@article{tiwari_tulbure_caineta_gaines_perin_kamal_krupnik_aziz_islam_2024, title={Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh}, volume={351}, ISSN={["1095-8630"]}, DOI={10.1016/j.jenvman.2023.119615}, abstractNote={High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April-May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.}, journal={JOURNAL OF ENVIRONMENTAL MANAGEMENT}, author={Tiwari, Varun and Tulbure, Mirela G. and Caineta, Julio and Gaines, Mollie D. and Perin, Vinicius and Kamal, Mustafa and Krupnik, Timothy J. and Aziz, Md Abdullah and Islam, A. F. M. Tariqul}, year={2024}, month={Feb} }
 @article{perin_tulbure_gaines_reba_yaeger_2022, title={A multi-sensor satellite imagery approach to monitor on-farm reservoirs}, volume={270}, ISSN={["1879-0704"]}, DOI={10.1016/j.rse.2021.112796}, abstractNote={Fresh water stored by on-farm reservoirs (OFRs) is an important component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season and for crop irrigation during the dry season, yet their seasonal and inter-annual variability and downstream impacts are not quantified. Therefore, OFRs' sub-weekly surface area changes are critical to understanding their dynamics and mitigating their downstream impacts. However, prior to the recent increase in satellite imagery availability and improvement in sensors' spatial resolution, monitoring the OFRs' sub-weekly surface area changes across space and time was challenging because OFRs occur in high numbers (i.e. hundreds) and are small water bodies (< 50 ha). We propose a novel multi-sensor approach to monitor OFRs surface areas, developed based on 736 OFRs in eastern Arkansas, USA, which leverages the use of PlanetScope (PS), RapidEye (RE), Sentinel 2 (S2), and Sentinel 1 (S1). First, we estimate the uncertainties in surface area for each sensor by comparing the surface area estimates to a validation dataset, and by comparing RE, S2 and S1 to PS—the sensor with the highest spatial resolution (i.e. 3.125 m). Second, we use the uncertainties of each sensor with a data assimilation algorithm based on the Kalman filter to obtain sub-weekly surface area time series for all OFRs. Our results show the lowest uncertainties for PS, followed by RE, S2 and S1. These uncertainties varied according to the OFRs' size and shape complexities. The surface area estimates derived from the Kalman filter including only the optical sensors resulted in high agreement (r2 > 0.95) and small uncertainties (4–8%) when compared to the validation dataset. We found higher uncertainties (5–14%) when adding S1 to the Kalman filter—this is related to the higher uncertainties found for S1 (~20%). The algorithm can assimilate optical and radar satellite data to increase the OFRs' surface area time series cadence allowing us to investigate sub-weekly surface area changes. The algorithm is not sensor-specific, and it accounts for the uncertainties in both the sensors observations and the resulting surface areas, which are key advantages when compared to other algorithms used to combine satellite data. By improving the surface area observations cadence and providing the surface area uncertainties, the approach presented in this study has the potential to enhance water conservation plans by allowing better assessment and management of the OFRs.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Perin, Vinicius and Tulbure, Mirela G. and Gaines, Mollie D. and Reba, Michele L. and Yaeger, Mary A.}, year={2022}, month={Mar} }
 @article{tulbure_broich_perin_gaines_ju_stheman_pavelsky_masek_yin_mai_et al._2022, title={Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone?}, volume={185}, ISSN={["1872-8235"]}, DOI={10.1016/j.isprsjprs.2022.01.021}, abstractNote={Spatiotemporal quantification of surface water and flooding is essential given that floods are among the largest natural hazards. Effective disaster response management requires near real-time information on flood extent. Satellite remote sensing is the only way of monitoring these dynamics across vast areas and over time. Previous water and flood mapping efforts have relied on optical time series, despite cloud contamination. This reliance on optical data is due to the availability of systematically acquired and easily accessible optical data globally for over 40 years. Prior research used either MODIS or Landsat data, trading either high temporal density but lower spatial resolution or lower cadence but higher spatial resolution. Both MODIS and Landsat pose limitations as Landsat can miss ephemeral floods, whereas MODIS misses small floods and inaccurately delineates flood edges. Leveraging high temporal frequency of 3–4 days of the existing Landsat-8 (L8) and two Sentinel-2 (S2) satellites combined, in this research, we assessed whether the increased temporal frequency of the three sensors improves our ability to detect surface water and flooding extent compared to a single sensor (L8 alone). Our study area was Australia’s Murray-Darling Basin, one of the world’s largest dryland basins that experiences ephemeral floods. We applied machine learning to NASA’s Harmonized Landsat Sentinel-2 (HLS) Surface Reflectance Product, which combines L8 and S2 observations, to map surface water and flooding dynamics. Our overall accuracy, estimated from a stratified random sample, was 99%. Our user’s and producer’s accuracy for the water class was 80% (±3.6%, standard error) and 76% (±5.8%). We focused on 2019, one of the most recent years when all three HLS sensors operated at full capacity. Our results show that water area (permanent and flooding) identified with the HLS was greater than that identified by L8, and some short-lived flooding events were detected only by the HLS. Comparison with high resolution (3 m) PlanetScope data identified extensive mixed pixels at the 30 m HLS resolution, highlighting the need for improved spatial resolution in future work. The HLS has been able to detect floods in cases when one sensor (L8) alone was not, despite 2019 being one of the driest years in the area, with few flooding events. The dense optical time-series offered by the HLS data is thus critical for capturing temporally dynamic phenomena (i.e., ephemeral floods in drylands), highlighting the importance of harmonized data such as the HLS.}, journal={ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING}, author={Tulbure, Mirela G. and Broich, Mark and Perin, Vinicius and Gaines, Mollie and Ju, Junchang and Stheman, Stephen V. and Pavelsky, Tamlin G. and Masek, Jeffrey and Yin, Simon and Mai, Joachim and et al.}, year={2022}, month={Mar}, pages={232–246} }
 @article{gaines_tulbure_perin_2022, title={Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States}, volume={58}, ISSN={["1944-7973"]}, DOI={10.1029/2021WR031484}, abstractNote={Surface water is the most readily accessible water resource and provides an array of ecosystem services, but its availability and access are stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000–2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the southeastern United States. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean −0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land‐use policies can be highly effective tools in controlling surface water variations in the Southeast.}, number={3}, journal={WATER RESOURCES RESEARCH}, author={Gaines, Mollie D. and Tulbure, Mirela G. and Perin, Vinicius}, year={2022}, month={Mar} }
 @article{mcquillan_tulbure_martin_2022, title={Forest water use is increasingly decoupled from water availability even during severe drought}, volume={2}, ISSN={["1572-9761"]}, url={https://doi.org/10.1007/s10980-022-01425-9}, DOI={10.1007/s10980-022-01425-9}, abstractNote={Key to understanding forest water balances is the role of tree species regulating evapotranspiration (ET), but the synergistic impact of forest species composition, topography, and water availability on ET and how this shapes drought sensitivity across the landscape remains unclear. Our aims were to quantify (1) the effect of forest composition and topography including elevation and hillslope gradients on the relationship between ET and water availability, and (2) whether the relationship has changed over time. We used remotely sensed Landsat and MODIS ET to quantify forest ET across the Blue Ridge ecoregion of the southeastern USA. Then quantified metrics describing ET responses to water availability and trends in responses over time and assessed how these metrics varied across elevation, hillslope, and forest composition gradients. We demonstrated forest ET is becoming less constrained by water availability at the expense of lateral flow. Drought impacts on ET diverged along elevation and hillslope gradients, and that divergence was more pronounced with increasingly severe drought, indicating high elevation and drier, upslope regions tend to maintain ET rates even during extreme drought. We identified a decoupling of ET from water availability over time, and found this process was accelerated at higher elevations and in areas with more diffuse-porous trees. Given the large proportion of forests on the landscape distributed across high elevation and upslope positions, reductions in downslope water availability could be widespread, amplifying vulnerability of runoff, the health of downslope vegetation, and aquatic biodiversity.}, journal={LANDSCAPE ECOLOGY}, author={McQuillan, Katie A. and Tulbure, Mirela G. and Martin, Katherine L.}, year={2022}, month={Feb} }
 @article{ordway_elmore_kolstoe_quinn_swanwick_cattau_taillie_guinn_chadwick_atkins_et al._2021, title={Leveraging the NEON Airborne Observation Platform for socio-environmental systems research}, volume={12}, ISSN={["2150-8925"]}, DOI={10.1002/ecs2.3640}, abstractNote={. During the 21st century, human – environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human – environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urban-ization, and wild fi res, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of mul-titemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network ’ s Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining speci fi c applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.}, number={6}, journal={ECOSPHERE}, author={Ordway, Elsa M. and Elmore, Andrew J. and Kolstoe, Sonja and Quinn, John E. and Swanwick, Rachel and Cattau, Megan and Taillie, Dylan and Guinn, Steven M. and Chadwick, K. Dana and Atkins, Jeff W. and et al.}, year={2021}, month={Jun} }
 @article{perin_roy_kington_harris_tulbure_stone_barsballe_reba_yaeger_2021, title={Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets}, volume={13}, ISSN={["2072-4292"]}, DOI={10.3390/rs13245176}, abstractNote={Basemap and Planet Fusion—derived from PlanetScope imagery—represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)—small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 ≥ 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 ≥ 0.55, and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.}, number={24}, journal={REMOTE SENSING}, author={Perin, Vinicius and Roy, Samapriya and Kington, Joe and Harris, Thomas and Tulbure, Mirela G. and Stone, Noah and Barsballe, Torben and Reba, Michele and Yaeger, Mary A.}, year={2021}, month={Dec} }
