The newly launched Landsat 8 satellite continues the long and extremely important record of Earth observation from the Landsat program. We analyzed differences between Landsat 7 and Landsat 8 surface ...reflectances and cirrus cloud characterization to address how substitutable Landsat 8 observations are within this long archive. Comparison of surface reflectance estimates acquired near simultaneously during Landsat 8's underflight orbital placement shows that Landsat 8 surface reflectance is consistently darker in the blue, green, and red bands and brighter in the near infrared than in Landsat 7. Vegetation indices that rely on the visible and near infrared bands should be used with caution as individual biases in index components can be amplified to create large biases in vegetation indices. We also analyzed time series datasets from the Landsat Climate Data Record (CDR) surface reflectance product across four scenes that contained only Landsat 7 data, Landsat 7 data and only Landsat 8 data post-launch, and Landsat 7 data and data from both sensors post-launch to investigate how sensor differences propagate in time series analysis. If left uncorrected or unexplained, the difference in reflectance between Landsat 7 and Landsat 8 creates spurious time trends in visible wavelengths and in the Normalized Difference Vegetation Index (NDVI). The introduction of Landsat 8 into time series of Landsat 7 data also biases the mean reflectance or vegetation index value as measured by a time series model intercept while increasing the Root Mean Squared Error of such models. We characterized the spectral reflectance of cirrus clouds in the underflight data that were omitted from Landsat 7 cloud masks but were detected in Landsat 8's cloud mask due to the use of the newly added cirrus band. While these cirrus cloud observations missed in Landsat 7's cloud mask are only slightly brighter in the visible bands, a simulation of time series containing Landsat 8 data that does not use the cirrus band shows that omission of cirrus clouds can result in anomalously brighter time series intercepts and positive time trends. Our results indicate that while Landsat 8 has improved on the legacy of previous sensors through increased radiometric resolution, better cloud identification, and better geometric accuracy, difference in reflectance between sensors in the current Landsat CDR product must be corrected or explained within time series analysis to avoid deleterious consequences. Future efforts should identify the contributions of target specific effects versus differences in atmospheric correction methods to better inform approaches to synthesize the two sensors.
•We compare the Landsat Climate Data Record surface reflectance from Landsats 7 and 8.•Landsat 8 is consistently darker in the blue, green, and red bands than Landsat 7.•Sensor specific differences in reflectance bias time series model estimates.•Landsat 8's cirrus band can detect subtle contamination by cirrus clouds.•Cirrus cloud contamination can produce spurious results in time series analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Multi-spectral imagery from the Landsat family of satellites has been used to map forest properties for decades, but accurate forest type characterizations at a 30-m Landsat resolution have remained ...an ongoing challenge, especially over large areas. We combined existing Landsat time series algorithms to quantify both harmonic and phenological metrics in a new set of spectral-temporal features that can be produced seamlessly across many Landsat scenes. Harmonic metrics characterize mean annual reflectance and seasonal variability, while phenological metrics quantify the timing of seasonal events. We assessed the performance of spectral-temporal features derived from time series of all available observations (1985–2015) relative to more conventional single date and multi-date inputs. Performance was determined based on agreement with a reference dataset for eight New England forest types at both the pixel and polygon scale. We found that spectral-temporal features consistently and significantly (paired t-test, p ≪ 0.01) outperformed all feature sets derived from individual images and multi-date combinations in all measures of agreement considered. Harmonic features, such as annual amplitude and model fit error, aid in distinguishing deciduous hardwoods from conifer species, while phenology features, like the timing of autumn onset and growing season length, were useful in separating hardwood classes. This study represents an important step toward large-scale forest type mapping using spectral-temporal Landsat features by providing a quantitative assessment of the advantages of harmonic and phenology features derived from time series of Landsat data as compared with more conventional single-date and multi-date classification inputs.
