@conference {JRN55424, title = {Hacking food waste with 4-H youth}, booktitle = {American Geophysical Union}, year = {2021}, month = {8/4/2021}, abstract = {A team of 4-H Cooperative Extension agents, educational media professionals, and academics at New Mexico State University are collaborating to produce a novel food waste Hackathon for 4-H youth. 4-H (Head, Heart, Hands and Health) is a positive youth development organization with the primary purpose of empowering youth to reach their full potential; a guiding principle of 4-H is learning by doing. The goal of the food waste hackathon is to help youth learn about climate change, and to discover how they can make a difference by designing strategies to motivate sustainable behaviors in their communities. Our team selected food waste as the focus for the hackathon for three main reasons. Firstly, according to the UN Food and Agriculture Organization (FAO), food waste contributes 8 percent of total global greenhouse gas emissions. Included in this total is the wasted energy for growing, harvesting, packaging, and transporting food as well as the methane produced by food entering and rotting in landfills. Secondly, the issue of food waste is closely tied to food security. The FAO estimate that globally, food production must increase by 60 percent by 2050, yet more than 30 percent of food is currently lost or wasted. Thirdly, hacking food waste remains true to the 4-H mission {\textquotedblleft}[to provide] kids with community, mentors, and learning opportunities to develop the skills they need to create positive change in their lives and communities.{\textquotedblright} For this presentation we share our package of Hackathon materials and animations, and we reflect on successes, challenges and opportunities arising from the first event. }, keywords = {4-H Cooperative Extension, 4-H youth, academics, agents, collaborating, educational, food, Hackathon, media, New Mexico State University, novel, produce, professionals, waste}, author = {Caitriana Steele and Amanda Benton and Sierra Cain and Nicole Jacobs and Eva Madrid and Courtney Mitchell and Brittany Sonntag and Peter Skelton and Laura Bittner and Adri{\'a}n Aguirre Mart{\'\i}nez and Matheus Cezarotto and Amy Smith Muise and Barbara Chamberlin} } @conference {JRN55066, title = {Recent streamflow declines and snow drought in the Upper Rio Grande tributary basins}, booktitle = {Universities Council on Water Resources}, year = {2019}, month = {06/2019}, address = {Snowbird, UT}, abstract = {

To understand how future climate change will impact agricultural communities in the southwest, it is essential that we know how water availability has varied in the past. Over the last half century, there has been a trend of declining streamflow across the southwestern United States. For rivers such as the Rio Grande, with more than 65\% of the native flow originating as snowpack in mountainous headwaters, this trend has been driven by changes in winter precipitation, decreasing snow water equivalent (SWE), rising temperatures and earlier spring snowmelt. Researchers have already documented how lower SWE contributes to reduced streamflow volume from the entire Upper Rio Grande (URG) headwaters to the state line between Colorado and New Mexico. In this paper, we analyze the relationship between SWE and streamflow volume over the last 30 - 40 years at a finer spatial scale, looking at individual high elevation watersheds in New Mexico that contain tributaries of the Rio Grande. In addition, we use longer term datasets to identify years of snow drought that resulted from significantly lower winter precipitation. Two years of critically low snowpack (2002 and 2018) provide insights into potential future snow drought scenarios. We intend to use these findings to summarize potential impacts from climate change on the rural, acequia communities who live in and cultivate the narrow valleys situated just below the high elevation watersheds.

