scholarly journals Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

2014 ◽  
Vol 11 (6) ◽  
pp. 1449-1459 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Fire Spread ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Fractal Properties ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.

scholarly journals Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burned area

2013 ◽  
Vol 10 (8) ◽  
pp. 14141-14167 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein
Keyword(s):  
Forest Fires ◽  
Fire Spread ◽  
Burned Area ◽  
Burnt Area ◽  
Pareto Model ◽  
Fractal Properties ◽  
Vegetation Models ◽  
Area Product ◽  
Global Vegetation ◽  
Tapered Pareto

Abstract. Current methods for modelling burnt area in Dynamic Global Vegetation Models involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation. Using observed fractal distributions of fire scars in Brazilian Amazonia, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. Several parameterizations of two possible distributions are calibrated at multiple spatial resolutions using a satellite-derived burned area map, and compared. The tapered Pareto model most accurately simulates the total area burnt (only 3.5 km2 larger than the recorded 16 387 km2) and its spatial distribution. When tested pan-tropically using MODIS MCD14ML fire counts, the model accurately predicts temporal and spatial fire trends, but produces generally higher estimates than the GFED3.1 burnt area product, suggesting higher pan-tropical carbon emissions from fires than previously estimated.

scholarly journals LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model

2019 ◽  
Vol 12 (3) ◽  
pp. 893-908 ◽  
Author(s):  
Veiko Lehsten ◽  
Michael Mischurow ◽  
Erik Lindström ◽  
Dörte Lehsten ◽  
Heike Lischke
Keyword(s):  
Large Scale ◽  
Fourier Transforms ◽  
Grid Cells ◽  
Large Area ◽  
Climate Conditions ◽  
Small Areas ◽  
Seed Supply ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Dynamic global vegetation models are a common tool to assess the effect of climate and land use change on vegetation. Though most applications of dynamic global vegetation models use plant functional types, some also simulate species occurrences. While the current development aims to include more processes, e.g. the nitrogen cycle, the models still typically assume an ample seed supply allowing all species to establish once the climate conditions are suitable. Pollen studies have shown that a number of plant species lag behind in occupying climatological suitable areas (e.g. after a change in the climate) as they need to arrive at and establish in the newly suitable areas. Previous attempts to implement migration in dynamic vegetation models have allowed for the simulation of either only small areas or have been implemented as a post-process, not allowing for feedbacks within the vegetation. Here we present two novel methods simulating migrating and interacting tree species which have the potential to be used for simulations of large areas. Both distribute seeds between grid cells, leading to individual establishment. The first method uses an approach based on fast Fourier transforms, while in the second approach we iteratively shift the seed production matrix and disperse seeds with a given probability. While the former method is computationally faster, it does not allow for modification of the seed dispersal kernel parameters with respect to terrain features, which the latter method allows. We evaluate the increase in computational demand of both methods. Since dispersal acts at a scale no larger than 1 km, all dispersal simulations need to be performed at maximum at that scale. However, with the currently available computational power it is not feasible to simulate the local vegetation dynamics of a large area at that scale. We present an option to decrease the required computational costs through a reduction in the number of grid cells for which the local dynamics are simulated only along migration transects. Evaluation of species patterns and migration speeds shows that simulating along transects reduces migration speed, and both methods applied on the transects produce reasonable results. Furthermore, using the migration transects, both methods are sufficiently computationally efficient to allow for large-scale DGVM simulations with migration.

Burnt area estimation for the year 2005 in Borneo using multi-resolution satellite imagery

2007 ◽  
Vol 16 (1) ◽  
pp. 45 ◽  
Author(s):  
Jukka Miettinen ◽  
Andreas Langner ◽  
Florian Siegert
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Agricultural Land ◽  
Fire Detection ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Medium Resolution ◽  
Significant Difference ◽  
Degraded Ecosystems

Humid tropical South-East Asia suffers significant yearly biomass burning. This paper evaluates and compares the results of medium-resolution (MODIS) burnt area mapping and hotspot-based assessment of fire affected areas in Borneo in 2005, using field observations and high resolution Landsat ETM+ data as reference. Based on burnt area mapping, over 600 000 ha burnt in large-scale vegetation fires. Approximately 90% of this burning took place in degraded ecosystems and was related to agricultural land clearing activities or logged over forests. The estimation based on active fire detection (hotspots) resulted in a total burnt area of more than 1.1 million hectares. The reason for this significant difference was that small scale shifting cultivation fires could not be detected in MODIS images. These results indicate that a combination of both methods is required to reliably assess burnt areas in Borneo using medium-resolution MODIS satellite imagery.

