The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between 1985 and 2014, and subsequently determine the trends and drivers of these changes, we used a series of Landsat images and field data obtained from the LVB. Landsat image pre-processing and band combinations were done in ENVI 5.1. A supervised classification was applied on 118 Landsat scenes using the maximum likelihood classifier in ENVI 5.1. The overall accuracy of classified images was computed for the 2014 images using 124 reference data points collected through stratified random sampling. Computations of area under various land cover classes were calculated between the 1985 and 2014 images. We also correlated the area from natural vegetation classes to farmlands and settlements (urban areas) to explore relationships between land use land cover conversions among these classes. Based on our land cover classifications, we obtained overall accuracy of 71% and a moderate Kappa statistic of 0.56. Our results indicate that the LVB has undergone drastic changes in land use land cover, mainly driven by human activities that led to the conversion of forests, woodlands, grasslands, and wetlands to either farmlands or settlements. We conclude that information from this work is useful not only for basin-scale assessments and monitoring of land cover changes but also for targeting, prioritizing, and monitoring of small scale, community led efforts to restore degraded and fragmented areas in the basin. Such efforts could mitigate the loss of ecosystem services previously derived from large contiguous land covers which are no longer tenable to restore. We recommend adoption of a basin scale, operational, Earth observation-based, land use change monitoring framework. Such a framework can facilitate rapid and frequent assessments of gains and losses in specific land cover classes and thus focus strategic interventions in areas experiencing major losses, through mitigation and compensatory approaches.

Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction.

The 2019–2020 Desert Locust (DL) upsurge in East Africa threatened food security for millions in the region. This highlighted the need to track and quantify the damaging impacts of the swarming insects on cropland and rangelands. Satellite Earth observations (EO) data have the potential to contribute to DL damage assessments that can inform control measures, aid distribution and recovery efforts. EO can complement traditional ground based surveys (which are currently further limited due to COVID-19), by rapidly and cost effectively capturing the full spatial scale of the DL upsurge. However, EO-based techniques struggled to accurately quantify damage from this DL upsurge due to the sporadic and localized nature of infestations impacting scale, timing, and anomalous vegetation conditions. This study analyzed time series data from MODIS, the harmonized Landsat Sentinel-2 product, and C-band radar data from Sentinel-1 to distinguish DL damage from normal senescence or other confounding factors from January to June 2020. These data were compared to in situ locust swarm, band, and non locust observations collected by the Food and Agriculture Organization (FAO) and PlantVillage. The methods presented did not produce results that could confidently differentiate senescence from locust activity, and may represent a limitation of publicly available remotely sensed data to detect DL damage. However, the higher spatial resolution data sets showed promise, and there is potential to explore commercially available satellite products such as Planet Labs for damage assessment protocols.

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.

In 2018, UNESCO reported just under 30% of all science, technology, engineering, arts, and math (STEAM) positions are filled by women. Despite increasing global demand for higher paying, highly technical STEAM careers, women continue to face substantial barriers to STEAM. There are many obstacles women may face throughout their journey to a STEAM career, including lack of resources and role models, and discrimination. Studies show a near equivalent interest of boys and girls in STEAM fields in early childhood, however, this ratio declines sharply through time due to various pressures girls and boys face. This compounds into considerably less women participating in participate in STEAM careers. An increase in women in STEAM fields would not only increase economic activity but also provide a diverse perspective to develop and produce unique and innovative technology solutions. The Women in Science ‘WiSci’ science, technology, engineering, arts, and math (STEAM) camp, organized by the United States Department of State and implemented by World Learning and the United Nations Foundation Girl Up program, supports young women on their journey to STEAM careers. Since 2015, Google, Intel, and others deliver hands-on, STEAM focused curriculum around the world. These events bring together high school girls from around the world, to expose them to STEAM concepts and role models. NASA has participated since 2017 through the SERVIR program. Since then, the NASA SERVIR team has introduced Earth science materials to participants. This paper will provide an overview of the materials presented and lessons learned from this important event.