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Droughts are slow-moving natural hazards that gradually spread over large areas and capable of extending to continental scales, leading to severe socio-economic damage. A key challenge is developing accurate drought forecast model and understanding a models’ capability to examine different drought characteristics. Traditionally, forecasting techniques have used various time-series approaches and machine learning models. However, the use of deep learning methods have not been tested extensively despite its potential to improve our understanding of drought characteristics. The present study uses a deep learning approach, specifically the Long Short-Term Memory (LSTM) to predict a commonly used drought measure, the Standard Precipitation Evaporation Index (SPEI) at two different time scales (SPEI 1, SPEI 3). The model was compared with other common machine learning method, Random Forests, Artificial Neural Networks and applied over the New South Wales (NSW) region of Australia, using hydro-meteorological variables as predictors. The drought index and predictor data were collected from the Climatic Research Unit (CRU) dataset spanning from 1901 to 2018. We analysed the LSTM forecasted results in terms of several drought characteristics (drought intensity, drought category, or spatial variation) to better understand how drought forecasting was improved. Evaluation of the drought intensity forecasting capabilities of the model were based on three different statistical metrics, Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The model achieved R2 value of more than 0.99 for both SPEI 1 and SPEI 3 cases. The variation in drought category forecasted results were studied using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) approach. The analysis revealed an AUC value of 0.83 and 0.82 for SPEI 1 and SPEI 3 respectively. The spatial variation between observed and forecasted values were analysed for the summer months of 2016–2018. The findings from the study show an improvement relative to machine learning models for a lead time of 1 month in terms of different drought characteristics. The results from this work can be used for drought mitigation purposes and different models need to be tested to further enhance our capabilities.
Abhirup Dikshit; Biswajeet Pradhan; Alfredo Huete. An improved SPEI drought forecasting approach using the long short-term memory neural network. Journal of Environmental Management 2021, 283, 111979 .
AMA StyleAbhirup Dikshit, Biswajeet Pradhan, Alfredo Huete. An improved SPEI drought forecasting approach using the long short-term memory neural network. Journal of Environmental Management. 2021; 283 ():111979.
Chicago/Turabian StyleAbhirup Dikshit; Biswajeet Pradhan; Alfredo Huete. 2021. "An improved SPEI drought forecasting approach using the long short-term memory neural network." Journal of Environmental Management 283, no. : 111979.
The intertidal habitat of mangroves is very complex due to the dynamic roles of land and sea drivers. Knowledge of mangrove phenology can help in understanding mangrove growth cycles and their responses to climate and environmental changes. Studies of phenology based on digital repeat photography, or phenocams, have been successful in many terrestrial forests and other ecosystems, however few phenocam studies in mangrove forests showing the influence and interactions of water color and tidal water levels have been performed in sub-tropical and equatorial environments. In this study, we investigated the diurnal and seasonal patterns of an equatorial mangrove forest area at an Andaman Sea site in Phuket province, Southern Thailand, using two phenocams placed at different elevations and with different view orientations, which continuously monitored vegetation and water dynamics from July 2015 to August 2016. The aims of this study were to investigate fine-resolution, in situ mangrove forest phenology and assess the influence and interactions of water color and tidal water levels on the mangrove–water canopy signal. Diurnal and seasonal patterns of red, green, and blue chromatic coordinate (RCC, GCC, and BCC) indices were analyzed over various mangrove forest and water regions of interest (ROI). GCC signals from the water background were found to positively track diurnal water levels, while RCC signals were negatively related with tidal water levels, hence lower water levels yielded higher RCC values, reflecting brownish water colors and increased soil and mud exposure. At seasonal scales, the GCC profiles of the mangrove forest peaked in the dry season and were negatively related with the water level, however the inclusion of the water background signal dampened this relationship. We also detected a strong lunar tidal water periodicity in seasonal GCC values that was not only present in the water background, but was also detected in the mangrove–water canopy and mangrove forest phenology profiles. This suggests significant interactions between mangrove forests and their water backgrounds (color and depth), which may need to be accounted for in upscaling and coupling with satellite-based mangrove monitoring.
