New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss

Hewson, Jenny; Crema, Stefano; González‐Roglich, Mariano; Tabor, Karyn; Harvey, Célia A.

doi:10.3390/land8010014

Cited by 23 publications

(23 citation statements)

References 42 publications

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“…Countries were split into risk categories based on the quartile ranges of the data and are presented as “low” risk, “low‐medium,” “medium‐high” and “high” risk depending on the rank in which they fell. Finally, the risks of slavery causing tree loss from the illegal logging analysis, and the slavery estimates (Figures and ) were compared with predicted tree cover loss, modeled at continental and global levels by Hewson, Crema, González‐Roglich, Tabor, and Harvey () as part of a new 1 km resolution dataset. These data are freely available to download (from http://futureclimates.conservation.org/index.html).…”

Section: Methodsmentioning

confidence: 99%

“…The four countries which have been investigated in the discussion. Clips of the global prediction model by Hewson et al () clearly demonstrate the areas of these nations which are most at risk of tree cover loss through their prediction of tree loss transitional potential…”

Section: Methodsmentioning

confidence: 99%

“…The countries chosen using these parameters are: Brazil, Ghana, Indonesia and Mozambique. While each have differing vulnerabilities; all are found across the tropics where Hewson et al's () model predicts some of the highest losses. Past tree loss/tree gains for these countries were then extracted using the Hansen et al () derived dataset (GFW, ; accessed via Google Earth Engine [GEE]).…”

Section: Methodsmentioning

confidence: 99%

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Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

Jackson

Sparks

Brown

et al. 2020

Conservat Sci and Prac

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Locations where populations are most reliant on forests and their ecosystem services for subsistence and development are also areas where modern slavery persists. These issues are noted within the Sustainable Development Goals (SDGs), both target 15.2 and 8.7 respectively. Often activities using slavery perpetuate deforestation, bolstering a slavery-environment nexus; which has been examined by comparing modern slavery estimates against environmental protection levels. This study assesses the relationship between tree loss and modern slavery focusing on four countries: Brazil, Ghana, Indonesia, and Mozambique. Previously mapped levels of tree loss and predicted future levels of loss have been compared against modern slavery estimates from the Global Slavery Index 2016 and illegal logging analyses to determine an estimate of the risk for slavery related tree loss. These results provide an insight in to the cooccurrence between modern slavery and tree loss due to a number of activities that are highlighted, including mining, illegal logging, and agricultural practices. The co-occurrence is both complex, and yet, beyond coincidental. Implications for both national and global policy are noted assessing the benefits that could be achieved by limiting tree loss and ending modern slavery; of benefit to both the conservation and antislavery communities.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

Jackson

Sparks

Brown

et al. 2020

Conservat Sci and Prac

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“…In order 231 to identify which areas will allow the long-term persistence of a given organism, these future dynamics need 232 to be incorporated, as much as possible, into the mapping of Anthropocene refugia. Spatially-explicit 233 forecasts of future human population [64], urban expansion [65], global habitat conversion [66] and 234 deforestation [67] are readily available and can be used as a first approximation to understand how the 235 distribution of anthropogenic pressures will change in the future. These datasets however summarise future 236 risks at coarse scales and for different points in time (e.g.…”

Section: Anthropogenic Pressures 200mentioning

confidence: 99%

“…While it may prove difficult 228 to obtain robust predictions for the direction of these changes for all relevant anthropogenic pressures, 229 forecasts of future spatial patterns of human activities under different scenarios for some pressure types are 230 available (e.g. [61,[64][65][66]) and can be used as a first approximation to understand how the distribution of 231 anthropogenic pressures will change in the future. 232…”

Section: Anthropogenic Pressures 201mentioning

confidence: 99%

Anthropocene refugia: integrating history and predictive modelling to assess the space available for biodiversity in a human-dominated world

Monsarrat

Jarvie

Svenning

2019

Preprint

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13During periods of strong environmental change, some areas may serve as refugia, where components of 14 biodiversity can find protection, persist and potentially expand from should conditions again become 15 favourable. The refugia concept has previously been used in the context of climatic change, to describe 16 climatically stable areas in which taxa survived past Quaternary glacial-interglacial oscillations, or where they 17 might persist in the future under anthropogenic climate change. However, with the recognition that Earth 18 has entered the Anthropocene, an era in which human activities are the dominant driving force on 19 ecosystems, it is critical to also consider human pressures on the environment as factors limiting species 20 distributions. Here, we present a novel concept, Anthropocene refugia, to refer to areas that provide spatial 21 and temporal protection from human activities and that will remain suitable for a given taxonomic unit in 22 the long-term. It integrates a deep-time perspective on species biogeography that provides information on 23 the natural rather than current-day relictual distribution of species, with spatial information on modern and 24 future anthropogenic threats. We define the concept and propose a methodology to effectively identify and 25 map realised and potential current and future refugia, using examples for two megafauna species as a proof 26 of concept. We argue that identifying Anthropocene refugia will improve biodiversity conservation and 27 restoration by allowing better prediction of key areas for conservation and potential for re-expansions today 28 and in the future. More generally, it forms a new conceptual framework to assess and manage the impact of 29 anthropogenic activities on past, current and future patterns of species distributions. 30 31

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Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Chen

Liu

et al. 2021

Ecology and Evolution

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Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.

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New 1 km Resolution Datasets of Global and Regional Risks of Tree Cover Loss

Cited by 23 publications

References 42 publications

Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

Understanding the co‐occurrence of tree loss and modern slavery to improve efficacy of conservation actions and policies

Anthropocene refugia: integrating history and predictive modelling to assess the space available for biodiversity in a human-dominated world

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

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