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An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop. [ABSTRACT FROM AUTHOR]

This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the global warming hiatus. In the first period (1982–2002), SOS and EOS advanced (−7.5 and −3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios. [ABSTRACT FROM AUTHOR]

Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate–vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model's performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 ≥ 0.915, RMSE ≤ 0.033, MAE ≤ 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems. [ABSTRACT FROM AUTHOR]

We developed a new algorithm for computing radiometrically and spectrally consistent surface reflectances from multiple sensors. The algorithm approximates surface reflectances of reference sensors directly from top-of-atmosphere (TOA) reflectances of sensors-to-be-transformed. A unique characteristic of the algorithm is that coefficients in the algorithm are computed independently using statistics of time-series reflectance data for each sensor; thus, no regressions or optimizations using pairs of data from different sensors are required. This characteristic can lead to a substantial reduction in the number of computational tasks required for calibrating models when numerous satellite sensors or datasets are used. First, a system of equations relating TOA reflectances of one sensor and surface reflectances of another sensor in the red and near-infrared bands was analytically approximated using a linear mixture model of three land-cover types and radiative transfer in the atmosphere. The equations were subsequently used to develop an unmixing-based algorithm for radiometric corrections and spectral transformations. The algorithm was evaluated using synchronous observation data and long-term time-series data with middle spatial resolution, which were obtained from the Landsat 4–5 Multispectral Scanner (MSS) and Thematic Mapper (TM) sensors. Results obtained using contemporaneous data from the two sensors indicated that cross-sensor differences in reflectances and in a spectral index, the normalized difference vegetation index (NDVI), between the MSS and TM sensors were reduced to reasonable levels after the algorithm was applied; the magnitudes of remaining biases were less than 0.005 in reflectance units and less than 0.03 in NDVI units. Results obtained using time-series data for four regions of interest with different land-cover types indicated that the transformed MSS time-series data well synchronized with the TM data used as a reference. Reflectance differences remaining after implementation of the algorithm were possibly due to instability of the algorithm for computing parameters, sensor-dependent quality assurance (QA) data and QA accuracy, and geolocation errors, among others. The concept of the developed algorithm might be applicable universally to various combinations of spectral bands and sensors/missions, which should be further evaluated for cross-sensor radiometric and spectral harmonization with the aim of multi-sensor analysis. [ABSTRACT FROM AUTHOR]

Improving agricultural nitrogen management is one of the key objectives of the recent Green Deal in Europe. Current technological developments in agriculture offer new opportunities to improve nitrogen fertilization practices. The aim of this study was to adapt to Sentinel-2 data a proven delineation method initially developed for yield maps, in order to facilitate precise nitrogen management by farmers. The study was conducted in two steps. Firstly, an analysis at annual level was conducted to assess the relationship between vegetation indices and yield at the subfield scale, for different sensing period. The second step consisted in performing a pluri- annual analysis through the delineation of management zones and compare the results achieved from yield maps and from NDVI maps. Among different vegetation indices, NDVI proved to be an interesting candidate for subfield detection of yield variation, specifically when the index was sensed was sensed around the second half of May. In this area, this period usually corresponds to phenological development between the flag leaf stage and heading stage, just prior the initiation of winter wheat flowering. Using NDVI maps within Blackmore's delineation approach instead of yield maps. Allowed to reach an accuracy of 69% on zone classification. However, as yields and NDVI distribution do not respond to similar statistical distributions, we considered that the delineation threshold used to differentiate high from low yielding zones had to be adapted. The adaptation of the "performance threshold" in favor of the median NDVI, made it possible to achieve a higher accuracy (71%) of the delineation. But above all, the improvement lies also in a more robust satellite-based delineation. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to examine the influence of forest vegetation changes on water-sediment dynamics, and to provide a scientific basis for formulating and adjusting ecological environment development policies in the Yellow River Basin. [Methods] Based on the runoff and sediment discharge data from the Dacun hydrological station in the Shiwangchuan River Basin from 1959 to 2018, as well as precipitation data within the basin, the hydrological changes over a span of 60 years were analyzed. By combining NDVI data from 1985 to 2018 and land use data from five periods, the vegetation underlying surface driving effects on water-sediment changes in the watershed were analyzed and discussed. [Results] The runoff and sediment discharge in the Shiwangchuan River Basin had shown a significant decreasing trend over the past 60 years（p＜0.01）, with abrupt changes occurring in 1988 and 1983, respectively. However, there had been no significant trends in land use and precipitation within the watershed during this period. The NDVI during the growing season showed little variation before 2000 but exhibited a significant increasing trend after 2000. [Conclusion] Before 2000, the implementation of various soil and water conservation measures was the main reason for the decrease of runoff and sediment transport in the basin; After entering the 21st century, the improvement of vegetation quality in the basin has further reduced runoff and sediment transport. [ABSTRACT FROM AUTHOR]

