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Despite conservation efforts in the U.S. Great Plains, woody species have continued to expand at an unprecedented rate, threatening key ecosystem services and resilience. Cross-scale monitoring of these grasslands is key to successful integrative management strategies. In this study we measured plant optical traits derived from hyperspectral proximal sensing techniques with a field spectrometer, coupled with field-based measurements, including fluorescence and chlorophyll content, to determine the impacts of Juniperus virginiana and Pinus ponderosa expansion on grasslands health in Nebraska Sandhills, and investigated the use of optical-based approaches as indicators of successful monitoring of grasslands. Our results showed that higher woody species cover in grasslands was associated with lower soil moisture, decline in forbs, shrubs, and grasses cover and productivity, as well as herbaceous chlorophyll content and fluorescence, compared to non-invaded grasslands. We derived 13 vegetation indices (VIs) from optical-based methods and validated them against traditional handheld measurements of plant ecophysiological traits and vegetation biomass and composition. VIs, including Normalized Difference Vegetation Index (NDVI), Water Index (WI) and Chlorophyll Index at red edge (CIred edge) performed best when tested against biomass, and chlorophyll content and fluorescence (Fv/Fm), suggesting their potential use for assessing grasslands vegetation health. We demonstrate that optical-based approaches can serve as efficient non-invasive tools that can be part of multi-scale successful integrative management strategies.
Anastasios Mazis; Julie A. Fowler; Jeremy Hiller; Yuzhen Zhou; Brian D. Wardlow; David Wedin; Tala Awada. Ecophysio-optical traits of semiarid Nebraska grasslands under different Juniperus virginiana and Pinus ponderosa canopy covers. Ecological Indicators 2021, 131, 108159 .
AMA StyleAnastasios Mazis, Julie A. Fowler, Jeremy Hiller, Yuzhen Zhou, Brian D. Wardlow, David Wedin, Tala Awada. Ecophysio-optical traits of semiarid Nebraska grasslands under different Juniperus virginiana and Pinus ponderosa canopy covers. Ecological Indicators. 2021; 131 ():108159.
Chicago/Turabian StyleAnastasios Mazis; Julie A. Fowler; Jeremy Hiller; Yuzhen Zhou; Brian D. Wardlow; David Wedin; Tala Awada. 2021. "Ecophysio-optical traits of semiarid Nebraska grasslands under different Juniperus virginiana and Pinus ponderosa canopy covers." Ecological Indicators 131, no. : 108159.
Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a given compositing time interval. However, VI time series with high temporal resolution were preferred by many applications such as vegetation phenology and land change detections. This study presents a novel strategy named DAVIR-MUTCOP (DAily Vegetation Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference vegetation index (NDVI) time-series reconstruction with high temporal resolution. The core of the DAVIR-MUTCOP method is a combination of the advantages of both original daily and temporally composite products, and selecting more daily observations with high quality through the temporal variation of temporally corrected composite data. The DAVIR-MUTCOP method was applied to reconstruct high-quality NDVI time-series using MODIS multi-temporal products in two study areas in the continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska from 2001 to 2012 and forty-six AmeriFlux sites evenly distributed across CONUS from 2006 to 2010. In these two study areas, the DAVIR-MUTCOP method was also compared to several commonly used methods, i.e., the Harmonic Analysis of Time-Series (HANTS) method using original daily observations, Savitzky–Golay (SG) filtering using daily observations with cloud mask products as auxiliary data, and SG filtering using temporally corrected composite data. The results showed that the DAVIR-MUTCOP method significantly improved the temporal resolution of the reconstructed NDVI time series. It performed the best in reconstructing NDVI time-series across time and space (coefficient of determination (R2 = 0.93 ~ 0.94) between reconstructed NDVI and ground-observed LAI). DAVIR-MUTCOP method presented the highest robustness and accuracy with the change of the filtering parameter (R2 = 0.99 ~ 1.00, bias = 0.001, root mean square error (RMSE) = 0.020). Only MODIS data were used in this study; nevertheless, the DAVIR-MUTCOP method proposed a universal and potential way to reconstruct daily time series of other VIs or from other operational sensors, e.g., AVHRR and VIIRS.
