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Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of the required processing time pose challenges to real-time applications. NDVI prediction emerges as an innovative solution to address these limitations. It allows for proactive decision-making by providing accurate estimates, enabling farmers and land managers to plan essential agronomic activities such as irrigation, fertilization, and pest control, based on anticipated future conditions. This study introduces an Artificial Neural Network (ANN) model incorporating NDVI, Normalized Difference Water Index (NDWI), temperatures, and precipitation as predictive variables. The model employs a novel time series slicing algorithm, Boosting Adaptive Time Series Slicer (BATS), to enhance the input training dataset's variability, presenting the model with a broader range of examples. A 2-Bidirectional Long Short-Term Memory (LSTM) forecasting model was developed to predict future NDVI values over short and medium-term horizons. The study area used to train, test and validate the ANN corresponds to a diverse landscape of cultivated corn fields located in Piemonte (NW-Italy). Results showed that NDVI future estimates were accurate; considering three time horizons for predictions (5, 10, and 15 days) RMSE values resulted to be 0.028, 0.038 and 0.050, respectively. Additionally, ablation tests proved that the most important variable for enhancing the model's accuracy is the NDWI, and the most useful timesteps are the four most recent ones. To preliminary investigate the capability of the ANN to operate over a wider and different area it was applied over the entire Europe, using the LUCAS dataset as reference map to locate corn fields. Results show RMSE of 0.062, 0.083 and 0.105 for the 5, 10 and 15 days forecasting horizons, respectively. The methodology proposed in this paper can be a possible alternative to more ordinary approaches for NDVI forecasting that nowadays appears to be a fundamental step for a proactive precision agriculture where crop management can be significantly improved. Future developments should explore the use of sequence-to-sequence ANNs to predict the development of multiple spectral indices over multiple crop types simultaneously. [ABSTRACT FROM AUTHOR]

A regular, dense time series of the normalized difference vegetation index (NDVI) is a crucial remote sensing parameter that provides insights into the growth status of plants across different temporal and spatial scales. However, the production of high-resolution, equal-interval, and dense-time-series NDVI products currently faces technical challenges, limiting their application in agriculture and forestry-related fields. This study proposes a new method for reconstructing a dense Sentinel-2 NDVI time series based on the spatiotemporal optimal weighted combination estimation model (SOWCEM). The SOWCEM is developed using state-of-the-art spatial and temporal reconstruction algorithms. This model considers the distribution characteristics of errors in spatiotemporal dimensions, allowing for adaptive determination of the optimal combination weight at the pixel level. Then, the reconstructed regular dense-time-series NDVI data covering the region for five days/time are evaluated through a comparative analysis. The experimental results demonstrate that the regional NDVI reconstruction image based on the SOWCEM algorithm has a determination coefficient (R2) of 0.9082, a root mean square error (RMSE) of 0.0403, and a comprehensive overall error (ERGAS) of 5.7726. Compared with single spatiotemporal dimension models, the SOWCEM algorithm shows an average improvement of 5.78% in R2, while the RMSE and ERGAS decrease by 6.5% and 6.86%, respectively. Moreover, the stability and robustness of the models are also enhanced. These results indicate that the SOWCEM algorithm can effectively achieve a high-quality fusion reconstruction of regional images, significantly enhancing the accuracy of high-spatial-resolution NDVI dense temporal spectral reconstruction. Furthermore, it exhibits clear superiority and good applicability. This algorithmic framework provides a feasible approach for the dense reconstruction of high-spatial-resolution regular time-series NDVI data, enabling more accurate and efficient high-resolution NDVI remote sensing monitoring services in related field applications. The core code of SOWCEM is available at https://github.com/GISerZkun/SOWCEM. • Developed the SOWCEM algorithm technical framework for NDVI image reconstruction; • Improved the accuracy and stability of high spatial resolution NDVI reconstruction; • Implemented NDVI production with 10 m spatial resolution and dense time series. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is the most commonly used index for assessing vegetation. However, significant differences among various satellite datasets, complex terrain, and the impact of clouds on optical sensors limit vegetation change assessment based on NDVI. To address these issues, this study utilizes multi-source satellite data (GIMMS3g NDVI, CDR AVHRR NDVI, SPOT NDVI, and MODIS NDVI) to monitor vegetation dynamics at different time scales from 1990 to 2020 in Sichuan Province, China. The results indicate that over time, NDVI values from the four NDVI products in Sichuan Province have shown an upward trend. There are certain differences in the spatial distribution and spatial heterogeneity of the change rate of NDVI values among the four NDVI products at different time scales, and the differences are mainly concentrated in the Sichuan Basin (SB) and the Western Sichuan alpine plateau region (WS). Compared with the other three NDVI products, GIMMS NDVI has the highest value but the smallest increase during the study period. The SPOT NDVI value is the smallest, but the increase is relatively large. However, within the overlapping period of the four NDVI datasets, only the annual average of CDR AVHRR NDVI showed a downward trend (slope 2000–2013 = −0.0001·a−1). The annual fluctuation of CDR AVHRR NDVI is the smallest, and compared to other NDVI datasets, its correlation with climate factors shows significantly weaker spatial variability. Moreover, the ability of CDR AVHRR NDVI to distinguish different vegetation land cover types is significantly poor (STD = 0.045). The findings of this study will provide a reference for further research on vegetation changes in Sichuan Province and NDVI reconstruction in cloudy areas. [Display omitted] • Thirty-year spatio-temporal pattern of vegetation over cloudy areas of Southwest China was firstly revealed by NDVI products. • Four kinds of NDVI products were compared from the perspective of different seasons and times. • There was a significant difference between CDR AVHRR NDVI product and the others in cloudy areas. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and there has been a substantial shift in global vegetation cover in recent decades. China is recognized for its substantial impact on global vegetation cover changes, which are influenced by both climate change and human activities. Therefore, this research aims to assess the respective influences of climate modification and human activities on vegetation variations in China. First, the changes in vegetation cover are explored between 1982 and 2020 using satellite-image derived vegetation index, known as the Normalized Difference Vegetation Index (NDVI). Second, a multiple regression model based on time-lag analysis is used to simulate NDVI. In addition to common climatic factors such as temperature, precipitation, solar radiation intensity and relative humidity, the atmospheric CO 2 concentration to directly reflect climate change is considered in this model. Finally, the relative influence of climate variation and human activities on the alteration of vegetation cover is determined based on reconstructed NDVI. Results: (1) Precipitation and solar radiation are the most important influences, while carbon dioxide concentration and relative humidity have the least influence. (2) The simulation error before 2000 was 0.875%, which was considerably lower than the error after 2000. (3) After 2000, human activities favorably affected vegetation recovery in most of the study area, with an average degree of influence of >30%. • Explored the change in vegetation cover in China from 1982 to 2020 using NDVI; • A multiple regression model based on time-lag analysis was used to simulate NDVI; • Tried to quantify the human activities effects on the change of vegetation in China. [ABSTRACT FROM AUTHOR]

Understanding the intricate relationship between climate variables and the Normalized Difference Vegetation Index (NDVI) is essential for effective ecosystem management. This study focuses on the spatiotemporal dynamics of NDVI and its interaction with climate variables in the ecologically diverse Khyber Pakhtunkhwa (KPK) Province, Pakistan, from 2000 to 2022. The research methodology involves analyzing satellite images and meteorological datasets to examine NDVI and surface latent heat flux (SHF), total precipitation (TPP), temperature (T), soil temperature (ST), and total pressure (TP). KPK Province's ecological significance and complex climate-vegetation interactions drive the selection of this study area. The study uses multiple linear regression analysis to investigate how T, TPP, SHF, and TP influence NDVI. The Mann-Kendall test detects trends, with Sen's slope estimator quantifying trend magnitudes. Additionally, correlation coefficients provide insights into long-term changes and association strengths. The findings highlight a consistent upward trend in mean NDVI over the 23 years, revealing an overall increase in NDVI, particularly in vegetation-dense areas where it rose from 0.27 to 0.32. The research showed an annual growth rate of 0.84% in the entire area, with specific vegetated zones exhibiting a slightly lower rate of 0.80%. However, the average yearly increase in NDVI is higher in vegetation-specific zones (0.00237) compared to the whole area (0.00151). This increase in NDVI occurs alongside a statistically significant decrease in SHF and PPT, suggesting a complex adaptation of vegetation to changing climate conditions in the KPK Province. In contrast, SHF exhibits a statistically significant negative slope of −5.952e-06 (p < 0.05), indicating a pronounced downward trend. Similarly, Sen's slope estimate for precipitation demonstrates a significant negative trend of −0.0001 (p < 0.05), showing diminishing precipitation. The study uncovers intricate linkages between climate variables and vegetation dynamics within KPK Province. These insights have far-reaching implications, guiding decision-making in land management, conservation efforts, and global climate resilience strategies. Ultimately, the research underscores the critical role of data-driven approaches in shaping a greener and more sustainable future. • Upward NDVI trend shows dense area growth over 23 years. • Zones show 0.80% annual growth, revealing nuanced vegetation dynamics in Province. • Clear SHF decrease & diminishing precipitation show complex vegetation adaptation. • Complex climate-NDVI dynamics revealed, highlighting changing conditions' impact. • Data insights guide global land, conservation, and climate strategies. [ABSTRACT FROM AUTHOR]

