All posts by keco

Anthropogenic Risk


Context

As of 2015, forests were estimated to cover around 31% of the global land area or 3999 M ha. This represents a decline of around 3% since 1990 due to anthropogenic and natural causes, although this rate has halved in the last 25 years (Keenan et al., 2015). Anthropogenic factors are a primary cause of this deforestation. For example, deliberate policy to clear land for agriculture or other purposes, and logging concessions can create large-scale deforestation. Even within sustainably managed concessions and protected areas, illegal logging still has the potential to cause wide-scale forest loss.


Approaches to assessing risk

There are a number of approaches to predicting the risk of deforestation. However, these typically focus on identifying the variables closely associated with deforestation. Such variables tend to relate to accessability to the forest, such as the presence of nearby roads, and also to the value of the forests themselves. Application of these approaches to looking at the risks to commercial timber and carbon forestry projects is however problematic. By definition, commercial timber and carbon projects must be accessible and timber projects must be of value. Furthermore, some variables such as political and regulatory risk do not lend themselves to a probability type approach required for risk assessment e.g. it is unlikely to be possible to identify the annual probability of a reversal of support for e.g. sustainable forest management or carbon offsets. Finally, forest carbon projects, particularly those such as REDD+ projects, which are aimed at reducing deforestation and degradation, are often located in areas known to be at high risk. Approaches to assessing risk are therefore not of high relevance to this audience.

In this section we therefore focus primarily on presenting approaches to assessing and quantifying deforestation, and changes to biomass/carbon stocks, rather than approaches to predicting the risks of deforestation.


Remote sensing of canopy height and biomass to track deforestation

Prof. Balzter has developed Synthetic Aperture Radar (SAR) and LIDAR techniques for mapping forest canopy height and forest biomass, which can be used to track deforestation (Lynch et al., 2013). He is the principal investigator in a range of satellite programmes including TerraSAR-X, Disaster Monitoring Constellation, ENVISAT, ERS-1/2, JERS-1 and ALOS PALSAR. He is also scientist-in-charge of the European Centre of Excellence for Earth Observation Research Training “GIONET” and has had a long involvement with the European Copernicus initiative (formerly GMES), which delivers operational data services from remote sensing and ancillary observing networks. This was through the land-monitoring projects GEOLAND and GEOLAND-2 and in 2014 his group completed the delivery of the UK CORINE 2012 land cover map under the GIO-Land programme to the European Environment Agency and DEFRA. The ESA project GLOBBIOMASS, is aiming to improve forest aboveground biomass estimates by developing innovative synergistic mapping approaches in five regional sites for the epochs 2005, 2010 and 2015 and for one global map for the year 2010, and includes the leading Earth Observation experts of Europe. Prof. Balzter is leading the task on Regional Case Studies, with the aim to produce 3 global forest biomass maps. He is an advocate of using satellite information to monitor deforestation (Lynch et al., 2013). Contact: Prof. Heiko Balzter


Deforestation and degradation detection algorithm – Congo

Dr Mitchard has current grants from the UK’s Natural Environment Research Council (NERC) and Innovate UK to develop a deforestation and degradation detection algorithm using C-band radar data; from the US Forest Service to work on the relations between fire return period and carbon storage in Congo. Contact: Dr Ed Mitchard


Remote sensing of deforestation – general

As Director of Forestry at DMCii Prof. Lynch had responsibility for developing partnerships and tools. DMCii, primarily focuses on disasters and has used its Disaster Monitoring Constellation (DMC) to provide wide-scale mapping of tropical forests such as the Amazon Basin and sub-Saharan Africa since 2005. He is a strong advocate of using satellite technology to monitor deforestation, REDD delivery and illegal logging (Lynch et al., 2013). He believes current monitoring is too infrequent e.g. annual observation does not provide seasonal variability in carbon stock, and ideally optical measurements should be every 1-2 weeks. To act as an early warning system to stop illegal logging they need to be daily. Both optical and radar sensors are needed: radar can scan down to 5-20m resolution regardless of the weather as it can penetrate cloud cover – a particular issue in equatorial rainforest – which optical imaging can not. Optical satellites can detect changes in chlorophyll to detect pest and disease impact and can provide resolution down to 20m of vegetation greenness and density, tree cover and forest type. At DMCii he led a multi-disciplinary consortium of universities, companies and public sector organisations inFORm which supports UK efforts to be involved in REDD+. He is continuing to develop this led from the Universities of Surrey and Leicester, but also engaging a consortium ASTROTROP led from Edinburgh and Leeds Universities to create a virtual forestry observatory. In a NERC funded SCENARIO project at Surrey and Reading Universities he is working with the University of Sao Paulo and the Brazilian Space Agency INPE to generate indicators for the monitoring and management of forests in a sustainable land use context. Contact: Prof. Jim Lynch


Landcover, landcover change and forestry classes

Prof Balzter led the creation of the new Corine land cover map for 2012 for the European Environment Agency and Defra, an Open Access dataset. It shows land cover and forestry classes at 25 ha minimum mapping unit, and land cover change over a 6-year time frame at 5 ha minimum mapping unit including forest to non-forest and regrowth and replanting. It will be available from the NERC Environmental Information Centre (Link). Contact: Prof. Heiko Balzter


Monitoring forest inventory via remote sensing – general

Prof Juan Suárez  has been working with airborne LiDAR for 15 years. He has developed techniques for analysing the point clouds generated by the sensor to produce stand and tree level estimates. At stand level, it is possible to calculate biomass, volume, fractional cover, canopy height, site index and yield class. At tree level, he has developed an algorithm over eCognition to delineate individual tree canopies. Canopy area and height is used to estimate diameter at breast height (DBH) and stem volume (Suárez, 2014). The location of individual trees and its characteristics is used in combination with timber quality models and competition indices to estimate stem density and straightness (Suárez, 2009). Stand level predictions have been calculated for the Cowal and Trossachs Forest District to update the Sub-compartment Database (SCDB) and to run the Production Forecast more accurately.  Juan was the Principal Investigator for  projects to: use airborne LiDAR for British forest inventory (2014-16);  to update the National Forest Inventory (GB) using satellite imagery (2016 DEFRA project); and to develop new methods for biomass assessment and forest inventory using airborne LiDAR and Hyperspectral imagery in China (Newton Grant 2015-16). He was also part of a NERC-funded Partnership Research Grant with University of Swansea to use satellite LiDAR to enhance Forest Inventory and Production Forecast Capabilities (2008-14). Whilst working at the NASA Goddard Space Flight Centre (Maryland, US) he worked on the application of small footprint LiDAR systems to support  the Carbon Monitoring System project (2011-12). Contact: Prof. Juan Suárez-Minguez


Our Ecosystem: Web-based Pan-Tropic GIS maps of forest carbon stocks

Dr  Mitchard is an expert in using GIS to provide benchmarks of carbon stock information. Two medium resolution (500m-1,000m) maps have recently been generated that both use the same spaceborne LIDAR dataset to calculate pan-tropical carbon stocks but different algorithms. Dr Mitchard was involved in one of these (Saatchi et al., 2011) but not the other (Baccini et al., 2012). In conjunction with the company Ecometrica, he has produced a freely available web-based interactive map which displays the two maps and compares the difference in measurements (Mitchard et al., 2013) (Open access (free): link). A higher resolution (100m) biomass map has been generated for part of the Columbian Amazon using forest information from the FAO. The maps are widely used by REDD+ stakeholders at national and sub-national level. Users can select an area to view the carbon stock information (link).  Contact: Dr Ed Mitchard.


Distinguishing land degradation caused by climatic vs anthropogenic factors

Prof. Balzter has also worked on an approach that uses remote sensing to distinguish vegetation change due to climatic factors from that caused by anthropogenic factors on the basis of vegetation greenness and rainfall trends and anomalies (Hoscilo et al., 2015). If over a 10 year period conditions become wetter and greener or drier and browner then the approach assumes that the cause was climatic, however, if areas become wetter and browner then the cause is likely to be non-climatic i.e. due to anthropogenic causes. He was involved in developing a method for land degradation mapping using Normalised Difference Vegetation Index data from satellite data and soil moisture trends (Ibrahim et al., 2015). Contact: Prof. Heiko Balzter


Illegally logged timber (FLEGT)

Prof. Lynch is an expert in FLEGT (Forest Law Enforcement, Governance and Trade), which aims to reduce the import of illegal logging to the EU. He was the lead Board Member in setting up the FLEGT facility in the European Forest Institute. Contact: Prof. Jim Lynch


REDD+: Advice on minimising risk in sustainable community forestry

Prof. Lynch has spent 17 years working for OECD in relation to sustainable agriculture and forestry. He is therefore able to offer expertise on optimising productive community forest projects and minimising risks. He believes community involvement is essential to preventing deforestation which cannot be solved by Governments alone. This includes appropriate species selection to minimise risks such as pest and disease risk, and to maximise community benefits e.g. he advised on planting Jatropha in Ghana as a productive crop but which also solved the problem of a lack of lighting in local schools as they could burn Jatropha. oil. He has also has experience in other parts of Africa; he is a Member of the Board of the Council for the Frontiers of Knowledge in Africa, based at Makere University, Uganda. This experience includes bioenergy generation and horticulture specific to Africa (Lynch and Harvey, 2011; Lynch and Von Lampe, 2011). Contact: Prof. Jim Lynch

