Forest type distribution on a calcareous terrain in western Newfoundland

1996 ◽  
Vol 72 (2) ◽  
pp. 185-192
Author(s):  
A. R. van Kesteren
Keyword(s):  
Forest Type ◽  
Forest Types ◽  
Forest Land ◽  
Photo Interpretation ◽  
Type Distribution ◽  
Mapping System ◽  
Maximum Elevation ◽  
Elevation Data ◽  
Log Linear ◽  
Air Photo

Terrain factors influencing forest type distribution on a calcareous terrain in western Newfoundland were investigated. Landform elements were mapped at a scale of 1:12,500 utilizing air photo interpretation. Minimum and maximum elevation data along with dominant forest type occurrence were determined in the field. Frequencies of landform element and forest type correspondence were tested using a log-linear G2 analysis. Additionally, elevational differences of both landform elements and forest types were analyzed using the Kruskal-Wallis test. Null hypotheses of no significant landform influence on forest type distribution and no significant elevational differentiation of landform elements were rejected. However, no significant direct elevational differentiation of forest types was detected. Results are supportive of the observations of Damman (1967), indicating a primary toposequence control on forest type distribution. Verified forest type–landform associations could aid the development of a statistically based phytogeomorphic mapping system for forest land use management in Newfoundland. Key words: forest type, landform element, phytogeomorphic mapping, air photo interpretation

scholarly journals Global Forest Types Based on Climatic and Vegetation Data

2022 ◽  
Vol 14 (2) ◽  
pp. 634
Author(s):  
Chen Xu ◽  
Xianliang Zhang ◽  
Rocío Hernandez-Clemente ◽  
Wei Lu ◽  
Rubén D. Manzanedo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Type ◽  
Tree Cover ◽  
Small Range ◽  
Vegetation Types ◽  
Forest Types ◽  
Type Distribution ◽  
Forest Attributes ◽  
Descriptive Names

Forest types are generally identified using vegetation or land-use types. However, vegetation classifications less frequently consider the actual forest attributes within each type. To address this in an objective way across different regions and to link forest attributes with their climate, we aimed to improve the distribution of forest types to be more realistic and useful for biodiversity preservation, forest management, and ecological and forestry research. The forest types were classified using an unsupervised cluster analysis method by combining climate variables with normalized difference vegetation index (NDVI) data. Unforested regions were masked out to constrict our study to forest type distributions, using a 20% tree cover threshold. Descriptive names were given to the defined forest types based on annual temperature, precipitation, and NDVI values. Forest types had distinct climate and vegetation characteristics. Regions with similar NDVI values, but with different climate characteristics, which would be merged in previous classifications, could be clearly distinguished. However, small-range forest types, such as montane forests, were challenging to differentiate. At macroscale, the resulting forest types are largely consistent with land-cover types or vegetation types defined in previous studies. However, considering both potential and current vegetation data allowed us to create a more realistic type distribution that differentiates actual vegetation types and thus can be more informative for forest managers, conservationists, and forest ecologists. The newly generated forest type distribution is freely available to download and use for non-commercial purposes as a GeoTIFF file via doi: 10.13140/RG.2.2.19197.90082).

scholarly journals The woody flora of Shettihalli Wildlife Sanctuary, central Western Ghats of Karnataka, India - A checklist

2021 ◽  
Vol 13 (13) ◽  
pp. 20033-20055
Author(s):  
Naveen Babu Kanda ◽  
Kurian Ayushi ◽  
Vincy K. Wilson ◽  
Narayanan Ayyappan ◽  
Narayanaswamy Parthasarathy
Keyword(s):  
Western Ghats ◽  
Forest Cover ◽  
Forest Type ◽  
Human Impacts ◽  
Forest Types ◽  
Wildlife Sanctuary ◽  
Peninsular India ◽  
Ecological Features ◽  
Central Western Ghats ◽  
The Western Ghats

