ORCID Profile
0000-0002-3555-4295
Current Organisation
University of Oxford
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Publisher: Frontiers Media SA
Date: 17-01-2020
Publisher: Cold Spring Harbor Laboratory
Date: 18-12-2022
DOI: 10.1101/2022.12.15.520573
Abstract: Tropical forests are threatened by degradation and deforestation but the consequences for these ecosystems are poorly understood, particularly at the landscape scale. We present the most extensive ecosystem analysis to date of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 79 variables categorized into four hierarchical ecological ‘levels’: 1) structure and environment, 2) species traits, 3) bio ersity and 4) ecosystem functions. Variables at the lowest levels that were directly impacted by the physical processes of timber extraction, such as soil characteristics, were sensitive to even moderate amounts of logging, whereas bio ersity and ecosystem functions proved remarkably resilient to logging in many cases, but were more affected by conversion to oil palm plantation. Logging tropical forest mostly impacts structure while bio ersity and functions are more vulnerable to habitat conversion
Publisher: Wiley
Date: 09-07-2018
DOI: 10.1002/ECE3.4268
Publisher: MDPI AG
Date: 07-2023
DOI: 10.3390/RS15133374
Abstract: In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of s les in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of s les in the subclass labels was sufficient. Next, the enriched data are ided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for David Hemprich-Bennett.