•Spectral-temporal features were derived from time series of Landsat observations.•Harmonic and phenology features were combined for forest type classification.•Spectral-temporal features consistently outperformed more conventional inputs.•Spectral-temporal features provide more consistent inputs for large area mapping.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
An ensemble of time series algorithms improves land change monitoring. The methodology combines the Continuous Change Detection and Classification (CCDC; Zhu & Woodcock, 2014) and Cumulative Sum of ...Residuals (CUSUM) algorithms for break detection and the Chow Test (Chow, 1960) for removing false positives (or breaks in time series not representing land change). The algorithms included are based on fundamentally different approaches to change detection and therefore offer unique advantages. The ensemble, or the combination of the three algorithms, was applied to 3 Landsat scenes in the United States and the results were assessed based on their ability to correctly discern structural breaks from stable time periods. The CUSUM test was shown to detect significant breaks 84.18% of the time and the Chow Test correctly removed breaks in 87.4% of the breaks analyzed. The ensemble produced results with lower frequency of errors of omission and commission (Type-I and Type-II errors) than a single algorithm approach. These results indicate that using a combination of break detection algorithms can be an improvement over typical approaches that utilize only one algorithm.
•Multiple approaches for detecting breaks in a time series are combined sequentially.•The algorithms are applied in the context of land change monitoring.•The break detection algorithms are CCDC, CUSUM, and the Chow Test.•Two tests are used to detect breaks and one is used to identify false positives.•Break detection is improved in over 84% of break locations tested.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A new algorithm for generating synthetic Landsat images is developed based on all available Landsat data. This algorithm is capable of predicting Landsat surface reflectance for any desired date. It ...first excludes cloud, cloud shadow, and snow observations, and then uses the remaining clear observations to estimate time series models for each Landsat pixel. Three time series models (a simple model, advanced model, and full model) are used for estimating surface reflectance for each pixel, and the selection of a time series model is dependent on the number of clear observations available: the more clear observations, the more complex the model will be that is used. For each time series model there are three components (seasonality, trend, and breaks), that are used for modeling intra-annual and inter-annual differences and abrupt surface change. Abrupt surface changes are detected by differencing predicted and observed Landsat observations, and if the difference is larger than twice the Root Mean Square Error (RMSE) for six consecutive observations, it will be detected as a “break” in the time series model. The RMSE values are temporally adjusted to provide better threshold range. For each “synthetic” image, a Quality Assessment (QA) Band is provided that contains information on how the time series model was estimated and used for generating the synthetic data. We have applied this approach to six Landsat scenes within the United States. We visually compared the synthetic images with real Landsat images for different kinds of environments and they are similar for all image pairs. We also quantitatively assessed the accuracy of the synthetic data by calculating the RMSE value for all clear Landsat observations. The RMSE values for the three visible bands are the lowest (approximately 0.01), and the Short-wave Infrared (SWIR) bands are slightly higher in magnitude (between 0.01 and 0.02). The Near Infrared (NIR) band has the highest RMSE values (between 0.02 and 0.03). The goal of this paper is to provide Landsat images that are free of cloud, cloud shadow, snow, and Scan Line Corrector (SLC)-off gaps that can be used to derive land cover and bio-physical products.
•All available Landsat data are used for predicting surface reflectance.•Synthetic Landsat images can be generated for any given time.•Model selection, LASSO, and temporally-adjusted RMSE are used for better modeling.•Synthetic images are similar to real Landsat images for all image pairs.•The prediction accuracy is similar in magnitude to the noise levels in Landsat data.
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•The reliability of a measure previously untested on Mechanical Turk is assessed.•Strong test–retest reliability was found between administration dates.•This article offers a conceptual replication ...of previous work.
Amazon’s online service, Mechanical Turk (MTurk) has become a popular option for data collection among social scientists. Early work (Buhrmester, Kwang, & Gosling, 2011) indicated that data collection through MTurk was faster and less expensive than traditional collection methods (undergraduate human subject pool), as well as being reliable when administered at different dates. Building on their work, we sought to extend this investigation of reliability to a larger measure. For the current research we chose a 120-item measure of personality. After collecting data through MTurk, it was determined that our MTurk sample had strong test–retest reliability, indicating that they did not significantly change between administration dates.