}, author = {Caitriana Steele and Emile H. Elias and J.T. Reyes} } @conference {JRN55064, title = {Vulnerability of acequia communities to climate change}, booktitle = {Universities Council on Water Resources}, year = {2019}, month = {06/2019}, address = {Snowbird, UT}, abstract = {

Vulnerability to climate change can be conceptualized either as an outcome resulting from biophysical changes to a system, or as a condition that already exists in a system and is subject to complex interactions between biophysical and human factors. Much of the research on agricultural systems in the U.S. has used the outcome vulnerability framework and focuses on producing technical, infrastructural, or policy-level solutions to climate change impacts. These types of assessment do not necessarily consider a community{\textquoteright}s ability to apply these solutions. The alternative framework often applies the terms {\textquotedblleft}contextual{\textquotedblright} or {\textquotedblleft}starting point{\textquotedblright} vulnerability. Assessments using a contextual vulnerability framework incorporate biophysical, social, cultural, political, institutional and economic factors to define pre-existing vulnerabilities. Contextual vulnerability frameworks are well-suited for place-based research that seeks to formulate community-level responses to climate change impacts, especially where there are strong institutional factors to consider. In this paper, we first summarize the biophysical factors that acequia communities in Northern New Mexico will be exposed to under a changing climate, notably impacts on snowpack, water supply and the health of surrounding forests. We then review contextual vulnerabilities using a framework that draws on the five capitals model. Some of the key factors that reduce the vulnerability of acequia communities to climate impacts include the institutions associated with their community-based irrigation systems, and the social capital and {\textquotedblleft}reciprocal interdependence{\textquotedblright} that acequia membership generates. Contemporary factors that increase acequia vulnerability include land use change, property-rights fragmentation and population decline.

}, author = {Caitriana Steele and J.T. Reyes and Emile H. Elias} } @conference {JRN55252, title = {What makes southwestern communities resilient to climate change?}, booktitle = {American Geophysical Union}, year = {2019}, month = {12/2019}, address = {San Francisco, California}, abstract = {With multiple interpretations of resilience in the physical and social science literature, and little consensus on its measurement and evaluation, determining what makes communities resilient to climate change can be challenging. Our goal is to identify drivers of community resilience in the southwest United States to inform outreach needs. These drivers may be physical traits of the community infrastructure and the surrounding environment, socio-economic and demographic characteristics of the community itself, or actions taken by public or private entities, individuals or groups to build community resilience. In pursuit of this goal, we have stepped outside of the conventional literature review to inventory programs that aim to improve community climate change readiness. In doing so, we establish how practitioners see resilience and consider climate change not in isolation but as a problem that co-exists with multiple issues such as available energy, transport, employment, housing, health services, access to food etc. We highlight challenges specific to the southwest region, where water is scarce, population is concentrated in urban and exurban settlements, and agriculture is important both economically and culturally. Further, the southwest sees some of the highest levels of poverty and deprivation in the country, most notably in tribal communities which have long been subject to institutional inequities and discrimination. Economically-disadvantaged communities will be disproportionately impacted by climate change and this is an environmental justice issue. To conclude, we summarize the infrastructural, demographic and socio-economic characteristics of resilient communities and we identify priority actions for outreach to build greater resilience for vulnerable southwestern communities. Poster $\#$624120.}, author = {Caitriana Steele and Gutierrez, Paaul and Skye Aney and Emile H. Elias and J.T. Reyes and Helena Deswood} } @article {JRN54927, title = {Cascading impacts of climate change on southwestern US cropland agriculture}, journal = {Climatic Change}, volume = {148}, year = {2018}, month = {05/2018}, pages = {437-450}, chapter = {437}, abstract = {

The interior southwest United States is one of the hottest, driest regions on the planet, yet irrigated cropland agriculture is successfully practiced where there is access to surface water and/or groundwater. Through climate change, the southwest is projected to become even hotter and drier, increasing the challenges faced by farmers across the region. We can assess the vulnerability of cropland agriculture, to assist in developing potential solutions to these challenges of warming temperatures and water scarcity. However, these types of biophysical vulnerability assessment usually generate technological or policy-level solutions that do not necessarily account for farmers{\textquoteright} ability to respond to climate change impacts. Further, there are non-climatic factors that also threaten the future of agriculture in the region, such as population increase, loss of agricultural land, and increasing competition for depleting water resources. In this paper, we assert that to fully address how southwestern farmers may respond to climate change impacts, we must consider both biophysical outcome and contextual vulnerabilities. Future research on individual localities and/or specific commodities and including cross-disciplinary analysis of socio-economic, institutional, cultural, and political factors alongside biophysical factors will help to develop more substantive understanding of system vulnerabilities and feasible adaptive solutions.