The use of dynamic global vegetation models for simulating hydrology and the potential integration of satellite observations

2012 ◽  
Vol 37 (1) ◽  
pp. 63-97 ◽  
Author(s):  
S.J. Murray ◽  
I.M. Watson ◽  
I.C. Prentice
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climatic Forcing ◽  
The Family ◽  
Dynamic Global Vegetation Models ◽  
Explicit Representations ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Derived Data

Dynamic global vegetation models (DGVMs) offer explicit representations of the land surface through time and have been used to research large-scale hydrological responses to climate change. These applications are discussed and comparisons of model inputs and formulations are made among and between DGVMs and global hydrological models. It is shown that the configuration of process representations and data inputs are what makes a given DGVM unique within the family of vegetation models. The variety of available climatic forcing datasets introduces uncertainty into simulations of hydrological variables. It is proposed that satellite-derived data, validated thoroughly, could be used to improve the quality of model evaluations and augment ground-based observations, particularly where spatial and temporal gaps are present. This would aid the reduction of model uncertainties and thus potentially enhance our understanding of global hydrological change.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models

2016 ◽  
Vol 76 (2) ◽  
pp. 341-351
Author(s):  
L. F. C. Rezende ◽  
B. C. Arenque-Musa ◽  
M. S. B. Moura ◽  
S. T. Aidar ◽  
C. Von Randow ◽  
...  
Keyword(s):  
Data Mining ◽  
Native Species ◽  
Classification And Regression Tree ◽  
Northeastern Brazil ◽  
Semiarid Region ◽  
Global Changes ◽  
Data Mining Techniques ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

scholarly journals Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170315 ◽  
Author(s):  
Cleiton B. Eller ◽  
Lucy Rowland ◽  
Rafael S. Oliveira ◽  
Paulo R. L. Bittencourt ◽  
Fernanda V. Barros ◽  
...  
Keyword(s):  
Tropical Forest ◽  
El Niño ◽  
Optimization Model ◽  
El Nino ◽  
Soil Drought ◽  
Forest Sites ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration ( E ) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Where is the carbon going? - A multi-model comparison

2021 ◽  
Author(s):  
Michael O'Sullivan ◽  
Pierre Friedlingstein ◽  
Stephen Sitch
Keyword(s):  
Model Comparison ◽  
Net Primary Productivity ◽  
Relative Increase ◽  
Historical Period ◽  
Residence Times ◽  
Vegetation Carbon ◽  
Dynamic Global Vegetation Models ◽  
Spatio Temporal ◽  
Vegetation Models ◽  
Global Vegetation

<p>Net terrestrial carbon uptake is primarily driven by increases in net primary productivity (NPP) and/or the residence time of carbon in vegetation and soil. As such, it is of critical importance to accurately quantify spatio-temporal variation in both terms and determine their drivers. Both NPP and residence times are modulated by changing environmental conditions, including climate change and variability, atmospheric CO<sub>2</sub>, and Land Use and Land Cover Changes (LULCC). For the historical period, 1901-2019, outputs from a suite of Dynamic Global Vegetation Models (DGVMs) from the TRENDY consortium, driven with observed changes in climate, CO<sub>2</sub>, and LULCC are analysed. Changes in global and regional carbon fluxes, stocks, and residence times are quantified, as well as an attribution to the underlying drivers. We find that over the historical period the majority of models simulate an increase in NPP, predominantly driven by enhanced atmospheric CO<sub>2</sub> concentrations. This generally leads to increased carbon storage in both vegetation and soils, however there is no agreement across models on the partitioning between vegetation and soils. This increased storage also acts to reduce soil carbon residence times due to a relative increase in carbon allocated in the faster decomposing soil pools. LULCC over this period has acted to reduce carbon inputs to the system and reduce vegetation carbon residence times due to conversion of forests to shorter vegetation. We find there is a large variation in simulated global and regional fluxes, stocks, and residence times in resonse to changes in climate, implying there are considerable uncertainties in current DGVMs. We therefore use long-term global observations of productivity and biomass change to constrain model estimates and provide insight into a process attribution for biospheric change as well as highlighting areas for future model improvement.</p>

Dynamic Global Vegetation Models

Plant Ecology ◽  
2018 ◽  
pp. 843-863
Author(s):  
Ernst-Detlef Schulze ◽  
Erwin Beck ◽  
Nina Buchmann ◽  
Stephan Clemens ◽  
Klaus Müller-Hohenstein ◽  
...  
Keyword(s):  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation
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