The tyranny of choice : comparing land cover maps for Uganda

Last week, i posted about land cover for Burkina Faso, and i came across this post that remained as a draft on my blog for half a year at least. Time to get it out there! Joanne Morris, a colleague of mine from SEI York did quite some work to compare land cover maps for Uganda. I thought this comparison might be interesting to others and asked her to write it up into this blog! So here it is!

Joanne Morris, from SEI York and the author of this post

I am privileged to have been invited to post on Catherine's blog, which I particularly enjoy reading for frank insights that are much easier to absorb and relate to than many of the dry research reports I have read elsewhere. A bit about myself - I am Joanne Morris, working for the Stockholm Environment Institute at the University of York, UK. I am broadly interested in natural resource use and management in semi-arid and arid regions, for balancing the demands for food, energy, livelihood and above all looking after the natural resource base so that we can continue providing for those demands into the future. I am especially interested in the use of biomass for agriculture, livestock and energy (e.g. as mulch, manure, compost, feed, fuel for burning, or biogas) - what is the most effective use of biomass to serve all three? And which form of biomass is best to use?

Anyway, back to the actual question at hand: during the year, we had to choose a landuse map to use as the base for the calculations we are doing for the Uganda pig value chain in Hoima. Perhaps you and others will be interested in the comparison of current landcover products out there that led to our final choice to use the SERVIR-Uganda 2000 Scheme II map.

From Google, a USGS list of Land Cover Data Links for Africa and a good review paper of global land cover maps (Gong et al 2013), I gathered the following list of possible candidates with some of their key features. We were interested in the resolution first and foremost - within even 1km in Uganda we saw very different landcovers, with fields mixed in with natural forest, unlike in Europe or the US where you can have fields of wheat of maize stretching for thousands of hectares. Therefore, ideally we needed a product with a higher resolution than 1km. Second was the date of the data - landuse changes quickly in Africa - as we found on our field trip, where large swathes of commercial forest have been planted in the last 10-15 years, so ideally something more recent that the last best global landcover layer which was the Global Land Cover 2000 layer from the JRC. Finally we were interested in how many landcover types were differentiated for Hoima - could we get cropland, bushland and wetland from it, as well as forest?

Landcover map	Resolution and satellite source	Year and projection of data	Coverage – Available for Uganda?	Landcover classes in Hoima
SERVIR – GHG Landcover Maps for Eastern and Southern Africa	0.000271dd (~30m) Landsat 30m – supervised classification using maximum likelihood algorithm on LandSat thematic mapper (LandSat 5, 7 and 8 – Uganda)	2000 & 2014 (Uganda) 2000, 2005, 2010 – other countries   WGS 1984	Uganda Land Cover 2014 Scheme I and Scheme II same for 9 (ESA) countries namely: Ethiopia, Botswana, Lesotho, Malawi, Namibia, Rwanda, Tanzania, Uganda, and Zambia.	Scheme II – 14 classes Scheme I – the 6 IPCC classes
FROM-GLC - (Finer Resolution Observation and Monitoring of Global Land Cover)	30m Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+)	mainly 2009-2010, filled with 2007, few 1998	Global	Level 1 –11 classes Level 2 –25 classes to combine with L1 predominantly forest, doesn’t differentiate in Hoima well enough
GLCN Africover (FAO)	na (shapefile) LANDSAT TM images (Bands 4,3,2)	mainly 2000 (Uganda) - shapefile, not raster WGS 1984	Africover Eastern Africa module, 10 countries: Burundi, Democratic Republic of Congo, Egypt, Eritrea, Kenya, Rwanda, Somalia, Sudan, Tanzania and Uganda	LCCS classification, 8 major landcover classes, can download all or selections e.g. agriculture
GLCN Globcover-SHARE (FAO)	30 arc second/ 0.00833dd (~1km) combination – see comments	varies by country – 1990 – 2012 WGS 1984	Global	Thematic layers: 10 LCCS classification
GLCN Globcover by country (ESA) - Uganda	~300m/ 0.00278dd, ENVISAT, 300m MERIS sensor	2005 and 2009	Africa	46 LCCS classes Cropland by lake
GLCF MODIS (NASA/ USGS)	raw tiles: 0.0041667dd (~500m) OR 5’ (~10km) or 0.5dd (~60km) – aggregated	2001 – 2012 (every year)	global – by tile: Uganda PN3536	16 classes IGBP classification
GLC 2000 (JRC)	0.00893dd (~1km) SPOT 4, VEGETATION instrument	2000 WGS 1984	World	22 categories - LCCS classification no wetland, cropland mainly by lake
IFPRI World Land use – Land cover	30 arc seconds, 0.00833 (~1km) based on a reinterpretation of the Global Land Cover Characteristics Database (GLCC ver. 2.0), EROS Data Centre (EDC, 2000)	2000  GCS_Clarke_1866	World	17 classes IGBP classification agriculture focus no wetland shown
IGBP-DISCOVER	1km, resampled to 0.25, 0.5 and 1.0 degree grids AVHRR	1992-1993  geographic	Global	17 classes, IGBP classification
University of Maryland (UMD)	1 degree, 8 kilometer and 1 kilometer pixel resolutions AVHRR	1981-1994	Global	14 classes
CORINE-land cover	Scale of 1:100 000	1990 and 2000	Europe	44 classes
AVHRR – Global Land Cover Characterisation	AVHRR – 1km	1992 – 1993	Global	7 classes
GOFC/ GOLD Land Cover Project office	coordinating agency

