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The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets. [ABSTRACT FROM AUTHOR]

Previous studies have shown that climate change and human activities play an important role in the vegetation dynamics in the Qinling-Daba Mountains of central China. However, which environmental factors including climate, topography, soil and human activities play an important role in the vegetation dynamics and its spatial pattern in the Qinling-Daba Mountains remains to be further clarified. Based on the normalized difference vegetation index (NDVI) data of the growing season from 1986 to 2019 synthesized by Landsat series satellite data on Google Earth Engine, this study aimed to further investigate the spatial pattern of NDVI and its dynamics, and clarify its environmental controlling factors in the Qinling-Daba Mountains using the methods of spatial analysis and Geodetector. The results showed that: (1) the spatial pattern of NDVI in the study area had a U-shaped NDVI distribution in latitude, anti-U-shaped patterns in longitude and with increasing altitude. (2) 2005 was the year of NDVI breakthrough increase, and the vegetation dynamics was divided into two periods according to the result of MK mutation test: the slow increasing period with an increasing rate of 0.25%/a from 1986 to 2004 (R2 0.74), and the rapid increasing period with an increasing rate of 0.30%/a from 2005 to 2019 (R2 0.92). (3) Topography regulating local hydrothermal conditions and soil enriching nutritions played more important influence on NDVI spatial pattern than climate factors (temperature and precipitation) at the regional scale. The effect of land use on NDVI change was stronger than that of climate warming (temperature), and the climate warming in recent decades played a more important role than precipitation on the NDVI dynamics. Research on vegetation patterns, changes and their environmental influencing factors will help the government and other related agencies to formulate plans or policies for infrastructure development and land management, ecological restoration.

Coal is one of the most important fossil energy sources and is ensuring global energy security. Annual maximum NDVI (Normalized Difference Vegetation Index) data is an important indicator for the research in balancing coal mining and vegetation conservation. However, the existing annual maximum NDVI data displayed lower values with temporally inconsistent and a noticeable mosaic line. Here we propose an algorithm for automatically generating the annual maximum NDVI of China's coal bases in Google Earth Engine called: Auto-NDVIcb. The accuracy of the Auto-NDVIcb algorithm has been verified with an average RMSE of 0.087 for the 14 coal bases from 2013 to 2022. Based on the proposed Auto-NDVIcb algorithm, an annual maximum NDVI dataset for all 14 coal bases in China from 2013 to 2022 was publicly released. This dataset can be fast and automatically updated online. Hence, the public dataset will continuously serve to monitor the vegetation change induced by coal mining, exploring the mechanism of vegetation degradation, and providing scientific data for developing vegetation protection policies in coal mines. [ABSTRACT FROM AUTHOR]

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

The eco-hydrological process related to vegetation on the Qinghai–Tibet Plateau is special, and the impact of snow cover on the growth of vegetation is unique and important. In this study, we analyzed the multi-year variations in the normalized difference vegetation index (NDVI) and snow cover on the Qinghai–Tibet Plateau from spatial and temporal perspectives and determined the relationship between the changes in the NDVI and snow cover. Results showed that in the last 40 years, the rate of change in the snow depth on the plateau was −0.016 mm/a, and the NDVI changed by 0.0005/a. The correlations (|R| values) between the different factors and the NDVI followed the order of precipitation (0.77) > snow depth (0.76) > temperature (0.67) > solar radiation (0.21). The responses of the NDVI to changes in meteorological elements were synchronous, whereas the opposite was found for the snow cover. The snow cover had more significant impacts on vegetation at higher elevations. The NDVI had a lag of about 2 months from the onset of the snow cover, and heavy snow events had negative impacts on the NDVI for more than 3 years. Our findings will facilitate studies of ecological vulnerability and the predictions of changes in vegetation on the plateau. [ABSTRACT FROM AUTHOR]

Glasswort (Salicornia herbacea) is an annual succulent plant that grows widely around intertidal zone of Gomishan Lagoon in eastern boarders of the Caspian Sea in Iran. Due to its medicinal, industrial, and economic values, tendency of its plantation is growing in recent years. One of the challenges to manage a glasswort farm is to know the appropriate date to harvest since glasswort seeds ripe quickly and seed fall happens considerably by little shake. One way of defining appropriate harvesting date can be found through correlating the amount of visible greenness of the plant, and it's ripening stage with NDVI values derived from remote sensing imageries . This experimental research was conducted to answer this problem by relating the changes in Glasswort visually estimated color classes and its amount of seedfall to the amount of Normalized difference Vegetation Index (NDVI) values that were obtained from Sentinel-2 remotely sensed imagery during the seedfall period of Glasswort community either in the field or in its natural habitat. The maximum NDVI values of 41 Sentinel-2 images during 2018-2020 within Glasswort phenological period were extracted and were correlated with the color class of the plant and the weight of seedfall in sample plots. Results showed a strong correlation between NDVI and brownness color class of the plant (R2=0.80) and a strong negative correlation with amount of its seed fall (R2=-0.83).