Documenting the biodiversity of protected areas and reserve forests is important to researchers, academicians and forest departments in their efforts to establish policies to protect regional biodiversity. Shettihalli Wildlife Sanctuary (SWS) is an important protected area located in the central Western Ghats of Karnataka state known for its diverse flora and fauna with distinct ecological features. For the last four decades the sanctuary has witnessed the loss of forest cover, yet the vegetation in few locations is relatively undisturbed. The current inventory was undertaken during 2019–2020 to provide a checklist of woody species from SWS under-researched earlier. The list comprises 269 species of trees, lianas and shrubs distributed in 207 genera and 68 families. The most diverse families are Fabaceae, Moraceae, Rubiaceae, Rutaceae, Lauraceae, Apocynaceae, Meliaceae, Malvaceae, Phyllanthaceae, and Anacardiaceae, representing 48% of total woody flora. The sanctuary shelters 263 native and six exotic plant species. Thirty-nine species were endemic to the Western Ghats, five species to peninsular India and one species to the Western Ghats and Andaman & Nicobar Islands. Four forest types, i.e., dry deciduous, moist deciduous, semi-evergreen, and evergreen forests, are represented in the sanctuary. Of the total species, only seven occurred in all forest types, while 111 species are exclusive to a single forest type. One-hundred-and-four taxa were assessed for the International Union for Conservation of Nature & Natural Resources (IUCN) Red List. Ten species that fall under Near Threatened, Vulnerable, and Endangered categories were encountered occasionally. The baseline data generated on plant diversity will be useful in highlighting the importance of these forests for species conservation and forest management. Such data form a cornerstone for further research. For instance, to understand the effect of invasive species and human impacts on the diversity of the region. 

Habitat Use by Arboreal Mammals along an Environment Gradient in North-eastern Victoria

1991 ◽  
Vol 18 (2) ◽  
pp. 125 ◽  
Author(s):  
AF Bennett ◽  
LF Lumsden ◽  
JSA Alexander ◽  
PE Duncan ◽  
PG Johnson ◽  
...  
Keyword(s):  
Habitat Use ◽  
Environmental Gradient ◽  
Forest Type ◽  
High Elevation ◽  
Annual Rainfall ◽  
Distributional Pattern ◽  
Forest Types ◽  
Phascolarctos Cinereus ◽  
North Eastern ◽  
Arboreal Mammal

A total of 1487 observations of nine species of arboreal mammal, Acrobates pygmaeus, Phascolarctos cinereus, Petauroides volans, Petaurus australis, P. breviceps, P. norfolcensis, Pseudocheirusperegrinus, Trichosurus caninus and T. vulpecula, were made during surveys of the vertebrate fauna of northeastern Victoria. Habitat use by each species was examined in relation to eight forest types that occur along an environmental gradient ranging from sites at high elevation with a high annual rainfall, to sites on the dry inland and riverine plains. Arboreal mammals were not evenly distributed between forest types. Three species (P. australis, P. volans and T. caninus) were mainly associated with moist tall forests; two species (P. norfolcensis and T. vulpecula) were primarily associated with drier forests and woodlands of the foothills; the remaining three species (A. pygmaeus, P. breviceps and P. peregrinus) occurred widely throughout the forests. The composition of the arboreal mammal assemblage changed along the environmental gradient, but species displayed gradual changes in abundance with forest type rather than marked discontinuities in distributional pattern. The highest overall frequencies of occurrence of arboreal mammals were in forests typically dominated by a mixture of eucalypt species. The position at first sighting of an animal, and the relative height in the forest stratum, were used to describe the micro-habitats utilised. In general, the microhabitats occupied by each species are consistent with the distribution of their known food resources.

