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Riparian ecosystems are biodiversity hotspots and provide crucial services to human wellbeing. Currently, the knowledge of how riparian ecosystems respond to and in turn influence the variations of the environment remains considerably limited. As a first step toward filling the gap, this research aims to characterize the dynamics of riparian vegetation during the past several decades across multiple aquatic sites operated by the National Ecological Observatory Network (NEON) of the US. Specifically, it leverages high-resolution hyperspectral and lidar data collected by NEON’s airborne observational platform (AOP) surveys, the long-term records of satellite optical and radar imagery, and advanced data fusion and classification techniques to generate a time-series record of riparian vegetation on a seasonal-to-yearly basis. The maps derived will provide a new basis for understanding how riparian vegetation has changed across continental US, and for predicting how it is likely to change in the future. This work is sponsored by NSF’s Macrosystems Biology and NEON-Enabled Science (MSB-NES) Program (2021/9–2024/8), and the overarching goal of the project is to mechanistically link riparian vegetation dynamics to hydroclimate variations and assess the functional importance of riparian ecosystems to macrosystem fluxes of carbon and water.

Cu-based functional materials are excellent candidates for the elimination of iodine anions. However, the low utilization rate of Cu and its unsatisfactory adsorption performance limit its large-scale practical applications. This paper proposes a co-gelation method to obtain Cu/Al

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM

Surface water is crucial to ecosystems in Alaska. Precisely mapping the dynamic surface water extent in this region is required by a wide range of environmental studies. However, most existing inundation products cannot reveal the distribution of surface water in mainland Alaska at high spatiotemporal scales. To bridge this gap, this study developed a framework to generate subpixel surface water fraction (SWF) maps from the 8-day VIIRS surface reflectance composites at the 1 km resolution through a random forest regression. Assessment of map accuracy resulted in an r2 value of 0.839 and a root mean square error (RMSE) of 12.17%. With the adoption of a proper terrain shadow preprocessing procedure and more training samples in rugged terrain, the developed framework can be easily extended to produce accurate time-series SWF maps over the entire Arctic-Boreal region on a weekly basis, particularly during the summer season.

With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated fro...

Wetlands provide important ecosystem services, the provision of which is largely controlled by fluctuations in inundation and soil saturation. Inundation is highly dynamic and can vary substantially through time in response to multiple drivers, including precipitation and evapotranspiration. This research focused on developing a practical and effective framework for regional, long-term monitoring of wetland inundation dynamics using airborne LiDAR intensity data (Lang et al., 2013) and Landsat time-series imagery. Subpixel water fraction (SWF) maps indicating the percent of surface water within each 30-m pixel were generated on an annual basis over the entire Delmarva Peninsula on the East Coast of the United States from 1985 to 2011. Comprehensive accuracy assessments of the SWF maps were conducted using historical high-resolution aerial photography to determine the reference condition. The assessment resulted in an estimated root mean square error (RMSE) of 7.78% for the sample of open water areas (mean SWF was ~ 40% for this region of the map). Moreover, a separate accuracy assessment targeting inundation in wetlands (i.e. presence or absence of water) yielded an overall accuracy of 93%. Accuracies derived indicated that Landsat data can be calibrated to accurately extract long-term water information at the regional scale. Characteristics of inundation were examined with respect to different wetland types defined by water regime and dominant vegetation types, as well as different physical drivers. Results showed that tidal wetlands typically exhibited more intensive inundation than nontidal wetlands, and a higher degree of inundation was associated with emergent wetlands compared to wetland areas dominated by woody vegetation. Analysis of change drivers revealed that tide exerted a statistically significant influence on coastal inundation with r 2 values of 32–36% and p r 2 values of 25–34% and p