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera's NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products. [ABSTRACT FROM AUTHOR]

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Background: The time‐series data of the Normalized Difference Vegetation Index (NDVI) is a crucial indicator for global and regional vegetation monitoring. However, the current assessment of global and regional long‐term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time‐series data sets. Methods: In this study, a long time‐series monthly NDVI data set with a spatial resolution of 250 m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR (Advanced Very High‐Resolution Radiometer) time‐series NDVI products using the Random Forest (RF) downscaling model. Results: Compared to the MODIS NDVI product, the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05, respectively, with R2 values mostly above 0.7. Conclusions: The long time‐series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long‐term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Long-term, daily, and gap-free Normalized Difference Vegetation Index (NDVI) is of great significance for a better Earth system observation. However, gaps and contamination are quite severe in current daily NDVI datasets. This study developed a daily 0.05° gap-free NDVI dataset from 1981–2023 in China by combining valid data identification and spatiotemporal sequence gap-filling techniques based on the National Oceanic and Atmospheric Administration daily NDVI dataset. The generated NDVI in more than 99.91% of the study area showed an absolute percent bias (|PB|) smaller than 1% compared with the original valid data, with an overall R2 and root mean square error (RMSE) of 0.79 and 0.05, respectively. PB and RMSE between our dataset and the MODIS daily gap-filled NDVI dataset (MCD19A3CMG) during 2000 to 2023 are 7.54% and 0.1, respectively. PB between our dataset and three monthly NDVI datasets (i.e., GIMMS3g, MODIS MOD13C2, and SPOT/PROBA) are only −5.79%, 4.82%, and 2.66%, respectively. To the best of our knowledge, this is the first long-term daily gap-free NDVI in China by far. [ABSTRACT FROM AUTHOR]

Ecological protection in the Yellow River Basin (YRB) is a major strategy for China's sustainable development. Amid global warming, droughts have occurred more frequently, severely affecting vegetation growth. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) at different time scales from 2003 to 2020, this study employed the linear trend method and the Spearman correlation coefficient method to calculate the trends and correlation coefficients of NDVI and SPEI at different scales at the pixel scale and explored the spatial distribution pattern of the sensitivity of vegetation growth in the YRB to drought. The results show that: (1) NDVI and SPEI are positively correlated in 77% of the area, negatively correlated in 9%, and are positively correlated in the arid and semi-arid areas, while negatively correlated in the humid and subhumid areas. The significant negative correlation between NDVI and drought at high altitudes may be due to the fact that Gramineae vegetation is more sensitive to drought, with heat being more affected than water. (2) Urbanization has a relatively obvious impact on the distribution of drought. Extreme drought mainly occurs in the middle and upper reaches of the Wei River; severe drought mainly occurs in the central area of the Guanzhong Plain centered on Xi'an; the central area of the Loess Plateau; and the surrounding areas of the Zhengzhou-centered Central Plains City Group. (3) The NDVI showed an upward trend from 2003 to 2020, indicating an increase in vegetation density or an expansion of vegetation coverage. From the temporal trend, SPEI decreased at a rate of −0.17/decade, indicating that the entire watershed has a drought trend on an annual scale. (4) Spring NDVI is more sensitive to the water supply provided by SPEI-1, while the positive correlation between SPEI and NDVI begins to rise in June and reaches its peak in July, then starts to decline in August. In autumn and winter, NDVI is more sensitive to 3–6-month accumulated drought. (5) From the dynamic transmission laws of different levels of positive correlation, the positive impact of the 3-month accumulated drought on NDVI is most significant, and the influence of SPEI-1 on the negative correlation between SPEI and NDVI is most significant. This paper aims to clarify the sensitivity of vegetation to different time-scale droughts, provide a basis for alleviating drought in the YRB, and promote sustainable development of ecological environmental protection. The research findings enable us to gain a profound insight into the responsiveness of vegetation growth to drought in the context of global warming and offer a valuable theoretical foundation for devising pertinent measures to alleviate stress on vegetation growth in regions prone to frequent droughts. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications. [ABSTRACT FROM AUTHOR]

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry–clear, humid–hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh). [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin. [ABSTRACT FROM AUTHOR]

The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification. [ABSTRACT FROM AUTHOR]

