Analysis of Spatiotemporal Changes in NDVI-Derived Vegetation Index and Its Influencing Factors in Kunming City (2000 to 2020)

Advanced very high resolution radiometer (AVHRR) data provide the longest available time series of global satellite observations and have been extensively used. The Land Long-Term Data Record (LTDR) project has generated daily surface reflectance and normalized difference vegetation index (NDVI) products from AVHRR. However, residual cloud and aerosol contamination in the LTDR AVHRR surface reflectance and NDVI products significantly limits their applications and results in temporal and spatial inconsistencies in subsequent downstream products. Based on the LTDR AVHRR surface reflectance, a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method was refined in this study to generate Global LAnd Surface Satellite (GLASS) AVHRR NDVI and surface reflectance products from 1982 to 2015. The daily LTDR AVHRR surface reflectance data were first aggregated into eight-day intervals. The aggregated surface reflectance data were used to calculate NDVI, and a robust smoothing algorithm was used to reconstruct continuous and smooth NDVI upper envelopes, which were used to identify cloud-contaminated surface reflectance values. Then the surface reflectance time series was reconstructed from cloud-free surface reflectance values by incorporating the upper envelopes of the NDVI time series as constraints. The results show that the refined VIRR method successfully removes NDVI and surface reflectance values contaminated by clouds and can reconstruct temporally continuous NDVI and land-surface reflectance time series. Comparison of the GLASS AVHRR NDVI product with the third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) and the moderate resolution imaging spectroradiometer (MODIS) NDVI products indicates that these NDVI products exhibit similar spatial patterns, but the GIMMS3g NDVI values were clearly higher than the GLASS AVHRR and MODIS NDVI values in tropical forest regions and the 50°N−60°N latitude band, particularly in July. Comparisons with the MODIS NDVI values over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites demonstrate that the GLASS AVHRR NDVI product provides better performance (RMSE = 0.1007 and Bias = 0.0518) than the GIMMS3g NDVI product (RMSE = 0.1288 and Bias = 0.0852). The temporal profiles of all these NDVI products exhibited consistent seasonal variations, but the temporal smoothness of the GLASS AVHRR NDVI product was superior to that of the GIMMS3g and MODIS NDVI products. The GLASS AVHRR and GIMMS3g NDVI products show consistent trends in most situations, but the trends of the GLASS AVHRR NDVI product were slightly more pronounced than those of the GIMMS3g NDVI product for each biome type. Comparison of the GLASS AVHRR surface reflectance product with MODIS surface reflectance product indicates the GLASS AVHRR and MODIS surface reflectance showed similar seasonal and interannual variations and the GLASS AVHRR surface reflectance was in good agreement with the MODIS surface reflectance, especially in the red band.

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.

The normalized difference vegetation index (NDVI) is a simple but powerful indicator, that can be used to observe green live vegetation efficiently. Since its introduction in the 1970s, NDVI has been used widely for land management, food security, and physical models. For these applications, acquiring NDVI in both high spatial resolution and high temporal resolution is preferable. However, there is generally a trade-off between temporal and spatial resolution when using satellite images. To relieve this problem, a convolutional neural network (CNN) based downscaling model was proposed in this research. This model is capable of estimating 10-m high resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area. Then, the generality of this model was validated by applying it to test data acquired in 2020, with the result that the model predicted the NDVI with reasonable accuracy (MAE = 0.090, ρ = 0.734 on average). Next, 250-m NDVI from MODIS data was used as input to confirm this model under conditions replicating an actual application case. Although there were mismatch in the original MODIS and Sentinel-2 NDVI data, the model predicted NDVI with acceptable accuracy (MAE = 0.108, ρ = 0.650 on average). Finally, this model was applied to predict high spatial resolution NDVI using MODIS and Sentinel-1 data acquired in target area from 1 January 2020~31 December 2020. In this experiment, double cropping of cabbage, which was not observable at the original MODIS resolution, was observed by enhanced temporal resolution of high spatial resolution NDVI images (approximately ×2.5). The proposed method enables the production of 10-m resolution NDVI data with acceptable accuracy when cloudless MODIS NDVI and Sentinel-1 SAR data is available, and can enhance the temporal resolution of high resolution 10-m NDVI data.

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series

ABSTRACTBased on the vegetation map of Mongolia, Global Inventory Monitoring and Modelling Studies (GIMMS) normalized difference vegetation index (NDVI) (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000–2010), and temperature and precipitation data derived from 60 meteorological stations, this study has thoroughly examined vegetation dynamics in Mongolia and their responses to regional climate change at biome scale. To ensure continuity and consistency between the two NDVI datasets, the MODIS NDVI was first calibrated to the GIMMS NDVI based on the overlapping period of 2000–2006. Good calibration results with R2 values of 0.86–0.98 between the two NDVI datasets were obtained and can detect subtle trends in the long‐term vegetation dynamics of Mongolia. The results indicated that for various biomes, although NDVI changes during 1982–2010 showed great variation, vegetation greening for all biomes in Mongolia seem to have stalled or even decreased since 1991–1994, particularly for meadow steppe (0.0015 year−1), typical steppe (−0.0010 year−1), and desert steppe (−0.0008 year−1), which is an apparent turning point (TP) of the vegetation growth trend in Mongolia. A pronounced drying trend (from −4.399 mm year−1 in meadow steppe since 1990 to −2.445 mm year−1 in alpine steppe since 1993) occurred between 1990 and 1994, and persistently warming temperatures (0.015 °C year−1 in alpine steppe to 0.070 °C year−1 in forest and meadow steppe) until recently have likely played a major role in this NDVI trend reversal. However, the NDVI TP varied by biome, month, and climate and was not coupled exactly with climatic variables. The impact on climate of both same‐time and lagged‐time temperature and precipitation effects also varied strongly across biomes and months. On the whole, climate‐related vegetation decline and associated potential desertification trends will likely be among the major sources of ecological pressure for each biome in Mongolia, which could intensify environmental problems like sandstorms in other East Asian regions.