Background: Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. Methods: The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. Results: Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. Conclusions: Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control. [ABSTRACT FROM AUTHOR]

Culicoides imicola is the main vector of viral diseases of livestock in Europe such as bluetongue (BT), African horse sickness and epizootic haemorrhagic disease. Climatic factors are the main drivers of C. imicola occurrence and its distribution might be subject to rapid shifts due to climate change. Entomological data, collected during BT surveillance, and climatic/environmental variables were used to analyse ecological niche and to model C. imicola distribution and possible future range shifts in Italy. An ensemble technique was used to weigh the performance of machine learning, linear and profile methods. Updated future climate projections from the latest phase of the Climate Model Intercomparison Project were used to generate future distributions for the next three 20‐year periods, according to combinations of general circulation models and shared socioeconomic pathways and considering different climate change scenarios. Results indicated the minimum temperature of the coldest month (BIO 6) and precipitation of the driest‐warmest months (BIO 14) as the main limiting climatic factors. Indeed, BIO 6 and BIO 14 reported the two highest values of variable importance, respectively, 9.16% (confidence interval [CI] = 7.99%–10.32%), and 2.01% (CI = 1.57%–2.44%). Under the worst‐case scenario of climate change, C. imicola range is expected to expand northward and shift away from the coasts of central Italy, while in some areas of southern Italy, environmental suitability will decrease. Our results provide predictions of C. imicola distribution according to the most up‐to‐date future climate projections and should be of great use to surveillance management at regional and national scales. [ABSTRACT FROM AUTHOR]

Prioritizing restoration opportunities effectively across entire riverscape networks (i.e., riverine landscape including floodplain and stream channel networks) can be difficult when relying on in‐channel, reach‐scale monitoring data, or watershed‐level summaries that fail to capture riverscape heterogeneity and the information necessary to implement restoration actions. Leveraging remote sensing and geospatial tools to develop spatially continuous information across nested hierarchical scales may support increased understanding of local riverscape reaches in their broader network context. Using riparian (vegetation) and geomorphic (elevation) indicators to assess status of riverscape health, along with a measure of restoration capacity (valley bottom area), could be adapted to fit specific management goals related to riverscape restoration. Frameworks using remotely sensed vegetation and elevation data to prioritize restoration continuously across riverscapes at restoration‐relevant, reach‐scales may uphold the ecosystem services provided by riverscapes. By incorporating local knowledge and identifying caveats for using these datasets, continuous inferences can be applied at network scales (watershed to regional extent and reach‐scale resolution) to prioritize restoration over a wide variety of ecoregions. This article is categorized under:Water and Life > Conservation, Management, and AwarenessWater and Life > MethodsWater and Life > Stresses and Pressures on Ecosystems [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) is one of the three great plains in China and also serves as a vital region for grain, cotton, and oil production. Under the influence of regional hydrothermal changes, groundwater overexploitation, and seawater intrusion, the vegetation coverage is undergoing continuous alterations. However, a comprehensive assessment of impacts of precipitation, temperature, and groundwater on vegetation in marine sedimentary regions of the NCP is lacking. Heilonggang Basin (HB) is located in the low-lying plain area in the east of NCP, which is part of the NCP. In this study, the HB was chosen as a typical area of interest. We collected a series of data, including the Normalized Difference Vegetation Index (NDVI), precipitation, temperature, groundwater depth, and Total Dissolved Solids (TDS) from 2001 to 2020. Then the spatiotemporal variation in vegetation was analyzed, and the underlying driving mechanisms of vegetation variation were explored in this paper. The results show that NDVI experiences a rapid increase from 2001 to 2004, followed by stable fluctuations from 2004 to 2020. The vegetation in the HB has achieved an overall improvement in the past two decades, with 76% showing improvement, mainly in the central and eastern areas, and 24% exhibiting deterioration in other areas. From 2001 to 2020, NDVI correlates positively with precipitation, whereas its relationship with temperature fluctuates between positive and negative, and is not statistically significant. There is a threshold for the synergistic change of NDVI and groundwater depth. When the groundwater depth is lower than 3.8 m, NDVI increases sharply with groundwater depth. However, beyond this threshold, NDVI tends to stabilize and fluctuate. In the eastern coastal areas, NDVI exhibits a strong positive correlation with groundwater depth, influenced by the surface soil TDS controlled by groundwater depth. In the central regions, a strong negative correlation is observed, where NDVI is primarily impacted by soil moisture under the control of groundwater. In the west and south, a strong positive correlation exists, with NDVI primarily influenced by the intensity of groundwater exploitation. Thus, precipitation and groundwater are the primary driving forces behind the spatiotemporal variability of vegetation in the HB, while in contrast, the influence of temperature is uncertain. This study has elucidated the mechanism of vegetation response, providing a theoretical basis for mitigating adverse factors affecting vegetation growth and formulating rational water usage regulations in the NCP. [ABSTRACT FROM AUTHOR]