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in numerous climatological and ecological models. The targeted accuracy of FAPAR products is 10%, or 0.05, for many applications. However, most of the FAPAR products in current usage have not yet fulfilled the accuracy requirement, thus requiring further improvements. In this study, a new FAPAR estimation model is developed on the basis of the radiative transfer (RT) for a horizontally homogeneous continuous canopy. The spatially explicit parameterization of leaf-scattering and soil background reflectance is derived from a 13-year Moderate Resolution Imaging Spectroradiometer (MODIS) albedo database. The new algorithm requires the input of leaf area index (LAI), which is estimated by a hybrid geometric optical-RT model suitable for both continuous and discrete vegetation canopies in this study. The model calculated radiative surface fluxes, i.e., canopy reflectance, absorption, and transmittance, are compared with the reference data from Radiation transfer Model Intercomparison (RAMI) exercise. The evaluation results show that the model estimated FAPAR has an uncertainty of 0.08 over homogeneous and heterogeneous canopies. The FAPAR estimates from the new model are intercompared with reference satellite FAPAR products and validated with ground-based measurements at the Validation of Land European Remote Sensing Instruments (VALERI) AmeriFlux experimental sites. The validation results show that the FAPAR estimates from the new model are comparable to or slightly better in performance than the MODIS and the Multi-angle Imaging SpectroRadiometer (MISR) FAPAR products when using corresponding satellite LAI product values as the input. The FAPAR estimates are further improved when using the new LAI estimates from the hybrid model as the input. The new model adequately identifies the growing seasons and produces smooth time series curves of estimated FAPAR during a specific duration. The uncertainty is reduced to 0.1 when validating with total FAPAR measurements, and 0.08 when validating with green FAPAR measurements. The improvements are apparent in grasslands and forests with an uncertainty reduction of 0.06. The regional-scale application of the presented model generates consistent FAPAR maps at spatial resolutions of 30 m, 500 m, and 1 km from the Landsat, MODIS, and MISR data, respectively.

Although multispectral images acquired during the summer season have been used extensively in impervious surface extraction with relatively high accuracy, the area of impervious surface extracted is generally underestimated. In this study, a quantitative comparison of urban impervious surface extraction was conducted using WorldView-2 images of the summer and winter seasons over two urban areas in a temperate region of Northern China. A hierarchical object-based classification method was adopted to extract urban impervious surfaces. The results showed that the impervious surface extraction from the winter image achieved an accuracy comparable with that from the summer image. However, the area of impervious surface extracted from the winter image was much greater than that from the summer image, which was mainly attributed to seasonal variations of deciduous trees. Therefore, winter images are recommended for impervious surface mapping in temperate regions using very high resolution images.

Aboveground forest biomass estimation is an integral component for climate change, carbon stocks assessment, biodiversity and forest health. LiDAR (Light Detection And Ranging), specifically NASA's...

This study used the Night Light Development Index (NLDI) to measure the regional inequality of public services in Mainland China at multiple scales. The NLDI was extracted based on a Gini Coefficient approach to measure the spatial differences of population distribution and night light distribution. Population data were derived from the dataset of China’s population density grid, and night light data were acquired from satellite imagery. In the multi-scale analysis, we calculated the NLDI for China as a whole, eight economic regions, 31 provincial regions, and 354 prefectural cities for the two years of 2005 and 2010. The results indicate that Southwest China and Northwest China are the regions with the most unequal public services, with NLDI values of 0.7116 and 0.7251 for 2005, respectively, and 0.6678 and 0.6304 for 2010, respectively. In contrast, Northern Coastal China had the lowest NLDI values of 0.4775 and 0.4312 for 2005 and 2010, respectively, indicating that this region had the most equal public services. Also, the regional inequality of Mainland China in terms of NLDI has been reduced from 0.6161 to 0.5743 during 2005–2010. The same pattern was observed from the provincial and prefectural analysis, suggesting that public services in Mainland China became more equal within the five-year period. A regression analysis indicated that provincial and prefectural regions with more public services per capita and higher population density had more equal public services.

