Detecting intra-site patterns with systematic sampling strategies. Archaeobotanical grid sampling of the lakeshore settlement Bad Buchau-Torwiesen II, southwest Germany

2011 ◽  
Vol 20 (5) ◽  
pp. 349-365 ◽  
Author(s):  
Ursula Maier ◽  
Arno Harwath
Keyword(s):  
Systematic Sampling ◽  
Sampling Strategies ◽  
Grid Sampling ◽  
Southwest Germany

Incremental Grid Sampling Strategies in Robotics

2005 ◽  
pp. 313-328 ◽  
Author(s):  
Stephen R. Lindemann ◽  
Anna Yershova ◽  
Steven M. LaValle
Keyword(s):  
Sampling Strategies ◽  
Grid Sampling

Charred plant remains of the Archaic period from the Forum and Palatine

2001 ◽  
Vol 14 ◽  
pp. 239-248 ◽  
Author(s):  
Lorenzo Costantini ◽  
John Giorgi
Keyword(s):  
Cereal Grains ◽  
Systematic Sampling ◽  
Sampling Strategies ◽  
Archaeological Evidence ◽  
Agricultural Economy ◽  
Literary Sources ◽  
Plant Remains ◽  
Weed Seeds ◽  
Incomplete Picture ◽  
By Products

Our understanding of the arable economy of the early centuries of Rome has been based largely on ancient literary sources to give an idea of the range of crops grown in the area and their possible uses. For Archaic Rome in particular, very little evidence from the physical remains of crops has been available, and this has limited any serious comparison between archaeological evidence and what the literary sources may suggest about the arable agriculture of the early city. Archaeobotanical evidence for the agricultural economy of Rome and its surrounds in the pre-urban period has largely depended upon the work of Hans Helbaek: in the 1950s and 1960s he carried out the study of plant remains recovered in three areas of the Forum (Helbaek 1953, 1956, 1960). His studies were limited, however, by the absence of systematic sampling strategies and particularly by inadequate retrieval methods. Flotation techniques were not employed, and this prevented the potential recovery of smaller plant items such as small cereal grains (e.g., millet) and crop by-products (e.g., chaff fragments and small weed seeds). This led to an incomplete picture of the range of crops used and also led to difficulties in the identification of cereal grains owing to the absence of chaff fragments.

scholarly journals Estimating Pollution Loads in Snow Removed from a Port Facility: Snow Pile Sampling Strategies

2021 ◽  
Vol 232 (2) ◽  
Author(s):  
Arya Vijayan ◽  
Heléne Österlund ◽  
Jiri Marsalek ◽  
Maria Viklander
Keyword(s):  
Sampling Strategy ◽  
Sampling Strategies ◽  
Grid Sampling ◽  
Pollutant Loads ◽  
Pollutant Concentrations ◽  
Port Area ◽  
Guideline Values ◽  
Mass Load ◽  
Entire Depth ◽  
Composite Samples

AbstractChoosing the appropriate sampling strategy is significant while estimating the pollutant loads in a snow pile and assessing environmental impacts of dumping snow into water bodies. This paper compares different snow pile sampling strategies, looking for the most efficient way to estimate the pollutant loads in a snow pile. For this purpose, 177 snow samples were collected from nine snow piles (average pile area − 30 m2, height − 2 m) during four sampling occasions at Frihamnen, Ports of Stockholm’s port area. The measured concentrations of TSS, LOI, pH, conductivity, and heavy metals (Zn, Cu, Cd, Cr, Pb, and V) in the collected samples indicated that pollutants are not uniformly distributed in the snow piles. Pollutant loads calculated from different sampling strategies were compared against the load calculated using all samples collected for each pile (best estimate of mass load, BEML). The results/study showed that systematic grid sampling is the best choice when the objective of sampling is to estimate the pollutant loads accurately. Estimating pollutant loads from single snow column samples (collected at a point from the snow pile through the entire depth of the pile) produced up to 400% variation from BEML, whereas samples composed by mixing volume-proportional subsamples from all samples (horizontal composite samples) produced only up to 50% variation. Around nine samples were required to estimate the pollutant loads within 50% deviation from BEML for the studied snow piles. Converting pollutant concentrations in snow to equivalent concentrations in snowmelt and comparing it with available guideline values for receiving water, Zn was identified as the critical pollutant.

scholarly journals Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

2019 ◽  
Vol 11 (16) ◽  
pp. 1906
Author(s):  
Siqi Li ◽  
Lindi J. Quackenbush ◽  
Jungho Im
Keyword(s):  
New York ◽  
Aboveground Biomass ◽  
Forest Type ◽  
New York State ◽  
Airborne Lidar ◽  
Systematic Sampling ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Sampling Strategies ◽  
York State

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies.

scholarly journals The Application of Systematic Sampling Strategies for Bioarchaeological Studies in the Early Neolithic Lakeshore Site of La Draga (Banyoles, Spain)

