Moderate Resolution Imaging Spectroradiometer Time Research Articles

Greening on the Bathurst caribou range in northern Canada: are erect shrubs responsible for remotely sensed trends?

Remote sensing has detected recent trends of increased vegetation productivity on the Bathurst caribou herd’s range. The cause of this spectral greening is unknown but is hypothesized to be the result of a change in the composition, establishment, structure, and/or growth of erect deciduous shrubs. We combined field investigation and dendroecology to compare shrubs between areas where productivity has increased (Greening sites) and areas where productivity has remained stable (No Change sites) based on a Moderate Resolution Imaging Spectroradiometer time series for the period from 2000 to 2017. Contrary to expectations, species composition, timing of stem establishment, stem density, stem length, and vertical and diameter growth rates did not differ between the two site types. However, shrub cover was 13% greater at Greening sites and shrubs at No Change sites had 9% more dead stems regardless of the size of the shrub. This suggests that the differences observed remotely are due to a combination of increased foliage production or survival at Greening sites and a dimming of the vegetation index at No Change sites. Our findings offer a population-level validation of specific shrub characteristics contributing to trends in spectral greening in an understudied region of the southern Arctic tundra.

Clear-Sky Composites over Canada from Visible Infrared Imaging Radiometer Suite: Continuing MODIS Time Series into the Future

The Visible Infrared Imaging Radiometer Suite (VIIRS) represents a new generation of satellite imagers for global operational observations. In many aspects, it is comparable to the Moderate Resolution Imaging Spectroradiometer (MODIS) operated since 2000, i.e. almost for two decades. The Canada Centre for Remote Sensing has developed a unique MODIS processing chain to produce a long-term time series of clear-sky composites and some terrestrial products at 250 m spatial resolution over a 5700 km × 4800 km region centered on Canada. The paper describes an extension of the MODIS time series at the top of the atmosphere level using VIIRS data. The VIIRS clear-sky composites are produced on a 250-m spatial grid for I-bands and a 500-m grid for M-bands. Nominal products are generated as 10-day composites, while snow mask and normalized difference vegetation index are generated as daily products. Preliminary assessment of VIIRS versus MODIS composites has been conducted through comparison of value-added warm season snow/ice probability maps and minimum snow/ice extent. The results demonstrate a high level of consistency (with the average different difference around 0.12%), which indicates that the developed VIIRS processing technology produces results that can potentially be used to extend MODIS time series into the future.

Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China

Aeolian desertification has become one of the most serious environmental and socioeconomic problems facing the world today. Quantitative remote sensing technology is an important means to achieve the development trends of aeolian desertified land (ADL). To compensate for the shortcomings in the time scale of Landsat Thematic Mapper and other high-spatial-resolution remote sensing data, this study introduces Moderate Resolution Imaging Spectroradiometer time series data and products to invert the monitoring indicators of ADL. The QUEST (quick, unbiased, and efficient statistical tree) classification method was used to establish the extraction model of ADL based on multiple indicators. The ADL time series dataset was extracted from 2000 to 2015, and the characteristics of ADL and its spatial-temporal dynamics were analyzed. These results were combined with meteorological data and socioeconomic statistics to discuss the main factors influencing ADL. The results showed that, by the end of 2015, the total area of ADL was 32,633 km2, accounting for 26.02% of the study area. The slight, moderate, severe, and extremely severe ADL accounted for 51.39%, 34.11%, 10.31%, and 4.20%, respectively. The total area of ADL decreased significantly at a rate of 2388.60 km2 y−1 from 2000 to 2015. The decreasing area was dominated by the slight and moderate ADL. The reversal of ADL exhibited significant correlations with an increase of annual precipitation and a decrease of annual maximum wind velocity (p < 0.01). The impact of annual maximum wind velocity on ADL is more important than annual precipitation. Increases in population density and the number of livestock did not promote the development of ADL. A series of ecological protection projects and policies created advantageous conditions for the reversal of ADL. This research provides a new method for monitoring ADL and useful information for controlling and managing aeolian desertification in this region.

Spatiotemporal drought evaluation of Hyrcanian deciduous forests and semi‐steppe rangelands using moderate resolution imaging spectroradiometer time series in Northeast Iran

AbstractAdvanced MODIS data have provided diverse products for assessing and monitoring natural vegetation affected by droughts. Between 2000 and 2016, we estimated monthly precipitation anomalies in the deciduous forests and semi‐steppe rangelands of northeastern Iran using kriging models, and analyzed 16‐day vegetation anomalies using vegetation greenness and water content indices—including the enhanced vegetation index, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). Vegetation anomalies showed high positive responses to interseasonal precipitation anomalies over the 17‐year period, and low positive responses to monthly precipitation deficits during critical droughts. Forest and rangeland anomalies recorded higher Moran's coefficients based on the NDVI (I = 0.253 ± 0.102) and NDWI (I = 0.284 ± 0.087) with interseasonal precipitation anomalies, respectively. Throughout critical droughts, the NDWI anomalies showed higher coefficients with monthly precipitation deficits for both forests (I = 0.0716 ± 0.059) and rangelands (I = 0.125 ± 0.0615). Nevertheless, there were only significant differences between the Moran's coefficients of the three vegetation indices for rangelands (F = 2.873; p &lt; 0.05). BiLISA maps indicated that sparse forests show higher spatial associations with drought conditions (High‐high cluster), whereas dense forests experienced lower stresses by severe droughts (High‐low outlier) during drought periods. Meanwhile, some severe vegetation stresses occurred at locations with low droughts (Low‐high outlier), which indicate the impact of other significant climate‐induced disturbances on vegetation anomalies.