Aflatoxin B1 (AFB1) and its metabolite aflatoxin M1 (AFM1) are well-known carcinogens for humans and animals health. In this study, an ultra-high performance liquid chromatography linked with fluorescence detection (UPLC-FLD) method was optimized and validated. In addition, we investigated for the first time, the influence of curcumin on residue depletion of AFB1 and AFM1 in liver, kidney and muscle tissues of broiler chickens and estimated a necessary clearance time required for AFB1 and AFM1 residues. The results showed that the average recoveries of AFB1 varied in liver, kidney and muscles between 82.32-85.56%, 85.34-88.45% and 84.88-89.73% respectively, while the average recoveries of AFM1 in liver, kidney and muscles varied between 92.17-95.03%, 94.12-97.21% and 95.32-98.51%, respectively. The detection limit of aflatoxin B1 was 0.008 ng/ml, while for aflatoxin M1 was 0.003 ng/ml. The limit of quantification for AFB1 and AFM1 was 0.02 ng/ml and 0.01 ng/ml, respectively. Clearance time for AFB1 and AFM1 residues were analyzed in two experimental groups of broilers. One group fed with dietary AFB1 (5.0 mg/kg feed) and other with curcumin+AFB1 diet (curcumin; 300 mg/kg feed, AFB1; 5.0 mg/kg feed). AFB1 and AFM1 residues clearance time was calculated based on limit of quantification (LOQ) using withdrawal time calculation software (WT1.4). Clearance time analyzed for AFB1 ranged from 11 to 19 days and for AFM1 ranged from 10 to 12 days at 95% confidence level. Interestingly, curcumin supplementation in the diet reduced the clearance time of AFM1 in liver and kidney but not in muscle tissues. Conclusively, the developed method can be appropriately used for the quality control testing of commercial broiler-meat processing companies, food manufacturers and quality control laboratories.

Clay aerogels have many attractive properties, such as low thermal conductivities, good thermal stabilities and low flammability. But the generally weak mechanical property severely restrains their practical applications. Incorporation of polymers into clay aerogel could achieve desirable strength, unfortunately, the fire resistance or insulating properties were sacrificed to some extent. In this work, fire-resistant palygorskite/wood fiber composite aerogels with low densities were prepared by an eco-friend freeze-drying method. In the aerogels, palygorskite contents were up to 80 wt% while only a small amount of wood fiber were used as reinforced filler. The composite aerogel had an array of porous structure with intertwined palygorskite fibers as pore walls, which resulted in a low thermal conductivity of 0.033 W/mK. In addition, the composite aerogel exhibited good mechanical strength with the Young's modulus up to 4.7 MPa while the density was lower than 0.08 g/cm3. Overall, an outstanding combination of excellent machinability, thermal stability, high limiting oxygen index, and prominent flame-retardant properties has been achieved in the composite aerogels. This work represents a significant progress of porous materials development and makes the practical applications of clay-based aerogel insulators realistic.

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.

Understanding the factors that influence the performance of classifications over urban areas is of considerable importance to applications of remote-sensing-derived products in urban design and planning. We examined the impact of training sample selection on a binary classification of urban and nonurban for the Denver, Colorado, metropolitan area. Complete coverage reference data for urban and nonurban cover were available for the year 1997, which allowed us to examine variability in accuracy of the classification over multiple repetitions of the training sample selection and classification process. Four sampling designs for selecting training data were evaluated. These designs represented two options for stratification (spatial and class-specific) and two options for sample allocation (proportional to area and equal allocation). The binary urban and nonurban classification was obtained by employing a decision tree classifier with Landsat imagery. The decision tree classifier was applied to 1000 training ...

This study presents an integrated wetland-watershed modeling approach that capitalizes on inundation maps and geospatial data to improve spatial prediction of wetland inundation and assess its prediction uncertainty. We outline problems commonly arising from data preparation and parameterization used to simulate wetlands within a (semi-) distributed watershed model. We demonstrate how wetland inundation can be better captured by the wetland parameters developed from remotely sensed data. We then emphasize assessing model prediction using inundation maps derived from remotely sensed data. This integrated modeling approach is tested using the Soil and Water Assessment Tool (SWAT) with an improved riparian wetlands (RWs) extension, for an agricultural watershed in the Mid-Atlantic Coastal Plain, US. This study illustrates how spatially distributed information is necessary to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place.

In this paper, non-aqueous Fe(OH)3/palygorskite dispersion was prepared and its liquid crystal phase behavior was investigated. Positively charged Fe(OH)3 colloidal particles were put in to non-aqueous and organically modified palygorskite dispersion. The effects of organically treated palygorskite and Fe(OH)3 on the preparation of stable palygorskite dispersion were characterized with FT-IR, XRD, Contact Angle measurement, Zeta potential detection, Settlement measurement and SEM, respectively. The addition of Fe(OH)3 particles shifted the concentrations of the coexisting isotropic and nematic phases to higher values while at the same time markedly accelerated the phase separation. The influence of Fe(OH)3 particles on the dispersion in magnetic field was also discussed.

