Shrub clumps of the Chilean matorral vegetation: structure and possible maintenance mechanisms

Oecologia ◽  
1984 ◽  
Vol 62 (3) ◽  
pp. 405-411 ◽  
Author(s):  
Eduardo R. Fuentes ◽  
Ricardo D. Otaiza ◽  
M. Catalina Alliende ◽  
Alicia Hoffmann ◽  
Aldo Poiani
Keyword(s):  
Vegetation Structure ◽  
Chilean Matorral

Analisis Keragaman Tanaman Sebagai Jasa Lingkungan Pada Lanskap Agroforestri di Daerah Aliran Sungai Krueng Aceh

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Zulkifli Aiyub Kadir ◽  
Bahagia Bahagia
Keyword(s):  
Water Conservation ◽  
Vegetation Structure ◽  
Swot Analysis ◽  
Diversity Index ◽  
Middle Stream ◽  
Land Management System ◽  
Study Sites ◽  
Aleurites Moluccana ◽  
Lansium Domesticum ◽  
Agroforestry Practices

<p>Humans have utilized landscape for  produces a diverse character of the wider area of the watershed. Agroforestry is a land management system in addressing the problems that arise due to changes in land use of soil and water conservation. The aim of the study was to analyze plant diversity in agroforestry practices that have services in the Krueng watershed landscape in Aceh watershed. Develop strategies in the Krueng Aceh DAS agroforestry service. This research was conducted in the upper, middle and downstream of the Krueng Aceh watershed, with a rapid method of Agro-Biodiversity Appraisal and SWOT. The results showed that the composition of the vegetation structure found in the study sites tended to vary with the diversity index of agroforestry that was currently in the upstream and middle of the Krueng Aceh watershed. Based on SWOT analysis, internal scores are 2.45 and external scores are 3.21. Agroforestry practices in the upper stream of Krueng Aceh watershed were dominated by <em>Aleurites moluccana</em>, <em>Areca cathecu</em>, and  <em>Averrhoa bilimbi</em> L  species with the highest INP in the upper stream of Krueng Aceh watershed. Vegetation at the middle stream of Krueng Aceh watershed dominated by <em>Areca cathecu,</em> <em>Lansium domesticum</em> and Musa<em> paradisiaca</em>.  </p>

The Vegetation Structure of Evergreen Coniferous Forests of Daehuksan Island in the Dadohaehaesang National Park

2018 ◽  
Vol 30 (1) ◽  
pp. 173-193 ◽  
Author(s):  
Yeongjun Cho ◽  
Hasong Kim ◽  
Hyeonho Myeong ◽  
Jungwon Park ◽  
Janggeun Oh
Keyword(s):  
Vegetation Structure ◽  
National Park ◽  
Coniferous Forests

Vegetation structure and environmental gradients in the Sallum area, Egypt

2005 ◽  
Vol 31 (1) ◽  
pp. 15-32 ◽  
Author(s):  
Fawzy M. Salama ◽  
Monier Abd El-Ghani ◽  
Salah El Naggar ◽  
Khadija A. Baayo
Keyword(s):  
Vegetation Structure ◽  
Environmental Gradients

scholarly journals Effects of habitat edges on vegetation structure and the vulnerable golden-brown mouse lemur (Microcebus ravelobensis) in northwestern Madagascar

BMC Ecology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bertrand Andriatsitohaina ◽  
Daniel Romero-Mujalli ◽  
Malcolm S. Ramsay ◽  
Frederik Kiene ◽  
Solofonirina Rasoloharijaona ◽  
...  
Keyword(s):  
Body Mass ◽  
Edge Effects ◽  
Vegetation Structure ◽  
Species Abundance ◽  
Tree Height ◽  
Logistic Function ◽  
Study Region ◽  
Large Trees ◽  
Significant Difference ◽  
Vegetation Parameters

Abstract Background Edge effects can influence species composition and community structure as a result of changes in microenvironment and edaphic variables. We investigated effects of habitat edges on vegetation structure, abundance and body mass of one vulnerable Microcebus species in northwestern Madagascar. We trapped mouse lemurs along four 1000-m transects (total of 2424 trap nights) that ran perpendicular to the forest edge. We installed 16 pairs of 20 m2 vegetation plots along each transect and measured nine vegetation parameters. To determine the responses of the vegetation and animals to an increasing distance to the edge, we tested the fit of four alternative mathematical functions (linear, power, logistic and unimodal) to the data and derived the depth of edge influence (DEI) for all parameters. Results Logistic and unimodal functions best explained edge responses of vegetation parameters, and the logistic function performed best for abundance and body mass of M. ravelobensis. The DEI varied between 50 m (no. of seedlings, no. of liana, dbh of large trees [dbh ≥ 10 cm]) and 460 m (tree height of large trees) for the vegetation parameters, whereas it was 340 m for M. ravelobensis abundance and 390 m for body mass, corresponding best to the DEI of small tree [dbh < 10 cm] density (360 m). Small trees were significantly taller and the density of seedlings was higher in the interior than in the edge habitat. However, there was no significant difference in M. ravelobensis abundance and body mass between interior and edge habitats, suggesting that M. ravelobensis did not show a strong edge response in the study region. Finally, regression analyses revealed three negative (species abundance and three vegetation parameters) and two positive relationships (body mass and two vegetation parameters), suggesting an impact of vegetation structure on M. ravelobensis which may be partly independent of edge effects. Conclusions A comparison of our results with previous findings reveals that edge effects are variable in space in a small nocturnal primate from Madagascar. Such an ecological plasticity could be extremely relevant for mitigating species responses to habitat loss and anthropogenic disturbances.

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals The history of vegetation and landscapes of the lake Ilchir basin for understanding the modern vegetation structure in the Okinsky plateau

2021 ◽  
Vol 629 ◽  
pp. 012046
Author(s):  
E V Volchatova ◽  
E V Bezrukova ◽  
N V Kulagina ◽  
O V Levina ◽  
A A Shchetnikov ◽  
...  
Keyword(s):  
Vegetation Structure ◽  
History Of Vegetation ◽  
History Of

scholarly journals Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

2021 ◽  
Vol 13 (2) ◽  
pp. 257 ◽  
Author(s):  
Shaun R. Levick ◽  
Tim Whiteside ◽  
David A. Loewensteiner ◽  
Mitchel Rudge ◽  
Renee Bartolo
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Structural Diversity ◽  
Point Clouds ◽  
Large Area ◽  
Calibration And Validation ◽  
Savanna Vegetation ◽  
Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

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