Characterizing Canopy Openness Across Large Forested Landscapes Using Spherical Densiometer and Smartphone Hemispherical Photography

2021 ◽  
Author(s):  
Katie L Beeles ◽  
Jordon C Tourville ◽  
Martin Dovciak
Keyword(s):  
United States ◽  
Large Range ◽  
Forest Type ◽  
High Elevation ◽  
Canopy Openness ◽  
Hemispherical Photography ◽  
Forest Types ◽  
Northeastern United States ◽  
Closed Canopy ◽  
The Right

Abstract Canopy openness is an important forest characteristic related to understory light environment and productivity. Although many methods exist to estimate canopy openness, comparisons of their performance tend to focus on relatively narrow ranges of canopy conditions and forest types. To address this gap, we compared two popular approaches for estimating canopy openness, traditional spherical densiometer and modern smartphone hemispherical photography, across a large range of canopy conditions (from closed canopy to large gaps) and forest types (from low-elevation broadleaf to high-elevation conifer forests) across four states in the northeastern United States. We took 988 field canopy openness measurements (494 per instrument) and compared them across canopy conditions using linear regression and t-tests. The extensive replication allowed us to quantify differences between the methods that may otherwise go unnoticed. Relative to the densiometer, smartphone photography overestimated low canopy openness (<10%) but it underestimated higher canopy openness (>10%), regardless of forest type. Study Implications We compared two popular ways of measuring canopy openness (smartphone hemispherical photography and spherical densiometer) across a large range of forest structures encountered in the northeastern United States. We found that, when carefully applied, the traditional spherical densiometer can characterize canopy openness across diverse canopy conditions (including closed canopies) as effectively as modern smartphone canopy photography. Although smartphone photography reduced field measurement time and complexity, it was more susceptible to weather than the densiometer. Although selection of the right method depends on study objectives, we provide a calibration for these two popular methods across diverse canopies.

scholarly journals Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables

2020 ◽  
Vol 12 (12) ◽  
pp. 2049
Author(s):  
Joongbin Lim ◽  
Kyoung-Min Kim ◽  
Eun-Hee Kim ◽  
Ri Jin
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Tree Species ◽  
North Korea ◽  
Forest Type ◽  
Forest Area ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Forest Types

The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea).

Within-Patch Structures Influence Small Mammal Abundance in Managed Forests of Northern California, USA

2019 ◽  
Vol 65 (6) ◽  
pp. 796-804 ◽  
Author(s):  
Steven M Gray ◽  
Gary J Roloff ◽  
Andrew J Dennhardt ◽  
Brian P Dotters ◽  
Thomas T Engstrom
Keyword(s):  
Small Mammal ◽  
Forest Type ◽  
Ground Cover ◽  
Ground Squirrels ◽  
Northern California ◽  
Managed Forests ◽  
Forest Types ◽  
Forest Patches ◽  
Downed Wood ◽  
Intensively Managed

Abstract We evaluated how forest type, vegetation structure in trapping webs, and proximate forest types influenced localized (~6.35 hectares) abundances for commonly captured small mammals in northern California, USA. We trapped from May to August of 2011–13 in 69 forest patches that represented: (1) clearcuts (3–5 years postharvest), (2) 10–20 year-old conifer plantations, (3) rotation-aged conifer stands, and (4) Watercourse and Lake Protection Zones. We captured 11 species; four in sufficient numbers for regression modeling. Our average abundance estimates for the study were 4.57 (standard error [SE] = 0.43), 0.32 (SE = 0.11), 0.90 (SE = 0.30), and 0.25 (SE = 0.09) individuals per web location (~0.75 hectares) for Peromyscus spp., Neotoma spp., California ground squirrels, and Allen’s chipmunks. We found that web-level ground cover (shrubs and grass), downed wood, and types of forests containing our trapping webs best described small mammal abundances, whereas proximate forest types were not important. Our results indicated that retaining localized structures in the form of understory shrub cover and downed wood positively influences small mammal abundance in intensively managed forests of northern California.

scholarly journals Mono-specific forest plantations are valuable bat habitats: implications for wind energy development

2020 ◽  
Vol 67 (1) ◽  
Author(s):  
Sascha Buchholz ◽  
Volker Kelm ◽  
Simon J. Ghanem
Keyword(s):  
Species Composition ◽  
Wind Energy ◽  
Forest Type ◽  
Energy Development ◽  
Forest Plantations ◽  
Forest Types ◽  
Bat Activity ◽  
Power Stations ◽  
Bat Conservation ◽  
Wind Energy Development

