Tag Archives: environment

A step by step guide to making maps of vegetation carbon stocks

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A follow on from the 5th Building Carbon Bridges across Africa workshop 

I’ve just come back from leading a training workshop in Accra, Ghana, as part of the Building Carbon Bridges across Africa project. For me this was a very useful and informative workshop, and I hope very much that the 12 participants from African government ministries and forestry departments learned some useful new skills. I’m writing this blog post to share some of the training I conducted in that workshop more widely. 

Workshopimage

I want to start by explaining a bit about the Building Carbon Bridges initiative. Building Carbon Bridges is one of an increasing number of South-South collaborations for REDD+, funded by western organisations. Ghana has recently developed a high-resolution carbon map, and has considerable expertise on other aspects of the Monitoring, Reporting and Validation (MRV) process, creating registries, and integrating sub-national projects (“nesting”). This project was set up to allow Ghana to share its knowledge with some East African countries (Ethiopia, Kenya, Uganda & Tanzania) and Nigeria. Building Carbon Bridges was funded by the Clinton Climate Initiative and run by Ghana’s Nature Conservation Research Centre (NCRC) in partnership with the Common Market for East and Southern Africa (COMESA).

This workshop, the 5th and final in the series, was run by NCRC and Ghana’s Forestry Commission and held in the excellent remote sensing training facility in the Center for Remote Sensing and Geographic Information Systems in the University of Ghana. I did the majority of the teaching at the workshop, but sessions were also led by Rebecca Asare and Winston Asante of NCRC.

Below I summarise some of what we discussed over the 3-day workshop – in particular I will go over links to some existing datasets, discuss how they can be used, and introduce the concepts of how you can develop your own carbon maps.

Why carbon maps?

So, why would you need a map of vegetation carbon? I can think of several reasons, that apply whether you are responsible for a small forest reserve, a forest concession, a community forest, a province or the forest/environmental resources of a whole country:

  1. Mapping carbon over your area of interest gives you an estimate of the total carbon locked up in the vegetation. This carbon has considerably value to the world: if it is released to the atmosphere it will contribute to climate change. Only by measuring this asset can it be valued. Forests have much more to offer the world than just their carbon stores (ecosystem services including flood protection, rain generation, evaporative cooling, and their store of biodiversity), but their carbon can be easily measured and doing so provides a part of the case for their preservation.
  2. Carbon maps show the areas of high and low biomass within a region. This allows efforts at forest protection to be targeted to the higher carbon areas, or potential can provide a warning sign of areas subject to degradation.
  3. Repeat carbon maps (for example annually, or every five years) allow calculations of net changes in carbon stocks. These net change numbers directly relate to payments under REDD+ or carbon sequestration schemes, and also can be used to set up historical baselines for carbon stock changes.

Why remote sensing?

The only way to accurately estimate the aboveground biomass of a forest is to set up field inventory plots, measuring the diameter and identifying the species of every stem (usually above a minimum size of 10 cm, as small trees do not contribute much to biomass), and measure the height of a subset. Standard methodologies exist for this, for example as described in the RAINFOR Plot Establishment and Remeasurement Manual.

Lidar dataHowever, most field plots only cover 1 hectare (or often less), and are very time intensive and expensive to set up. Remote sensing allows the whole landscape to be sampled equally, regularly, and with little or no cost to the user. Only remote sensing can provide the continuous and regular view of a landscape essential for carbon stock monitoring. But remote sensing instruments do not directly measure biomass – they only provide indirect estimates. So field data remains essential – the guidance for forest carbon projects is clearly that remote sensing and field data should be combined (for example as stated in the excellent GOFC-GOLD sourcebook, the authority on estimating and reporting carbon stocks.).


