Assessing reduction of cluster size to estimate wood volume in an Amazonian forest

ABSTRACT While the Brazilian National Forest Inventory (NFI) is in progress, there is a growing demand to understand the effect of cluster size on the accuracy and precision of forest-attribute estimation. We aimed to find the minimum cluster size (in area) to estimate merchantable volume (MV) with the same accuracy and precision as the estimates derived from the original cluster of 8,000 m2. We used data from an inventory carried out in a forest unit (Bom Futuro National Forest) in the southwestern Brazilian Amazon, where 22 clusters were distributed as a two-stage sampling design. Three products were evaluated: (i) MV of trees with a diameter at breast height (DBH) ≥ 20 cm (P1); (ii) MV of trees with DBH ≥ 50 cm (P2); and (iii) MV of commercial species with DBH ≥ 50 cm and stem quality ‘level 1’ or ‘level 2’ (P3). We assessed ten scenarios in which the cluster size was reduced from 8,000 m2 to 800 m2. The accuracy of P1, P2 and P3 was highly significantly lower for reductions < 2,400 m². The precision was more sensitive to variations in cluster size, especially for P2 and P3. Minimum cluster sizes were ≥ 2,400 m² to estimate P1, ≥ 4,800 m² to estimate P2, and ≥ 7,200 m² to estimate P3. We concluded that it is possible to reduce the cluster size without losing the accuracy and precision given by the original NFI cluster. A cluster of 2,400 m² provides estimates as accurate as the original cluster, regardless of the evaluated product.

Instantaneous Approach for Evaluating the Initial Centers in the Agricultural Databases Using K-Means Clustering Algorithm

Clustering algorithms are most probably and widely used analysis method for grouping agricultural data with high similarity. For example, one of the most widely used approaches in previous study is K-means, which is simpler, more versatile, and easier to understand and formulate. The only disadvantage of the K-means algorithm has always been that the predetermined set of cluster centres must be prepared ahead of time and provided as feedback. This paper addresses the issue of estimating cluster random centres for data segmentation and proposes a new method for locating appropriate random centres based on the frequency of attribute values. As a consequence of calculating cluster random centres, the number of iterations required to achieve optimum clusters in K-means will be reduced, as will the time required to shape the final clusters. The experimental findings show that our approach is efficient at estimating the right random cluster centres that indicate a fair separation of objects in the given database. The technique observation and comparative test results showed that the new strategy does not use present manual cluster centres, is more efficient in determining the original cluster centres, and therefore more successful in terms of time to converge the actual clusters especially in agricultural data bases.

Density functional theory study of structural, magnetic and spectral properties of the CrmFen (m + n = 6) and CrmFenCu (m + n = 5) clusters

Using Density functional theory (DFT) to study the geometries, stability, magnetic properties and infrared spectroscopy of CrmFen (m + n = 6) and CrmFenCu (m + n = 5) clusters at the BP86/SDD level. The ground state structures of CrmFen (m + n = 6) and CrmFenCu (m + n = 5) clusters are determined according to the principles of lowest energy and no virtual frequency. On this basis, the structural and chemical stabilities are obtained by the average binding energies (Eb), chemical hardness (η) and HOMO-LUMO energy gap (Eg). The average binding energies show the substitution of a copper atom is beneficial to improve the structural stability; It can be seen Cr4Fe2 and Cr3Fe2Cu have the best chemical stability in the two cluster series from the chemical hardness and HOMO-LUMO energy gap. By calculating the magnetic moment, it is shown that Cr5Fe and CrFe4Cu have large magnetic moments, which can be understood by the spin distribution. Finally, infrared spectroscopy of the clusters are calculated, we find a copper atom substitutes the CrmFen (m + n = 6) does not change the range of vibration frequency a lot because it does not significantly change the molecular structure of the original cluster, but it changes the vibration mode of the original cluster, resulting in the strongest infrared absorption peak intensity of Cr3Fe2Cu being lower than that of Cr3Fe3.

Luster Sampling to Improve Classifier Accuracy for Numeric Data

Clustering is one of the essential techniques to group similar data. Improving model accuracy is still a challenge for all variety of data. Training and testing a classifier on entire data is not possible for large scale of data. Sampling of the data is necessary for any modeling and is an important aspect in data mining. All models train and test on different samples taken by traditional techniques like random forest ensemble method. In this paper, we propose cluster sampling which is superior to any other sampling methods in improving classifier accuracy. Sampling the data from usual methods cannot cover all variety of data from the original. Cluster sampling is a two-step approach. First it clusters the entire data, second it selects samples from each cluster. These samples consists all verity of data with equal proportion. Cluster sampling leverages the tree based ensemble to handle categorical, numerical and mixed type of data. Classifiers modeled on cluster sampling samples shown superior in accuracy than modeled on other sampling techniques.

Anvil Productivities of Tropical Deep Convective Clusters and Their Regional Differences

The anvil productivities of tropical deep convection are investigated and compared among eight climatological regions using 4 yr of collocated and combined CloudSat and CALIPSO data. For all regions, the convective clusters become deeper while they become wider and tend to be composed of multiple rainy cores. Two strong detrainment layers from deep convection are observed at 6–8 km and above 10 km, which is consistent with the trimodal characteristics of tropical convection that are associated with different divergence, cloud detrainment, and fractional cloudiness. The anvil productivity of tropical deep convection depends on the convection scale, convective life stage or intensity, and large-scale environment. Anvil ice mass ratio related to the whole cluster starts to level off or decrease when the cluster effective scales Weff (the dimension of an equivalent rectangular with the same volume and height as the original cluster) increase to about 200 km wide, while the ratios of anvil scale and volume keep increasing from 0.4 to 0.6 and 0.15 to 0.4, respectively. The anvil clouds above 12 km can count for more than 20% of cluster volume, or more than 50% of total anvil volume, but they only count less than about 2% of total ice mass in the cluster. Anvil production of younger convection of the same Weff is higher than that of the decaying convection. The regional difference in the composite anvil productivities of tropical convective clusters sorted by Weff is subtle, while the occurrence frequencies of different scales of convection vary substantially.

Muffin-tin potentials in EXAFS analysis

Muffin-tin potentials are the standard tool for calculating the potential surface of a cluster of atoms for use in the analysis of extended X-ray absorption fine-structure (EXAFS) data. The set of Cartesian coordinates used to define the positions of atoms in the cluster and to calculate the muffin-tin potentials is commonly also used to enumerate the scattering paths used in the EXAFS data analysis. In this paper, it is shown that these muffin-tin potentials are sufficiently robust to be used to examine quantitatively contributions to the EXAFS data from scattering geometries not represented in the original cluster.

A typological study of gambling and substance use among adolescent students

Cluster analysis was used to define subpopulations of youth involved in drugs, alcohol, and gambling. Data from a 2001 cross-sectional survey of Ontario grade 7 to 13 students (N = 2,243; mean age 15 years; 51% males) were examined. The analysis suggested four clusters: Mainstreamers (66.0%), Party Goers (26.2%), Drug Takers (5.9%), and Heavy Gamblers (1.9%). This cluster structure was validated across a number of additional external variables that were not used in the original cluster analysis. The findings indicated that Drug Takers and Heavy Gamblers formed two distinct clusters. Probable pathological gamblers were found in all four clusters, but they were most concentrated in the heavy gambling cluster. The results suggest that troubled youths are not a single entity, but display heterogeneity in their configuration of risk behaviours.