}, doi = {doi.org/10.1007/s10584-018-2220-4}, url = {files/bibliography/18-010.pdf}, author = {Caitriana Steele and J.T. Reyes and Emile H. Elias and Sierra Aney and A. Rango} } @article {JRN54791, title = {Evaluating MODIS snow products for modelling snowmelt runoff: case study of the Rio Grande headwaters}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {63}, year = {2017}, month = {09/2017}, pages = {234-243}, chapter = {234}, abstract = {

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS{\textquoteright} coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered- Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4\% and 4.7\%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCAMG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11\% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91. In conclusion, we recommend that when simulating snowmelt runoff from small basins (\< 4000 km2) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.

}, keywords = {MOD10A1, MODIS snow products, MODSCAG, snow covered area, Snowment Runoff Model}, doi = {dx.doi.org/10.1016/j.jag.2017.08.007}, url = {files/bibliography/17-018.pdf}, author = {Caitriana Steele and Jake Dialesandro and D. K. James and Emile H. Elias and A. Rango and M. Bleiweiss} } @conference {JRN54640, title = {Assessing Crop Vulnerability to Climate Change: A Southwest Perspective}, booktitle = {Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting}, year = {2016}, month = {11/2016}, publisher = {Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting}, organization = {Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting}, address = {Phoenix, AZ}, abstract = {
The USDA Southwest Regional Climate Hub is one of ten Climate Hubs and Sub-hubs established in 2014. The Hub region includes Arizona, California (partnering with the California Sub-Hub), Nevada, New Mexico and Utah. \ Beyond the mainland States, the SW hub also serves Hawaii and the US affiliated Pacific Islands. The Hub{\textquoteright}s primary mission is to deliver science-based knowledge and practical information to farmers, ranchers and forest landowners to help them adapt to changing environmental conditions. One of the recent activities of the hub was to review climate change impacts on Southwestern agriculture, with a specific focus on vulnerability. In considering vulnerability, we reviewed factors relating to (i) exposure of systems to the impacts of climate change, (ii) the inherent ability of systems to withstand exposure to the changing climate and (iii) adaptive capacity. In this presentation, we highlight some of the key findings from our review of regional vulnerabilities and we discuss a potential framework for vulnerability assessment of southwestern cropping systems. If vulnerability is a function of exposure, sensitivity, and adaptive capacity then in quantifying vulnerability, it is essential to recognize how these factors combine and interact at different scales within the system. For example at the scale of an individual crop, we can assess how changes in yield are affected by rising temperatures or drought, how sensitive the crop is to temperature or drought stress, and what potential adaptations might be practical at the scale of the crop (e.g., changing sowing dates, using different crop cultivars etc.) An alternative but complimentary approach is to assess contextual vulnerability, which incorporates the socio-political and economic context alongside climate change in evaluating the security of a human system (such as a farm, farming community or regional economy).\ 
}, author = {Caitriana Steele and A. Rango and Emile H. Elias and J.T. Reyes} } @conference {JRN54507, title = {Climate change and the water cycle: A new southwest regional climate hub curriculum unit for 6th-12th grade students}, booktitle = {American Geophysical Union Fall Meeting}, year = {2015}, month = {12/2015}, publisher = {AGU}, organization = {AGU}, address = {San Francisco, CA}, abstract = {

