High-quality Normalized Difference Vegetation Index (NDVI) time-series data are important for many regional and global ecological and environmental applications. Unfortunately, residual noise in ...current NDVI time-series products greatly hinders their further applications. Several noise-reduction methods have been proposed during the past two decades, but two important issues remain to be resolved. First, the methods usually perform poorly for cases of continuous missing data in the NDVI time series. Second, they generally assume negatively biased noise in the NDVI time series and thus erroneously raise some local low NDVI values in certain cases (e.g., the harvest period for multi-season crops).We therefore developed a new noise-reduction algorithm called the Spatial-Temporal Savitzky-Golay (STSG) method. The new method assumes discontinuous clouds in space and employs neighboring pixels to assist in the noise reduction of the target pixel in a particular year. The relationship between the NDVI of neighboring pixels and that of the target pixel was obtained from multi-year NDVI time series thanks to the accumulation of NDVI data over many years, which would have been impossible a decade ago. We tested STSG on 16-day composite MODIS NDVI time-series data from 2001 to 2016 in regions of mainland China and 11 phenology camera sites in North American. The results showed that STSG performed significantly better compared with four previous widely used methods (i.e., the Asymmetric Gaussian, Double Logistic, Fourier-based, and Savitzky-Golay filter methods). One obvious advantage was that STSG was able to address the problem of temporally continuous NDVI gaps. STSG effectively increased local low NDVI values and simultaneously avoided overcorrecting low NDVI values during the crop harvest period. In addition, implementing STSG required only raw MODIS NDVI time-series products without any additional burden of data requirements. All of these advantages make STSG a promising noise-reduction method for generating high-quality NDVI time-series data.
•We developed a new method to reconstruct high-quality NDVI time-series data.•The new method integrates spatiotemporal information with the Savitzky-Golay method.•Continuous missing data in the NDVI time series are well addressed by the new method.•The new method effectively increases local low NDVI values without overcorrection.
Display omitted
Normalized Difference Vegetation Index (NDVI) data derived from Landsat satellites are important resources for vegetation monitoring. However, Landsat NDVI time-series data are ...usually temporally discontinuous owing to the nominal 16-day revisit cycle, frequent cloud contamination, and other factors. Although several methods have been proposed to reconstruct continuous Landsat NDVI time-series data, some challenges remain in the existing reconstruction methods. In this study, we developed a simple but effective Gap Filling and Savitzky–Golay filtering method (referred to as “GF-SG”) to reconstruct high-quality Landsat NDVI time-series data. This new method first generates a synthesized NDVI time series by filling missing values in the original Landsat NDVI time-series data by integrating the MODIS NDVI time-series data and cloud-free Landsat observations. Then, a weighted Savitzky-Golay filter was designed to remove the residual noise in the synthesized time series. Compared with three previous typical methods (IFSDAF, STAIR, and Fill-and-Fit) in two challenging areas (the Coleambally irrigated area in Australia and the Taian cultivated area in China) with heterogeneous parcels and complex NDVI profiles, we found that GF-SG performed the best with three obvious improvements. First, GF-SG improved the reconstruction of long-term continuous missing values in Landsat NDVI time series, whereas the other methods were less reliable for reconstructing these long data gaps. Second, the performance of GF-SG was less affected by the residual noise caused by cloud detection errors in the Landsat image, which is due to the incorporation of the weighted SG filter in the new method. Third, GF-SG was simple and could be implemented on the computing platform Google Earth Engine (GEE), which is particularly important for the practical application of the new method at a large spatial scale. The GEE code is freely available at https://code.earthengine.google.com/3a883c9e84ad119045bcb88e4de77b47?noload=true. We expect that this practical approach can further popularize the use of Landsat NDVI time-series data in ecological, geographical, and environmental research.