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing 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]

Cropland mapping using remote sensing data is the basis for effective crop monitoring, crop rotation control, and the detection of irrational land use. Classification using Normalized Difference Vegetation Index (NDVI) time series from multi-year data requires additional time costs, especially when sentinel data are sparse. Approximation by nonlinear functions was proposed to solve this problem. Time series of weekly NDVI composites were plotted using multispectral Sentinel-2 (Level-2A) images at a resolution of 10 m for sites in Khabarovsk District from April to October in the years 2021 and 2022. Missing values due to the lack of suitable images for analysis were recovered using cubic polynomial, Fourier series, and double sinusoidal function approximation. The classes that were considered included crops, namely, soybean, buckwheat, oat, and perennial grasses, and fallow. The mean absolute percentage error (MAPE) of each class fitting was calculated. It was found that Fourier series fitting showed the highest accuracy, with a mean error of 8.2%. Different classifiers, such as the support vector machine (SVM), random forest (RF), and gradient boosting (GB), were comparatively evaluated. The overall accuracy (OA) for the site pixels during the cross-validation (Fourier series restored) was 67.3%, 87.2%, and 85.9% for the SVM, RF, and GB classifiers, respectively. Thus, it was established that the best result in terms of combined accuracy, performance, and limitations in cropland mapping was achieved by composite construction using Fourier series and machine learning using GB. Similar results should be expected in regions with similar cropland structures and crop phenological cycles, including other regions of the Far East. [ABSTRACT FROM AUTHOR]

Hurricanes Ian and Nicole caused devastating destruction across Florida in September and November 2022, leaving widespread damage in their wakes. This study focuses on the assessment of barrier islands' shorelines, encompassing natural sand dunes and dune vegetation as well as armored dunes with man-made infrastructure such as seawalls. High-resolution satellite imagery from Planet was used to assess the impacts of these hurricanes on the beach shorelines of Volusia, Flagler, and St. Johns Counties on the Florida Central East Coast. Shorefront vegetation was classified into two classes. Normalized Difference Vegetation Index (NDVI) values were calculated before the hurricanes, one month after Hurricane Ian, one month after Hurricane Nicole, and one-year post landfall. LiDAR (Light Detection and Ranging) was incorporated to calculate vertical changes in the shorelines before and after the hurricanes. The results suggest that natural sand dunes were more resilient as they experienced less impact to vegetation and elevation and more substantial recovery than armored dunes. Moreover, the close timeframe of the storm events suggests a compound effect on the weakened dune systems. This study highlights the importance of understanding natural dune resilience to facilitate future adaptive management efforts because armored dunes may have long-term detrimental effects on hurricane-prone barrier islands. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of food cultivation status is important for guaranteeing food security and planning rational cultivation. Significant progress has been seen in crop classification using remote sensing data with high temporal resolution over large areas. However, improvement is needed for cultivation monitoring at the scale of small watersheds. This study combined high-temporal resolution moderate resolution imaging spectroradiometer (MODIS) data and high-spatial resolution Sentinel-2 data to identify key phenological stages of winter wheat and summer corn. MODIS data were used to construct indicators for differences in stages. Extraction thresholds for winter wheat and summer corn were determined using indicators for field sample sites. Different thresholds for key phenological stages were employed to determine the spatial distribution of these crops using Sentinel-2. The spatial distribution of winter wheat and summer corn rotation lands was generated using an overlay. User accuracies of the spatial extraction of wheat and corn rotation lands were 95%, 88.89%, and 86.96% for 2017-2018, 2018-2019, and 2019-2020, respectively (kappa coefficients: 74.24%, 54.47%, and 68.21%). The key phenological stage difference indicator method is suitable for crop classification and spatial extraction at the scale of small watersheds and can allow dynamic monitoring of grain crop cultivation in small-scale areas. [ABSTRACT FROM AUTHOR]