As climate change intensifies, increased temperatures and altered precipitation will make water, a limited resource in the arid southwestern United States, even scarcer in many locations. The USDA Southwest Regional Climate Hub (SWRCH) developed Climate Change and the Water Cycle, an engaging and scientifically rigorous education unit for 6th -12th grade students. The unit is aligned with Common Core State Standards and Next Generation Science Standards. Nine activities can be conducted over 10 instruction hours. Each activity can also stand alone.\  In partnership with SWRCH, the Asombro Institute for Science Education developed the unit. Each activity was reviewed by an educator for educational practices and by a scientist for scientific accuracy. The unit was pilot tested with 524 students in 2014, and pre- and post-tests were administered. Ninety-one percent of students were able to name a greenhouse gas on the post-test, compared to only 48\% on the pre-test. On the post-test, 86\% of students identified the relationship between average global temperature and carbon dioxide levels in the atmosphere, compared to only 52\% on the pre-test. A student commented: {\textquotedblleft}I loved all of the activities! They are fun and help us understand about what goes on in the world.{\textquotedblright} Educators who participated in pilot testing said: {\textquotedblleft}the entire curriculum is great, but I was particularly impressed with the progression of ideas and the variety of lessons,{\textquotedblright} and {\textquotedblleft}students could see the relevance and importance of these real life issues.{\textquotedblright}

}, author = {Caitriana Steele and Emile H. Elias and Stephanie Bestelmeyer and Stephanie Haan-Amato and Helena Deswood and A. Rango and Kris Havstad} } @conference {JRN54402, title = {Climate change, diversified agriculture and adaptive capacity in Hawaii}, booktitle = {13th Annual Climate Predictions Workshop, NOAA National Weather Service}, year = {2015}, month = {03/2015}, address = {Las Cruces, NM}, abstract = {

Export-oriented sugar cane and pineapple plantation agriculture once dominated Hawaii{\textquoteright}s economy but over the latter half of the 20th Century, there was a rapid decline in the production of these crops as Hawaii{\textquoteright}s competitive advantage over foreign producers dwindled. The decline of the plantations corresponds with the rise in more diversified agricultural activities which emphasize food production for local consumption. This diversification is key to maximizing the adaptive capacity of Hawaii{\textquoteright}s agriculture within a changing climate. In the context of the increasing temperatures and uncertain water availability projected for the 21st century, we present the expected exposures, sensitivity and adaptive capacity of economically and culturally important field and tree crops.

}, author = {Caitriana Steele and Emile H. Elias and A. Rango and Kris Havstad} } @conference {JRN54504, title = {Dust-on-snow and the timing of peak streamflow in the upper Rio Grande}, booktitle = {American Geophysical Union Fall Meeting}, year = {2015}, month = {12/2015}, publisher = {AGU}, organization = {AGU}, address = {San Francisco, CA}, abstract = {

Dust radiative forcing on high elevation snowpack is well-documented in the southern Rockies. Various field studies show that dust deposits decrease snow albedo and increase absorption of solar radiation, leading to earlier snowmelt and peak stream flows. These findings have implications for the use of temperature-index snow runoff models (such as the Snowmelt Runoff Model [SRM]) for predicting streamflow. In previous work, we have used SRM to simulate historical streamflow from 26 Upper Rio Grande sub-basins. Because dust radiative forcing can alter the relation between temperature and snowmelt, we wanted to find out if there is evidence of dust radiative forcing and earlier snowmelt in our study basins, particularly for those years where SRM was less successful in simulating streamflow. To accomplish this we have used openly-available data such as EPA air quality station measurements of particulate matter up to 10 micrometers (PM10); streamflow data from the USGS National Water Information System and Colorado Division of Water Resources; temperature, precipitation and snow water equivalent (SWE) from NRCS SNOTEL stations and remotely sensed data products from the MODIS sensor. Initial analyses indicate that a connection between seasonal dust concentration and streamflow timing (date of onset of warm-season snowmelt, date of streamflow center-of-volume) can be detected. This is further supported by time series analysis of MODIS-derived estimates of snow albedo and dust radiative-forcing in alpine and open subalpine snow fields.