Applying Model Parameters as a Driving Force to a Deterministic Nonlinear System to Detect Land Cover Change

In this paper, we propose a new method for extracting features from time-series satellite data to detect land cover change. We propose to make use of the behavior of a deterministic nonlinear system driven by a time-dependent force. The driving force comprises a set of concatenated model parameters regressed from fitting a model to a Moderate Resolution Imaging Spectroradiometer time series. The goal is to create behavior in the nonlinear deterministic system, which appears predictable for the time series undergoing no change, while erratic for the time series undergoing land cover change. The differential equation used for the deterministic nonlinear system is that of a large-amplitude pendulum, where the displacement angle is observed over time. If there has been no change in the land cover, the mean driving force will approximate zero, and hence the pendulum will behave as if in free motion under the influence of gravity only. If, however, there has been a change in the land cover, this will for a brief initial period introduce a nonzero mean driving force, which does work on the pendulum, changing its energy and future evolution, which we demonstrate is observable. This we show is sufficient to introduce an observable change to the state of the pendulum, thus enabling change detection. We extend this method to a higher dimensional differential equation to improve the false alarm rate in our experiments. Numerical results show a change detection accuracy of nearly 96% when detecting new human settlements, with a corresponding false alarm rate of 0.2% (omission error rate of 4%). This compares very favorably with other published methods, which achieved less than 90% detection but with false alarm rates all above 9% (omission error rate of 66%).

Improved forest-cover mapping based on MODIS time series and landscape stratification

ABSTRACTDetailed forest-cover mapping at a regional scale by supervised classification is technically limited by various factors. This study evaluates the ability of a landscape stratification method to improve classification accuracy. An object-based segmentation technique (OBIA) was performed to delineate radiometrically homogeneous regions into the study area, used as strata for the classification of a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. As a reduction of the spatial variability of the signatures of the vegetation classes is expected, Maximum Likelihood Classifier (MLC) was used to analyse potential effects on classification accuracy. Accuracy assessment was based on the calculation of kappa coefficient (κ) and reject fraction (RF). The values obtained with and without stratification were compared, to assess their global and per-stratum influence on the quality of a detailed forest-cover map (20 different classes). To study the influence of topographical and landscape stratum characteristics on classification accuracy, eight indicators were calculated. Their correlation with κ and RF differences due to stratification was analysed. Our study showed that stratification improved global and per-stratum classification accuracy and in parallel caused an RF increase. Both these evolutions are not conditioned by the stratum topographical and landscape characteristics but strongly influenced by stratum and classified vegetation area.

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Drought Monitoring by remotely sensed moisture vegetation indexes is being an active research subject as the vegetation spectral responses are showed to be highly correlated to water content. The MODIS (MODerate resolution Imaging Spectro-radiometer) sensor of the Terra satellite provides MOD09A1 product of BRDF (Bidirectional Reflectance Distribution Function) used in computing moisture vegetation indexes (MVI). The exploration of an MVI time-series in the Kroumirie forest in Northern Tunisia showed important noise due to both clouds contamination and sensor defaults that had to be removed. Amongst methods for removing these imperfections, TIMESAT tool was designed for correcting time-series of satellite data and also to retrieve seasonal parameters from smoothed vegetation indexes. The methodology of smoothing functions to fit the time series data is based on two stages. First, a least square fit to the upper envelope of the vegetation indices series is applied. The second stage is achieved by local and adaptive fitting functions. The corrections have been made by spikes removal due to abrupt change of MVI variations and by fitting the MVI time-series to the upper envelop to correct the negative biases of remote sensing vegetation indexes. The adaptive Savitsky-Golay function filter compared to local filtering process produces variations that conserve local variations for all the tested MVI. Seasonal vegetation parameters were extracted for each year of the time-series analysis and compared to the Standardized Precipitation Index (SPI) calculated at meteorological station level and for different time scales. Positive relations were found between SPI and the seasonal parameters expressed by the length and the amplitude of the season, indicating MODIS derived MVI sensitivity to water deficit or surplus conditions. The 6-month SPI showed the best performance when related to water sensitive indexes suggesting that MODIS derived indexes are more correlated to the precipitation variations over seasons.

