Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index Research Articles

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5’s spatial resolution and at MODIS’s temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R2 of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R2 of the SOS ranging from 0.68 to 0.86 and with an R2 of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate–NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.

Effect of water level fluctuations on wintering goose abundance in Poyang Lake wetlands of China

The Poyang Lake is a Ramsar site and is the important over-wintering site for migratory waterbirds along the East Asian–Australasian Fly way. Examining the effects of water level fluctuations on waterbird abundance and analyzing the influencing mechanism is critical to waterbird protection in the context of hydrological alteration. In this study, the effect of water level regime on wintering goose abundance was examined and the influencing mechanism was interpreted. Synchronous waterbirds survey data, hydrological data, Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI) data and habitat data derived from Landsat TM/ETM data and HJ/CCD data were combined. The satellite-derived Green Wave Index (GWI) based on MODIS-NDVI dataset was applied to detect changes in goose food resources. It was found that habitat size and vegetation conditions are key factors determining goose abundance. Geese numbers were positively correlated with habitat area, while intermediate range of vegetation productivity might benefit the goose abundance. Water level affects goose abundance by changing available habitat areas and vegetation conditions. We suggested that matching hydrological regime and exposed meadows time to wintering geese dynamics was crucial in the Poyang Lake wetlands. Our study could provide sound scientific information for hydrological management in the context of waterbird conservation.

Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands

ABSTRACTMonitoring is essential for appropriate rangeland management. The present study aimed to examine the potential of moderate resolution imaging spectroradiometer (MODIS) satellite imagery in rangeland condition assessment and monitoring within and across vegetation types in the arid and semi-arid rangelands of central Iran. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were calculated from MODIS Aqua/Terra Level 1B data (related to 2003–2013). The obtained values were compared with vegetation cover measurements and rangeland condition classes at 110 sampling sites using linear regression, one-way analysis of variance (ANOVA), independent-samples t-tests, and Tukey’s pairwise comparisons. The results showed that two indices made stronger predictions of vegetation cover within a vegetation type (R2 > 0.87, P < 0.001) than across vegetation types (R2 > 0.51, P < 0.001). Both NDVI and EVI worked well across vegetation types (P ≤ 0.001) in predicting rangeland condition classes (poor, fair, and good), but their performance varied between vegetation types. The NDVI classified about 73, 19, and 7.5% of the rangelands in poor, fair, and good condition classes, respectively. The good performance of MODIS NDVI index at different landscapes indicates that this index has high potential in detecting vegetation cover and discriminating different condition classes, therefore, it can be used to aid field- based techniques in rangeland condition assessment and monitoring.

Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR

Abstract This article describes a Bayesian approach for ensuring the continuity of remote sensing records (1982–2014) by back-prediction of Moderate Resolution Imaging Spectroradiometer (MODIS) using historical Advanced Very High Resolution Radiometer (AVHRR) data. First, a historical 8 km snow data was generated using MODIS snow Quality Assurance field and North American Regional Reanalysis snow depth and air temperature outputs. Second, the relationships between coarsened 8 km MODIS and AVHRR Normalized Difference Vegetation Index (NDVI) were modeled pixel by pixel using spatial autocorrelation. Then the NDVI was back-predicted to 1982 from historical AVHRR observations and snow data. The back-predicted NDVI was validated against those data held-out from MODIS in 2001 and 2002 from four distinct biogeographic state regions (California, Kansas, Minnesota and Mississippi) in the continental United States. The results show consistency of the derived NDVI from two sensors, with a Root Mean Square Error within 0.05 over most land cover classes. This validation results suggest potential application of this approach for generating consistent long term multi-sensor NDVI data records for ecology and global climate change studies.

A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr–1.

Diurnal and Seasonal Variation of Surface Urban Cool and Heat Islands in the Semi-Arid City of Erbil, Iraq

The influence of land surface temperature (LST) makes the near-surface layer of the troposphere a key driver of urban climate. This paper assesses the temporal formation of the daytime Surface Urban Cool Island (SUCI) and night-time Surface Urban Heat Island (SUHI) effect in Erbil, Iraq, situated in a semi-arid climate region. LST retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra and MODIS Normalized Difference Vegetation Index (NDVI) from January 2003 to December 2014 are analysed. The relationships of LST with NDVI and the Normalized Multi-band Drought Index (NMDI) are investigated in order to assess the influence of vegetation and moisture on the observed patterns of LST and the SUCI/SUHI. The results indicate that during the daytime, in summer, autumn and winter, densely built-up areas had lower LST acting as a SUCI compared to the non-urbanised area around the city. In contrast, at night-time, Erbil experienced higher LST and demonstrated a significant SUHI effect. The relationship between LST and NDVI is affected by seasonality and is strongly inverted during spring (r2 = 0.73; p &lt; 0.01). Contrary to previous studies of semi-arid cities, a SUCI was detected, not only in the morning, but also during the afternoon.