References

BACCINI, A., GOETZ, S. J., WALKER, W. S., LAPORTE, N. T., SUN, M., SULLA-MENASHE, D., HACKLER, J., BECK, P. S. A., DUBAYAH, R., FRIEDL, M. A., SAMANTA, S. & HOUGHTON, R. A. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change, 2, 182-185.
GRAINGER, A. & LINDQUIST, E. 2015. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. Forest Ecology and Management, 352, 9-20.
HOSCILO, A., BALZTER, H., BARTHOLOME, E., BOSCHETTI, M., BRIVIO, P. A., BRINK, A., CLERICI, M. & PEKEL, J. F. 2015. A conceptual model for assessing rainfall and vegetation trends in sub-Saharan Africa from satellite data. International Journal of Climatology, 35, 3582-3592.
IBRAHIM, Y. Z., BALZTER, H., KADUK, J. & TUCKER, C. J. 2015. Land Degradation Assessment Using Residual Trend Analysis of GIMMS NDVI3g, Soil Moisture and Rainfall in Sub-Saharan West Africa from 1982 to 2012. Remote Sensing, 7, 5471-5494.
KEENAN, R. J., REAMS, G. A., ACHARD, F., DE FREITAS, J. V.,
LYNCH, J., MASLIN, M., BALZTER, H. & SWEETING, M. 2013. Choose satellites to monitor deforestation. Nature, 496, 293-294.
LYNCH,J.M. & HARVEY, P.H. 2011. Opportunities and problems with bioenergy. Biochemist, 33, 39-43
LYNCH,J.M. & VON LAMPE, M. 2011. The need for bioenergy policy analysis. Biomas & Bioenergy, 35, 2311-2314.
LYNCH, J., MASLIN, M., BALZTER, H. & SWEETING, M. 2013. Choose satellites to monitor deforestation. Nature, 496, 293-294.
MITCHARD, E., SAATCHI, S., BACCINI, A., ASNER, G., GOETZ, S., HARRIS, N. & BROWN, S. 2013. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance and Management, 8, 10. Open access: link
SAATCHI, S. S., HARRIS, N. L., BROWN, S., LEFSKY, M., MITCHARD, E. T. A., SALAS, W., ZUTTA, B. R., BUERMANN, W., LEWIS, S. L., HAGEN, S., PETROVA, S., WHITE, L., SILMAN, M. & MOREL, A. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America, 108, 9899-9904.
SUÁREZ, J.C. 2009. ‘An analysis of the consequences of stand variability in Sitka spruce plantations in Britain using a combination of airborne LiDAR analysis and models’. PhD thesis. University of Sheffield.
SUÁREZ, J.C. 2014. ‘An individual canopy delineation algorithm based on Object-Oriented segmentation and classification’. Book chapter in ‘Challenges and opportunities for the world’s forests in the 21st century’. Springer.

 

Wind Risk


Context

Wind is one of the largest causes of forest loss in the world, especially in temperate and boreal forests. For example, in the EU alone it accounts for more than half of all catastrophic damage by wood volume to forests (Schelhaas et al., 2003). Hurricanes affecting regions such as the US are particularly devastating. Hurricane Hugo in 1989 damaged more than a third of standing timber in South Carolina, and Hurricanes Katrina and Rita damaged over 2M hectares on the gulf coast (Beach, 2010 link). In Amazonia in 2005, a single squall from a convective storm destroyed 0.3-0.5m trees in the Manaus district, equating to 30% of the observed annual deforestation in that region in 2005 and the second highest deforestation rate in 15 years (Negron-Juarez et al., 2010).

Furthermore, a study following the 2005 Gudrun storm in Sweden, found that the impacts of storms on forests continue long after the actual event. The growth of Norway Spruce forests was found to be reduced by over 10% in the worst hit regions over the following 3 year period (Seidl and Blennow, 2012).

Whilst nothing can be done to reduce the likelihood of strong winds, forest management can have a strong bearing on the impact of such storms. Research to date has revealed that the level of impact depends on a number of key factors that determine the susceptibility of a forest to wind risk. These include: trees species; tree height (and height/diameter ratio); age; crown properties; stand edge; thinning characteristics; root properties; topography and soil properties (Hanewinkel et al., 2011). It is generally agreed that tree height is the most important variable governing susceptibility to wind. Other factors vary in importance depending on the study/model. Conifer species in general are more vulnerable than broadleaves but since local conditions vary and species adapt to their surroundings it is difficult to generalise on species vulnerability (Gardiner et al., 2011 Link).

Climate change adds an additional challenge to predicting wind loss. In 2012, the Intergovernmental Panel on Climate Change (IPCC) published a special report link  on managing the risks of extreme events, which concluded that it had very low confidence in the ability of the approaches used in the few studies that exist to predict strong and extreme winds with the exception of some predictions for cyclones. Tropical cyclones, were said to be likely to decrease or remain constant in number but to see an increase in mean maximum wind speed, whereas extra tropical cyclones were said to be likely to have moved pole-wards in the Northern and Southern hemispheres in the last 50 years and there was medium confidence that they would decrease in number and continue to move pole-wards (Seneviratne et al., 2012).


Approaches to assessing risk

To date, we have found that the only UK approach to wind risk assessment of relevance to our audience, is the widely used mechanistic model ForestGALES developed by Forest Research. We therefore focus on this tool.


ForestGALES  Version 2.5 (released October 2015)

Website: link  User Guide: link
For online demonstration, click on ‘Forest DSS’ tab (above right) and select ForestGALES 2.5 from the pull down menu
Primary Contact: Dr. Bruce Nicoll, Forest Research
Other: Professor Barry Gardiner (original developer / recent upgrade to version 2.5).


Description

ForestGALES is a computer-based tool to assess forest wind risk. The tool assists forest managers in developing silvicultural practices to minimise wind risk, and provides return intervals of winds that can cause damage to stands either by uprooting or stem breakage. It does not require historical data and so can be adapted to new forest projects and locations lacking this information. Simplified versions requiring fewer inputs can provide lower resolution, regional scale wind risk assessments.


Availability
The latest web based version 2.5 (Nicoll et al., 2015) is available free from the Forest Research web site (link) . The full version is also available to download and costs £50 plus vat for commercial use, but it may be provided free of charge for academic use and for research in collaboration with Forest Research. A research version is also available which allows the adjustment of input parameters and a complete range of output data useful for research purposes. ForestGALES outputs can be read into ArcGIS to provide results in GIS format. An R version of ForestGALES has been developed with  Locatelli to allow easy incorporation in calculations developed in R. All versions are fully documented on the website.


Reducing wind risk through silvicultural practice
ForestGALES was originally developed to assist forest managers in developing silvicultural practices to minimise wind risk. Key data on forest projects can be input and GALES will advise on which stands are at the highest risk.


Identification of return intervals of windspeeds causing uprooting or stem breakage
GALES identifies the critical wind speeds at which trees are subject to uprooting or stem breakage, and provides the expected return period of such winds i.e. average number of years between wind speeds exceeding the critical level. Risk assessments can easily be derived from these outputs. If all of the information is readily available an assessment may be done in half an hour.
Note: Before ForestGALES was developed, the UK forest industry used a system called “Windthrow Hazard Classification” (WHC). WHC scores are provided as a ForestGALES output for comparison.


Impacts/Vulnerability assessment
ForestGALES provides the probability of an average tree being damaged within a stand. It does not, however, estimate the % of the stand that is likely to be damaged but damage to an average tree implies substantial damage within the stand that would normally require the whole stand to be cleared.
Gardiner and Locatelli are investigating whether vulnerability information can be added to the model in future, and are developing a probabilistic version of GALES with expressions to describe the variability. Contact: Gardiner


Inputs
The full version of GALES typically requires data to be input from field measurements or yield models on a range of tree and stand characteristics as well as upwind edge effects, however, tree height, stem diameter, spacing, and species are the most important of these variables. Provided these data are available for a given investment a reasonable estimation of risk can be provided using default values for the other factors.
The model contains default wind values for the UK but it can be adapted to other countries provided ‘a’ and ‘k’ values for the Weibull distribution of local windspeed can be obtained (wind is assumed to be normally distributed and a is the mid-point of the distribution and k the standard deviation. In general, for different regions, a tends to move but k stays relatively constant).


Countries covered
The model is provided for the UK but has also been adapted for use in New Zealand, Canada (Quebec and British Columbia), Southwest France, Denmark and Japan. If wind data is available (see previous section) GALES could be adapted to other countries.


Lower resolution regional maps of wind risk
Lower resolution regional maps of wind risk to forests can also be derived from GALES. As part of the European MOTIVE project (link) , Nicoll worked with Gardiner and Dr Mart-Jan Schelhaas to produce European maps of critical wind speeds that would produce risk to forest stands across Europe (link to full report including maps). These outputs were derived from ForestGALES using forest structure from the Synthetic European Forest Structure Database, and soil information derived from the FAO soil maps. These results can be combined with past and future climates from the EU ENSEMBLES project to estimate future wind risk to forests (Gardiner et al., 2013). This work is intended to support adaptive forest management in the face of climate change. (Contact: Nicoll/Gardiner)
A simplified version of ForestGALES designed to work at large spatial scales (national or continental) with a reduced input dataset was developed by Dr. Ferenc Pasztor for use in a land-surface exchange model, ORCHIDEE. (Contact: Gardiner).


