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urban resilience

What can satellite imagery tell us about secondary cities? (Part 2/2)

Sarah Elizabeth Antos's picture
In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.

Can we rely only on satellite? How accurate are these results?

It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.

So, it’s important to learn how well the resulting maps reflect the reality on the ground.

Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.

Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.
 
Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).

What can satellite imagery tell us about secondary cities? (Part 1/2)

Sarah Elizabeth Antos's picture

The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.

But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.

The question remains: How can we efficiently transform big data into valuable products that help urban planners?

In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.

A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.

Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.

What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.

Want to explore our maps? Download our data here. Click here for an interactive land cover map of La Ceiba.

Urban jungles in jeopardy

Ivo Germann's picture
Why the world’s cities are at risk – and what we can do to make them more resilient



We may not know exactly what the world will look like in two decades, but we know this: it is going to be a world of cities.
 
The global population is becoming increasingly urban, and at an astonishing rate. Each year, urban areas are growing by an average of more than 75 million people – more than the population of the world’s 85 smallest countries combined.
 
For the world’s economy, this is great news, since cities produce 80 percent of global GDP, despite currently being home to only 55 percent of the population. But it is a problem for urban infrastructure, which can’t keep up with such fast-paced growth. As a result, cities, already vulnerable, are becoming increasingly susceptible to natural disasters – from flooding and landslides that can decimate informal housing settlements, to earthquakes that can devastate power grids and water systems.
 
These risks could be disastrous for the urban poor, 881 million of whom currently live in slums (up 28 percent since 2000). And climate change – which is increasing the intensity and frequency of natural disasters – will only exacerbate the problem. For this reason, multilateral and government institutions now see resilience and climate adaptation as integral pillars of development.
 
The Swiss State Secretariat for Economic Affairs (SECO), for example, considers low-emission and climate-resilient economies to be key to global competitiveness. A recent report by the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) found that climate change may force up to 77 million urban residents into poverty by 2030 – unless we take action to improve the resilience of cities around the world.

Investing in resilient cities can help the urban poor

Ede Ijjasz-Vasquez's picture
By 2030, without efforts to boost urban resilience, climate change may push up to 77 million urban residents into poverty.
 
The good news is that the world has a brief window of opportunity to make cities more resilient to climate change, natural disasters, and other stresses, as almost 60% of the urban area that will be built by 2030 is yet to be developed.

Cities: the best place to strive for sustainability

Xiaomei Tan's picture

Also available in: Español | Français | العربية

 
Cities are a puzzle for some and inspiration for others. As engines of economic growth, they are also hubs of rapid urbanization, a rising middle class, and a growing population. These three mega-trends drive global environmental degradation yet are only part of the important challenge facing cities today.

While consuming over two-thirds of global energy supply and emitting 70% of all carbon dioxide, cities are also uniquely vulnerable to climate change. Fourteen of the world’s 19 largest cities are located in port areas. With sea level rise and increased storm activity, these areas are likely to face coastal flooding, damage to infrastructure, and compromised water and food security. Under these conditions, meeting urban population’s growing production and consumption needs for food, energy, water, and infrastructure will overload rural and urban ecosystems.

To tackle these issues, the Global Environment Facility (GEF), in collaboration with the World Bank Group (WBG), launched the Sustainable Cities Program to engage 23 cities in 11 developing countries. Hailing from one of such countries, two urban development specialists working on each side of the Program explain why making cities more sustainable appeals to them.