Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling

Mechenich, Michael; Žliobaitė, Indrė

doi:10.1038/s41597-023-01966-x

Cited by 10 publications

(3 citation statements)

References 71 publications

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“…To simulate a landscape scope, a regular grid of hexagons was created, with a constant area of 3091 ha, which corresponds to the average size of the lowest-level administrative unit in Portugal (freguesia) for which some statistical data are available. The hexagonal shape was chosen attending to several topological and compactness advantages to represent landscapes [49]. These hexagonal polygons were used as the analysis unit for all subsequent calculations.…”

Section: Landscape Spatial Datamentioning

confidence: 99%

Estimating the Trade-Offs between Wildfires and Carbon Stocks across Landscape Types to Inform Nature-Based Solutions in Mediterranean Regions

Simões,

Ribeiro,

Santos

2023

Preprint

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Climate and land-use changes have been contributing to increase the occurrence of extreme wild-fires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the effects of land-use change on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini index) between 1984 and 2019, which was combined with 2018 carbon stock data (above- and below-ground), and a landscape typology re-sulting from cluster analysis over land-use composition, diversity and configuration, to explore relationships between landscape types and the two major ecosystem services at stake: wildfire reduction and carbon stock. Cross tabulations, logistic and linear regressions were performed on these data and results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt forest plantations and high-hazardous fire regime but hold the highest carbon stock. Shrubland-mixed landscapes were associated with lower carbon stocks but less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agro-forestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes however, this good wildfire performance is achieved at the cost of the poorest carbon stock, de-noting land degradation, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

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Section: Landscape Spatial Datamentioning

confidence: 99%

Estimating the Trade-Offs between Wildfires and Carbon Stocks across Landscape Types to Inform Nature-Based Solutions in Mediterranean Regions

Simões,

Ribeiro,

Santos

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To simulate a landscape scope, a regular grid of hexagons was created, with a constant area of 3091 ha, which corresponds to the average size of the lowest-level administrative unit in Portugal (freguesia) for which some statistical data are available. The hexagonal shape was chosen based on several topological and compactness advantages associated with representing landscapes [48]. These hexagonal polygons were used as the analysis unit for all subsequent calculations.…”

Section: Landscape Spatial Datamentioning

confidence: 99%

Estimating the Trade-Offs between Wildfires and Carbon Stocks across Landscape Types to Inform Nature-Based Solutions in Mediterranean Regions

Simões,

Ribeiro,

Santos

2023

Fire

View full text Add to dashboard Cite

Climate and land-use changes have been contributing to the increase in the occurrence of extreme wildfires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the influence of land use/cover on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini Index) between 1984 and 2019. This typology was then combined with carbon stock data and different landscapes to explore relationships between landscape types and two important ecosystem services: wildfire reduction and carbon stock. Multivariate analyses were performed on these data and the results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt plantations and highly hazardous fire regimes, which in turn hold the highest carbon stocks. Shrubland and mixed landscapes were associated with low carbon stocks and less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agroforestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes, however, this good wildfire performance is achieved at the cost of the poorest carbon stock, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

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“…Two recent examples of open-source DGGS libraries that are based on hexagonal grid structures are the H3 system, developed by Uber (2022) and DGGRID (Barnes and Sahr, 2017). Mechenich and Zliobaite (2023) recently presented the Eco-ISEA3H database that consists of global spatial data characterizing climate, geology, land cover, physical and human geography, and the geographic ranges of nearly 900 large mammalian species. In contrast to grid cells induced by the longitude-latitude graticule, hexagonal cells are able to cover almost the entire surface of the Earth without suffering from area distortion.…”

mentioning

confidence: 99%

Drivers of global irrigation expansion: the role of discrete global grid choice

Wagner,

Stenzel,

Krüger

et al. 2023

Preprint

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Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude-latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify main drivers of historical global irrigation expansion amongst others, also using outputs from the global dynamic vegetation model LPJmL. Irrigation is critical for food security amidst growing population, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the natural water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and predicting future trends. We compare the predictive accuracy, the simulated irrigation patterns and identification of irrigation drivers between the two grid choices. Results demonstrate that using the ISEA3H geodesic grid increases the predictive accuracy by 29 % compared to the longitude-latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. GDP per capita also shows minimal influence. We conclude that the geodesic discrete global grid significantly affects predicted irrigation patterns and identification of drivers, and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

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Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling

Cited by 10 publications

References 71 publications

Estimating the Trade-Offs between Wildfires and Carbon Stocks across Landscape Types to Inform Nature-Based Solutions in Mediterranean Regions

Estimating the Trade-Offs between Wildfires and Carbon Stocks across Landscape Types to Inform Nature-Based Solutions in Mediterranean Regions

Estimating the Trade-Offs between Wildfires and Carbon Stocks across Landscape Types to Inform Nature-Based Solutions in Mediterranean Regions

Drivers of global irrigation expansion: the role of discrete global grid choice

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