}, author = {Caitriana Steele and Emile H. Elias and Moffitt, Ashlie and Beltran, Ismael and A. Rango} } @conference {JRN54430, title = {The NDVI: Back to basics}, booktitle = {American Geophysical Union}, year = {2014}, month = {12/2014}, address = {San Francisco, CA}, abstract = {

Ease of access to satellite sensor imagery and image products has driven the use of remote sensing data in many disciplines, including landscape ecology, forestry, environmental and wildlife management, agriculture, and epidemiology. A common format of these data is as vegetation indices and of these the most widely used is the Normalized Difference Vegetation Index (NDVI). There are many examples where the NDVI has been assessed as a covariate in spatial models of ecosystem health, faunal habitat, faunal distribution and condition, forage quality and disease risk, to name but a few. Occasionally, such applications detach the NDVI from its theoretical underpinnings; there may be no consideration of surface phenomena affecting the radiation environment and causing change in red and near infrared (NIR) reflectance. In this paper, we revisit classic remote sensing research to determine for which environmental modeling applications the NDVI is likely to be meaningful. We collated data from multiple published research studies to characterize the relationships between NDVI and vegetation canopy variables (leaf area index, biomass, percentage cover, and the fraction of absorbed radiation) over different vegetation types. Using these collated data sets, we discuss the physical basis of the relationships between NDVI and vegetation canopy variables, how and why these relationships differ, and what this means for the usefulness of the NDVI as a proxy for Earth surface or climate variables. We conclude with a critique of the variables that NDVI has been used to replace as a proxy and show its success or failure as a proxy variable relates back to the simple relationships known to exist between NDVI and vegetation canopy variables.\  Abstract B42C-01.

