EcoSat is a first of its kind semi-automated satellite imagery processing tool that’s part of the BioBase cloud mapping platform (Figures 1 and 2). EcoSat is helping several US states and countries map and monitor the status of shallow growing aquatic vegetation and benthic habitats. In this blog, we discuss several tips and tricks about how practitioners can maximize the accuracy and precision of their EcoSat vegetation maps.
What is EcoSat?
EcoSat delivers a one-of-it’s-kind semi-automated cloud processing of very high resolution satellite imagery to map nearshore vegetation and coastal benthic habitats. EcoSat uses the latest multi-spectral imagery from reputable providers such as Digital Globe (World View 2,3 and 4), Airbus Defence and Space (Pleiades), and ESA’s Sentinel program and industry standard image processing techniques. Sophisticated Amazon Web Service cloud infrastructure rapidly processes imagery, creates reports and imagery tiles, and delivers detailed habitat maps to user’s BioBase dashboard where it can be analyzed and shared. Average turnaround time from imagery tasking order to delivery of results is 60 days. The rapid and standard processing methods are allowing entities like the Florida Fish and Wildlife Conservation Commission to establish regular monitoring programs for emergent vegetation. The extremely long and expensive one-off nature of conventional remote sensing mapping projects using non-repeatable tailored techniques has prevented natural resource entities from assessing the degree that habitats are changing as a result of environmental stressors such as invasive species invasions and climate change.
Another FAQ we get is wondering if there are published studies using BioBase technology? There are many legacy applications on which the BioBase technology is based. Further, now that a sufficient passage of years has accumulated to support the “research to publication” cycle, we’re happy to share several BioBase-specific studies published in the peer-reviewed literature. This is far from an exhaustive list and we’ve intentionally left out the niche growth in consumer side-scan technology for creating habitat maps. If there are good published papers you know of that are not on this list, please share in the comments.
A primary strength of BioBase EcoSound is its simplicity and that is reflected in the easy 3 step process of “Collect,” “Upload,” and “Analyze” (Figure 1).
|Figure 1. The core process of EcoSound depicting the 3 Steps of “Collect,” “Upload,” and “Analyze.”|
But there are many strategies that users can employ that will ensure that they will get the best EcoSound outputs possible. We’ll focus on several questions under each of the three categories
Budgets are tight, time is short, labor resources and technical know-how are scarce. These truths are the motivating force behind the ciBioBase system. Recently, we ran an analysis that demonstrates the cost-effectiveness of ciBioBase. We selected 3 peer-reviewed studies that demonstrated three alternative methods for whole lake assessments of vegetation abundance and compared the costs of producing a vegetation biovolume map with ciBioBase. The first two studies Valley and Drake (2007) and Sabol et al. (2009) used a scientific-grade echosounder, associated software, and required expertise in hydroacoustics and Geographic Information Systems (GIS). Hardware and software costs were adjusted to 2012 dollars which actually brought costs down to a total of $18,400. These costs were amortized over 5 years at 5% interest and scaled to daily costs assuming use in a season would not typically exceed 45 days. For both methods, hardware and software costs amounted to approximately $84 a day. We did not factor in time on the water for any of these analyses, or the cost of training in hydroacoustics, geostatistics, and GIS.
Labor costs were relatively large in the Valley and Drake (2007) study because the authors were working in environments that exceeded the capability of the vegetation-detecting algorithm they were using. Specifically, noisy signals generated in surface-growing vegetation canopies were thrown out and thus biasing biovolume (i.e., percent of the water column occupied by vegetation) downward. Consequently, Valley and Drake did ping-by-ping verification and reclassification where signals were obscured by surface-growing vegetation. Summing the modest hardware and high labor costs to manually verify thousands of pings, the cost of producing a vegetation map in a 500-acre lake using methods described in Valley and Drake (2007) was approximately $1,288.
|Method||Amortized Hardware||Maint-enance||Labor||Subscription Cost||Total Cost|
|Valley and Drake (2007)||$84||$23||$1,181||NA||$1,288|
|Sabol et al. (2009)||$84||$23||$250||NA||$357|
|*High resolution vegetationmapping was not an objective of Hoyer (2009) and thus the following scaled-up cost estimates should be viewed as a hypothetical scenario for an equal comparison to other methods|
The low rate of ciBioBase doesn’t consider any of the value-added features of ciBioBase such as:
Sabol, B.M., Kannenberg, J., and Skogerboe, J.G. 2009. Integrating acoustic mapping into
operational aquatic plant management : a case study in Wisconsin. Journal of Aquatic Plant