• We compared NDVI and OpenStreetMap data to people's visual perceptions of greenness. • NDVI and OpenStreetMap data often diverge from human perceptions of greenness. • OpenStreetMap captures greenness best in short distance, NDVI best in longer distance. • Vegetation configuration, variety, and natural elements enhance perceived greenness. • Vegetated space dominated by built-environment elements may not be perceived as green. The study of urban greenspaces typically relies on three types of data: people's subjective perceptions collected via questionnaires , vegetation indices derived from satellite imagery, such as the Normalized Difference Vegetation Index (NDVI), and Land Use or Land Cover maps, such as OpenStreetMap (OSM). Data on people's perceptions are essential when researching human activities, yet they scale poorly. NDVI and OSM data, on the other hand, are freely available worldwide, thus valuable for assessing cities at scale or prioritizing locations for interventions. However, it is unclear how effectively NDVI and OSM data capture people's visual perceptions of urban greenspaces. In this work, we collect people's visual perceptions of public spaces in three major European cities through crowdsourcing, quantitatively compare them to NDVI and OSM data, and qualitatively investigate disparities. We found that NDVI moderately correlates with perceived greenness and that not only OSM greenspaces but also pocket parks and play spaces are often considered green. Furthermore, we found that people's perceptions correspond best to OSM data in small radius distances and NDVI data in larger radius distances and that combining NDVI and OSM data can improve identification of places in OSM that are commonly considered green. Our qualitative analysis revealed that configuration and variety of vegetation, and presence of other natural or built-up features, influence people's perceptions of greenspace. With our findings we aim to help researchers and practitioners make more informed decisions when collecting greenspace data for their specific context, ultimately contributing to green urban environments that reflect people's perspectives. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) and tree rings provide different approaches that allow evaluating the productivity of ecosystems. Following this premise, we evaluated the relationship between NDVI and tree-ring widths of Austrocedrus chilensis forests at sectors of the Argentinian northwestern foothills of the Patagonian Andes. To proceed with this study, wood core samples were taken from 153 trees at 6 sites located at different latitudes and along a strong west-east precipitation gradient. The NDVI presented a significant and positive spatial correlation with precipitation, the Standardized Precipitation-Evapotranspiration Index (SPEI-3), soil moisture, and a negative correlation with air temperature at large geographical coverages. Stand location in the latitudinal and meridian coordinates showed a species-specific effect, with greater sensitivity to NDVI in sites located at higher latitudes and under drier conditions. We found stronger associations between stem biomass (tree rings) and canopy biomass (NDVI) in drier sites, assuming a closer link to stressful water conditions. This behavior was accentuated since 1988, which coincides with the increase in air temperature as a result of regional warming processes. These results suggest that site environmental conditions influence how NDVI and ring width are linked, showing the complex dynamics between climate and growth. The results indicate that ring widths of A. chilensis trees have the potential to assess current NDVI variations in the study area, allowing reconstructions of past productivity and potential predictive models of future changes. • The width of the rings of A. chilensis is positively associated with NDVI. • The correlation between ring width and NDVI has been presented since 1988. • The association between ring width and NDVI presents spatial variability. [ABSTRACT FROM AUTHOR]

Vegetation indices computed from spectral signatures are vastly used for monitoring the terrestrial biosphere. Indices are convenient proxies for canopy structure, and leaf pigment content, and consequently to estimate the photosynthetic activity of vegetation. Owing to its simplicity, the celebrated Normalized Difference Vegetation Index (NDVI) has been used as a proxy for greenness and canopy structure. Unfortunately, NDVI can only capture linear relationships of the near infrared (NIR) - red difference with the parameter of interest. To account for higher-order relations between the spectral channels, kernel NDVI (kNDVI) was proposed in (Camps-Valls et al., 2021). In this work, we give useful prescriptions for its proper use and show its good performance in a wider set of applications. We discuss the good characteristics of the index like boundedness, low error propagation. Furthermore, we give empirical evidence of performance in estimating in-situ vegetation parameters (leaf area index (LAI), gross primary productivity (GPP), leaf, and canopy chlorophyll content, green and total LAI and fraction of absorbed photosynthetically active radiation (fAPAR)) as well as the estimation of latent heat at flux tower level. We confirm the generally good performance of the index (correlation coefficient of kNDVI and canopy chlorophyll content is 0.919 and 0.933 for maize over two sites, as well as the correlation coefficient between kNDVI and carotenoid, is 0.816, 0.520 and 0.579 for three forest sites) and highlight its convenience in monitoring terrestrial ecosystems. To foster wider adoption of the new family of the index, we provide source code in 6 programming languages as well as efficient implementations in the Google Earth Engine (GEE) platform at https://github.com/IPL-UV/kNDVI. • The nonlinear kNDVI generalizes standard vegetation indices. • kNDVI propagates lower uncertainty and addresses saturation issues. • Prescriptions for the correct use of the index are given. • kNDVI yields improved performance to estimate in-situ vegetation parameters. • kNDVI improves estimation of latent heat at flux tower level. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of IRS LISS III satellite image based on NDVI (Normalized Difference Vegetation Index) for extracting the crop pattern of rabi season of Kangsabati commanded area located at Bankura district of West Bengal, India. NDVI was calculated through creation of FCC (False Colour Composite) followed by supervised classification by applying ground truth data obtained after physical survey on three districts, covered under Kangsabati reservoir commanded area. Final crop map is generated to extract different crop classes from which crop sown in rabi season is estimated. It has been found that potato crop is sown on much more area than rice. Finally, accuracy is checked, and 87% accuracy was achieved after successful identification of crop. This study suggest that this approach will be effective with an acceptable accuracy for crop map generation to classify the different crops and thereby calculate the actual crop water demand of commanded area of Kangsabati reservoir and thereby evaluate the performance of the canal system in near future. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is a valuable indicator of plant vigor that is frequently used in agronomic practices to make timely and targeted decisions with the aim of increasing the productivity of the system. NDVI measurements of large-scale fields are typically performed using remote sensing from satellite and aerial imaging devices. However, due to their low spatial and temporal resolution, these technologies may have limitations in precision viticulture. This paper investigates the potential of a proximal sensing system to characterize the vine foliage that makes use of data collected by a farmer robot equipped with an Intel RealSense D435 camera. The camera includes two infrared (IR) sensors in stereoscopic configuration and one RGB sensor, which provide, for each observation, both infrared and visible red channel information, thus making possible pixel-per-pixel NDVI calculation. Solutions to IR filtering and radiometric calibration issues are proposed that significantly improve measurement accuracy and reliability. Since the camera also provides stereo-based 3D scene reconstruction, depth information can be used to separate the plant canopy from the background before NDVI measurement. At the same time, range data can be employed to extract geometric properties of the crop, such as plant height and/or volume. The system is validated in the field in a commercial vineyard at different phenological stages, from the formation of the berries to leaf discoloration and fall. Experimental results show good agreement compared with ground truth provided by a GreenSeeker, with a mean absolute percentage error in the NDVI estimation of 4.6% and a R 2 of 0.87 tested over the whole grapevine cycle. Therefore, the proposed sensing system could be a feasible solution to automated NDVI estimation at plant-scale. [Display omitted] • Consumer-grade imaging system using a RealSense RGB-D camera for NDVI estimation. • Robotic platform for automated in-field monitoring at plant scale in viticulture. • Proximal sensing system to characterize the vine foliage. • Solutions to IR filtering and radiometric calibration issues under natural light. • Validation in the field in a commercial vineyard at different phenological stages. [ABSTRACT FROM AUTHOR]

