Search NASA⌕ Search

SEARCH · Search NASA

Results for “Phenology”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

Monitoring Phenology as Indicator for Timing of Nutrient Inputs in Northern Gulf Watersheds

Nutrient over-enrichment defined by the U.S. Environmental Protection Agency as the anthropogenic addition of nutrients, in addition to any natural processes, causing adverse effects or impairments to the beneficial uses of a water body has been identified as one of the most significant environmental problems facing sensitive estuaries and coastal waters. Understanding the timing of nutrient inputs into those waters through remote sensing observables helps define monitoring and mitigation strategies. Remotely sensed data products can trace both forcings and effects of the nutrient system from landscape to estuary. This project is focused on extracting nutrient information from the landscape. The timing of nutrients entering coastal waters from the land boundary is greatly influenced by hydrologic processes, but can also be affected by the timing of nutrient additions across the landscape through natural or anthropogenic means. Non-point source nutrient additions to watersheds are often associated with specific seasonal cycles, such as decomposition of organic materials in fall and winter or addition of fertilizers to crop lands in the spring. These seasonal cycles or phenology may in turn be observed through the use of satellite sensors. Characterization of the phenology of various land cover types may be of particular interest in Gulf of Mexico estuarine systems with relatively short pathways between intensively managed systems and the land/estuarine boundary. The objective of this study is to demonstrate the capability of monitoring phenology of specific classes of land, such as agriculture and managed timberlands, at a refined watershed level. The extraction of phenological information from the Moderate Resolution Imaging Spectroradiometer (MODIS) data record is accomplished using analytical tools developed for NASA at Stennis Space Center: the Time Series Product Tool and the Phenological Parameters Estimation Tool. MODIS reflectance data (product MOD09) were used to compute the Normalized Difference Vegetation Index, which is sensitive to changes in vegetation canopies. The project team is working directly with the Mississippi Department of Environmental Quality to understand end-user requirements for this type of information product. Initial focus areas are identification of time frames for pre-plant fertilizer applications (prior to start of season), side-dress fertilizer applications (during rapid green-up), and periods of plant decomposition (during and after senescence). Prototypical maps of phenological stages related to these time frames have been generated for watersheds in the northern Gulf of Mexico. Where feasible, these maps have been compared to existing in situ nutrient monitoring data, but the in situ data is temporally sparse (monthly frequency or less), which makes interpretation challenging. Future work will include integrating effects of rainfall and seeking couplings with estuarine remote sensing.

Ross, Kenton W.↗

Tracking Climate Effects on Plant-Pollinator Interaction Phenology with Satellites and Honey Bee Hives

Background/Question/Methods: The complexity of plant-pollinator interactions, the large number of species involved, and the lack of species response functions present challenges to understanding how these critical interactions may be impacted by climate and land cover change on large scales. Given the importance of this interaction for terrestrial ecosystems, it is desirable to develop new approaches. We monitor the daily weight change of honey bee (Apis mellifera) colonies to record the phenology of the Honey Bee Nectar Flow (HBNF) in a volunteer network (honeybeenet.gsfc.nasa.gov). The records document the successful interaction of a generalist pollinator with a variety of plant resources. We extract useful HBNF phenology metrics for three seasons. Sites currently exist in 35 states/provinces in North America, with a concentration in the Mid-Atlantic region. HBNF metrics are compared to standard phenology metrics derived from remotely sensed vegetation indices from NASA's MODIS sensor and published results from NOAA's A VHRR. At any given time the percentage of plants producing nectar is usually a sma11 fraction of the total satellite sensor signal. We are interested in determining how well the 'bulk' satellite vegetation parameters relate to the phenology of the HBNF, and how it varies spatially on landscape to continental scales. Results/Conclusions: We found the median and peak seasonal HBNF dates to be robust, with variation between replicate scale hives of only a few days. We developed quality assessment protocols to identify abnormal colony artifacts. Temporally, the peak and median of the HBNF in the Mid-Atlantic show a significant advance of 0.58 d/y beginning about 1970, very similar to that observed by the A VHRR since 1982 (0.57 d/y). Spatially, the HBNF metrics are highly correlated with elevation and winter minimum temperature distribution, and exhibit significant but regionally coherent inter-annual variation. The relationship between median of the spring HBNF with the "Green-up" metric from the 500 meter MODIS NDVI phenology product, for sites throughout the Eastern US 2000-2009, is well described by a single linear fit (r(exp 2) = 0.72). We conclude.that for the tree-dominated areas of the Eastern US at least the spring HBNF can be tracked very well by MODIS phenology. Analysis of other regions and seasons is presently underway but with more limited data. Spatial patterns in the eastern US and management implications will be presented and discussed.

