Journal articles on the topic 'Forest species mapping'
To see the other types of publications on this topic, follow the link: Forest species mapping.
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 'Forest species mapping.'
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
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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.
5
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.
Full textAbstract:
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.
6
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.
Full text
7
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.
Full textAbstract:
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.
8
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.
Full textAbstract:
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.
9
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.
Full textAbstract:
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.
10
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.
Full textAbstract:
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.
11
Chiang, Shou-Hao, and Miguel Valdez. "Tree Species Classification by Integrating Satellite Imagery and Topographic Variables Using Maximum Entropy Method in a Mongolian Forest." Forests 10, no. 11 (November 1, 2019): 961. http://dx.doi.org/10.3390/f10110961.
Full textAbstract:
Forests are an important natural resource that achieve ecological balance by regulating water regimes and promoting soil conservation. Based on forest inventories, the government is able to make decisions to sustainably conserve, improve, and manage forests. Fieldwork for forestry investigation requires intensive physical labor, which is costly and time-consuming, especially for surveys in remote mountainous regions. Remote sensing technology has been recently used for forest investigation on a large scale. An informative forest inventory must include forest attributes, including details of tree species; however, tree species mapping is not always applicable due to the similarity of surface reflectance and texture between tree species. Topographic variables such as elevation, slope, aspect, and curvature are crucial in allocating ecological niches to different species; therefore, this study suggests that integrating topographic information and optical satellite image classification can improve mapping accuracy for tree species. The main purpose of this study is to classify forest tree species in Erdenebulgan County, Huwsgul Province, Mongolia, by integrating Landsat satellite imagery with a Digital Elevation Model (DEM) using a Maximum Entropy algorithm. A forest tree species inventory from the Forest Division of the Mongolian Ministry of Nature and Environment was used as training data and as ground truth to perform the accuracy assessment. In this study, the classification was made using two different experimental approaches. First, classification was done using only Landsat surface reflectance data; and second, topographic variables were integrated with the Landsat surface reflectance data. The integration approach showed a higher overall accuracy and kappa coefficient, indicating that an accurate forest inventory can be achieved by integrating satellite imagery data and other topographic information to enhance the practice of forest management in remote regions.
12
Alonso, L., J. Picos, and J. Armesto. "MAPPING EUCALYPTUS SPECIES USING WORLDVIEW 3 AND RANDOM FOREST." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 819–25. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-819-2022.
Full textAbstract:
Abstract. Recent advances in remote sensing technologies have allowed the development of new innovative methodologies to obtain geospatial information about Eucalyptus genus distribution. This is an important task for forest stakeholders due to the high presence of this genus in forest plantations worldwide. Therefore, the next step in research should focus on exploring remote sensing possibilities to discern between Eucalytpus species. It would be an important step forward in forest management since different Eucalyptus species present different characteristics and properties that imply different management plans and industrial usages. This study accomplish the classification of E. nitens and E. globulus, the most common Eucalyptus species in the Iberian Peninsula. Worldview-3 images and random forest are used in a forest area placed in Galicia (Northwest of Spain). The differentiation of Eucalyptus species resulted in a producer’s accuracy of 84% and a users’ accuracy of 70% for E. nitens, while for E. globulus accuracy metrics did not reach 70%. The most important bands in the classification were the coastal blue and the blue, followed by the red related ones. The resulting unequal accuracy metrics might be caused by an imbalanced presence of both species in the selected study area. Therefore, further studies might be developed in different locations.
13
Gyamfi-Ampadu, Enoch, and Michael Gebreslasie. "Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review." Forests 12, no. 6 (June 4, 2021): 739. http://dx.doi.org/10.3390/f12060739.
Full textAbstract:
Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.
14
Behera, Mukunda Dev, Surbhi Barnwal, Somnath Paramanik, Pulakesh Das, Bimal Kumar Bhattyacharya, Buddolla Jagadish, Parth S. Roy, Sujit Madhab Ghosh, and Soumit Kumar Behera. "Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data." Remote Sensing 13, no. 11 (May 21, 2021): 2027. http://dx.doi.org/10.3390/rs13112027.
Full textAbstract:
Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.
15
Xu, Kaijian, Qingjiu Tian, Zhaoying Zhang, Jibo Yue, and Chung-Te Chang. "Tree Species (Genera) Identification with GF-1 Time-Series in A Forested Landscape, Northeast China." Remote Sensing 12, no. 10 (May 13, 2020): 1554. http://dx.doi.org/10.3390/rs12101554.
Full textAbstract:
Forests are the most important component of terrestrial ecosystem; the accurate mapping of tree species is helpful for the management of forestry resources. Moderate- and high-resolution multispectral images have been commonly utilized to identify regional tree species in forest ecosystem, but the accuracy of recognition is still unsatisfactory. To enhance the forest mapping accuracy, this study integrated the land surface phenological metrics and text features of forest canopy on tree species identification based on Gaofen-1 (GF-1) wide field of view (WFV) and time-series images (36 10-day NDVI data), conducted at a forested landscape in Harqin Banner, Northeast China in 2017. The dominant tree species include Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica in the study region. The result of forest mapping derived from a 10-day dataset was also compared with the outcome based upon a commonly utilized 30-day dataset in tree species identification. The results indicate that tree species identification accuracy is significantly (p < 0.05) improved with higher temporal resolution (10-day, 79.4%) of images than commonly used monthly data (30-day, 76.14%), and the accuracy can be further increased to 85.13% with a combination of the information derived from principal component analysis (PCA) transformation, phenological metrics (standing for the information of growing season) and texture features. The integration of higher dimensional NDVI data, vegetation growth dynamics and feature of canopy simultaneously will be beneficial to map tree species at the landscape scale.