Linglin Zeng; Brian Wardlow; Shun Hu; Xiang Zhang; Guoqing Zhou; Guozhang Peng; Daxiang Xiang; Rui Wang; Ran Meng; Weixiong Wu. A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products. Remote Sensing 2021, 13, 1397 .
AMA StyleLinglin Zeng, Brian Wardlow, Shun Hu, Xiang Zhang, Guoqing Zhou, Guozhang Peng, Daxiang Xiang, Rui Wang, Ran Meng, Weixiong Wu. A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products. Remote Sensing. 2021; 13 (7):1397.
Chicago/Turabian StyleLinglin Zeng; Brian Wardlow; Shun Hu; Xiang Zhang; Guoqing Zhou; Guozhang Peng; Daxiang Xiang; Rui Wang; Ran Meng; Weixiong Wu. 2021. "A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products." Remote Sensing 13, no. 7: 1397.
Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
Trenton Benedict; Jesslyn Brown; Stephen Boyte; Daniel Howard; Brian Fuchs; Brian Wardlow; Tsegaye Tadesse; Kirk Evenson. Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions. Remote Sensing 2021, 13, 1210 .
AMA StyleTrenton Benedict, Jesslyn Brown, Stephen Boyte, Daniel Howard, Brian Fuchs, Brian Wardlow, Tsegaye Tadesse, Kirk Evenson. Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions. Remote Sensing. 2021; 13 (6):1210.
Chicago/Turabian StyleTrenton Benedict; Jesslyn Brown; Stephen Boyte; Daniel Howard; Brian Fuchs; Brian Wardlow; Tsegaye Tadesse; Kirk Evenson. 2021. "Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions." Remote Sensing 13, no. 6: 1210.
Vegetation growth plays a crucial role in the carbon cycle and climate change mitigation. However, the relative contribution of hydroclimatic variables (relative humidity, terrestrial water storage, day and night‐time land surface temperatures) on vegetation growth of agricultural and non‐agricultural areas at the global scale remains unexplored. Using satellite‐based datasets, we examined the changes in Normalized Difference Vegetation Index (NDVI) and the four hydroclimatic variables during 2003‐2014. Also, the relative contribution of the four hydroclimatic variables on vegetation growth in agricultural and non‐agricultural areas was estimated. A significant (p‐value < 0.05) greening has occurred in the agricultural regions of India and Brazil during 2003‐2014. Whereas in non‐agriculture areas, a considerable greening occurred only in India and China during the 2003‐2014 period. Among the four hydroclimatic variables, both day‐time and night‐time land surface temperature are the significant contributors of vegetation growth in the two‐thirds of the global landmass. Terrestrial water storage is a substantial contributor to the vegetation growth in the tropics and sub‐tropics. Night‐time land surface temperature is strongly associated with the vegetation growth in the colder regions. The hydroclimatic variables do not explain the considerable amount of the total variance of vegetation growth over the agricultural areas in China, which is due to human agricultural management practices. Generally, the response of hydroclimate variables on vegetation growth in the agricultural and non‐agricultural areas has significant implications in many areas, including food security, carbon sequestration, water resource management, and climate change. This article is protected by copyright. All rights reserved.
Akarsh Asoka; Brian Wardlow; Tadesse Tsegaye; Matthew Huber; Vimal Mishra. A Satellite‐Based Assessment of the Relative Contribution of Hydroclimatic Variables on Vegetation Growth in Global Agricultural and Nonagricultural Regions. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .
AMA StyleAkarsh Asoka, Brian Wardlow, Tadesse Tsegaye, Matthew Huber, Vimal Mishra. A Satellite‐Based Assessment of the Relative Contribution of Hydroclimatic Variables on Vegetation Growth in Global Agricultural and Nonagricultural Regions. Journal of Geophysical Research: Atmospheres. 2021; 126 (5):1.
Chicago/Turabian StyleAkarsh Asoka; Brian Wardlow; Tadesse Tsegaye; Matthew Huber; Vimal Mishra. 2021. "A Satellite‐Based Assessment of the Relative Contribution of Hydroclimatic Variables on Vegetation Growth in Global Agricultural and Nonagricultural Regions." Journal of Geophysical Research: Atmospheres 126, no. 5: 1.