Arid zone ecosystems, integral to terrestrial systems, exhibit relatively low stability and are prone to influences from human activities and climate change. To elucidate the influence on the ecological environment of the arid zone by climate change and human activities, the paper takes normalized difference vegetation index (NDVI) as an evaluation index of the ecosystem and uses trend analysis to evaluation of NDVI variation characteristics in the Shiyang River Basin (SRB) from 1990 to 2020. Simultaneously using methods such as partial correlation analysis and residual analysis to evaluate the impact of climate change and human activities on NDVI changes. This study yielded several key findings: (1) The NDVI in the SRB exhibits an increasing trend of 0.034/10a in the interannual variation. (2) The relation cooperatives between NDVI and the deviation of precipitation and temperature in the SRB range from −0.735 to 0.770 and −0.602 to 0.773, respectively. (3) The changes in land use and groundwater depth in the SRB have a certain impact on NDVI changes. (4) The proportion of areas with significant contributions (contribution rate greater than 60%) from climate change and human activities to NDVI change is 33.5% and 22.5%, respectively. [ABSTRACT FROM AUTHOR]

The high-resolution temporal records (1738–2018 CE) of the dendrochronological analysis on Cedrus deodara from Uttarakhand and Himachal Pradesh in the western Himalayan region were generated. An analysis of the association between the Normalized Difference Vegetation Index (NDVI) and tree-ring radial growth indicated a significant positive relationship from April to July. The association between tree rings and other hydroclimatic variables was used to further evaluate this relationship. Based on the observation, mean April–July NDVI variability since 1790 CE was reconstructed based on the regional tree-ring chronology. Comparisons of the reconstructed NDVI with gridded precipitation, drought records, and proxy-based hydroclimatic reconstructions revealed a consistent agreement over a large spatial domain extending beyond the study region. We observed a correlation between the region's NDVI browning (reduction) and past droughts and famines. Comparison has been made to the regional proxy-based hydroclimatic reconstruction and stable oxygen isotope datasets with the present NDVI reconstruction. This revealed the commonalities in the regional hydroclimatic scenario recorded in these data sets over the last few centuries. This first tree-ring-based NDVI reconstruction from the western Himalayas infers regional climate-vegetation dynamics during the past two centuries. This reconstruction provides valuable input for modeling vegetation dynamics for predicting vegetation changes due to climate variations in the western Himalaya. [ABSTRACT FROM AUTHOR]

Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith), damage was monitored at a regional scale using time series data in Western and Southern African countries. The study employed the normalized difference vegetation index (NDVI) computed from Landsat 8 imagery using the Google Earth Engine (GEE) using image composites for the years 2013 to 2020 for the study areas. The index was then reclassified based on the NDVI threshold values into low, sparse, moderate, and dense classes. FAW prevalence data were then utilized to validate the correlation between the FAW infestation and NDVI values. FAW was associated with a decrease in vegetation productivity between the years 2016, 2017, and 2018 when the pest infestation was reported in the study areas. The validation results showed that there is a correlation between FAW infestation and NDVI (R20.83). Our study highlighted that NDVI can be used as a proxy to quantify pest damage to vegetation productivity.

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI's capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat. [ABSTRACT FROM AUTHOR]

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans. [ABSTRACT FROM AUTHOR]

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI's lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation's vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation's diverse adaptability to climatic variations. [ABSTRACT FROM AUTHOR]

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon the normalized difference vegetation index (NDVI) from 2001 to 2020. The correlation between the NDVI and standardized precipitation evapotranspiration index (SPEI) at disparate time scales was used to assess the response of vegetation growth to drought in the Yinshanbeilu region. The drought levels of SPEI1, SPEI3, SPEI6, and SPEI12 increased prominently in the eastern region of the country, while the NDVI decreased significantly from east to west in spring, summer, and autumn but was reversed in the winter. The area with an upward trend (33.86%) was slightly lower than that with a downward trend (66.14%). The correlation coefficients between the NDVI and SPEI over the entire year increased with the SPEI timescale. The elevated values were concentrated in the southeastern and western regions of the survey region. Additionally, the best correlation timescales were SPEI6 and SPEI12. Grassland was the most sensitive vegetation type to the SPEI response in the NDVI. The correlation coefficients of NDVI and SPEI1–12 were 0.313, 0.459, 0.422, and 0.406. Both spring and summer were more responsive to SPEI12, whereas autumn and winter were more responsive to SPEI3. The correlation of disparate time scales exhibited complex soil texture features with respect to different seasonal scales, and the soil texture showed a strong response to vegetation in both summer and autumn. Loam, sandy loam, and silty loam all exhibited the highest response to SPEI12, with coefficients of 0.509, 0.474, and 0.403, respectively.

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.

