Back in the 1960s and 1970s, scientists were familiar with data from weather satellites and the kinds of questions they could answer. But what else could be seen from space? And how useful was it?
So, when the first Landsat satellite was launched on 23rd July 1972, the biggest questions were about the type of sensors, how useful the data were and the kinds of applications that could use the data. That’s why the first satellite carried 2 sensors that would be used to monitor the Earth’s landmass - an RBV camera to capture images and an experimental multi-spectral scanning (MSS) sensor that scientists hoped would be useful but had no idea if it would be. And, in the fun and annoying way that science often works - scientists discovered that the experimental MSS sensor produced data that were much more useful and accurate than the RBV camera! So, while the RBV camera system was carried by the next 3 satellites in the Landsat program, the MSS data were what scientists focused on.
NASA asked a team of over 300 scientists from all over the world to discover different applications of the Landsat data to their fields. And the scientists found that satellite data could be used for all kinds of applications - identifying different crops from space so that governments could make better decisions about their agricultural sector, monitoring the behavior of cyclones and storm systems, mapping deforestation, exploring changes in how land was used by people around the world and even identifying unknown mountains in the Arctic and Antarctic.
The Landsat program continued adding more sensors over the years - with Landsat 7 having the most accurate and detailed data of all the satellites. The data were finally unlocked and offered for free in 2008 - and since then, we’ve seen an explosion of both commercial and scientific uses for the data. Fun fact - Google Earth uses Landsat data to visualize the planet!
Today, we have data from all kinds of sources - satellites, aircraft, drones, and submarines.All these come under the banner of remote sensing - that is, any data collected at a distance from the source. And they are equipped with a wide variety of sensors that help us collect different types of data at different resolutions and solve a range of problems.Together they help us understand different aspects of our planet - what happens on the surface, below ground, in the ocean, in the atmosphere, in forests, in crop fields - all kinds of things.
The last couple of months have been interesting from a climate viewpoint - we’ve seen a record number of climate related disasters around the globe - drought, floods, fires, heat waves…..and it looks like this is probably going to be what our planet will look like in the near future. Add to that the COP26 conference that is scheduled for October 31st - and climate, sustainability and technology are front page news! So, let’s talk about one of the technologies in the news - artificial intelligence (AI) and its impact on climate, water, agriculture, energy, forestry, ecosystems and other sectors in clean technology . AI and its subset of tools - machine learning (ML), data science and statistics - are being touted as one of the key technologies in solving the problems facing the planet today. And while these technologies are certainly powerful - applying them effectively to solve problems in clean tech is another issue altogether. AI has been used by scientists in different clean tech se
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Every Earth Day we hear from companies around the world about their sustainability efforts - the gallons of water they have saved, the reductions in energy use, the systems they have converted from fossil-fuel to renewable energy, the ecosystems they have restored and, more recently the amount of carbon they have offset. On the face of it, these are amazing and hopeful numbers - the very fact that corporations are paying so much attention to sustainability and the environment is testament to the fact that consumer and citizen pressure is yielding results. In fact, with every successive year, it seems that companies and organizations are able to sequester more carbon, minimize greenhouse gas emissions and thus mitigate climate change. What allows companies to make these claims - especially with respect to carbon and greenhouse gas emissions? The way that most companies claim these credits is through carbon markets or “cap and trade” programs. In these programs, a community or a no