Veeranun Songsom; Werapong Koedsin; Raymond Ritchie; Alfredo Huete. Mangrove Phenology and Water Influences Measured with Digital Repeat Photography. Remote Sensing 2021, 13, 307 .
AMA StyleVeeranun Songsom, Werapong Koedsin, Raymond Ritchie, Alfredo Huete. Mangrove Phenology and Water Influences Measured with Digital Repeat Photography. Remote Sensing. 2021; 13 (2):307.
Chicago/Turabian StyleVeeranun Songsom; Werapong Koedsin; Raymond Ritchie; Alfredo Huete. 2021. "Mangrove Phenology and Water Influences Measured with Digital Repeat Photography." Remote Sensing 13, no. 2: 307.
Soil erosion is a severe threat to food production systems globally. Food production in farming systems decreases with increasing soil erosion hazards. This review article focuses on geo-informatics applications for identifying, assessing and predicting erosion hazards for sustainable farming system development. Several researchers have used a variety of quantitative and qualitative methods with erosion models, integrating geo-informatics techniques for spatial interpretations to address soil erosion and land degradation issues. The review identified different geo-informatics methods of erosion hazard assessment and highlighted some research gaps that can provide a basis to develop appropriate novel methodologies for future studies. It was found that rainfall variation and land-use changes significantly contribute to soil erosion hazards. There is a need for more research on the spatial and temporal pattern of water erosion with rainfall variation, innovative techniques and strategies for landscape evaluation to improve the environmental conditions in a sustainable manner. Examining water erosion and predicting erosion hazards for future climate scenarios could also be approached with emerging algorithms in geo-informatics and spatiotemporal analysis at higher spatial resolutions. Further, geo-informatics can be applied with real-time data for continuous monitoring and evaluation of erosion hazards to risk reduction and prevent the damages in farming systems.
Sumudu Senanayake; Biswajeet Pradhan; Alfredo Huete; Jane Brennan. A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management. Remote Sensing 2020, 12, 4063 .
AMA StyleSumudu Senanayake, Biswajeet Pradhan, Alfredo Huete, Jane Brennan. A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management. Remote Sensing. 2020; 12 (24):4063.
Chicago/Turabian StyleSumudu Senanayake; Biswajeet Pradhan; Alfredo Huete; Jane Brennan. 2020. "A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management." Remote Sensing 12, no. 24: 4063.
Urban heat islands (UHIs) can present significant risks to human health. Santiago, Chile has around 7 million residents, concentrated in an average density of 480 people/km2. During the last few summer seasons, the highest extreme maximum temperatures in over 100 years have been recorded. Given the projections in temperature increase for this metropolitan region over the next 50 years, the Santiago UHI could have an important impact on the health and stress of the general population. We studied the presence and spatial variability of UHIs in Santiago during the summer seasons from 2005 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and data from nine meteorological stations. Simple regression models, geographic weighted regression (GWR) models and geostatistical interpolations were used to find nocturnal thermal differences in UHIs of up to 9 °C, as well as increases in the magnitude and extension of the daytime heat island from summer 2014 to 2017. Understanding the behavior of the UHI of Santiago, Chile, is important for urban planners and local decision makers. Additionally, understanding the spatial pattern of the UHI could improve knowledge about how urban areas experience and could mitigate climate change.
Daniel Montaner-Fernández; Luis Morales-Salinas; José Rodriguez; Luz Cárdenas-Jirón; Alfredo Huete; Guillermo Fuentes-Jaque; Waldo Pérez-Martínez; Julián Cabezas. Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005–2017. Remote Sensing 2020, 12, 3345 .