Monitoring drought impacts in forest ecosystems is a complex process because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.
Tsegaye Tadesse; David Hollinger; Yared Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian Wardlow; Gil Bohrer; Kenneth Clark; Ankur Desai; Lianhong Gu; Asko Noormets; Kimberly Novick; Andrew Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. Remote Sensing 2020, 12, 3605 .
AMA StyleTsegaye Tadesse, David Hollinger, Yared Bayissa, Mark Svoboda, Brian Fuchs, Beichen Zhang, Getachew Demissie, Brian Wardlow, Gil Bohrer, Kenneth Clark, Ankur Desai, Lianhong Gu, Asko Noormets, Kimberly Novick, Andrew Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. Remote Sensing. 2020; 12 (21):3605.
Chicago/Turabian StyleTsegaye Tadesse; David Hollinger; Yared Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian Wardlow; Gil Bohrer; Kenneth Clark; Ankur Desai; Lianhong Gu; Asko Noormets; Kimberly Novick; Andrew Richardson. 2020. "Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States." Remote Sensing 12, no. 21: 3605.
Monitoring drought impacts in forest ecosystems is a complex process, because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.
Tsegaye Tadesse; David Y. Hollinger; Yared A. Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian D. Wardlow; Gil Bohrer; Kenneth L. Clark; Ankur R. Desai; Lianhong Gu; Asko Noormets; Kimberly A. Novick; Andrew D. Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. 2020, 1 .
AMA StyleTsegaye Tadesse, David Y. Hollinger, Yared A. Bayissa, Mark Svoboda, Brian Fuchs, Beichen Zhang, Getachew Demissie, Brian D. Wardlow, Gil Bohrer, Kenneth L. Clark, Ankur R. Desai, Lianhong Gu, Asko Noormets, Kimberly A. Novick, Andrew D. Richardson. Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States. . 2020; ():1.
Chicago/Turabian StyleTsegaye Tadesse; David Y. Hollinger; Yared A. Bayissa; Mark Svoboda; Brian Fuchs; Beichen Zhang; Getachew Demissie; Brian D. Wardlow; Gil Bohrer; Kenneth L. Clark; Ankur R. Desai; Lianhong Gu; Asko Noormets; Kimberly A. Novick; Andrew D. Richardson. 2020. "Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States." , no. : 1.
Wildfires are ecosystem‐level drivers of structure and function in many vegetated biomes. While numerous studies have emphasized the benefits of fire to ecosystems, large wildfires have also been associated with the loss of ecosystem services and shifts in vegetation abundance. The size and number of wildfires are increasing across a number of regions, and yet the outcomes of large wildfire on vegetation at large‐scales is still largely unknown. We introduce an exhaustive analysis of wildfire‐scale vegetation response to large wildfires across North America’s grassland biome. We use 18 years of a newly released vegetation dataset combined with 1390 geospatial wildfire perimeters and drought data to detect large‐scale vegetation response among multiple vegetation functional groups. We found no evidence of persistent declines in vegetation driven by wildfire at the biome level. All vegetation functional groups exhibited relatively rapid recovery to pre‐wildfire ranges of variation (ROV) across Great Plains ecoregions, with the exception being a persistent decrease in the abundance of trees in the Northwestern Great Plains. Drought intensity magnified immediate vegetation response to wildfire. Persistent declines in vegetation cover were observed at the scale of single pixels (30‐m), suggesting that these responses were localised and represent extreme cases within larger wildfires. Our findings echo over a century of research demonstrating a biome resilient to wildfire.
Victoria M. Donovan; Dirac Twidwell; Daniel R. Uden; Tsegaye Tadesse; Brian D. Wardlow; Christine H. Bielski; Matthew O. Jones; Brady W. Allred; David E. Naugle; Craig R. Allen. Resilience to Large, “Catastrophic” Wildfires in North America's Grassland Biome. Earth's Future 2020, 8, 1 .
AMA StyleVictoria M. Donovan, Dirac Twidwell, Daniel R. Uden, Tsegaye Tadesse, Brian D. Wardlow, Christine H. Bielski, Matthew O. Jones, Brady W. Allred, David E. Naugle, Craig R. Allen. Resilience to Large, “Catastrophic” Wildfires in North America's Grassland Biome. Earth's Future. 2020; 8 (7):1.