Single species vs mixed stands/age distribution
The web-based version determines wind risk for a single uniform stand of single-aged single species conifers. The full version, supports analysis of multiple stands and can be run over time to show how wind risk changes as a stand develops. It can support thousands of sub-compartments with different species but not mixed species, mixed age stands, or continuous cover stands although a version able to do this is in preparation. A basic assessment of the risk to mixed stands or continuous cover could be obtained by running GALES for each species separately to find which is the most vulnerable component, and this risk applied to the whole stand.

Yield models containing projected growth information can be input to GALES to model how wind risk will change over the rotation period. Forest yield models are included for the UK, but can be replaced by the user’s own yield models.

Nicoll is investigating the ability for GALES to work with complex stands including those managed for continuous cover for ForestGALES v3.4.
Gardiner is working on a research version of GALES for individual trees that accounts for local competition from neighbours and can handle mixed aged and mixed species stands.


Canopy level versus stand level analysis
ForestGALES normally works at forest stand level but it has been modified to run at individual canopy level too. The model can incorporate estimates of canopy dimensions produced by airborne LiDAR. The result is a more detail view of the variations of risk within a forest stand (Suárez et al., 2014). This new approach has been tested in the Cowal and Trossachs Forest District, using LiDAR data taken in 2008 and 2012. The model has been able to locate most of the areas affected by wind damage after the storm in January 2012. The identification of windthrow gaps allows a better estimation of timber loses and a more accurate production forecast.


Tree species covered
Conifers – GALES was originally developed to assess the risk to commercial conifers. Twelve conifer species are included: Scots pine; Corsican pine; Lodgepole pine; European larch; Japanese larch; Hybrid larch; Douglas fir; Noble fir; Grand fir; Sitka spruce; Norway spruce; and Western hemlock.
Broadleaves –Nicoll is exploring the future incorporation of broadleaf species. In the interim:
Locatelli has been working with GALES to assess Eucalyptus.
Gardiner has added Beech and hopes to add Oak soon.


Simplified GALES for additional species/ages
Wind risk is often similar for uprooting or breakage of trees in storms. Nicoll believes that using tree dimensions (if available), and manuals on the wood properties of trees (including the moduli of rupture and elasticity) around the world, it could be possible to determine critical wind speeds without having to undertake expensive research including mechanically overturning trees down to determine anchorage. In this way look-up tables could be provided which estimated the wind speed at which trees of a range of species, at a range of heights or ages, would be vulnerable. This project could be considered on request if funding is available. Contact: Nicoll


GALES linked to CAPSIS forest model
A version of ForestGALES in a Java library has been incorporated in the CAPSIS forest modelling framework with Celine Meredieu and Thierry Labbé at INRA. It allows calculation of wind risk to stands and individual trees. Contact: Gardiner


Accounting for variability of windspeed during a storm

A version that properly accounts for variability of wind speed during a storm and propagation of damage and storm duration is under development with Sophie Hale, Bruce Nicoll (Forest Research) and Sylvain Dupont (INRA). It is similar to the GALES-BC model developed by Ken Bryne and Steve Mitchell at UBC but uses new calculations of airflow over forests. Contact: Nicoll


Comparison of ForestGALES 2.5 with previous versions
The new version 2.5 reflects the latest science and has full documentation. A comparison of the old Windthrow Hazard Classification system with ForestGALES 2.1 and the new version, ForestGALES 2.5, based on a validation exercise using recorded wind damage in a recent winter storm (Hale et al., 2015) shows that the WHC is the most pessimistic, and ForestGALES 2.5 is the least pessimistic. Version 2.5 provided predictions of damage across a large forest area that were close to that observed. The difference between ForestGALES 2.1 and 2.5 is equivalent to reducing the DAMS (windiness) score by 2 points.

References

BEACH, R. H. S. E. O. L. T. P. S. 2010. The influence of forest management on vulnerability of forests to severe weather. In: Pye, John M.; Rauscher, H. Michael; Sands, Yasmeen; Lee, Danny C.; Beatty, Jerome S., tech. eds. 2010. Advances in threat assessment and their application to forest and rangeland management, Gen. Tech. Rep. PNW-GTR-802. Portland.
GARDINER, B., BLENNOW, K., CARNUS, J., FLEISCHER, P., INGEMARSON, F., LANDMANN, G., LINDNER, M., MARZANO, M., NICOLL, B., ORAZIO, C., PEYRON, J., REVIRON, M., SCHELHAAS, M., SCHUCK, A., SPIELMANN, M. & USBECK, T. 2011. Destructive Storms in European Forests: Past and Forthcoming Impacts. European Forest Institute, Atlantic European Regional Office – Efiatlantic.
GARDINER, B., SCHELHAAS, M. & NICOLL, B. 2013. Chapter V: Mapping the risk to European forests with a changing climate. In: FITZGERALD, J. & LINDNER, M. (eds.) Adapting to climate change in European forests – results of the MOTIVE project. Sofia: Pensoft Publishers.
HALE, S. E., GARDINER, B., PEACE, A., NICOLL, B., TAYLOR, P. & PIZZIRANI, S. 2015. Comparison and validation of three versions of a forest wind risk model. Environmental Modelling & Software, 68, 27-41.
HANEWINKEL, M., HUMMEL, S. & ALBRECHT, A. 2011. Assessing natural hazards in forestry for risk management: a review. European Journal of Forest Research, 130, 329-351.
NEGRON-JUAREZ, R. I., CHAMBERS, J. Q., GUIMARAES, G., ZENG, H. C., RAUPP, C. F. M., MARRA, D. M., RIBEIRO, G., SAATCHI, S. S., NELSON, B. W. & HIGUCHI, N. 2010. Widespread Amazon forest tree mortality from a single cross-basin squall line event. Geophysical Research Letters, 37.
NICOLL, B., HALE, S., GARDINER, B., PEACE, A. & RAYNER, B. 2015. ForestGALES: A wind risk decision support tool for forest management in Britain. Version 2.5. In: COMMISSION, F. (ed.). Edinburgh.
SCHELHAAS, M. J., NABUURS, G. J. & SCHUCK, A. 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology, 9, 1620-1633.
SEIDL, R. & BLENNOW, K. 2012. Pervasive Growth Reduction in Norway Spruce Forests following Wind Disturbance. Plos One, 7.
SENEVIRATNE, S. I., NICHOLLS, N., EASTERLING, D., GOODESS, C. M., KANAE, S., KOSSIN, J., LUO, Y., MARENGO, J., MCINNES, K., RAHIMI, M., REICHSTEIN, M., SORTEBERG, A., VERA, C. & X. ZHANG, X. 2012. Changes in climate extremes and their impacts on the natural physical environment. In:. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC).. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.
SUÁREZ, J.C. 2014. ‘An individual canopy delineation algorithm based on Object-Oriented segmentation and classification’. Book chapter in ‘Challenges and opportunities for the world’s forests in the 21st century’. Springer.

 

 

Pest & Disease Risk


Context

Pests and diseases (P&D) are one of the largest causes of forest loss and damage, affecting around 35M hectares of forest a year globally – mainly in temperate and boreal forests (FAO 2010). For example, across Europe between 1950 and 2000, biotic factors as a whole are estimated to have accounted for 16% of the total wood damaged by disturbances of which 8% was attributed to bark beetles (Schelhaas et al., 2003).

Regional trends for P&D show high variability with the percentage of forest areas affected by pests running as high as 5% in North America in the 1990s during the spruce budworm outbreak, although the average had fallen to 3.4% in the latest figures for 2005. Northern Africa, Western and Central Asia and Europe (excluding the Russian Federation) also had affected areas of around 2-3%. Overall insect infestations have shown a decline but again data is variable. Diseases have affected much smaller proportions of forest overall with only Oceania showing high levels up to 3.9%. (FAO 2010). Limited data is available but show in general that: insects are the most frequent pests impacting on forests; in most regions the majority of pests are indigenous and are prevalent in planted as opposed to naturally regenerated forests; and in all regions broadleaf species had a higher number of pests than conifers (FAO, 2009).

Historic trends offer little guidance on the future risks of P&D for a number of reasons: e.g. new P&D can arise;  existing ones can jump species; and most importantly new P&D can arrive from other countries. Climate change can have both positive and negative impacts on P&D, but trees under climatic stress are more vulnerable to them.