AMA StyleDaniel Montaner-Fernández, Luis Morales-Salinas, José Rodriguez, Luz Cárdenas-Jirón, Alfredo Huete, Guillermo Fuentes-Jaque, Waldo Pérez-Martínez, Julián Cabezas. Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005–2017. Remote Sensing. 2020; 12 (20):3345.
Chicago/Turabian StyleDaniel Montaner-Fernández; Luis Morales-Salinas; José Rodriguez; Luz Cárdenas-Jirón; Alfredo Huete; Guillermo Fuentes-Jaque; Waldo Pérez-Martínez; Julián Cabezas. 2020. "Spatio-Temporal Variation of the Urban Heat Island in Santiago, Chile during Summers 2005–2017." Remote Sensing 12, no. 20: 3345.
The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies.
Ngoc Tran; Alfredo Huete; Ha Nguyen; Ian Grant; Tomoaki Miura; Xuanlong Ma; Alexei Lyapustin; Yujie Wang; Elizabeth Ebert. Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites. Remote Sensing 2020, 12, 2494 .
AMA StyleNgoc Tran, Alfredo Huete, Ha Nguyen, Ian Grant, Tomoaki Miura, Xuanlong Ma, Alexei Lyapustin, Yujie Wang, Elizabeth Ebert. Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites. Remote Sensing. 2020; 12 (15):2494.
Chicago/Turabian StyleNgoc Tran; Alfredo Huete; Ha Nguyen; Ian Grant; Tomoaki Miura; Xuanlong Ma; Alexei Lyapustin; Yujie Wang; Elizabeth Ebert. 2020. "Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites." Remote Sensing 12, no. 15: 2494.
The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.
Mingxi Zhang; Bin Wang; James Cleverly; De Li Liu; Puyu Feng; Hong Zhang; Alfredo Huete; Xihua Yang; Qiang Yu. Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau. Remote Sensing 2020, 12, 1722 .
AMA StyleMingxi Zhang, Bin Wang, James Cleverly, De Li Liu, Puyu Feng, Hong Zhang, Alfredo Huete, Xihua Yang, Qiang Yu. Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau. Remote Sensing. 2020; 12 (11):1722.
Chicago/Turabian StyleMingxi Zhang; Bin Wang; James Cleverly; De Li Liu; Puyu Feng; Hong Zhang; Alfredo Huete; Xihua Yang; Qiang Yu. 2020. "Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau." Remote Sensing 12, no. 11: 1722.
This study aims to identify the vulnerable landscape areas using landslide frequency ratio and land-use change associated soil erosion hazard by employing geo-informatics techniques and the revised universal soil loss equation (RUSLE) model. Required datasets were collected from multiple sources, such as multi-temporal Landsat images, soil data, rainfall data, land-use land-cover (LULC) maps, topographic maps, and details of the past landslide incidents. Landsat satellite images from 2000, 2010, and 2019 were used to assess the land-use change. Geospatial input data on rainfall, soil type, terrain characteristics, and land cover were employed for soil erosion hazard classification and mapping. Landscape vulnerability was examined on the basis of land-use change, erosion hazard class, and landslide frequency ratio. Then the erodible hazard areas were identified and prioritized at the scale of river distribution zones. The image analysis of Sabaragamuwa Province in Sri Lanka from 2000 to 2019 indicates a significant increase in cropping areas (17.96%) and urban areas (3.07%), whereas less dense forest and dense forest coverage are significantly reduced (14.18% and 6.46%, respectively). The average annual soil erosion rate increased from 14.56 to 15.53 t/ha/year from year 2000 to 2019. The highest landslide frequency ratios are found in the less dense forest area and cropping area, and were identified as more prone to future landslides. The river distribution zones Athtanagalu Oya (A-2), Kalani River-south (A-3), and Kalani River- north (A-9), were identified as immediate priority areas for soil conservation.
Sumudu Senanayake; Biswajeet Pradhan; Alfredo Huete; Jane Brennan. Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka. Remote Sensing 2020, 12, 1483 .