Highlights • LTDRV4 NDVI day of year statistics were retrieved for clear and cloudy observations. • A validation dataset for time series reconstruction methods is presented (TISSBERT). • Different parameterizations for 5 gap-filling techniques are evaluated. • These techniques are compared for recommended and best parameterizations. • Best performing techniques must be fine-tuned locally and fail in cloud-prone areas. Abstract NDVI (Normalized Difference Vegetation Index) time series usually suffer from remaining cloud presence, even after data pre-processing. To address this issue, numerous gap-filling (or reconstruction) techniques have been developed in the literature, although their comparison has mainly been local to regional, with only two global studies to date, and has led to sometimes contradictory results. This study builds on these different comparisons, by testing different parameterizations for five NDVI temporal profile reconstruction techniques, namely HANTS (Harmonic Analysis of Time Series), IDR (iterative Interpolation for Data Reconstruction), Savitzky-Golay, Asymmetric Gaussian and Double Logistic, and then comparing them as generally parameterized, and then with the best of the tested parameterizations. These comparisons show that the HANTS reconstruction technique provides lower errors in cloud prone areas, while the IDR method works best with shorter cloud covers. However, the remaining errors in cloud prone areas are still high, and there is room for new reconstruction techniques. Although these results are only applicable to the range of the tested parameterizations, these latter have been chosen within widely used configurations, and should provide interested users with a better understanding of the different methods and the best parameterization for their needs, as well as an estimate of the expected error in the reconstruction of NDVI time series. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Relationships between non-photosynthetic vegetation (NPV) coverage and 6 NPVIs were explored. • Develop an NDVI-optimal NPVI model and quantitatively assess the accuracy of the model retrieval of f PV , f NPV and f BS. • Produce a MODIS-based time series of f PV , f NPV and f BS at 500 m resolution for the typical steppe. • Discussed several difficult factors for estimating f PV , f NPV and f BS using remote sensing. Abstract Quantitative estimations of the fractional cover of photosynthetic vegetation (f PV), non-photosynthetic vegetation (f NPV) and bare soil (f BS) are critical for soil wind erosion, desertification, grassland grazing, grassland fire, and grassland carbon storage studies. At present, regional and large-scale f PV , f NPV and f BS estimations have been carried out in many areas. However, few studies have used moderate resolution imaging spectroradiometer (MODIS) data to perform large-scale, long-term f PV , f NPV and f BS estimations in the Xilingol grassland of China. The objective of this study was to quantitatively estimate the time series of f PV , f NPV and f BS in the typical grassland region of Xilingol from MODIS image data. Field measurement spectral and coverage data from May and September 2017 were combined with the 8-day composite product (MOD09A1) acquired during 2017. We established an empirical linear model of different non-photosynthetic vegetation indices (NPVIs) and f NPV based on the sample scale. The linear correlation between the dead fuel index (DFI) and f NPV was best (R2 = 0.60, RMSE = 0.15). A normalized difference vegetation index (NDVI)-DFI model based on MODIS data was proposed to accurately estimate the f PV , f NPV and f BS (estimation accuracies of 44%, 71%, and 74%, respectively) in the typical grasslands of Xilingol in China. The f PV , f NPV and f BS values for the typical grassland time series estimated by the NDVI-DFI model were consistent with the phenological characteristics of the grassland vegetation. The results show that the application of the NDVI-DFI model to the Xilingol grassland is reasonable and appropriate, and it is of great significance to the monitoring of soil wind erosion and fires in grasslands. [ABSTRACT FROM AUTHOR]

• Predict the normalized difference vegetation index (NDVI) using RGB drone imagery data. • The curvelet coefficients were analysed using Empirical Curvelet Transform method from RGB. • The deep DenseNet model predict the NDVI using the output of Empirical Curvelet Transform method. • This work is helpful to monitor the growth, plant health, and identify crop problems. Predicting accurately the Normalized Difference Vegetation Index (NDVI) trends from RGB images are essential to monitor crops and identify issues related to plant diseases, and water shortages. The current NDVI prediction models are primarily based on traditional machine learning models which lack reliability due to the problem related to atmospheric conditions. To predict NDVI in Prince Edward Island using RGB drone imagery data, this paper proposed a novel framework integrating empirical curvelet transform and DenseNet models. Each channel of RGB drone imagery data was passed through empirical curvelet transform method where the curvelet coefficients were analysed which result in creating a new formula to design NDVI. The output of the new formula was sent to the deep DenseNet to predict the final NDVI. The proposed model was evaluated using quantitative metrics including, Q-Q plot, regression, correlation coefficients, structural similarity (SSIM), peak signal to noise ratio (PSNR) and mean square error (MSE) as well as accuracy (A C C) , sensitivity (S E N) , f1-score, specificity. The obtained results showed that the proposed model outperformed the previous models by scoring the highest values of SSIM = 0.98, and lowest MSE = 120. It is believed that the proposed model is helpful to support farmers in monitoring the growth and plant health as well as to identify crops problems. [ABSTRACT FROM AUTHOR]

[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 G ap F illing and S avitzky– G olay 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. [ABSTRACT FROM AUTHOR]

A significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However, a major hurdle in achieving this goal is the discrepancy in spatial resolution between early satellite measurements and eddy flux measurements. By using a large set of growing season data covering 100 site-years in North and Central America, we explored the potential of transforming incident and reflected shortwave (R g) and photosynthetically active radiation (PAR) measurements into a broadband normalized difference vegetation index (NDVI) and near-infrared (NIR) reflectance of vegetation (NIRv) which simultaneously explains the GPP variability. We found that the broadband NDVI and NIRv derived from R g and PAR measurements at the daily time scale were highly correlated with Planet Fusion, Landsat-8/9, and Sentinel-2 narrowband NDVI and NIRv across a wide range of climate and ecological gradients. The differences between satellite and broadband NDVI and NIRv were found to be significantly associated with soil background variations, phenological stages, water stress and signal saturation of broadband NIR reflectance at high biomass. The seasonal variability of broadband NDVI and NIRv remarkably captured the seasonality of vegetation phenology, evaporative fraction, GPP and rainfall in different ecosystems. Although saturation of GPP at high NDVI was evident, a linear relationship between broadband NIRv times incident PAR versus GPP indicated the effectiveness of NIRv-based approach to capture the hidden light use efficiency impacts on GPP. Our study concludes that inexpensive measurement of R g and PAR components can provide reliable information on NDVI, NIRv, and GPP uninterruptedly. This enhances the sensing capability of flux tower sites without requiring additional spectrometer measurements. The proposed in-situ vegetation indices make a compelling case on using radiation signals for handshaking between ecosystem-scale measurements and remote sensing observables relevant to carbon uptake. • Shortwave radiation and PAR measurements transformed into a broadband NDVI and NIRv. • Broadband NDVI highly correlated with NDVI from Planet, Sentinel-2, Landsat8/9. • PAR*NIRv linearly explained GPP variability across ecosystems of varying water stress. • Broadband NDVI from proximal sensing is a proxy of satellite NDVI at flux tower sites. [ABSTRACT FROM AUTHOR]

Monitoring soil erosion in the Arctic tundra is complicated by the highly fragmentated nature of the landscape and the limited spatial resolution of even high-resolution satellite data. The expansion of shrubs across the Arctic has led to substantial changes in vegetation composition that alter the spectral reflectance and directly affect vegetation indices such as the normalized difference vegetation index (NDVI), which is widely applied for environmental monitoring. This change can mask soil erosion if datasets with too coarse spatial resolutions are used, as increases in NDVI driven by shrub expansion can obscure concurrent increases in barren land cover. Here we created land cover maps from a multispectral uncrewed aerial vehicle (UAV) and land cover survey and assessed satellite imagery from PlanetScope, Sentinel-2 and Landsat-8 for several areas in north-eastern Iceland. Additionally, we used a novel application of the Shannon evenness index (SHEI) to evaluate levels of pixel mixing. Our results show that shrub expansion can lead to spectral confusion, which can obscure soil erosion processes and emphasize the importance of considering spatial resolution when monitoring highly fragmented landscapes. We demonstrate that remote sensing data with a resolution < 3 m greatly improves the amount of information captured in an Icelandic tundra environment. The spatial resolution of Landsat data (30 m) is inadequate for environmental monitoring in our study area. We found that the best platform for monitoring tundra land cover is Sentinel-2 when used in combination with multispectral UAV acquisitions for validation. Our study has the potential to improve environmental monitoring capabilities by introducing the use of SHEI to assess pixel mixing and determine optimal spatial resolutions. This approach combined with comparing remote sensing imagery of different spatial and time scales significantly advances our comprehension of land cover changes, including greening and soil degradation, in the Arctic tundra. • Shrub expansion can mask increases in eroded areas in remote sensing data. • Shannon evenness index is a useful metric to assess pixel mixture. • Spatial resolutions < 3 m show a significant increase in information gain in Iceland. • Sentinel-2 performed best in combination with UAV in Arctic tundra monitoring. [ABSTRACT FROM AUTHOR]