16
Galidaki, Georgia, and Ioannis Gitas. "Mediterranean forest species mapping using classification of Hyperion imagery." Geocarto International 30, no. 1 (March 31, 2014): 48–61. http://dx.doi.org/10.1080/10106049.2014.883439.
Full text
17
Ehlers, Dekker, Chao Wang, John Coulston, Yulong Zhang, Tamlin Pavelsky, Elizabeth Frankenberg, Curtis Woodcock, and Conghe Song. "Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data." Remote Sensing 14, no. 5 (February 24, 2022): 1115. http://dx.doi.org/10.3390/rs14051115.
Full textAbstract:
The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R2 of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables.
18
Wijana, I. Nyoman, and Gede Astra Wesnawa. "THE MAPPING OF RARE PLANT SPECIES DISTRIBUTION IN MONKEY FOREST, UBUD, GIANYAR, BALI." Media Komunikasi Geografi 19, no. 1 (July 20, 2018): 23. http://dx.doi.org/10.23887/mkg.v19i1.13749.
Full textAbstract:
The purpose of this research was to know the species of rare plants existing in forest tourism Monkey Forest, Ubud, Gianyar, Bali and their mapping distributions in the original nature. This is an explorative research. The populations of this research were all species of plants in Monkey Forest. This research samples were the plant species covered by the squares. The sampling method used was quadratic method with systematic sampling technique. The mapping of rare plant species distribution used simple mapping method which was simple polygon compass and GPS. Identification of rare plant species was conducted through interviews, questionnaires, observations, and document studies. The results showed that the distribution of rare plant species in Monkey Forest, Ubud, Gianyar, Bali was in groups. The total number of rare plant species their nature were 33 species with the details that there were as many as six species of plants belonging to the National Rare category, 18 species of Bali Rare category, eight species of Regency Rare category, and one species of Rare Sub-District category.
19
Mohamed Elhag, Mohamed Elhag. "Tropical Forests Mapping of Bioko Island Using Remote Sensing Techniques." journal of King Abdulaziz University - Meteorology, Environment and Arid Land Agriculture Sciences 26, no. 2 (August 16, 2015): 95–109. http://dx.doi.org/10.4197/met.26-2.10.
Full textAbstract:
Forest sustainable management requires basically adequate vegetation mapping. Remote sensing techniques delivers reliable classification scheme of medicinal species Prunus africana located in Bioko Island -Equatorial Guinea. Prunus africana sustainable management relies principally on the population’s quantification of the sustainable trade volume. Unsupervised and supervised image classifications techniques were implemented on Landsat OLI-8 (Operational Land Imager-8) to produce P. africana thematic maps on Bioko. Primarily, Support Vector Machine classification algorithm realized overall accuracy of 82.01%, with kappa coefficient of 0.79. Forests misclassification was mainly confined between two interconnected classes of Guineo-Congolian/ Afromontane forest classes and lowland forest classes. Therefore an extra rule of determent altitude (>1400 m) was added to the classification decision rule to improve the classification accuracies to be estimated as overall accuracy of 80.01% and a kappa coefficient of 0.81. Regular ground truth data collection from nine transects found that both of P. africana and Schefflera sp. were dominantly the two arboreal species located in Bioko’s forests. Thematic classification maps illustrated in the conducted research is an essential data for the sustainable management of P. africana bark extraction. These results may also be valuable for various future studies ranging from primate research to genetic variation of P. africana on Bioko Island.
20
Chiang, Shou Hao, Miguel Valdez, and Chi-Farn Chen. "FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 593–96. http://dx.doi.org/10.5194/isprs-archives-xli-b8-593-2016.
Full textAbstract:
Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. <br><br> In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled with terrain variables produced better result, with the higher overall accuracy and kappa coefficient than first experiment. The results indicate that the Maximum Entropy method is an applicable, and to classify tree species using satellite imagery data coupled with terrain information can improve the classification of tree species in the study area.
21
Chiang, Shou Hao, Miguel Valdez, and Chi-Farn Chen. "FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 593–96. http://dx.doi.org/10.5194/isprsarchives-xli-b8-593-2016.
Full textAbstract:
Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. <br><br> In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled with terrain variables produced better result, with the higher overall accuracy and kappa coefficient than first experiment. The results indicate that the Maximum Entropy method is an applicable, and to classify tree species using satellite imagery data coupled with terrain information can improve the classification of tree species in the study area.
22
Vlasenko, V. I. "The mapping of vegetation cover dynamics in the Sayan-Shushensky Reserve." Geobotanical mapping, no. 1998-2000 (2000): 32–49. http://dx.doi.org/10.31111/geobotmap/1998-2000.32.