Definitions and units

Before we start, we should clear up some definitions:

  • Aboveground Biomass (AGB): this is the oven-dry mass of all aboveground living plant material in a plot. Often in the literature only stems > 10 cm diameter (DBH) are included in calculations of AGB – this is fine in most forest ecosystems as most biomass is held in the large trees, but before reporting results a correction factor must be made to account for seedlings, shrubs and small trees. Units are normally in tonnes per hectare (Mg ha-1)
  • Carbon stocks: carbon makes up approximately 50 % of plant biomass, and so biomass can be converted to carbon by multiplying by 0.5. Stocks over a landscape are normally expressed in units of carbon, not biomass, as this makes more sense when collating other carbon pools not covered here (e.g. soil carbon). Normal units are MgC, TgC or PgC, depending on the size of area being measured. 
  • tCO2e: this is the number of tonnes of carbon dioxide that would have the same global warming potential as the material being discussed. In our case this is easy to calculate: we simply multiply the number of tonnes (Mg) of carbon by 3.667. tCO2e are useful when comparing different types of carbon project, as the prevention of emission of other greenhouse gases (e.g. methane) can be converted to their CO2 equivalent.

What existing biomass maps are available?

There are two relatively high resolution maps of carbon stocks covering the tropics that can be freely downloaded by projects and used. A brief summary of how they are derived is summarised in a website I wrote here.

The first was developed by Sassan Saatchi and his collaborators, and was published in 2011 in the journal Proceedings of the National Academy of Sciences. A link to the original article can be found here. I should declare an interest at this point – I assisted in developing this map during my PhD, and am an author on the paper.

The second was developed by Alessandro Baccini and was published in the journal Nature Climate Change in 2012. A link to the original article can be found here.

I have developed a simple web tool that allows you to visualise, compare and query the maps. I have blogged about this tool before. Alternatively, the authors have made the raw data for both their maps available online to any user. The Saatchi et al. map is available here, the Baccini et al. map here.

Carbon Map Comparison website

Some country-level maps also exist, for example Ghana has produced its own 100 m resolution map, but these are unfortunately rarely freely available online.

Simple map comparisons

These different carbon maps can be directly compared using the web tool described above.

However, to do more detailed comparisons and create maps, you will need to use a remote sensing or GIS software package. There are a number of open source GIS systems that are powerful and easy to use, including (in no particular order) GRASS, SAGA, and QGIS. There are also commercial packages: ArcGIS is the most widely used GIS software package, and due to its ubiquity this was used for the workshop. Most suited to developing carbon maps are true remote sensing packages, that can easily open, manipulate and analyse satellite datasets. I use IDL-ENVI for my research, and another packages with similar capabilities are ERDAS IMAGINE and IDRISI.

Any of the above packages should enable you to open the carbon maps described above and:

  • Create output maps over an area of interest using the same stretch.
  • Extract the mean and total biomass over your area of interest, or a subset
  • Create maps of the differences between maps (using the Raster Calculator or similar function to subtract one map from the other)
  • Use a landcover map (see below) to discover the mean biomass of different landcover types, and from that use a spreadsheet to estimate the likely carbon losses (or possibly gains) that would result from landcover change. For example, if your ‘broadleaved forest’ class cover 1000 ha and had an aboveground biomass of 300 Mg ha-1, and ‘farmland’ had an AGB of 0 Mg ha-1, if you lost 10 % of forest (i.e. 100 ha) you could calculate that you would lose 300 x 100 = 30,000 tonnes of biomass, or 15,000 tonnes of carbon, or and 54,900 tCO2e (confused about the units? See the definitions sections above).
Comparison of two carbon maps over Uganda from BCB5 training workshop. Note the similarities (placements of high biomass areas) and differences (some SW forests are very different).

Comparison of two carbon maps over Uganda from BCB5 training workshop. Note the similarities (the location of high biomass areas are similar) and differences (e.g. more high biomass forest in the SW in Baccini et al. map).

Creation of maps from landcover classifications

If you would like to build your own carbon maps, rather than rely on published datasets, one of the simplest way is to use a landcover classification, and assign carbon values to different landcover classes. You may already have a landcover map for your region/country, or you can download an existing one. Two that are widely used are:

  1. the Global Landcover 2000 (GLC2000) dataset, which was a 1 km resolution dataset developed for the year 2000. The data can be downloaded here.
  2. the European Space Agency (ESA) GlobCover product, which is 300 m resolution and is available for the years 2005 and 2009. The datasets can be downloaded here.