Brazil, as a global player in soybean production, contributes about 35% to the world's supply and over half of its agricultural exports. Therefore, reliable information about its development becomes imperative to those who follow the market. Thus, this study estimates three phenological stages of soybean crops (sowing, beginning seed, and harvesting dates), identifying spatial–temporal patterns of soybean phenology using phenological metric extraction techniques from Normalized Difference Vegetation Index (NDVI) time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Focused on the state of Paraná, this study validates the methodology using reference data from the Department of Rural Economics (DERAL). Subsequently, the model was applied to the major Brazilian soybean area cultivation. The results demonstrate strong agreement between the phenological estimates and reference data, showcasing the reliability of phenological metrics in capturing the stages of the soybean cycle. This study represents the first attempt, to the best of our knowledge, to correlate the vegetative peak of soybeans with the beginning seed stage at a large scale within Brazilian territory. Amidst the urgent need for the accurate estimation of agricultural crop phenological stages, particularly considering extreme weather events threatening global food security, this research emphasizes the continual importance of advancing techniques for soybean monitoring. [ABSTRACT FROM AUTHOR]

Vegetation phenology models still rely on temperature as the primary limiting factor to growth. They generally do not recognize the importance of photoperiod and water availability, which can cause them to under-perform. Moreover, few models have used machine learning algorithms to find relationships in the data. In this paper, four Vegetation Indexes (VIs), namely the green chromatic coordinate (GCC), the vegetation contrast index (VCI), the normalized difference vegetation index (NDVI) and the two-band enhanced vegetation index (EVI2), are predicted for the North American Great Plains. This is possible by using six PhenoCams, Daily Surface Weather and Climatological Summaries (DAYMET), processing them with the machine learning algorithm XGBoost (XGB) and comparing them with seven phenophase stages throughout a growth cycle. Examining the results, GCC was the best fitting model with an R2 of 0.946, while EVI2 was the poorest with an R2 of 0.895. Also, the results indicate that changing temperature and precipitation patterns are driving a significant change in phenology of the grasslands. We developed a model capable of explaining 90 to 93% of the variability in four VIs across six grassland PhenoCam sites over the growing season using the XGB regression. Our model demonstrates the importance of including photoperiod, temperature, and precipitation information when modeling vegetation phenology. Finally, we were able to construct a 38-year phenology record at each PhenoCam location. [ABSTRACT FROM AUTHOR]

Objective The increase in atmospheric CO2 and the associated global warming is expected to affect plant growth and development. This paper investigates the spatiotemporal variation in vegetation phenological traits and peak NDVI of plants in Hetao Irrigation District in Inner Mongolia. [Method] The analysis was based on MOD13Q1 data from 2001 to 2021. The impact of climatic factors on spatiotemporal variation in the start of season SOS, the end of season (EOS), the length of season (LOS), the peak of season (POS), and the normalized difference vegetation index (NDVI) of the vegetations in the region was analyzed using the two-logistic and four-parameter model. [Result] The SOS started from the end of May to the middle of June, and ended from the middle of September to the beginning of October. The annual peak LOS and POS ranged from 95 to 116 days, and from 200 to 220 days, respectively. The annual peak NDVI ranged from 0.5-0.7. Climate change delayed the onset of SOS, reduced LOS, and elevated peak NDVI. Additionally, the delayed SOS contributed to the decrease in LOS. The changes in EOS and POS with climate were consistent. SOS, EOS and POS were negatively correlated to temperature and positively correlated to precipitation. The peak NDVI was positively correlated to temperature and precipitation. [Conclusion] The vegetation phenology and peak NDVI in the Hetao Irrigation District were spatially heterogeneous, and the impact of climatic factors on them also varied. [ABSTRACT FROM AUTHOR]

Under the background of global climate change, characterizing correlations between vegetation dynamics and bioclimatic indices (calculations based on temperature, precipitation, and reference evapotranspiration) are important for vegetation conservation and the restoration of fragile ecosystems. The Southwest China (SWC), which is a hotspot of global biodiversity with complex and diverse terrain, stereoscopic climatic characteristics, and pronounced spatial heterogeneity. Here, spatial–temporal changes in bioclimatic factors and annual normalized difference vegetation index (NDVI) and the effects of bioclimatic factors on the NDVI of different vegetation types at different elevations in SWC were examined during 1961(1982)–2019. Temperature-related bioclimatic indices had significant positive and negative effects on the NDVI of different types of vegetation, and the effects of temperature-related bioclimatic indices were stronger than those of precipitation and reference evapotranspiration-related indices in the three subregions. The effects of changes in temperature and reference evapotranspiration-related bioclimatic indices on the NDVI of different types of vegetation were significant in different elevation bins in the three subregions. The rate of change in high-value and low-value NDVI pixels of different types of vegetation significantly increased and decreased over the study period in the three subregions, respectively. The NDVI of grass vegetation significantly increased in the three subregions with elevation, and it was highest in the higher elevation bins. The interaction effects of several bioclimatic factors on the NDVI of different vegetation types varied, and the responses of the same vegetation type to interactions between different bioclimatic factors also varied. These findings provide new insights into the complex mechanisms underlying the relationships between bioclimatic indices and vegetation dynamics and have implications for the conservation of vegetation and the restoration of fragile ecosystems.