Chicago/Turabian StyleVictoria M. Donovan; Dirac Twidwell; Daniel R. Uden; Tsegaye Tadesse; Brian D. Wardlow; Christine H. Bielski; Matthew O. Jones; Brady W. Allred; David E. Naugle; Craig R. Allen. 2020. "Resilience to Large, “Catastrophic” Wildfires in North America's Grassland Biome." Earth's Future 8, no. 7: 1.
The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.
Sneha Kulkarni; Brian Wardlow; Yared Bayissa; Tsegaye Tadesse; Mark Svoboda; Shirishkumar Gedam. Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India. Remote Sensing 2020, 12, 2091 .
AMA StyleSneha Kulkarni, Brian Wardlow, Yared Bayissa, Tsegaye Tadesse, Mark Svoboda, Shirishkumar Gedam. Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India. Remote Sensing. 2020; 12 (13):2091.
Chicago/Turabian StyleSneha Kulkarni; Brian Wardlow; Yared Bayissa; Tsegaye Tadesse; Mark Svoboda; Shirishkumar Gedam. 2020. "Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India." Remote Sensing 12, no. 13: 2091.
Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.
Dong-Hyun Yoon; Won-Ho Nam; Hee-Jin Lee; Eun-Mi Hong; Song Feng; Brian D. Wardlow; Tsegaye Tadesse; Mark D. Svoboda; Michael J. Hayes; Dae-Eui Kim. Agricultural Drought Assessment in East Asia Using Satellite-Based Indices. Remote Sensing 2020, 12, 444 .
AMA StyleDong-Hyun Yoon, Won-Ho Nam, Hee-Jin Lee, Eun-Mi Hong, Song Feng, Brian D. Wardlow, Tsegaye Tadesse, Mark D. Svoboda, Michael J. Hayes, Dae-Eui Kim. Agricultural Drought Assessment in East Asia Using Satellite-Based Indices. Remote Sensing. 2020; 12 (3):444.
Chicago/Turabian StyleDong-Hyun Yoon; Won-Ho Nam; Hee-Jin Lee; Eun-Mi Hong; Song Feng; Brian D. Wardlow; Tsegaye Tadesse; Mark D. Svoboda; Michael J. Hayes; Dae-Eui Kim. 2020. "Agricultural Drought Assessment in East Asia Using Satellite-Based Indices." Remote Sensing 12, no. 3: 444.
Unmanned aerial systems (UAS) for collecting multispectral imagery of agricultural fields are becoming more affordable and accessible. However, there is need to validate calibration of sensors on these systems when using them for quantitative analyses such as evapotranspiration, and other modeling for agricultural applications. The results of laboratory testing of a MicaSense (Seattle, WA, USA) RedEdge™ 3 multispectral camera and MicaSense Downwelling Light Sensor (irradiance sensor) system using a calibrated integrating sphere were presented. Responses of the camera and irradiance sensor were linear over many light levels and became non-linear at light levels below expected real-world, field conditions. Simple linear corrections should suffice for most light conditions encountered during the growing season. Using an irradiance sensor or similar system may not properly account for light variability in cloudy or partly cloudy conditions as also identified by others. A simple stand for aiding in reference panel imaging was also described, which may facilitate repetitive, consistent reference panel imaging.
J. Burdette Barker; Wayne E. Woldt; Brian D. Wardlow; Christopher M. U. Neale; Mitchell S. Maguire; Bryan C. Leavitt; Derek M. Heeren. Calibration of a common shortwave multispectral camera system for quantitative agricultural applications. Precision Agriculture 2019, 21, 922 -935.
AMA StyleJ. Burdette Barker, Wayne E. Woldt, Brian D. Wardlow, Christopher M. U. Neale, Mitchell S. Maguire, Bryan C. Leavitt, Derek M. Heeren. Calibration of a common shortwave multispectral camera system for quantitative agricultural applications. Precision Agriculture. 2019; 21 (4):922-935.
