In a short time geospatial analytics have made big strides in the space of environmental consulting. From international to local, administrations tasked with responding to the environmental challenges posed by climate change have looked to the analysis of Earth observation data to help guide their strategies and decision-making.
A wide range of organizations have already begun to make large impacts in this space. At an international scale groups have partnered with nations and UN bodies to implement this work. like Lobelia has worked with such groups as the FAO and the European Space Agency to tackle large scale climate resilience challenges, while groups like MapAction and Humanitarian OpenStreetMap have helped national institutions implement this data in their disaster risk management and response capabilities.
At the local level within the US groups like Fernleaf and the institution it broke off from, NEMAC, have partnered with local city resilience planners to apply these tools to local disaster mitigation and response systems. There is no shortage of macro-level data accessible to these US-based administrators. Federal institutions such as NASA, NOAA, the USGS and the EPA, just to name a few, provide them with plenty of high quality satellite data. Yet there continues to be significant difficulty translating that data into action. However, as a software designer at NEMAC, Dave Michelson, shares in a Resilience Journey Map tool he created to chart common experiences expressed by city planners utilizing this data who they've worked with, they receive guidance from the data during hazard exploration and risk assessment, but are left wanting local, ground-truthed information when it comes time to investigate options, plan and take action.
The reason has to do with the scope of this data compared with the scope of their work. A local administrator planning resilience efforts for their city can benefit a great deal from the types of data provided by available macro-level satellite sources, including national models and projections, in their planning stage, but the direction they receive from it often ends there. Once it comes to implementing a plan of action and monitoring the impact of their efforts, they are frequently left in the dark when it comes to measuring the effectiveness of their response. Even further, many resilience planners are hesitant (and rightly so) to put complete faith in models created from data collected at the global scale which is often outdated.
The available federal data is very good at helping outline issues at a large scale, but the gap exists in understanding exactly how bad the problem is in the planners' specific area of concern and how effective their responses are.
At Precision Ecology our mission is to bridge this gap between macro-level projections and hyper-local monitoring for outcomes by helping teams interpret satellite models, then taking it a step further to use the latest mapping technologies to ground-truth and assess impact. Using autonomous UAVs to efficiently map large areas, our team is able to offer the in depth view needed to strategize, assess and adjust efforts to maximize impact at the local scale.
Satellite imagery is typically between 1.5 and 3.5 meter to the pixel, with the highest resolution options being 30cm and very expensive to acquire, costing tens of thousands of dollars for a small area. The local mapping data we provide is between 1 and 2 centimeters to the pixel, providing much more detailed information at a fraction of the cost.
Our current case study for FEMA's 2022 TOP Project will be focused on flood mapping and reporting. While plenty of data is provided to local resilience planners on sea level rise from a global scale and alerts exist for sea level gauges reaching critical levels, there is currently no method for understanding where flooding is actually occurring during an extreme weather event. City administrations pay A&E firms six figure project budgets to model flooding extents in the preparation stage, but have limited faith in their accuracy when flooding really occurs.
Our project will be focused on helping administrators bridge the gap between predicting and understanding when these gauges will be surpassed and predicting and understanding where water is in the city to enable them to respond as efficiently as possible. While models are certainly useful and are constantly improving, hyper-local reporting mechanisms will allow planners to understand where their people are truly being affected in time to respond appropriately.
Ultimately effective disaster risk management cycles in the age of environmental crises are all about information, but which type of data and when will make all the difference in creating better outcomes. Macro level data and models to predict trends over long periods are critical tools for planning and have already come incredibly far. But to maximize their impact there is a critical need to bridge the gap between them and hyper-local data to understand the true effects being experienced on the ground to best respond to disasters and evaluate responses.