AMA StyleSumudu Senanayake, Biswajeet Pradhan, Alfredo Huete, Jane Brennan. Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka. Remote Sensing. 2020; 12 (9):1483.
Chicago/Turabian StyleSumudu Senanayake; Biswajeet Pradhan; Alfredo Huete; Jane Brennan. 2020. "Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka." Remote Sensing 12, no. 9: 1483.
Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.
Xuanlong Ma; Alfredo Huete; Ngoc Tran; Jian Bi; Sicong Gao; Yelu Zeng. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sensing 2020, 12, 1339 .
AMA StyleXuanlong Ma, Alfredo Huete, Ngoc Tran, Jian Bi, Sicong Gao, Yelu Zeng. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sensing. 2020; 12 (8):1339.
Chicago/Turabian StyleXuanlong Ma; Alfredo Huete; Ngoc Tran; Jian Bi; Sicong Gao; Yelu Zeng. 2020. "Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8." Remote Sensing 12, no. 8: 1339.
Both the sensor viewing angle and the solar angle influence the remote sensing signal of terrestrial ecosystems. This influence is characterized by the bidirectional reflectance distribution function (BRDF). Knowledge of this BRDF is needed to correctly interpret the signal, but can also provide information on vegetation characteristics and structure. Obtaining the BRDF is far from straightforward: at leaf scale, laboratory goniometers can measure reflected radiation over a range of sensor-solar angle; for very homogeneous ecosystems, such as grassland or agricultural cropland, unmanned aerial vehicles (UAVs) can be programmed as giant goniometer, scanning the BRDF of an area of up to a few m². For heterogeneous ecosystems such as forests, this is not feasible. In this case, BRDF could so far only be derived from theoretical radiation transfer models or semi-empirical models; yet these models do not always agree.
We here propose a new method for measuring BRDF of forest ecosystems with UAVs, by measuring a star-shaped area of the ecosystem, covering in total about 3600m² and capturing 6 different sensor-solar azimuth angle and three different zenith angles. This approach was applied over two sites of tropical rainforests in Queensland, Australia, with measurements with a RGB camera and a spectrometer. By repeating the flights several times during the day, we were able to test the Helmholtz reciprocity principle – that states the BRDF function of ecosystems remains the same, regardless of the solar angle – and are able to increase the range of sensor-solar angles. Our results present the first strictly empirical BRDF of tropical rainforests and confirm the importance of accurate BRDF correction of remote sensing products from forest ecosystems.
Wouter Maes; Lisa Bovend'Aerde; Marlies Lauwers; Kathy Steppe; Alfredo Huete. Empirical acquisition of bidirectional reflectance of tropical forest ecosystems using unmanned aerial vehicles. 2020, 1 .
AMA StyleWouter Maes, Lisa Bovend'Aerde, Marlies Lauwers, Kathy Steppe, Alfredo Huete. Empirical acquisition of bidirectional reflectance of tropical forest ecosystems using unmanned aerial vehicles. . 2020; ():1.
Chicago/Turabian StyleWouter Maes; Lisa Bovend'Aerde; Marlies Lauwers; Kathy Steppe; Alfredo Huete. 2020. "Empirical acquisition of bidirectional reflectance of tropical forest ecosystems using unmanned aerial vehicles." , no. : 1.
The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R² from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.
Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 2571 -2589.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Alfredo R. Huete, Baodong Xu, Gaofei Yin, Weiliang Fan, Yixuan Ouyang, Kai Yan, Dalei Hao, Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (4):2571-2589.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. 2019. "A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4: 2571-2589.
Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.
Dailiang Peng; Helin Zhang; Liangyun Liu; Wenjiang Huang; Alfredo R. Huete; Xiaoyang Zhang; Fumin Wang; Le Yu; Qiaoyun Xie; Cheng Wang; Shezhou Luo; Cunjun Li; Bing Zhang. Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables. Remote Sensing 2019, 11, 2270 .