}, author = {Caitriana Steele and Alistair Smith and Dawn M. Browning} } @article {49907, title = {Spatially explicit representation of state-and-transition models}, journal = {Rangeland Ecology and Management}, volume = {65}, year = {2012}, month = {05/2012}, pages = {213-222}, chapter = {213}, abstract = {The broad-scale assessment of natural resource conditions (e.g., rangeland health, restoration needs) requires knowledge of their spatial distribution. We argue that creating a database that links state-and-transition models (STMs) to spatial units is a valuable management tool for structuring ground-based observations, management planning for landscapes, and for housing information on the responses of land areas to management actions. To address this need, we introduce a multi-factor classification system based on ecological sites and STMs that is directly linked to recent concepts of vegetation dynamics in rangelands. We describe how this classification was used as a basis for creating a spatial database and maps of ecological states. We provide an example of how the classification and mapping has been applied in over 1.2 million ha of public rangelands in southern New Mexico using aerial photo interpretation supplemented with existing inventory data and rapid field assessments. The resulting state map has been used by the Bureau of Land Management (BLM) to design landscape-level shrub control efforts, structure and report district-wide rangeland health assessments, and to evaluate locations for energy development. We conclude by discussing options for the development of state maps and their current limitations, including the use of satellite imagery and concepts for defining states. We argue that cataloging ecological states in a spatial context has clear benefits for rangeland managers because it connects STM concepts to specific land areas. State mapping also provides a means to generate and store spatially-explicit data resulting from tests of the propositions in STMs and conservation practices.}, keywords = {alternative states, ecological sites, geographic information systems, land classification, Soil Survey Geographic (SSURGO) Database}, doi = {10.2111/REM-D-11-00047.1}, url = {/files/bibliography/12-022.pdf}, author = {Caitriana Steele and Brandon T Bestelmeyer and Laura M. Burkett and Phil Smith and Yanoff, Steve} } @proceedings {JRN54528, title = {Modeling streamflow from snowmelt in the upper Rio Grande}, volume = {Session 16, p 17.}, year = {2011}, month = {11/2011}, publisher = {American Water Resources Association}, address = {Albuquerque, New Mexico}, abstract = {Annual snowpack in the high elevation snowsheds of the Upper Rio Grande (URG) Basin is a vital source of surface water for irrigated agriculture in New Mexico. Maximum streamflow from the annual snowpack usually occurs in early May for the southernmost snowsheds (e.g., Ojo Caliente) and at the end of May for northernmost snowsheds (e.g., Del Norte). Increases in average daily temperatures due to climate warming will affect the timing of maximum streamflow from snowmelt and this will have repercussions for water storage and supply during the growing season. Moreover, different climate change scenarios indicate less reliable precipitation, which could affect the quantity of water from melting snow. It is essential that we understand and plan for potential changes in the amount of water from snowmelt. We have been assembling input data for the Snowmelt Runoff Model (SRM) for use in 24 important snow basins in the URG. We present the results of this model for a good snow year (2005) and a poor snow year (2002) for a selection of basins along a south-north transect. SRM is driven with climate data (temperature and precipitation) and remotely estimated snow cover data. Once SRM simulates runoff at acceptable levels for snow basins for both good and poor snow years, we will be able to use data from global climate models to predict how the timing of maximum streamflow and quantity of water from snowmelt will change.}, author = {Caitriana Steele and A. Rango} } @conference {JRN54481, title = {Performance of the snowmelt runoff model when remotely-sensed estimates of snow covered area are not available}, booktitle = {American Geophysical Union}, year = {2010}, month = {12/2010}, publisher = {American Geophysical Union}, organization = {American Geophysical Union}, address = {San Francisco, CA}, abstract = {The Snowmelt Runoff Model (SRM) is usually run with snow cover depletion data. These daily depletion data can be derived through interpolation of periodic, remotely sensed measurements of basin snow covered area (SCA). It is also possible to run SRM successfully without snow depletion data in {\textquotedblleft}no snow cover{\textquotedblright} mode. In this mode, accumulated winter precipitation is carried forward into the melt season. SRM will start to melt the accumulated winter precipitation incrementally when a degree-day threshold is passed. It is these data that are used in place of the daily snow cover depletion data.
We compared results from SRM run in three modes of operation: (i) {\textquotedblleft}no snow cover{\textquotedblright} mode, (ii) with depletion curves interpolated from SCA estimated using fine resolution satellite sensor data (30 m), and (iii) with depletion curves interpolated from SCA estimated using coarse resolution satellite sensor data (500 m). Comparisons were made for an average snow year (2001), a heavy snow year (2008) and a low snow year (2002) over 3 watersheds in the Upper Rio Grande basin. In this paper, we discuss the reasons why estimates of runoff differ between the three modes of operation. Factors that may contribute to the variation in the results include the quantity of precipitation preceding the melt season, basin size, basin elevation, proximity of climate stations and the quality of remotely sensed SCA data.
These results have relevance for water managers. If reasonable estimates of runoff from snowmelt can be obtained in {\textquotedblleft}no snow cover{\textquotedblright} mode, the usability of SRM is improved for the non-expert in remote sensing or when remotely-sensed SCA data are unreliable (e.g., in years of extreme cloudiness).}, author = {Caitriana Steele and A. Rango} } @proceedings {JRN54483, title = {Sensitivity of the snowmelt runoff model to underestimates of remotely sensed snow covered area}, year = {2010}, month = {07/2010}, pages = {WEP2.PL.10}, address = {Honolulu, HI}, abstract = {

Three methods for estimating snow covered area (SCA) from Terra MODIS data were used to derive conventional depletion curves for input to the Snowmelt Runoff Model (SRM). We compared the MOD10 binary and fractional snow cover products and a method for estimating sub-pixel snow cover using spectral mixture analysis (SMA). All three methods underestimated SCA and this contributed to underestimates in runoff modeled by SRM. The closest relationship between measured and computed runoff was achieved when SRM was run with conventional depletion curves derived from the MODIS binary snow cover product (R2 \ = 0.91). Although the MODIS fractional snow cover product and SMA did not perform as well as the binary snow cover product (R2 \ = 0.70 and R2 \ = 0.72 respectively) we anticipate that either of these methods may be reworked to better account for forest cover in our study area and so improve SCA estimates.