 @article{perin_tulbure_gaines_reba_yaeger_2021, title={On-farm reservoir monitoring using Landsat inundation datasets}, volume={246}, ISSN={["1873-2283"]}, DOI={10.1016/j.agwat.2020.106694}, abstractNote={On-farm reservoirs (OFRs)—artificial water impoundments that retain water from rainfall and run-off—enable farmers to store water during the wet season to be used for crop irrigation during the dry season. However, monitoring the inter- and intra-annual change of these water bodies remains a challenging task because they are typically small (< 10 ha) and occur in high numbers. Therefore, we used two existing Landsat inundation datasets—the U.S. Geological Survey Dynamic Surface Water Extent (DSWE) and the European Commission’s Joint Research Centre (JRC) Global Monthly Water History—to assess surface water area change of OFRs located in eastern Arkansas, the third most irrigated state in the U.S. that has seen a rapid increase of OFRs occurrence. We used an existent OFRs dataset as ground-truth. We aimed (i) to compare the performance of the DSWE and the JRC when characterizing OFRs of varied sizes and (ii) to assess the impact of climate variables (i.e., precipitation and temperature) on surface water area of OFRs. We found the highest mean percent errors (MPE) in size (~20%) for OFRs between 0 and 5 ha, the smallest size class in our study. The DSWE had a smaller MPE and higher agreement with our ground-truth dataset when compared to the JRC for OFRs smaller than 5 ha (p-value < 0.05). Both inundation datasets enabled us to estimate the seasonality in surface area change of OFRs, with the highest surface water extent between March–May, the months when the region receives most of the annual precipitation. Our results showed that both DSWE and JRC can be used to enhance hydrological assessments in poorly monitored basins that have a concentration of OFRs, and the methods can be applied to other study regions if the inundation datasets are available.}, journal={AGRICULTURAL WATER MANAGEMENT}, author={Perin, Vinicius and Tulbure, Mirela G. and Gaines, Mollie D. and Reba, Michele L. and Yaeger, Mary A.}, year={2021}, month={Mar} }
 @article{tulbure_hostert_kuemmerle_broich_2021, title={Regional matters: On the usefulness of regional land-cover datasets in times of global change}, volume={12}, ISSN={["2056-3485"]}, DOI={10.1002/rse2.248}, abstractNote={Unprecedented amounts of analysis‐ready Earth Observation (EO) data, combined with increasing computational power and new algorithms, offer novel opportunities for analysing ecosystem dynamics across large geographic extents, and to support conservation planning and action. Much research effort has gone into developing global EO‐based land‐cover and land‐use datasets, including tree cover, crop types, and surface water dynamics. Yet there are inherent trade‐offs between regional and global EO products pertaining to class legends, availability of training/validation data, and accuracy. Acknowledging and understanding these trade‐offs is paramount for both developing EO products and for answering science questions relevant for ecology or conservation studies based on these data. Here we provide context on the development of global EO‐based land‐cover and land‐use datasets, and outline advantages and disadvantages of both regional and global datasets. We argue that both types of EO‐derived land‐cover datasets can be preferable, with regional data providing the context‐specificity that is often required for policy making and implementation (e.g., land‐use and management, conservation planning, payment schemes for ecosystem services), making use of regional knowledge, particularly important when moving from land cover to actors. Ensuring that global and regional land‐cover and land‐use products derived based on EO data are compatible and nested, both in terms of class legends and accuracy assessment, should be a key consideration when developing such data. Open access high‐quality training and validation data derived as part of such efforts are of utmost importance. Likewise, global efforts to generate sets of essential variables for climate change, biodiversity, or eventually land use, which often require land‐cover maps as inputs, should consider regionalized, hierarchical approaches to not sacrifice regional context. Global change impacts manifest in regions, and so must the policy and planning responses to these challenges. EO data should embrace that regions matter, perhaps more than ever, in an age of global data availability and processing.}, journal={REMOTE SENSING IN ECOLOGY AND CONSERVATION}, author={Tulbure, Mirela G. and Hostert, Patrick and Kuemmerle, Tobias and Broich, Mark}, year={2021}, month={Dec} }
 @article{zeller_lewsion_fletcher_tulbure_jennings_2020, title={Understanding the Importance of Dynamic Landscape Connectivity}, volume={9}, ISSN={["2073-445X"]}, DOI={10.3390/land9090303}, abstractNote={Landscape connectivity is increasingly promoted as a conservation tool to combat the negative effects of habitat loss, fragmentation, and climate change. Given its importance as a key conservation strategy, connectivity science is a rapidly growing discipline. However, most landscape connectivity models consider connectivity for only a single snapshot in time, despite the widespread recognition that landscapes and ecological processes are dynamic. In this paper, we discuss the emergence of dynamic connectivity and the importance of including dynamism in connectivity models and assessments. We outline dynamic processes for both structural and functional connectivity at multiple spatiotemporal scales and provide examples of modeling approaches at each of these scales. We highlight the unique challenges that accompany the adoption of dynamic connectivity for conservation management and planning in the context of traditional conservation prioritization approaches. With the increased availability of time series and species movement data, computational capacity, and an expanding number of empirical examples in the literature, incorporating dynamic processes into connectivity models is more feasible than ever. Here, we articulate how dynamism is an intrinsic component of connectivity and integral to the future of connectivity science.}, number={9}, journal={LAND}, author={Zeller, Katherine A. and Lewsion, Rebecca and Fletcher, Robert J., Jr. and Tulbure, Mirela G. and Jennings, Megan K.}, year={2020}, month={Sep} }
 @article{spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of landsat satellite data_2019, url={https://doi.org/10.1016/j.scitotenv.2018.11.390}, DOI={https://doi.org/10.1016/j.scitotenv.2018.11.390}, abstractNote={Spatiotemporal distribution and systematic quantification of surface water and their drivers of change are critical. However, quantifying this distribution is challenging due to a lack of spatially explicit and temporally dynamic empirical data of both surface water and its drivers of change at large spatial scales. We focused on one of the largest dryland basins in the world, Australia's Murray-Darling Basin (MDB), recently identified as a global hotspot of water decline. We used a new remotely sensed time-series of surface water extent dynamics (SWD) data to quantify spatiotemporal patterns in surface water across the entire MDB and catchments and to assess natural and anthropogenic drivers of SWD, including climate and historical land use change. We show high intra- and inter-annual dynamics in surface water with a rapid loss during the Millennium Drought, the worst, decade-long drought in SE Australia. We show strong regional and catchment differences in SWD, with the northern basin showing high variability compared to the southern basin which shows a steady decline in surface water. Linear mixed effect models including climate and land-use change variables explained up to 70% variability in SWD with climate being more important in catchments of the northwestern MDB, whereas land-use was important primarily in the central MDB. Increase in fraction of dryland agriculture in a catchment and maximum temperature was negatively related to SWD, whereas precipitation and soil moisture were positively related to SWD. The fact that land-use change was an important explanatory variable of SWD in addition to climate is a significant result as land-use can be managed more effectively whereas climate-mitigation actions can be intractable, with global change scenarios predicting drier conditions for the area followed by a further reduction in surface water availability.}, journal={Science of The Total Environment}, year={2019} }
 @misc{surface water and flooding dynamics based on seasonally continuous landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)_2019, DOI={http://doi.org/10.5281/zenodo.2438110}, year={2019} }
 @misc{surface water and flooding dynamics data set based on seasonally continuous landsat data (1986-2011) in a dryland river basin (version version v1.0.0)_2019, DOI={http://doi.org/10.5281/zenodo.2441784}, abstractNote={Animations of the data are available here: https://doi.org/10.5281/zenodo.2438110 If you are using this data set, please cite the following publication: Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean.}, year={2019} }
 @article{the role of grace total water storage anomalies, streamflow and rainfall in stream salinity trends across australia’s murray-darling basin during and post the millennium drought_2019, DOI={https://doi.org/10.1016/j.jag.2019.101927}, abstractNote={By influencing water tables of saline aquifers, multiyear dry or wet periods can significantly delay or accelerate dryland salinity, but this effect remains poorly quantified at the large river basin scale. The Gravity and Climate Recovery Experiment (GRACE) satellite measures changes in the total water storage of river systems, providing a unique opportunity for better understanding connections between stream salinity and changes in catchment water storages at the large river basin scale. Here, we quantified the role of GRACE total water storage anomalies (TWSA) in stream salinity variability across Australia’s Murray-Darling Basin (∼1 million km2), while also accounting for streamflow and rainfall. We used the MERRA-2 global land surface model to i) place our findings in the context of the longer-term hydroclimatology (1980-present) and ii) to decompose TWSA into groundwater storage as an alternative driver variable. Multivariate time series regression models (generalized additive mixed models or GAMM) showed that the driver variables could explain 20–50% of the variability in stream salinity across 8 sub-catchments in the Murray Darling Basin. TWSA commonly explained as much variability as streamflow, while groundwater storage and TWSA had very similar explanatory power and rainfall only negligible contributions. The 2000–2009 Millennium Drought and the subsequent La Nina Floods had a predominantly decelerating and accelerating effect on stream salinity respectively and these trends were partially explained by trends in TWSA. Our study illustrates that GRACE can be a useful addition for monitoring and modeling dryland salinity over large river basins.}, journal={International Journal of Applied Earth Observation and Geoinformation}, year={2019} }