An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements

In this study, a compound technique was developed using eight denoising techniques for reconstructing high-quality normalized difference vegetation index (NDVI) time series data. The new algorithm consists of two major procedures: 1) detecting noisy data according to variation in the modification rates of eight selected denoising techniques and 2) using the medians of the denoised values of the eight techniques to replace the noisy data. The eight techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky–Golay (S–G) technique, the mean value iteration (MVI) filter, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight (CW) filter, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. The technique was tested with moderate resolution imaging spectroradiometer (MODIS) NDVI time series data derived from MOD09GQ of the Heihe River Basin in China. In situ NDVI data were obtained during one nearly complete growing season for six land-use types in the study area. Analysis of the temporal and spatial characteristics of the reconstructed data revealed that the compound technique performs better than the other techniques. In addition, the lower root-mean-square error (RMSE) of the compound technique, which was calculated using ground measurements, demonstrated the improved performance of the new technique. The main advantage of the new technique is its ability to effectively denoise data and maintain fidelity such that it can be widely used for other NDVI time series data and for other study areas.

Detecting Different Types of Directional Land Cover Changes Using MODIS NDVI Time Series Dataset

This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs’ test eliminated short-lived changes by considering them outliers. Then, the Brown-Forsythe test and the combination of Tomé’s method and the Chow test were applied to determine abrupt changes. Finally, Sen’s slope estimation coordinated with the Mann-Kendall test detection method was used to detect trend changes. Results demonstrated that both abrupt and trend land cover changes could be detected accurately and automatically. The overall accuracy of abrupt land cover changes was 87.0% and the kappa index was 0.74. Detected trends of land cover change indicated high consistency between NDVI (Normalized Difference Vegetation Index), change trends from LTS (Landsat Thematic Mapper and Enhanced Thematic Mapper Plus time series dataset), and MODIS (Moderate Resolution Imaging Spectroradiometer) time series datasets with the percentage of samples indicating consistency of 100%. For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p &lt; 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with specific ecosystem changes, reducing the risk of false changes in the area with frequent and strong interannual fluctuations.

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.

Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series

Reliable drought information is of utmost importance for efficient drought management. This paper presents a fully operational processing chain for mapping drought occurrence, extent and strength based on Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data at 250 m resolution. Illustrations are provided for the territory of Kenya. The processing chain was developed at BOKU (University of Natural Resources and Life Sciences, Vienna, Austria) and employs a modified Whittaker smoother providing consistent (de-noised) NDVI “Monday-images” in near real-time (NRT), with time lags between zero and thirteen weeks. At a regular seven-day updating interval, the algorithm constrains modeled NDVI values based on reasonable temporal NDVI paths derived from corresponding (multi-year) NDVI “climatologies”. Contrary to other competing approaches, an uncertainty range is produced for each pixel, time step and time lag. To quantify drought strength, the vegetation condition index (VCI) is calculated at pixel level from the de-noised NDVI data and is spatially aggregated to administrative units. Besides the original weekly temporal resolution, the indicator is also aggregated to one- and three-monthly intervals. During spatial and temporal aggregations, uncertainty information is taken into account to down-weight less reliable observations. Based on the provided VCI, Kenya’s National Drought Management Authority (NDMA) has been releasing disaster contingency funds (DCF) to sustain counties in drought conditions since 2014. The paper illustrates the successful application of the drought products within NDMA by providing a retrospective analysis applied to droughts reported by regular food security assessments. We also present comparisons with alternative products of the US Agency for International Development (USAID)’s Famine Early Warning Systems Network (FEWS NET). We found an overall good agreement (R2 = 0.89) between the two datasets, but observed some persistent (seasonal and spatial) differences that should be assessed against external reference information.

Assessing Seasonal and Inter-Annual Variations of Lake Surface Areas in Mongolia during 2000-2011 Using Minimum Composite MODIS NDVI.

A minimum composite method was applied to produce a 15-day interval normalized difference vegetation index (NDVI) dataset from Moderate Resolution Imaging Spectroradiometer (MODIS) daily 250 m reflectance in the red and near-infrared bands. This dataset was applied to determine lake surface areas in Mongolia. A total of 73 lakes greater than 6.25 km2in area were selected, and 28 of these lakes were used to evaluate detection errors. The minimum composite NDVI showed a better detection performance on lake water pixels than did the official MODIS 16-day 250 m NDVI based on a maximum composite method. The overall lake area detection performance based on the 15-day minimum composite NDVI showed -2.5% error relative to the Landsat-derived lake area for the 28 evaluated lakes. The errors increased with increases in the perimeter-to-area ratio but decreased with lake size over 10 km2. The lake area decreased by -9.3% at an annual rate of -53.7 km2 yr-1 during 2000 to 2011 for the 73 lakes. However, considerable spatial variations, such as slight-to-moderate lake area reductions in semi-arid regions and rapid lake area reductions in arid regions, were also detected. This study demonstrated applicability of MODIS 250 m reflectance data for biweekly monitoring of lake area change and diagnosed considerable lake area reduction and its spatial variability in arid and semi-arid regions of Mongolia. Future studies are required for explaining reasons of lake area changes and their spatial variability.