Esaias, Wayne E.↗

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Ulsig, Laura↗

Bhutan Water Resources II: Comparing Phenology, Precipitation, and Temperature Data in Bhutan to Assist the Himalayan Environmental Rhythm Observation and Evaluation System (HEROES) Project

Bhutan is vulnerable to climate fluctuations that can affect vegetation phenology patterns. Changes in the climate have raised concerns from local farmers about altered growing seasons. In response, the DEVELOP team assessed annual vegetation phenology trends across Bhutan from 1981-2014 by comparing vegetation phenology-derived data and meteorological data. The project assessed phenology change using Vegetation Index and Phenology (VIP) Normalized Difference Vegetation Index (NDVI) products from the Advanced Very High-Resolution Radiometer (AVHRR) and the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. We also used Climate Hazards Center Infrared Precipitation with Station (CHIRPS) precipitation data and Famine Early Warning System Network Land Data Assimilation System (FLDAS) temperature data to assess climate trends in the country. The team assessed VIP phenology data for 1981-2014 to assess trends for the start of season, day of peak, and length of season. For the main growing season, the results indicated that the start of season and day of peak were delayed, while the length of season increased by 22 days. Analysis of temperature and precipitation data for the early 1980s to present indicted that Bhutan’s temperature has become warmer and precipitation has increased. Satellite-based precipitation and temperature data were compared to in situ precipitation and temperature data, yielding high correlations for both precipitation (R=0.85) and temperature (R=0.9). The project results and methods were shared with the Ugyen Wangchuck Institute for Conservation and Environmental Research (UWICER) to help assess climate change impacts in Bhutan.

DEVELOP Tech Paper↗

Plant Phenology Evaluation of CRESCENDO Land Surface Models–Part 1: Start and End of the Growing Season

Plant phenology plays a fundamental role in land–atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of the beginning and end of the growing season, simulated by the land component of seven state-of-the-art European Earth system models participating in the CMIP6, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. The difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broadleaf deciduous trees, while high variability is noted in regions dominated by broadleaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

Phenology↗

Flowering Date of Taxonomic Families Predicts Phenological Sensitivity to Temperature: Implications for Forecasting the Effects of Climate Change on Unstudied Taxa

Premise of the study: Numerous long-term studies in seasonal habitats have tracked interannual variation in fi rst fl owering date (FFD) in relation to climate, documenting the effect of warming on the FFD of many species. Despite these efforts, long-term phenological observations are still lacking for many species. If we could forecast responses based on taxonomic affi nity, however, then we could leverage existing data to predict the climate-related phenological shifts of many taxa not yet studied; Methods: We examined phenological time series of 1226 species occurrences (1031 unique species in 119 families) across seven sites in North America and England to determine whether family membership (or family mean FFD) predicts the sensitivity of FFD to standardized interannual changes in temperature and precipitation during seasonal periods before fl owering and whether families differ signifi cantly in the direction of their phenological shifts; Key results: Patterns observed among species within and across sites are mirrored among family means across sites; earlyfl owering families advance their FFD in response to warming more than late-fl owering families. By contrast, we found no consistent relationships among taxa between mean FFD and sensitivity to precipitation as measured here; Conclusions: Family membership can be used to identify taxa of high and low sensitivity to temperature within the seasonal, temperate zone plant communities analyzed here. The high sensitivity of early-fl owering families (and the absence of earlyfl owering families not sensitive to temperature) may refl ect plasticity in fl owering time, which may be adaptive in environments where early-season conditions are highly variable among years.