2013 ◽  
Vol 13 (1) ◽  
pp. 29-49 ◽  
Author(s):  
Ferran AntolÍn ◽  
Àngel Blanco ◽  
Ramon Buxó ◽  
Laura Caruso ◽  
Stefanie Jacomet ◽  
...  
Keyword(s):  
Systematic Sampling ◽  
Sampling Strategies ◽  
Early Neolithic

scholarly journals Improving the Accuracy of Land Cover Mapping by Distributing Training Samples

2021 ◽  
Vol 13 (22) ◽  
pp. 4594
Author(s):  
Chenxi Li ◽  
Zaiying Ma ◽  
Liuyue Wang ◽  
Weijian Yu ◽  
Donglin Tan ◽  
...  
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Land Cover Classification ◽  
Training Sample ◽  
Stratified Sampling ◽  
Systematic Sampling ◽  
Sampling Strategies ◽  
Sample Distribution ◽  
Training Samples ◽  
Sampling Approach

High-quality training samples are essential for accurate land cover classification. Due to the difficulties in collecting a large number of training samples, it is of great significance to collect a high-quality sample dataset with a limited sample size but effective sample distribution. In this paper, we proposed an object-oriented sampling approach by segmenting image blocks expanded from systematically distributed seeds (object-oriented sampling approach) and carried out a rigorous comparison of seven sampling strategies, including random sampling, systematic sampling, stratified sampling (stratified sampling with the strata of land cover classes based on classification product, Latin hypercube sampling, and spatial Latin hypercube sampling), object-oriented sampling, and manual sampling, to explore the impact of training sample distribution on the accuracy of land cover classification when the samples are limited. Five study areas from different climate zones were selected along the China–Mongolia border. Our research identified the proposed object-oriented sampling approach as the first-choice sampling strategy in collecting training samples. This approach improved the diversity and completeness of the training sample set. Stratified sampling with strata defined by the combination of different attributes and stratified sampling with the strata of land cover classes had their limitations, and they performed well in specific situations when we have enough prior knowledge or high-accuracy product. Manual sampling was greatly influenced by the experience of interpreters. All these sampling strategies mentioned above outperformed random sampling and systematic sampling in this study. The results indicate that the sampling strategies of training datasets do have great impacts on the land cover classification accuracies when the sample size is limited. This paper will provide guidance for efficient training sample collection to increase classification accuracies.

Spatial and temporal variation in soil inorganic n concentration, and soil test P and K, in red raspberry fields and implications for soil sampling strategies

2002 ◽  
Vol 82 (3) ◽  
pp. 355-364 ◽  
Author(s):  
B J Zebarth ◽  
D M Dean ◽  
C G Kowalenko ◽  
J W Paul ◽  
K. Chipperfield
Keyword(s):  
Sampling Strategy ◽  
Systematic Sampling ◽  
Soil Test ◽  
Sampling Strategies ◽  
Red Raspberry ◽  
Inorganic N ◽  
N Concentration ◽  
Soil Inorganic N ◽  
Soil Test P ◽  
Soil Inorganic

Fertilizer is commonly applied as a band in red raspberry (Rubus idaeus L.) fields, resulting in complex spatial and temporal variation in soil inorganic N concentration, and in soil test P and K. The objectives of this study were to determine the spatial and temporal distribution of soil inorganic N in red raspberry fields receiving different N fertility treatments, to use the data to determine the most appropriate sampling strategies for estimating the quantity of soil inorganic N at various times during the growing season, and to evaluate the same sampling strategies for soil test P and K. Treatments were a control that received no manure or fertilizer N, 55 kg N ha-1 as urea or as Duration T60, a slow release N fertilizer, banded in mid-April, or 100 kg total N ha-1 as solid broiler manure broadcast or banded in early March, or banded in mid-April. Soil inorganic N was sampled at 10 inter-row locations 8, 23, 38, 53, 68, 83, 98, 113, 128, and 143 cm from the crop row, and for 0–15, 15–30, and 30–60 cm depth, for four sampling dates for the control and urea treatments, and for 0–15 and 15–30 cm depth on one sampling date for the remaining treatments. Random sampling and four systematic sampling strategies were evaluated for their bias in estimating soil inorganic N concentration and soil test P and K, and with respect to the number of soil cores required to achieve a given precision and probability level combination. The random sampling strategy gave unbiased estimates of soil inorganic N and soil test P and K, however, the number of cores required to obtain a given precision at a given probability level were generally greater than for the systematic sampling strategies. The systematic sampling strategy involving sampling only in the crop row and in the centre of the inter-row, the current industry standard, gave expected values that could sometimes be substantially lower than the true value, and was therefore not recommended for use in raspberry fields. The best systematic sampling strategy used samples collected from the crop row, from the fertilizer band, from the centre of the inter-row, and from midway between the fertilizer band and the centre of the inter-row. Key words: Rubus idaeus, nitrate leaching, nitrification, nitrate, ammonium

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