Investigating collection 4 versus collection 5 MODIS 250 m NDVI time-series data for crop separability in Kansas, USA

ABSTRACTThe primary objective of this research was to analyse collection 5 versus collection 4 time-series normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m for the purpose of separating crop types. Using extensive ground reference data from the state of Kansas in the central USA, NDVI value profiles were extracted from different collection versions for 2001 (collections 4 and 5) and 2005 (collection 5 only). Phenological curves for all crops and all data sets were created and visually inspected. Jeffries–Matusita (J-M) distance statistical analysis was performed to assess crop separability. Contrary to expectations, collection 5 time-series MODIS 250 m NDVI data were found to be inferior to collection 4 with respect to crop separability. Specifically, collection 4 data exhibited a greater dynamic range across the growing seasons of the various crop types, and this discriminatory advantage was supported by J-M distance analysis. Though the analysis did not suggest reasons for the outcome, it corroborates the conclusion of the only other similar study in the literature comparing data from collections 4 and 5. Considering the pervasive use of these data for land-cover mapping, it is recommended that MODIS NDVI data from collection 4 should be used where possible for crop type mapping in agricultural regions with climate, geography, and crops similar to Kansas.

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland

Abstract. The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002–2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days, respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation and increasing temperature at the early growing period because of global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.

Soil Moisture Retrieval Using Neural Networks: Application to SMOS

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures, $T_{b} $ 's) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using $T_{b} $ 's measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis

Abstract. A long-term decline in ecosystem functioning and productivity, often called land degradation, is a serious environmental challenge to Ethiopia that needs to be understood so as to develop sustainable land use strategies. This study examines inter-annual and seasonal trends of vegetation cover in the Upper Blue Nile (UBN) or Abbay Basin. The Advanced Very High Resolution Radiometer (AVHRR)-based Global Inventory, Monitoring, and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) was used for long-term vegetation trend analysis at low spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (MOD13Q1) were used for medium-scale vegetation trend analysis. Harmonic analyses and non-parametric trend tests were applied to both GIMMS NDVI (1981–2006) and MODIS NDVI (2001–2011) data sets. Based on a robust trend estimator (Theil–Sen slope), most parts of the UBN (~ 77 %) showed a positive trend in monthly GIMMS NDVI, with a mean rate of 0.0015 NDVI units (3.77 % yr−1), out of which 41.15 % of the basin depicted significant increases (p &lt; 0.05), with a mean rate of 0.0023 NDVI units (5.59 % yr−1) during the period. However, the MODIS-based vegetation trend analysis revealed that about 36 % of the UBN showed a significant decreasing trend (p &lt; 0.05) over the period 2001–2011 at an average rate of 0.0768 NDVI yr−1. This indicates that the greening trend of the vegetation condition was followed by decreasing trend since the mid-2000s in the basin, which requires the attention of land users and decision makers. Seasonal trend analysis was found to be very useful to identify changes in vegetation condition that could be masked if only inter-annual vegetation trend analysis was performed. Over half (60 %) of the Abay Basin was found to exhibit significant trends in seasonality over the 25-year period (1982–2006). About 17 and 16 % of the significant trends consisted of areas experiencing a uniform increase in NDVI throughout the year and extended growing season, respectively. These areas were found primarily in shrubland and woodland regions. The study demonstrated that integrated analysis of inter-annual and intra-annual trends based on GIMMS and MODIS enables a more robust identification of changes in vegetation condition.

Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data

Data from the U.S. Defense Meteorological Satellite Program’s Operational Line-scan System are often used to map impervious surface area (ISA) distribution at regional and global scales, but its coarse spatial resolution and data saturation produce high inaccuracy in ISA estimation. Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) with its high spatial resolution and dynamic data range may provide new insights but has not been fully examined in mapping ISA distribution. In this paper, a new variable—Large-scale Impervious Surface Index (LISI)—is proposed to integrate VIIRS-DNB and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for mapping ISA distribution. A regression model was established, in which LISI was used as an independent variable and the reference ISA from Landsat images was a dependent variable. The results indicated a better estimation performance using LISI than using a single VIIRS-DNB or MODIS NDVI variable. The LISI-based approach provides accurate spatial patterns from high values in core urban areas to low values in rural areas, with an overall root mean squared error of 0.11. The LISI-based approach is recommended for fractional ISA estimation in a large area.

Tracking MODIS NDVI time series to estimate fuel accumulation

Patterns of post-fire recovery in southern California chaparral shrublands are important for understanding fuel available for future fires. Satellite remote sensing provides an opportunity to examine these patterns over large spatial extents and at high temporal resolution. The relatively limited temporal range of satellite remote sensing products has previously constrained studies of post-fire recovery to chronosequence approaches, in which space is substituted for time, but the lengthening satellite data record is easing this limitation. In this study, we tracked vegetation recovery using a pixel-explicit approach from 2000 to 2013 using normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We attempted to control for inter-annual precipitation variation and examined the influence of shrub cover on MODIS NDVI during the post-fire recovery process. We find strong variation in recovery trajectories associated with site differences, which would have been lost in a chronosequence approach. Shrub cover plays a larger role in explaining annual NDVI variation during the early stages of post-fire recovery, and is a less important factor in more mature stands.