The Loess Plateau experiences the most serious soil erosion problems in China and possibly even globally. Many measures have been taken to increase the vegetation cover and to control soil erosion. ...Of these measures, the Grain for Green Project, launched in 1999, has been the most effective, changing the ecological and socio‐economic situation of the Plateau significantly. Using time series normalized difference vegetation index (NDVI) data from 1981 to 2013, this study analysed spatial patterns of changes in vegetation cover, detected spatial patterns of the abrupt‐change time of NDVI trends, and investigated factors that may contribute to these changes. The results showed that (a) vegetation restoration was obvious over the last decades, especially in the 2000s, and the increase in NDVI during 2000–2013 was more than 3‐times that during 1990–2000. Approximately 54.99% of the Plateau during 2000–2013 had a statistically significant increase, mainly distributing in loess hilly and gully region and loess gully region. (b) Vegetation restoration was comprehensively affected by climate change and human activities (e.g., the Grain for Green Project, urbanization and policies), and the effects of each factor varied for different regions. (c) Vegetation in most of the Plateau began to increase around 2007–2010, and 4 discrete areas with different timings of such break points were detected. The difference in the timing of break points in NDVI may be related with precipitation, reproducing tree species, management measures, and survival rate. These results imply that integrating natural and human factors is important when making effective and suitable measures and policies.
Residential greenness is considered beneficial to human health, and its association with respiratory function has been found in previous studies. However, its link with pneumonia remains unclear. To ...explore the association of residential greenness with incident pneumonia, we conducted a prospective cohort study based on participants of the UK Biobank, followed from 2006 to 2010 to the end of 2019. Residential greenness was measured by Normalized Difference Vegetation Index (NDVI) within 500 m and 1000 m buffer. Cox proportional hazard models were conducted to assess the association, and restricted cubic spline models were also constructed to estimate their exposure-response relationship. Results demonstrate that residential greenness was negatively related to the risk of incident pneumonia. An interquartile (IQR) increase in NDVI 500-m buffer was associated with 4 % HR (95 % CI) =0.96 (0.94, 0.97), P < 0.001 lower risk of incident pneumonia. Compared to the lowest greenness quartile (Q1), the highest quartile (Q4) had a lower risk of incident pneumonia, with the HR (95 % CI) estimated to be 0.91 (0.87, 0.95) (P values <0.001). Analyses based on NDVI 1000-m buffer obtained similar results. Furthermore, a significant effect of modifications by age and income on the linkage between residential greenness and incident pneumonia was found. These findings propose a potential effective prevention of incident pneumonia and provide the scientific basis for promoting the construction of residential greenness.
Display omitted
•Residential greenness was associated with a lower risk of incident pneumonia.•The exposure-response relationship between greenness and pneumonia was linear.•Age and income modified the association of greenness with pneumonia.
This study aims to develop a plant health monitoring system suitable for vertical farms. It began by creating a multispectral LED with UVA and NIR light sources and an IoT device capable of ...controlling the LED spectrum. The IoT device integrates a camera with switchable filters and RGB CMOS sensor to simultaneously calculate SI-NDVI (single-image normalized difference vegetation index) and UV-NDVI (UV induce red chlorophyll fluorescence normalized difference vegetation index).
A lettuce cultivation experiment was conducted on a planting shelf in a controlled environmental room, with two cultivation planters containing the same nutrient solution and red lettuce variety. SI-NDVI and UV-NDVI were calculated every three hours to compare differences. Results showed that both UV-NDVI and SI-NDVI trends were similar in monitoring lettuce growth. However, UV-NDVI was more sensitive to plant health than SI-NDVI. By inducing drought stress, the UV-NDVI indicator was able to detect water deficiency anomalies earlier than visual observation.
Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date ...based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the "similarity" criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the "consistency" criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the "consistency" criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called "cross-fusion") to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the "similarity" criterion and then takes the "consistency" criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.
This study analyzes the temporal change of Normalized Difference Vegetation Index (NDVI) for temperate grasslands in China and its correlation with climatic variables over the period of 1982–1999. ...Average NDVI of the study area increased at rates of 0.5%
yr
−1 for the growing season (April–October), 0.61%
yr
−1 for spring (April and May), 0.49%
yr
−1 for summer (June–August), and 0.6%
yr
−1 for autumn (September and October) over the study period. The humped-shape pattern between coefficient of correlation (
R) of the growing season NDVI to precipitation and growing season precipitation documents various responses of grassland growth to changing precipitation, while the decreased
R values of NDVI to temperature with increase of temperature implies that increased temperature declines sensitivity of plant growth to changing temperature. The results also suggest that the NDVI trends induced by climate changes varied between different vegetation types and seasons.
The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been ...developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial, temporal and periodical correlations. In this paper, an adaptive Spatio-Temporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative ℓ1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST-Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions.