Approaches to assessing risk

General – proposal for daily monitoring of general pest and disease impacts via remote sensing

Prof. Lynch has promoted the concept of daily monitoring of the state and health of forests by using optical and radar satellites that work in different parts of the electromagnetic spectrum (Lynch et al., 2013). Optical satellites could detect changes in the amount of chlorophyll, which can help detect the onset and spread of pests and diseases. Images would need processing into datasets for end-users, via an intermediary organisation, including orthorectification. Daily information is needed to identify threats on a timely basis, whereas currently information is typically provided weekly. This could be provided using the latest satellite constellations recently launched including DMC-2 and the radar systems soon to be launched (resolution 1m). Prof. Lynch has discussed this concept with the commercial and public sector, including Defra. Insurers have been receptive to the idea, provided consensus on data interpretation is achieved by providers. In a letter to the Times dated 30 October 2012 under the general head ‘Defra and the timber trade share the blame for ash dieback’, he argued that this could have helped in the detection of Ash Dieback caused by Hymenoscyphus fraxineus (formerly Chalara fraxinea). There is precedence in that precision agriculture already makes significant use of satellite technology e.g. for irrigation and fertiliser application. Contact: Prof. Jim Lynch



Using remote sensing to detect stress factors/forest health indicating possible P&D impact

  • Prof. Juan Suárez (Forest Research) is working with Dr. Jacqueline Rosette and Professor North (University of Swansea) in the development of new techniques for detecting changes in the foliage associated to stress factors. This project is funded by the Royal Society and involves the use of different sensors and the calibration of the radiative transfer model Flight, developed by Peter North and his collaborators. A recently awarded project by DEFRA aims at the adaptation of the system developed by NASA and the US Forest Service to monitor trajectories of change using satellite imagery. This project is due to start in April 2016.
  • Prof. Juan Suárez is leading the participation of Forest Research  in a consortium with the Chinese Academy of Forestry and the University of Swansea in the Focus-Kanlin project. This work is focused on multi-sensor analysis, data assimilation techniques and modelling. The project aims to develop an operational platform to improve forest inventories and monitor forest health and condition. The project is due to start at the end of 2016.

For both projects contact: Prof Juan Suárez-Minguez


Using remote sensing to detect specific P&D (general approach)

Juan Suárez works with Professor Heiko Baltzer and others at the University of Leicester and Dr. Jackie Rosette at the University of Swansea, on identifying and mapping tree physiology from remotely sensed data. Projects aim to identify stress symptoms that can be associated to the characteristics of individual P&D. The physiological activity of trees under stress can be detected through multispectral and hyperspectral scanning from different types of airborne and satellite sensors. At this point, it is not yet possible to detect the spectral ‘signature’ of the disease. Instead, work is focused on the detection of carotenoid and chlorophyll composition of the foliage as well as temperature differences as indicators of stress. Data assimilation techniques between sensor data and an energy balance model allow a guided search for forest condition. The goal is to gain a better understanding of what sensors depict at a point in time compared to expected signal. So, by a better estimation of anomalies, it will be possible to improve the detection of stress in our forests. Projects in this area include:


Using remote sensing to detect: Phytophthora ramorum

  • The Bluesky Project (link), funded by NERC and  supported by Forest Research is led by Prof. Heiko Baltzer, with Juan Suárez-Minguez as a co-supervisor of Chloe Barnes (PhD student). Bluesky is a UK-based company specialising in aerial survey. This project investigates the use of aerial photography, thermal imaging and airborne laser mapping systems to improve the identification of Phytophthora ramorum in larch. It aims to ‘use a state of the art aerial mapping system to collect data for already infected trees and compare this with data for healthy sites nearby and historic, pre diseased, baseline data.’ The project aims to integrate hyperspectral and thermal imagery with LiDAR information to develop a spectral signature of infected vs uninfected larch. Contact: Prof. Heiko Balzter
  • There is also a Forest Research project aiming to construct the spectral signatures of change depicting the progression of P. ramorum in larch since the point of infection. An experiment will be conducted in the spring and summer of 2016 on a group of saplings. They will be infected with the disease in the control environment of a polytunnel. Temperatures and reflectance will be monitored in a group of infected trees and control individuals. In parallel, foliar samples will be analysed using chromatography to determine changes in pigment composition that will be associated to the changes in reflectance. The hypothesis is that the disease will affect pigments due to changes in carotenoid content and will also affect chemical composition as detected by the sensors and lower stomata activity. This information will be used to calibrate airborne campaigns using the Flight radiative transfer model developed at Swansea University. Contact: Prof Juan Suárez-Minguez


Using remote sensing to detect: Dothistroma septosporum (Red Band Needle Blight)

  • Prof. Juan Suárez is co-supervising PhD student Magdalena Smigaj  with Dr. Rachel Gaulton ( University of Newcastle). Magdalena is using a combination of field and airborne sensors aimed at the detection of Dothistroma septosporum in Scots pine. Work is focused on thermal differences between individuals affected by different degrees of defoliation and multispectral characteristics of the foliage. The airborne sensors are mounted on UAV platforms. Other work is looking at the use of the multispectral waveform LiDAR system (SALCA) developed by Professor Danson at Salford University.
  • Prof. Juan Suárez is also co-supervising PhD student William Cornforth, with Dr. Caroline Nichol (University of Edinburgh). William has been undertaking a set of experiments in a polytunnel with Scots pines infected by Dothistroma septosporum and exposed to nutrient and water deficiencies. The aim of this project is the creation of spectral signatures of plant response associated to different stressors.

For both projects contact: Prof Juan Suárez-Minguez


Quantifying losses from P&D via remote sensing

Prof. Balzter previously worked on techniques to detect forest scars from remote sensing and identify which are caused by P&D damage through elimination of other causes. For example, if remote sensing shows no history of heat emissions from these scars then the cause is not fire related and may be due to P&D damage. High resolution of remaining trees and topographical information can also enhance the analysis. This technique could be particularly useful in areas where there is known P&D damage e.g. from pine beetles, and can be used to assess the extent. Whilst the feasibility of this approach was tested some time ago over Siberia the work has not been followed up in depth and was not published. Contact: Prof. Heiko Balzter


Biological control of pests and diseases

Prof. Lynch is an expert in the use of biological control i.e. the use of natural predators and pathogens to control crop plant and tree pest and diseases ( Hokkanen and Lynch , 1986). This can be economically attractive but most importantly it can pose less environmental and human health problems than many chemicals which have been used. Contact: Prof. Jim Lynch


A new approach to assessing the overall threat to woodland and individual tree species

Davies has developed a new approach that supports a systematic, risk-based assessment of the future threat to woodland from all known individual pest and diseases, based on a risk management approach taken from the finance sector (Davies et al, Forestry 2017). As well as a comprehensive assessment of the threat to woodland, it also provides an assessment of the comparative threat to individual tree species. The approach has been demonstrated through a case study of the evaluation of pest and disease threats to projects certified under the UK’s Woodland Carbon Code. It is currently being expanded to look at all threats to the top 5 tree species in the UK: Sitka Spruce, Scots Pine, Oak, Ash and Birch. The approach can be adapted to any woodland resource worldwide – whether managed for timber, carbon, biodiversity, or public enjoyment – provided the requisite data is available. Its novelty lies in the simplification of complex threats, from numerous pests and diseases, to measures that can be used by a range of stakeholders including those involved in policy, forest asset management and investment. Contact: Susan Davies

References

DAVIES, S.A., PATENAUDE, G., SNOWDON, P. IN PRESS. A new approach to assessing the risk to woodland from pest and diseases. Forestry Journal. Open access article available here.
FAO 2009. Global review of forest pests and diseases. FAO Forestry Paper 156. Rome: Food and Agriculture Organization of the United Nations.
FAO 2010. Global Forest Resources Assessment 2010. FAO Forestry Paper 163. Rome: Food and Agriculture Organization of the United Nations.
HOKKANEN,H.M.T. & LYNCH,J.M. (Eds) 1995. Biological Control Agents. Cambridge University Press.
LYNCH, J., MASLIN, M., BALZTER, H. & SWEETING, M. 2013. Choose satellites to monitor deforestation. Nature, 496, 293-294.
SCHELHAAS, M. J., NABUURS, G. J. & SCHUCK, A. 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology, 9, 1620-1633.

Drought Risk

Photo: Kindly provided by Mark Harpur 


Context

Drought causes stress to trees that can impact on rates of growth and mortality, and thereby timber production and carbon sequestration values. Within the UK, for example, trees such as Sitka and Norway spruce, Larch and Beech are more vulnerable to drought than species such as Scots pine, Douglas-fir and Ash. Long-term drought can lead to loss of trees and also increases the susceptibility of trees to other risks especially fire and P&D. Extreme drought may also lead to reduced productivity and even large-scale die-offs on a regional scale.

One of the difficulties in predicting the impacts of droughts on forests is the uncertainty around forecasting future drought extent and severity. It is therefore useful to conceptually separate climate risk (here risk of drought) from the sensitivity of vegetation to that climate risk (Meir and Woodward, 2010). A recent IPCC report, for example, concluded that issues with existing drought models meant that there was only ‘medium confidence’ in projections for some regions including southern Europe and the Mediterranean region, central Europe, central North America, Central America and Mexico, northeast Brazil, and southern Africa. Elsewhere there was only a ‘low confidence’ in projections due to disagreement in projections (Seneviratne et al., 2012).

The susceptibility of trees to drought depends primarily not only on the ecological characteristics of the species but also on the soils present. The ability of soils to store and also to release water to plants is affected by a number of factors including soil (and rooting) depth, permeability, sand/silt and organic matter content, capillarity etc. A period of drought can have a greater or lesser impact depending on the type of soil and the pre-existing level of the water table.