Full textAbstract:
The character of forest dynamics in the last century may be traced on the key area of the Altai-Sayan mountain country — the Sayan-Shushensky Biosphere Reserve of total area 389 000 ha. It is situated at the border of humid mountain region of South Siberia and the arid areas of Tuva. The basement for creation of prognosis map of potential state of the Reserve vegetation cover for 2050 year includes: 1) the data on inventory, ages structure, and conditions of forests; 2) the results of the earlier studies of dynamics of mountain open woodlands; 3) materials of dendroclimatic investigations at the upper and lower forest boundaries in the Sayan Mts.; 4) prognosis of climatologists. While classifying the Reserve vegetation, 1500 biogeocoenoses have been united into 362 types of biogeocoenoses which have been included into 112 groups of associations identified in altitudinal vegetation belts: goltsy, meadows and shrub thickets; high elevation open woodlands; mountain taiga; subtaiga—forest- steppe complexes. Local expansion of forest boundaries, at the expense of forming the 40— 80 years old forest stands in goltsy and open woodlands, testifies to climate warming, and a rise of the subtaiga—forest-steppe pine and larch forests of the 40—80 years old is the evidence of climate humification in this region during the last century. According to the prognoses of climatologists this tendency will continue in the future 50 years. On the territory of the Sayan-Shushensky Reserve, within the strip of 200 m width at the age of steppefied forests as well as within the steppes with shrubs, mesophilous species will be developed in the shrub, herb-dwarf shrub and moss cover. Under protection of shrubs, young open stands of larch and pine will spring up (on the area of 200 ha) and in subtaiga forests Abies sibirica and Pinus sibirica will appear among the regrowth. Under canopy of the mountain taiga and the subtaiga larch forests, the second layer of dark needle-leaved species will be formed. In the places of present pyrogenic derivatives — birch and aspen forests — the fir forest with admixture of Pinus sibirica and Picea obovata will be restored. On the area of 49 322 ha, occupied by mountain woodlands (87% of the total area), the closed mountain taiga forest of Pinus sibirica will spread. The area of goltsy and shrub thickets will decrease by 44.4% (19 214 ha), being replaced by the high elevation open woodlands of Pinus sibirica and Larix sibirica. In accordance with changes in vegetation cover the boundary between the Altai-Sayan and the Central-Asian forest areas within the Reserve's territory will deviate from the modern one approximately by 25 km.
23
Fu, Yuanyuan, Hong S. He, Todd J. Hawbaker, Paul D. Henne, Zhiliang Zhu, and David R. Larsen. "Evaluating k-Nearest Neighbor (kNN) Imputation Models for Species-Level Aboveground Forest Biomass Mapping in Northeast China." Remote Sensing 11, no. 17 (August 25, 2019): 2005. http://dx.doi.org/10.3390/rs11172005.
Full textAbstract:
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.
24
Cumming, Steven G., C. Ronnie Drever, Mélina Houle, John Cosco, Pierre Racine, Erin Bayne, and Fiona K. A. Schmiegelow. "A gap analysis of tree species representation in the protected areas of the Canadian boreal forest: applying a new assemblage of digital Forest Resource Inventory data." Canadian Journal of Forest Research 45, no. 2 (February 2015): 163–73. http://dx.doi.org/10.1139/cjfr-2014-0102.
Full textAbstract:
We undertook a gap analysis of how protected areas represent the tree-species diversity within the Canadian boreal forest, as measured from Forest Resource Inventory (FRI) maps. We used a new compilation of Forest Resource Inventory designed to support ecological analyses over large areas and across jurisdictional boundaries. The analysis was stratified into four analytical regions determined by terrestrial ecozones. We calculated the relative abundance of species within regions, developed rarity criteria, and evaluated the relative abundances and prevalence of rare species. We characterized representation gaps when the abundance of a tree species in protected areas within an analytical region differed markedly (by more than a factor of 2) from the expectation, calculated as the product of regional abundance and the proportional area protected. Most species were well represented in the most species-diverse region (n = 33), the Boreal Shield in eastern Canada, due apparently to a large number of relatively small protected areas in the southern part of the region. Some marked gaps existed in the more species-depauperate western zones, notably for montane conifers in the Boreal Plains. As is common for species abundance distributions, as few as five species accounted for 90% of total abundance in each zone. Relatively rare species were more numerous. Mostly associated with southern temperate or hemiboreal forests, these reached their highest prevalence and abundance in the managed forests of the Boreal Shield. Our work identified some gaps in representation in the protected areas network of Canada in western Canada, substantiates the use of species distribution mapping based on FRI data to inform conservation planning — including the identification of high conservation biodiversity elements for forest certification — and demonstrates the need for improved vegetation mapping in National Parks.
25
Shikhov, Andrey N., and Anastasia V. Semakina. "MAPPING OF THE VEGETATION COVER OF THE PERM REGION BASED ON LANDSAT SATELLITE IMAGES." Географический вестник = Geographical bulletin, no. 1(60) (2022): 150–64. http://dx.doi.org/10.17072/2079-7877-2022-1-150-164.
Full textAbstract:
The paper deals with the methodology and results of Landsat-based vegetation cover mapping for the Perm region. Initial Landsat images were obtained in 2016–2020. The map building technique is based on the supervised classification of satellite images and subsequent post-processing. This technique involves the use of a number of additional sources, in particular, the results of global-Landsat-based mapping of forest disturbances, water surface, and arable lands, as well as reforestation areas on abandoned agricultural lands. As a result, a map with a spatial resolution of 30 m (which corresponds to a scale of 1:100,000) has been created. The map legend includes 19 thematic classes, 11 of them contain information on forest vegetation. The accuracy assessment of the obtained data was carried out with the use of a MODIS-based map of the vegetation cover of Russia and also forest inventory data on two forestries of the Perm region. The highest classification accuracy is typical for dark-coniferous and pine forests (it is about 70% according to the map of the vegetation cover of Russia, and up to 75% according to the forest inventory data). Deciduous forests are recognized with the lowest accuracy since, according to the classification results, they were partly categorized as mixed forests (with a predominance of deciduous species). The practical use of the created map of the vegetation cover may include estimation of long-term changes for individual vegetation classes (in particular, for intact forest landscapes), or various calculations based on the species composition and age structure of the forests. The compiled map of the vegetation cover of the Perm region is available at https://figshare.com/s/98d29e83d1f2039b2528.
26
Farber, Sergyey K., Nastassia V. Sokolova, and Artem G. Nevodinykh. "MAPPING OF BIOTOPES OF RARE AND ENDANGERED PLANT SPECIES IN THE SOUTHERN TAIGA FORESTS OF SIBERIA (EVIDENCE FROM KRASFAN LLC LEASED FOREST AREA)." Interexpo GEO-Siberia 4, no. 2 (May 21, 2021): 160–65. http://dx.doi.org/10.33764/2618-981x-2021-4-2-160-165.