Chicago/Turabian StyleJ. Burdette Barker; Wayne E. Woldt; Brian D. Wardlow; Christopher M. U. Neale; Mitchell S. Maguire; Bryan C. Leavitt; Derek M. Heeren. 2019. "Calibration of a common shortwave multispectral camera system for quantitative agricultural applications." Precision Agriculture 21, no. 4: 922-935.
Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed ‘land surface phenology’, as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization.
Linglin Zeng; Brian D. Wardlow; Daxiang Xiang; Shun Hu; Deren Li. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment 2019, 237, 111511 .
AMA StyleLinglin Zeng, Brian D. Wardlow, Daxiang Xiang, Shun Hu, Deren Li. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment. 2019; 237 ():111511.
Chicago/Turabian StyleLinglin Zeng; Brian D. Wardlow; Daxiang Xiang; Shun Hu; Deren Li. 2019. "A review of vegetation phenological metrics extraction using time-series, multispectral satellite data." Remote Sensing of Environment 237, no. : 111511.
In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving; 2) Fostering flexible reliable, and integrated urban water supply; 3) Improving efficiency of urban water management; 4) Investing in sustainability science research for urban drought; and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.
Xiang Zhang; Nengcheng Chen; Hao Sheng; Chris Ip; Long Yang; Yiqun Chen; Ziqin Sang; Tsegaye Tadesse; Tania Pei Yee Lim; Abbas Rajabifard; Cristina Bueti; Linglin Zeng; Brian Wardlow; Siqi Wang; Shiyi Tang; Zhang Xiong; Deren Li; Dev Niyogi. Urban drought challenge to 2030 sustainable development goals. Science of The Total Environment 2019, 693, 133536 .
AMA StyleXiang Zhang, Nengcheng Chen, Hao Sheng, Chris Ip, Long Yang, Yiqun Chen, Ziqin Sang, Tsegaye Tadesse, Tania Pei Yee Lim, Abbas Rajabifard, Cristina Bueti, Linglin Zeng, Brian Wardlow, Siqi Wang, Shiyi Tang, Zhang Xiong, Deren Li, Dev Niyogi. Urban drought challenge to 2030 sustainable development goals. Science of The Total Environment. 2019; 693 ():133536.
Chicago/Turabian StyleXiang Zhang; Nengcheng Chen; Hao Sheng; Chris Ip; Long Yang; Yiqun Chen; Ziqin Sang; Tsegaye Tadesse; Tania Pei Yee Lim; Abbas Rajabifard; Cristina Bueti; Linglin Zeng; Brian Wardlow; Siqi Wang; Shiyi Tang; Zhang Xiong; Deren Li; Dev Niyogi. 2019. "Urban drought challenge to 2030 sustainable development goals." Science of The Total Environment 693, no. : 133536.
Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses.
Markéta Poděbradská; Bruce K. Wylie; Michael J. Hayes; Brian D. Wardlow; Deborah J. Bathke; Norman B. Bliss; Devendra Dahal. Monitoring Drought Impact on Annual Forage Production in Semi-arid Grasslands: A Case Study of Nebraska Sandhills. Remote Sensing 2019, 11, 2106 .
AMA StyleMarkéta Poděbradská, Bruce K. Wylie, Michael J. Hayes, Brian D. Wardlow, Deborah J. Bathke, Norman B. Bliss, Devendra Dahal. Monitoring Drought Impact on Annual Forage Production in Semi-arid Grasslands: A Case Study of Nebraska Sandhills. Remote Sensing. 2019; 11 (18):2106.
Chicago/Turabian StyleMarkéta Poděbradská; Bruce K. Wylie; Michael J. Hayes; Brian D. Wardlow; Deborah J. Bathke; Norman B. Bliss; Devendra Dahal. 2019. "Monitoring Drought Impact on Annual Forage Production in Semi-arid Grasslands: A Case Study of Nebraska Sandhills." Remote Sensing 11, no. 18: 2106.
Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.
Li Hua; Huidong Wang; Haigang Sui; Brian Wardlow; Michael J. Hayes; Jianxun Wang. Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region. Remote Sensing 2019, 11, 1873 .