Aim The FAO land‐cover classification system (LCCS) represents an innovative approach to standardizing and harmonizing land‐cover classifications based on remote sensing data. The thematic information considered by the LCCS, however, is intrinsically related to vegetation physiognomy and does not report important eco‐climatic features. Our aim is to develop a methodology to enrich LCCS maps with information on vegetation productivity and phenology derived from Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data.Location The LCCS has recently been applied in East Africa by the Africover project. The proposed methodology is developed and tested in Tanzania using MODIS NDVI data for a 5‐year period (2001–05).Methods Annual NDVI profiles of Africover polygons were extracted from MODIS imagery. These profiles, composed of 23 NDVI values per year, were averaged over the study period, purified for possible land‐cover errors and converted into a more manageable format composed of 24 half‐month values. The resulting NDVI profiles were first analysed visually and then evaluated statistically against rainfall measurements taken at 12 Tanzanian stations. The steps involved were as follows: NDVI values were aggregated on a monthly basis and represented with a one‐digit integer to obtain an extended code; a subset of parameters describing vegetation development and phenology was identified, thus obtaining a restricted codification; and finally, the information loss resulting from both the extended and restricted codification was evaluated with respect to the original NDVI profiles.Results NDVI profiles of different Africover classes can differ in mean values but tend to have a similar shape, linked to the seasonality of local vegetation. Both NDVI annual averages and seasonal variations are strictly dependent on rainfall patterns, particularly in arid zones. The tested codifications effectively summarize the eco‐climatic information contained in the polygon NDVI profiles, with the extended and restricted codifications retaining > 90% and 80% of such information, respectively.Main conclusions The proposed methodology is capable of enriching LCCS polygons with eco‐climatic information derived from MODIS NDVI data. Such information is related to vegetation development and seasonality, and can be efficiently condensed at various levels of detail.

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.

ABSTRACTThis article discusses an evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data for monitoring vegetation variation in Qaidam Basin, Northwest China. In this study, 16 day composite 250 m normalized difference vegetation index (NDVI) products (MOD13Q1) acquired from 2000 to 2011 were processed to determine vegetation cover fraction (VCF) for detecting the annual dynamics of different types of vegetation cover in the basin and the products were validated by comparing field measurement in spatial distribution. The results show that the annual NDVI value increased from 0.126 to 0.172 on average between 2000 and 2011. The basin interior is dominated by desert and 74% of the area is covered by low-density shrubs and bare soil. Both areas of bare soil and low-density vegetation present a decreased rate, whereas medium-, medium-high-, and high-density vegetation show increase trends in the vegetation cover. Generally, the vegetation fluctuation depends on various attributes such as climate change, elevation, water table depth, and total dissolved solids (TDS) in arid areas. We found strong statistical correlation between NDVI time series and climatic factors such as air temperature and precipitation. There is also an agreement between the spatial distribution of NDVI value and elevation, because elevation has important impacts on the distribution of vegetation pattern, which are different in coverage. The vegetation dependent on water table depth is more complicated: shrubs of Phragmites australis, Artemisia desertorum, and Tamarix ramossissima Ledeb. are sensitive to water table depth and the maximum NDVI occurred at a water table depth shallower than 2 m. However, high-height shrub such as Nitraria Schoberi L. reflects less dependence on water table depth. Normally, vegetation can develop well at TDS between 0 and 3 g l−1 whereas Tamarix ramossissima Ledeb. can still survive when the TDS is larger than 8 g l−1.

Moderate Resolution Imaging Spectroradiometer (MODIS) data offer great potential for monitoring vegetation dynamics in Alaska. However, certain MODIS image quality issues, such as geometric distortion, have been analyzed and documented in high latitudes and regions distant from the Greenwich Meridian. To improve MODIS data usability, the Canada Centre for Remote Sensing (CCRS) developed a seven-band reflectance dataset (10 day composite) at 250 m resolution for Canada and North America. More recently, the US Geological Survey Earth Resources Observation and Science Center produced an eMODIS dataset that includes a 7 day composite of normalized difference vegetation index (NDVI) and surface reflectance data at 250 m, 500 m, and 1 km resolutions for the conterminous United States and Alaska. Although these two datasets are based on the same MODIS level 1B data as those of the standard MODIS products, they are processed to improve their use in high-latitude regions. In this study, we conducted a comparative analysis of the standard MODIS, CCRS MODIS, and eMODIS Alaska 250 m NDVI products using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images as a reference source. CCRS MODIS and eMODIS Alaska NDVI images have significantly improved geometric features over those of the standard MODIS product. Pixel-by-pixel comparisons of the MODIS datasets indicated that all retained the original MODIS radiometric characteristics, but considerable mismatches at the pixel level were found due to geometric distortions caused by resampling. All three MODIS datasets agreed well as images were degraded to 5 or 10 km resolution.

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.

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R 2 values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1 : 1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R 2 values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.

The spatial and temporal correlation analysis between MODIS NDVI and SWAT predicted soil moisture during forest NDVI increasing and decreasing periods

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.

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 < 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 < 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.