Moderate Resolution Imaging Spectroradiometer Time Series Research Articles

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece

The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies was studied for the case of the large 2007 Peloponnese wildfires in Greece. Fire severity is defined as the degree of environmental change as measured immediately post-fire, whereas burn severity combines the direct fire impact and ecosystems responses. Geo Composite Burn Index (GeoCBI), two pre-/post-fire differenced Thematic Mapper (TM) dNBR assessments and a Moderate Resolution Imaging Spectroradiometer (MODIS) dNBR time series were used to analyze the temporal dimension. MODIS dNBR time series were calculated based on the difference between the NBR of the burned and control pixels, which were retrieved using time series similarity of a pre-fire year. The analysis incorporated the optimality statistic, which evaluates index performance based on displacements in the mid-infrared–near infrared bi-spectral space. Results showed a higher correlation between field and TM data early post-fire (R 2 = 0.72) than one-year post-fire (R 2 = 0.56). Additionally, mean dNBR (0.56 vs. 0.29), the dNBR standard deviation (0.29 vs. 0.19) and mean optimality (0.65 vs. 0.47) were clearly higher for the initial assessment than for the extended assessment. This is due to regenerative processes that obscured first-order fire effects impacting the suitability of the dNBR to assess burn severity in this case study. This demonstrates the importance of the lag timing, i.e. time since fire, of an assessment, especially in a quickly recovering Mediterranean ecosystem. The MODIS time series was used to study intra-annual changes in index performance. The seasonal timing of an assessment highly impacts what is actually measured. This seasonality affected both the greenness of herbaceous resprouters and the productivity of the control pixels, which is land cover specific. Appropriate seasonal timing of an assessment is therefore of paramount importance to anticipate false trends (e.g. caused by senescence). Although these findings are case study specific, it can be expected that similar temporal constraints affect assessments in other ecoregions. Therefore, within the limitations of available Landsat imagery, caution is recommended for the temporal dimension when assessing post-fire effects. This is crucial, especially for studies that aim to evaluate trends in fire/burn severity across space and time. Also, clarification in associated terminology is suggested.

Land Cover Classification Based on Time-series MODIS NDVI Data in Heihe River Basin

Temporal changes in the normalized difference vegetation index(NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI datasets in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover.The Time-series Moderate Resolution Imaging Spectroradiometer(MODIS) NDVI datasets hold considerable promise for large-area land cover classification given their global coverage,intermediate spatial resolution,high temporal resolution(16-day composite period),and cost-free status.This study focused on generating effective classification features from multi-temporal MODIS NDVI datasets to improve classification accuracy in the Heihe River Basin.Two types of features were derived from reconstructed multi-temporal MODIS NDVI datasets.The first are the basic parameters including the annual maximum NDVI,the mean NDVI during the growing season,the inter-annual variability of NDVI and the annual mean NDVI.The second are the amplitude and phase information of the first and second harmonic components derived from the shape of the time-series NDVI profile.Additionally,DEM with 1km resolution has also been used to simplify the current scheme.According to the validated results with 469 ground truth survey samples,the overall land cover classification accuracy using the decision tree was 78% and a Kappa coefficient is 0.74.The results support using decision tree classification based on 1km MODIS NDVI temporal and derived parameters to provide an up-to-date land cover mapping.However,the current decision tree does not work well in the downstream of the Heihe River Basin since the NDVI of non-vegetation types can not represent the temporal feature of these types.Thus,new effort is necessary in the future in order to improve the overall performance on this issue.

Evaluation of NRCS curve number and MODIS time-series proxies for antecedent moisture condition

Soil moisture plays a vital role in a watershed's hydrologic response to a precipitation event. Soil moisture condition antecedent to an event in empirical runoff estimation models, such as the 5-day antecedent moisture model for natural resources conservation service (NRCS) curve number (CN) method, is generalised and spatially inexplicit. This study assesses the potential to parameterise a statistical streamflow estimation model utilising next generation weather radar (NEXRAD) precipitation records and time-series biophysical proxies for soil moisture from moderate resolution imaging spectroradiometer (MODIS) on board Terra satellite. This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A least squares regression model, accounting for 83% of the variance of observed streamflow for calendar year 2004, was developed based on radar precipitation estimates, land surface temperature, and a vegetation index, on an 8-day interval. Estimated 8-day mean streamflows from the remote sensing model represented an improvement over CN modelled streamflows, developed with the standard and a continuum-based 5-day antecedent moisture condition model. Assessment of remotely sensed proxies of soil moisture has potential for complementing traditional CN method and for estimating streamflow in watersheds for which the CN or other empirical methods are limited by data constraints.