Remote sensing applications in biodiversity research often rely on the establishment of relationships between spectral information from the image and tree species diversity measured in the field. Most studies have used normalized difference vegetation index (NDVI) to estimate tree species diversity on the basis that it is sensitive to primary productivity which defines spatial variation in plant diversity. The NDVI signal is influenced by photosynthetically active vegetation which, in the savannah, includes woody canopy foliage and grasses. The question is whether the relationship between NDVI and tree species diversity in the savanna depends on the woody cover percentage. This study explored the relationship between woody canopy cover (WCC) and tree species diversity in the savannah woodland of southern Africa and also investigated whether there is a significant interaction between seasonal NDVI and WCC in the factorial model when estimating tree species diversity. To fulfil our aim, we followed stratified random sampling approach and surveyed tree species in 68 plots of 90 m × 90 m across the study area. Within each plot, all trees with diameter at breast height of >10 cm were sampled and Shannon index − a common measure of species diversity which considers both species richness and abundance − was used to quantify tree species diversity. We then extracted WCC in each plot from existing fractional woody cover product produced from Synthetic Aperture Radar (SAR) data. Factorial regression model was used to determine the interaction effect between NDVI and WCC when estimating tree species diversity. Results from regression analysis showed that (i) WCC has a highly significant relationship with tree species diversity ( r 2 = 0.21; p < 0.01), (ii) the interaction between the NDVI and WCC is not significant, however, the factorial model significantly reduced the error of prediction (RMSE = 0.47, p < 0.05) compared to NDVI (RMSE = 0.49) or WCC (RMSE = 0.49) model during the senescence period. The result justifies our assertion that combining NDVI with WCC will be optimal for biodiversity estimation during the senescence period. [ABSTRACT FROM AUTHOR]

Most uses of remotely sensed satellite data to characterize wildlife habitat have used metrics such as mean NDVI (Normalized Difference Vegetation Index) in a year or season. These simple metrics do not take advantage of the temporal patterns in NDVI within and across years and the spatial arrangement of cells with various temporal NDVI signatures. Here we use 13 years of data from MODIS (Moderate Resolution Imaging Spectroradiometer) to bin individual MODIS pixels (5.3 ha) into phenoclasses, where each phenoclass consists of pixels with a particular temporal profile of NDVI, regardless of spatial location. We present novel procedures that assign sites to phenoclusters, defined as particular composition of phenoclasses within a 1 km radius. We apply these procedures to Mexican spotted owl ( Strix occidentalis lucida ) nesting locations in the Sacramento Mountain range in south-central New Mexico. Phenoclasses at owl nest sites and phenoclusters around owl nest sites differed from those at and around points randomly placed in forest types that are known to support nesting owls. Stand exam data showed that the phenoclasses associated with owl nest sites are dominated by Douglas-fir ( Pseudotsuga menziesii ) and white fir ( Abies concolor ). The availability of phenoclusters and phenoclasses on Mescalero Apache tribal lands differed from those on adjacent National Forest lands within the Sacramento Mountain, consistent with different elevations and forest management practices. Nonetheless owls predominately used the same phenoclasses and phenoclusters in both land ownerships. MODIS phenoclasses and phenoclusters offer a useful means of remotely identifying forest conditions suitable for wildlife. Because the remote sensing data are freely available and regularly updated, they can be part of a cost effective approach to monitor and assess forested wildlife habitat over large temporal and spatial scales. [ABSTRACT FROM AUTHOR]

This study examines the impact of green space health on local flooding based on the analysis of eighty-two watersheds in four Texas metropolitan statistical areas: Dallas, Houston, San Antonio, and Austin. The runoff records in October 2007 and October 2012 were selected for the assessment. The study met the methodological challenge posed by comparison by using the Normalized Difference Vegetation Index (NDVI) datasets produced based on the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery of the 250-m resolution as a proxy to represent the health of green space. Two linear regression models were employed to explain the variation in mean daily runoff depth in 2007 and 2012, while controlling multiple contextual variables.Results indicate that watersheds containing healthier green spaces were likely to generate lower amounts of runoff in both periods. Standardized coefficients of green space health also show that the NDVI is a powerful and significant predictor to explain variation in runoff. These findings illustrate the important role of urban green spaces in attenuating local flooding and may provide planners and decision-makers with a method to consider, using this kind of objective greenery index in further developing local and regional green infrastructure and land-use plans. [ABSTRACT FROM AUTHOR]

• NDVI time series predictions for the Yellow River Basin, China. • Proposed multivariate Long-Short Term Memory neural network incorporating climate elements. • The common constraining effects of multiple climatic factors on NDVI are fully accounted for in the model proposed in the study. Accurate prediction of the trend of Normalized Difference Vegetation Index (NDVI) time series in the Yellow River Basin (YRB) is crucial for the assessment of the hydrological and ecological environment in this region. Currently, the NDVI time series prediction model is primarily based on traditional models and single-variable neural network models. Nevertheless, these models present challenges in considering the limitations of multiple factors, causing the NDVI time series prediction results to lack reliability. To predict NDVI time-series in the YRB of China, this study constructed a multilayer multivariate Long-Short Term Memory (LSTM) neural network model including climatic components. The initial important climatic elements in this region were identified using GeoDetector. Then, the relationship between NDVI and climatic factors in the YRB of China is established. Finally, numerical scale data are used to train and predict a multilayer multivariate LSTM model with climatic components. According to the results, the three-layer multivariate LSTM neural network NDVI time series prediction model developed in this study has the best performance among the evaluated indices. When compared to existing time series prediction models, the proposed model in this study takes into account the common constraint effect of various climate factors on NDVI. This leads to a significantly improved prediction accuracy, presenting new opportunities for enhancing the prediction model. By analyzing the NDVI time series prediction outcomes for the YRB, it has been determined that the ecological environment of the area will continuously improve in the future. This study offers significant technological and theoretical backing for assessing the hydrological and ecological environment of the YRB and comparable ecologically vulnerable regions in China. [ABSTRACT FROM AUTHOR]

• The percentile method in statistics was introduced into the crop growth monitor. • Many years' crop NDVI data make the percentile lookup tables more reasonable. • Division of large monitoring areas reduced the effects of spatial heterogeneity. • Crop NDVI percentiles had a good relationship with its aboveground part dry weight. The Normalized Difference Vegetation Index (NDVI) is a widely used remote sensing indicator for crop growth monitoring, farmland management and crop production prediction. Traditional monitoring methods that are based on the NDVI difference (d NDVI) are sensitive to variations in the crop growth status in the reference year and to the classification standard to obtain crop growth "grades", however, they are only applicable for the same crop area. To overcome this limitation, this study developed a new approach that is based on crop NDVI percentiles (p NDVI) for real-time monitoring of crop growth. The method utilizes NDVI data for the same day in the past five years over the same region and is based on crop distribution maps of these years, thereby creating a large sample of crop NDVI. According to the lookup table for each NDVI and its percentile (P %), which were retrieved from this sample, the current NDVI value is converted to its P and used for real-time growth evaluation, and the P score indicates that the growth of the crop at this pixel on the monitoring day is better than the P % of the crops for the whole monitoring region over the last 5 years. This approach was applied and validated for a representative agricultural region in the North China Plain (NCP), and winter wheat was used as the test crop. The results were satisfactory, with a significant and sound correlation between the scores that were obtained by the p NDVI method and the sampled winter wheat aboveground biomass. The correlation was higher for the p NDVI compared to d NDVI , although the correlation was significant for both methods. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • 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. [ABSTRACT FROM AUTHOR]

Curve fitting of noisy NDVI time series is studied in this research. Six fitting functions with numbers of parameters from 3 to 10 are assessed: piecewise linear, asymmetric Gaussian, double logistic, Fourier series, a polynomial and a cubic spline. Root mean square error, maximum error and a number of incorrect curve fitting procedures are used as the quality criteria. It has been found that the cubic spline is the best fitting function and can be used to generate NDVI time series. Examples of generated NDVI time series by data of the previous years are realized. Generated data can be used for early crops identification by NDVI time series when learning sample of the current year is small. [ABSTRACT FROM AUTHOR]

Topography is a key factor affecting numerous environmental phenomena, including Arctic and alpine aboveground biomass (AGB) distribution. Digital Elevation Model (DEM) is a source of topographic information which can be linked to local growing conditions. Here, we investigated the effect of DEM derived variables, namely elevation, topographic position, radiation and wetness on AGB and Normalized Difference Vegetation Index (NDVI) in a Fennoscandian forest–alpine tundra ecotone. Boosted regression trees were used to derive non-parametric response curves and relative influences of the explanatory variables. Elevation and potential incoming solar radiation were the most important explanatory variables for both AGB and NDVI. In the NDVI models, the response curves were smooth compared with AGB models. This might be caused by large contribution of field and shrub layer to NDVI, especially at the treeline. Furthermore, radiation and elevation had a significant interaction, showing that the highest NDVI and biomass values are found from low-elevation, high-radiation sites, typically on the south-southwest facing valley slopes. Topographic wetness had minor influence on AGB and NDVI. Topographic position had generally weak effects on AGB and NDVI, although protected topographic position seemed to be more favorable below the treeline. The explanatory power of the topographic variables, particularly elevation and radiation demonstrates that DEM-derived land surface parameters can be used for exploring biomass distribution resulting from landform control on local growing conditions. [ABSTRACT FROM AUTHOR]

