Dating lava flows of tropical volcanoes by means of spatial modeling of vegetation recovery

Abstract

The age of past lava flows is crucial information for evaluating the hazards and risks posed by effusive volcanoes, but traditional dating methods are expensive and time‐consuming. This study proposes an alternative statistical dating method based on remote sensing observations of tropical volcanoes by exploiting the relationship between lava flow age and vegetation cover. First, the factors controlling vegetation density on lava flows, represented by the normalized difference vegetation index (NDVI), were investigated. These factors were then integrated into pixel‐based multi‐variable regression models of lava flow age to derive lava flow age maps. The method was tested at a pixel scale on three tropical African volcanoes with considerable recent effusive activity: Nyamuragira (Democratic Republic of Congo), Mt Cameroon (Cameroon) and Karthala (the Comoros). Due to different climatic and topographic conditions, the parameters of the spatial modeling are volcano‐specific. Validation suggests that the obtained statistical models are robust and can thus be applied for estimating the age of unmodified undated lava flow surfaces for these volcanoes. When the models are applied to fully vegetated lava flows, the results should be interpreted with caution due to the saturation of NDVI. In order to improve the accuracy of the models, when available, spatial data on temperature and precipitation should be included to directly represent climatic variation. Copyright © 2017 John Wiley & Sons, Ltd.

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

“…Stepwise regression (SR) is essentially a multiple linear regression method, but it is different from the general multiple linear regression in the selection of variables. In a stepwise regression analysis, the most significant or least significant variable is added to or removed with iteration from the multiple linear regression model based on its statistical significance [56,57]. At each iteration of adding or removing a potential independent variable, resultant models are assessed by means of the p-value of an F-statistic (p-value < 0.05 for statistical significance) [56,57].…”

“…In a stepwise regression analysis, the most significant or least significant variable is added to or removed with iteration from the multiple linear regression model based on its statistical significance [56,57]. At each iteration of adding or removing a potential independent variable, resultant models are assessed by means of the p-value of an F-statistic (p-value < 0.05 for statistical significance) [56,57]. Stepwise regression has proved effective in selecting variables for modeling and has been widely used in different fields [58,59], including forest biomass estimation [60].…”

“…As it is likely that collinearity exists in the predictive variables, the variance inflation factor (VIF) [57,61] is used to examine it in this study: Although 192 vegetation quadrats were initially selected, only 140 quadrats of them (shown in Figure 1) were visited and investigated in practice-because some of the pre-selected quadrats were not accessible for various reasons (e.g., physical barriers and refusal to access). Among the 140 quadrats were 35 dominated by coniferous forest, 73 by broadleaved forest, and 32 by low vegetation.…”

“…As it is likely that collinearity exists in the predictive variables, the variance inflation factor (VIF) [57,61] is used to examine it in this study:…”

Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model

“…Although both spectral vegetation indices and spectral mixture analysis modeled vegetation fractions [57][58][59] can be used to calculate the C factor values [60,61], it is easier to extract spectral vegetation indices than vegetation fractions. In this study, the mostly commonly used vegetation index, Normalized Difference Vegetation Index (NDVI), which is the ratio of the difference between spectral reflectance in near infrared and red regions [62,63], was used to calculate C factor values according to the following equation [64]:…”