first flowering date↗

Learning the Rhythm of the Seasons in the Face of Global Change: Phenological Research in the 21st Century

Phenology is the study of recurring life-cycle events, of which classic examples include flowering by plants as well as 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 challenges include refined landscape-scale phenology estimates from satellite data, advanced instrument-based approaches for field measurements, and new cyber-infrastructure for archiving and distribution of products. These advances are aiding in diverse areas including modeling land-surface exchange, evaluating climate-phenology relationships, and aiding land management decisions.

Morisette, Jeffrey T.↗

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

McKellip, Rodney D.↗

Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

Vegetation↗

Satellite Observation of El Nino Effects on Amazon Forest Phenology and Productivity

Climate variability may affect the functioning of Amazon moist tropical forests, and recent modeling analyses suggest that the carbon dynamics of the region vary interannually in response to precipitation and temperature anomalies. However, due to persistent orbital and atmospheric artifacts in the satellite record, remote sensing observations have not provided quantitative evidence that climate variation affects Amazon forest phenology or productivity, We developed a method to minimize and quantify non-biological artifacts in NOAA AVHRR satellite data, providing a record of estimated forest phenological variation from 1982-1993. The seasonal Normalized Difference Vegetation Index (NDVI) amplitude (a proxy for phenology) increased throughout much of the basin during El Nino periods when rainfall was anomalously low. Wetter La Nina episodes brought consistently smaller NDVI amplitudes. Using radiative transfer and terrestrial biogeochemical models driven by these satellite data, we estimate that canopy-energy absorption and net primary production of Amazon forests varied interannually by as much as 21% and 18%, respectively. These results provide large-scale observational evidence for interannual sensitivity to El Nino of plant phenology and carbon flux in Amazon forests.

Asner, Gregory P.↗

Integration for Airborne Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

The objective of the program is to assess the feasibility of combining a dust transport model with MODIS derived phenology to study pollen transport for integration with a public health decision support system. The use of pollen information has specifically be identified as a critical need by the New Mexico State Health department for inclusion in the Environmental Public Health Tracking (EPHT) program. Material and methods: Pollen can be transported great distances. Local observations of plan phenology may be consistent with the timing and source of pollen collected by pollen sampling instruments. The Dust REgional Atmospheric Model (DREAM) is an integrated modeling system designed to accurately describe the dust cycle in the atmosphere. The dust modules of the entire system incorporate the state of the art parameterization of all the major phases of the atmospheric dust life such as production, diffusion, advection, and removal. These modules also include effects of the particles size distribution on aerosol dispersion. The model was modified to use pollen sources instead of dust. Pollen release was estimated based on satellite-derived phenology of key plan species and vegetation communities. The MODIS surface reflectance product (MOD09) provided information on the start of the plant growing season, growth stage, and pollen release. The resulting deterministic model is useful for predicting and simulating pollen emission and downwind concentration to study details of phenology and meteorology and their dependencies. The proposed linkage in this project provided critical information on the location timing and modeled transport of pollen directly to the EPHT> This information is useful to support the centers for disease control and prevention (CDC)'s National EPHT and the state of New Mexico environmental public health decision support for asthma and allergies alerts.