Full textAbstract:
The stage of mapping of biotopes is a preliminary to decision-making aimed at the conservation of forest biodiversity. The presence of rare and endangered plant species within the leased forest area of a logging company is identified by analysis of literature report and is confirmed under field surveys. To outline the biotopes of vegetation species, it is assumed to use mass forest inventory materials that contain all the information required. A field of biotopes is formed in the GIS attribute table and determines the possibility of mapping.
27
Ferreira, M. P., M. Zortea, D. C. Zanotta, J. B. Féret, Y. E. Shimabukuro, and C. R. Souza Filho. "ON THE USE OF SHORTWAVE INFRARED FOR TREE SPECIES DISCRIMINATION IN TROPICAL SEMIDECIDUOUS FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 473–76. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-473-2015.
Full textAbstract:
Tree species mapping in tropical forests provides valuable insights for forest managers. Keystone species can be located for collection of seeds for forest restoration, reducing fieldwork costs. However, mapping of tree species in tropical forests using remote sensing data is a challenge due to high floristic and spectral diversity. Little is known about the use of different spectral regions as most of studies performed so far used visible/near-infrared (390-1000 nm) features. In this paper we show the contribution of shortwave infrared (SWIR, 1045-2395 nm) for tree species discrimination in a tropical semideciduous forest. Using high-resolution hyperspectral data we also simulated WorldView-3 (WV-3) multispectral bands for classification purposes. Three machine learning methods were tested to discriminate species at the pixel-level: Linear Discriminant Analysis (LDA), Support Vector Machines with Linear (L-SVM) and Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF). Experiments were performed using all and selected features from the VNIR individually and combined with SWIR. Feature selection was applied to evaluate the effects of dimensionality reduction and identify potential wavelengths that may optimize species discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved the highest average accuracy (77.4%). Inclusion of the SWIR increased accuracy to 85% with LDA. The same pattern was also observed when WV-3 simulated channels were used to classify the species. The VNIR bands provided and accuracy of 64.2% for LDA, which was increased to 79.8 % using the new SWIR bands that are operationally available in this platform. Results show that incorporating SWIR bands increased significantly average accuracy for both the hyperspectral data and WorldView-3 simulated bands.
28
Reyes-Palomeque, G., J. M. Dupuy, C. A. Portillo-Quintero, J. L. Andrade, F. J. Tun-Dzul, and J. L. Hernández-Stefanoni. "Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests." Ecological Indicators 120 (January 2021): 106955. http://dx.doi.org/10.1016/j.ecolind.2020.106955.
Full text
29
Furniss, John, Parinaz Rahimzadeh-Bajgiran, Tawanda W. Gara, John Daigle, and Kara K. L. Costanza. "Mapping ash species across a mixed forest using hyperspectral imagery." Remote Sensing Letters 13, no. 5 (February 16, 2022): 441–51. http://dx.doi.org/10.1080/2150704x.2022.2040753.
Full text
30
Pu, Ruiliang. "Mapping urban forest tree species using IKONOS imagery: preliminary results." Environmental Monitoring and Assessment 172, no. 1-4 (February 6, 2010): 199–214. http://dx.doi.org/10.1007/s10661-010-1327-5.
Full text
31
Rąkowski, Grzegorz, and Krzysztof Czarnocki. "Breeding avifauna of the forest interior and forest edge in the Borki Forest." Forest Research Papers 80, no. 1 (March 1, 2019): 23–43. http://dx.doi.org/10.2478/frp-2019-0003.
Full textAbstract:
Abstract The composition and structure of breeding bird communities in the Borki Forest in North-Eastern Poland were investigated separately in the forest interior (years 2012–2014) and at the forest edge (years 2016–2018). In both areas, bird censuses were carried out on three plots located in mature oak-hornbeam, ash-alder and mixed coniferous forest stands. Plots were selected to cover similar forest types, encompass stands of similar age and to have similar acreage, both, in the forest interior and at the forest edge. A standard combined mapping technique for estimating the number of breeding birds was applied and a total of 97 bird species were found to have bred at least once within any plot. Regardless of the forest type, both the number of breeding bird species and the population densities were higher on plots situated at the forest edge than in the forest interior. The mean number of breeding species was over 20% higher and the mean total density of breeding pairs was higher by over 30%. Total population densities on the plots located at the forest edge were higher as a result of an increase in population densities of some individual bird species combined with an increase in the number of breeding species, including non-forest and non-typical forest interior species. The number of nesting species in the edge zone was higher than in the forest interior with common species and generalists clearly dominating. Specialist species typical of natural forests as well as rare and endangered species, such as three-toed woodpecker (Picoides tridactylus), white-backed woodpecker (Dendrocopos leucotos), collared flycatcher (Ficedula albicollis) and red-breasted flycatcher (Ficedula parva), for whom the Borki Forest is an important breeding site in Poland were less numerous. Despite the observed differences and a clear edge effect, bird assemblages inhabiting research plots in the forest interior and at the edge were not fundamentally different. We conclude that the edge zone is inhabited by a poorer-quality variant of bird assemblage typical of forest interior, enriched quantitatively by non-forest species associated with open and/or semi-open areas as well as by synanthropic species.
32
Franklin, Steven E. "Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles." Journal of Unmanned Vehicle Systems 6, no. 4 (December 1, 2018): 195–211. http://dx.doi.org/10.1139/juvs-2017-0022.
Full textAbstract:
Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.