 @article{addressing spatio-temporal resolution constraints in landsat and modis-based mapping of large-scale floodplain inundation dynamics_2018, DOI={https://doi.org/10.1016/j.rse.2018.04.016}, abstractNote={Recent studies have developed novel long-term records of surface water (SW) maps on continental and global scales but due to the spatial and temporal resolution constraints of available satellite sensors, they are either of high spatial and low temporal resolution or vice versa. In this study, we address this limitation by exploring two approaches for generating an 8-day series of Landsat resolution (30 m) SW maps for three floodplain sites in south-eastern Australia during the 2010 La Nina Floods. Firstly, we applied a generalized additive regression model (GAM) that empirically relates Landsat-based SW extent to in-situ river flow to then predict additional time steps. Secondly, we used the STARFM and ESTARFM blending algorithms for predicting the Open Water Likelihood at 8-daily intervals and 30 m resolution from Landsat and MODIS imagery. ESTARFM outperformed STARFM and best blending accuracies were achieved in the floodplain site with the slowest changes in inundation extent through time. There was good agreement between the blended and statistically-modeled 8-day SW series and both series provided new and temporally consistent information about changes in inundation extent throughout the flooding cycles. After careful consideration of accuracy limitations and model assumptions, these SW records hold great potential for assimilation into hydrodynamic and hydrological models as well as improved management of terrestrial freshwater ecosystems.}, journal={Remote Sensing of Environment}, year={2018} }
 @article{heimhuber_tulbure_broich_2018, title={Comparing Landsat-MODIS blending with a statistical model for improved spatio-temporal quantification of large-scale floodplain inundation dynamics}, volume={211}, journal={Remote Sensing of Environment}, author={Heimhuber, V. and Tulbure, M.G. and Broich, M.}, year={2018}, pages={307–320} }
 @article{bishop-taylor_tulbure_broich_2018, title={Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series}, volume={33}, ISSN={0921-2973 1572-9761}, url={http://dx.doi.org/10.1007/s10980-018-0624-1}, DOI={10.1007/s10980-018-0624-1}, number={4}, journal={Landscape Ecology}, publisher={Springer Nature}, author={Bishop-Taylor, Robbi and Tulbure, Mirela G. and Broich, Mark}, year={2018}, month={Feb}, pages={625–640} }
 @article{bishop-taylor_tulbure_broich_2018, title={Impact of hydroclimatic variability on regional-scale landscape connectivity across a dynamic dryland region}, volume={94}, ISSN={1470-160X}, url={http://dx.doi.org/10.1016/j.ecolind.2017.07.029}, DOI={10.1016/j.ecolind.2017.07.029}, abstractNote={In dynamic dryland regions, accounting for spatiotemporal landscape dynamics is essential to understanding how ecological habitat networks are affected by hydroclimatic variability at regional or sub-continental scales. Here we assess how changes in the distribution and availability of surface water influence potential landscape connectivity for water-dependent organisms by combining graph theory network analysis with a Landsat-derived, seasonally continuous 25-year surface-water time-series. We focused on Australia’s Murray Darling Basin (MDB), a globally significant and ecologically stressed 1 million km2 semi-arid region recently affected by two unprecedented hydroclimatic extremes: the 1997–2010 Millennium Drought and 2010–2012 La Niña floods. We constructed potential habitat networks for two dispersal abilities using circuit theory resistance distances, and used ‘habitat availability’ graph theory metrics as indicators of regional-scale connectivity. We analysed 792 unique potential habitat networks containing over 6.6 million nodes, making our study one of the largest spatially explicit ecological network analyses yet conducted. Our indicators of connectivity revealed consistently positive but spatially heterogeneous relationships between flooded habitat area and landscape connectivity. Connectivity increased by over two orders of magnitude along the spectrum from severe drought to flood, associated with a transition from connectivity driven by intra-habitat or short-distance dispersal during drought to long-distance dispersal during wet conditions. Reductions in connectivity during drought were lower than expected given equivalent decreases in surface water habitat area, suggesting habitat network structure provides a degree of resistance to dry conditions. By providing insights into the processes driving connectivity during different phases along the drought-flood spectrum, our approach may assist in guiding conservation management aimed at maintaining or improving landscape connectivity within dynamic environments faced with increasing hydroclimatic variability.}, journal={Ecological Indicators}, publisher={Elsevier BV}, author={Bishop-Taylor, Robbi and Tulbure, Mirela G. and Broich, Mark}, year={2018}, month={Nov}, pages={142–150} }
 @article{shendryk_broich_tulbure_2018, title={Multi-sensor airborne and satellite data for upscaling tree number information in a structurally complex eucalypt forest}, volume={73}, ISSN={0303-2434}, url={http://dx.doi.org/10.1016/j.jag.2018.07.011}, DOI={10.1016/j.jag.2018.07.011}, abstractNote={Detailed information on the number and density of trees is important for conservation and sustainable use of forest resources. In this respect, remote sensing technology is a reliable tool for deriving timely and fine-scale information on forest inventory attributes. However, to better predict and understand the functioning of the forest, fine-scale measures of tree number and density must be extrapolated to the forest plot or stand levels through upscaling. In this study, we compared and combined three sources of remotely sensed data, including low point density airborne laser scans (ALS), synthetic aperture radar (SAR) and very-high resolution WorldView-2 imagery to upscale the total number of trees to the plot level in a structurally complex eucalypt forest using random forest regression. We used information on number of trees previously derived from high point density ALS as training data for a random forest regressor and field inventory data for validation. Overall, our modelled estimates resulted in significant fits (p < 0.05) with goodness-of-fit (R2) of 0.61, but systematically underestimated tree numbers. The ALS predictor variables (e.g. canopy cover and height) were the best for estimating tree numbers (R2 = 0.48, nRMSE = 61%), as compared to WorldView-2 and SAR predictor variables (R2 < 0.35). Overall, the combined use of WorldView-2, ALS and SAR predictors for estimating tree numbers showed substantial improvement in R2 of up to 0.13 as compared to their individual use. Our findings demonstrate the potential of using low point density ALS, SAR and WorldView-2 imagery to upscale high point density ALS derived tree numbers at the plot level in a structurally complex eucalypt forest.}, journal={International Journal of Applied Earth Observation and Geoinformation}, publisher={Elsevier BV}, author={Shendryk, Iurii and Broich, Mark and Tulbure, Mirela G.}, year={2018}, month={Dec}, pages={397–406} }
 @article{broich_tulbure_verbesselt_xin_wearne_2018, title={Quantifying Australia's dryland vegetation response to flooding and drought at sub-continental scale}, volume={212}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/j.rse.2018.04.032}, DOI={10.1016/j.rse.2018.04.032}, abstractNote={Vegetation response to flooding across large dryland areas such as Australia's Murray Darling Basin (MDB) is not understood synoptically and with locally relevant detail. We filled this knowledge gap by quantifying vegetation dynamics, defined here as greening and browning due to changing chlorophyll content and leaf area index, in response to flooding and rainfall across the floodplains of the entire MDB. We quantified vegetation and flooding dynamics using the same data source, namely 26 years of high resolution, wall-to-wall satellite data, in a top down statistical modeling approach, where we controlled for rainfall. Our time series (1986–2011) covered a period of extreme hydroclimatic variability, including the South East Australian Millennium Drought, thus providing a research opportunity to investigate how the relationship between vegetation and flooding changed during wet and dry periods. Our results showed that besides rainfall, flooding plays a key role in driving floodplain vegetation dynamics, yet the role of flooding varied across the MDB floodplains. We quantified a change in the relationship of how vegetation responds to rainfall and flooding with an unprecedented level of spatial detail. The change in the relationships coincided primarily with the onset of the Millennium Drought, yet local and regional differences in the timing of the change did occur, suggesting that the beginning of the Millennium Drought did not impact all floodplain areas at the same time. Our synoptic while locally relevant quantification of the changing response of vegetation to rainfall and flooding is a first step to help underpin Australia's investment into environmental water allocations.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Broich, Mark and Tulbure, Mirela G. and Verbesselt, Jan and Xin, Qinchuan and Wearne, Jack}, year={2018}, month={Jun}, pages={60–78} }
 @misc{bishop-taylor_tulbure_broich_2017, title={Data from: Surface-water dynamics and land use influence landscape connectivity across a major dryland region}, url={https://datadryad.org/resource/doi:10.5061/dryad.qf83q}, DOI={10.5061/dryad.qf83q}, publisher={Dryad Digital Repository}, author={Bishop-Taylor, Robbi and Tulbure, Mirela G. and Broich, Mark}, year={2017} }
 @book{brioch_wearne_tulbure_heimhuber_chandra_firmansyah_wijaya_weisse_stolle_2017, title={Exploring the utility of combining Landsat8, Sentinel2 and Sentinel1 time series for the quantification of forest degradation for test sites across Indonesia}, institution={World Resource Institute}, author={Brioch, M. and Wearne, J. and Tulbure, M.G. and Heimhuber, V. and Chandra, A. and Firmansyah, R. and Wijaya, A. and Weisse, M. and Stolle, F.}, year={2017} }
 @article{heimhuber_tulbure_broich_2017, title={Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth-observation and river flow data}, volume={53}, ISSN={0043-1397}, url={http://dx.doi.org/10.1002/2016WR019858}, DOI={10.1002/2016wr019858}, abstractNote={Periodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long‐term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat‐derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray‐Darling Basin (∼1 million km2). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain‐lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain‐lakes (r2 > 0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r2 > 0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud‐affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r2 > 0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods.}, number={2}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Heimhuber, V. and Tulbure, M. G. and Broich, M.}, year={2017}, month={Feb}, pages={1251–1269} }