Abstract To explore precision farming profits, the variability within a plot can be evaluated using digital technology by different remote means. The objectives of this study were to determine crop coverage (CC) of soybean (Glycine max (L.) Merril) with Normalized Difference Vegetation Index (NDVI) data obtained by digital photographs on the field and from the satellites LANDSAT (7 and 8), with an overpass each 16 days and a pixel of 30 m, and PROBA-V, which has daily frequency and 100 m of spatial resolution, in order to evaluate productivity differences between sectors of a 45 ha rainfed plot located at south of Córdoba city, Argentina. In the plot, sowed on 22/11/2014 and harvested on 10/04/2015, 16 sampling areas were established to record periodically photographs with a modified camera and, in 8 of them, supplementary crop information. A non-linear model was developed from NDVI data of digital camera (NDVI C) to estimate the soybean CC that showed an appropriate predictive performance. Furthermore, NDVI data of LANDSAT (7 and 8) (NDVI L) and PROBA-V (NDVI P-V) were also applied to estimate CC, resulting in models whose structure and accuracy was similar to that obtained with the digital camera (R2 = 0.956 and 0.939, respectively). According to the radiometric information the two instruments provide, the digital images classification procedure to determine CC requires increasing the threshold from 0.0 to 0.05 when soybean progresses towards the maturation and senescence stages and green material is mixed with the senescent one. Growing conditions were very favorable for soybean in 2014–2015, since precipitation (PP) not only showed a marked continuity with 60 rainy days during the cycle, but also 642 mm accumulated in this period far exceeded maximum evapotranspiration (ETmax) of 389 mm. The CC had a major development in all sectors, maintaining a complete coverage condition for more than 50 days during most of the reproductive stage. However, prevalent overcast sky restricted significantly solar radiation (SR) and reduced potential yield (PY) to an average value close to 6000 kg ha−1 which, according to the plot yield map, produced a reduced yield gap (YG) between 10.6 and 19.8%. From the proposed model and with the NDVI data of LANDSAT 7 (NDVI 7), soybean CC was estimated in the same plot for 2010–2011. Water availability were less favorable in this case, with accumulated values of 584 mm and 460 mm, for PP and ETmax, respectively, while a higher availability of SR during the crop season increased notably PY that reached a range between 7347 and 8224 kg ha−1. Moreover, lower water availability was evidenced increasing YG in the plot (40–53%). From the spatial evaluation carried out, only one-third of the plot located at the south reached the highest productivity in both crop seasons, leaving open the question about the weather influence in each productive cycle with respect to the effectiveness of the site-specific management. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new integrated framework was proposed for simulating NDVI patterns precisely. • Social factors caused encroachment on growth space through changing land use. • Natural factors affected NDVI through changing growth conditions of vegetation. • Social and natural factors jointly shaped spatial aggregations of NDVI patterns. • SD-FLUS and RF models were integrated into nonlinear socio-natural system analysis. Due to climate change and socio-economic development, the growth and distribution of wetland vegetation have been destroyed, resulting in a degradation trend of wetland ecosystem. It is critical to simulate variations in the normalized difference vegetation index (NDVI) patterns under changing environments to guide targeted restoration and protection. Considering that current NDVI pattern simulation focused only on the linear driving forces of nature or society and had a low simulation efficiency, an integrated simulation framework with dual society-nature drives was proposed. In the framework, the spatial and NDVI patterns were simulated by system dynamics-future land use simulation (SD-FLUS) and random forest (RF) models, respectively. Baiyangdian Wetland in North China was taken as a case study, and its typical vegetation—reed—was taken as the simulation object. The NDVI patterns were simulated under the effects of socio-economic development, climate change and ecological protection policies. The results showed that the spatial patterns were affected by social factors, while the NDVI patterns were affected by natural factors. The spatial aggregations of the NDVI were affected by social and natural factors. Besides, the growth space of reeds was easily encroached under the existing development and protection policies. If ecological protection policies were implemented, the reed area would be restored, but the growth status would be poorer, requiring further protection measures and more attention to the growth status of reeds in sensitive areas. This study provides a new research perspective for simulating NDVI patterns and scientific supports for refined management of wetland vegetation. [ABSTRACT FROM AUTHOR]

The integration of remote sensing data and vegetation indices can enable the investigation of dynamic changes in ecological environments. This study utilized SPOT satellite images from different periods as raw materials and combined geographic information system (GIS), normalized difference vegetation index (NDVI), and related statistical analyses to examine the condition of the ecological environment at the main terrestrial attractions of Kenting National Park in Taiwan. The results showed that changes in the ecological environment at the main attractions are presented to varying degrees on spatial-temporal scales. The changes are also relevant with respect to the numbers of artificial facilities and tourists. Moreover, change analysis via RGB WFM (write function memory insertion) can create spatial layouts for management. Well-conserved natural resources for sustainable development play a key role in attracting tourists. This study's investigation of the change in the main terrestrial attractions in the ecological environment of Kenting National Park may facilitate the preservation of natural resources. [ABSTRACT FROM AUTHOR]

• A robust NDVI estimation RF model was established by inputting SAR and auxiliary environmental data. • An improved feature importance measurement method based on RF was proposed to suggest model input parameters. • An ensemble-based uncertainty quantification method was proposed to optimize NDVI time series construction results. • Temperature-related data can effectively improve the accuracy and generalization ability of the SAR-NDVI relationship model. • The weighted least squares (WLS) approach outperformed the baseline method in NDVI time series construction. Satellite-based Normalized Difference Vegetation Index (NDVI) time series data are conducive for near-real-time (NRT) monitoring of crop progress, but they commonly suffer from data gaps due to cloudy weather conditions. Fortunately, Synthetic Aperture Radar (SAR) data, which are cloud-insensitive, has high potentials to fill these data gaps. However, the relationship between NDVI and SAR data is not generic and affected by various factors (e.g., crop canopy structure and phenology, soil exposure and humidity). Therefore, it is worth exploring to evaluate the relationships and develop a more robust SAR-NDVI fusion method for NDVI time series construction. In this study, we proposed and evaluated an ensemble-based data fusion framework that accounts for these factors, based on which dense NDVI time series for crop monitoring were constructed. The framework consisted of three steps: (1) NDVI was predicted from Sentinel-1 SAR data and auxiliary environmental factors using a random forest (RF) model; (2) an improved feature importance measurement method was proposed to reveal the different contributions of multisource data to the modeling, and suggestions for selecting optimal input parameters were presented; (3) the uncertainty of the predicted data was quantified by an ensemble-based method and incorporated into a Weighted Least Squares (WLS) method to construct dense NDVI time series. Results showed that the RF models were generally improved by the auxiliary data and achieved a satisfactory accuracy of NDVI estimation (R2 > 0.93, RMSE < 0.075) for both corn and soybean crops in Southwestern Ontario, Canada. The proposed method performed well in filling data gaps in both the vegetative and reproductive stages for corn and soybean, providing a practical and promising solution for continuous crop monitoring. [ABSTRACT FROM AUTHOR]

Abstract To provide a long-term perspective of drought variability from 1981 to present, we develop a new monthly agriculturally-based drought index called the Integrated Scaled Drought Index (ISDI). This index integrates Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) data (available from 1981 to present), land surface temperature (LST), precipitation (PCP), and soil moisture (SM) data from North American Regional Reanalysis (NARR) project (available from 1979 to present). This new agriculturally-based drought index incorporates important components controlling agricultural drought, particularly soil moisture, for which there are limited in-situ observations through time and across space. The optimum weights for each component of the ISDI are determined by correlation analysis with commonly used in-situ drought indices, such as the Palmer Drought Severity Index (PDSI), the Palmer Modified Drought Index (PMDI), the Palmer's Z-index, and the Standardized Precipitation Index (SPI) at different time scales. Resulting ISDI maps are also visually compared with United States Drought Monitor (USDM) and Vegetation Drought Response Index (VegDRI) maps for empirical validation. ISDI shows strong agreement with these two national-wide drought monitoring systems. ISDI also shows strong linear correlations with corn yield anomalies in July and with soybean yield anomalies in August and strong spatial correspondence with county-level corn/soybean yield anomalies during major drought events. These results illustrate the robustness and usefulness of ISDI. This agriculturally-based drought index integrates the benefits of numerical model simulation and remote sensing technology to account for interannual variability of drought for the longest possible time-frame in the satellite era. This long-term monthly drought index provides a longer historical perspective of drought impacts since 1981. It can be generalized to incorporate other satellite data or in-situ observation and has the potential for operational drought monitoring and assessment. Highlights • ISDI incorporates important components controlling agricultural drought. • ISDI integrates the benefits of remote sensing technology and numerical model. • ISDI covers the longest possible time-frame in the satellite era. • We prove the robustness and usefulness of ISDI using various sources of information. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI), derived from multi-spectral (MS) images, is a metric widely used to evaluate the growth status of vegetation in remote sensing. Existing methods for generating high-resolution (HR) NDVI are typically based on pan-sharpening, which often result in huge errors even in case of tiny spectral distortions. To overcome this challenge, from a novel perspective, this paper introduces an HR vegetation index (HRVI) to realize direct fusion with a low-resolution NDVI rather than pan-sharpening an HRMS image. In particular, we propose a two-branch network based on the multi-scale and attention mechanism, termed as NDVI-Net, to obtain the HRNDVI with small distortion. In our network, the multi-scale channel enhancement blocks are used in both NDVI and HRVI branches, in which multi-scale convolution is used to capture structural information with different reception fields and channel attention mechanism is adopted to perform feature selection. Meanwhile, the spatial features are injected unidirectionally from the HRVI into NDVI branches, so as to further improve the quality of features in the NDVI branch. Subsequently, the spatial intensify block is adopted only in the NDVI branch to implement selective enhancement for the previously obtained features along the spatial position, strengthening the retention of local detail features. Finally, HRNDVI is reconstructed based on the high-representation NDVI features, which contains clear texture details and precise intensity. Experimental results demonstrate the significant advantage of our method over the current state-of-the-art in terms of both subjective visual effect and quantitative metrics. Moreover, we apply the HRNDVI generated by our method to vegetation detection and enhancement, and land cover mapping in remote sensing, which can achieve the best performance. [ABSTRACT FROM AUTHOR]

