A New and Automated Method for Improving Georeferencing in Nighttime Thermal ECOSTRESS Imagery

Georeferencing accuracy plays a crucial role in providing high-quality ready-to-use remote sensing data. The georeferencing of nighttime thermal satellite imagery conducted by matching to a basemap is challenging due to the complexity of thermal radiation patterns in the diurnal cycle and the coarse resolution of thermal sensors in comparison to sensors used for imaging in the visual spectral range (which is typically used for creating basemaps). The presented paper introduces a novel approach for the improvement of the georeferencing of nighttime thermal ECOSTRESS imagery: an up-to-date reference is created for each to-be-georeferenced image, derived from land cover classification products. In the proposed method, edges of water bodies are used as matching objects, since water bodies exhibit a relatively high contrast with adjacent areas in nighttime thermal infrared imagery. The method was tested on imagery of the East African Rift and validated using manually set ground control check points. The results show that the proposed method improves the existing georeferencing of the tested ECOSTRESS images by 12.0 pixels on average. The strongest source of uncertainty for the proposed method is the accuracy of cloud masks because cloud edges can be mistaken for water body edges and included in fitting transformation parameters. The georeferencing improvement method is based on the physical properties of radiation for land masses and water bodies, which makes it potentially globally applicable, and is feasible to use with nighttime thermal infrared data from different sensors.

Detecting patterns of post-fire pine regeneration in a Madrean Sky Island with field surveys and remote sensing

The American Southwest is experiencing drastic increases in aridity and wildfire incidence, triggering conversion of some frequent surface forests to non-forest. Extensive research has focused on these dynamics in regional ponderosa pine forests, but we know much less about Madrean pine-oak forests, which are broadly distributed from the Sierra Madre in Mexico to the Sky Island mountain ranges in the U.S. Increased fire incidence and drought in these forests are limiting pine regeneration and driving conversion of biodiverse forests to oak shrublands. We investigated regeneration patterns in Pinus engelmannii and P. leiophylla during severe drought 10 years after the Horseshoe Two Megafire in the Chiricahua Mountains, Arizona-a follow-up to an assessment five years post-fire. In long-term plots, we examined changes in pine seedling and resprout recruitment. Past research demonstrated that topography and fire severity influenced pine recruitment across environmental gradients. We investigated here whether Landsat-8 normalized difference vegetation index (NDVI) and evapotranspiration estimated by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) added explanatory value to our understanding of these patterns. Conversion of Madrean pine-oak forest to oak shrublands continued 6-10 years post-fire. A dense, low oak canopy continued to coalesce in sites subject to severe fire. The importance of resprouts in P. leiophylla regeneration accelerated because these plants outgrew competing oak resprouts. Topography and fire severity (dNBR) were important predictors of 2021 patterns of pine recruitment. NDVI added explanatory value to these models, suggesting its potential in tracking forest dynamics. Evapotranspiration did not add value, likely because ECOSTRESS' larger pixel sizes and moving pixel locations created excessive subpixel heterogeneity in this highly dissected landscape. These models suggest that P. engelmannii is more drought sensitive, was more negatively affected by drought and fire, and is more at risk to shifts in climate and wildfires than P. leiophylla.

LST-R: A method for assessing land surface temperature reduction in urban, hot and semi-arid Global South

Over the next 30 years, temperatures are expected to increase in hot semi-arid zones. Despite increasing studies on urban heat, cooling measures suitable for this climate zone remain poorly investigated. The proposed method is innovative because it focuses on significant landscape metrics for determining the land surface temperature (LST) and evaluating cooling measures. Recurrence of warm spells was identified analysing the daily air temperatures. Daytime and night-time LST data acquired from space were correlated with landscape metrics extracted from very high-resolution satellite imagery. Stepwise linear regression was used to identify the significant metrics that affected it. Cooling measures were selected considering implementation leeway; performance of existing measures; strengths, weaknesses, opportunities, and threats, equity analyses. Although the method was tested in Niamey, Niger, it can be applied to any city or town in hot semi-arid Global South, requiring decision-making support on cooling policies.•Landscape metrics are consistent with development standard and general requirements.•Evaluation of measures to reduce land surface temperature includes experts... advice.•Equity of measures to reduce land surface temperature is considered.

Evaluation of ECOSTRESS Thermal Data over South Florida Estuaries

Operational coarse-resolution satellite thermal sensors designed for global oceans are often insufficient for evaluating surface temperature of small water bodies. Here, the quality of the thermal data, collected by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), over several South Florida estuaries, Chesapeake Bay, and Lake Okeechobee is evaluated using both in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) Sea Surface Temperature (SST) data. Overall, for SST between ~6 and ~32 °C, ECOSTRESS LST (Land Surface Temperature, used as a surrogate for SST in this study) appears to be slightly underestimated, with the underestimation being more severe at night (−1.13 °C) than during the day (−0.64 °C), in spring and summer (−1.25 ± 1.39 °C) than in autumn and winter (−0.57 ± 0.98 °C), and after May 2019 when two of the five bands failed. The root-mean-square uncertainties of ECOSTRESS SST are generally within 1–2 °C. Spatial analysis further suggests that ECOSTRESS SST covers waters closer to shore and reveals more spatial features than MODIS, with comparable image noise. From these observations, after proper georeferencing and empirical correction of the negative bias, ECOSTRESS SST may be used to evaluate the thermal environments of small water bodies, thus filling gaps in the coarse-resolution satellite data.

Use of Geospatial Surveillance and Response Systems for Vector-Borne Diseases in the Elimination Phase

The distribution of diseases caused by vector-borne viruses and parasites are restricted by the environmental requirements of their vectors, but also by the ambient temperature inside the host as it influences the speed of maturation of the infectious agent transferred. The launch of the Soil Moisture Active Passive (SMAP) satellite in 2015, and the new ECOSTRESS instrument onboard the International Space Station (ISS) in 2018, established the leadership of the National Aeronautics Space Administration (NASA) in ecology and climate research by allowing the structural and functional classification of ecosystems that govern vector sustainability. These advances, and the availability of sub-meter resolution data from commercial satellites, contribute to seamless mapping and modelling of diseases, not only at continental scales (1 km²) and local community or agricultural field scales (15–30 m²), but for the first time, also at the habitat–household scale (<1 m²). This communication presents current capabilities that are related to data collection by Earth-observing satellites, and draws attention to the usefulness of geographical information systems (GIS) and modelling for the study of important parasitic diseases.