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A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest

Abstract : Monitoring biodiversity on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.
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Submitted on : Wednesday, June 23, 2021 - 10:56:24 AM
Last modification on : Tuesday, September 7, 2021 - 3:44:28 PM


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Eric Chraibi, Haley Arnold, Sandra Luque, Amy Deacon, Anne Magurran, et al.. A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest. Remote Sensing, MDPI, 2021, 13 (11), pp.2148. ⟨10.3390/rs13112148⟩. ⟨hal-03268430⟩



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