In order to create a biomass map from a landcover map you need estimates of the biomass of each class. These are best obtained by locating a large number of field plots within each class; if that is not possible then a first guess can be made by matching the landcover types to values in standard tables, for example in forest inventory or FAO Forest Resource Assessment (FRA) reports for your country, the values in Annexe 3 of the IPCC’s Good Practice Guidance on Land Use, Land Use Change and Forestry.

Then a biomass map can be created using your GIS package, by assigning an AGB value to each class using a Reclass Table (or similar depending on your software).

Here is an example map produced at the workshop:

Botswana biomass map from GLC2000 landcover map and IPCC values

Botswana biomass map from GLC2000 landcover map and IPCC values

As you can see maps produced using this method are blocky, having a ‘painting by numbers’ look. This is because rather than each pixel being given a unique value, each class has the same value. Such maps have their uses, but clearly do not represent reality as do maps with different values for each pixel, which better reproduce the heterogeneity of ecosystems.

Creation of maps from active remote sensing data

By and large, optical datasets (e.g. Landsat, MODIS, SPOT) cannot be used for directly mapping biomass. They can be used for mapping landcover and landcover change, which is useful, but often have little sensitivity to biomass. This is a shame, as the vast majority of remote sensing data is optical.

However, fortunately, there also exists active remote sensing datasets, LiDAR and radar, which can map biomass at a pixel level (not having to go via landcover). Radar and LiDAR sensors have the capacity to produce accurate maps of biomass and biomass change, as shown in for example this paper of mine from Cameroon (radar), this from Gabon (lidar and radar fusion), or this paper by Greg Asner over Amazonian Peru (lidar and optical fusion).

In the workshop, we downloaded radar data over Africa from the ALOS PALSAR satellite, which is available freely form the Kyoto and Carbon Initiative website at a 50 m resolution for 2008 and 2009. This data is also available for SE Asia for 2008, 2009 and 2010; however unfortunately no free South America data is available currently. We then applied an equation from one of my papers (this one) relating radar backscatter to biomass, and from this were able to make biomass maps of the lower biomass regions and countries.

Backscatter:biomass equation from Mitchard et al. (2009):
EXP [(-2.73 + sqrt(7.45-(0.623(22+”Sigma0″))))/-0.311]

Unfortunately the ALOS PALSAR sensor is only sensitive to biomass up to about 150 Mg ha-1, so we were unable to map biomass over high biomass regions using this sensor. We were however able to use point estimates from the ICESat GLAS LiDAR sensor to estimate forest height, and hence biomass, in high biomass areas. ICESat GLAS data is freely available to download, but data was only collected from 2003-2009.

Biomass map over Mbam Djerem National Park in Cameroon. Derived from ALOS PALSAR data from 2007 and local field plot calibration. Details here.

Biomass map over Mbam Djerem National Park in Cameroon. Derived from ALOS PALSAR data from 2007 and local field plot calibration. Details here.

Creation of maps from nonlinear models

When mapping biomass over very large areas, wall-to-wall coverage using active remote sensing data is not currently possible. Therefore combinations of field data, LiDAR/radar data, and optical data are normally used, combined using complex nonlinear models. This is the method followed by the two pantropical biomass maps described above.

There is not space here to describe the precisely methodologies used in detail – this is a complex process involving specialist software. See the Baccini et al. and Saatchi et al. papers for descriptions of how they did it – maybe I will expand further giving a step-by-step guide in a further blog post if there is interest.

Wrap up

I presented a summary of first steps for using aboveground biomass maps that already exist, and then mapping aboveground biomass in your study site. Obviously this is a very big topic, but I hope the above has provided help on the first steps, and links to useful resources. Please get in touch if this has been useful. Or if you have any questions or comments, please send me an email, or better still comment below so everyone can benefit.

What next for forest conservation after disappointing Doha?