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Urban areas are expanding, changing the structure and productivity of landscapes. While some urban areas have been shown to hold substantial biomass, the productivity of these systems is largely ...unknown. We assessed how conversion from forest to urban land uses affected both biomass structure and productivity across eastern Massachusetts. We found that urban land uses held less than half the biomass of adjacent forest expanses with a plot level mean biomass density of 33.5 ± 8.0 Mg C ha(-1). As the intensity of urban development increased, the canopy cover, stem density, and biomass decreased. Analysis of Quercus rubra tree cores showed that tree-level basal area increment nearly doubled following development, increasing from 17.1 ± 3.0 to 35.8 ± 4.7 cm(2) yr(-1). Scaling the observed stem densities and growth rates within developed areas suggests an aboveground biomass growth rate of 1.8 ± 0.4 Mg C ha(-1) yr(-1), a growth rate comparable to nearby, intact forests. The contrasting high growth rates and lower biomass pools within urban areas suggest a highly dynamic ecosystem with rapid turnover. As global urban extent continues to grow, cities consider climate mitigation options, and as the verification of net greenhouse gas emissions emerges as critical for policy, quantifying the role of urban vegetation in regional-to-global carbon budgets will become ever more important.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Remote sensing has proven a useful way of evaluating long-term trends in vegetation “greenness” through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced ...Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends.
An assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.
•All available Landsats 5–8 data were used to analyze greenness trends.•Data from Landsat 8 were not completely consistent with the data from Landsats 5–7.•Landsat 8 EVI values were less biased than Landsat 8 NDVI values.•The total EVI change estimated by SLT was 14.3% higher than CCDC estimation.•On average Guangzhou experienced a 0.0567 increase in EVI from 2000 to 2014.
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New England has lost more than 350,000 ha of forest cover since 1985, marking a reversal of a two-hundred-year trend of forest expansion. We a cellular land-cover change model to project a ...continuation of recent trends (1990-2010) in forest loss across six New England states from 2010 to 2060. Recent trends were estimated using a continuous change detection algorithm applied to twenty years of Landsat images. We addressed three questions: (1) What would be the consequences of a continuation of the recent trends in terms of changes to New England's forest cover mosaic? (2) What social and biophysical attributes are most strongly associated with recent trends in forest loss, and how do these vary geographically? (3) How sensitive are projections of forest loss to the reference period-i.e. how do projections based on the period spanning 1990-to-2000 differ from 2000-to-2010, or from the full period, 1990-to-2010? Over the full reference period, 8201 ha yr-1 and 468 ha yr-1 of forest were lost to low- and high-density development, respectively. Forest loss was concentrated in suburban areas, particularly near Boston. Of the variables considered, 'distance to developed land' was the strongest predictor of forest loss. The next most important predictor varied geographically: 'distance to roads' ranked second in the more developed regions in the south and 'population density' ranked second in the less developed north. The importance and geographical variation in predictor variables were relatively stable between reference periods. In contrast, there was 55% more forest loss during the 1990-to-2000 reference period compared to the 2000-to-2010 period, highlighting the importance of understanding the variation in reference periods when projecting land cover change. The projection of recent trends is an important baseline scenario with implications for the management of forest ecosystems and the services they provide.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We report a novel hybrid polyamidoamine (PAMAM) dendrimer hydrogel/poly(lactic-co-glycolic acid) (PLGA) nanoparticle platform (HDNP) for codelivery of two antiglaucoma drugs, brimonidine and timolol ...maleate. This platform was not cytotoxic to human corneal epithelial cells. Cellular uptake of Nile red-encapsulating PLGA nanoparticles was significantly increased by dendrimer hydrogel. A prolonged residence time of nanoparticles was demonstrated through investigation of FluoSpheres loaded into dendrimer hydrogel. Both brimonidine and timolol maleate were slowly released in vitro over a period of 28–35 days. Following topical administration of one eye drop (30 μL of 0.7% w/v brimonidine and 3.5% w/v timolol maleate) in normotensive adult Dutch-belted male rabbits, the HDNP formulation resulted in a sustained and effective IOP reduction (18% or higher) for 4 days. Furthermore, the HDNP maintained significantly higher concentrations of brimonidine in aqueous humor and cornea as well as timolol maleate in the aqueous humor, cornea, and conjunctiva up to 7 days as compared to saline, DH, and PLGA nanoparticle dosage forms, without inducing ocular inflammation or discomfort. Histological analysis of the cornea and conjunctiva did not reveal any morphological or structural changes. Our work demonstrated that this new platform is capable of enhancing drug bioavailability and sustaining effective IOP reduction over an extended period of time. This newly developed platform can greatly reduce dosing frequency of topical formulations, thus, improving long-term patient compliance and reducing enormous societal and economic costs. Given its high structural adaptability, many other chronic ocular diseases would benefit from long-lasting drug delivery of this new platform.
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