Vegetation Index Time Series Research Articles

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

Satellite remote sensing for monitoring citrus orchards water requirements at the irrigation district scale

In Italy, irrigated agriculture is often managed by Reclamation Consortia, but they generally lack the proper tools for monitoring the irrigation water requirements (IWR), forbidding the required sustainability level by the European Water Framework Directive. In this context, the study aims at monitoring the IWR of citrus orchards of a Sicilian irrigation district by implementing a satellite-based methodological approach, during the irrigation seasons 2019 and 2020. Firstly, neural networks were implemented to map the citrus orchards, by using Normalized Difference Vegetation Index time series as input, and obtaining accuracy values of 94% and 87%, in 2019 and 2020, respectively. Then, the satellite-based ArcDualKc model was used to estimate crop evapotranspiration (ETc) over the identified citrus orchards. The estimated ETc rates were validated at the farm scale in terms of irrigation volumes and compared to those obtained by the traditional FAO-56 approach. Then, the spatially distributed irrigation volumes estimated at district level were compared with those declared by the Reclamation Consortium, obtaining absolute error values of 3.28 Mm3 (294%) and 7.08 Mm3 (647%) for the years 2019 and 2020, respectively. The results of the study confirmed the usefulness of the satellite-based methodological approach for determining spatial distributed IWR estimates.

Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning.

Revealing Cropping Intensity Dynamics Using High-Resolution Imagery: A Case Study in Shaanxi Province, China

Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management.

Effects of Climate Changes and Crop Phenological Responses on Soil Organic Carbon of Cultivated Land in Fujian Province

Research on the mechanism of how climate change affects cultivated soil organic carbon is the basis for the management of cultivated land quality in the context of climate change. Crop phenological responses to climate change have an important effect on cultivated soil organic carbon as well. However, previous research primarily focused on the independent effects of climate change or crop phenological responses on the changes in soil organic carbon, and few studies have analyzed the changes in cultivated soil organic carbon under the combined influence of both factors or quantified their contribution rates to the changes in cultivated soil organic carbon. Based on topsoil samples in 2008 and 2021, annual pre-season and mid-season climate data from 2008 to 2021, and the phenological parameters extracted from the enhanced vegetation index （EVI） time series from 2007 to 2022, a soil organic carbon predictive model was constructed using the random forest algorithm. The total change in soil organic carbon from 2008 to 2021, the change in soil organic carbon under climate change alone, and the change in soil organic carbon under the synergistic influence of climate change and crop phenological responses were simulated. Furthermore, the contributions of climate change and crop phenological responses to the changes in cultivated soil organic carbon were distinguished and quantified. Moreover, the dominant influencing factors of soil organic carbon changes and their spatial distributions were identified and analyzed. The results were as follows： ① Under the synergistic influence of climate change and crop phenological responses, a decrease was observed in soil organic carbon in 74.15% of the cultivated land area in Fujian Province during the years 2008-2021, with an average decrease of 2.20 g·kg-1. Additionally, there was an increase in soil organic carbon in 25.85% of the cultivated area, with an average increase of 1.48 g·kg-1. ②The average contribution rates of pre-season climate, crop phenological responses to climate change, mid-season climate, and phenological changes resulting from cultivars shifts or other adjustments of agricultural measures to soil organic carbon changes were 34.08%, 28.56%, 22.75%, and 14.61%, respectively. Overall, climate change had a greater impact on the changes in cultivated soil organic carbon in Fujian Province than the crop phenological response to climate change. ③ The regions where climate change and phenological response jointly acted as dominant influencing factors held the largest area, accounting for 47.06% of the total cultivated land area in Fujian Province, and the regions where climate change was the dominant influencing factor alone held the second-largest area, accounting for 28.64% of the total cultivated land area. ④ Higher contribution rates of pre-season climate factors and phenological changes resulting from cultivar shifts or other adjustments of agricultural measures tended to be distributed in higher-altitude areas, whereas higher contribution rates of mid-season climate factors and phenological responses to climate change tended to be distributed in lower-altitude areas. These research findings can provide a theoretical basis for decision making regarding the management of cultivated land quality and the safeguarding of food security in the context of climate change.