}, keywords = {snow covered area, snowmelt runoff model, Terra MODIS}, url = {/files/bibliography/10-026.pdf}, author = {Caitriana Steele and A. Rango and D. K. Hall and M. Bleiweiss} } @conference {JRN54476, title = {An analysis of MODIS fractional snow cover estimates for snowmelt runoff modeling.}, booktitle = {American Geophysical Union}, year = {2009}, month = {12/2009}, publisher = {American Geophysical Union}, organization = {American Geophysical Union}, address = {San Francisco, CA}, abstract = {

Rio Grande streamflow is fed primarily by water from the melting snowpack in mountainous basins in the Southern Rockies. The snowpack accumulates mass in winter months and melts continuously through Spring and Summer. It is estimated that snowpack contributes 50\% of the annual water supply of Colorado and New Mexico. A warmer winter climate anomaly, earlier snowmelt and declining snowpack in mountainous basins has been observed since the 1950s throughout the Western United States. The effect of this warming climate pattern on snowpack mass, extent and duration has serious implications for water resources in the entire Rio Grande basin.

We use the Snowmelt Runoff Model (SRM: Martinec et al., 2008) to provide seasonal estimates of runoff contributions from snow cover in the upper Rio Grande basin and to predict runoff under different future climate scenarios. Sub-basin snow cover at a range of dates through the melt season is a key input variable driving SRM. Remotely sensed estimates of fractional snow cover, such as the MODIS snow cover product (MOD10A1 and MYD10A1, are the most available sources of snow cover but the accuracy of these estimates is highly variable. The aim of our current research is to compare existing snow cover products against alternative methods for estimating sub-pixel snow cover. Because we intend to share our snow mapping method with local water resource managers, we also aim to identify the most accurate, least complex algorithm to use for estimating snow cover from remotely sensed data.

In this paper we compare four methods for estimating fractional snow cover from 500 m MODIS data: (i) the empirical model of Kaufman et al., (2002) (ii) linear mixture modeling (Andersen, 1982), (iii) the Normalized Difference Snow Index (NDSI; Salomonson and Appel, 2002) calculated from MODIS daily reflectance products (MOD09GA and MYD09GA) and (iv) the MODIS daily fractional snow product (Salomonson and Appel, 2002) available from the National Snow and Ice Data Center. To evaluate accuracy of each method, all snow cover estimates from MODIS data were compared with snow cover estimates derived from 30 m Landsat TM data for a range of dates and snow conditions between 2001 and 2008. We also compare the results for both the Aqua and Terra sensors. Further, to ascertain the degree of error that could be introduced into snowmelt runoff predictions by each mapping method, we run SRM for the snowmelt season of 2009.

