Design Thinking for the Applied Sciences: Developing a Novel Approach to Encourage the Use of Synthetic Aperture Radar (SAR) and Open Source Tools for Forest Monitoring

Earth observations from Synthetic Aperture Radar, or SAR, have yet to be fully leveraged for forest monitoring applications. While SAR sensors are uniquely able to capture components of forest structure over optical imagery, especially in cloud-heavy regions, there is a shortage of freely-available applied training materials and related case studies. With the wealth of available datasets from Sentinel-1 and other missions, such as ALOS-Palsar open historical archive, and in preparation for upcoming opendata policy SAR missions (e.g. NISAR and BIOMASS), the applied forestry community would benefit from increased access to relevant, understandable SAR training materials. This work documents lessons learned and best practices for creating EO capacity building/training materials gleaned from the SAR Handbook project. Strategies for increasing legibility for both print and online applications, illustration and editing guidelines for original and modified figures, and the development of quick-reference guides will be shared. Additionally, the conception and use of companion explainer videos, using cartoon characters and humor to outline relevant SAR concepts will be explored. Preliminary results indicate the SAR Handbook and supplemental project materials are already having an impact in training sessions. Increased uptake of SAR technologies in SERVIR Hub regions, where Hubs are leading follow-on SAR trainings, has also been noted. In addition, a review of download statistics from the SERVIR global website indicates widespread worldwide access. We conclude similar holistic approaches integrating design concepts into future content development would help increase uptake of EO applications by the earth science community

GC13I-0857: Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about$200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USD annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through our partner network developed at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frost forecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the key decision makers - the small-scale farmers of East Africa

How to Leverage the Power of SAR Observations for Forest Monitoring Systems

Earth observations from Synthetic Aperture Radar (SAR) can provide unique observations related to forest structure and condition. Furthermore, SAR has many potential applications in forest monitoring systems, particularly where clouds have impeded optical observations. Currently, there is a reliable, freely-available, provision of SAR datasets, such as Sentinel-1, and there are plans to have more observations in the near- future (NISAR, BIOMASS). Given SARs enhanced earth observation characteristics, there is broad interest in using SAR datasets for decision support systems, such as deforestation early warning systems. However, applications of SAR are still underutilized. What is preventing users from using SAR data in their decision support systems? This study documents the experiences and lessons learned from the SERVIR network on the main limitations of incorporating SAR datasets into existing forest monitoring systems. This research also focuses on the major technical and scientific barriers we experience and best practices to address them. The results of this study are part of the SERVIR- SilvaCarbon collaboration. The primary goal of this collaboration is to build capacity in the applied use of SAR for forest monitoring and biomass estimation. The products of this effort aim to start closing the gap between SAR-science and forest applications. We will also present results to generate applied-ready knowledge for SAR

Hyperspectral remote sensing of water quality in Lake Atitlan, Guatemala

Lake Atitlan in Guatemala is a vital source of drinking water. The deteriorating conditions of water quality in this lake threaten human and ecological health as well as the local and national economy. Given the sporadic and limited measurements available, it is impossible to determine the changing conditions of water quality. The goal of this thesis is to use Hyperion satellite images to measure water quality parameters in Lake Atitlan. For this purpose in situ measurements and satellite-derived reflectance data were analyzed to generate an algorithm that estimated Chlorophyll concentrations. This research provides for the first time a quantitative application of hyperspectral satellite remote sensing for water quality monitoring in Guatemala. This approach is readily transferable to other countries in Central America that face similar issues in the management of their water resources