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President of the Conference of the Parties (COP 18), Abdullah bin Hamad Al-Attiyah address the UN Climate Change Conference in Doha, Qatar. Photo: UNFCCC

President of the Conference of the Parties (COP 18), Abdullah bin Hamad Al-Attiyah address the UN Climate Change Conference in Doha, Qatar. Photo: UNFCCC

Now the dust has settled the consensus appears to be that COP 18, held in Doha, was  yet another disappointment in the stalled UNFCCC process. Yes, the Kyoto Protocol was extended to 2020: but with only Europe and Australia are now parties to it, between them responsible for just 12 % of global greenhouse gas emissions, this is hardly grounds for optimism. Countries made next to no progress in closing the ‘Emissions Gap’ identified in a recent UNEP report, with current policies likely to take the world far beyond the 2 degree agreed maximum warming target of the UNFCCC. Furthermore next to no progress was made in talks on REDD+ (Reducing Emissions from Deforestation and forest Degradation): no agreement was made on the two most important areas – how it would be financed, and how it would be monitored.

This is disappointing. The ‘Durban Platform’, agreed at the COP17 conference in Durban in 2011, envisaged an all-encompassing agreement to limit global emissions, including for the first time developing as well as developed economies. It was to be signed by 2015, and to take effect in 2020. However, agreement on an effective comprehensive deal is now looking less and less likely, with developed and developing nations seemingly further apart after Doha than before. In 2012 atmospheric carbon dioxide concentrations reached a peak of 396 ppm (41 % higher than pre-industrial concentrations), while summer Arctic sea ice reached its smallest recorded extent ever,  the US had a record-breaking wildfire season triggered by record-breaking droughts, Hurricane Sandy wrecked havoc in the Caribbean and US East Coasts, and floods caused tragedies in places as diverse as the Philippines, North Korea, the UK, India, Peru and Nigeria. While floods and hurricanes cannot be directly attributed to the rise in carbon dioxide concentration, it is clear that recent global warming, strongest at the poles, led directly led to the reduction in Arctic sea ice. Hurricane Sandy was in all likelihood made more powerful due to exceptionally high sea surface temperatures and more devastating due to rising sea levels, and floods have always been predicted to become more common in a high CO2 world. Yet politicians failed to act, instead concentrating more on apportioning blame for past emissions. Indeed, as the case for action on climate change becomes stronger, political will to do something appears to be becoming weaker.

The carbon dioxide concentration in the atmosphere continued its inexorable rise in 2012

The inexorable rise in atmospheric carbon dioxide concentration continued in 2012.

But what does all this mean for the protection of tropical forests? REDD+ was meant to become a key part of a comprehensive climate change deal, and receive its funding from that process. If the Durban platform leads to nothing, does REDD+ die too, and with it the last hope for the world’s tropical forests?

I sincerely hope not. There seems to be a consensus forming that perhaps forest preservation is best done outside the main UN system. This is already happening at two different scales: at a local scale there are approximately 200 voluntary-sector REDD+ projects either currently functioning or in an advanced state of development; and at a national scale a number of programs exist to support whole countries in monitoring and managing their forests (e.g. UN-REDD and the FPCC), and a number of bilateral agreements exist, most famously the $1 billion Norway-Indonesia REDD+ Partnership. As small-scale projects grow and merge, and bilateral and regional deals become more numerous, it is possible that an international system to fund the protection of tropical forests could come to exist even without any overarching international framework. It will undoubtedly be more complicated, messier, and harder to monitor than a UNFCCC deal would have been, but that does not necessarily mean it will be less effective.

I’ll end this blog post on that positive note. However, a decentralised REDD+ scheme is not without pitfalls, and I’ll write about these (and potential solutions) at a later date.

Are Cameroon’s forests doomed?

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A new map shows mining permits overlap many protected areas in Cameroon

The Interactive Forestry Atlas of Cameroon Version 3.0 has just been released by the World Resources Institute (WRI), giving spatial details on all the management units of Cameroon’s forests for 2011. The data have been released in the form of a report, an excellent interactive map, and the raw GIS files1. While the report does not give data on rates of deforestation per se, it does show the location and status of forest and mining concessions, as well as the various categories of protected and non protected other forests.