33
Lombard, Leighton, Riyad Ismail, and Nitesh K. Poona. "Modelling forest species using LiDar-derived metrics of forest canopy gaps." South African Journal of Geomatics 9, no. 1 (September 8, 2022): 31–43. http://dx.doi.org/10.4314/sajg.v9i1.3.
Full textAbstract:
LiDAR intensity and texture features have reported high accuracies for discriminating forest species, particularly with the utility of the random forest (RF) algorithm. To date, limited studies has utilized LiDAR-derived forest gap information to assist in forest species discrimination. In this study, LiDAR intensity and texture features were extracted from forest canopy gaps to discriminate Eucalyptus grandis and Eucalyptus dunnii within a forest plantation. Additionally, LiDAR intensity and texture information was extracted for both canopy gaps and forest canopy and utilized for species discrimination. Using LiDAR intensity and texture information extracted for both canopy gap and forest canopy, resulted in a model accuracy of 94.74% (KHAT = 0.88). Using only canopy gap information, the RF model obtained an overall accuracy of 90.91% (KHAT = 0.81). The results highlight the potential for using canopy gap information for commercial species discrimination and mapping.
34
CORNILS, JESSICA S., ISABELL RIEDL, JULIAN FRICKE, MORITZ KATZ, and CHRISTIAN H. SCHULZE. "Population density and habitat preferences of the Black-cheeked Ant-tanager Habia atrimaxillaris." Bird Conservation International 25, no. 3 (September 23, 2014): 306–21. http://dx.doi.org/10.1017/s0959270914000215.
Full textAbstract:
SummaryThe lowland forest on the southern Pacific slope of Costa Rica has an extremely diverse avifauna, including the Black-cheeked Ant-tanager Habia atrimaxillaris. The only known remaining populations of this highly range-restricted species occur in the areas of Piedras Blancas and Corcovado National Park. It is assumed that the population is decreasing due to habitat loss and fragmentation. We assessed the species’ population density in a part of the Piedras Blancas National Park using distance sampling (in February–April 2009 and November 2010–January 2011) and territory mapping (November 2010–January 2011). We also examined habitat preferences based on vegetation structure at point count locations. Black-cheeked Ant-tanagers were exclusively found in old-growth forest. The species’ likelihood of occurrence at census points increased with forest cover (within a radius of 200 m around census points), canopy closure, and density of trees (with diameter at breast height >10 cm). Average population density estimated by distance sampling was 24–27 individuals per km², which is in accordance with the population size estimated by territory mapping (17–25 birds per km²). Based on these estimates, an overall population size of 12,432–20,720 birds is predicted for the remaining 592 km² lowland forest area of the Golfo Dulce region. The Black-cheeked Ant-tanager was only recorded in old-growth forest, but not in gallery forests embedded in a human-dominated landscape matrix. Since the species appears to avoid forest edges, further forest degradation and fragmentation will have a strong negative impact and should be rapidly reduced by adequate conservation measures.
35
Hościło, Agata, and Aneta Lewandowska. "Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data." Remote Sensing 11, no. 8 (April 17, 2019): 929. http://dx.doi.org/10.3390/rs11080929.
Full textAbstract:
There are a limited number of studies addressing the forest status, its extent, location, type and composition over a larger area at the regional or national levels. The dense time series and a wide swath of Sentinel-2 data are a good basis for forest mapping and tree species identification over a large area. This study presents the results of the classification of the forest/non-forest cover, forest type (broadleaf and coniferous) and the identification of eight tree species (beech, oak, alder, birch, spruce, pine, fir, and larch) using the multi-temporal Sentinel-2 data in combination with topographic information. The study was conducted over the large mountain area located in southern Poland. The Random Forest classifier was used to first derive a forest/non-forest map. Second, the forest was classified into broadleaf and coniferous. Finally, the tree species classification was carried out following two approaches: (i) Non-stratified, where all species were classified together within the forest mask and (ii) stratified, where the broadleaf and coniferous tree species were classified separately within the forest type masks. The overall accuracy for the forest/non-forest cover reached 98.3% and declined slightly to 94.8% for the classification of the forest type. The use of the topographic information did not increase the accuracy of either result. The role of the topographic variables increased significantly in the process of tree species delineation. By combining the topographic information (in particular, digital elevation model) with the multi-temporal Sentinel-2 data, the classification of eight tree species improved from 75.6% to 81.7% (approach 1). A further increase in accuracy to 89.5% for broadleaf and 82% for coniferous species was observed following the stratified approach number 2. The highest overall accuracy (above 85%) was obtained for beech, oak, birch, alder, and larch. The study confirmed the potential of the multi-temporal Sentinel-2 data for accurate delineation of the forest cover, forest type, and tree species at the regional scale.
36
Moe, Kyaw Thu, Toshiaki Owari, Naoyuki Furuya, Takuya Hiroshima, and Junko Morimoto. "Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests." Remote Sensing 12, no. 17 (September 3, 2020): 2865. http://dx.doi.org/10.3390/rs12172865.
Full textAbstract:
High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.
37
Gorodnichev, V. A., M. L. Belov, V. V. Shvygina, and D. S. Sitnikov. "Two-wave Laser Method for Monitoring the Species Composition of Forest." Radio Engineering, no. 2 (May 17, 2020): 27–38. http://dx.doi.org/10.36027/rdeng.0220.0000162.