Luvall, J. C.↗

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

Pollen can be transported great distances. Van de Water et. al., 2003 reported Juniperus pollen, a significant aeroallergen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. The DREAM (Dust REgional Atmospheric Model, Yin 2007) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust (Yin 2007). The use of satellite data products for studying phenology is well documented (White and Nemani 2006). We are modifying the DREAM model to incorporate pollen transport. The linkages already exist with DREAM through PHAiRS (Public Health Applications in remote Sensing) to the public health community. This linkage has the potential to fill this data gap so that health effects of pollen can better be tracked for linkage with health outcome data including asthma, respiratory effects, myocardial infarction, and lost work days. DREAM is based on the SKIRON/Eta modeling system and the Eta/NCEP regional atmospheric model. The dust modules of the entire system incorporate the state of the art parameterizations of all the major phases of the atmospheric dust life such as production, diffusion, advection, and removal. These modules also include effects of the particle size distribution on aerosol dispersion. The dust production mechanism is based on the viscous/turbulent mixing, shear-free convection diffusion, and soil moisture. In addition to these sophisticated mechanisms, very high resolution databases, including elevation, soil properties, and vegetation cover are utilized. The DREAM model was modified to use pollen sources instead of dust (PREAM). Pollen release will be estimated based on satellite-derived phenology of Juniperus spp. communities. The MODIS surface reflectance product (MOD09) will provide information on the start of the plant growing season, growth stage, peak greenness, dry-down and pollen release. Ground based observational records of pollen release timing and quantities will be used as verification. Techniques developed using MOD09 surface reflectance products will be directly applicable to the next generation sensors such as VIIRS. The resulting deterministic model for predicting and simulating pollen emission and downwind concentration to study details of phenology and meteorology and their dependencies. This information will be used to support the Centers for Disease Control and Prevention (CDC)'s National Environmental Public Health Tracking Program (EPHT) and the State of New Mexico environmental public health decision support for asthma and allergies alerts

Luvall, Jeffrey C.↗

Hydroclimatic Controls on the Means and Variability of Vegetation Phenology and Carbon Uptake

Long-term, global offline (land-only) simulations with a dynamic vegetation phenology model are used to examine the control of hydroclimate over vegetation-related quantities. First, with a control simulation, the model is shown to capture successfully (though with some bias) key observed relationships between hydroclimate and the spatial and temporal variations of phenological expression. In subsequent simulations, the model shows that: (i) the global spatial variation of seasonal phenological maxima is controlled mostly by hydroclimate, irrespective of distributions in vegetation type, (ii) the occurrence of high interannual moisture-related phenological variability in grassland areas is determined by hydroclimate rather than by the specific properties of grassland, and (iii) hydroclimatic means and variability have a corresponding impact on the spatial and temporal distributions of gross primary productivity (GPP).

GPP↗

Variability of Phenology and Fluxes of Water and Carbon with Observed and Simulated Soil Moisture in the Ent Terrestrial Biosphere Model (Ent TBM Version 1.0.1.0.0)

The Ent Terrestrial Biosphere Model (Ent TBM) is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models (GCMs). This study describes the leaf phenology submodel implemented in the Ent TBM version 1.0.1.0.0 coupled to the carbon allocation scheme of the Ecosystem Demography (ED) model. The phenology submodel adopts a combination of responses to temperature (growing degree days and frost hardening), soil moisture (linearity of stress with relative saturation) and radiation (light length). Growth of leaves, sapwood, fine roots, stem wood and coarse roots is updated on a daily basis. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites, with both observed and prognostic hydrology, and observed and prognostic seasonal leaf area index. The phenology submodel is able to capture the timing and magnitude of leaf-out and senescence for temperate broadleaf deciduous forest (Harvard Forest and Morgan- Monroe State Forest, US), C3 annual grassland (Vaira Ranch, US) and California oak savanna (Tonzi Ranch, US). For evergreen needleleaf forest (Hyytiäla, Finland), the phenology submodel captures the effect of frost hardening of photosynthetic capacity on seasonal fluxes and leaf area. We address the importance of customizing parameter sets of vegetation soil moisture stress response to the particular land surface hydrology scheme. We identify model deficiencies that reveal important dynamics and parameter needs.