Global climate change has altered the timing of seasonal events (i.e., phenology) for a diverse range of biota. Within and among species, however, the degree to which alterations in phenology match climate variability differ substantially. To better understand factors driving these differences, we evaluated variation in timing of nesting of eight Arctic‐breeding shorebird species at 18 sites over a 23‐year period. We used the Normalized Difference Vegetation Index as a proxy to determine the start of spring (SOS) growing season and quantified relationships between SOS and nest initiation dates as a measure of phenological responsiveness. Among species, we tested four life history traits (migration distance, seasonal timing of breeding, female body mass, expected female reproductive effort) as species‐level predictors of responsiveness. For one species (Semipalmated Sandpiper), we also evaluated whether responsiveness varied across sites. Although no species in our study completely tracked annual variation in SOS, phenological responses were strongest for Western Sandpipers, Pectoral Sandpipers, and Red Phalaropes. Migration distance was the strongest additional predictor of responsiveness, with longer‐distance migrant species generally tracking variation in SOS more closely than species that migrate shorter distances. Semipalmated Sandpipers are a widely distributed species, but adjustments in timing of nesting relative to variability in SOS did not vary across sites, suggesting that different breeding populations of this species were equally responsive to climate cues despite differing migration strategies. Our results unexpectedly show that long‐distance migrants are more sensitive to local environmental conditions, which may help them to adapt to ongoing changes in climate. [ABSTRACT FROM AUTHOR]

The Indo-Pacific, a vast biogeographic of Earth, is influenced by both the Indian and East Asian monsoons. Despite its geographical importance, this region has been less studied compared to East Asia and India. Here, we present speleothem records from southern Thailand that cover the last 1500 years, including a hiatus during the Little Ice Age, providing insights into the interactions among climate dynamics, human influences, and ecological responses to climate change. Notably, our records lack the characteristic cold and warm periods observed in other regions, such as the Dark Ages Cold Period and Medieval Warm Period, which may reflect the complexity of the tropical climate system or the region's unique topography. The analysis reveals a link between ENSO multi-decadal variability and hydroclimate conditions in southern Thailand, as evidenced by speleothem δ18O. Furthermore, a comparison between speleothem δ13C and the Normalized Difference Vegetation Index (NDVI) indicates significant vegetation changes in the last three decades, corresponding with increased atmospheric CO2 levels and expansion of agricultural land due to human activities during the Current Warm Period. Additionally, our study suggests that an abrupt increase in sea surface temperatures may enhance vegetation growth in the Indo-Pacific by influencing atmospheric circulation and increasing precipitation. [ABSTRACT FROM AUTHOR]

There are spatial differences in the correlation between urbanization (NL) and vegetation cover (NDVI), and the driving mechanism is not clear. This study aims to explore the spatial correlation between NL and NDVI in China and identify the influencing factors. The results showed that there was a north-south difference in the correlation, and this correlation was strongest in winter. Meteorological factors were the main influencing factors, Bivariate enhancement is observed for TEM-DEM at the national scale in winter, TEM-relative humidity (RH) in cold spot areas, and TEM-RH in hot spot areas in summer. [ABSTRACT FROM AUTHOR]

This study is the first to examine vegetation change on Kilimanjaro from 2000 to 2022, using the Normalised Difference Vegetation Index (NDVI) at a spatial resolution of 250 m via the Moderate Resolution Imaging Spectrometer (MODIS). There is a long-term increase in vegetation on an annual basis, but with decreases in the long/short rains at low/high elevations. Temperature significantly influences NDVI across all elevations and seasons. Precipitation effects are more variable and often delayed, especially in the forest zone. The study explores how El Nino Southern Oscillation (ENSO) events affect NDVI differently at contrasting elevations, indicating the need for further elevation-specific remote sensing research and field data. [ABSTRACT FROM AUTHOR]