Tropical forests are particularly vulnerable to drought as the severe droughts in Amazonia in 2005 and 2010, and others in SE Asia in earlier periods (e.g. 1982/3, 1997/8) highlighted. The two recent droughts in Amazonia were both described as ‘1 in a 100 year’ events according to preceding records and yet they occurred within 6 years of each other. The 2005 drought was estimated to have caused a total biomass carbon loss of 1.2 to 1.6 petagrams including estimates of below-ground losses (Phillips et al., 2009) . There is considerable uncertainty around how 21st century climate change will affect Amazonia but what is virtually certain is that there will be warmer temperatures and more extreme rainfall patterns. Extreme drought and rainfall events have both been observed in the last 7 years in the region consistent with this trend.

Whilst tropical forests are particularly vulnerable, temperate forests are not immune from drought. A study of the impacts of the 2003 heatwave and drought in Europe found that there was a 30% reduction in gross primary production[3] in the region that year, with a reduction in rainfall being the primary cause of loss in Eastern Europe (Ciais et al., 2005).


Approaches to assessing risk


Ecological Site Classification Tool (ESC) for tree species selection under current and future climate scenarios in Britain

Weblink: click here
The Ecological Site Classification Tool (ESC) was developed by Forest Research to assist forest practitioners in assessing the maximum yield expectations for a range of key broadleaved and conifer tree species for any potential site in Great Britain (Pyatt et al.,2001). The system uses four climatic variables and two edaphic (soil) properties to estimate potential yields. Climate variables are accumulated temperature; continentality; detailed aspect method scoring (DAMS – a measure of exposure and windiness); and moisture deficit. Pre-calculated climate variables have been loaded into ESC for the baseline period from 1961-90. Edaphic properties are soil moisture and soil nutrient regimes. Default values are contained within ESC but the user is recommended to input site specific values. The Tool is widely used within the UK forest sector. The Tool has also been developed to provide yield forecasts for Sitka Spruce, Scots Pine and Oak under different future climate scenarios (Petr et al., 2014). Moisture deficit is an indicator of ‘droughtiness’ under these scenarios. Work is currently underway to process this latter information into a form of use to end-users across the forest sector and to adapt the decision support tool to include it. A demonstration version of ESC can be accessed via the ‘Forest DSS’ tab on this site and clicking on the pull-down menu to select the Tool. Clicking on a site on the map provides sample outputs. Contact: Duncan Ray, Dr Michal Petr.


Using Thermal Lidar to identify trees under drought and disease stress

Professor Juan Suárez is the UK contact for the ThermoLiDAR project (Link), funded under the EU 7th Framework Programme (finished in 2014), which aimed to improve the early detection of forest health through the provision of new tools for sustainable forest management based on LiDAR and THERMAL data integration. This project combined the use of LiDAR to retrieve estimates of canopy structure (height, fractional cover, biomass, etc), with thermal imagery to create estimates of relative temperature differences that can be used as a proxy for stomata conductance. The general principle is that under normal conditions, the stomata release water through respiration and therefore the canopy is kept relatively cool. However, at times of stress, stomata close earlier during the day and the canopy shows a relative higher temperature compared to healthier ones. Time series measuring thermal outputs at noon at the beginning, middle and end of a growing season can identify if trees are under drought stress. Link to thermolidar project. Contact: Prof. Juan Suárez-Minguez.


Susceptibility of different tree species to drought and drought-induced mortality – Northern Australia, Malaysia, Amazon

Prof. Patrick Meir, Prof. Oliver Phillips and Ms Adriane Esquivel Muelbert at the University of Leeds are investigating the underlying differences in response to drought of different tree species, their susceptibility to mortality, and suitability in different locations. They have a number of observational and experimental plots in Northern Australia, Malaysia and the Amazon. The plots in the Amazon complement Patrick’s work-to-date in eastern Amazonia focusing on experimental manipulation. Whilst around 150 species are included in the multiple-plot study, for focused physiological studies they have narrowed the list down to a small number of drought-tolerant and drought-intolerant taxa. Measurements are focused on 6 species, 3 of each tolerance group. This latter work is being conducted with Prof. Maurizio Mencuccini. Contact: Prof. Patrick Meir.


Susceptibility of different tree species to precipitation change  – Tropical dry forest biomes

The above  projects focus on investigating tolerances for tropical rain forests. However, more than 50% of the tropics globally have climates that are too seasonally dry to support rain forest. In these areas, two other major biomes are found: tropical dry forests and tropical savannas. Prof. Toby Pennington (Royal Botanic Garden Edinburgh) is investigating extending monitoring of biodiversity and ecological processes into dry forest biomes in Brazil with his co-investigator Dr Kyle Dexter (Edinburgh).   Tropical savannas and dry forests contain species already adapted to extreme droughts, and in the savannas, to frequent natural fires. Thus these species may be threatened by increased rainfall, which is predicted for some tropical regions. Conversely, they house species that could be useful resources under increasing drought. This project could provide information to assist in tree species selection to reduce risk of tree loss under future climate conditions.  Contact: Prof. Toby Pennington or Dr Kyle Dexter


Susceptibility of different species to drought  – Utility of Inga spp. for afforestation/reforestation and agroforestry

Prof Pennington and Dr Dexter also investigate the use of the genus Inga (around 300 species) commonly used in agroforestry, afforestation and reforestation projects. Ing species are particularly good at rejuvenating degraded soils. Planting species with a greater tolerance to a range of climates and thereby precipitation levels could significantly reduce risk to forest projects in an uncertain climate future. Contact: Prof. Toby Pennington and Dr Kyle Dexter


Susceptibility of different tree species to drought  – Mediterranean incl. Scots Pine

Prof. Mencuccini also leads on a project with Prof. Meir focusing on drought-induced mortality in the Mediterranean and in particular the susceptibility of Scots Pine to drought. (Salmon et al., 2015). Contact: Prof. Maurizio Mencuccini


Mechanisms and climate factors causing susceptibility to drought-induced mortality

One key area of focus in relation to drought and forestry is the causal mechanisms behind drought-induced mortality. There are two main hypotheses of causation: the first is that drought causes the water column to ‘snap’ under negative pressure, resulting in irreversible hydraulic failure and leaf desiccation; the second relates to leaf stomata closing to prevent water loss and thereby preventing sufficient CO2 to be taken in through the same stomata for photosynthesis, with the result that there is a progressive decline in the availability of carbon to metabolism, ultimately resulting in death by ‘carbon starvation’. Both mechanisms cause physiological weakening and potentially mortality, either directly through hydraulic failure or carbon starvation, or indirectly through greater susceptibility to pest and diseases or windthrow. Prof. Meir is working on how such mechanisms might be modelled (Meir et al., 2015) using input from sample plots. There is evidence for both mechanisms in different parts of the world. In particular, if the point at which carbon starvation or hydraulic failure occurs can be isolated this could in future be used in conjunction with probabilistic climate forecasts to derive mortality estimates. Prof Meir’s group published a significant advance in this area in Dec 2015 (Rowland 2015, Nature), showing data which favoured the hydraulic explanation rather than the carbon starvation explanation as the ‘trigger’ for drought-induced mortality. Contact: Prof. Patrick Meir.


Sensitivity of Amazonian forest ecosystems to drought – General expertise

Prof. Patrick Meir co-leads a large NERC-funded project to understand and model the drought impacts on rainforests pan-tropically. The work combines work in SE Asia with long-term ecosystem-scale experimental work in Amazonia. The latter project in eastern Amazonia involves using field data and other field-based sources of evidence to develop understanding and to allow the estimation of drought risk. The work also builds towards advancing the UK’s land surface model that is used by the Hadley Centre in the IPCC reports on the future of the Earth system. For this work he is joined by a colleague at Exeter University (Prof Stephen Sitch). More recently he also led the University of Edinburgh’s contribution to the EU’s AmazAlert consortium looking at critical land-climate feedbacks in the region, and related policy responses. He led and edited a special feature in the subject-leading journal New Phytologist summarising the state of knowledge on Amazonian rainforests and drought (Meir and Woodward, 2010) and co-authored a paper overviewing existing modelled predictions of the response by tropical rainforests globally to 21st century climate change (Huntingford et al., 2013). Contact: Prof. Patrick Meir.


Using Dynamic Global Vegetation Models to model the sensitivity of Amazonian forest ecosystems to severe drought

Prior to the experimental work in Amazonia (see section above on Sensitivity of Amazonian forest ecosystems to drought), Prof Patrick Meir led a small team comparing the ability of different Dynamic Global Vegetation Models (DGVMs) to model the sensitivity of Amazonian forest ecosystems to climate, especially severe drought (Galbraith et al., 2010). This work demonstrated the need for the current experimental work. His team have more recently published ground-breaking work from the Amazon experiment to show that tropical rainforests are not resistant to long term drought, and that the very large loss of biomass that occurs through increased mortality associated with drought is triggered by deterioration in the transport of water from soil to leaves, rather than through reduced metabolic capacity (Rowland et al. 2015, Nature). This result is now being used to improve the predictive capacity of DGVMs by improving their mechanistic representation of the response to drought. Statistical models can help us predict future change, but this approach is considered unreliable under future climates which don’t replicate the combination of preceding climate variables from which statistical models are derived. For this reason the advance made by Rowland et al. 2015 is of particular note, as it helps modellers focus their development of their models on a particular group of processes. Contact: Prof. Patrick Meir.