 @article{bishop-taylor_tulbure_broich_2017, title={Surface-water dynamics and land use influence landscape connectivity across a major dryland region}, volume={27}, ISSN={1051-0761}, url={http://dx.doi.org/10.1002/eap.1507}, DOI={10.1002/eap.1507}, abstractNote={Landscape connectivity is important for the long-term persistence of species inhabiting dryland freshwater ecosystems, with spatiotemporal surface-water dynamics (e.g., flooding) maintaining connectivity by both creating temporary habitats and providing transient opportunities for dispersal. Improving our understanding of how landscape connectivity varies with respect to surface-water dynamics and land use is an important step to maintaining biodiversity in dynamic dryland environments. Using a newly available validated Landsat TM and ETM+ surface-water time series, we modelled landscape connectivity between dynamic surface-water habitats within Australia's 1 million km2 semiarid Murray Darling Basin across a 25-yr period (1987-2011). We identified key habitats that serve as well-connected "hubs," or "stepping-stones" that allow long-distance movements through surface-water habitat networks. We compared distributions of these habitats for short- and long-distance dispersal species during dry, average, and wet seasons, and across land-use types. The distribution of stepping-stones and hubs varied both spatially and temporally, with temporal changes driven by drought and flooding dynamics. Conservation areas and natural environments contained higher than expected proportions of both stepping-stones and hubs throughout the time series; however, highly modified agricultural landscapes increased in importance during wet seasons. Irrigated landscapes contained particularly high proportions of well-connected hubs for long-distance dispersers, but remained relatively disconnected for less vagile organisms. The habitats identified by our study may serve as ideal high-priority targets for land-use specific management aimed at maintaining or improving dispersal between surface-water habitats, potentially providing benefits to biodiversity beyond the immediate site scale. Our results also highlight the importance of accounting for the influence of spatial and temporal surface-water dynamics when studying landscape connectivity within highly variable dryland environments.}, number={4}, journal={Ecological Applications}, publisher={Wiley}, author={Bishop-Taylor, Robbi and Tulbure, Mirela G. and Broich, Mark}, year={2017}, month={Apr}, pages={1124–1137} }
 @article{dafforn_johnston_ferguson_humphrey_monk_nichols_simpson_tulbure_baird_2016, title={Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems}, volume={67}, ISSN={1323-1650}, url={http://dx.doi.org/10.1071/mf15108}, DOI={10.1071/mf15108}, abstractNote={Aquatic ecosystems are under threat from multiple stressors, which vary in distribution and intensity across temporal and spatial scales. Monitoring and assessment of these ecosystems have historically focussed on collection of physical and chemical information and increasingly include associated observations on biological condition. However, ecosystem assessment is often lacking because the scale and quality of biological observations frequently fail to match those available from physical and chemical measurements. The advent of high-performance computing, coupled with new earth observation platforms, has accelerated the adoption of molecular and remote sensing tools in ecosystem assessment. To assess how emerging science and tools can be applied to study multiple stressors on a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was held on 10–12 September 2014 at the Sydney Institute of Marine Sciences, Australia. Here we introduce a conceptual framework for assessing multiple stressors across ecosystems using emerging sources of big data and critique a range of available big-data types that could support models for multiple stressors. We define big data as any set or series of data, which is either so large or complex, it becomes difficult to analyse using traditional data analysis methods.}, number={4}, journal={Marine and Freshwater Research}, publisher={CSIRO Publishing}, author={Dafforn, K. A. and Johnston, E. L. and Ferguson, A. and Humphrey, C.L. and Monk, W. and Nichols, S. J. and Simpson, S. L. and Tulbure, M. G. and Baird, D. J.}, year={2016}, pages={393} }
 @article{shendryk_broich_tulbure_alexandrov_2016, title={Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest}, volume={173}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/j.rse.2015.11.008}, DOI={10.1016/j.rse.2015.11.008}, abstractNote={Full-waveform airborne laser scanning (ALS) is a powerful tool for characterizing and monitoring forest structure over large areas at the individual tree level. Most of the existing ALS-based algorithms for individual tree delineation from the point cloud are top-down, which are accurate for delineating cone-shaped conifers, but have lower delineation accuracies over more structurally complex broad-leaf forests. Therefore, in this study we developed a new bottom-up algorithm for detecting trunks and delineating individual trees with complex shapes, such as eucalypts. Experiments were conducted in the largest river red gum forest in the world, located in the south-east of Australia, that experienced severe dieback over the past six decades. For detection of individual tree trunks, we used a novel approach based on conditional Euclidean distance clustering that takes advantage of spacing between laser returns. Overall, the algorithm developed in our study was able to detect up to 67% of field-measured trees with diameter larger than or equal to 13 cm. By filtering ALS based on the intensity, return number and returned pulse width values, we were able to differentiate between woody and leaf tree components, thus improving the accuracy of tree trunk detections by 5% as compared to non-filtered ALS. The detected trunks were used to seed random walks on graph algorithm for tree crown delineation. The accuracy of tree crown delineation for different ALS point cloud densities was assessed in terms of tree height and crown width and resulted in up to 68% of field-measured trees being correctly delineated. The double increase in point density from ~ 12 points/m2 to ~ 24 points/m2 resulted in tree trunk detection increase of 11% (from 56% to 67%) and percentage of correctly delineated crowns increase of 13% (from 55% to 68%). Our results confirm an algorithm that can be used to accurately delineate individual trees with complex structures (e.g. eucalypts and other broadleaves) and highlight the importance of full-waveform ALS for individual tree delineation.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Shendryk, Iurii and Broich, Mark and Tulbure, Mirela G. and Alexandrov, Sergey V.}, year={2016}, month={Feb}, pages={69–83} }
 @article{malone_tulbure_pérez-luque_assal_bremer_drucker_hillis_varela_goulden_2016, title={Drought resistance across California ecosystems: evaluating changes in carbon dynamics using satellite imagery}, volume={7}, ISSN={2150-8925}, url={http://dx.doi.org/10.1002/ecs2.1561}, DOI={10.1002/ecs2.1561}, abstractNote={Drought is a global issue that is exacerbated by climate change and increasing anthropogenic water demands. The recent occurrence of drought in California provides an important opportunity to examine drought response across ecosystem classes (forests, shrublands, grasslands, and wetlands), which is essential to understand how climate influences ecosystem structure and function. We quantified ecosystem resistance to drought by comparing changes in satellite-derived estimates of water-use efficiency (WUE = net primary productivity [NPP]/evapotranspiration [ET]) under normal (i.e., baseline) and drought conditions (ΔWUE = WUE2014 − baseline WUE). With this method, areas with increasing WUE under drought conditions are considered more resilient than systems with declining WUE. Baseline WUE varied across California (0.08 to 3.85 g C/mm H2O) and WUE generally increased under severe drought conditions in 2014. Strong correlations between ΔWUE, precipitation, and leaf area index (LAI) indicate that ecosystems with a lower average LAI (i.e., grasslands) also had greater C-uptake rates when water was limiting and higher rates of carbon-uptake efficiency (CUE = NPP/LAI) under drought conditions. We also found that systems with a baseline WUE ≤ 0.4 exhibited a decline in WUE under drought conditions, suggesting that a baseline WUE ≤ 0.4 might be indicative of low drought resistance. Drought severity, precipitation, and WUE were identified as important drivers of shifts in ecosystem classes over the study period. These findings have important implications for understanding climate change effects on primary productivity and C sequestration across ecosystems and how this may influence ecosystem resistance in the future.}, number={11}, journal={Ecosphere}, publisher={Wiley}, author={Malone, Sparkle L. and Tulbure, Mirela G. and Pérez-Luque, Antonio J. and Assal, Timothy J. and Bremer, Leah L. and Drucker, Debora P. and Hillis, Vicken and Varela, Sara and Goulden, Michael L.}, year={2016}, month={Nov}, pages={e01561} }
 @article{shendryk_broich_tulbure_mcgrath_keith_alexandrov_2016, title={Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest}, volume={187}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/j.rse.2016.10.014}, DOI={10.1016/j.rse.2016.10.014}, abstractNote={Declining forest health can affect crucial ecosystem functions, such as carbon storage in biomass and soils, the regulation of water regimes, the modulation of regional climate and conservation of biodiversity. Airborne laser scanning (ALS) and imaging spectroscopy (IS) are two potentially complementary remote sensing technologies capable of characterizing and monitoring regional forest health. However, the combined use of ALS and IS data to classify the health of individual trees has not yet been assessed. In this study we propose a new approach utilizing ALS and IS combined to characterize the health of individual trees. Firstly, we applied a recently developed bottom-up individual tree delineation algorithm across a structurally complex floodplain eucalypt forest that has experienced episodes of severe dieback over the past six decades. We further calculated ALS and IS indices for delineated tree crowns and used them as predictor variables in machine learning models. We trained and evaluated an object-oriented random forest classifier against field-measured tree crown dieback and transparency ratios, as indicators of eucalypt tree health and crown density, respectively. Our results showed that dieback levels of individual trees can be classified using ALS and IS with an overall accuracy of 81% and a kappa score of 0.66, while the classification of tree crown transparency levels had an overall accuracy of 70% and a kappa score of 0.5. Returned pulse width, intensity and density related ALS indices were the most important predictors in the tree health classification, as they accounted for > 40% of the variance in the data. At the forest level in terms of dieback, 81.5% of correctly delineated trees were classified as healthy, 12.3% as declining and 6.2% as dying or dead. Dieback occurred primarily in areas that were flooded < 5% of the time, as quantified by Landsat derived flooding frequency (1986–2011). Our results provide a novel application of ALS and IS to accurately classify the health of individual trees in a structurally complex eucalypt forest, enabling us to prioritize areas for forest health promotion and conservation of biodiversity.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Shendryk, Iurii and Broich, Mark and Tulbure, Mirela G. and McGrath, Andrew and Keith, David and Alexandrov, Sergey V.}, year={2016}, month={Dec}, pages={202–217} }
