scholarly journals The effect of sampling density and study area size on landscape genetics inferences for the Mississippi slimy salamander ( Plethodon mississippi )

2021 ◽  
Author(s):  
Stephanie M. Burgess ◽  
Ryan C. Garrick
Keyword(s):  
Landscape Genetics ◽  
Sampling Density

scholarly journals The Effect of LiDAR Sampling Density on DTM Accuracy for Areas with Heavy Forest Cover

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 265
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru
Keyword(s):  
Forest Cover ◽  
Initial Point ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Morphological Data ◽  
Sampling Density ◽  
Terrain Model ◽  
Digital Terrain ◽  
Point Density ◽  
The Impact

Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.

Comparison of geochemical patterns from different sampling density geochemical mapping in Altay, Xinjiang Province, China

2021 ◽  
pp. 106761
Author(s):  
Dongsheng Liu ◽  
Xueqiu Wang ◽  
Lanshi Nie ◽  
Hanliang Liu ◽  
Bimin Zhang ◽  
...  
Keyword(s):  
Geochemical Mapping ◽  
Sampling Density

Performance of partial statistics in individual-based landscape genetics

2014 ◽  
Vol 15 (3) ◽  
pp. 512-525 ◽  
Author(s):  
E. M. Kierepka ◽  
E. K. Latch
Keyword(s):  
Landscape Genetics

scholarly journals Spatial Genetic Structure of a Symbiotic Beetle-Fungal System: Toward Multi-Taxa Integrated Landscape Genetics

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e25359 ◽  
Author(s):  
Patrick M. A. James ◽  
Dave W. Coltman ◽  
Brent W. Murray ◽  
Richard C. Hamelin ◽  
Felix A. H. Sperling
Keyword(s):  
Genetic Structure ◽  
Landscape Genetics ◽  
Spatial Genetic Structure

scholarly journals USE OF SCALED SEMIVARIOGRAMS IN THE PLANNING SAMPLE OF SOIL CHEMICAL PROPERTIES IN SOUTHERN AMAZONAS, BRAZIL

2015 ◽  
Vol 39 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Ivanildo Amorim de Oliveira ◽  
Milton César Costa Campos ◽  
José Marques Junior ◽  
Renato Eleotério de Aquino ◽  
Daniel de Bortoli Teixeira ◽  
...  
Keyword(s):  
Chemical Properties ◽  
Spherical Model ◽  
Exponential Model ◽  
Soil Samples ◽  
Soil Chemical Properties ◽  
Sampling Density ◽  
Sugarcane Crop ◽  
Lack Of Information ◽  
Set Up ◽  
Almost All

The lack of information concerning the variability of soil properties has been a major concern of researchers in the Amazon region. Thus, the aim of this study was to evaluate the spatial variability of soil chemical properties and determine minimal sampling density to characterize the variability of these properties in five environments located in the south of the State of Amazonas, Brazil. The five environments were archaeological dark earth (ADE), forest, pasture land, agroforestry operation, and sugarcane crop. Regular 70 × 70 m mesh grids were set up in these areas, with 64 sample points spaced at 10 m distance. Soil samples were collected at the 0.0-0.1 m depth. The chemical properties of pH in water, OM, P, K, Ca, Mg, H+Al, SB, CEC, and V were determined at these points. Data were analyzed by descriptive and geostatistical analyses. A large part of the data analyzed showed spatial dependence. Chemical properties were best fitted to the spherical model in almost all the environments evaluated, except for the sugarcane field with a better fit to the exponential model. ADE and sugarcane areas had greater heterogeneity of soil chemical properties, showing a greater range and higher sampling density; however, forest and agroforestry areas had less variability of chemical properties.

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