AMA StyleLi Hua, Huidong Wang, Haigang Sui, Brian Wardlow, Michael J. Hayes, Jianxun Wang. Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region. Remote Sensing. 2019; 11 (16):1873.
Chicago/Turabian StyleLi Hua; Huidong Wang; Haigang Sui; Brian Wardlow; Michael J. Hayes; Jianxun Wang. 2019. "Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region." Remote Sensing 11, no. 16: 1873.
Remnant populations of Betula papyrifera Marshall have persisted in the Great Plains after the Wisconsin Glaciation along the Niobrara River Valley, Nebraska. Population health has declined in recent years, which has been hypothesized to be due to climate change. We used dendrochronological techniques to assess the response of B. papyrifera to microclimate (1950–2014) and the normalized difference vegetation index (NDVI) derived from satellite imagery (Landsat 5 TM (1985–2011) and MODIS (2000–2014)) as a proxy for population health. Growing-season streamflow and precipitation were positively correlated with raw and standardized tree-ring widths and basal area increment increase. Increasing winter and spring temperatures were unfavorable for tree growth, while increasing summer temperatures were favorable in the absence of drought. The strongest predictor for standardized tree rings was the Palmer Drought Severity Index, suggesting that B. papyrifera is highly responsive to a combination of temperature and water availability. The NDVI from the vegetation community was positively correlated with standardized tree-ring growth, indicating the potential of these techniques to be used as a proxy for ex situ monitoring of B. papyrifera. These results aid in forecasting the dynamics of the species in the face of climate variability and change in both remnant populations and across its current distribution in northern latitudes of North America.
E. Bumann; T. Awada; Brian Wardlow; M. Hayes; J. Okalebo; C. Helzer; A. Mazis; J. Hiller; P. Cherubini. Assessing responses of Betula papyrifera to climate variability in a remnant population along the Niobrara River Valley in Nebraska, U.S.A., through dendroecological and remote-sensing techniques. Canadian Journal of Forest Research 2019, 49, 423 -433.
AMA StyleE. Bumann, T. Awada, Brian Wardlow, M. Hayes, J. Okalebo, C. Helzer, A. Mazis, J. Hiller, P. Cherubini. Assessing responses of Betula papyrifera to climate variability in a remnant population along the Niobrara River Valley in Nebraska, U.S.A., through dendroecological and remote-sensing techniques. Canadian Journal of Forest Research. 2019; 49 (5):423-433.
Chicago/Turabian StyleE. Bumann; T. Awada; Brian Wardlow; M. Hayes; J. Okalebo; C. Helzer; A. Mazis; J. Hiller; P. Cherubini. 2019. "Assessing responses of Betula papyrifera to climate variability in a remnant population along the Niobrara River Valley in Nebraska, U.S.A., through dendroecological and remote-sensing techniques." Canadian Journal of Forest Research 49, no. 5: 423-433.
Developing a robust drought monitoring tool is vital to mitigate the adverse impacts of drought. A drought monitoring system that integrates multiple agrometeorological variables into a single drought indicator is lacking in areas such as Ethiopia, which is extremely susceptible to this natural hazard. The overarching goal of this study is to develop a combined drought indicator (CDI-E) to monitor the spatial and temporal extents of historic agricultural drought events in Ethiopia. The CDI-E was developed by combining four satellite-based agrometeorological input parameters – the Standardized Precipitation Index (SPI), Land Surface Temperature (LST) anomaly, Standardized Normalized Difference Vegetation Index (stdNDVI) and Soil Moisture (SM) anomaly – for the period from 2001 to 2015. The method used to combine these indices is based on a quantitative approach that assigns a weight to each input parameter using Principal Component Analysis (PCA). The CDI-E results were evaluated using satellite-based gridded rainfall (3-month SPI) and crop yield data for 36 intra-country crop growing zones for a 15-year period (2001 to 2015). The evaluation was carried out for the main rainfall season, Kiremt (June-September), and the short rainfall season, Belg (February-May). The results showed that moderate to severe droughts were detected by the CDI-E across the food insecure regions reported by FEWS NET during Kiremt and Belg rainfall seasons. Relatively higher correlation coefficient values (r > 0.65) were obtained when CDI-E was compared with the 3-month SPI across the majority of Ethiopia. The spatial correlation analyses of CDI-E and cereal crop yields showed relatively good correlations (r > 0.5) in some of the crop growing zones in the northern, eastern and southwestern parts of the country. The CDI-E generally mapped the spatial and temporal patterns of historic drought and non-drought years and hence the CDI-E could potentially be used to develop an agricultural drought monitoring and early warning system in Ethiopia. Moreover, decision makers and donors may potentially use CDI-E to more accurately monitor crop yields across the food-insecure regions in Ethiopia.