 @inproceedings{shendryk_broich_tulbure_mcgrath_keith_alexandrov_2016, title={Multiscale forest health mapping: the potential of air- and space-borne sensors}, author={Shendryk, I. and Broich, M. and Tulbure, M.G. and McGrath, A. and Keith, D. and Alexandrov, S.V.}, year={2016} }
 @article{broich_tulbure_2016, title={RESPONSE OF RIPARIAN VEGETATION IN AUSTRALIA"S LARGEST RIVER BASIN TO INTER AND INTRA-ANNUAL CLIMATE VARIABILITY AND FLOODING AS QUANTIFIED WITH LANDSAT AND MODIS}, volume={XLI-B8}, ISSN={2194-9034}, url={http://dx.doi.org/10.5194/isprs-archives-xli-b8-577-2016}, DOI={10.5194/isprs-archives-xli-b8-577-2016}, abstractNote={Abstract. Australia is a continent subject to high rainfall variability, which has major influences on runoff and vegetation dynamics. However, the resulting spatial-temporal pattern of flooding and its influence on riparian vegetation has not been quantified in a spatially explicit way. Here we focused on the floodplains of the entire Murray-Darling Basin (MDB), an area that covers over 1M km2, as a case study. The MDB is the country’s primary agricultural area with scarce water resources subject to competing demands and impacted by climate change and more recently by the Millennium Drought (1999–2009). Riparian vegetation in the MDB floodplain suffered extensive decline providing a dramatic degradation of riparian vegetation. We quantified the spatial-temporal impact of rainfall, temperature and flooding patters on vegetation dynamics at the subcontinental to local scales and across inter to intra-annual time scales based on three decades of Landsat (25k images), Bureau of Meteorology data and one decade of MODIS data. Vegetation response varied in space and time and with vegetation types, densities and location relative to areas frequently flooded. Vegetation degradation trends were observed over riparian forests and woodlands in areas where flooding regimes have changed to less frequent and smaller inundation extents. Conversely, herbaceous vegetation phenology followed primarily a ‘boom’ and ‘bust’ cycle, related to inter-annual rainfall variability. Spatial patters of vegetation degradation changed along the N-S rainfall gradient but flooding regimes and vegetation degradation patterns also varied at finer scale, highlighting the importance of a spatially explicit, internally consistent analysis and setting the stage for investigating further cross-scale relationships. Results are of interest for land and water management decisions. The approach developed here can be applied to other areas globally such as the Nile river basin and Okavango River delta in Africa or the Mekong River Basin in Southeast Asia.}, journal={ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, publisher={Copernicus GmbH}, author={Broich, M. and Tulbure, M. G.}, year={2016}, month={Jun}, pages={577–578} }
 @article{tulbure_broich_stehman_2016, title={SPATIOTEMPORAL DYNAMICS OF SURFACE WATER EXTENT FROM THREE DECADES OF SEASONALLY CONTINUOUS LANDSAT TIME SERIES AT SUBCONTINENTAL SCALE}, volume={XLI-B8}, ISSN={2194-9034}, url={http://dx.doi.org/10.5194/isprs-archives-xli-b8-403-2016}, DOI={10.5194/isprs-archives-xli-b8-403-2016}, abstractNote={Abstract. Surface water is a critical resource in semi-arid areas. The Murray-Darling Basin (MDB) of Australia, one of the largest semi-arid basins in the world is aiming to set a worldwide example of how to balance multiple interests (i.e. environment, agriculture and urban use), but has suffered significant water shrinkages during the Millennium Drought (1999-2009), followed by extensive flooding. Baseline information and systematic quantification of surface water (SW) extent and flooding dynamics in space and time are needed for managing SW resources across the basin but are currently lacking. To synoptically quantify changes in SW extent and flooding dynamics over MDB, we used seasonally continuous Landsat TM and ETM+ data (1986 – 2011) and generic machine learning algorithms. We further mapped flooded forest at a riparian forest site that experienced severe tree dieback due to changes in flooding regime. We used a stratified sampling design to assess the accuracy of the SW product across time. Accuracy assessment yielded an overall classification accuracy of 99.94%, with producer’s and user’s accuracy of SW of 85.4% and 97.3%, respectively. Overall accuracy was the same for Landsat 5 and 7 data but user’s and producer’s accuracy of water were higher for Landsat 7 than 5 data and stable over time. Our validated results document a rapid loss in SW bodies. The number, size, and total area of SW showed high seasonal variability with highest numbers in winter and lowest numbers in summer. SW extent per season per year showed high interannual and seasonal variability, with low seasonal variability during the Millennium Drought. Examples of current uses of the new dataset will be presented and include (1) assessing ecosystem response to flooding with implications for environmental water releases, one of the largest investment in environment in Australia; (2) quantifying drivers of SW dynamics (e.g. climate, human activity); (3) quantifying changes in SW dynamics and connectivity for water dependent organisms; (4) assessing the impact of flooding on riparian vegetation health. The approach developed here is globally applicable, relevant to areas with competing water demands (e.g. Okavango River delta, Mekong River Basin). Future work should incorporate Landsat 8 and Sentinel-2 data for continued quantification of SW dynamics.}, journal={ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, publisher={Copernicus GmbH}, author={Tulbure, M. G. and Broich, M. and Stehman, Stephen V.}, year={2016}, month={Jun}, pages={403–404} }
 @article{tulbure_broich_stehman_kommareddy_2016, title={Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region}, volume={178}, ISSN={0034-4257}, url={http://dx.doi.org/10.1016/j.rse.2016.02.034}, DOI={10.1016/j.rse.2016.02.034}, abstractNote={Seasonally continuous long-term information on surface water and flooding extent over subcontinental scales is critical for quantifying spatiotemporal changes in surface water dynamics. We used seasonally continuous Landsat TM/ETM + data and generic random forest-based models to synoptically map the extent and dynamics of surface water and flooding (1986–2011) over the Murray–Darling Basin (MDB). The MDB is a large semi-arid basin with competing demands for water that has recently experienced one of the most severe droughts in the southeast of Australia. We used a stratified random probability sampling design with 500 sample pixels each observed across time to assess the accuracy of the surface water maps. We further developed models to map flooded forest at a riparian site that experienced severe tree dieback. Water indices and bands 5 and 6 were among the top 10 explanatory variables most important for mapping surface water. Surface water extent per season per year showed high inter-annual and seasonal variability, with low extent and variability during the Millennium Drought (1999–2009). Accuracy assessment yielded an overall classification accuracy of 99.9% (± 0.02% standard error) with 87% (± 3%) and 96% (± 2%) producer's and user's accuracy of water, respectively. User's and producer's accuracies of water were higher for Landsat 7 than Landsat 5 data. Both producer's and user's accuracies of water were lower in wet years compared to dry years. The approach presented here can be further developed for global application and is relevant to areas with competing water demands. Quantifying the uncertainty of the accuracy assessment and providing an unbiased accuracy estimate are imperative steps when remotely sensed products are intended to be used for follow on applications.}, journal={Remote Sensing of Environment}, publisher={Elsevier BV}, author={Tulbure, Mirela G. and Broich, Mark and Stehman, Stephen V. and Kommareddy, Anil}, year={2016}, month={Jun}, pages={142–157} }
 @article{tulbure_broich_stehman_kommareddy_2016, title={Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region}, volume={178}, DOI={http://dx.doi.org/10.1016/j.rse.2016.02.034}, abstractNote={Seasonally continuous long-term information on surface water and flooding extent over subcontinental scales is critical for quantifying spatiotemporal changes in surface water dynamics. We used seasonally continuous Landsat TM/ETM + data and generic random forest-based models to synoptically map the extent and dynamics of surface water and flooding (1986–2011) over the Murray–Darling Basin (MDB). The MDB is a large semi-arid basin with competing demands for water that has recently experienced one of the most severe droughts in the southeast of Australia. We used a stratified random probability sampling design with 500 sample pixels each observed across time to assess the accuracy of the surface water maps. We further developed models to map flooded forest at a riparian site that experienced severe tree dieback. Water indices and bands 5 and 6 were among the top 10 explanatory variables most important for mapping surface water. Surface water extent per season per year showed high inter-annual and seasonal variability, with low extent and variability during the Millennium Drought (1999–2009). Accuracy assessment yielded an overall classification accuracy of 99.9% (± 0.02% standard error) with 87% (± 3%) and 96% (± 2%) producer's and user's accuracy of water, respectively. User's and producer's accuracies of water were higher for Landsat 7 than Landsat 5 data. Both producer's and user's accuracies of water were lower in wet years compared to dry years. The approach presented here can be further developed for global application and is relevant to areas with competing water demands. Quantifying the uncertainty of the accuracy assessment and providing an unbiased accuracy estimate are imperative steps when remotely sensed products are intended to be used for follow on applications.}, journal={Remote Sensing of Environment}, author={Tulbure, Mirela G. and Broich, Mark and Stehman, Stephen V. and Kommareddy, Anil}, year={2016}, pages={142–157} }
 @article{broich_huete_paget_ma_tulbure_coupe_evans_beringer_devadas_davies_et al._2015, title={A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications}, volume={64}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2014.11.017}, DOI={10.1016/j.envsoft.2014.11.017}, abstractNote={Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN). We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Broich, Mark and Huete, Alfredo and Paget, Matt and Ma, Xuanlong and Tulbure, Mirela and Coupe, Natalia Restrepo and Evans, Bradley and Beringer, Jason and Devadas, Rakhesh and Davies, Kevin and et al.}, year={2015}, month={Feb}, pages={191–204} }