This study develops an early warning system for crop yield (CY) failure based on meteorological drought and vegetation health conditions. The framework combines three drought indices – the Standardized Precipitation Evapotranspiration Index (SPEI), standardized Normalized Difference Vegetation Index (stdNDVI), and standardized CY (stdCY) values – using copulas. Datasets for five major wheat-producing cities in Turkey between 2000 and 2022 are used for analysis. Results indicate that the time periods used to calculate SPEI and NDVI are critical in determining agricultural drought and CY conditions. The critical threshold values for SPEI and NDVI, with a 10% probability of causing agricultural drought, are found to be ~0.28 and ~0.42, respectively. Using a three-dimensional copula model resulted in more precise CY simulations than a two-dimensional model. The validation efforts showed that all of the observed CYs fell within the simulated range, indicating the robustness of the methodology in capturing drought impacts on CY conditions. [ABSTRACT FROM AUTHOR]

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels. [ABSTRACT FROM AUTHOR]

The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha−1). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop's biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha−1, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R2 = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R2 = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha−1. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R2 = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses. [ABSTRACT FROM AUTHOR]

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (Rrs) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and Rrs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited. [ABSTRACT FROM AUTHOR]

This study, employing the AquaCrop model, demonstrated notable efficacy in assessing and predicting crop yields for winter wheat, maize, winter rapeseed, and sugar beets in the Joint Experiment for Crop Assessment and Monitoring (JECAM) test area of Poland from 2018 to 2023. In-situ measurements, conducted through field campaigns, included parameters such as electromagnetic radiation reflectance, Leaf Area Index (LAI), soil moisture, accumulated photosynthetically active radiation, chlorophyll content, and plant development phase. The model was calibrated with input data covering daily climatic parameters from the ERA5-land Daily Aggregated repository, crop details, and soil characteristics. Specifically, for winter wheat, the Root Mean Square Error (RMSE) values ranged from 1.92% to 14.26% of the mean yield per hectare. Maize cultivation showed RMSE values ranging from 0.21% to 1.41% of the mean yield per hectare. Winter rapeseed exhibited RMSE values ranging from 0.58% to 17.15% of the mean yield per hectare. In the case of sugar beets, the RMSE values ranged from 0.40% to 1.65% of the mean yield per hectare. Normalized Difference Vegetation Index (NDVI)-based predictions showed higher accuracy for winter wheat, similar accuracy for maize and sugar beets, but lower accuracy for winter rapeseed compared to Leaf Area Index (LAI). The study contributes valuable insights into agricultural management practices and facilitates decision-making processes for farmers in the region. [ABSTRACT FROM AUTHOR]

Summary: The climatic significance and ecosystem implications of latewood intra-annual density fluctuations (IADFs) are still not fully understood in Mediterranean ring-porous oak species. To solve this issue, we investigated: (i) the climate drivers of radial growth and latewood IADFs in the Mediterranean oak Quercus faginea , and (ii) whether they were correlated to increased canopy cover and greenness as recorded by the Normalized Difference Vegetation Index (NDVI). IADFs were formed in the mid to late latewood and they were characterized by rows of parenchyma cells. Such IADFs were uncommon and only present in 30–50% of sampled trees during 1999 and 2014, both years characterized by high precipitation in late July and early August. This relationship between IADFs formation and summer wet conditions differed from the conditions that enhance Q. faginea annual growth, wet winter-spring conditions, and low summer temperatures. Furthermore, IADFs formation showed a positive relationship with August NDVI values, indicating a correspondence of canopy greenness with secondary growth reactivation. We conclude that latewood IADFs in Q. faginea are a robust proxy of rare late-summer wet conditions and enhanced canopy activity as reflected by increased August NDVI values. Further monitoring of xylogenesis in other Mediterranean hardwoods along climatic gradients is suggested to mechanistically explain IADF production in seasonally dry biomes. [ABSTRACT FROM AUTHOR]