I downloaded the data yesterday, and expected to spend this morning overlaying the active logging concessions on satellite imagery, to test my ability to detect forest degradation. However, opening the full dataset, I was immediately horrified by a different spatial layer. It appears that mining permits have now been issued for almost 10 million hectares of Cameroon, about a fifth of the country’s total area. 85 % of these overlap with forested areas, and 40 % of these concessions overlap forest designated by the government as ‘Permanent Forest Estate’, that is land that is meant to remain forest in perpetuity. Incredibly, 20 % overlap some form of protected area, and nine concessions overlap with National Parks.

Figure produced by Ed Mitchard using data from Data from the WRI Interactive Forest Atlas of Cameroon 3.0.

Mining permits are not all the same: most of these are exploratory, and do not allow for widespread extraction. However, for the government to issue them at all there must be an assumption in the Ministry of Mines that these exploratory permits could be converted to extractive permits if the companies involved find significant deposits. This surely should not be allowed within the official Permanent Forest Estate, let alone in national parks. It seems likely that officials high up in Cameroon’s government have given authority for the supposedly concrete forest protection areas to be changed if there are economically important mineral deposits underneath.

For me, the most breathtaking overlap is the two permits that cover 20 % of the Dja Biosphere Reserve in southern Cameroon. The Dja Reserve  is a UNESCO World Heritage Site and is one of the most important national parks in central Africa. It contains over 600,000 hectares of near-undisturbed rainforest, an area twice the size of Yosemite National Park, and has a unique assemblage of species. UNESCO has recently published a damming report stating that the Dja Reserve is critically threatened by a combination of mining, commercial agriculture, flooding from a planned hydropower dam, bushmeat hunting and illegal logging. I am part of an effort to set up a Reducing Emissions from Deforestation and Forest Degradation (REDD+) project in the area, funded by voluntary sector carbon credits, but such a project has little chance of success if Cameroon does not obey its own laws.

It is difficult to know what to suggest as a solution. I believe that Cameroon has the perfect right to develop, and to develop fast. It is a very poor country with significant unexploited mineral and forest resources, and the reasoning behind this push into mining and commercial agriculture is to build up its economy. This rush towards industrial development is a key pillar in Cameroon’s Vision 2035, a 2009 document which tracks a path to Cameroon becoming a major emerging economy by 2035. However there is a concern, which I share, that the costs of this industrialisation will be borne mostly by the poor, who will be displaced from their land, have their drinking water polluted, and lose their livelihoods. Meanwhile, the benefits will be felt mostly by those who are already rich. Equally I am concerned by the rapid destruction of the country’s natural heritage, with no national or international debate. Open cast mines elsewhere have caused significant environmental damage, removing all vegetation, causing massive erosion, and permanently scaring the landscape.

Open Cast mine in the Philippines.
Picture (c) Storm Crypt, reproduced here under a Creative Commons license.

Cameroon shouldn’t need to grow at the expense of its natural environment. As a country I believe it has so much potential – it has plentiful natural resources, incredible wildlife and a wealth of cultures (over 230 languages are spoken). The wildlife and cultures remain largely intact, and it’s possible to extract the other natural resources without destroying them. Cameroon’s process for allocating forestry concessions is much more transparent than it once was, and Cameroon is now actively involved in reducing illegal logging through the EU’s FLEGT program. If that were to continue and expand, Cameroon could earn a significant income from the sustainable logging of its precious hardwoods while maintaining its protected area network, safeguarding biodiversity and earning further income through ecotourism. Agroforestry programs, involving for example the sustainable growth of cacao to feed the world’s ever-increasing demand for chocolate, could be expanded. Local people, including traditional forest dwellers, could be involved in decisions of whether to explore for minerals or oil, how to extract them, and could share the proceeds.

The external world can help by monitoring what is happening (hence the great utility of this WRI effort) and applying pressure, as well as providing sensible solutions that work for the poor as well as the rich (in terms of both people and countries , for example a well-designed international REDD+ agreement.

1The GIS files that make this map can all be downloaded here, for 2004-2011. WRI and Cameroon’s Ministry of Forestry and Wildlife (MINFOF) should be congratulated for providing these: there is a huge difference between making a report visible online and releasing the underlying data. Having been involved in many conservation projects in African countries I know how difficult and frustrating it can be to obtain baseline spatial data – I have for example before had to spend days painstakingly hand-digitising pdf maps. I am sure the shapefiles will be widely used.

