Analysis of other toxins secreted by U. maydis, KP1 and KP4, have demonstrated that, unlike KP6, they are composed of a single polypeptide. Finally, KP6 has been expressed in transgenic tobacco plants, indicating that accurate processing by Kex2p-like activity occurs in plants as well. Using tobacco as a model system, we determined that active antifungal toxins can be synthesized and targeted to the outside of transgenic plant cells. If this methodology can be applied to other agronomically crop species, then U. maydis toxins may provide a novel means for biological control of pathogenic fungi.