Vegetation dynamics play a crucial role in understanding the complex interactions between climate variability, ecosystem changes and human land use. In this study, we use the Hindcasting Ecosystems Model (HEMO) to reconstruct the paleo Normalized Difference Vegetation Index (paleo-NDVI), which provides valuable insights into the historical vegetation patterns. Our research focuses on the El Alto Ancasti mountain range, a region characterized by its rich environmental diversity, and archaeological evidence of pre-Hispanic agricultural village landscapes, showcasing the knowledge and management of diverse environments and their fluctuations. To reconstruct the paleo-NDVI, we employed a Neuronal Network trained with two datasets. First, we utilized NDVI data extracted from AVHRR/NOAA satellite images spanning from 1982 to 2015, representing 8 km pixels from the Global Inventory Modeling and Mapping Studies. Second, we incorporated growth ring chronologies from Northwest Argentina sourced from the International Tree Ring Data Bank, enabling the calibration of the NDVI-ring relationship. This methodology allowed us to reconstruct the paleo-NDVI back to 442 AD and to calculate anomalies. Our analysis revealed significant negative anomalies around 600 AD, coinciding with previous research conducted in the nearby Ambato valley. These anomalies may be attributed to the eruption of Tres Cruces volcano in Catamarca during that period. Additionally, extensive positive anomalies emerged from 1600 AD onwards, which align with anomalies detected in earlier studies. The eruption of Huaynaputina volcano in southern Peru in 1600 AD is a possible factor contributing to these positive anomalies. Ongoing investigations focusing on the paleoenvironment and human occupations in the El Alto-Ancasti area aim to provide further insights into how these detected changes could have influenced environmental management decisions. By integrating multidisciplinary approaches, we can better comprehend the complex relationship between vegetation dynamics, climatic events and human activities, ultimately enhancing our understanding of past environmental transformations and their implications for sustainable management strategies. • Use of tree rings and NDVI data for paleoenvironmental retrodictions by means of artificial neural networks (NN). • Retrodiction of paleo-NDVI up to 442DC in Alto Ancasti (Catamarca, Argentina) by means of artificial neural networks (NN). • Coherence between paleo-NDVI retrodiction of Sierra Alto Ancasti with that of the Ambato Valley. • Finding of relation between paleo-NDVI variations and climate forcings such as volcanic events and ENSO. • Evidence of interrelations between ecosystem dynamics and environmental management of past occupations. [ABSTRACT FROM AUTHOR]

Deterioration of the environmental quality of mangrove ecosystem must be identified as early as possible for management and threat mitigation. Degraded mangrove areas have to be distinguished from healthy ones. Low value of Mangrove Index (MI) corresponded to the degraded mangrove area where dominated by two species (Derris trifoliata and Acanthus ilicifolius), which are indicators of mangrove degradation at Segara Anakan mangrove area. The Normalized Difference Vegetation Index (NDVI) failed to detect the degaradation due to existing of these species by detecting as high index value. The study was designed to (1) test the performance of MI at Alas Purwo mangrove area which categorized as a non-degraded mangrove forest, (2) compare the spatial pattern of MI images with that of NDVI images, and (3) further investigate the ability of MI to detect areas of degraded mangrove when it occurs in only small areas. We used Landsat-8 OLI imagery because it includes the short-wave infrared (SWIR) bands required to generate MI. Both direct visual comparison and correlation analysis of the resulting maps were conducted to evaluate the ability of these indices to detect degraded and non-degraded areas of mangroves. In general, both indices detected a similar pattern, which is contrary to the results from Segara Anakan images. However, a difference value between MI and NDVI of >0.2 indicated the presence of small areas of vegetation with a spectral reflectance similar to that of species indicating degradation at Segara Anakan. This finding was confirmed by field observation of mangrove species. A difference of >0.2 between MI and NDVI indicates mangrove degradation for small areas. This study provides a new tool to determine the condition of mangrove forest remotely, allowing for rapid and targeted management action to tackle degradation. • Without land-use conversion, degraded mangrove areas are undetected using NDVI. • MI indicates the different characteristic of habitat adaptation of mangrove species. • Mangrove degradation can be detected from the combination of MI and NDVI. [ABSTRACT FROM AUTHOR]

[Display omitted] • Validated and compared the effects of multiple spatial and temporal fusion methods on annual-scale NDVI reconstruction. • Proposed a new idea to improve the accuracy of traditional spatial and temporal fusion methods. • Analyzed the spatial and temporal changes of vegetation in Danjiang River Basin from 2001 to 2021. • Analyzed the influence of different driving factors on vegetation growth in Danjiang River Basin. The Normalized Difference Vegetation Index (NDVI) is an essential metric in vegetation monitoring for remote sensing applications. While there are numerous long-term low-resolution NDVI datasets available there remains an unmet need for high-resolution NDVI reconstructions over extended time frames. Existing research has not comprehensively assessed the efficacy of various temporal fusion techniques for NDVI reconstruction at large regional scales. Traditional Spatiotemporal Image Fusion (TSTIF) methods often suffer from limited fusion accuracy due to input data quality constraints. To address these limitations this study introduces an innovative Deep Learning-Enhanced Spatiotemporal Fusion Method. Deep learning algorithms are employed to refine the spatial resolution of input data thereby facilitating the separation of complex image elements located at feature boundaries. This improved approach significantly enhances fusion accuracy as validated against five established TSTIF techniques through empirical analysis. As a case study we generate long-term Fractional Vegetation Cover (FVC) datasets to investigate the ecological dynamics of the Danjiang River Basin. Our findings reveal substantial gains in NDVI reconstruction accuracy through the incorporation of deep learning into traditional fusion techniques. Among the methods tested the STRUM algorithm showed the greatest improvement with its R2 value increasing from 0.872 to 0.894 and a consistent reduction in Root Mean Square Error (RMSE). The FSDAF technique emerged as the most effective boasting an R2 value of 0.953 and an RMSE of 0.012. These enhanced NDVI datasets provide more accurate representations of the basin's vegetation cover thus enriching long-term observational archives and facilitating further quantitative remote sensing analyses. Moreover we report a 21-year mean FVC of 0.702 for the Danjiang River Basin characterized by a "north-high south-low" distribution that varies seasonally. Improvements in vegetation cover were observed in Xichuan and Neixiang counties while environmental degradation is evident in the broader Danjiang River watershed. Key factors influencing these spatial variations include elevation and soil composition in addition to climate variables such as rainfall and temperature. Infrastructure projects like the South-to-North Water Diversion Project and targeted ecological conservation policies have also positively impacted vegetation within the basin. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is often used as a marker of surrounding greenness in epidemiological studies aiming to evaluate the health effects of green space in urban settings. However, it is not clear the relationship between built environment characteristics, including green space, and NDVI. We aimed to evaluate the relationship between built environment characteristics, based on land-use and land-cover maps, and NDVI as a marker of surrounding greenness in the city of Barcelona. We used data from an already existing cohort of pregnant women in Barcelona (N = 8402). NDVI was derived and averaged within buffers of 100 m and 300 m for each participant, and categories of the built environment (m 2 ) were derived from land-use and land-cover maps of Barcelona. We conducted ANOVA models to calculate the contribution (R 2 ) of each land-use (or land-cover) category. The variability in NDVI in Barcelona was mainly explained by urban green (R 2 between 0.32 and 0.53) and natural green areas (R 2 between 0.19 and 0.52), although for the latter less than 4% of the participants were exposed to this. Both land-use and land-cover maps explained NDVI at 300 m better (full models explaining 76% and 78%, respectively) than at 100 m buffers (full models explaining 55% and 54%, respectively). Results of the present study indicate that NDVI can be a useful greenness metric depending on the hypothesis and area of study. However, for certain sizes of study areas (buffers smaller than 100 m), NDVI might have a lower predictive value. Results of the present study should be replicated in studies from other cities with different urban characteristics and climate conditions. [ABSTRACT FROM AUTHOR]