R.I.P. Envisat

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Are space agencies investing enough in Earth Observation?

ENVISAT photographed in orbit by Pleidas, 15th April 2012

Envisat, the Eurpean Unions’ flagship earth observation satellite, stopped communicating  with ground stations on 8th April 2012. Though it is still in orbit, and appears intact (as this incredible image from Pleidas – a normally earth-facing satellite that passed within 100 km of ENVISAT on the 15th April –  showed), the European Space Agency has now given up attempts to make it respond to ground commands. This triggered the Economist to write a Leader bemoaning the lack of investment in Earth Observation satellites for environmental monitoring by the EU and the USA, the two dominant traditional players. Envisat has provided over 10 years of consistent data on many aspects of the earth system, its 10 instruments providing data from areas as diverse as sea-ice extent, atmospheric composition, landcover change and ocean temperature. All these appear to be changing rapidly (indeed more rapidly than the most pessimistic scientists used to predict), as they represent different aspects of global climate change: whatever an individual’s views are on the cause of these changes, it is hard to argue against the importance of collecting consistent global data to study the trends.

The worrying thing is that even though Envisat was 5 years beyond its 5-year design life, there is no replacement in orbit. Many of its functions are covered to a certain extent by sensors on other satellites. But some functions are not duplicated at all, and none collect data directly comparable to the sensors on Envisat. This will at best result in long-term environmental datasets having added uncertainties due to issues of cross-calibration and changing metrics, and at worse result in large data-gaps.

Image copyright ESA & ASTRIUM

The EU had aimed to secure Envisat’s legacy through the Sentinal satellite series, a series of operational satellites planned through  Global Monitoring for Environmental Security (GMES) program. However the first of these will not be launched until 2013; and they may be further delayed due to a lack of commitment from the EU to pay their considerable operating costs. I don’t want to criticise this program too much though: provided the funding can be guaranteed, the GMES Sentinel series mark a big step forward in this area, envisaging new satellites being launched every 3-4 years to ensure that these essential climate variables are monitored consistently into the 2030’s and beyond. This is in sharp contrast to the USA, which has been consistently reducing its (still considerable) civilian earth observation budgets over the last few years. Indeed, Professor Dennis Hartmann of the University of Washington, who led a report on the subject, stated in May 2012 that:

It’s likely our capabilities will decline fairly precipitously at just the time they’re most needed. If nothing is changed, we’re predicting to be down to 25 percent of our current capabilities by 2020.

There must be a hope among policy makers that private companies and developing countries will help make up the gap. This is likely to work to a certain extent: for example sub-meter resolution imagery is now widely available due to a series of satellites produced by GeoEye and DigitalGlobe.

Image of Dakar, Senegal, captured by the WorldView-2 satellite. Sample image provided by DigitalGlobe.

Other commercial companies, often in collaboration with universities and research institutions, can also now produce satellites at a very low cost, leading to for example the Disaster Monitoring Constellation and Cubesat initiatives (more about these in a later post). Finally emerging economies are indeed putting many satellites into orbit, most notably China, India and Brazil. But these satellites will only be put up there for reasons of commercial returns, research interests and national priorities respectively: for example China has an is launching a large number of satellites to monitor the disputed seas around its coast, and Brazil runs the CBERS satellites (developed in partnership with China) to monitor its forests. What these will not do is provide the long-term consistent measurements of the earth system that are needed to monitor our changing planet.

Envisat could continue orbiting the planet for 150 years before re-entering the atmosphere, provided it does not hit one of many pieces of space junk identified to pass nearby and spread its 8 tonnes of components across its orbit. But the question is whether governments will wake up to their responsibility to continue its legacy and measure the basic variables consistently throughout that period.

I leave you with one single striking image from one of the 10 sensors on ENVISAT.

This shows the carbon monoxide concentration globally over a single year from the SCIAMACHY sensor. The sources of carbon monoxide from African fires and SE Asian industry are especially obvious; but no directly comparable data will be collected this year nor in the near future.