}, author = {Caitriana Steele and A. Rango and M. Bleiweiss} } @conference {50284, title = {The contribution of vegetation cover and bare soil to pixel reflectance in an arid ecosystem}, booktitle = {AGU 2008 Fall Meeting}, series = {AGU 2008 Fall Meetings}, year = {2008}, month = {December 15-19, }, address = {San Francisco, CA}, abstract = {The heterogeneity of vegetation and soils in arid and semi-arid environments complicates the analysis of medium spatial resolution remotely sensed imagery. A single pixel may contain several different types of vegetation, as well as a sizeable proportion of bare soil. We have used linear mixture modeling to explore the contribution of vegetation cover and bare soil to pixel reflectance. In October, 2006, aerial imagery (0.25 m spatial resolution) was acquired for our study sites in the Jornada Experimental Range, southern New Mexico. Imagery was also acquired from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for June and November, 2006. These data corresponded with pre- and post monsoon conditions. Object-based feature extraction was used to classify the aerial imagery to shrub, grass and bare ground cover classes. Percent cover was then calculated for each cover class. Visible-near-infrared and shortwave infrared ASTER reflectance data from both dates were combined into a single 18-band dataset (30 m spatial resolution). A vector overlay from the classification results of the aerial imagery was used to define pure endmember pixels in the ASTER imagery. Estimates of the proportions of shrub, grass and bare ground cover from the linear mixture modeling approach were compared with cover calculated using feature extraction from the aerial imagery. The results indicate that reflectance in ASTER pixels is likely to be a linear combination of the cover proportions of the three main cover types (shrubs, grass, bare ground). However, noticeable outliers in the relationship between cover calculated from each method, indicate there may be other variables that affect the accuracy with which can estimate cover using linear mixture modeling.}, keywords = {aerial imagery, ASTER, pixel}, author = {Caitriana Steele and Alistair Smith and A. Campanella and A. Rango} } @conference {50386, title = {Remote sensing of threshold conditions in an arid ecosystem}, booktitle = {Amereican Geophysical Union Fall Meeting}, year = {2007}, month = {December 10-14, }, pages = {B42B-1160}, address = {San Francisco, CA}, abstract = {Land management in the arid southwestern USA increasingly addresses thresholds in response to recent concepts adopted by private and public lands agencies and conservation organizations. Vegetation in arid rangelands typically presents as distinctive mosaics of vegetation patches, which persist in dynamic equilibrium with the abiotic environment and facilitative-competitive interactions between organisms. Theory and observation suggest that as an area approaches a threshold in response to disturbance, there is a concomitant change in the spatial arrangement of vegetation patches. This change is readily identifiable on fine spatial resolution aerial photography or satellite sensor imagery. We propose a classification method for identifying threshold-inducing change in vegetation pattern. To illustrate this method, we have applied an object-oriented, supervised classification to subsets of Quickbird imagery (70 cm ground resolution) over the Jornada basin in southern New Mexico. The imagery covers several land management regimes (private, public, federal) and provides spatial variation in ecosystem conditions. Imagery was first segmented to create fine and coarse resolution image objects. Fine resolution image objects are defined as having within-object spectral homogeneity at the scale of the shrub or single patch of grass or soil. Coarse resolution image objects are defined as containing spectral homogeneity at the scale of the vegetation stand. A classification tree was used to classify coarse resolution image objects to high risk of a threshold, low risk of a threshold, or post-threshold according to the content and spatial arrangement of shrub, grass and soil patches within them. Ground-based monitoring to detect localized threshold conditions across broad management areas is intractable so the use of remote sensing is essential to successful prevention of threshold development.}, keywords = {management, private, public lands, threshold}, author = {Caitriana Steele and Brandon T Bestelmeyer and A. Rango and Phil Smith and Andrea S. Laliberte} } @proceedings {50605, title = {Current and future options for the use of remote sensing for rangeland management and monitoring}, year = {2005}, month = {April 20-21, 200}, pages = {84}, address = {Zacatecas, Mexico}, abstract = {Many of the existing rangeland health, remotely sensed products are derived from medium spatial resolution imagery and, while these products may provide useful ancillary information for rangeland management, they do not provide information at spatial resolution that is fine enough to correspond with the field assessments conducted by rangeland management specialists. In this paper we present an overview of ongoing remote-sensing research at the ARS-USDA Jornada Experimental Range (the Remotely Sensed Indicators and Stratification (RSIS) Project) that aims to provide (1) relevant contextual information for optimizing ground sampling for field assessments and (2) accurate, fine-resolution vegetation maps and associated analyses for deriving remotely sensed indicators of rangeland health. Further, we provide a synopsis of other remote sensing activities at the Jornada Experimental Range.}, keywords = {management, monitoring, rangeland, remote sensing}, author = {Caitriana Steele and Andrea S. Laliberte and Brandon T Bestelmeyer and Jeffrey E. Herrick and A. Rango} }