    *dd = decimal degrees; GLCF – Global Landcover Facility; GLCN – Global Landcover Network; JRC – Joint Research Centre; AVHRR - Advanced Very High Resolution Radiometer; FAO – Food and Agricultural Organisation; ESA – European Space Agency; NASA - National Aeronautics and Space Administration; USGS – US Geological Survey; IFPRI – International Food Policy Research Institute; IGBP – International Geosphere-Biosphere Programme; LCCS - ; GOFC-GOLD – Global Observations of Forest and Land Cover Dynamics

Here are examples of how a selection of the most recent layers look for Hoima. SERVIR came out quite clearly as the most useful because of its high resolution (30m) and the landcover classes resonated with what we saw on a round route drive through the district - that much of the area around Hoima town is mixed smallscale crop and bush/ forest land, while towards the south it gets drier and more bushland. Along the lake shore was very dry with no fields. The only landcover missing is the large commercial pine and eucalyptus forests that we saw on our drive. It would be ideal if it was Africa wide so that we could very easily transfer our calculations to another country without worrying about the change in landcover maps, perhaps by the time we move away from the ESA (East and Southern Africa) countries, SERVIR will have expanded :)

SERVIR - Scheme 2, 2000

The SERVIR Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF) sector for each country, and were developed with country representatives, informed by country specific interest, definitions, descriptions, mapping goals and policy statements and documents with guidance from IPCC Good Practice guidelines.
SERVIR Scheme I - 6 IPCC classes:  Forestland, Grassland, Wetland, Cropland, Settlement and Other land; Scheme II - 14 classes


SERVIR - Scheme 2, 2014
 

GlobCover - 2009, Uganda

GlobCover is an ESA initiative which began in 2005 in partnership with JRC, EEA, FAO, UNEP, GOFC-GOLD and IGBP, and incorporates input from the global community. The aim of the project was to develop a service capable of delivering global composites and land cover maps using as input observations from the 300m MERIS sensor on board the ENVISAT satellite mission. ESA makes available the land cover maps, which cover 2 periods: December 2004 - June 2006 and January - December 2009.
We decided not to use it because it is not as detailed within Hoima as SERVIR.


GlobCover SHARE
 The GlobCover SHARE dataset was created by FAO-Land and Water Division, in partnership with various institutions, and makes use of several sources (GLC-Share report ):

• Global Landcover Datasets: Globcover 2009 (MERIS 300m), MODIS VCF (Vegetation Continuous Fields), CROPLAND Hybrid database (mixed resolution), GLC2000 (SPOT Vegetation 1km), Mangroves (Landsat 30m, FAO Global Database of Mangroves)
• By country – variety of satellite sources, including: Landsat 30m, MODIS 250m, FAO LCCS, SPOT 10m, AirPhotos 1m, Ikonos 4m – majority is Landsat 30m 

11 classes: 1: Artificial Surfaces; 2: Cropland; 3: Grassland; 4: Tree covered Area; 5: Shrubs Covered Area; 6: Herbaceous vegetation; 7: Mangroves; 8: Sparse vegetation; 9: BareSoil; 10: Snow and glaciers; 11: Water bodies
 We decided not to use it because of the coarse resolution (1km).
 