AMA StyleDailiang Peng, Helin Zhang, Liangyun Liu, Wenjiang Huang, Alfredo R. Huete, Xiaoyang Zhang, Fumin Wang, Le Yu, Qiaoyun Xie, Cheng Wang, Shezhou Luo, Cunjun Li, Bing Zhang. Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables. Remote Sensing. 2019; 11 (19):2270.
Chicago/Turabian StyleDailiang Peng; Helin Zhang; Liangyun Liu; Wenjiang Huang; Alfredo R. Huete; Xiaoyang Zhang; Fumin Wang; Le Yu; Qiaoyun Xie; Cheng Wang; Shezhou Luo; Cunjun Li; Bing Zhang. 2019. "Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables." Remote Sensing 11, no. 19: 2270.
Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was ( R 723 − R 738 ) / ( R 723 − R 722 ) with a cross-validation R2cv of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were ( R 735 − R 753 ) / ( R 715 − R 819 ) for estimating LCC from the adaxial surface (R2cv = 0.96, RMSEcv = 2.37 μg/cm2) and ( R 732 − R 754 ) / ( R 724 − R 773 ) for estimating LCC from reflectance of both sides (R2cv = 0.94, RMSEcv = 2.81 μg/cm2). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.
Mengmeng Xie; Zhongqiang Wang; Alfredo Huete; Luke A. Brown; Heyu Wang; Qiaoyun Xie; Xinpeng Xu; Yanling Ding. Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces. Remote Sensing 2019, 11, 2148 .
AMA StyleMengmeng Xie, Zhongqiang Wang, Alfredo Huete, Luke A. Brown, Heyu Wang, Qiaoyun Xie, Xinpeng Xu, Yanling Ding. Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces. Remote Sensing. 2019; 11 (18):2148.
Chicago/Turabian StyleMengmeng Xie; Zhongqiang Wang; Alfredo Huete; Luke A. Brown; Heyu Wang; Qiaoyun Xie; Xinpeng Xu; Yanling Ding. 2019. "Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces." Remote Sensing 11, no. 18: 2148.
The Australian AusPollen Partnership provides respiratory allergy patients with accurate, relevant and localised pollen information via smartphone Apps. This study aims to evaluate public perceptions of need and benefit of providing local pollen information. Individuals aged 18 years and older were contacted through AusPollen Smartphone Apps (Brisbane, Sydney, Canberra and Melbourne), Australian Society for Clinical Immunology and Allergy, Asthma Australia and social media. A pilot questionnaire was developed in consultation with partner organisations, including select questions drawn from the National Young People and Asthma Survey. The questionnaire consisted of four sections: participant demographics, allergic rhinitis and asthma symptoms, symptom management and App utility. One hundred and twenty-seven people completed the survey, of whom 53% had access to local pollen information. Most (97%) participants without access to local pollen information indicated that they wanted such a service. Pollen information was most commonly perceived by participants to be useful for prevention and avoidance as well as preparation and planning. This preliminary study identified a public demand for local pollen information. Users identified practical ways in which pollen information assisted them. Publicised pollen concentrations and forecasts have the potential to improve awareness of allergy triggers and empower patient self-management, reducing symptoms and burden of disease.
Danielle E. Medek; Marko Simunovic; Bircan Erbas; Constance H. Katelaris; Edwin R. Lampugnani; Alfredo Huete; Paul J. Beggs; Janet M. Davies. Enabling self-management of pollen allergies: a pre-season questionnaire evaluating the perceived benefit of providing local pollen information. Aerobiologia 2019, 35, 777 -782.
AMA StyleDanielle E. Medek, Marko Simunovic, Bircan Erbas, Constance H. Katelaris, Edwin R. Lampugnani, Alfredo Huete, Paul J. Beggs, Janet M. Davies. Enabling self-management of pollen allergies: a pre-season questionnaire evaluating the perceived benefit of providing local pollen information. Aerobiologia. 2019; 35 (4):777-782.