AbstractNear-natural or semi-natural forests such as relatively undisturbed and old deciduous or mixed woodland are considered worth protecting and ecologically valuable habitats for bat conservation. In contrast, mono-specific forest plantations are considered ecologically less valuable; thus, decision-makers recommend these plantations as suitable locations for wind power stations and therefore want to further expand wind turbines in these habitats. This is expected to have a strong negative impact on the landscape because forests would be cleared for wind turbine pads and access roads and wind turbines rise above the trees with adverse impacts for bats. Therefore, we argue that, in light of bat conservation, the suitability of forest plantations for wind energy development is not, per se, warranted and that implications of wind power stations, even in mono-specific forest plantations, should be assessed and evaluated. We conducted long-term bat activity monitoring and recorded bat echolocation calls above the canopies of different forest sites (coniferous monoculture plantations and semi-natural mixed deciduous forests) in Germany and compared different forest types in terms of species richness, total bat activity, activity of the three bat species groups and species composition. Generalised linear models revealed that forest type and the amount of forest biotopes did not enhance bat activity. Ordination showed that species composition was not affected by forest type, location and connectivity. Mono-specific forest plantations can harbour a diverse bat fauna with high species activity and are, therefore, valuable bat habitats just as near-natural or semi-natural woodlands are. Environmental impact assessment and mitigation measures are vital in all forest types before and after planning for wind energy turbines. In particular, future planning and approval processes must consider the importance of mono-specific forest plantations for bat species protection.

scholarly journals Forest Site and Type Variability in ALS-Based Forest Resource Inventory Attribute Predictions over Three Ontario Forest Sites

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 226 ◽  
Author(s):  
Karin van Ewijk ◽  
Paul Treitz ◽  
Murray Woods ◽  
Trevor Jones ◽  
John Caspersen
Keyword(s):  
Prediction Accuracy ◽  
Forest Type ◽  
Stem Density ◽  
Forest Site ◽  
Forest Resource ◽  
Forest Types ◽  
Resource Inventory ◽  
Forest Resource Inventory ◽  
Forest Attributes ◽  
The Impact

Over the last decade, spatially-explicit modeling of landscape-scale forest attributes for forest inventories has greatly benefitted from airborne laser scanning (ALS) and the area-based approach (ABA) to derive wall-to-wall maps of these forest attributes. Which ALS-derived metrics to include when modeling forest inventory attributes, and how prediction accuracies vary over forest types depends largely on the structural complexity of the forest(s) being studied. Hence, the purpose of this study was to (i) examine the usefulness of adding texture and intensity metrics to height-based ALS metrics for the prediction of several forest resource inventory (FRI) attributes in one boreal and two Great Lakes, St. Lawrence (GLSL) forest region sites in Ontario and (ii) quantify and compare the site and forest type variability within the context of the FRI prediction accuracies. Basal area (BA), quadratic mean diameter-at-breast height (QMD), and stem density (S) were predicted using the ABA and a nonparametric Random Forests (RF) regression model. At the site level, prediction accuracies (i.e., expressed as RMSE (Root Mean Square Error), bias, and R2) improved at the three sites when texture and intensity metrics were included in the predictor set, even though no significant differences (p > 0.05) could be detected using the nonparametric RMANOVA test. Stem density benefitted the most from the inclusion of texture and intensity, particularly in the GLSL sites (% RMSE improved up to 6%). Combining site and forest type results indicated that improvements in site level predictions, due to the addition of texture and intensity metrics to the ALS predictor set, were the result of changes in prediction accuracy in some but not all forest types present at a site and that these changes in prediction accuracy were site and FRI attribute specific. The nonparametric Kruskal–Wallis test indicated that prediction errors between the different forest types were significantly different (p ≤ 0.01). In the boreal site, prediction accuracies for conifer forest types were higher than for deciduous and mixedwoods. Such patterns in prediction accuracy among forest types and FRI attributes could not be observed in the GLSL sites. In the Petawawa Research Forest (PRF), we did detect the impact of silvicultural treatments especially on QMD and S predictions.

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