Assessment of environmental changes induced by internally displaced person (IDP) camps in the Darfur region, Sudan, based on multitemporal MODIS data

Darfur’s complex conflict situation led to large-scale internal displacement of people with significant implications for the environment as well as for the conflict situation itself. Time series analyses of medium-resolution satellite data for monitoring large regions were carried out over a longer period to detect the impact of camps for internally displaced persons (IDPs) on their surrounding environment. Four regions of interest were defined in the Darfur region, Sudan, which cover the main and most dynamic IDP camps of the region and are characterized by different environmental conditions. Hotspots of anthropogenic impacts on the vegetation are revealed based on the Seasonal Kendall test applied to quality-enhanced enhanced vegetation index and normalized difference vegetation index Moderate Resolution Imaging Spectroradiometer time series. Overlaying the resulting trend pattern with the locations of IDP camps suggests significant impact on the vegetation, especially – but not exclusively – in those areas with very high population densities. A comparison of the temporal development of the detected hotspots with precipitation and population figures over the investigated period help differentiate between natural and human-induced impacts. Subsequent to the analysis of the medium resolution data, the resulting trends in vegetation cover were correlated with changes detected with very-high-resolution satellite imagery for the two areas around Zalingei and Zam Zam. Areas of decreased vegetation cover in the resulting trend pattern can be correlated with logging of trees as well as removal of shrubs and grass, while positive changes in vegetation cover are related to agricultural land expansion.

Meta-Optimization of the Extended Kalman Filter’s Parameters Through the Use of the Bias Variance Equilibrium Point Criterion

The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods.

Improved alpine grassland mapping in the Tibetan Plateau with MODIS time series: a phenology perspective

The Tibetan Plateau is primarily composed of alpine grasslands. Spatial distributions of alpine grasses, however, are not well documented in this remote, highly uninhabited region. Taking advantage of the frequently observed moderate resolution imaging spectroradiometer (MODIS) images (500-m, 8-day) in 2010, this study extracted the phenological metrics of alpine grasses from the normalized difference vegetation index time series. With the Support Vector Machine, a multistep classification approach was developed to delineate alpine meadows, steppes, and desert grasses. The lakes, permanent snow, and barren/desert lands were also classified with a MODIS scene acquired in the peak growing season. With ground data collected in the field and aerial experiments in 2011, the overall accuracy reached 93% when alpine desert grasses and barren lands were not examined. In comparison with the recently published national vegetation map, the alpine grassland map in this study revealed smoother transition between alpine meadows and steppes, less alpine meadows in the southwest, and more barren/deserts in the high-cold Kunlun Mountain in the northeast. These variations better reflected climate control (e.g. precipitation) of different climatic divisions on alpine grasslands. The improved alpine grassland map could provide important base information about this cold region under the pressure of rapidly changing climate.

Dynamic monitoring of drought using HJ-1 and MODIS time series data in northern China

From early November 2008 to February 2009, lack of rainfall led to severe drought in northern China. More than 9.3 million ha of wheat in six major crop production provinces, including Henan, Anhui, Shandong, Shanxi, Gansu, and Shaanxi, were hit by drought. Supported by Chinese HJ-1 satellite images together with NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, dynamic monitoring of the drought was conducted. HJ-1 CCD data with 30-m resolution were used to identify cropland information. Spatial–temporal variation of drought was detected using Vegetation Index and Water Index time series data derived from MODIS visible, infrared, and short-wave infrared bands. The influences of drought were classified into five levels based on MODIS-derived 8-day composite Anomaly Water Index (AWI) and field survey data. The results indicated that the drought deteriorated beginning in November 2008 and became most serious in late January 2009. HJ-1 data together with MODIS data proved to be valuable data sources for monitoring soil moisture and drought at a both regional and national scale.

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km2. AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.

Variability of atmospheric precipitable water in northern Chile: Impacts on interpretation of InSAR data for earthquake modeling

The use of Synthetic Aperture Radar interferometry (InSAR) in northern Chile, one of the most seismically active regions in the world, is of great importance. InSAR enables geodesists not only to accurately measure Earth's motions but also to improve fault slip map resolution and our knowledge of the time evolution of the earthquake cycle processes. Fault slip mapping is critical to better understand the mechanical behavior of seismogenic zones and has fundamental implications for assessing hazards associated with megathrust earthquakes. However, numerous sources of errors can significantly affect the accuracy of the geophysical parameters deduced by InSAR. Among them, atmospheric phase delays caused by changes in the distribution of water vapor can lead to biased model parameter estimates and/or to difficulties in interpreting deformation events captured with InSAR. The hyper-arid climate of northern Chile might suggest that differential delays are of a minor importance for the application of InSAR techniques. Based on GPS, Moderate Resolution Imaging Spectroradiometer (MODIS) data our analysis shows that differential phase delays have typical amplitudes of about 20 mm and may exceptionally exceed 100 mm and then may impact the inferences of fault slip for even a Mw 8 earthquakes at 10% level. In this work, procedures for mitigating atmospheric effects in InSAR data using simultaneous MODIS time series are evaluated. We show that atmospheric filtering combined with stacking methods are particularly well suited to minimize atmospheric contamination in InSAR imaging and significantly reduce the impact of atmospheric delay on the determination of fundamental earthquake parameters.