Chicago/Turabian StyleDanielle E. Medek; Marko Simunovic; Bircan Erbas; Constance H. Katelaris; Edwin R. Lampugnani; Alfredo Huete; Paul J. Beggs; Janet M. Davies. 2019. "Enabling self-management of pollen allergies: a pre-season questionnaire evaluating the perceived benefit of providing local pollen information." Aerobiologia 35, no. 4: 777-782.
Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.
Xuanlong Ma; Alfredo Huete; Ngoc Nguyen Tran. Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS. Remote Sensing 2019, 11, 1398 .
AMA StyleXuanlong Ma, Alfredo Huete, Ngoc Nguyen Tran. Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS. Remote Sensing. 2019; 11 (12):1398.
Chicago/Turabian StyleXuanlong Ma; Alfredo Huete; Ngoc Nguyen Tran. 2019. "Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS." Remote Sensing 11, no. 12: 1398.
Vegetation phenology is the annual cycle timing of vegetation growth. Mangrove phenology is a vital component to assess mangrove viability and includes start of season (SOS), end of season (EOS), peak of season (POS), and length of season (LOS). Potential environmental drivers include air temperature (Ta), surface temperature (Ts), sea surface temperature (SST), rainfall, sea surface salinity (SSS), and radiation flux (Ra). The Enhanced vegetation index (EVI) was calculated from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD13Q1) data over five study sites between 2003 and 2012. Four of the mangrove study sites were located on the Malay Peninsula on the Andaman Sea and one site located on the Gulf of Thailand. The goals of this study were to characterize phenology patterns across equatorial Thailand Indo-Malay mangrove forests, identify climatic and aquatic drivers of mangrove seasonality, and compare mangrove phenologies with surrounding upland tropical forests. Our results show the seasonality of mangrove growth was distinctly different from the surrounding land-based tropical forests. The mangrove growth season was approximately 8–9 months duration, starting in April to June, peaking in August to October and ending in January to February of the following year. The 10-year trend analysis revealed significant delaying trends in SOS, POS, and EOS for the Andaman Sea sites but only for EOS at the Gulf of Thailand site. The cumulative rainfall is likely to be the main factor driving later mangrove phenologies.
Veeranun Songsom; Werapong Koedsin; Raymond J. Ritchie; Alfredo Huete. Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand. Remote Sensing 2019, 11, 955 .
AMA StyleVeeranun Songsom, Werapong Koedsin, Raymond J. Ritchie, Alfredo Huete. Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand. Remote Sensing. 2019; 11 (8):955.
Chicago/Turabian StyleVeeranun Songsom; Werapong Koedsin; Raymond J. Ritchie; Alfredo Huete. 2019. "Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand." Remote Sensing 11, no. 8: 955.
In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSEdaily was 54 W/m2, which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSEdaily = 82 W/m2, GLDAS: RMSEdaily = 151 W/m2). However, LE RMSE values at 8-day temporal scale for GMA are RMSE8-days = 32 W/m2, similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations.
Verónica Barraza; Francisco Grings; Mariano Franco; Vanesa Douna; Dara Entekhabi; Natalia Restrepo-Coupe; Alfredo Huete; María Gassmann; Esteban Roitberg. Estimation of latent heat flux using satellite land surface temperature and a variational data assimilation scheme over a eucalypt forest savanna in Northern Australia. Agricultural and Forest Meteorology 2019, 268, 341 -353.
AMA StyleVerónica Barraza, Francisco Grings, Mariano Franco, Vanesa Douna, Dara Entekhabi, Natalia Restrepo-Coupe, Alfredo Huete, María Gassmann, Esteban Roitberg. Estimation of latent heat flux using satellite land surface temperature and a variational data assimilation scheme over a eucalypt forest savanna in Northern Australia. Agricultural and Forest Meteorology. 2019; 268 ():341-353.