Spatio-temporal graph modelling is a new, prominent predictive tool to use on datasets with complex spatial and temporal relationships. Normalized Difference Vegetation Index (NDVI) is a remote measure offering these complex relationships, used by agricultural producers and researchers due to its strong correlation with crop growth. Accurate periodic field-level NDVI forecasting helps project crop yield, crucial for planning agricultural production. This NDVI forecasting problem was previously studied, with best results obtained by Convolutional Long Short-Term Memory (ConvLSTM) architecture. We modify the ConvLSTM architecture, improving over the original paper. Additionally, we propose a new architecture based on Graph WaveNet (GWNN). GWNN captures spatial relationships in the non-tabular data with an adaptive dependency matrix and long-range temporal relationships with stacked spatial-temporal layers. We test each model (original ConvLSTM, new ConvLSTM, and GWNN) over the same geographical points. Under Root Mean Square Error metric, GWNN outperforms original ConvLSTM by 31% and our new one by 15%. Moreover, the GWNN is more than 170 times faster at training. We compare these models on other NDVI datasets, up to 50 times larger than the original set. The consistent results show the GWNN is most efficient in both quality and runtime for the NDVI forecasting problem.

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons' NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced "greening" process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant "browning" trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of "greening in the east and browning in the west" of Central Asia. During spring, the dominant theme was the "greening" of vegetation NDVI, although there was noticeable "browning" observed in southwest region of Central Asia. During summer, the "browning" of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the "browning" trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the "greening" trend of vegetation NDVI in arid regions, which further aggravates the "browning" trend of vegetation NDVI. [ABSTRACT FROM AUTHOR]

The purpose of the article was to describe the outcomes of an in-field evaluation of the NDVI Converter application, which was created to assess the normalized difference vegetation index (NDVI) using estimates of the percentage of green canopy cover. The study was carried out in 2023 on fields of winter wheat, winter barley, and winter oilseed rape in the phenological stages BBCH 21-32 and BBCH 18-39, respectively. The fraction of green canopy cover was estimated using the application Canopeo. By comparing the mean absolute percentage error and Pearson's correlation coefficient to the actual values of the spatial vegetation index, as determined by the platform OneSoil, the quality of the vegetation index evaluation was assessed. Thus, it has been established that NDVI Converter offers accurate vegetation index assessment for cereal crops, with mean absolute percentage error 16.23% and Pearson's correlation coefficient within 0.99, and reasonable quality for oilseed rape (statistical indicators of 46.61% and 0.99, respectively). After the adjustment, the accuracy score of the NDVI Converter increased up to 27.62% for oilseed rape, and 8.71% for winter cereals. Therefore, NDVI Converter could be recommended for practical use in case the spatial vegetation index is not provided by satellite imagery services. [ABSTRACT FROM AUTHOR]

The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development.

Remote sensing plays a vital role in analyzing urban changes. In this regard, various datasets collected from satellites today serve as a foundation for decision-makers and urban planners. This study compares the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) as indicators for the creation of surface heat islands. Using Landsat 8 OLI/TIRS C2 L2 images, spatial correlations between land surface temperature (LST) were examined for August 2013, 2019 and 2023. Urban heat islands (UHI) are a contemporary phenomenon and increasingly common in large urban areas compared to surrounding, less populated areas. With the advancement in remote sensing, it is possible to adequately determine the spatial differentiation and prevalence of urban heat islands (UHI). The study is based on Landsat 8 satellite image sets for the Sarajevo basin in August 2013, 2019 and 2023, which were used to analyze LST, NDVI, and NDBI indices. This work indicates a relationship between LST and NDVI but varies depending on the analyzed year. Normalized Difference Built-up Index (NDBI) serves as a suitable indicator for surface UHI effects and can be used as an indicator to assess its spatial distribution within a larger urban environment. [ABSTRACT FROM AUTHOR]

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman-Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5-6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them. [ABSTRACT FROM AUTHOR]

Most ecological studies use remote sensing to analyze broad‐scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost‐effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30‐meter resolution EVI and NDVI values of the pixels within a 50‐m buffer of bird sample points using Google Earth Engine (32‐day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities. [ABSTRACT FROM AUTHOR]

Extreme weather events and frequent meteorological disasters pose significant threats to vegetation growth in the context of climate change. However, information regarding the impact of extreme climate events on vegetation in the ecologically fragile Karst area has been lacking. This study aims to fill this knowledge gap by quantifying changes in extreme temperature and the normalized difference vegetation index (NDVI) while exploring the relationship between extreme temperature and NDVI in Guangxi's Karst area, China. Our analysis reveals a pronounced trend toward extreme warming in the study region from 2000 to 2019. Warm days TX90d, TX95d, and TX99d (count of days where daily maximum temperature TX > 90th, 95th, and 99th percentile) significantly (P < 0.05) increased 7.1, 5.5, and 2.0 days per decade. Warm nights TN90d, TN95d, and TN98d (count of nights where daily minimum temperature TN > 90th, 95th, and 98th percentile) significantly (P < 0.05) increased 9.7, 6.8, and 4.2 days per decade, with the increase rate greater than warm days. Annual days for daily maximum temperature higher than 35 °C also increased 3.4 days per decade. The annual NDVI increased 0.04 per decade. We have identified a negative linear relationship between the trends of very cold and very warm days (i.e., very cold days TX01d and hot days TX99d) and NDVI. In contrast, the annual NDVI demonstrates a positive correlation with the annual days for daily maximum temperature higher than 35 °C. The results suggested that climate warming might to some extent benefit the vegetation growth. Nevertheless, it is imperative to enhance vegetation protection efforts, particularly in regions experiencing extreme warm and cold. [ABSTRACT FROM AUTHOR]