The Great Lakes region of North America has warmed by 1–2 °C on average since pre-industrial times, with the most pronounced changes observable during winter and spring. Interannual variability in temperatures remains high, however, due to the influence of ocean-atmosphere circulation patterns that modulate the warming trend across years. Variations in spring temperatures determine growing season length and plant phenology, with implications for whole ecosystem function. Studying how both internal climate variability and the "secular" warming trend interact to produce trends in temperature is necessary to estimate potential ecological responses to future warming scenarios. This study examines how external anthropogenic forcing and decadal-scale variability influence spring temperatures across the western Great Lakes region and estimates the sensitivity of regional forests to temperature using long-term growth records from tree-rings and satellite data. Using a modeling approach designed to test for regime shifts in dynamic time series, this work shows that mid-continent spring climatology was strongly influenced by the 1976/1977 phase change in North Pacific atmospheric circulation, and that regional forests show a strengthening response to spring temperatures during the last half-century. [ABSTRACT FROM AUTHOR]

The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas. [ABSTRACT FROM AUTHOR]

Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is associated with a lower incidence of dementia. However, many definitions of green space exist, and the effects of its use may differ with the type of green space. Therefore, two types of green space measures were considered in this study to assess the differences in their impact on the prevalence of dementia among females and males. This study used five years of data (2017–2021) from 235 districts in South Korea. The two green space measures used were open space density and normalized difference vegetation index (NDVI), which were derived from satellite images. The analysis utilized a combination of traditional and spatial panel analyses to account for the spatial and temporal effects of independent variables on dementia prevalence. The spatial autocorrelation results revealed that both measures of greenness were spatially correlated with dementia prevalence. The spatial panel regression results revealed a significant positive association between NDVI and dementia prevalence, and open space had a negative association with dementia prevalence in both genders. The difference in the findings can serve as the basis for further research when choosing a greenspace measure, as it affects the analysis results, depending on the objective of the study. This study adds to the knowledge regarding improving dementia studies and the application of spatial panel analysis in epidemiological studies. [ABSTRACT FROM AUTHOR]

When combined with conducive atmospheric conditions, air pollution caused by fossil fuel consumption associated with transportation, industry and electricity production for households, create and sustain continuous pollution over the megalopolis of Tehran, Iran. In conjunction with daily meteorological forecasts, remote sensing can be used to identify and predict days with hazardous levels of air pollution, providing an opportunity for air quality alert systems to be triggered and warnings circulated to reduce health risks to citizens of Tehran province. Combining remotely sensed ozone column density (OCD) data from the Sentinel-5 TROPOMI sensor with NASA Giovanni data concerning meteorological parameters (temperature (T), wind speed (WS) and specific humidity (SH)), geographical parameters and population data, this study considers the drivers and effects of ozone pollution on the urban climate and vegetation condition (normalized difference vegetation index (NDVI)) of 16 counties in Tehran province, Iran during 12 months (i.e., January 2021 to December 2021). Future monthly forecasts of the OCD, climatic and terrestrial factors in 2022 are also presented. Google Earth Engine and the NASA Giovanni platforms were employed for the processing and analysis of data using an interpolation technique. Additionally, a Box–Jenkins ARIMA and Exponential Smoothing (ETS) models were compared and tailored to generate monthly forecasts of OCD, T, WS, SH and NDVI. The highest and lowest OCD was obtained in June and December 2021, with a concentration of 0.14277 mol/m2 and 0.12383 mol/m2, respectively. However, the annual average OCD was higher in the cities of Shahriar and Pakdasht in March, with values of 0.13237 mol/m2 and 0.13244 mol/m2, respectively. The lowest OCD recorded was 0.13105 mol/m2, in Shemiranat city, in the north of Tehran. The results indicate a positive correlation between OCD and NDVI, and a negative correlation between OCD, SH, WS and T. A strong seasonal trend in OCD was identified for all cities, but across the entire province, altitude and population size were the most significant explanatory variables for spatial variations in OCD. This research demonstrates that an effective OCD monitoring and forecasting model may be generated from remote sensing and meteorological variables. The implementation and utilization of these models are of paramount importance as they offer vital information to authorities for continuous air quality monitoring and strategic planning, particularly for days with hazardous air pollution. By effectively implementing the OCD model, it has the potential to directly contribute to improved health outcomes in major cities across Iran. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the spatiotemporal changes of NDVI in the middle reaches of the Yellow River, to clarify the impact and driving forces of natural and human factors on NDVI changes, and to provide scientific basis for ecological protection and development. [Methods] Based on the AVHRR GIMMS NDVI dataset, linear regression analysis, Mann-Kendall test and Geodetector-mode were used to analyze the spatiotemporal evolution characteristics of vegetation NDVI in the middle reaches of Yellow River from 1982 to 2015. The driving forces of natural and human factors affecting vegetation NDVI were also analyzed. [Results] From 1982 to 2015,the NDVI of the middle reaches of Yellow River showed upward trend, with spatial distribution of high level in the southeast and low level in the northwest. The improved vegetation coverage area reached to 70.79%,with a significant improvement area of 49.2%. Annual precipitation was the dominant factor affecting NDVI changes in the middle reaches of Yellow River, and factors such as vegetation type, geomorphic type, land use type and soil type could also explain the vegetation cover status in the middle reaches of Yellow River. The impact of natural factors on vegetation change was much higher than that of human factors, and the result of factor interaction was a dual factor enhancement or non-linear enhancement. [Conclusion] The NDVI of the vegetation in the middle reaches of the Yellow River is mainly affected by annual precipitation, and the vegetation had improved significantly during the 34-year period under the interaction of natural and human factors, and more attention should be paid to the influence of human activities on the vegetation cover in the future. [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]