This article presents an enhanced Change Detection method for the analysis of Satellite image based on Normalized Difference Vegetation Index (NDVI). NDVI employs the Multi-Spectral Remote Sensing data technique to find Vegetation Index, land cover classification, vegetation, water bodies, open area, scrub area, hilly areas, agricultural area, thick forest, thin forest with few band combinations of the remote sensed data. Land Resources are easily interpreted by computing their Normalized Difference Vegetation Index for Land Cover classification. Remote Sensing data from Landsat TM image along with NDVI and DEM data layers have been used to perform multi-source classification. The Change Detection method used was NDVI differencing. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4 and 0.5. The Simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for policy makers in decision making. The Vegetation analysis can be helpful in predicting the unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies. From the empirical study, the forest or shrub land and Barren land cover types have decreased by about 6% and 23% from 2001 to 2006 respectively, while agricultural land, built-up and water areas have increased by about 19%, 4% and 7% respectively. Curvature, Plan curvature, Profile curvature and Wetness Index areas are also estimated. [ABSTRACT FROM AUTHOR]

Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [ G CC ]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVI C ) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVI C was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVI C values and recommend the use of site-specific NDVI from MODIS in order to scale NDVI C . We also compared G CC extracted from red-green-blue images to NDVI C and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVI C lags behind G CC in deciduous broad-leaf forests and grasslands, suggesting that G CC is more sensitive to changes in leaf color and NDVI C is more sensitive to changes in leaf area. In evergreen forests, NDVI C peaks later than G CC in spring, probably tracking the processes of shoot elongation and new needle formation. Both G CC and NDVI C can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVI C is more comparable than G CC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems. Our results demonstrate that NDVI C is in excellent agreement with NDVI obtained from spectral measurements, and that NDVI C and G CC can complement each other in describing ecosystem phenology. Additionally, NDVI C allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three NDVI vegetation correction schemes with CRNS were provided. • The ratio of parameter N 0 between different landscapes reached 1.42. • N 0 was negatively correlated with NDVI (y = 373.9 (x + 0.015)−0.136, R2 = 0.77) • Three methods reduced RMSE from 0.093 to 0.063, 0.054, and 0.032 g g−1, respectively. Cosmic-ray neutron sensors (CRNS) are a powerful tool for measuring soil moisture on a hectometer scale, and mobile cosmic-ray neutron technology holds significant importance for upscaling soil moisture. However, vegetation strongly affects with CRNS soil moisture measurements. Variations in vegetation over time change how the CRNS is affected, which is more challenging than a stable vegetation cover, particularly during CRNS roving, where one crosses many vegetation covers. To correct vegetation, we hypothesized that Normalized Difference Vegetation Index (NDVI) can represent vegetation hydrogen pools. From 2020 to 2023, at the 74 plots with varying vegetation cover near the Qilian Mountains, the experiments for soil moisture measurements were conducted in shrubs, forests, deserts, farmlands, and grasslands using a Cosmic-ray Neutron Rover. The measured neutron intensity clearly varied across different landscapes, and the calibrated parameter N 0 differed among 74 plots. The variations in vegetation cover resulted in low measurement accuracy for CRNS. All three NDVI vegetation correction methods (NDVI-θ NDVI method, NDVI-NDVI ave method, and NDVI-N 0 method) offered by the study could improve accuracy of soil moisture measurements. In the optimal NDVI-N 0 method, the relationship between NDVI and N 0 was established using a power function y = 373.9 (x + 0.015)−0.136 (R2 = 0.77), which had the capability to decrease the root mean square error between oven-dried and measured soil moisture from 0.093 to 0.032 g g−1 in our study area. Employing NDVI instead of biomass for CRNS vegetation correction can substantially reduce workload and effectively enhance CRNS soil moisture measurements, showing the potential for upscaling soil moisture. [ABSTRACT FROM AUTHOR]

• NDVI has been estimated from sRGB co-registered images. • NDVI was estimated using a shallow-regressive neural network performing a pixel-to-pixel regression. • Calibrated RGB images have been used obtaining a correlation with NDVI in validation r = 0.91. • The model was applied to a dataset acquired with a different non-co-registered obtaining a r = 0.71. • The obtained model is ready to be applicable being available as supplementary material (GitHub). The Normalized Difference Vegetation Index (NDVI) is the most common index to measure vegetation in agriculture and create classified prescription maps used for several purposes. It is obtained as the ratio between information from the visible and near infrared spectral bands. The calculation of NDVI often uses very expensive hyperspectral and multispectral optical sensors. This study aims to develop an efficient model to extract NDVI data from RGB images. During the work were acquired images, of different plant species at different vegetation status, through a snapshot hyperspectral camera (Specim IQ) featuring natively superimposed RGB sensor whose images were calibrated a posteriori (sRGB). NDVI was predicted from sRGB images using a Shallow-regressive neural network that performs a pixel-pixel regression. The model has been then tested on around 1000 drone images acquired by a 6x Sentera sensor to test the efficiency of the applied model. The model has been used to estimate the numerical value of NDVI and the classes produced by a clustering method (k -means). The results of this work showed that the calibrated images had a very high correlation with the NDVI in validation (r = 0.91), maintaining good performances (r = 0.71) when applied to a set of data acquired with a different non-co-registered sensor. Therefore, for the first time this work has shown that the health of vegetation through the NDVI could be calculated using unexpensive RGB device adopting a pixel-pixel regression AI approach. The approach shows its importance especially for small-sized farms where profit would not allow for the budget to access multispectral cameras and heavy carriers (i.e., UAV). [ABSTRACT FROM AUTHOR]

• NDVI from pure canopy pixels is more correlated to leaf chlorophyll than to LAI. • The simulated satellite NDVI is more correlated to LAI than to leaf chlorophyll. • Canopy volume estimated from UAV images is a better predictor of LAI than NDVI. Despite the wide use of the Normalized Difference Vegetation Index (NDVI) in precision viticulture, there are no studies aimed at discriminating the contribution of plant biomass from that of leaf chlorophyll on canopy NDVI. Leaf area index (LAI) and leaf chlorophyll (Chl) were concomitantly monitored by ground measurements and projected canopy area (PCA), canopy volume (CV) and NDVI by high resolution UAV multispectral images in fully productive "Sangiovese" grapevines either grown in containers or in the field and subjected to different irrigation regimes over two consecutive years. NDVI values calculated from only vine canopy pixels (NDVI UAV) and NDVI obtained from mixed ground-canopy pixels (simulated Satellite NDVI, NDVI SAT) were both evaluated as potential predictor of LAI and leaf Chl concentration. The seasonal patterns of LAI and leaf Chl concentration were affected by irrigation, showing differences depending on field vs container-grown conditions. In situations where a decoupling between LAI and leaf Chl occurred, NDVI UAV and NDVI SAT showed different responses: NDVI UAV patterns strictly followed the leaf Chl ones, whereas NDVI SAT was more affected by LAI. The coefficient of determination between NDVI UAV and leaf Chl ranged between 0.51 and 0.78, that between NDVI UAV and leaf Chl from 0.01 to 0.76, depending on the irrigation-growing conditions combination. NDVI SAT was a better predictor of LAI (R2=0.69) than NDVI UAV (R2=0.42). In field-grown vines the relationships between NDVI (both UAV and SAT) and LAI was stronger than in potted ones. The relationships between LAI and PCA (R2=0.44) or LAI and canopy volume (R2=0.77) were both significant. The results allowed to confirm the two main hypotheses behind this experiment: i) leaf Chl concentration had a greater impact than LAI on NDVI values obtained from vine canopy pixels (NDVI UAV), whereas NDVI SAT was more affected by LAI; ii) The canopy volume from UAV images was a better predictor of LAI than NDVI and the resulting relationship showed a better temporal stability. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is one of the significant classification methods widely used in detecting land cover and land use changes. For this purpose, two Landsat TM images from 1990 to 2010 are used to extract NDVI values. The NDVI values are initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Afterwards, a Difference NDVI map between 1990 and 2010 is generated to negatively or positively identify the values of land cover changes. Results confirmed that the area without vegetation, such as bodies of water, as well as built up areas and barren lands, increased from 3.55% in 1990 to 7.25% in 2010. The dense vegetation area also decreased from 78.57% to 65.44%, indicating the necessity to create new policies in the city for the protection of vegetation areas during the urban and economic development in Seremban. [ABSTRACT FROM AUTHOR]