Chicago/Turabian StyleVerónica Barraza; Francisco Grings; Mariano Franco; Vanesa Douna; Dara Entekhabi; Natalia Restrepo-Coupe; Alfredo Huete; María Gassmann; Esteban Roitberg. 2019. "Estimation of latent heat flux using satellite land surface temperature and a variational data assimilation scheme over a eucalypt forest savanna in Northern Australia." Agricultural and Forest Meteorology 268, no. : 341-353.
Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts.
Wouter H. Maes; Alfredo R. Huete; Michele Avino; Matthias M. Boer; Remy Dehaan; Elise Pendall; Anne Griebel; Kathy Steppe. Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees? Remote Sensing 2018, 10, 2062 .
AMA StyleWouter H. Maes, Alfredo R. Huete, Michele Avino, Matthias M. Boer, Remy Dehaan, Elise Pendall, Anne Griebel, Kathy Steppe. Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees? Remote Sensing. 2018; 10 (12):2062.
Chicago/Turabian StyleWouter H. Maes; Alfredo R. Huete; Michele Avino; Matthias M. Boer; Remy Dehaan; Elise Pendall; Anne Griebel; Kathy Steppe. 2018. "Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees?" Remote Sensing 10, no. 12: 2062.
Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.
Shangrong Lin; Jing Li; Qinhuo Liu; Alfredo Huete; Longhui Li. Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing. Remote Sensing 2018, 10, 1329 .
AMA StyleShangrong Lin, Jing Li, Qinhuo Liu, Alfredo Huete, Longhui Li. Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing. Remote Sensing. 2018; 10 (9):1329.
Chicago/Turabian StyleShangrong Lin; Jing Li; Qinhuo Liu; Alfredo Huete; Longhui Li. 2018. "Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing." Remote Sensing 10, no. 9: 1329.
Land surface phenology, especially spring phenology, has been reported as a powerful indicator of ecosystem responses to climate change. It also exerts strong control on the carbon, water and energy balances and, hence, climatic feedbacks. Researchers have produced numerous spring phenology products from various coarse-resolution remote sensing data at regional or global scales. Scaling up observations of spring phenology from plot-level (or finer resolution) to coarser resolution is important for the validation, synthesis, and evaluation of those products. The best method for scaling up is unclear although coarse resolution data can be obtained by averaging across fine-scale pixels, or selecting the start of spring phenology (SOS) date associated with the earliest 30% (or another percentile) of fine-scale pixels within a coarse-scale pixel. In this study, we tested different methods that were average and percentile approaches to aggregate SOS as measured at 250 m (SOS (250 m)) resolution to 8 km (SOS (8 km)) resolution pixels, and then to ecosystems and national scales for the continental United States. The results indicated that the average absolute difference (AAD) between SOS (250 m) and SOS (8 km) from the average approach was close to that achieved by the percentile approach. Relatively large AAD values occurred in the western and southern regions of the continental United States. The distribution of AAD was positively related to landscape heterogeneity. The percentile approach generally yielded smaller AADs than the average approach did, but these two approaches performed similarly. Across landscapes and ecosystems, the optimal percentile usually ranged from 30–45th instead of a single value. Our findings indicated that the percentile approach may be best for finer scale areas, but that the average approach is an adequate alternative for scaling up SOS in most circumstances. In addition, the detailed error distributions of scaling up spring phenology across scales are helpful to identify the appropriate method of scaling up for validating the coarse SOS products derived from remote sensing images.
Dailiang Peng; Chaoyang Wu; Xiaoyang Zhang; Le Yu; Alfredo R. Huete; Fumin Wang; Shezhou Luo; Xinjie Liu; Helin Zhang. Scaling up spring phenology derived from remote sensing images. Agricultural and Forest Meteorology 2018, 256-257, 207 -219.
AMA StyleDailiang Peng, Chaoyang Wu, Xiaoyang Zhang, Le Yu, Alfredo R. Huete, Fumin Wang, Shezhou Luo, Xinjie Liu, Helin Zhang. Scaling up spring phenology derived from remote sensing images. Agricultural and Forest Meteorology. 2018; 256-257 ():207-219.