Soil moisture↗

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

Seasonal Forecasting↗

Bhutan Water Resources Tech Paper: Comparing Precipitation, Temperature, and Phenology Data Trends in Bhutan to Assist the Himalayan Environmental Rhythm Observation and Evaluation System (HEROES) Project

Himalayan countries, including Bhutan, have become vulnerable to warming trends which result in increasing temperature and variable rainfall and snowfall. By combining phenological and meteorological data, Earth observation platforms and sensors were used to assess trends in precipitation, temperature, and vegetation phenology in Bhutan from 1996 through 2017. This project studied precipitation using Climate Hazards Center Infrared Precipitation with Station data, analyzed temperature using the Famine Early Warning System Network Land Data Assimilation System, incorporated elevation data from the Shuttle Radar Topography Mission, and assessed vegetation phenology using Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) to evaluate climate variability and its effects on Bhutan. In situ temperature and precipitation data were also collected from the Himalayan Environmental Rhythm Observation and Evaluation System (HEROES) project and Bhutan’s National Center for Hydrology and Meterology. Modeled and in situ data showed similar trends in precipitation and temperature for the Thimpu, Chhukha, and Gasa regions. Aqua and Terra MODIS phenology data were variable from year to year. However, the data suggested a late onset of spring green up. Partners at the Bhutan Foundation and locally in Bhutan can use this information to better understand and monitor climate variability and replicate these methods for future analysis.

Kinley Dorji↗

Bhutan Water Resources Project Summary - Comparing Precipitation, Temperature, and Phenology Data Trends in Bhutan to Assist the Himalayan Environmental Rhythm Observation and Evaluation System (HEROES) Project

Himalayan countries, including Bhutan, have become vulnerable to warming trends which result in increasing temperature and variable rainfall and snowfall. By combining phenological and meteorological data, Earth observation platforms and sensors were used to assess trends in precipitation, temperature, and vegetation phenology in Bhutan from 1996 through 2017. This project studied precipitation using Climate Hazards Center Infrared Precipitation with Station data, analyzed temperature using the Famine Early Warning System Network Land Data Assimilation System, incorporated elevation data from the Shuttle Radar Topography Mission, and assessed vegetation phenology using Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) to evaluate climate variability and its effects on Bhutan. In situ temperature and precipitation data were also collected from the Himalayan Environmental Rhythm Observation and Evaluation System (HEROES) project and Bhutan’s National Center for Hydrology and Meterology. Modeled and in situ data showed similar trends in precipitation and temperature for the Thimpu, Chhukha, and Gasa regions. Aqua and Terra MODIS phenology data were variable from year to year. However, the data suggested a late onset of spring green up. Partners at the Bhutan Foundation and locally in Bhutan can use this information to better understand and monitor climate variability and replicate these methods for future analysis.

DEVELOP Project Summary↗

Colorado River Basin Water Resources: Pairing Phenology Citizen Science Observations with NASA Earth Observations to Monitor Riparian Vegetation

Native plant species in the Colorado River Basin are currently threatened by non-native species that are changing resource competition dynamics. Monitoring native species is essential for understanding changes in plant health throughout the year as well as how species’ distributions change inter-annually. However, current monitoring of species is limited to local efforts and cannot be efficiently conducted across more extensive regions of the basin. Project partners, including the USA National Phenology Network, United States Geological Survey, and Educating Children Outdoors, provided data and on-the-ground expertise to assist the project. NASA Earth observations, including Landsat 8 Operational Land Imager (OLI), Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI) from the European Space Agency, provided remote sensing data that covers the large spatial extent of the basin. This study combined both data sources over the study period of 2013 – 2019 and explored the relationship between on-the-ground phenology observations and remotely sensed phenology signals. The team found that pairing the datasets required spatial coordinates to be accurately logged and that it was necessary to have consistent observations throughout the year by citizen scientists. The team found promising relationships between phenology observations and the two vegetation indices, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), when study locations were in an area with a high proportion of vegetation within a satellite pixel. The results and methods outlined in this study create new opportunities to leverage remote sensing alongside citizen science data for widespread monitoring of non-native species.

Tyler Pantle↗