In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale, and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) products (the MOD13A1 and MYD13A1 collections) from both Terra and Aqua sensors from the years 2000 and 2002, respectively. to detect change in vegetation, we define an EVI baseline period, and then draw results at a planetary scale using the web-based application by measuring improvement or degradation in vegetation based on the user-defined baseline periods. We also used EcoDash to measure the impact of deforestation and mitigation efforts by the Vietnam Forests and Deltas (VFD) program for the Nghe An and Thanh Hoa provinces in Vietnam. Using the period before 2012 as a baseline, we found that as of March 2017, 86% of the geographical area within the VFD program shows improvement, compared to only a 24% improvement in forest cover for all of Vietnam. Overall, we show how using satellite imagery for monitoring vegetation in a cloud-computing environment could be a cost-effective and useful tool for land managers and other practitioners

Floods cause significant damage and loss of lives worldwide, more so than any other type of natural disaster.¹ In an effort to minimize flood impact and the impact of all disasters, international bodies have come together to set goals and priorities for disaster risk reduction, as presented in the Hyogo Framework for Action and its successor, the Sendai Framework for Disaster Risk Reduction.^2,3 The goals outlined in these frameworks have encouraged the development of risk reduction strategies by a wide array of sectors. One such approach is the humanitarian community’s forecast-based early action (FbA), which endeavors one to enable action based on forecasts of extreme events. Forecast-based Financing for early action is gaining momentum as an approach to manage changing climate risks, including through United Nations Framework Convention on Climate Change (UNFCCC)-related processes and the Green Climate Fund.⁴

Most modern airplanes include automated systems to improve both the efficiency and safety of flight. By the standards of the automotive industry, these avionics systems are both highly complex and highly automated. Ongoing advancements in computing systems and machine learning lead the SAE to publish a Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles that has become the standard for classifying levels of driving automation. This standardization has provided the automotive industry and the regulatory agencies a useful framework for exploring and promoting the development of more advanced automated and autonomous systems. The aviation industry is also benefiting from advancements in technology, with growing interest in even more advanced flight systems and ultimately fully autonomous solutions. The higher demands of the piloting task relative to the driving task and the more complex nature of modern airplane systems lead the authors to re-evaluate and tailor the SAE classifications of autonomy specifically for pilots and avionics solutions. This paper summarizes a taxonomy of flight automation system levels that Rockwell Collins has derived from the SAE driving automation system levels. The intent is to provide a similar framework for the exploration and development of advanced automation and autonomous solutions for avionics. Similar to the SAE classifications, this paper clarifies the role of both the (human) pilot, and the automated systems during flight operations at each level of automation. The objective is to provide scope definition and a useful framework for the development of technical specifications and related policies, regulations and standards.

In arid and semi-arid regions of Eastern and Southern Africa, drought can be devastating to pastoralists who depend on healthy vegetation for their herds. The Kenya Livestock Insurance Program (KLIP) addresses this challenge through its insurance program that relies on a vegetation index product derived from eMODIS NDVI (enhanced Normalized Difference Vegetation Index). Insurance payouts are triggered when index values fall below a certain threshold for a Unit Area of Insurance (UAI). The objective of this study is to produce an updated, cloud-based NDVI product, potentially allowing for earlier payouts that may help herders to prevent, minimize, or offset drought-induced losses. The new product, named reNDVI (rapid enhanced NDVI), provides an updated cloud filtering algorithm and brings the entire processing chain to the cloud. Access to the scripts used for the processing described and resulting data is openly available. To test the performance of the new product, we provide a robust evaluation of reNDVI and eMODIS NDVI and their derived payout indices against historical drought, payouts provided, and mortality data. The implications of potential payout differences are also discussed. The products show good comparability; the monthly average NDVI per UAI has correlation values over 0.95 and MAPD under 5% for most UAIs. However, there are moderate differences when assessing year-to-year payout amounts triggered. Because the payouts are currently calculated based on the 20th and first percentile of index values from 2003–2016, payouts are very sensitive to even small changes in NDVI. Where livestock mortality was available, payouts for reNDVI and eMODIS had similar correlations (r = 0.453 and r = 0.478, respectively) with mortality rates. Therefore, with the potential reduced latency and updated cloud filtering, the reNDVI product could be a suitable replacement for eMODIS in the Kenya Livestock Insurance Program. The updated reNDVI product shows promise as a vegetation index that could address a pressing drought insurance challenge.