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Primary forest (PF) is critical in supporting biodiversity and mitigating greenhouse gas emissions. However, the continuous monitoring of PF loss through remote sensing time-series observations remains largely unexplored, particularly in undeveloped and developing countries. In this study, we use the COntinuous monitoring of Land Disturbance (COLD) algorithm and Landsat time-series data to quantify PF loss on the island of Hispaniola, including Haiti and the Dominican Republic, from 1996 to 2022. The major findings include: (1) Haiti experienced a more pronounced PF loss compared to the Dominican Republic despite its lower PF coverage. From 1996 to 2022, PF in Haiti decreased from 0.64% to 0.35%, while PF in Dominican Republic decreased from 7.17% to 4.89%. (2) PF loss is observed both inside and outside protected areas. In Haiti, more PF loss occurs within protected areas than outside those areas. In the Dominican Republic, PF loss rates inside and outside protected areas are comparable. (3) The mean topographic slope of PF shows an increasing trend through time in both Haiti and Dominican Republic, suggesting slope plays a key role in PF loss. Despite the disparities between Haiti and Dominican Republic in preserving PF, urgent conservation policies are needed for the whole island. The land cover maps framework can be extended beyond the island of Hispaniola to larger regions for evaluating the impacts of PF loss on biodiversity conservation and ecosystem services.
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A framework for near-real time monitoring of diversity patterns based on indirect remote sensing, with an application in the Brazilian Atlantic rainforest
Monitoring biodiversity change is key to effective conservation policy. While it is difficult to establish in situ biodiversity monitoring programs at broad geographical scales, remote sensing advances allow for near-real time Earth observations that may help with this goal. We combine periodical and freely available remote sensing information describing temperature and precipitation with curated biological information from several groups of animals and plants in the Brazilian Atlantic rainforest to design an indirect remote sensing framework that monitors potential loss and gain of biodiversity in near-real time. Using data from biological collections and information from repeated field inventories, we demonstrate that this framework has the potential to accurately predict trends of biodiversity change for both taxonomic and phylogenetic diversity. The framework identifies areas of potential diversity loss more accurately than areas of species gain, and performs best when applied to broadly distributed groups of animals and plants.
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- Award ID(s):
- 1745562
- PAR ID:
- 10355719
- Date Published:
- Journal Name:
- PeerJ
- Volume:
- 10
- ISSN:
- 2167-8359
- Page Range / eLocation ID:
- e13534
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7717/peerj.13534