References

CIAIS, P., REICHSTEIN, M., VIOVY, N., GRANIER, A., OGEE, J., ALLARD, V., AUBINET, M., BUCHMANN, N., BERNHOFER, C., CARRARA, A., CHEVALLIER, F., DE NOBLET, N., FRIEND, A. D., FRIEDLINGSTEIN, P., GRUNWALD, T., HEINESCH, B., KERONEN, P., KNOHL, A., KRINNER, G., LOUSTAU, D., MANCA, G., MATTEUCCI, G., MIGLIETTA, F., OURCIVAL, J. M., PAPALE, D., PILEGAARD, K., RAMBAL, S., SEUFERT, G., SOUSSANA, J. F., SANZ, M. J., SCHULZE, E. D., VESALA, T. & VALENTINI, R. 2005. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529-533.
HUNTINGFORD C, ZELAZOWSKI P, MERCADO, LM, SITCH, S, GALBRAITH, D, FISHER R, LOMAS M, WALKER A, JONES CD, BOOTH BBB, MALHI Y, COX PM, HEMMING D, KAY G, GOOD P, LEWIS S, ATKIN OK, LLOYD J, GLOOR M, ZARAGOZA-CASTELLS J, MEIR P & BETTS R. 2013. Simulated resilience of tropical rainforest to CO2–induced climate change. Nature Geoscience6, 268-273
MEIR, P. & WOODWARD, F. I. 2010. Amazonian rain forests and drought: response and vulnerability. New Phytologist, 187, 553-557.
PETR, M., BOERBOOM, L. G. J., VAN DER VEEN, A. & RAY, D. 2014. A spatial and temporal drought risk assessment of three major tree species in Britain using probabilistic climate change projections. Climatic Change, 124(4), pp.791–803.
PYATT, G., RAY, D. & FLETCHER, J. 2001. An Ecological Site Classification for Forestry in Great Britain, Norwich, HMSO, Crown Copyright. Link
ROWLAND L, DA COSTA ACL, MENCUCCINI M, GALBRAITH DR, OLIVEIRA RS, BINKS OJ, OLIVEIRA AAR, PULLEN AMM, DOUGHTY CE, METCALFE DB, VASCONCELOS SS, FERREIRA LV, MALHI Y, GRACE J and MEIR P. 2015.  ‘Death from drought in tropical forests is triggered by hydraulics not carbon starvation’. Nature, 528, 119-125.
SALMON Y, TORRES-RUIZ JM, POYATOS R, MARTINEZ-VILALTA J, MEIR P, COCHARD H, MENCUCINNI M. (2015). Balancing the risks of hydraulic failure and carbon starvation: a twig scale analysis in declining Scots pine. Plant Cell and Environment, 38, 2575-2588
SENEVIRATNE, S. I., NICHOLLS, N., EASTERLING, D., GOODESS, C. M., KANAE, S., KOSSIN, J., LUO, Y., MARENGO, J., MCINNES, K., RAHIMI, M., REICHSTEIN, M., SORTEBERG, A., VERA, C. & X. ZHANG, X. 2012. Changes in climate extremes and their impacts on the natural physical environment. In:. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC).. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.
PHILLIPS, O. L., ARAGAO, L., LEWIS, S. L., FISHER, J. B., LLOYD, J., LOPEZ-GONZALEZ, G., MALHI, Y., MONTEAGUDO, A., PEACOCK, J., QUESADA, C. A., VAN DER HEIJDEN, G., ALMEIDA, S., AMARAL, I., ARROYO, L., AYMARD, G., BAKER, T. R., BANKI, O., BLANC, L., BONAL, D., BRANDO, P., CHAVE, J., DE OLIVEIRA, A. C. A., CARDOZO, N. D., CZIMCZIK, C. I., FELDPAUSCH, T. R., FREITAS, M. A., GLOOR, E., HIGUCHI, N., JIMENEZ, E., LLOYD, G., MEIR, P., MENDOZA, C., MOREL, A., NEILL, D. A., NEPSTAD, D., PATINO, S., PENUELA, M. C., PRIETO, A., RAMIREZ, F., SCHWARZ, M., SILVA, J., SILVEIRA, M., THOMAS, A. S., TER STEEGE, H., STROPP, J., VASQUEZ, R., ZELAZOWSKI, P., DAVILA, E. A., ANDELMAN, S., ANDRADE, A., CHAO, K. J., ERWIN, T., DI FIORE, A., HONORIO, E., KEELING, H., KILLEEN, T. J., LAURANCE, W. F., CRUZ, A. P., PITMAN, N. C. A., VARGAS, P. N., RAMIREZ-ANGULO, H., RUDAS, A., SALAMAO, R., SILVA, N., TERBORGH, J. & TORRES-LEZAMA, A. 2009. Drought Sensitivity of the Amazon Rainforest. Science, 323, 1344-1347.

Fire Risk

Primarily UK focused work:


Context

Fire is one of the largest causes of forest loss in the world. E.g. across Europe between 1950 and 2000, for example, fire is estimated to have accounted for 16% of the total wood damaged by disturbances (Schelhaas et al., 2003). Standardised global information on fire losses in forests is poor. FAO data supplied by national authorities suffers from inconsistencies in definition and quality; however, it shows that less than 1% of forest is affected by fire each year in most regions. Fires are most prevalent in Africa, particularly Western and Central areas, where 12% of land was affected. Many of these fires will not, however, be the tall plantation forests normally invested in by investors. Oceania was the only other region where fires affected more than 1% of land at 2.4% (FAO, 2010). Where fires do occur at the national and local level, they can of course be devastating.

The likelihood of fire is primarily driven by the presence of an ignition source and the availability of dry fuel to burn. The spread and intensity of a fire depends on a number of other factors including forest type, management regime (e.g. fire-fighting capability), topography, and meteorological conditions – especially temperature and humidity.


Approaches to assessing risk

The most promising development in terms of monitoring fire risk is the development of appropriate remote sensing technologies. Remotely sensed data allows the monitoring of active fires as well as the historical analysis of the scale and extent of past fires through the identification of burned areas using specially developed algorithms. Many of these datasets are now available in a widely usable form e.g. The FAO’s Global Fire Information Management System (Link), and the European Commission’s European Forest Fire Information System (EFFIS) (Link),  provide fire information from remote sensors in a simple format for a wide range of stakeholders. The information can provide basic trend information for countries and regions where there is an absence of quality historical data.

 There are complex interactions and feedbacks between fire, vegetation type, land use and climate. Researchers have been developing approaches to modelling these interactions however it is early days for this sort of modelling and the ability to predict future risk is compounded by uncertainties in future climate projections of key variables such as precipitation and drought. It does, however, have high potential.

 A number of fire danger indices exist which indicate the scale of the current threat to forests. Many national authorities publish daily indices for different areas of the country from these.

We present below a summary of the research being undertaken by leading academics in this field which seek to enhance and combine these approaches to derive new means of estimating historic and future fire risk.


General: Analysis of remotely sensed active/burned area data to derive historical fire series

Global historical fire data is now readily available from sensors on board satellites. Two different methods are used: either mapping the intense heat and light triggered by active fires, giving the location of fires occurring as the satellites passed over, or looking at changes in land surface colour after fires have passed over, producing maps of the area burned. The resolution and historical availability varies by sensor. The most commonly used sensors to provide historical fire data are MODIS and Landsat. MODIS fire information is available globally at 1km resolution for active fires, and 500m resolution for burned areas from 2000 (link). Landsat scenes are available at a far higher resolution than MODIS, and for a far longer time period: from 1972 at a 60 m resolution, and from 1982 at 30m resolution, but are not routinely processed for fire data so significant post-processing is necessary. The latest Landsat satellites (Landsat 7 & 8, from 2000 onwards) provide 15m resolution data in their panchromatic bands, theoretically allowing very small burn scars to be detected: though in all likelihood in practice fires would need to be at least a hectare in size (100 x 100 m or similar) to be easily detectable even from the latest Landsat data. Datasets from all these sensors are free to download and use, even for commercial purposes. The non-technical end user can view active fires from the last 24/48hours and fire history information for a particular location from MODIS using the online FIRMS tool (link). Academics working with GIS can provide tailored history and statistical analyses by downloading the historic datasets and processing them. As an example, processing historic MODIS data from 2000 for a country such as Cameroon could take half a day and around a day for a medium sized country. Coarser resolution data is available using the Meteosat satellites, which have low spatial resolution but very high temporal resolution (15 minutes), as these are geostationary satellites looking at the same area continuously. These satellites also allow an idea of fire intensity, rather than just extent, though their Fire Radiative Power products.

Example: Dr Thomas Smith processed GIS fire data for ForestRe Ltd (insurance). He analysed MODIS burned area data on China for the purposes of quantifying premiums for forest fire insurance. He provided datasets on monthly and annual burned areas, for each of the 22 provinces covering the whole of China, from 1997-2011. This took 7 days to complete.

Academics in our network with expertise in processing fire data via GIS for forestry include:
– Balzter, Prof. Heiko
– Mitchard, Dr Edward.  (use of processed fire data for analysis)
– McMorrow, Julia  – (particular expertise in the UK)
– Smith, Dr Thomas
– Wooster, Prof. Martin – (particular expertise in the development of new algorithms to process satellite data)
For additional areas of expertise see sections below.