 @article{dafforn_johnston_ferguson_humphrey_monk_nichols_simpson_tulbure_baird_2015, title={Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems}, journal={Marine and Freshwater Research}, author={Dafforn, K. A. and Johnston, E. L. and Ferguson, A. and Humphrey, C. L. and Monk, W. and Nichols, S. J. and Simpson, S. L. and Tulbure, M. G. and Baird, D. J.}, year={2015} }
 @article{heimhuber_tulbure_broich_2015, title={Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of earth observation data}, volume={12}, ISSN={1812-2116}, url={http://dx.doi.org/10.5194/hessd-12-11847-2015}, DOI={10.5194/hessd-12-11847-2015}, abstractNote={Abstract. The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.}, number={11}, journal={Hydrology and Earth System Sciences Discussions}, publisher={Copernicus GmbH}, author={Heimhuber, V. and Tulbure, M. G. and Broich, M.}, year={2015}, month={Nov}, pages={11847–11903} }
 @article{bishop-taylor_tulbure_broich_2015, title={Surface water network structure, landscape resistance to movement and flooding vital for maintaining ecological connectivity across Australia’s largest river basin}, volume={30}, ISSN={0921-2973 1572-9761}, url={http://dx.doi.org/10.1007/s10980-015-0230-4}, DOI={10.1007/s10980-015-0230-4}, number={10}, journal={Landscape Ecology}, publisher={Springer Science and Business Media LLC}, author={Bishop-Taylor, Robbi and Tulbure, Mirela G. and Broich, Mark}, year={2015}, month={Jun}, pages={2045–2065} }
 @article{broich_huete_tulbure_ma_xin_paget_restrepo-coupe_davies_devadas_held_et al._2014, title={Land surface phenological response to decadal climate variability across Australia using satellite remote sensing}, volume={11}, ISSN={1810-6285}, url={http://dx.doi.org/10.5194/bgd-11-7685-2014}, DOI={10.5194/bg-11-5181-2014}, abstractNote={Land surface phenological cycles of vegetation greening and browning are influenced by variability in cli- matic forcing. Quantitative spatial information on pheno- logical cycles and their variability is important for agricul- tural applications, wildfire fuel accumulation, land manage- ment, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows an- nually recurring patterns. However, precipitation-driven non- annual phenology of arid and semi-arid systems (i.e., dry- lands) received much less attention, despite the fact that they cover more than 30 % of the global land surface. Here, we focused on Australia, a continent with one of the most vari- able rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing. To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the South- ern Oscillation Index (SOI). We conducted a continent- wide investigation and a more detailed investigation over the Murray-Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles across Australia. The peak of pheno- logical cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and par- ticularly in floodplain and wetland areas. Integrated green- ness of the phenological cycles (surrogate of vegetation pro- ductivity) showed positive anomalies of more than 2 stan- dard deviations over most of eastern Australia in 2009-2010, which coincided with the transition from the El Nino-induced decadal droughts to flooding caused by La Nina.}, number={5}, journal={Biogeosciences Discussions}, publisher={Copernicus GmbH}, author={Broich, M. and Huete, A. and Tulbure, M. G. and Ma, X. and Xin, Q. and Paget, M. and Restrepo-Coupe, N. and Davies, K. and Devadas, R. and Held, A. and et al.}, year={2014}, month={May}, pages={7685–7719} }
 @article{shendryk_tulbure_broich_2014, title={Mapping tree health using airborne full-waveform laser scans and hyperspectral imagery: a case study for floodplain eucalypt forest}, volume={1}, journal={AGU Fall Meeting Abstracts}, author={Shendryk, I. and Tulbure, M. G. and Broich, M.}, year={2014}, pages={0081} }
 @article{tulbure_kininmonth_broich_2014, title={Spatiotemporal dynamics of surface water networks across a global biodiversity hotspot—implications for conservation}, volume={9}, ISSN={1748-9326}, url={http://dx.doi.org/10.1088/1748-9326/9/11/114012}, DOI={10.1088/1748-9326/9/11/114012}, abstractNote={The concept of habitat networks represents an important tool for landscape conservation and management at regional scales. Previous studies simulated degradation of temporally fixed networks but few quantified the change in network connectivity from disintegration of key features that undergo naturally occurring spatiotemporal dynamics. This is particularly of concern for aquatic systems, which typically show high natural spatiotemporal variability. Here we focused on the Swan Coastal Plain, a bioregion that encompasses a global biodiversity hotspot in Australia with over 1500 water bodies of high biodiversity. Using graph theory, we conducted a temporal analysis of water body connectivity over 13 years of variable climate. We derived large networks of surface water bodies using Landsat data (1999–2011). We generated an ensemble of 278 potential networks at three dispersal distances approximating the maximum dispersal distance of different water dependent organisms. We assessed network connectivity through several network topology metrics and quantified the resilience of the network topology during wet and dry phases. We identified ‘stepping stone’ water bodies across time and compared our networks with theoretical network models with known properties. Results showed a highly dynamic seasonal pattern of variability in network topology metrics. A decline in connectivity over the 13 years was noted with potential negative consequences for species with limited dispersal capacity. The networks described here resemble theoretical scale-free models, also known as ‘rich get richer’ algorithm. The ‘stepping stone’ water bodies are located in the area around the Peel-Harvey Estuary, a Ramsar listed site, and some are located in a national park. Our results describe a powerful approach that can be implemented when assessing the connectivity for a particular organism with known dispersal distance. The approach of identifying the surface water bodies that act as ‘stepping stone’ over time may help prioritize surface water bodies that are essential for maintaining regional scale connectivity.}, number={11}, journal={Environmental Research Letters}, publisher={IOP Publishing}, author={Tulbure, Mirela G and Kininmonth, Stuart and Broich, Mark}, year={2014}, month={Nov}, pages={114012} }
 @misc{tulbure_broich_2013, title={Data from: Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011}, url={https://datadryad.org/resource/doi:10.5061/dryad.50003}, DOI={10.5061/dryad.50003}, publisher={Dryad Digital Repository}, author={Tulbure, Mirela G. and Broich, Mark}, year={2013} }
 @article{tulbure_broich_2013, title={Data from: Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011}, journal={http://datadryad.org/resource/doi:10.5061/dryad.50003}, author={Tulbure, Mirela G. and Broich, M.}, year={2013} }
 @article{chintala_wimberly_djira_tulbure_2013, title={Interannual variability of crop residue potential in the north central region of the United States}, volume={49}, ISSN={0961-9534}, url={http://dx.doi.org/10.1016/j.biombioe.2012.12.018}, DOI={10.1016/j.biombioe.2012.12.018}, abstractNote={Crop residue is potentially a major biomass feedstock for bio-based industries. Spatial and interannual variability of crop residue yield potential in relation to climatic variability in average of daily mean temperature and total precipitation during crop growing season at regional scale has not previously been investigated. Crop yield data were used to estimate crop residue yield potential and quantify its spatial and temporal variability across the North Central Region of the USA. A correlation analysis was also conducted to examine the relationship between temporal stability of crop residue yield and climatic variability. Temporal variability in crop residue and climate parameters was quantified by the coefficient of variation (CV). Based on this observational study, the counties in the south- eastern part of the North Central Region were observed to have relatively stable crop residue yield potential and also have a relatively low CV of average of daily mean temperature and total precipitation during the crop growing season. The CV of crop residue yield potential was positively correlated with the CVs of average of daily mean temperature and total precipitation. These findings highlight the influences of climatic variability on the spatial and temporal patterns of crop residue yield potential, and emphasize that these factors should be taken into account when developing regional strategies for sustainable bioenergy production.}, journal={Biomass and Bioenergy}, publisher={Elsevier BV}, author={Chintala, Rajesh and Wimberly, Michael C. and Djira, Gemechis D. and Tulbure, Mirela G.}, year={2013}, month={Feb}, pages={231–238} }
 @article{tulbure_broich_2013, title={Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011}, volume={79}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000318889600004&KeyUID=WOS:000318889600004}, DOI={10.1016/j.isprsjprs.2013.01.010}, abstractNote={Detailed information on the spatiotemporal dynamic in surface water bodies is important for quantifying the effects of a drying climate, increased water abstraction and rapid urbanization on wetlands. The Swan Coastal Plain (SCP) with over 1500 wetlands is a global biodiversity hotspot located in the southwest of Western Australia, where more than 70% of the wetlands have been lost since European settlement. SCP is located in an area affected by recent climate change that also experiences rapid urban development and ground water abstraction. Landsat TM and ETM+ imagery from 1999 to 2011 has been used to automatically derive a spatially and temporally explicit time-series of surface water body extent on the SCP. A mapping method based on the Landsat data and a decision tree classification algorithm is described. Two generic classifiers were derived for the Landsat 5 and Landsat 7 data. Several landscape metrics were computed to summarize the intra and interannual patterns of surface water dynamic. Top of the atmosphere (TOA) reflectance of band 5 followed by TOA reflectance of bands 4 and 3 were the explanatory variables most important for mapping surface water bodies. Accuracy assessment yielded an overall classification accuracy of 96%, with 89% producer’s accuracy and 93% user’s accuracy of surface water bodies. The number, mean size, and total area of water bodies showed high seasonal variability with highest numbers in winter and lowest numbers in summer. The number of water bodies in winter increased until 2005 after which a decline can be noted. The lowest numbers occurred in 2010 which coincided with one of the years with the lowest rainfall in the area. Understanding the spatiotemporal dynamic of surface water bodies on the SCP constitutes the basis for understanding the effect of rainfall, water abstraction and urban development on water bodies in a spatially explicit way.}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, author={Tulbure, Mirela G. and Broich, Mark}, year={2013}, pages={44–52} }