Full textAbstract:
Today the monitoring of forests is one of the actual tasks of environmental control. The most important problems of monitoring of forest resources are mapping of forests, determining species and age composition of forests and analysis of sanitary condition of forests.An effective method of monitoring the state of vegetation (including forests) is optical aerospace sensing. The methods of optical sensing of vegetation cover are currently passive, for the most part.However, passive methods are available to use for daylight only. Therefore, laser methods which can be used in wide range of light and atmospheric conditions are of interest.In this article there was carried out the comparative analysis and selection of the most effective sensing wavelengths in atmospheric transparency windows for two-waves laser method for determining forest areas with prevalence of coniferous or deciduous wood species.As an information index (coniferous or deciduous wood species) in this article the ratio of reflection coefficients of parcels of forest at two wavelengths was used. Pairs of wavelengths 1,54μ and 0,532μ; 1,54μ and 0,355μ are the most relevant for detecting forest areas with prevalence of coniferous or deciduous wood species.For quantitative assessment the efficiency of the laser method mathematical modeling was carried out. The results of mathematical modeling show that that the wavelengths of 0.532μm and 1.54μm are the most effective and provide scanning with probability of correct detecting ~ 0.99 and with false-alarm probability ~ 0.04.However, in terms of eye safety it’s better to choose wavelengths of 0.355μm and 1.54μm, because they allow to solve satisfactory the problem of determining forest areas with prevalence of coniferous or deciduous wood species with probability of correct detecting ~ 0.9 and with false-alarm probability ~ 0.14.
38
Machar, Ivo, Martin Schlossarek, Vilem Pechanec, Lubos Uradnicek, Ludek Praus, and Ahmet Sıvacıoğlu. "Retention Forestry Supports Bird Diversity in Managed, Temperate Hardwood Floodplain Forests." Forests 10, no. 4 (April 1, 2019): 300. http://dx.doi.org/10.3390/f10040300.
Full textAbstract:
The retention forestry approach is considered as one of the potentially effective tools for sustainable forest management for conservation of biodiversity in managed temperate and boreal forests. Retention of old-growth forest structures (e.g., very large old living trees) in forest stands during clear-cutting provides maintenance of key habitats for many old-growth forest interior-species. Most of ecological studies on green tree retention (GTR) consequences for biodiversity have been focused on birds. However, the long-term studies of GTR impacts on forest birds are very poor. In this paper, we focused on assessment of the long-term consequences of leaving legacy oak trees on the cut areas for bird diversity 18–22 years after clear-cutting in managed temperate European hardwood floodplain forests. Results based on bird counting using mapping of bird nesting territories revealed a key importance of legacy oak trees for maintaining bird diversity in the study area. These results are widely applicable for managed temperate hardwood forests with serious dominance of oak (Quercus sp.) in forest stands. Legacy oak trees in this habitat type are keystone structures for bird diversity. Retention approach focused on these trees is potentially an important conservation tool for preserving forest bird diversity and other associated species in temperate hardwood forests managed by clear-cutting.
39
Vlasenko, V. I., M. G. Erunova, and I. S. Scerbinina. "Geobotanical mapping of vegetation in "Stolby" Reserve." Geobotanical mapping, no. 2001-2002 (2002): 32–43. http://dx.doi.org/10.31111/geobotmap/2001-2002.32.
Full textAbstract:
The reserve “Stolby” is characteristic key plot of the mountain-taiga and subtaiga-forest steppe altitudinal belts in the East Sayan Mountains, where anthropogenic influence is the least pronounced. It was founded in 1925, in 15 km southward of Krasnoyarsk city, on north-west spurs of the Western Sayan Mountains which adjoin closely to right bank of the Yenisei River bordering upon the Middle Siberian Plateau. Reserve's physiography is characterized by low mountain and middle mountain erosion-accumulation relief with absolute heights of 200-800 m. Low mountain part (200-500 m) is composed of loose sedimentary rocks. In the middle mountain part of the reserve (500-800 m) there are outcrops of sienite rocks of various stages of destruction. Vegetation and soils of the reserve change in agreement with absolute heights and climate. In low mountains spread the subtaiga and forest-steppe leaved-light needle forests on mountain grey forest soils (8.1 % of reserve territory); the middle mountain part is occupied by the light needle and dark needle taiga forests on mountain podzol soils (91.9 % of the area). As the basement for vegetation map we took the map of forest environments of reserve by T. N. Butorina compiled according to materials of land forest management of 1977 year. As the result of forest management near 2000 biogeocoenoses were distinguished. The type of biogeocoenosis, according to V. N. Sukachev, is selected as mapping unit. Biogeocoenoses were united into 70 groups of forest types, representing 21 series of associations which are reflected in the map legend (Fig. 1). The main goal of map is to show the territorial distribution of groups and series of types of biogeocoenoses in the main structural units - altitudinal be't complexes (ВПК) which are equivalents of altitudinal vegetation belts. For designation of forest tree species various kinds of hatches were used. Formations of Siberian pine, larch, pine, fir, spruce, birch and aspen forests are shown on the map. Within the ВПК arabic numerals show the groups of types of biogeocoenoses (forest types), united into series according to similarity of dominants in ground layer. The mountain-taiga ВПК includes the following series and groups of types of biogeocoenoses: dwarf-shrub-moss (1-4); sedge-moss (5-9); bilberry-low herb-moss (10-14); tall herb-sedge (15-19); tall herb-wood sour-moss (20-26); tall herb-small reed (27-32). The subtaiga-forest steppe ВГ1К embraces: shrub steppificated (33-34); shrub-forb steppificated (35-38): sedge- bilberry (39-40); sedge-forb (41-43); bracken (44); small reed-forb (45); bilberrv-forb- sedge (46, 47); forb-tall herb (48-51); tall herb (52-55); wet tall herb-small reed (56-59); fern-tall herb (60). Intrazonal phytocoenoses: brook tall herb (61-63); brook shrub (64-68); lichen-moss (69); cowberry (70). In 1999-2000 on the base of topographic map in a scale 1 : 25 000, map of forest environments, transformed by us into vegetation map of the reserve, M. J . Erunova and I. S. Scerbinina worked out an electronic variant. For this project the instrumental facilities of GIS, GeoDraw and GeoGraph (CGI IG RAS, Moscow) and programs of Geophyt were used.