•A new method was proposed to reconstruct long-term NDVI series in cloudy regions.•It is the first time to introduce low-rank tensor completion in NDVI reconstruction.•Information among spatiotemporal and periodic neighbors is synthesized simultaneously.•Robustness of iterative trend filtering method in keeping feature points is proved.•Continuous gaps are well addressed and NDVI seasonal trajectories are well tracked.
We used the third generation Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) and climate data (temperature and precipitation) to examine recent (1982–2012) ...spatial and temporal variations in vegetation, and relationships between climate and vegetation for both the growing period and for different seasons, on the Tibetan Plateau (TP). Across the whole plateau, trends calculated by linear regression showed that as temperature and precipitation increased, the growing season (May–September) NDVI values increased at rate of 0.002 decade−1 (p = 0.14) from 1982 to 2012. The ensemble empirical mode decomposition estimation method showed that the rates of increase in the NDVI gradually intensified until the end of the 1990s, and then decreased slightly in the following years. The autumn NDVI increased at a rate of 0.005 decade−1 (p = 0.04) and was a major contributor to the growing season NDVI. The NDVI and temperature were positively correlated at seasonal and monthly timescales during the growing season. The responses of vegetation growth to seasonal and monthly changes in precipitation, however, were complex. The NDVI trends showed obvious spatial heterogeneity and coincided well with regional and seasonal changes in climate. The growing season NDVI increased in 55% of the area of the TP. On a seasonal basis, the largest increase in the NDVI occurred in autumn and affected more than 61% of the TP, while the smallest increase in the NDVI occurred in spring, and affected over 41% of the area. Moreover, there were seasonal and spatial variations in the responses of different vegetation types to temperature and precipitation.
Crop yield data is critical for precision agriculture, breeding programs, and other activities, but collecting this data at fine scales is labor-intensive. Unmanned aerial systems (UAS) allow ...collection of imagery with unprecedented temporal, spatial, and spectral resolutions and could be better leveraged to estimate or predict yield while limiting labor requirements. Therefore, the objectives of this study were to develop a relatively simple pixel-based multispectral image classification technique for cotton (Gossypium hirsutum L.) yield estimation, termed “Boll Area Index” or BAI, which is collected after defoliation, and to identify in-season co-predictors derived from multispectral and thermal imagery to improve estimate accuracy. A field study was conducted over four growing seasons (2017–2020) at College Station, TX. The experimental treatments included three irrigation rates (0%, 40%, and 80% ETc replacement) and eight commercial cotton cultivars each year. Multispectral and thermal infrared imagery were captured biweekly. In addition to BAI, three vegetation indices (Normalized Difference Vegetation Index or NDVI, Normalized Difference Red Edge or NDRE, and Optimized Soil Adjusted Vegetation Index or OSAVI) and canopy temperature were derived from orthomosaics and analyzed. There were positive linear relationships between BAI and seed cotton yield each year (R2 = 0.61–0.79). Multiple linear regression including BAI, vegetation indices, and/or canopy temperature from two flight dates produced better yield estimates (R2 = 0.79–0.89) than BAI alone. Cameras or payloads with both optical and thermal sensors are ideal for strictly in-season yield estimation endeavors, but thermal was not necessary when BAI was included in the models because canopy temperature provided minimal improvement as a third predictor. Multiple regressions involving NDVI and BAI already had quite strong relationships with yield (R2 = 0.7–0.87) without including canopy temperature. Cross validation of multiple linear regression models derived from BAI and NDVI, using data from two years to predict yield in a third, had R2 values that varied from 0.51 to 0.88 and RMSE varied from 273 to 508 kg ha−1. This is a level of error that may be acceptable for some purposes, such as screening lines in early stages of cotton breeding selection, but may be unacceptable for screening of advanced lines when greater accuracy is crucial. Overall, the results indicate that derivatives from just two or three UAS flights presents a detailed dataset for cotton yield prediction, while limiting labor and required computational resources.
•BAI had linear relationships with seed cotton yield (R2 = 0.61–0.79).•Multiple regression using in-season and BAI data produced better yield estimates.•Cross validated yield predictions had RMSE of 273–508 kg ha−1.•Error may be acceptable for screening lines in early stages of cotton breeding.
PDF