Achanakmar Tiger Reserve (ATR), endowed with rich biological diversity and lush green vegetation in and around, makes it more unique. It is also an integral part of the Achanakmar Amarkantak Biosphere Reserve (AABR) and has been identified as one of the important tiger reserves of the Central Indian landscape due to its connectivity with other protected areas and tiger reserves in neighboring landscapes. Vegetation mapping and monitoring are important to understand changes in ecosystem processes and associated temporal and spatial impacts. Pre- and post-monsoon IRS, LISS III, and AWiFS satellite data from 2000, 2004, 2008, 2010, and 2013 were used for the present study. This paper is an attempt to examine the variation in the normalized difference vegetation index (NDVI) of ATR and its buffer zone on a seasonal and temporal basis. Climate conditions such as temperature, precipitation, relative humidity, etc. play an important role in the growth and development of healthy vegetation. The NDVI value of ATR has shown fluctuation and recorded positive growth over the past 14 years with few exceptions. The post-monsoon season recorded a higher NDVI value as compared to the pre-monsoon months. The maximum NDVI value was recorded in 2004 (+0.539) for the entire ATR and its buffer zone. [ABSTRACT FROM AUTHOR]

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon the normalized difference vegetation index (NDVI) from 2001 to 2020. The correlation between the NDVI and standardized precipitation evapotranspiration index (SPEI) at disparate time scales was used to assess the response of vegetation growth to drought in the Yinshanbeilu region. The drought levels of SPEI1, SPEI3, SPEI6, and SPEI12 increased prominently in the eastern region of the country, while the NDVI decreased significantly from east to west in spring, summer, and autumn but was reversed in the winter. The area with an upward trend (33.86%) was slightly lower than that with a downward trend (66.14%). The correlation coefficients between the NDVI and SPEI over the entire year increased with the SPEI timescale. The elevated values were concentrated in the southeastern and western regions of the survey region. Additionally, the best correlation timescales were SPEI6 and SPEI12. Grassland was the most sensitive vegetation type to the SPEI response in the NDVI. The correlation coefficients of NDVI and SPEI1–12 were 0.313, 0.459, 0.422, and 0.406. Both spring and summer were more responsive to SPEI12, whereas autumn and winter were more responsive to SPEI3. The correlation of disparate time scales exhibited complex soil texture features with respect to different seasonal scales, and the soil texture showed a strong response to vegetation in both summer and autumn. Loam, sandy loam, and silty loam all exhibited the highest response to SPEI12, with coefficients of 0.509, 0.474, and 0.403, respectively. [ABSTRACT FROM AUTHOR]

Monitoring the evolution of pasture availability and quality throughout the growing season is the basis of grazing management in extensive Mediterranean livestock systems. Remote sensing (RS) is an innovative tool that, among many other applications, is being developed for detailed spatial and temporal pasture quality assessment. The aim of the present study is to evaluate the potential of satellite images (Sentinel-2) to assess indicators of pasture quality (pasture moisture content, PMC, crude protein, CP and neutral detergent fiber, NDF) using the normalized difference vegetation index (NDVI). Field measurements were conducted over three years at eight representative fields of the biodiversity and variability of dryland pastures in Portugal. A total of 656 georeferenced pasture samples were collected and processed in the laboratory. The results show a significant correlation between pasture quality parameters (PMC, CP and NDF) obtained in standard laboratory methods and NDVI satellite-derived data (R2 of 0.72, 0.75, and 0.50, respectively). The promising findings obtained in this large-scale validation study (three years and eight fields) encourage further research (i) to test and develop other vegetation indexes for monitoring pasture nutritive value; (ii) to extend this research to pastures of the other Mediterranean countries, building large and representative datasets and developing more robust and accurate monitoring models based on freely available Sentinel-2 images; (iii) to implement an extension program for agricultural managers to popularize the use of these technological tools as the basis of grazing and pasture management. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm–12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4–7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of −0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area's normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area. [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]