Urban change detection using remotely sensed data has been extensively studied. One current application of detection products is the formulation of calibration data for urban change prediction models. As multi-temporal scenes become available and urban growth prediction models increase in popularity and accuracy, it is natural to envisage a bi-directional relationship where, in addition to detection products assisting prediction models, the prediction information acts as ancillary input to enhance spectral-based change detection products. This closed feedback loop has the potential to significantly increase the accuracy of both detection and prediction efforts. Consequently, our objective is to evaluate the integration of prediction information with spectral data for urban change monitoring. A case study was carried out in the Denver, Colorado metropolitan area. Probabilities of urban change generated from two existing urban prediction models based on decision trees and logistic regression are combined as additional information content with a Landsat Thematic Mapper TM scene. Detailed assessments at the pixel and block scales are implemented to evaluate urban change classification accuracy using different input data and training sample sizes. Results show that in pixel-based assessments, the fusion of decision tree change probabilities and TM spectral bands with sufficient training samples leads to improvements. In terms of overall accuracies, the improvement is 2.0–2.4%, from 87.3% spectral-only model and 87.7% prediction probability model to 89.7% for the fused model. Similarly, the corresponding kappa coefficients show increases of 0.07–0.08, from 0.60 for the spectral model and 0.61 for the urban prediction model to 0.68 for the fused model. Accuracies aggregated at the block scale present an approximate 2.1–4.3% increase when the fusion-based model is employed compared with the exclusive use of either spectral or prediction probability data, namely 87.6% fused vs 83.4% spectral and 85.7% prediction. It is also important to state that the standard deviation of accuracies between blocks is significantly reduced by more than 3% 11.5% vs 14.9% and 14.7%, suggesting higher consistency in classification performance. This is a desirable attribute for subsequent use of these products, for example by the urban planning community. Statistical tests at the block scale also demonstrate that such improvements are significant. It is also observed that to receive the integration benefits, the remote sensing classifier needs a large but reasonable training data set size, while the prediction model should be based on advanced modelling methods. Further assessments on block accuracy with respect to urbanization conditions i.e. urban presence and change sizes indicate the ability of the fusion to address spectral limitations, especially in blocks with high relative change. These initial results encourage the expansion of spectral/prediction data fusion to other sites, modelling techniques, and input data.

Most existing multi-temporal classification studies use spectral information alone and ignore the temporal correlation between two-date images. This article proposes a new method to characterize the local temporal correlation using multi-temporal texture measured with a geostatistical function called the pseudo cross variogram (PCV). The derived multi-temporal texture, as an additional band, was combined with the spectral information in multi-temporal classification. The performance of the multi-temporal texture was evaluated and compared with the use of multi-temporal spectral data alone and plus the traditional variogram texture in land cover classification using bitemporal hyperspectral Compact High Resolution Imaging Spectrometer/Project for On Board Autonomy (CHRIS/PROBA) images. The results show that although land cover classification using spectral information from bitemporal CHRIS/PROBA data alone had an acceptable overall accuracy of 85.66%, the inclusion of multi-temporal texture in land cover c...

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43–10.96% higher than that of the IGSRM method for different scale factors, and 1.09–3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42–4.92%, and 0.08–0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods.

Urban land cover (ULC) has a considerable impact on local, regional, and global environmental change, and has significant ecological, biophysical, social, and climatic effects (Seto and Shepherd, 2009; DeFries et al., 2010). These effects are further amplified by the temporal duration of urban changes that tend to last for decades and are often irreversible. Optical sensors on board various satellite platforms play a significant role in urban monitoring and assessment. Two representative examples are indicative of the importance of optical sensors. First, since 2009 after USGS made the Landsat archive freely available, a 60-fold increase was observed in data downloads (NASA, 2013). Second, in the last decade, there has been a strong interest from the commercial sector to launch satellite optical sensors. This interest is clearly driven by the constantly increasing demand for such products from governmental, military, nonprofit, and commercial sectors. 4