40
Tajuddin, Tajuddin, and Doddy Ari Suryanto. "SEBARAN POTENSI HUTAN PINUS DAN PERANNYA TERHADAP PERBAIKAN KONDISI HUTAN DI PROVINSI SULAWESI SELATAN." Wahana Forestra: Jurnal Kehutanan 17, no. 1 (January 28, 2022): 1–12. http://dx.doi.org/10.31849/forestra.v17i1.8448.
Full textAbstract:
Pine forests in South Sulawesi are evidence of the success of the government's reforestation program. The purpose of this study was to describe a map of the distribution of pine forests and their role in improving forest conditions in South Sulawesi Province. Mapping of the distribution of pine forests through an overlay of village administrative maps that have pine potential based on information from informants with maps of forest areas. An analysis of changes in land cover and land use to determine the role of pine forests in improving forest conditions, in locations identified as having pine forest potential. We find that the potential for pine forests is spread across 18 districts/cities in South Sulawesi Province. Furthermore, pine forests planted in reforestation programs have improved forest cover conditions. The choice of pine species that provide economic benefits makes people tend to maintain pine stands because they can be used as a source of livelihood.
41
Krzystek, Peter, Alla Serebryanyk, Claudius Schnörr, Jaroslav Červenka, and Marco Heurich. "Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery." Remote Sensing 12, no. 4 (February 17, 2020): 661. http://dx.doi.org/10.3390/rs12040661.
Full textAbstract:
Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.
42
Polyakova, Alika, Svetlana Mukharamova, Oleg Yermolaev, and Galiya Shaykhutdinova. "Automated Recognition of Tree Species Composition of Forest Communities Using Sentinel-2 Satellite Data." Remote Sensing 15, no. 2 (January 5, 2023): 329. http://dx.doi.org/10.3390/rs15020329.
Full textAbstract:
Information about the species composition of a forest is necessary for assessing biodiversity in a particular region and making economic decisions on the management of forest resources. Recognition of the species composition, according to the Earth’s remote sensing data, greatly simplifies the work and reduces time and labor costs in comparison with a traditional inventory of the forest, conducted through ground-based observations. This study analyzes the possibilities of tree species discrimination in coniferous–deciduous forests according to Sentinel-2 data using two automated recognition methods: random forest (RF) and generative topographic mapping (GTM). As remote sensing data, Sentinel-2 images of the Raifa section of Volga-Kama State Reserve in the Tatarstan Republic, Russia used: six images for the vegetation period of 2020. The analysis was carried out for the main forest-forming species. The training sample was created based on the cadastral data of the forest fund. The recognition quality was assessed using the F1-score, precision, recall, and accuracy metrics. The RF method showed a higher recognition accuracy. The accuracy of correct recognition by the RF method on the training sample reaches 0.987, F1-score = 0.976, on the control sample, accuracy = 0.764, F1-score = 0.709.
43
W. Wardell-Johnson, Grant, Ben E. Lawson, and Robert H. Coutts. "Are regional ecosystems compatible with floristic heterogeneity? A case study from Toohey Forest, south-east Queensland, Australia." Pacific Conservation Biology 13, no. 1 (2007): 47. http://dx.doi.org/10.1071/pc070047.
Full textAbstract:
The recognition and effective portrayal of floristic heterogeneity is a complex issue for land classification. This study in Toohey Forest, south-east Queensland, examines the effects of mapping scale and environmental variables on a floristically heterogeneous area. Current Version 4.1 regional ecosystem mapping at 1: 1 00 000 scale maps Toohey Forest as a single regional ecosystem unit "12.11.5", described as an "open forest complex with Corymbia citriodora, Eucalyptus siderophloia, E. major on metamorphics ± interbedded volcanics". Plant taxa data from 50, 20 x 20 m sites comprising 247 native vascular plant taxa were collected, along with data for 17 environmental variables and 10 species richness categories. A priori site groupings of 1 :12 500 scale vegetation mapping and a geomorphic classifications of the area were examined using cluster analysis (UPGMA, Bray-Curtis Metric, β = –0.1) and ordination (SSH MDS). Biplots of several variables (shrub species richness, total species richness, per cent rock cover, CEC, carbon and phosphorus) were significantly (P < 0.05) correlated with the ordination axes derived from each of the two strata levels and the total taxa, for both geomorphological and vegetation mapping. Several variables (shrub, vine, woody and introduced species richness, and carbon, nitrogen, phosphorus, pH and CEC) varied significantly (P < 0.05) across both geomorphic categories and 1:12 500 scale vegetation community mapping. The ongoing reduction in regional ecosystem mapping scale, centred on the use of fine-scale geomorphology mapping, is likely to improve the representation of floristic patterns in heterogeneous environments.
44
Jackson, Colbert M., and Elhadi Adam. "Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset." Remote Sensing 13, no. 24 (December 7, 2021): 4970. http://dx.doi.org/10.3390/rs13244970.