Yared A. Bayissa; Tsegaye Tadesse; Mark Svoboda; Brian Wardlow; Calvin Poulsen; John Swigart; Schalk Jan Van Andel. Developing a satellite-based combined drought indicator to monitor agricultural drought: a case study for Ethiopia. GIScience & Remote Sensing 2018, 56, 718 -748.
AMA StyleYared A. Bayissa, Tsegaye Tadesse, Mark Svoboda, Brian Wardlow, Calvin Poulsen, John Swigart, Schalk Jan Van Andel. Developing a satellite-based combined drought indicator to monitor agricultural drought: a case study for Ethiopia. GIScience & Remote Sensing. 2018; 56 (5):718-748.
Chicago/Turabian StyleYared A. Bayissa; Tsegaye Tadesse; Mark Svoboda; Brian Wardlow; Calvin Poulsen; John Swigart; Schalk Jan Van Andel. 2018. "Developing a satellite-based combined drought indicator to monitor agricultural drought: a case study for Ethiopia." GIScience & Remote Sensing 56, no. 5: 718-748.
Miroslav Trnka; Michael Hayes; FrantiEk Jurečka; L Bartosová; Martha Anderson; Rudolf Brázdil; Jesslyn Brown; Jesus J. Camarero; Pavel Cudlín; Petr Dobrovolný; Josef Eitzinger; Song Feng; Taryn Finnessey; Gregor Gregorič; Petr Havlik; Christopher Hain; Ian Holman; David Johnson; Kurt Christian Kersebaum; Fredrik Charpentier Ljungqvist; Jürg Luterbacher; Fabio Micale; Claudia Hartl-Meier; M Možný; Pavol Nejedlik; Jørgen E. Olesen; Margarita Ruiz-Ramos; Reimund P. Rötter; Gabriel Senay; Sergio M. Vicente-Serrano; Mark Svoboda; Andreja Susnik; Tsegaye Tadesse; Adam Vizina; Brian Wardlow; Zdenek Alud; Ulf Büntgen; Smv Serrano. Priority questions in multidisciplinary drought research. Climate Research 2018, 75, 241 -260.
AMA StyleMiroslav Trnka, Michael Hayes, FrantiEk Jurečka, L Bartosová, Martha Anderson, Rudolf Brázdil, Jesslyn Brown, Jesus J. Camarero, Pavel Cudlín, Petr Dobrovolný, Josef Eitzinger, Song Feng, Taryn Finnessey, Gregor Gregorič, Petr Havlik, Christopher Hain, Ian Holman, David Johnson, Kurt Christian Kersebaum, Fredrik Charpentier Ljungqvist, Jürg Luterbacher, Fabio Micale, Claudia Hartl-Meier, M Možný, Pavol Nejedlik, Jørgen E. Olesen, Margarita Ruiz-Ramos, Reimund P. Rötter, Gabriel Senay, Sergio M. Vicente-Serrano, Mark Svoboda, Andreja Susnik, Tsegaye Tadesse, Adam Vizina, Brian Wardlow, Zdenek Alud, Ulf Büntgen, Smv Serrano. Priority questions in multidisciplinary drought research. Climate Research. 2018; 75 (3):241-260.