Post-mined reclamation is an essential phase in coal mining operations, ensuring that the ex-mining area can function again according to its intended purpose in the future. This study aimed to elucidate the differences in planting years of post-mined reclamation at a coal mine in the Paringin area, South Kalimantan, Indonesia, and compared them to undisturbed areas for the years 2003 and 2023. NDVI was used for land cover analysis based on the Ministry of Energy and Mineral Resource Regulation, and the ultimate criterion for 100% success in post-mined reclamation is vegetation canopy coverage. The NDVI results showed an improvement in NDVI values for the post-mined reclamation area from 2003 to 2023, indicating an improvement in land cover due to the revegetation process. In 2023, the NDVI results of the post-mined reclamation for eight and twenty years of tree planting showed NDVI values of 0.6-0.7 (moderate vegetation). However, the results of field observations of the twenty-year post-mined reclamation tree condition indicate the non-survival of fast-growing tree species, such as Albizia chinensis. The composition of fast-growing and local tree species, with a minimum proportion of 60:40, and systematic tree distribution across the post-mined reclamation area are required to maintain canopy coverage of the post-mined reclamation area in the long term. Fastgrowing tree species, as pioneers, have short to medium life periods. Therefore, the ultimate criterion for 100% success in post-mined reclamation for canopy coverage should be based on local tree species, serving as the basis for releasing the reclamation bond. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems depend heavily on their vegetation; it is possible to forecast future growth trends of regional vegetation by keeping an eye on changes in vegetation dynamics. To circumvent the potential reduction in prediction accuracy caused by the non-stationarity of meteorological changes, we analyzed the characteristics of NDVI (Normalized Difference Vegetation Index) spatial and temporal changes and the influencing factors over the past 20 years in the Maowusu Sandland of China via attribution analysis. We also constructed a comprehensive analysis system for vegetation pre-restoration. Moreover, we combined meteorological data from 2000 to 2018 and presented a deep-learning NDVI-Informer prediction model with a self-attentive mechanism. We also used distillation operation and fusion convolutional neural network for NDVI prediction. Incorporating a probsparse self-attention method successfully overcomes Transformer weaknesses by lowering the memory use and complexity of large time series. It significantly accelerates the inference speed of long time series prediction and works well with non-smooth data. The primary findings were: (1) the Maowusu Sandland's 20-year average showed a consistent increasing trend in the NDVI at 0.0034 a−1, which was mostly caused by climate change, with a relative contribution rate of 55.47%; (2) The Informer-based model accurately forecasted the NDVI in the research region based on meteorological elements and conducted a thorough analysis of the MAPE (mean absolute percentage error) (2.24%). This suggests that it can effectively lower the data's volatility and increase prediction accuracy. The anticipated outcomes indicate that the trend will stabilize during the following ten years. To attain more sustainable and efficient agricultural production, the results of this study may be used to accurately estimate future crop yields and NDVI using previous data. [ABSTRACT FROM AUTHOR]

Iraq's climate is prone to frequent droughts, which have severely influenced the country in the past two decades. A study conducted using remote sensing and geographical information systems analyzed the spatiotemporal patterns of drought in Iraqi Kurdistan - specifically Erbil for 19 years from 2003 to 2021. The research found that the region had experienced severe drought episodes during this time, with an increase in severity and frequency, especially in 2008, 2012, and 2021. These years were noticeable by a decrease in vegetation area cover and lower average precipitation. The study utilized the Normalized Difference Vegetation Index (NDVI), Rainfall, and Rainfall Anomaly to produce multi-temporal classified drought maps, which showed that climate conditions played a significant role in the change of the vegetated cover area. The research also estimated the effect of rainfall variability on vegetation cover in Erbil, concluding that rainfall anomalies led to changes in the NDVI. The correlation matrix between rainfall anomalies and NDVI anomalies confirms that irregular rainfall patterns result in modifications to the NDVI. Productivity was found to increase with wet anomalies and decrease with dry anomalies. Overall, the study provides valuable insights into the impact of drought on agricultural productivity in the Iraqi Kurdistan Region (IKR)-Erbil and emphasizes the importance of implementing effective strategies to mitigate the impact of drought on agriculture in the region. [ABSTRACT FROM AUTHOR]

Evaluating the effectiveness of fertilizers on crops without traditional yield is difficult, as clipping collection is time-consuming and not indicative of the desired response to the fertilizer. Remote sensing techniques, like the normalized difference vegetation index (NDVI), have emerged as an effective tool to combat these issues. Canopy reflectance, measured by NDVI, is commonly used to differentiate turfgrass response to nitrogen (N) fertilization treatments; however, advancements in data processing are needed for greater differentiation between treatments and better testing of the effects of fertilizer responses. Presented here is an advancement in the processing of NDVI data by applying a compound exponential (pulse peak) function to changes in NDVI over time, relative to a control. This method’s effectiveness in differentiating between fertilizers was evaluated by applying polymer-coated urea (PCU), an enhanced efficiency fertilizer (EEF), and urea on turfgrass to show the usefulness of the novel curve fitting in highlighting differences in fertilizer response. The field study was carried out on an established Kentucky bluegrass (Poa pratensis L.) blend on a sandy loam soil rootzone maintained with a typical home lawn maintenance regime. Ratios of PCU to urea ranging from 100% urea to 100% PCU with increments of 20% of PCU were used to observe a continuum of response between the two fertilizer types. The N rate for all blends was 73 kg ha−1, with unfertilized plots used as a control to set baseline measurements for the curve fitting. Turfgrass response to N fertilization treatments was assessed through visual turfgrass quality ratings, dry matter yield, and canopy reflectance, where higher urea content resulted in the shortest time to maximum ∆NDVI (24 days), while 100% EEF reached maximum ∆NDVI at 56 days. The novel curve fitting analysis of the NDVI data allowed for differentiation between treatments, provided a measure of fertilizer effects, and differentiated between urea and enhanced efficiency fertilizers.

