Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm's pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD).

Chasing and surfing seasonal waves: Avian migration through the US tracks land surface phenology in fall, but not spring.

Climate change is altering the timing of seasonal events for many taxa. There is limited understanding of how northward/southward songbird migration follows or is limited by the latitudinal progression of seasonal transitions. Consistent environmental conditions that migrating birds encounter across latitudes likely represent or correlate with important resources or limiting factors for migration.

Artificial light at night is a top predictor of bird migration stopover density.

As billions of nocturnal avian migrants traverse North America, twice a year they must contend with landscape changes driven by natural and anthropogenic forces, including the rapid growth of the artificial glow of the night sky. While airspaces facilitate migrant passage, terrestrial landscapes serve as essential areas to restore energy reserves and often act as refugia-making it critical to holistically identify stopover locations and understand drivers of use. Here, we leverage over 10 million remote sensing observations to develop seasonal contiguous United States layers of bird migrant stopover density.

Urbanization imprint on land surface phenology: The urban-rural gradient analysis for Chinese cities.

Rising temperature shifts plant phenology. Chinese cities, experiencing extensive expansion and intensive warming, spanning a wide latitudinal range, might provide ideal experimental opportunities for observing and predicting phenological responses to warming temperature. Using the urban-rural gradient approach, we explored urbanization imprint on land surface phenology across the entire urbanization intensity (UI) gradient ranging from 0% to 100% in 343 Chinese cities using the VIIRS Land Surface Phenology along with MODIS Land Surface Temperature (LST) products.

Trends in land surface phenology across the conterminous United States (1982-2016) analyzed by NEON domains.

Tracking phenological change in a regionally explicit context is a key to understanding ecosystem status and change. The current study investigated long-term trends of satellite-observed land surface phenology (LSP) in the 17 National Ecological Observatory Network (NEON) domains across the conterminous United States (CONUS). Characterization of LSP trends was based on a high temporal resolution (3-d) time series of the two-band enhanced vegetation index (EVI2) derived from a long-term data record (LTDR) of the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS).

Drivers of urban expansion over the past three decades: a comparative study of Beijing, Tianjin, and Shijiazhuang.

Urban expansion is influenced by various natural and anthropogenic factors. Understanding the driving forces of urban expansion is crucial for modeling the process of urban expansion as well as guiding urban planning and management. Here, we quantified and compared the effects of natural, socioeconomic, and neighboring factors on urban expansion and their temporal dynamics in three large cities in the Jing-Jin-Ji Urban Agglomeration: Beijing, Tianjin, and Shijiazhuang.

Contemporary evolution and scaling of 32 major cities in China.

Most of the planet's population currently lives in urban areas, and urban land expansion is one of the most dramatic forms of land conversion. Understanding how cities evolve temporally, spatially, and organizationally in a rapidly urbanizing world is critical for sustainable development. However, few studies have examined the coevolution of urban attributes in time and space simultaneously and the adequacy of power law scaling across cities and through time, particularly in countries that have experienced abrupt, widespread, political and economic changes.

Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system.

Background: Early indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited.

Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics.

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically.

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia.

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010.

Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia.

Background: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics.

Landsat still contributing to environmental research.

Landsat data have enabled continuous global monitoring of both human-caused and other land cover disturbances since 1972. Recently degraded performance and intermittent service of the Landsat 7 and Landsat 5 sensors, respectively, have raised concerns about the condition of global Earth observation programs. However, Landsat imagery is still useful for landscape change detection and this capability should continue into the foreseeable future.

Ecophysiological consequences of contrasting microenvironments on the desiccation tolerant moss Tortula ruralis.

Tortula ruralis is a homoiochlorophyllous-desiccation-tolerant (HDT) moss that retains all pigments when dehydrated and rapidly recovers physiological function upon rehydration. This moss forms extensive cover in exposed and shaded areas in the sandy semi-arid grasslands of Central Europe. We hypothesized that contrasting drying regimes between these microhabitats would affect plant N status, constraints to gas exchange and growth, as well as result in altered pigment concentrations and ratios, and photochemical light-response dynamics.

Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis.

The structural integrity of vertebral trabecular bone is determined by the continuity of its trabecular network and the size of the holes comprising its marrow space, both of which determine the apparent size of the marrow spaces in a transaxial CT image. A model-independent assessment of the trabeculation pattern was determined from the lacunarity of thresholded CT images. Using test images of lumbar vertebrae from human cadavers, acquired at different slice thicknesses, we determined that both median thresholding and local adaptive thresholding (using a 7 x 7 window) successfully segmented the grey-scale images.