GLCN - Africover
Cultivated-agriculture layer highlighted above the general landcover layer .
The Africover map is highly detailed, however, the legend is quite confusing to decipher, and therefore SERVIR was chosen for ease of handling.


IFPRI 
We chose not to use this map as the data is a bit old (2000) and the resolution too coarse (1km). 


MODIS
Although very good for time-series, because it is available for every year at quite fine resolution (500m), is it still not as detailed as SERVIR, so we decided not to use it. 


GLC-2000

GLC 2000 makes use of the VEGA 2000 dataset: a dataset of 14 months of pre-processed daily global data acquired by the VEGETATION instrument on board the SPOT 4 satellite, made available through a sponsorship from members of the VEGETATION programme, including JRC.
As the data is from 2000, it is too old for our purposes. 


FROM-GLC
Although great for a forest/bushland study, we are more interested in the cropland and wetland landcovers, so we decided not to use this map.


I was pleasantly surprised and impressed at the range of data out there - openly accessible - with varying characteristics to suit different purposes. At national level there will no doubt be many more! The GlobCover SHARE project will be one to watch - the 2014 beta is out, although I did not yet find the data. And here's to hoping SERVIR continue expanding their maps to more countries! Merry Christmas all.
 
On a side note, I would have done a follow-up comparison of precipitation maps out there, but NCAR got there first, with a comprehensive set of pages about each of the main datasets available here.

I would like to thank Joanne for having shared her comparison here. The project we have been working on settled to work with the SEVIR map 2000 scheme II. The decision was based on a trade-off between high resolution (30m for SEVIR) and accuracy tested with our validation point from the field.

High resolution land cover maps for West Africa

Finding high resolution land cover maps for East Africa is relatively easy, as there a quite some numbers of product freely accessible online. However, for me West Africa remained quite a challenge. But only until today!



I came across a new set of high resolution land cover maps for selected West African countries done by OSS (Observatoire du Sahara et du Sahel). What a wonderful product!

They have created fantastic atlases for Senegal, Mauritania, Niger, Burkina Faso Chad and Mali. The one for Nigeria is on the way. Sad that the data is not directly accessible on their website, but the beautiful atlases are!


Have a look at the Burkina Faso one here!

Identifying dynamics of natural resources in Somaliland through participatory mapping

If i tell you Somaliland, what is your first thought? War? terrorists? Al-Shebab? But in reality Somaliland is all but this. It is a place, which GDP depends for more than 80% on livestock production, exiting for a livestock scientist. It is a young democracy of an country that has not been recognized by the international community, yet has its own currency, own visa procedure and own government. Much of the international aid focuses on emergency relief and food aid. Little money flows for development, but some does, for example with FAO-SWALIM, who create a lot of good maps.

Hargeisa livestock market

Also Terra Nuova is one of the NGOs that does development work, and works mainly in the livestock sector. ILRI was contracted by Terra Nuova to implement the research component of one of their livestock project. Next to collecting analyzing data and create documentation for a country that has lost it all in a war, we also build capacity of ISTVS the vet school in Sheik. 

This week, i have hosted a participatory mapping workshop with decision maker to discuss the current state of natural resource that are needed to support the livestock sector. Participatory mapping asks participants to map, i.e. tell us where a given natural resource is and tell us more about that resource, for example the current state of the resource, who the user is, if there are conflicts and if people somehow are changing behavior. 

This allows to identify dynamics that define changes of natural resource, so that we can predict changes that might occur in future and support policy maker to take actions now that could address future issues.  

In a first step we have asked participants to rank the resource needed for the livestock sector. All three groups came up with the following order : 1. land, 2.water, 3. livestock feed. Number four was different from the groups, some would say livestock, other soil.

Want to know more about what we learned from the workshop? We are still processing the reports! So keep posted! 

What is big data?

What do you think big data is? Please fill this pool before reading the rest of the post!

survey service

Really, have you filled the pool?


I had recently a discussion about Big Data with my colleagues, and realized that most of them believe that big data is about a lot of data... what about you? It is true that until today there is no accepted definition for the concept of Big Data, the only agreement across definitions one can find is : Big Data is about so much more than the big amount of data.

http://www.big-data-book.com/on-the-book

I have recently finished reading this book, "Big Data : A revolution that will transform how we, work and think." A read for anyone who is interested in this emerging hot topic.