Vegetation is an important component of the terrestrial ecosystem, and studying the rules of vegetation change and its driving factors is helpful to strengthen the ecological protection and sustainable development of regional vegetation. This study analyzes the changes in Normalized Difference Vegetation Index (NDVI) and its response to climate factors in the five regions of western Ordos and eastern Alxa in China between 2000 and 2020. The MODIS NDVI and meteorological data from 2000 to 2020 was used and the ordinary least squares, trend analysis, and correlation analysis methods were analyzed. The NDVI in this region shows spatial differentiation and is high in the east and low in the west. The overall NDVI has shown a significant increasing trend (p < 0.01), and the slope value of the rate of change also shows that the NDVI in 98.17% of the area is increasing. On a temporal scale, NDVI had a significant positive correlation with precipitation (p < 0.01), but no significant correlation with temperature changes. On a spatial scale, NDVI was positively correlated with precipitation, which accounted for 95.57% of spatial changes, of which a significant positive correlation accounted for 34.99% (p < 0.05). Meanwhile, the temperature and NDVI were negatively correlated but not significantly. A positive correlation accounted for 45.95% of the change, but the insignificant negative correlation accounted for 54.05%. Therefore, comprehensive analysis showed that precipitation played a leading role in the NDVI in the study area. The results are helpful to study the driving mechanism of vegetation growth and provide reference for vegetation protection in regions of western Ordos and eastern Alxa of Inner Mongolia, China. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of the sugarcane planting area is of vital importance to the country’s agricultural production and sugar development. In remote sensing crop mapping based on spectral similarity, there will be have a phenomenon of foreign matters with the same spectrum by obtaining an accurate crop reference curve for crop identification, limiting mapping accuracy. In this study, we improved the spectral reconstruction method based on singular value decomposition (SR-SVD). A decision tree model was established based on the similarity of the sugarcane Normalized Difference Vegetation Index (NDVI) time series curve and the fluctuation range of NDVI in different growth periods. Using the Sentinel-2 (Level-2A) image data set to extract sugarcane planting area in two regions of Chongzuo City, Guangxi, China, the overall accuracy was higher than 96%, respectively. The results show that through the spectral similarity and the determination of the threshold fluctuation range, not only high-precision mapping of sugarcane can be achieved, but the problem of “same spectrum with different objects” can also be solved. Therefore, this method can provide accurate information on the sugarcane planting areas and technical support for monitoring the structure of sugarcane planting in the region.

The normalized difference vegetation index (NDVI) is one of the most common metrics used to describe vegetation dynamics. Unfortunately, low-quality pixels resulting from contamination (by features including clouds, snow, aerosols, and mixed factors) have impeded NDVI products' widespread application. Researchers have thought of several ways to improve NDVI quality when contamination occurs. However, most of these algorithms are based on the noise-negative deviation principle, which aligns low-value NDVI products to an upper line but ignores cases where absolute surface values are low. Consequently, to fill in these research gaps, in this article, we use the random forest model to produce a set of high-quality NDVI products to represent actual surface characteristics more accurately and naturally. Climate and geographical products are used as model inputs to describe environmental factors. They represent the random forest (RF) model that establishes relationships between MODIS NDVI products and meteorological products in high-quality areas. In addition, auxiliary data and empirical knowledge are employed to meet filling requirements. Notably, the random forest (RF) algorithm exhibits a mean absolute error (MAE) of 0.024 and a root mean squared error (RMSE) of 0.034, in addition to a coefficient of determination (R2) value of 0.974. Furthermore, the MAE and RMSE of the RF-based method decreased by 0.014 and 0.019, respectively, when compared to those of the STSG (spatial–temporal Savitzky–Golay) plan and by 0.013 and 0.015, respectively, when compared to the LSTM (long short-term memory) method. R2 increased by 0.039 and 0.027, respectively, compared to the STSG and LSTM methods. We introduced a novel series of NDVI products that demonstrated consistent spatial and temporal connectivity. The novel product exhibits enhanced adaptability to intricate environmental conditions and promises the potential for utilization in investigating vegetation dynamics within the Chinese region. [ABSTRACT FROM AUTHOR]