 @article{tulbure_wimberly_boe_owens_2012, title={Climatic and genetic controls of yields of switchgrass, a model bioenergy species}, volume={146}, ISSN={0167-8809}, url={http://dx.doi.org/10.1016/j.agee.2011.10.017}, DOI={10.1016/j.agee.2011.10.017}, abstractNote={The U.S. Renewable Fuel Standard calls for 136 billion liters of renewable fuels production by 2022. Switchgrass (Panicum virgatum L.) has emerged as a leading candidate to be developed as a bioenergy feedstock. To reach biofuel production goals in a sustainable manner, more information is needed to characterize potential production rates of switchgrass. We used switchgrass yield data and general additive models (GAMs) to model lowland and upland switchgrass yield as nonlinear functions of climate and environmental variables. We used the GAMs and a 39-year climate dataset to assess the spatio-temporal variability in switchgrass yield due to climate variables alone. Variables associated with fertilizer application, genetics, precipitation, and management practices were the most important for explaining variability in switchgrass yield. The relationship of switchgrass yield with climate variables was different for upland than lowland cultivars. The spatio-temporal analysis showed that considerable variability in switchgrass yields can occur due to climate variables alone. The highest switchgrass yields with the lowest variability occurred primarily in the Corn Belt region, suggesting that prime cropland regions are the best suited for a constant and high switchgrass biomass yield. Given that much lignocellulosic feedstock production will likely occur in regions with less suitable climates for agriculture, interannual variability in yields should be expected and incorporated into operational planning.}, number={1}, journal={Agriculture, Ecosystems & Environment}, publisher={Elsevier BV}, author={Tulbure, Mirela G. and Wimberly, Michael C. and Boe, Arvid and Owens, Vance N.}, year={2012}, month={Jan}, pages={121–129} }
 @article{tulbure_ghioca-robrecht_johnston_whigham_2012, title={Inventory and Ventilation Efficiency of Nonnative and Native Phragmites australis (Common Reed) in Tidal Wetlands of the Chesapeake Bay}, volume={35}, ISSN={1559-2723 1559-2731}, url={http://dx.doi.org/10.1007/s12237-012-9529-4}, DOI={10.1007/s12237-012-9529-4}, number={5}, journal={Estuaries and Coasts}, publisher={Springer Science and Business Media LLC}, author={Tulbure, Mirela G. and Ghioca-Robrecht, Dana M. and Johnston, Carol A. and Whigham, Dennis F.}, year={2012}, month={Jul}, pages={1353–1359} }
 @inproceedings{tulbure_broich_2012, title={Modelling wetland extent as a function of climate using remote sensing imagery and spatially explicit climate data}, booktitle={Proceedings of the International Society for Photogrammetry and Remote Sensing}, publisher={CD-ROM Publication}, author={Tulbure, M.G. and Broich, M.}, year={2012} }
 @article{tulbure_wimberly_owens_2012, title={Response of switchgrass yield to future climate change}, volume={7}, ISSN={1748-9326}, url={http://dx.doi.org/10.1088/1748-9326/7/4/045903}, DOI={10.1088/1748-9326/7/4/045903}, abstractNote={A climate envelope approach was used to model the response of switchgrass, a model bioenergy species in the United States, to future climate change. The model was built using general additive models (GAMs), and switchgrass yields collected at 45 field trial locations as the response variable. The model incorporated variables previously shown to be the main determinants of switchgrass yield, and utilized current and predicted 1 km climate data from WorldClim. The models were run with current WorldClim data and compared with results of predicted yield obtained using two climate change scenarios across three global change models for three time steps. Results did not predict an increase in maximum switchgrass yield but showed an overall shift in areas of high switchgrass productivity for both cytotypes. For upland cytotypes, the shift in high yields was concentrated in northern and north-eastern areas where there were increases in average growing season temperature, whereas for lowland cultivars the areas where yields were projected to increase were associated with increases in average early growing season precipitation. These results highlight the fact that the influences of climate change on switchgrass yield are spatially heterogeneous and vary depending on cytotype. Knowledge of spatial distribution of suitable areas for switchgrass production under climate change should be incorporated into planning of current and future biofuel production. Understanding how switchgrass yields will be affected by future changes in climate is important for achieving a sustainable biofuels economy.}, number={4}, journal={Environmental Research Letters}, publisher={IOP Publishing}, author={Tulbure, Mirela G and Wimberly, Michael C and Owens, Vance N}, year={2012}, month={Nov}, pages={045903} }
 @book{broich_fontaine_tulbure_2011, place={Western Australia}, title={Bushfire Threat Analysis: Western Australia. A report for Fire and Emergency Services}, institution={Bushfire Protection Branch}, author={Broich, M. and Fontaine, J. and Tulbure, M.G.}, year={2011} }
 @article{broich_fontaine_tulbure_2011, title={Bushfire threat analysis: Western Australia. A report for Fire and Emergency Services, Bushfire Protection Branch, Western Australia}, author={Broich, M. and Fontaine, J. and Tulbure, M. G.}, year={2011} }
 @inproceedings{chambers_wilson_tulbure_clarke_2011, title={Investigation of nutrient thresholds for ecological regime change in the Vasse-Wonnerup Estuary in SW Australia}, author={Chambers, Jane and Wilson, Celeste and Tulbure, M.G. and Clarke, Alan}, year={2011} }
 @article{tulbure_johnston_2010, title={Environmental Conditions Promoting Non-native Phragmites australis Expansion in Great Lakes Coastal Wetlands}, volume={30}, ISSN={0277-5212 1943-6246}, url={http://dx.doi.org/10.1007/s13157-010-0054-6}, DOI={10.1007/s13157-010-0054-6}, number={3}, journal={Wetlands}, publisher={Springer Science and Business Media LLC}, author={Tulbure, Mirela G. and Johnston, Carol A.}, year={2010}, month={May}, pages={577–587} }
 @article{tulbure_2010, title={Invasion, environmental controls, and ecosystem feedbacks of Phragmites australis in coastal wetlands}, author={Tulbure, M. G.}, year={2010} }
 @article{johnston_zedler_tulbure_2010, title={Latitudinal gradient of floristic condition among Great Lakes coastal wetlands}, volume={36}, ISSN={0380-1330}, url={http://dx.doi.org/10.1016/j.jglr.2010.09.001}, DOI={10.1016/j.jglr.2010.09.001}, abstractNote={Coastal wetland vegetation along the Great Lakes differs strongly with latitude, but most studies of Great Lakes wetland condition have attempted to exclude the effect of latitude to discern anthropogenic effects on condition. We developed an alternative approach that takes advantage of the strong relationship between latitude and coastal wetland floristic condition. Latitude was significantly correlated with 13 of 37 environmental variables tested, including growing degree days, agriculture, atmospheric deposition, nonpoint-source pollution, and soil texture, which suggests that latitude is a good proxy for several environmental drivers of vegetation. Using data from 64 wetlands along the U.S. coast of Lakes Huron, Michigan, Erie, and Ontario, we developed linear regressions between latitude and two measures of floristic condition, the Floristic Quality Index (FQI, adj. r2 = 0.437, p < 0.001) and the first axis scores from a non-metric multidimensional scaling of wetland plant cover (MDS1, adj. r2 = 0.501, p < 0.001). Departures from the central tendency of these regression models represented wetlands of better or worse condition than expected for their latitude. This approach provides a means to identify wetlands worthy of preservation, to establish vegetation targets for wetland restoration, and to forecast changes in floristic quality associated with future climate change.}, number={4}, journal={Journal of Great Lakes Research}, publisher={Elsevier BV}, author={Johnston, Carol A. and Zedler, Joy B. and Tulbure, Mirela G.}, year={2010}, month={Dec}, pages={772–779} }
 @article{johnson_werner_guntenspergen_voldseth_millett_naugle_tulbure_carroll_tracy_olawsky_et al._2010, title={Prairie Wetland Complexes as Landscape Functional Units in a Changing Climate}, volume={60}, ISSN={0006-3568 1525-3244}, url={http://dx.doi.org/10.1525/bio.2010.60.2.7}, DOI={10.1525/bio.2010.60.2.7}, abstractNote={The wetland complex is the functional ecological unit of the prairie pothole region (PPR) of central North America. Diverse complexes of wetlands contribute high spatial and temporal environmental heterogeneity, productivity, and biodiversity to these glaciated prairie landscapes. Climatewarming simulations using the new model WETLANDSCAPE (WLS) project major reductions in water volume, shortening of hydroperiods, and less-dynamic vegetation for prairie wetland complexes. The WLS model portrays the future PPR as a much less resilient ecosystem: The western PPR will be too dry and the eastern PPR will have too few functional wetlands and nesting habitat to support historic levels of waterfowl and other wetland-dependent species. Maintaining ecosystem goods and services at current levels in a warmer climate will be a major challenge for the conservation community.}, number={2}, journal={BioScience}, publisher={Oxford University Press (OUP)}, author={Johnson, W. Carter and Werner, Brett and Guntenspergen, Glenn R. and Voldseth, Richard A. and Millett, Bruce and Naugle, David E. and Tulbure, Mirela and Carroll, Rosemary W. H. and Tracy, John and Olawsky, Craig and et al.}, year={2010}, month={Feb}, pages={128–140} }
 @article{tulbure_wimberly_roy_henebry_2010, title={Spatial and temporal heterogeneity of agricultural fires in the central United States in relation to land cover and land use}, volume={26}, ISSN={0921-2973 1572-9761}, url={http://dx.doi.org/10.1007/s10980-010-9548-0}, DOI={10.1007/s10980-010-9548-0}, number={2}, journal={Landscape Ecology}, publisher={Springer Science and Business Media LLC}, author={Tulbure, Mirela G. and Wimberly, Michael C. and Roy, David P. and Henebry, Geoffrey M.}, year={2010}, month={Nov}, pages={211–224} }