Accurate monitoring of vegetation dynamics is required to understand the inter-annual variability and long term trends in terrestrial carbon exchange in tundra and boreal ecoregions. In western North America, two Normalized Vegetation Index (NDVI) products based on spectral reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are available. The MOD/MYD13A2 NDVI product is available as a 16-day composite product in a sinusoidal projection as global hdf tiles. The eMODIS Alaska NDVI product is available as a 7-day composite geotif product in a regional equal area conic projection covering Alaska and the entire Yukon River Basin. These two NDVI products were compared for the 2012–2014 late May–late June spring green-up periods in Alaska and the Yukon Territory. Relative to the MOD/MYD13A2 NDVI product, it is likely that the eMODIS NDVI product contained more cloud-contaminated NDVI values. For example, the MOD/MYD13A2 product flagged substantially fewer pixels as “good quality” in each 16-day composite period compared to the corresponding MODIS Alaska NDVI product from a 7-day composite period. During the spring green-up period, when field-based NDVI increases, the eMODIS NDVI product averaged 43 percent of pixels that declined by at least 0.05 NDVI between 2 composite periods, consistent with cloud-contamination problems, while the MOD/MYD13A2 NDVI averaged only 6 percent of pixels. Based on a cloudy Landsat-8 scene, the eMODIS compositing process selected 23 percent pixels, while the MOD/MYD13A2 compositing process selected less than 0.003 percent pixels. Based on the results, it appears that the MOD/MYD13A2 NDVI product is superior for scientific applications based on NDVI phenology in the tundra and boreal regions of northwestern North America. [ABSTRACT FROM AUTHOR]

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data. [ABSTRACT FROM AUTHOR]

Many cities around the world have undertaken greening programs (e.g., planting urban trees, adding or enhancing parks, providing incentives for green roofs) to benefit from the amenities of urban green spaces. To evaluate the economic benefits of these programs, hedonic pricing is the approach of choice but its application depends on the availability of adequate land cover data. In this context, this paper contrasts results from Cliff–Ord spatial hedonic models applied to single family houses in Los Angeles (California, USA) with land use data at two spatial resolutions: (1) high resolution (0.6 m) but relatively expensive classified land cover (CLC) data; and (2) moderate-resolution (30 m) but low-cost Normalized Difference Vegetation Index (NDVI) data from satellite imagery. Our results show that elasticities of price with respect to NDVI and with respect to tree canopy cover (TCC) are weakly correlated and often give conflicting signals for prioritizing tree planting efforts. In addition, correlations between NDVI and CLC grass measures are low, which suggest that hedonic models based on NDVI would miss the benefits of grassy areas. Our spatial lag-Tobit models that predict tree canopy cover from NDVI, structural characteristics, neighborhood amenities, and socio-demographic variables highlight the relative importance of the latter in explaining tree canopy cover in the vicinity of single family houses in Los Angeles; these characteristics are much less important to explain parcel TCC. Overall, our results highlight the advantages of using high-resolution CLC data over moderate resolution NDVI data to estimate the economic benefits of urban greening programs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many studies have revealed that Normalized Difference Vegetation Index (NDVI) is of vital importance for research of ground surface processes and climatic changes. However, identification of the causes responsible for vegetation changes remain challenging. Using Geographical Detector, a new spatial statistical approach, individual and interactive influences of natural factors on vegetation NDVI changes were quantified for the Sichuan province. The optimal characteristics of key natural factors that are beneficial to vegetation growth were determined. The vegetation cover in Sichuan for 2000 and 2015 is good overall; areas with upper-intermediate and high vegetation cover account for more than 88% of the total area. Regions with an NDVI higher than 0.4 are noticeably transformed; regions with upper-intermediate vegetation cover (NDVI from 0.4 to 0.8) tend to decrease significantly, whereas regions with high vegetation cover (NDVI>0.8) tend to increase significantly. Spatiotemporal variation in vegetation cover is very significant. Regions with high vegetation cover are distributed in the northeast part of the Sichuan Basin and on the northwestern Sichuan Plateau, whereas regions with low vegetation cover are distributed in urban agglomerative regions in the central part of the Sichuan Basin. Soil types, elevation, and annual mean temperature can satisfactorily account for vegetation changes. Natural factors have an interactive influence on NDVI. The synergistic effect of natural factors is manifested as mutual enhancement and nonlinear enhancement, and the interaction of two natural factors strengthens the influence of each individual natural factor. Natural factors within a favourable value range or favourable landform factors serve to promote and intervene in vegetation changes, adapt to climatic changes, and buffer against the effects of vegetation changes. Image 1 • Examine spatiotemporal variation of vegetation coverage from 2000 to 2015. • Quantify individual and interactive influences of natural factors on vegetation NDVI changes. • Soil types, elevation, and annual mean temperature can satisfactorily account for vegetation changes. • Determines the optimal characteristics of key natural factors that are beneficial to vegetation growth. [ABSTRACT FROM AUTHOR]

Abstract Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. Highlights • More accurate forest maps were produced by integrating PALSAR/PALSAR-2 and MODIS NDVI images. • The missing PALSAR forest maps during 2011–2014 were reconstructed with a new spatial-temporal model. • Forest fractional maps were estimated automatically from time-series MODIS NDVI images. • The reconstructed forest maps have an overall accuracy >92%. [ABSTRACT FROM AUTHOR]

Abstract For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different types of NDVI anomalies are typically employed to assess the current state of crops and rangelands as compared to previous years. Timeliness and accuracy of such anomalies are critical factors to an effective monitoring. Temporal smoothing can efficiently reduce noise and cloud contamination in the time series of historical observations, where data points are available before and after each observation to be smoothed. With NRT data, smoothing methods are adapted to cope with the unbalanced availability of data before and after the most recent data points. These NRT approaches provide successive updates of the estimation of the same data point as more observations become available. Anomalies compare the current NDVI value with some statistics (e.g. indicators of central tendency and dispersion) extracted from the historical archive of observations. With multiple updates of the same datasets being available, two options can be selected to compute anomalies, i.e. using the same update level for the NRT data and the statistics or using the most reliable update for the latter. In this study we assess the accuracy of three commonly employed 1 km MODIS NDVI anomalies (standard scores, non-exceedance probability and vegetation condition index) with respect to (1) delay with which they become available and (2) option selected for their computation. We show that a large estimation error affects the earlier estimates and that this error is efficiently reduced in subsequent updates. In addition, with regards to the preferable option to compute anomalies, we empirically observe that it depends on the type of application (e.g. averaging anomalies value over an area of interest vs. detecting "drought" conditions by setting a threshold on the anomaly value) and the employed anomaly type. Finally, we map the spatial pattern in the magnitude of NRT anomaly estimation errors over the globe and relate it to average cloudiness. Highlights • We compute three types of near real-time coarse resolution NDVI anomalies. • Errors in near real-time anomaly estimation are assessed. • We investigate the trade-off between timeliness and accuracy. • We explore two options for the computation of near real-time anomalies • The choice of the best option depends on the type of application and the employed anomaly type. [ABSTRACT FROM AUTHOR]

Site-specific information of crop types is required for many agro-environmental assessments. The study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the Phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of >86% for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach. The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Improvement in vegetation cover in the upper reaches of the Minjiang River protects major freshwater sources and constructs ecological barriers in the upstream of Yangtze River located in southwest China. However, the spatially varying relationships between normalized difference vegetation index (NDVI) and topographic factors at multiple scales remain unclear. In this study, the two-dimensional discrete wavelet transform (2D-DWT) and geographically weighted regression (GWR) were combined to build a framework for examining the complex spatial relationships between NDVI and topographic factors at multiple scales. Based on the gridded datasets of NDVI and digital elevation model (DEM) of a small watershed located in the upper reaches of the Minjiang River situated in southwest China, the influence of topographic factors on NDVI variation in each grid and which topographic variables correlated most with the NDVI at each location were analyzed in this study. The grid data were decomposed into smooth and detail components using 2D-DWT. The results revealed that elevation, slope, and aspect exhibited different scale effects on NDVI in different wavelet components. In the smooth components, the spatial correlation of NDVI with elevation, slope, and south-facing index (SFI) of aspect increased with the decomposition scale, whereas topographic factors had different degrees of influence on NDVI. Elevation was found to be the most important topographic factor that determined the spatial distribution of NDVI on both sides of the valley, and SFI exhibited the highest effect on NDVI when elevation was higher than 2500 m. In the detail components, the spatial distribution of the most important topographic factor affecting the NDVI was not evident at a smaller scale, whereas it became obvious at 480 m scale. SFI was found to be more important than elevation and slope at a larger scale. These results will help in understanding the complex relationship between NDVI and topographic factors and determining the appropriate topographic location for ecological restoration. • A framework was built to explore the relationship between vegetation and terrain. • Terrain showed different scale effects on normalized difference vegetation index. • Elevation was the most important topographic factor in the river valley. • South-facing index was more important than elevation and slope above 2500 m. [ABSTRACT FROM AUTHOR]