Full textAbstract:
Accurate maps of the spatial distribution of tropical tree species provide valuable insights for ecologists and forest management. The discrimination of tree species for economic, ecological, and technical reasons is usually necessary for achieving promising results in tree species mapping. Most of the data used in tree species mapping normally have some degree of imbalance. This study aimed to assess the effects of imbalanced data in identifying and mapping trees species under threat in a selectively logged sub-montane heterogeneous tropical forest using random forest (RF) and support vector machine with radial basis function (RBF-SVM) kernel classifiers and WorldView-2 multispectral imagery. For comparison purposes, the original imbalanced dataset was standardized using three data sampling techniques: oversampling, undersampling, and combined oversampling and undersampling techniques in R. The combined oversampling and undersampling technique produced the best results: F1-scores of 68.56 ± 2.6% for RF and 64.64 ± 3.4% for SVM. The balanced dataset recorded improved classification accuracy compared to the original imbalanced dataset. This research observed that more separable classes recorded higher F1-scores. Among the species, Syzygium guineense and Zanthoxylum gilletii were the most accurately mapped whereas Newtonia buchananii was the least accurately mapped. The most important spectral bands with the ability to detect and distinguish between tree species as measured by random forest classifier, were the Red, Red Edge, Near Infrared 1, and Near Infrared 2.
45
Ahokas, E., J. Hyyppä, X. Yu, X. Liang, L. Matikainen, K. Karila, P. Litkey, et al. "TOWARDS AUTOMATIC SINGLE-SENSOR MAPPING BY MULTISPECTRAL AIRBORNE LASER SCANNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 155–62. http://dx.doi.org/10.5194/isprs-archives-xli-b3-155-2016.
Full textAbstract:
This paper describes the possibilities of the Optech Titan multispectral airborne laser scanner in the fields of mapping and forestry. Investigation was targeted to six land cover classes. Multispectral laser scanner data can be used to distinguish land cover classes of the ground surface, including the roads and separate road surface classes. For forest inventory using point cloud metrics and intensity features combined, total accuracy of 93.5% was achieved for classification of three main boreal tree species (pine, spruce and birch).When using intensity features – without point height metrics - a classification accuracy of 91% was achieved for these three tree species. It was also shown that deciduous trees can be further classified into more species. We propose that intensity-related features and waveform-type features are combined with point height metrics for forest attribute derivation in area-based prediction, which is an operatively applied forest inventory process in Scandinavia. It is expected that multispectral airborne laser scanning can provide highly valuable data for city and forest mapping and is a highly relevant data asset for national and local mapping agencies in the near future.
46
Ahokas, E., J. Hyyppä, X. Yu, X. Liang, L. Matikainen, K. Karila, P. Litkey, et al. "TOWARDS AUTOMATIC SINGLE-SENSOR MAPPING BY MULTISPECTRAL AIRBORNE LASER SCANNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 155–62. http://dx.doi.org/10.5194/isprsarchives-xli-b3-155-2016.
Full textAbstract:
This paper describes the possibilities of the Optech Titan multispectral airborne laser scanner in the fields of mapping and forestry. Investigation was targeted to six land cover classes. Multispectral laser scanner data can be used to distinguish land cover classes of the ground surface, including the roads and separate road surface classes. For forest inventory using point cloud metrics and intensity features combined, total accuracy of 93.5% was achieved for classification of three main boreal tree species (pine, spruce and birch).When using intensity features – without point height metrics - a classification accuracy of 91% was achieved for these three tree species. It was also shown that deciduous trees can be further classified into more species. We propose that intensity-related features and waveform-type features are combined with point height metrics for forest attribute derivation in area-based prediction, which is an operatively applied forest inventory process in Scandinavia. It is expected that multispectral airborne laser scanning can provide highly valuable data for city and forest mapping and is a highly relevant data asset for national and local mapping agencies in the near future.
47
Hastings, Jack H., Scott V. Ollinger, Andrew P. Ouimette, Rebecca Sanders-DeMott, Michael W. Palace, Mark J. Ducey, Franklin B. Sullivan, David Basler, and David A. Orwig. "Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest." Remote Sensing 12, no. 2 (January 17, 2020): 309. http://dx.doi.org/10.3390/rs12020309.
Full textAbstract:
The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands.
48
Hemmerling, Jan, Dirk Pflugmacher, and Patrick Hostert. "Mapping temperate forest tree species using dense Sentinel-2 time series." Remote Sensing of Environment 267 (December 2021): 112743. http://dx.doi.org/10.1016/j.rse.2021.112743.
Full text
49
Hemmerling, Jan, Dirk Pflugmacher, and Patrick Hostert. "Mapping temperate forest tree species using dense Sentinel-2 time series." Remote Sensing of Environment 267 (December 2021): 112743. http://dx.doi.org/10.1016/j.rse.2021.112743.
Full text
50
Cotsell, Nigel, Mark Fisher, David Scotts, and Mark Cameron. "Identifying High Value Arboreal Habitat in forested areas using high-resolution digital imagery." Pacific Conservation Biology 22, no. 4 (2016): 367. http://dx.doi.org/10.1071/pc15031.
Full textAbstract:
Old-growth forest is recognised as a high-value habitat in conservation assessment programs because of its importance to hollow-dependent species. Previous mapping undertaken at regional scales does not map patches of old forest smaller than 5 ha. While small patches of old forest may not be as ecologically important as large areas they provide opportunities for connectivity and specific habitat resources for arboreal wildlife within a broader landscape matrix. Previously, smaller patches of old forest have been overlooked because the tools have not been available to map at finer scales. This study incorporates a methodology using recent advances in technology, including aerial photography, to map old forest at a fine scale for the purposes of land-use assessment and planning. The term ‘High Value Arboreal Habitat’ is introduced to convey the ecological importance of hollow-bearing trees as part of a wider identification and mapping of high-value habitats across the landscape. The assessment was undertaken across the forested areas of the Coffs Harbour Local Government Area using high-resolution digital imagery. It is anticipated that the High Value Arboreal Habitat mapping process will be adopted by a range of stakeholders and natural resource managers to better manage and conserve these old forests across the landscape whatever their size.