A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

ABSTRACT Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable models due to unstable SAR-NDVI relationships caused by imaging mechanism differences and environmental complexities. In this study, we developed a new Bidirectional Recurrent Imputation for Optical-SAR fusion (BRIOS) model to reconstruct high-quality Sentinel-2 NDVI time series data. BRIOS designs a two-layer recurrent architecture that integrates the autocorrelation of discrete, cloud-free NDVI observations into the model for a more stable SAR-NDVI relationship. Evaluating BRIOS against three baseline methods (GF-SG spatiotemporal fusion, Harmonic regression interpolation, and MCNN-Seq deep learning) across three full Sentinel-2 tiles in reconstructing 8-day NDVI time series, BRIOS consistently outperformed in scenarios of either random or continuously missing data, as evidenced by lower RMSE values (e.g. 0.075 for BRIOS vs. 0.108 for GF-SG vs. 0.143 for Harmonic regression vs. 0.303 for MCNN-Seq), better Edge index, and high linear correlation coefficients (R values up to 0.97). Further ablation experiments revealed that deep integration of NDVI autocorrelation features and SAR temporal change patterns has improved the stability and generalization of BRIOS. Discussions on the model's scalability across various cloud sizes and training dataset sizes affirm its practicality for broad-scale application in vegetation monitoring under challenging cloudy conditions.

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

Assessing open-pit mining impacts on semi-arid grassland: A framework for boundary and intensity quantification

Coal mining in northern China's grasslands faces significant environmental challenges due to the fragile semi-arid ecosystem and the abundance of mineral resources. However, current research lacks clarity in both theoretical understanding and effective quantification of its impacts. Insufficient distinction between direct and indirect environmental effects, the oversight of interactions from neighboring activities, and inaccurate measurements of impact intensity all hinder a comprehensive understanding. These limitations also prevent the precise delineation of ecological protection compensation areas. In this study, we developed a comprehensive framework utilizing Landsat time-series images (1989–2017) and field sampling data (228 sample locations in 2017) to assess the boundaries and intensities of mining impacts. First, the k-means clustering algorithm was applied to identify the Normalized Difference Vegetation Index (NDVI) time-series trajectories and map the spatial information of direct impacts. Then, a buffer zone was established and an amplitude model was proposed for determined the cross-impacts and their intensities based on the volatility of NDVI trajectories. Accordingly, a Trend-thresholds model was proposed to automatically extract the indirect impacts boundary of mining-dominated on vegetation under varying intensities. The Soil Quality Index (SQI) ∼ distance curves were fitted and a derivative model was employed for determine the boundary of indirect impacted on soil. We applied this assess framework in Baorixile coalfield (i.e., Baorixile coal mine (BM), DongMing coal mine (DM), Shunxing coal mine (SX)) a typical semi-arid grassland mining area, and quantified three types of impacts: direct, indirect, and cross-impacts. Findings reveal negative coal mining impacts primarily confined to permitted mining areas and decreasing over time. Direct impacts boundary limited in the mining sites and its impact intensity shows a dual nature, with a negative weakening and a positive strengthening trend. Widespread negative indirect impacts exist outward the mining area displays spatial heterogeneity, and its intensity attenuation with distances. In there, the very severe effect (D1) is predominantly focusing on the grassland where near by the mining work face (BM maximum range 1.2 km, DM maximum range 0.55 km, SX maximum range 0.4 km). Moreover, our study indicated that vegetation and soil exhibited consistent responses to coal mining impacts within 1 km, demonstrating the feasibility of using remote sensing data to qualify the impact boundary on the grassland environment. Besides, we further identify significant cross-impacts surrounding the mining areas, characterized by strong negative effects, particularly in the southeastern region of BM. This research offers valuable insights for effective ecological compensation governance and scientific environmental management in similar global contexts.

Temporal Patterns of Vegetation Greenness for the Main Forest-Forming Tree Species in the European Temperate Zone

In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones.

Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020

AbstractCalifornia produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high‐resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high‐resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine‐class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant (p < 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16‐year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.

Unpacking satellite pixels: UAVs reveal fine-scale drivers of land surface phenology in a winter rainfall shrubland

Land surface phenology (LSP) can reveal important connections between vegetation dynamics and climate but remains poorly understood in evergreen winter-rainfall shrublands globally. Field-based studies have indicated diverse plant functional strategies in shrublands, but further work is required to link LSP to vegetation functional composition in these regions. We analysed time-series of the normalised difference vegetation index (NDVI) in fynbos shrublands of South Africa using multi-spectral imagery from satellites and unmanned aerial vehicles (UAVs). We investigated the climate drivers of seasonal vegetative phenology and long-term NDVI trends at multiple spatial scales ranging from the landscape to individual species. At coarse spatial resolutions, NDVI time-series indicated rainfall-driven vegetation dynamics in fynbos, both at inter and intra-annual time scales. However, high-resolution time-series from UAVs exposed an underlying divergence in vegetative phenology and long-term NDVI trends between shallow and deep-rooted growth forms. Phenophases and NDVI trends of isolated, deep-rooted, overstory shrubs were decoupled from rainfall relative to dense overstory patches and shallow-rooted understory growth forms. Variations in growth form phenology were not detected at coarse spatial scales due to scaling and competitive effects based on the functional composition of the vegetation.

