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Phenology is the study of recurring life‐cycle events, classic examples being the flowering of plants and animal migration. Phenological responses are increasingly relevant for addressing applied environmental issues. Yet, challenges remain with respect to spanning scales of observation, integrating observations across taxa, and modeling phenological sequences to enable ecological forecasts in light of future climate change. Recent advances that are helping to address these questions include refined landscape‐scale phenology estimates from satellite data, advanced, instrument‐based approaches for field measurements, and new cyberinfrastructure for archiving and distribution of products. These breakthroughs are improving our understanding in diverse areas, including modeling land‐surface exchange, evaluating climate–phenology relationships, and making land‐management decisions.

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Biodiversity and habitat face increasing pressures due to human and natural influences that alter vegetation structure. Because of the inherent difficulty of measuring forested vegetation three‐dimensional (3‐D) structure on the ground, this important component of biodiversity and habitat has been, until recently, largely restricted to local measurements, or at larger scales to generalizations. New lidar and radar remote sensing instruments such as those proposed for spaceborne missions will provide the capability to fill this gap. This paper reviews the state of the art for incorporatinginformation on vegetation 3‐D structure into biodiversity and habitat science and management approaches, with emphasis on use of lidar and radar data. First we review relationships between vegetation 3‐D structure, biodiversity and habitat, and metrics commonly used to describe those relationships. Next, we review the technical capabilities of new lidar and radar sensors and their application to biodiversity and habitat studies to date. We then define variables that have been identified as both useful and feasible to retrieve from spaceborne lidar and radar observations and provide their accuracy and precision requirements. We conclude with a brief discussion of implications for spaceborne missions and research programs. The possibility to derive vegetation 3‐D measurements from spaceborne active sensors and to integrate them into science and management comes at a critical juncture for global biodiversity conservation and opens new possibilities for advanced scientific analysis of habitat and biodiversity.

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BackgroundMalaria 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. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia.MethodsIn this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series.ResultsMalaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates.ConclusionsMalaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.

The International Geosphere-Biosphere Program has delineated five study areas that form a northern high-latitude network for the analyses of vegetation and carbon dynamics. We examined the magnitude and significance of changes in the land surface phenologies of ecoregions within these transects using the NASA Pathfinder Advanced Very High-Resolution Radiometer Land dataset. We applied the seasonal Mann-Kendall (SMK) trend test, a robust and nonparametric approach, to determine the significance of trends in the normalized difference vegetation index (NDVI) over the five transects. The SMK trend test provides an important alternative over the frequently used but unreliable trend analysis based on linear regression.In addition, we modeled the land surface phenology using quadratic or nonlinear spherical models to relate the NDVI data to accumulated growing degree-days (base 0 1C). Nonlinear spherical models parsimoniously describe the green-up dynamics in taiga and tundra ecoregions. Models for each ecoregion within each transect were fitted separately for two time periods (1985-1988 and 1995-1999) and their parameter coefficient estimates were compared. In 10 of 24 ecoregions that comprise 72% of the land area in the transects, the date of the peak NDVI value was significantly earlier (range 2-18 days) in the second study period than in the first study period. This progression was more pronounced in North America than in Siberia (weighted average of 9.3 vs. 6.3 days earlier).Understanding of what constitutes significant change in land surface phenology amidst background variation is a critical component of global change science. A diversity of datasets, techniques, and study areas has led to a range of conclusions about boreal phenology. We discuss statistical pitfalls in standard analyses and offer a framework to conduct statistically reliable change assessments of land surface phenologies.

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