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Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs' current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r2, bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation's linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r2s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology. [ABSTRACT FROM AUTHOR]

Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs’ current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r2, bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation’s linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r2s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology.

The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation's (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes. [ABSTRACT FROM AUTHOR]

The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation’s (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes.

Fire is one of the earliest and most common tools used by humans to modify the earth surface. Landscapes in the Yucatán Peninsula are composed of a mosaic of old growth subtropical forest, secondary vegetation, grasslands, and agricultural land that represent a well-documented example of anthropogenic intervention, much of which involves the use of fire. This research characterizes land use systems and land cover changes in the Yucatán during the 2000–2010 time period. We used an active fire remotely sensed data time series from the Moderate Resolution Imaging Spectroradiometer (MODIS), in combination with forest loss, and anthrome map sources to (1) establish the association between fire and land use change in the region; and (2) explore links between the spatial and temporal patterns of fire and specific types of land use practices, including within- and between-anthromes variability. A spatial multinomial logit model was constructed using fire, landscape configuration, and a set of commonly used control variables to estimate forest persistence, non-forest persistence, and change. Cross-tabulations and descriptive statistics were used to explore the relationships between fire occurrence, location, and timing with respect to the geography of land use. We also compared fire frequencies within and between anthrome groups using a negative binomial model and Tukey pairwise comparisons. Results show that fire data broadly reproduce the geography and timing of anthropogenic land change. Findings indicate that fire and landscape configuration is useful in explaining forest change and non-forest persistence, especially in fragmented (mosaicked) landscapes. Absence of fire occurrence is related usefully to the persistence of spatially continuous core areas of older growth forest. Fire has a positive relationship with forest to non-forest change and a negative relationship with forest persistence. Fire is also a good indicator to distinguish between anthrome groups (e.g., croplands and villages). Our study suggests that active fire data series are a reasonable proxy for anthropogenic land persistence/change in the context of the Yucatán and are useful to differentiate quantitatively and qualitatively between and within anthromes.