Enhancing remotely sensed fire data with fire weather indices to reconstruct longer-term historic fire records

Prof. Balzter has developed a new approach of analysing remotely sensed burned area datasets from satellites over periods of around 20 years, and comparing them to local fire danger weather indices to evaluate and calibrate the influence of climate feedbacks on the fire regime. Using this dynamic, fire histories can be estimated over longer periods than that for which remotely sensed data is available, using longer term weather records. He has worked extensively on such an approach in Siberia as part of the EU project SIBERIA-2. He compared two burned area datasets from satellite observations: the L3JRC daily global burned area dataset derived from SPOT-VEGETATION and the FFID burned area dataset from MODIS and compared them to the Russian fire danger index – the Nesterov index (Balzter et al., 2010). The index uses daily temperature and dew point measurements. Regression analysis of the results showed that the number of days in which the index exceeded 4,300 explained about half of the interannual variability in the burned area assessed (R2 = 0.49). Such an approach takes around 3 months to develop. Contact: Prof. Heiko Balzter


Quantification of biomass lost during forest fires (fire severity) via remote sensing

Prof. Wooster is developing remote sensing methods to quantify the emissions and impacts of forest and grassland fires – through the analysis of thermal imagery from aircraft and satellites. He has developed an algorithm which estimates the rate of fuel consumption from the rate of radiative heat output of burning carbon and converts this to estimates of carbon emissions. Vegetation is typically around 50% ±5% carbon and the heat yield from the carbon burned is relatively constant regardless of the type of vegetation burned. The algorithms are usually tested on prescribed fires being conducted for management purposes, where comparison data are available, and then can be applied to unplanned ‘wildfires’ whose impacts we want to assess. Maps of carbon emitted per unit area can be created by dividing the tons of carbon emitted (estimated from the thermal methods) by the area burned. This information could have a particular application in loss adjusting by determining how much material was burned, not just the area burned. Because the thermal sensors are very sensitive to the strong thermal emissions from fires the satellites can detect fires covering only 0.01% of the pixel area. So for example a 1 km resolution sensor can potentially detect the heat output coming from a 100 square meter fire. This is far more sensitive that mapping of burned area, but the fire cannot be seen through cloud cover. Prof. Wooster’s group is now working with a new satellite sensor that provides 10x the sensitivity than this. The quality and temporal range of information available is dependent on current satellites. MODIS burned area data is available back to 1998 at 250-500 m resolution, whereas Landsat data is available back to the 1970s at higher resolution. However, the radiative heat information required has only been available from MODIS since 1998. There is a dataset already processed that provides fuel consumption data at coarse (10 km) resolution. Geostationary satellites provide continuously streamed information every 15 – 60 minutes, and can thus estimate fire intensity and carbon emissions at very high temporal resolution, but they may miss some of the smaller fires due to the coarser resolution (around 3 km). There are currently geostationary satellites over Europe (1), the Americas (2) and Asia (1). A new generation of geostationary satellites is in the process of being launched including a third generation of Meteosat satellites which Prof. Wooster has been involved in and which will provide data over Europe every 2.5 mins at 1km. Contact: Prof. Martin Wooster


Remote sensing of fire risk enhanced with vegetation models

In the EU project CARBOAFRICA, Prof. Balzter studied the African fire regime, using MODIS burned area data at 500 m scale and the dynamic fire model SPITFIRE, coupled to the vegetation model LPJ-GUESS. This work has led to improved greenhouse gas emission estimates from fire by using a combined fire/dynamic vegetation model forced with satellite-derived burned area data (Lehsten et al., 2009). CARBOAFRICA was awarded the Italian WWF (World Wildlife Fund for Nature) prize for scientific research in 2009. He has also investigated fire and vegetation structure in Kruger National Park (Khalefa et al., 2013).  Contact: Prof. Heiko Balzter


Wildfire ensembles modelling – worst case scenarios for investments

Dr. Smith has developed a concept for worst-case scenario modelling for a particular investment. This would involve dropping random ignitions across a project location and modelling the spread of wildfire under different weather conditions, wind directions and so forth to determine expected average and worst-case losses. He is looking for funding and appropriate case studies to perform this modelling. Contact: Dr Thomas Smith.


Analysis of forest carbon losses post fire for pan-tropic regions

Dr Mitchard was involved in a project that mapped carbon stocks in the pan-tropic regions (see Anthropogenic risk page). He has proposed determining the amount of biomass lost post fire by comparing burn scars from the MODIS remote sensor with these carbon stock maps to determine how much carbon was lost. This would be particularly useful for mixed areas of grassland and forest. Contact: Dr Edward Mitchard


Analysis of the size distribution of fires and their impact

Some remote sensors work at a higher resolution than others and therefore only capture the larger fires. Whilst it might be thought that these are the most damaging, in fact Prof. Balzter has found through his work that this does not always hold true. In some areas there are many small fires that collectively damage more area than the fewer larger fires. He found that the size distribution of fires varies in different ecosystems (Lehsten et al., 2014).  Contact: Prof. Heiko Balzter


Fire behaviour modelling and practical application

Dr Smith has adapted fire behaviour models (i.e. models that predict the extent and rate of spread of wildfires post ignition) for use in the UK and Europe. Such models are widely used in the US, Canada, Spain and Australia. He has also helped train fire fighters to use these fire behaviour models along with remotely sensed fire information to enhance fire-fighting. Dr Smith has experience in adapting/customising freely available off-the-shelf fire spread models (e.g. FARSITE, developed in the US; and Prometheus, developed in Canada) and developing the necessary inputs for these models (e.g. fuel maps, digital terrain maps, meteorological data) for use in novel environments (e.g. heather moorlands in the UK, and lowland forest in Denmark). Examples of training models and protocols can be found on the FIREfficient website (link). Contact: Dr Thomas Smith.


Self-propagating mega-fires in the Mediterranean

Prof. Balzter is working with a visiting scientist from Spain on self-propagating mega-fires which are increasing in the Mediterranean area. These are fires which create their own fire weather as they change the way that local winds behave, suck in local oxygen, and create uplift into fire whorls. These fire tornadoes then start moving across the landscape and are a huge risk for fire fighters. They can transport embers and sparks high into the air and create new fire outbreaks half a kilometre from their source. They are more common in California and Australia but are a new phenomenon in the Mediterranean. Urban development close to abandoned agricultural land which is starting to develop woody vegetation are also at risk. This is a new research topic and a paper is in progress but it is not a funded project.  Contact: Prof. Heiko Balzter


Mapping spatial and temporal wildfire risk in the UK using: satellite & IRS data

Julia McMorrow’s expertise is in comparing the usefulness of different datasets for assessing the fire regime in Great Britain i.e. England, Wales and Scotland. She is aiming to determine the likelihood of fires of different sizes at the national and local scale. More recently she has begun work on assessing impact which will include burned area analysis and impacts on infrastructure. She has a particular interest in the new Incident Reporting System (IRS) dataset, which is a standardised record of the incidence of wildfires attended by the Fire and Rescue services in the UK since 2009. She has compared this data to that derived from the satellite-borne MODIS sensor on burned areas and active fires (available from 2002, via EFFIS and German Space Agency (DLR) Fire Service Statistics (McMorrow and Ogbechie, 2011. Link)  (McMorrow, 2013). Julia used 2006 landcover data from the CORINE sensor, which covers Europe at 250 m resolution to screen out non-vegetation fires (e.g. hot chimney plumes). Updated CORINE data for 2012 will be available soon. UK landcover data at 25m resolution is available for 2007 (link) (and will be available soon for 2013) but CORINE was used as the resolution is more compatible with MODIS data. Her analyses show that whilst MODIS information is available for a longer period than IRS, it misses many smaller or less intense fires because it only has a resolution of 1 km and is affected by cloud cover and aerosols. IRS records a single ignition point, whereas MODIS records the fire front at the time of overpass which moves during successive satellite passes, and so it can be difficult to link MODIS data to the IRS ignition point. Comparison of four financial years of IRS wildfires and MODIS data for Scotland showed that only about 1 in 10 fires are detected by MODIS (Critchley and McMorrow, 2015. Link). Contact: Julia McMorrow


Using radar data to detect UK fires

Julia McMorrow and her colleague, Gail Millin-Chalabi use synthetic aperture radar (SAR) to detect burn scars in peat moorland (Millin-Chalabi et al., 2014). Radar data sources include ESA’s Sentinel and Japan’s ALOS satellite’s PALSAR sensor. Radar has the advantage of penetrating cloud, particularly the C-band wavelength. The perimeter of a peat moorland fire can be detected up to 3 months after the fire. The dataset from Sentinel goes back to 2003 and is improving. The work focuses on moorland but could be adapted for forests. Airborne sensors can provide finer resolution and have been used to analyse fires on peatlands e.g. in the Peak District, but not forests. Gail uses SARscape – a tool for radar. Radar data from Sentinel is available via the Sentinel Toolbox. Contact: Julia McMorrow


Forest fire danger ratings/indices and associated risk management systems – especially application in the UK