Chicago/Turabian StyleDailiang Peng; Chaoyang Wu; Xiaoyang Zhang; Le Yu; Alfredo R. Huete; Fumin Wang; Shezhou Luo; Xinjie Liu; Helin Zhang. 2018. "Scaling up spring phenology derived from remote sensing images." Agricultural and Forest Meteorology 256-257, no. : 207-219.
Allergic diseases, including respiratory conditions of allergic rhinitis (hay fever) and asthma, affect up to 500 million people worldwide. Grass pollen are one major source of aeroallergens globally. Pollen forecast methods are generally site-based and rely on empirical meteorological relationships and/or the use of labour-intensive pollen collection traps that are restricted to sparse sampling locations. The spatial and temporal dynamics of the grass pollen sources themselves, however, have received less attention. Here we utilised a consistent set of MODIS satellite measures of grass cover and seasonal greenness (EVI) over five contrasting urban environments, located in Northern (France) and Southern Hemispheres (Australia), to evaluate their utility for predicting airborne grass pollen concentrations. Strongly seasonal and pronounced pollinating periods, synchronous with satellite measures of grass cover greenness, were found at the higher latitude temperate sites in France (46–50° N. Lat.), with peak pollen activity lagging peak greenness, on average by 2–3 weeks. In contrast, the Australian sites (34–38° S. Lat.) displayed pollinating periods that were less synchronous with satellite greenness measures as peak pollen concentrations lagged peak greenness by as much as 4 to 7 weeks. The Australian sites exhibited much higher spatial and inter-annual variations compared to the French sites and at the Sydney site, broader and multiple peaks in both pollen concentrations and greenness data coincided with flowering of more diverse grasses including subtropical species. Utilising generalised additive models (GAMs) we found the satellite greenness data of grass cover areas explained 80–90% of airborne grass pollen concentrations across the three French sites (p < 0.001) and accounted for 34 to 76% of grass pollen variations over the two sites in Australia (p < 0.05). Our results demonstrate the potential of satellite sensing to augment forecast models of grass pollen aerobiology as a tool to reduce the health and socioeconomic burden of pollen-sensitive allergic diseases.
Rakhesh Devadas; Alfredo R. Huete; Don Vicendese; Bircan Erbas; Paul J. Beggs; Danielle Medek; Simon G. Haberle; Rewi M. Newnham; Fay H. Johnston; Alison K. Jaggard; Bradley Campbell; Pamela K. Burton; Constance H. Katelaris; Ed Newbigin; Michel Thibaudon; Janet M. Davies. Dynamic ecological observations from satellites inform aerobiology of allergenic grass pollen. Science of The Total Environment 2018, 633, 441 -451.
AMA StyleRakhesh Devadas, Alfredo R. Huete, Don Vicendese, Bircan Erbas, Paul J. Beggs, Danielle Medek, Simon G. Haberle, Rewi M. Newnham, Fay H. Johnston, Alison K. Jaggard, Bradley Campbell, Pamela K. Burton, Constance H. Katelaris, Ed Newbigin, Michel Thibaudon, Janet M. Davies. Dynamic ecological observations from satellites inform aerobiology of allergenic grass pollen. Science of The Total Environment. 2018; 633 ():441-451.
Chicago/Turabian StyleRakhesh Devadas; Alfredo R. Huete; Don Vicendese; Bircan Erbas; Paul J. Beggs; Danielle Medek; Simon G. Haberle; Rewi M. Newnham; Fay H. Johnston; Alison K. Jaggard; Bradley Campbell; Pamela K. Burton; Constance H. Katelaris; Ed Newbigin; Michel Thibaudon; Janet M. Davies. 2018. "Dynamic ecological observations from satellites inform aerobiology of allergenic grass pollen." Science of The Total Environment 633, no. : 441-451.