Community development by forest understory plants after prolonged burial by tephra

Zobel, Donald B.; Antos, Joseph A.; Fischer, Dylan G.

doi:10.1007/s11258-021-01216-3

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…A species grows well in a favourable environment, according to (Ririhena, 2010). Furthermore, pioneer plants that grow densely on the forest floor will face competition for light, nutrients, and movement space; as a result of this competition, some vegetation will survive and adapt (Lorenz et al, 2022;Zobel et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Vegetation Structure, Biomass, and Carbon Stock of Urban Forest of Bongohulawa National Wirakarya Campground in Gorontalo Regency - Gorontalo Province

Baderan,

Rahim,

Manyo’e

2023

J. Ilmu Lingk.

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An urban forest is one type of Urban Green Open Space (RTHKP) that reflects the character of nature and/or local culture with ecological, historical, and panoramic values that are unique to the level of technology application. The urban forest of the National Wirakarya Campsite is quite attractive, has a lovely view, is close to the road, and is easily accessible by the residents. This study aims to determine the structure of vegetation, biomass, and carbon values stored in the urban forest of the National Wirakarya Campsite, Bongohulawa, Gorontalo Regency, Gorontalo Province. The method used is a survey method with a quantitative descriptive research design. The sampling method used is a purposive sampling model based on land cover in the urban forests. For the measurement of the vegetation structure, the sampling of vegetation in the observation plots is carried out by using the number of sample plots that are placed regularly by calculating the Relative Density (RD), Relative Frequency (RF), and Relative Dominance (RDo), then the data obtained are tabulated to provide IVI. To measure the carbon stocks on the surface (stems), a non-destructive sampling method is used with an Allometric equation based on the plant species. The results showed, there were 13 families, 20 species, and 824 individuals. The vegetation structure in the urban forest of Bongohulawa National Wirakarya Campsite (PWN) has an IVI above 15 percent on average. The sawlog level is dominated by the Gmelina arborea (beechwood) species with an IVI of 98.36 percent, the pole level is dominated by the Swietenia mahagagoni (mahogany) with an IVI of 165.37 percent, the sapling level was dominated by Swietenia mahagoni with an IVI of 160.99 percent and the seedling level was dominated by Lantana camara (tembelekan) with an IVI of 32.25 percent. The content of biomass stored in the standing trees is 1,190.45 tons/ha, with the largest amount of biomass is at the sawlog level of 1,135.43 tons/year or 95.43 percent, the total biomass at the pole level is 45.10 tons/year or 3.79 percent and the amount of long-term biomass is 9.32 tons/year or 0.78 percent. The carbon stock stored (C-Stock) is 559.37 tons/ha and can absorb CO2 (CO2eq) of 2,052.88 tons/ha and provides converted O2 of 1,498.60 tons/ha.

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Section: Discussionmentioning

confidence: 99%

Vegetation Structure, Biomass, and Carbon Stock of Urban Forest of Bongohulawa National Wirakarya Campground in Gorontalo Regency - Gorontalo Province

Baderan,

Rahim,

Manyo’e

2023

J. Ilmu Lingk.

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“…Explosive volcanic eruptions produce tephra, a generic term for pyroclasts originating from the fragmentation of parent magma, the fraction <2 mm diameter of which is referred to as ash. For sufficiently large eruptions, tephra deposits can alter the hydrology, vegetation cover and soil properties of entire regions, contributing to the perturbation of their ecosystems for months to years (Major et al, 2016;Pierson et al, 2013;Zobel et al, 2022). Direct negative impacts on and the ability of vegetation to recover from eruptions depend on complex interactions between biotic and abiotic parameters (Ayris and Delmelle, 2012;Arnalds, 2013).…”

Section: Impact Of Volcanic Hazards On Vegetationmentioning

confidence: 99%

“…This is especially true for volcanic risk. Our current knowledge of the vulnerability of agriculture to volcanic hazards comes from a combination of opportunistic field-based post-event impact assessments (post-EIAs; e.g., Blake et al, 2015;Le Pennec et al, 2012;Magill et al, 2013;Phillips et al, 2019;Stewart et al, 2016;Wilson et al, 2011a, b;Wilson et al, 2013) and rarer experimental studies (Hotes et al, 2004;Zobel et al, 2022;Ligot et al, 2022). However, the generalization of these empirical lessons is limited by two main aspects.…”

Section: Introductionmentioning

confidence: 99%

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Biass

Jenkins²,

Aeberhard³

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses. Here, we explore how cloud-based big Earth observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big Earth observation (EO) data and machine learning complement existing impact datasets and open the way to a new type of dynamic and large-scale vulnerability models.

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Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Biass

Jenkins

Aeberhard

et al. 2022

Preprint

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Abstract. Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses. Here, we explore how cloud-based big Earth Observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big EO data and machine learning complement existing impact datasets open the way to a new type of dynamic and large-scale vulnerability models.

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Community development by forest understory plants after prolonged burial by tephra

Cited by 3 publications

References 51 publications

Vegetation Structure, Biomass, and Carbon Stock of Urban Forest of Bongohulawa National Wirakarya Campground in Gorontalo Regency - Gorontalo Province

Vegetation Structure, Biomass, and Carbon Stock of Urban Forest of Bongohulawa National Wirakarya Campground in Gorontalo Regency - Gorontalo Province

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data

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