Enhancement of Comparative Assessment Approaches for Synthetic Aperture Radar (SAR) Vegetation Indices for Crop Monitoring and Identification—Khabarovsk Territory (Russia) Case Study

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories.

Machine-Learning model for estimating sugarcane production at crop level

Yield maps provide essential information for those who manage the field. The anticipated production data will be able to make better decisions on how resources should be used in harvesting, define market strategies and, above all, it will help evaluate treatments used on the crop. Sugar cane is the predominant crop in Huasteca Potosina, Mexico. The proposed Machine Learning model based on Random Forest Regressor integrates time series of vegetation indices extracted from Sentinel-2 images and meteorological data. The R2 and RMSE metrics (0.84 y 0.034) show the effectiveness of the model for prediction.

<span>Assessing the impact of land-use/land-cover change on provisioning ecosystem services in the Long Xuyen Quadrangle from 2000 to 2021</span>

Changes in land use/land cover (LULC) have a significant impact on the changes in ecosystem service values. This study aims to assess the impacts of LULC changes on provisioning ecosystem services (pES) in the Long Xuyen Quadrangle region (LXQ) from 2000 to 2021. Using Landsat 5 TM and Landsat 8 OLI imagery, this study analyzes the normalized difference vegetation index (NDVI) time series using MODIS imagery to monitor and evaluate the impacts of LULC changes in the LXQ region. Additionally, interviews with farmers were conducted to estimate the ecosystem service values of different rice cultivation models. The research results in the creation of LULC maps for the LXQ in 2000 and 2021, with relatively high reliability, including overall accuracy (OA) of 83.18% (K = 0.772) and 94.26% (K = 0.9284), respectively. The main LULC change observed in the LXQ region from 2000 to 2021 is the conversion from wetlands to double-cropped rice and triple-cropped rice systems. The study identified an area of 54,667.722 ha (26.24%) where wetlands were converted to double-cropped rice, and an area of 36,779.633 ha (17.65%) where wetlands were converted to triple-cropped rice. Furthermore, the estimated ecosystem service values after the conversion from wetlands to triple-cropped rice were approximately 122,504,931 VND/ha/year, and for double-cropped rice, it was approximately 87,772,604.88 VND/ha/year. The research findings indicate that wetland agriculturalization, including double-cropped rice and triple-cropped rice, has affected the ecosystem service provision in the LXQ region. These research results provide significant considerations for future comprehensive valuation of ecosystem service values in the LXQ, Vietnamese MD.

Vegetation response to large-scale mountain excavation and city construction projects on the Loess Plateau of China

Since 2012, the “Mountain Excavation and City Construction” (MECC) project has been implemented extensively on the Loess Plateau of China, transforming gullies into flat land for urban sprawl by leveling loess hilltops to fill in valleys. However, this unprecedented human activity has caused widespread controversy over its unknown potential ecological impacts. Quantitative assessment of the impacts of the MECC project on the vegetation is key to ecological management and restoration. Taking the largest MECC project area on the Loess Plateau, Yan'an New District (YND), as the study area, this study investigated the spatiotemporal pattern of vegetation dynamics before and after the implementation of the MECC project using a multitemporal normalized difference vegetation index (NDVI) time series from 2009 to 2023 and explored the response of vegetation dynamics to the large-scale MECC project. The results showed that the vegetation dynamics in the YND exhibited significant spatial and temporal heterogeneity due to the MECC project, with the vegetation in the project-affected areas showing rapid damage followed by slow recovery. Vegetation damage occurred only in the project-affected area, and 84 % of these areas began recovery within 10 years, indicating the limited impact of the large-scale MECC project on the regional vegetation. The strong correlation between vegetation dynamics and the MECC project suggested that the destruction and recovery of vegetation in the project-affected areas was mainly under anthropogenic control, which highlights the importance of targeted ecological policies. Specifically, the MECC project induced local anthropogenic damage to the plant population structure during the land creation period, but regeneration and rational allocation of the vegetation were achieved through urbanization, gradually forming a new balanced ecological environment. These findings will contribute to a full understanding of the response of vegetation to such large-scale engineering activities and help local governments adopt projects or policies that facilitate vegetation recovery.

Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series

Research on Damage Detection of Civil Structures Based on Machine Learning of Multiple Vegetation Index Time Series

Using Geostationary Satellite Observations to Improve the Monitoring of Vegetation Phenology

Geostationary satellite data enable frequent observations of the Earth’s surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring.

Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index

Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index