This book basically claims : 

It ushers in three big shifts: more, messy and correlations (the book’s chapters 2, 3 and 4). First, more. We can finally harness a vast quantity of information, and in some cases, we can analyze all the data about a phenomenon. This lets us drill down into the details we could never see before. Second, messy. When we harness more data, we can shed our preference for data that’s only of the best calibre, and let in some imperfections. The benefits of using more data outweighs cleaner but less data. Third, correlations. Instead of trying to uncover causality, the reasons behind things, it is often sufficient to simply uncover practical answers. So if some combinations of aspirin and orange juice puts a deadly disease into remission, it is less important to know what the biological mechanism is than to just drink the potion. For many things, with big data it is faster, cheaper and good enough to learn “what,” not “why.”

And why is it a revolution? 
A reason that we can do these things is that we have so much more data, and one reason for that is because we are taking more aspects of society and rendering it into a data form (discussed in chapter 5). With so much data around, and the ability to process it, big data is the bedrock of new companies.
The value of data is in its secondary uses, not simply in the primary purpose for which it was initially collected, which is the way we tended to value it in the past (noted in chapter 6). Hence, a big delivery company can reuse data on who sends packages to whom to make economic forecasts. A travel site crunches billions of old flight-price records from airlines, to predict whether a given airfare is a good one, or if the price is likely to increase or decrease. These extraordinary data services require three things: the data, the skills, and a big data mindset (examined in chapter 7). Today, the skills are lacking, few have the mindset even though the data seems abundant. But over time, the skills and creativity will become commonplace — and the most prized part will be the data itself.

What are the threats (or how societies will have to re-invent privacy)? 
Big data also has a dark side (chapter 8). Privacy is harder to protect because the traditional legal and technical mechanisms don’t work well with big data. And a new problem emerges: propensity — penalizing people based on what they are predicted to do, not what the have done. At the same time, there will be an increasing need to stay vigilant so that we don’t fall victim to the “dictatorship of data,” the idea that we shut off our reasoned judgment and endow in the data-driven decisions more than they deserve.
Solutions to these thorny problems (raised in chapter 9) include a fundamental rethink of privacy law and the technology to protect personal information. Also, a new class of professional called the “algorithmist” that will do for the big data age what accountants and auditors did for an era 100 years ago, when the cornucopia of information swamping society was in the form of financial data.
What role is left for humanity? For intuition, experience and acting in defiance of what the data suggests? Big data is set to change not only how we interact with the world, but ourselves.

So look at the pool again? Which answers would you choose now? If you are not ticking all of them, then get the book!

Reaching the last mile : how WeFarm is about to bring the revolution into communicating to smallholder farmers

Mondays morning can be though, but this week i had a fascinating one. I hosted a short presentation of WeFarm, a social entreprise that offers sms services for peer-to-peer farmer advice. Though many young people are tech oriented and many have access to internet and social media in Kenya, the vast majority of farmers still own feature phones, i.e. cannot access the internet. This is the gap that WeFarm adresses.

They have a very interesting business model :  all the sms are free to the farmers (at least on Safaricom), the services are funded by the organization that want to use the data WeFarm are collecting.  This is the first business model i have seen that is very realistic about farmer capability and willingness to pay!

At the back of WeFarm there are data analyst who develop machine learning approaches to improve on how feedback is sent to farmer but also to make their data valuable to potential customers. WeFarm is also hoping to introduce a peer-rating system where farmers will be able to 'upvote' good pieces of information, ensuring that other farmers receive quality advice and answers.

most farmers still have feature phones

It is a promising start up, clearly addressing the gap of bringing the information to the last mile namely the smallholder farmer. However, this morning we also raised many questions and challenges such as:

• How can WeFarm work in pastoral areas where farmers are likely to be moving and what to know about conditions on the other side of the country and not per se from their neighbors?
• How can expert knowlege be introduced in WeFarm business models?
• How can accuracy of advice be improved?

We had an interesting debate from which both sides, WeFarm and scientists could learn from each other! Wanna know more, check up WeFarm's site (and if you are a farmer, register to their services! it is free!)