 @article{johnston_zedler_tulbure_frieswyk_bedford_vaccaro_2009, title={A unifying approach for evaluating the condition of wetland plant communities and identifying related stressors}, volume={19}, ISSN={1051-0761}, url={http://dx.doi.org/10.1890/08-1290.1}, DOI={10.1890/08-1290.1}, abstractNote={Assessment of vegetation is an important part of evaluating wetland condition, but it is complicated by the variety of plant communities that are naturally present in freshwater wetlands. We present an approach to evaluate wetland condition consisting of: (1) a stratified random sample representing the entire range of anthropogenic stress, (2) field data representing a range of water depths within the wetlands sampled, (3) nonmetric multidimensional scaling (MDS) to determine a biological condition gradient across the wetlands sampled, (4) hierarchical clustering to interpret the condition results relative to recognizable plant communities, (5) classification and regression tree (CART) analysis to relate biological condition to natural and anthropogenic environmental drivers, and (6) mapping the results to display their geographic distribution. We applied this approach to plant species data collected at 90 wetlands of the U.S. Great Lakes coast that support a variety of plant communities, reflecting the diverse physical environment and anthropogenic stressors present within the region. Hierarchical cluster analysis yielded eight plant communities at a minimum similarity of 25%. Wetlands that clustered botanically were often geographically clustered as well, even though location was not an input variable in the analysis. The eight vegetation clusters corresponded well with the MDS configuration of the data, in which the first axis was strongly related (R2 = 0.787, P < 0.001) with floristic quality index (FQI) and the second axis was related to the Great Lake of occurrence. CART models using FQI and the first MDS axis as the response variables explained 75% and 82% of the variance in the data, resulting in 6-7 terminal groups spanning the condition gradient. Initial CART splits divided the region based on growing degree-days and cumulative anthropogenic stress; only after making these broad divisions were wetlands distinguished by more local characteristics. Agricultural and urban development variables were important correlates of wetland biological condition, generating optimal or surrogate splits at every split node of the MDS CART model. Our findings provide a means of using vegetation to evaluate a range of wetland condition across a broad and diverse geographic region.}, number={7}, journal={Ecological Applications}, publisher={Wiley}, author={Johnston, Carol A. and Zedler, Joy B. and Tulbure, Mirela G. and Frieswyk, Christin B. and Bedford, Barbara L. and Vaccaro, Lynn}, year={2009}, month={Oct}, pages={1739–1757} }
 @article{ghioca-robrecht_johnston_tulbure_2008, title={Assessing the use of multiseason QuickBird imagery for mapping invasive species in a Lake Erie coastal Marsh}, volume={28}, ISSN={0277-5212 1943-6246}, url={http://dx.doi.org/10.1672/08-34.1}, DOI={10.1672/08-34.1}, abstractNote={QuickBird multispectral satellite images taken in September 2002 (peak biomass) and April 2003 (pre-growing season) were used to map emergent wetland vegetation communities, particularly invasive Phragmites australis and Typha spp., within a diked wetland at the western end of Lake Erie. An unsupervised classification was performed on a nine-layer image stack consisting of all four spectral bands from both dates plus a September Normalized Difference Vegetation Index image. The resulting eight cover classes distinguished three monodominant genera (Phragmites australis, Typha spp., Nelumbo lutea), three multigenera plant communities (wet meadow, other non persistent emergents, woody vegetation), and two unvegetated cover types (water, bare soil). Field validation at 196 data points yielded an overall classification accuracy of 62%, with producer’s accuracy for the eight individual classes ranging from 41 to 91% and user’s accuracy from 17 to 90%. Three-fourths of areas designated as Phragmites were correctly mapped, but 14% were found to be cattail (Typha) during field validation. Lotus (Nelumbo lutea) beds were accurately mapped on multiseason imagery (producer’s accuracy = 91%); these beds had not yet emerged above water in April, but were fully developed in September. Other types of non persistent vegetation were confused with managed areas in which vegetation had been cut and burned to control invasive Phragmites. Multiseason QuickBird imagery is promising for distinguishing certain wetland plant species, but should be used with caution in highly managed areas where vegetation changes may reflect human alterations rather than phenological change.}, number={4}, journal={Wetlands}, publisher={Springer Nature}, author={Ghioca-Robrecht, Dana M. and Johnston, Carol A. and Tulbure, Mirela G.}, year={2008}, month={Dec}, pages={1028–1039} }
 @article{johnston_ghioca_tulbure_bedford_bourdaghs_frieswyk_vaccaro_zedler_2008, title={Partitioning vegetation response to anthropogenic stress to develop multi-taxa indicators of wetland condition}, volume={18}, ISSN={1051-0761}, url={http://dx.doi.org/10.1890/07-1207.1}, DOI={10.1890/07-1207.1}, abstractNote={Emergent plants can be suitable indicators of anthropogenic stress in coastal wetlands if their responses to natural environmental variation can be parsed from their responses to human activities in and around wetlands. We used hierarchical partitioning to evaluate the independent influence of geomorphology, geography, and anthropogenic stress on common wetland plants of the U.S. Great Lakes coast and developed multi-taxa models indicating wetland condition. A seven-taxon model predicted condition relative to watershed-derived anthropogenic stress, and a four-taxon model predicted condition relative to within-wetland anthropogenic stressors that modified hydrology. The Great Lake on which the wetlands occurred explained an average of about half the variation in species cover, and subdividing the data by lake allowed us to remove that source of variation. We developed lake-specific multi-taxa models for all of the Great Lakes except Lake Ontario, which had no plant species with significant independent effects of anthropogenic stress. Plant responses were both positive (increasing cover with stress) and negative (decreasing cover with stress), and plant taxa incorporated into the lake-specific models differed by Great Lake. The resulting models require information on only a few taxa, rather than all plant species within a wetland, making them easier to implement than existing indicators.}, number={4}, journal={Ecological Applications}, publisher={Wiley}, author={Johnston, Carol A. and Ghioca, Dana M. and Tulbure, Mirela and Bedford, Barbara L. and Bourdaghs, Michael and Frieswyk, Christin B. and Vaccaro, Lynn and Zedler, Joy B.}, year={2008}, month={Jun}, pages={983–1001} }
 @article{johnston_bedford_bourdaghs_brown_frieswyk_tulbure_vaccaro_zedler_2007, title={Plant Species Indicators of Physical Environment in Great Lakes Coastal Wetlands}, volume={33}, ISSN={0380-1330 0380-1330}, url={http://dx.doi.org/10.3394/0380-1330(2007)33[106:psiope]2.0.co;2}, DOI={10.3394/0380-1330(2007)33[106:psiope]2.0.co;2}, abstractNote={ABSTRACT Plant taxa identified in 90 U.S. Great Lakes coastal emergent wetlands were evaluated as indicators of physical environment. Canonical correspondence analysis using the 40 most common taxa showed that water depth and tussock height explained the greatest amount of species-environment interaction among ten environmental factors measured as continuous variables (water depth, tussock height, latitude, longitude, and six ground cover categories). Indicator species analysis was used to identify species-environment interactions with categorical variables of soil type (sand, silt, clay, organic) and hydrogeomorphic type (Open-Coast Wetlands, River-Influenced Wetlands, Protected Wetlands). Of the 169 taxa that occurred in a minimum of four study sites and ten plots, 48 were hydrogeomorphic indicators and 90 were soil indicators. Most indicators of Protected Wetlands were bog and fen species which were also organic soil indicators. Protected Wetlands had significantly greater average coefficient of conservatism (C) values than did Open-Coast Wetlands and River-Influenced Wetlands, but average C values did not differ significantly by soil type. Open-Coast and River-Influenced hydrogeomorphic types tended to have sand or silt soils. Clay soils were found primarily in areas with Quaternary glaciolacustrine deposits or clay-rich tills. A fuller understanding of how the physical environment influences plant species distribution will improve our ability to detect the response of wetland vegetation to anthropogenic activities.}, number={sp3}, journal={Journal of Great Lakes Research}, publisher={Elsevier BV}, author={Johnston, Carol A. and Bedford, Barbara L. and Bourdaghs, Michael and Brown, Terry and Frieswyk, Christin and Tulbure, Mirela and Vaccaro, Lynn and Zedler, Joy B.}, year={2007}, month={Dec}, pages={106–124} }
 @article{tulbure_johnston_auger_2007, title={Rapid Invasion of a Great Lakes Coastal Wetland by Non-native Phragmites australis and Typha}, volume={33}, ISSN={0380-1330 0380-1330}, url={http://dx.doi.org/10.3394/0380-1330(2007)33[269:rioagl]2.0.co;2}, DOI={10.3394/0380-1330(2007)33[269:rioagl]2.0.co;2}, abstractNote={ABSTRACT Great Lakes coastal wetlands are subject to water level fluctuations that promote the maintenance of coastal wetlands. Point au Sauble, a Green Bay coastal wetland, was an open water lagoon as of 1999, but became entirely vegetated as Lake Michigan experienced a prolonged period of below-average water levels. Repeat visits in 2001 and 2004 documented a dramatic change in emergent wetland vegetation communities. In 2001 non-native Phragmites and Typha were present but their cover was sparse; in 2004 half of the transect was covered by a 3 m tall, invasive Phragmites and non-native Typha community. Percent similarity between plant species present in 2001 versus 2004 was approximately 19% (Jaccard's coefficient), indicating dramatic changes in species composition that took place in only 3 years. The height of the dominant herbaceous plants and coverage by invasive species were significantly higher in 2004 than they were in 2001. However, floristic quality index and coefficient of conservatism were greater in 2004 than 2001. Cover by plant litter did not differ between 2001 and 2004. The prolonged period of below-average water levels between 1999 and early 2004 exposed unvegetated lagoon bottoms as mud flats, which provided substrate for new plant colonization and created conditions conducive to colonization by invasive taxa. PCR/RFLP analysis revealed that Phragmites from Point au Sauble belongs to the more aggressive, introduced genotype. It displaces native vegetation and is tolerant of a wide range of water depth. Therefore it may disrupt the natural cycles of vegetation replacement that occur under native plant communities in healthy Great Lakes coastal wetlands.}, number={sp3}, journal={Journal of Great Lakes Research}, publisher={Elsevier BV}, author={Tulbure, Mirela G. and Johnston, Carol A. and Auger, Donald L.}, year={2007}, month={Dec}, pages={269–279} }
 @inproceedings{hartel_demeter_cogălniceanu_tulbure_2006, title={The influence of habitat characteristics on amphibian species richness in two river basins of Romania}, booktitle={Herpetologia Bonnensis II. Proceedings of the 13th Congress of the Societas Europaea Herpetologica}, author={Hartel, T. and Demeter, L. and Cogălniceanu, D. and Tulbure, M.G.}, editor={Vences, M. and Köhler, J. and Ziegler, T. and Böhme, W.Editors}, year={2006}, pages={47} }
 @article{hartel¹_demeter_cogălniceanu_tulbure_2006, title={The influence of habitat characteristics on amphibian species richness in two river basins of Romania}, volume={47}, journal={Proceedings of the 13th Congress of the Societas Europaea Herpetologica. pp}, author={Hartel¹, T. and Demeter, L. and Cogălniceanu, D. and Tulbure, M.}, year={2006}, pages={50} }