Classification of vegetation in North Tibet Plateau based on MODIS time-series data

Based on the 16d-composite MODIS (moderate resolution imaging spectroradiometer)-NDVI(normalized difference vegetation index) time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days.

An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series

Ecological and climate models require high-quality consistent biophysical parameters as inputs and validation sources. NASA's moderate resolution imaging spectroradiometer (MODIS) biophysical products provide such data and have been used to improve our understanding of climate and ecosystem changes. However, the MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in the North American Carbon Program, that use MODIS data as inputs require gap-free data. This letter presents the algorithm used within the MODIS production facility to produce temporally smoothed and spatially continuous biophysical data for such modeling applications. We demonstrate the algorithm with an example from the MODIS-leaf-area-index (LAI) product. Results show that the smoothed LAI agrees with high-quality MODIS LAI very well. Higher R-squares and better linear relationships have been observed when high-quality retrieval in each individual tile reaches 40% or more. These smoothed products show similar data quality to MODIS high-quality data and, therefore, can be substituted for low-quality retrievals or data gaps.

Burned-Area Mapping of the Serengeti–Mara Region Using MODIS Reflectance Data

Fire is a key factor for vegetation structure and ecosystem functioning in the Serengeti-Mara region in East Africa. However, there is a lack of accurate and consistent information on fires. We developed an algorithm for mapping burned areas in the wider Serengeti-Mara region from remote-sensing data. The algorithm is automated and, once trained for one year, runs independently for all years. It uses daily measurements of red and near-infrared (NIR) reflectance acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at a spatial resolution of 250 m. The MODIS time series was first converted to ten-day minimum NIR composites. Each composite was then classified into new and old burned areas, by thresholding burned-area index and temporal difference of NIR reflectances. The algorithm adjusts detection thresholds dynamically using measures related to atmospheric and vegetation conditions. Having trained the algorithm once on 2003 data, it was applied to MODIS data from April 21, 2000 to November 10, 2005. Accuracy was improved by size- and location-sensitive filters. Overall accuracy was 90.3% as determined from Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite imagery from 2005 and 87.1% as determined using field data from 2005. The algorithm holds the potential to be applied to other savanna areas. This letter provides a reliable product useful for future investigations of fire ecology and fire management

Global monitoring of air pollution over land from the Earth Observing System‐Terra Moderate Resolution Imaging Spectroradiometer (MODIS)

Moderate Resolution Imaging Spectroradiometer (MODIS) measurements (7 channels: 0.47–2.1 μm, 250–500 m resolutions) provide us with new insights into the characteristics of global aerosols. MODIS retrieves not only aerosol loading but also the fraction of fine mode particle. In this paper we demonstrate MODIS capability for use in monitoring global, regional, and local air pollution. Three case studies in northern Italy, Los Angeles, and Beijing showed the conclusive results of applying MODIS‐derived aerosol optical depths (τa) to regional and local air pollution in terms of accuracy (Δτa = ±0.05 ± 0.2τa) and spatial sensitivity of the retrievals. Under stagnant condition, accumulated aerosol abundance can reach τa > 1 (at 0.55 μm) before being removed by wind or precipitation. The correlation found between Aerosol Robotic Network (AERONET) daily averaged τa and 24‐hour PM10 (particulate matter with diameter <10 μm) concentration (μg/m3) in northern Italy is encouraging with correlation coefficient ∼0.82. The derivation of PM concentration from satellite measurements may be possible once we know the detailed aerosol vertical distribution. To compare aerosol loading in different regions of the globe, we choose the two most populated regions (eastern China and India) and the two most industrialized regions (the eastern United States/Canada and western Europe). The time series of MODIS monthly mean τa from July 2000 to May 2001 depicts a strong seasonal variation with maxima in the spring/summer and minima in the winter. The clear separation between (1) the eastern United States/Canada and western Europe and (2) eastern China and India shows that the τa values in (2) are 50% to 2–3 times higher compared to those in (1). The enhancements of aerosol loading were due to smoke as originated from Montana/Idaho forest fires transported to the eastern United States in late August 2000 and dust outbreaks from Taklimakan and Gobi Deserts to eastern China as well as smoke from Southeast Asia to southern China in February–April 2001.