ORCID Profile
0000-0001-6464-3054
Current Organisation
University of Nottingham
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Publisher: Informa UK Limited
Date: 2001
Publisher: Cold Spring Harbor Laboratory
Date: 10-02-2021
DOI: 10.1101/2021.02.09.430391
Abstract: Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like ersity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package - raster - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The raster package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Publisher: Wiley
Date: 05-01-2015
DOI: 10.1002/JOC.4210
Publisher: Wiley
Date: 08-2005
DOI: 10.1890/04-1061
Publisher: Elsevier BV
Date: 02-2018
Publisher: Wiley
Date: 15-03-2021
DOI: 10.1111/GEB.13270
Publisher: Elsevier BV
Date: 09-2010
Publisher: Wiley
Date: 03-05-2021
Abstract: Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, ersity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package— raster —to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The raster package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open‐source algorithms.
Publisher: Informa UK Limited
Date: 2000
Publisher: Elsevier BV
Date: 04-2005
Publisher: Wiley
Date: 12-02-2022
Abstract: Lianas (woody vines) are abundant and erse, particularly in tropical ecosystems. Lianas use trees for structural support to reach the forest canopy, often putting leaves above their host tree. Thus they are major parts of many forest canopies. Yet, relatively little is known about distributions of lianas in tropical forest canopies, because studying those canopies is challenging. This knowledge gap is urgent to address because lianas compete strongly with trees, reduce forest carbon uptake and are thought to be increasing, at least in the Neotropics. Lianas can be difficult to study using traditional field methods. Their pliable stems often twist and loop through the understorey, making it difficult to assess their structure and biomass, and the sizes and locations of their crowns. Furthermore, liana stems are commonly omitted from standard field surveys. Remote sensing of lianas can help overcome some of these obstacles and can provide critical insights into liana ecology, but to date there has been no systematic assessment of that contribution. We review progress in studying liana ecology using ground‐based, airborne and space‐borne remote sensing in four key areas: (i) spatial and temporal distributions, (ii) structure and biomass, (iii) responses to environmental conditions and (iv) ersity. This demonstrates the great potential of remote sensing for rapid advances in our knowledge and understanding of liana ecology. We then look ahead, to the possibilities offered by new and future advances. We specifically consider the data requirements, the role of technological advances and the types of methods and experimental designs that should be prioritised. Synthesis . The particular characteristics of the liana growth form make lianas difficult to study by ground‐based field methods. However, remote sensing is well suited to collecting data on lianas. Our review shows that remote sensing is an emerging tool for the study of lianas, and will continue to improve with recent developments in sensor and platform technology. It is surprising, therefore, how little liana ecology research has utilised remote sensing to date—this should rapidly change if urgent knowledge gaps are to be addressed. In short, liana ecology needs remote sensing.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Giles Foody.