Chicago/Turabian StyleMiroslav Trnka; Michael Hayes; FrantiEk Jurečka; L Bartosová; Martha Anderson; Rudolf Brázdil; Jesslyn Brown; Jesus J. Camarero; Pavel Cudlín; Petr Dobrovolný; Josef Eitzinger; Song Feng; Taryn Finnessey; Gregor Gregorič; Petr Havlik; Christopher Hain; Ian Holman; David Johnson; Kurt Christian Kersebaum; Fredrik Charpentier Ljungqvist; Jürg Luterbacher; Fabio Micale; Claudia Hartl-Meier; M Možný; Pavol Nejedlik; Jørgen E. Olesen; Margarita Ruiz-Ramos; Reimund P. Rötter; Gabriel Senay; Sergio M. Vicente-Serrano; Mark Svoboda; Andreja Susnik; Tsegaye Tadesse; Adam Vizina; Brian Wardlow; Zdenek Alud; Ulf Büntgen; Smv Serrano. 2018. "Priority questions in multidisciplinary drought research." Climate Research 75, no. 3: 241-260.
In advance of the FLEX mission, experimental studies are needed to better understand the factors driving Solar-Induced Fluorescence (SIF) emission from vegetation across different temporal and spatial scales. Here, we present findings from boreal (evergreen and deciduous) forest trees and Midwestern (annual) crops, illustrating effects of seasonal downregulation and drought on the fluorescence signals. Further work is needed to develop defensible, quantitative fluorescence measurements, and to partition the drivers of the fluorescence signals into effects of structure and physiology.
John Gamon; Gabriel Hmimina; Guofang Miao; Kaiyu Guan; Kyle Springer; Ran Wang; Rong Yu; Hamed Gholizadeh; Ryan Moore; Elizabeth Walter-Shea; Tim Arkebauer; Andy Suyker; Trenton Franz; Brian Wardlow; David Wedin. Imaging Spectrometry and Fluorometry in Support of Flex: What Can We Learn from Multi-Scale Experiments? IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 3931 -3934.
AMA StyleJohn Gamon, Gabriel Hmimina, Guofang Miao, Kaiyu Guan, Kyle Springer, Ran Wang, Rong Yu, Hamed Gholizadeh, Ryan Moore, Elizabeth Walter-Shea, Tim Arkebauer, Andy Suyker, Trenton Franz, Brian Wardlow, David Wedin. Imaging Spectrometry and Fluorometry in Support of Flex: What Can We Learn from Multi-Scale Experiments? IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():3931-3934.
Chicago/Turabian StyleJohn Gamon; Gabriel Hmimina; Guofang Miao; Kaiyu Guan; Kyle Springer; Ran Wang; Rong Yu; Hamed Gholizadeh; Ryan Moore; Elizabeth Walter-Shea; Tim Arkebauer; Andy Suyker; Trenton Franz; Brian Wardlow; David Wedin. 2018. "Imaging Spectrometry and Fluorometry in Support of Flex: What Can We Learn from Multi-Scale Experiments?" IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 3931-3934.
Yaping Cai; Kaiyu Guan; Jian Peng; Shaowen Wang; Christopher Seifert; Brian Wardlow; Zhan Li. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment 2018, 210, 35 -47.
AMA StyleYaping Cai, Kaiyu Guan, Jian Peng, Shaowen Wang, Christopher Seifert, Brian Wardlow, Zhan Li. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment. 2018; 210 ():35-47.
Chicago/Turabian StyleYaping Cai; Kaiyu Guan; Jian Peng; Shaowen Wang; Christopher Seifert; Brian Wardlow; Zhan Li. 2018. "A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach." Remote Sensing of Environment 210, no. : 35-47.
Yang Yang; Martha C. Anderson; Feng Gao; Brian Wardlow; Christopher R. Hain; Jason A. Otkin; Joseph Alfieri; Yun Yang; Liang Sun; Wayne Dulaney. Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA. Remote Sensing of Environment 2018, 210, 387 -402.
AMA StyleYang Yang, Martha C. Anderson, Feng Gao, Brian Wardlow, Christopher R. Hain, Jason A. Otkin, Joseph Alfieri, Yun Yang, Liang Sun, Wayne Dulaney. Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA. Remote Sensing of Environment. 2018; 210 ():387-402.
Chicago/Turabian StyleYang Yang; Martha C. Anderson; Feng Gao; Brian Wardlow; Christopher R. Hain; Jason A. Otkin; Joseph Alfieri; Yun Yang; Liang Sun; Wayne Dulaney. 2018. "Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA." Remote Sensing of Environment 210, no. : 387-402.