Prof. Moffat has significant expertise in approaches to managing forest fire risk, the use of Forest Fire Danger Rating Systems, and, in particular, their usefulness and applicability to the UK context. He compared approaches in New Zealand with the UK, as both countries have similar temperate maritime climates and levels of wildfire occurrence. Whilst the UK’s approach is based around contingency planning and response, NZ’s encompasses a broader risk management framework to reduce and prepare for fire, as well as respond and recover from it (Moffat and Pearce, 2013a). His work has included a detailed evaluation of Forest Fire Danger Rating Systems, such as the Canadian and New Zealand approaches (adapted from the former). These approaches link fire indices (such as the Fire Weather Index module of the Canadian system), with fire behaviour and prediction modules (such as New Zealand’s fire behaviour predication module) to support operational activities. He has looked at how such approaches might be developed in the UK using the current Met Office Fire Severity Index (and/or outputs from the Natural Hazards Partnership)  – (Moffat and Pearce, 2013b) (Full report: link). An effective assessment of medium to high fire risk is a precursor to obtaining co-funding from the EU Rural Development Regulation to protect forests from fire and support recovery. The absence of an effective system in the UK prevents such funding being obtained. Contact: Prof. Andy Moffat


Enhanced calculation and communication of UK wildfire severity forecasts

Mark de Jong, working for Prof. Wooster, was seconded to the Met Office as part of the NERC PURE funding to develop better risk estimates for UK wildfires. Under the Crow Act (2000), the Canadian Fire Weather Index (CFWI) is used in the UK to determine the level of fire risk in the UK on a daily basis and landowners must close land that is at extreme risk of fire due to extremely dry conditions. However the thresholds for triggering this closure were uniform across the country, whereas a dry period in northern Britain that might be anomalous might be perfectly normal for southern Britain. The project worked to define local thresholds by determining the 80th, 90th, 95th, 97th and 99th percentiles at 2km resolution for the entire UK for each season. It then compared this to actual fires so that the daily forecasts could be set in local and historical context. 7,000 wildfires were analysed and the new calibrated index was valued at extreme for the majority of these fires whereas the old approach did not capture them. Contact: Prof. Martin Wooster


Wildfire Threat Analysis for the UK

A team from Manchester University consisting of Julia McMorrow, Aleksandra Kazmierczak and Jonathan Aylen, worked on a NERC-funded scoping project to evaluate the potential use of Wildfire Threat Analysis (WTA) for the UK, in conjunction with the Forestry Commission using a forest-urban interface case study of the Swinley Forest Fire 2011. WTA is used in Canada and New Zealand and sees the threat of fire as a combination of: ‘risk of ignition’ (RoI), ‘hazard’ of fire spread, and ‘values at risk’ (VaR) which includes valuations for forest, health, infrastructure etc.. RoI and VaR modules were successfully developed in a GIS system via a structured evaluation process involving 2 workshops with a wide range of stakeholders. A hazard module could not be developed but Dr Smith (KCL) provided multiple runs of a fire behaviour model as an alternative to model where the fire might spread to and how fast. The WTA work is seeking further funding to complete but has outlined the potential for this approach. Further details see the post project report: ‘Wildfire Threat Analysis (WTA): NERC-funded scoping project with Forestry Commission.’ Julia’s primary contribution focused on using IRS data to determine the risk of ignition (RoI) factor. Primary contact: Julia McMorrow


Developing a risk of ignition and wildfire hazard map for Britain using Wildfire Threat Analysis

Julia McMorrow would like to develop a national risk of ignition and wildfire hazard map along with colleagues at KCL as a follow-up to the Wildfire Threat Analysis project (see previous section). Julia would define risk of ignition using IRS data and geographic factors derived from the Swinley project. Hazard information would cover how intense a fire might be and how dangerous, using factors such as fire climate (provided at 2km resolution by Mark de Jong, KCL); slope (taken from a digital elevation model. fires move uphill more quickly than downhill) and fuel (from a landcover map). The final dimension for a full WTA would be the risk of a fire starting and the hazard of it spreading in relation to the value of assets at risk. The value of ecosystem services such as biodiversity is difficult to quantify and values at risk are best understood and evaluated at the local scale so, at present this, would not be done at the national scale. The project would focus instead on using the risk of ignition and hazard to identify those gridcells at most risk, which will pinpoint where more detailed analysis of VaR is needed. Local values at risk could be determined by e.g. mapping houses around proposed forest stands.  Contact: Julia McMorrow


Prediction of future UK fires under climate change scenarios (moorland approach that could be adapted to forests)

Julia McMorrow and colleagues, including Jonathan Aylen from Manchester Business School, have experience of developing future forecasts of the incidence of wildfire in moorlands. Daily weather, day of the week (as a proxy for density of human ignition sources) and fire data, if available for sufficiently long periods (~30 years), can be statistically modelled to give the probability of fire occurring (Albertson et al., 2009). This approach has not yet been done for forests, however, they would be interested in developing similar approaches for forests in the UK if there was a location with a sufficiently long fire record. They have also combined this statistical model for the Peak District National Park with the UKCP09-based weather generator to predict future fire trends based on the relationship between weather, people and historic fires as modelled by Probit regression (Albertson et al., 2010. Link)[2]. It is not yet possible to determine if this might work in other areas as there is insufficient fire data for most other locations. All fires need to be recorded, not just the large ones, and daily weather data is needed. Contact: Julia McMorrow


UK Climate Change Risk Assessment & Fire

Prof Moffat was the lead author of the first Defra Climate Change Risk Assessment for the Forestry sector in the UK and has a broad level of expertise on climate change risks to UK forests. His personal focus is on the risks to forestry and land regeneration from wildfire in the UK. Fire has recently been deprioritised as a risk due to the decline in large scale planting and the focus on threats to more mature trees such as wind and pests and diseases amongst other reasons. However, the last three decades have seen increasing wildfires in the UK and climate change is likely to significantly increase this further (Moffat et al., 2012). Contact: Prof. Andy Moffat

References

ALBERTSON, K., AYLEN, J., CAVAN, G. & MCMORROW, J. 2009. Forecasting the outbreak of moorland wildfires in the English Peak District. Journal of Environmental Management, 90, 2642-2651.
ALBERTSON, K., AYLEN, J., CAVAN, G. & MCMORROW, J. 2010. Climate change and the future occurrence of moorland wildfires in the Peak District of the UK. Climate Research, 45, 105-118.
BALZTER, H., TANSEY, K., KADUK, J., GEORGE, C., GERARD, F., GONZALEZ, M. C., SUKHININ, A. & PONOMAREV, E. 2010. Fire/Climate Interactions in Siberia. In: BALZTER, H. (ed.) Environmental Change in Siberia: Earth Observation, Field Studies and Modelling. Dordrecht: Springer.
FAO 2010. Global Forest Resources Assessment 2010. FAO Forestry Paper 163. Rome: Food and Agriculture Organization of the United Nations.
CRITCHLEY, T., MCMORROW, J. (2015) A Comparison of Fire Service IRS and MODIS-Detected Vegetation Fires in Scotland. Poster presented at Wildfires 2015, Cambuslang, Glasgow, 10-11 Nov. (Link)
KHALEFA, E., SMIT, I. P. J., NICKLESS, A., ARCHIBALD, S., COMBER, A. & BALZTER, H. 2013. Retrieval of Savanna Vegetation Canopy Height from ICESat-GLAS Spaceborne LiDAR With Terrain Correction. Ieee Geoscience and Remote Sensing Letters, 10, 1439-1443.
LEHSTEN, V., TANSEY, K., BALZTER, H., THONICKE, K., SPESSA, A., WEBER, U., SMITH, B. & ARNETH, A. 2009. Estimating carbon emissions from African wildfires. Biogeosciences, 6, 349-360.
LEHSTEN, V., DE GROOT, W. J., FLANNIGAN, M., GEORGE, C., HARMAND, P. & BALZTER, H. 2014. Wildfires in boreal ecoregions: Evaluating the power law assumption and intra-annual and interannual variations. Journal of Geophysical Research-Biogeosciences, 119, 14-23.
MCMORROW, J. 2013. MODIS-detected fire regime in Great Britain: potential and challenges of validating against national fire incident data. In: Tansey, Kevin. Quantifying the Environmental Impact of Forest Fires: EARSeL Forest Fire Special Interest Group workshop; 15 Oct 2013-17 Oct 2013; Coombe Abbey, Warwickshire. Leicester: University of Leicester; 2013. p. 136-140. Link
MCMORROW, J. & OGBECHIE, O. 2011. MODIS-detected fire regime for Great Britain; 2007-2011. In Wildfire 2011; 14 Oct 2013-15 Oct 2013; Buxton, Derbyshire. Ripon: Rural Development Initiatives (RDI).
MILLIN-CHALABI, G., MCMORROW, J. & AGNEW, C. 2014. Detecting a moorland wildfire scar in the Peak District, UK, using synthetic aperture radar from ERS-2 and Envisat ASAR. International Journal of Remote Sensing, 35, 54-69.
MOFFAT, A. J., MORISON, J. I. L., NICOLL, B. & BAIN, V. 2012. Climate Change Risk Assessment for the Forestry Sector. UK 2012 Climate Change Risk Assessment. Crown Copyright.
MOFFAT, A. J. & PEARCE, H. G. 2013a. Contrasting approaches to forest fire risk in New Zealand and Great Britain. Scottish Forestry, 67 (4), 10-17.
MOFFAT, A. J. & PEARCE, H. G. 2013b. Harmonising approaches to evaluation of forest fire risk. A report by Forest Resarch & Scion, supported by Tranzfor.
SCHELHAAS, M. J., NABUURS, G. J. & SCHUCK, A. 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology, 9, 1620-1633.