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In developing regions of the world, the communications infrastructure pose enormous challenges for using Earth observation data. Limited internet bandwidth along with the high costs make it almost impossible to process and extract zonal statistics over large periods of time for even small geographic areas. In such cases, downloading daily rainfall data or dekadal series of NDVI data would take days and consume all the bandwidth allocated to an organization (for reference, internet connections in Niger would cost thousands of dollars per month at a maximum - and unreliable - bandwidth of just 10 Mbps). Running crop models or hydrological models typically require several years of historic data over the area of interest (AOI). In some cases, these AOIs are relatively small compared to the footprint of individual earth observation granules. Hence, systems that let the stakeholders subset the data to download to a user specified area, or even submit processing requests that let them download small result files for the AOI become critical. The SERVIR program has developed a tool to provide this type of access to help decision makers in developing regions use long time series of adjusted rainfall data (CHIRPS), NDVI values, seasonal weather forecasts, evaporative stress indices and others in a very efficient manner. This system, named ClimateSERV (https://climateserv.servirglobal.net) ingests the datasets in an automated fashion and allows interactive access (through a web application), or automated access through a simple API that developers can quickly incorporate in independent applications. This way, the extraction of daily averages of rainfall over a 50 square Km area through 30 years of archived data takes only a few seconds to process, and the results can be presented on an online chart or downloaded in a comma separated file that's only a few Kb.

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 SAR’s 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.

From space, we can view our planet in new ways. SERVIR empowers people in developing countries to use that view for gaining knowledge and insights about their environments and adaptation to a changing climate. We work with regional decision-makers to foster use of Earth observation satellite data, GIS, and predictive models for addressing water and land use, natural disasters, agricultural problems, biodiversity, and more. These tools can improve the lives, livelihoods, safety, and future of people in communities around the world.

Forests represent a key natural resource, for which degradation or disturbance is directly associated to economic implications, particularly in the context of the United Nations program REDD+ in supporting national policies to fight illegal deforestation. SERVIR, a joint NASA-USAID initiative that brings Earth observations (EO) for improved environmental decision making in developing countries, works with established institutions, called SERVIR hubs, in four regions around the world. SERVIR is partnering with global programs with great experience in providing best practices in forest monitoring systems, such as SilvaCarbon and the Global Forest Observation Initiative (GFOI), to develop a capacity building plan that prioritizes user needs. Representatives from the SERVIR global network met in February 2017 with experts in the field of Synthetic Aperture Radar (SAR) for forest applications to envisage this capacity building plan that aims to leverage the state-of-the-art knowledge on remote sensing to enhance forest monitoring for user agencies in SERVIR regions.

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.

The Lower Mekong Basin (LMB) is an economically and ecologically important region that experiences hydrologic hazards such as floods and droughts, which can directly affect human well-being and limit economic growth and development. To effectively develop long-term plans for addressing hydrologic hazards, the regional hydrological response to climate variability and land cover change needs to be evaluated. This research aims to investigate how climate variability, specifically variations in the precipitation regime, and land cover change will affect hydrologic parameters both spatially and temporally within the LMB. The research goal is achieved by (1) modeling land cover change for a baseline land cover change scenario as well as changes in land cover with increases in forest or agriculture and (2) using projected climate variables and modeled land cover data as inputs into the Variable Infiltration Capacity (VIC) hydrologic model to simulate the changes to the hydrologic system. The VIC model outputs were analyzed against historic values to understand the relative contribution of climate variability and land cover to change, where these changes occur, and to what degree these changes affect the hydrology. This study found that the LMB hydrologic system is more sensitive to climate variability than land cover change. On average, climate variability was found to increase discharge and evapotranspiration (ET) while decreasing water storage. The change in land cover show that increasing forest area will slightly decrease discharge and increase ET while increasing agriculture area increases discharge and decreases ET. These findings will help the LMB by supporting individual country policy to plan for future hydrologic changes as well as policy for the basin as a whole.

SERVIR is a joint project of NASA and US Agency for International Development (USAID). Mission is to use satellite data and geospatial technology to help developing countries manage resources, land use, and climate risks. Means to serve, in Spanish.