Urban green spaces benefit physical, mental health, and reduses the risk of cardiovascular disease. A study in Kaunas, Lithuania collected health data from 100 patients with symptomatic heart failure (HF) during 2006–2009. Residential greenness was measured by the normalized difference vegetation index (NDVI). We assessed the impact of greenness on health indicators and on changes in health markers after 6 months. Higher greenness levels based on the NDVI 1-km radius were related to higher mean values of heart rate (HR) and ejection fraction and lower left ventricular (LV) end-diastolic diameter index (LV EDDI), LV end-systolic volume (ESV), left atrium size (LAS), and right atrium size (RAS) at baseline. After 6 months, a decrease in DBP and HR and an improvement in spiroergometric parameters were associated with exposure to high levels of greenness. The long-term rehabilitation group experienced significant changes in spiroergometric indicators. The results confirm that the greenness of the residential environment can improve health indicators in patients with HF. [ABSTRACT FROM AUTHOR]

The study conducted in Jammu District, India, investigates land use and land cover (LULC) transformations over the past three decades using satellite data and remote sensing techniques. Analyzing data from 1990 to 2020, significant changes were observed. Agricultural land expanded by 157.76 km2 (6.71%), barren land by 151.69 km2 (6.45%), and settlements by 96.97 km2 (4.12%). However, vegetation decreased by 389.77 km2 (16.57%), while water bodies experienced minimal changes. Land Surface Temperature (LST) analysis, utilizing MODIS data (2000-2020), revealed warming trends, with temperatures ranging from 15.92°C to 42.77°C in 2010 and 14.04°C to 37.01°C in 2020. Notably, NDVI values peaked in 2020 (0.759) and were lowest in 1990 (−0.243), indicating healthier vegetation and lower surface temperatures. This inverse correlation highlights NDVI's potential as an indicator for assessing vegetation health and its impact on local temperature conditions. Man-Kendall Z statistics indicated negative trends for Tmax and Tmin, while rainfall data showed significant positive trend. Population growth, urbanization, climate change and agricultural intensification emerged as principal drivers of the LULC changes in the region. This study underscores the importance of geospatial tools in monitoring LULC changes, providing valuable insights for policymakers and planners to formulate sustainable land use planning and management strategies. [ABSTRACT FROM AUTHOR]

Vegetation is one of the key components in terrestrial ecosystems, particularly in climate, carbon balance, and water cycling. The healthy growth of vegetation can depend mainly on the context of climate change, such as more frequent occurrences of droughts and heat waves. Moreover, the degradation of vegetation can cause irreversible damage to the structure and functionality of terrestrial ecosystems. Compound hot and dry events can further exacerbate their impact on the vegetation, due to the positive feedback between droughts and heatwaves. Therefore, it is very necessary to evaluate the response relationship between vegetation and compound hot and dry events, in order to clarify the ecosystem response to climate change for effective mitigation and adaptation strategies. The correlation between vegetation and drought or hot indices has been commonly used to determine their relationship in recent years. However, the overall correlation coefficient is often insufficient to capture the tail dependence between extreme events and vegetation conditions. In this study, a Vine Copula-based model was constructed to assess the vulnerability of vegetation using the detrended and standardized normalized vegetation index (SNDVI), standardized precipitation and evapotranspiration index (SPEI), and standardized temperature index (STI). The probability of vegetation loss was determined for the different land use and climatic regions in the Loess Plateau from 1982 to 2015. The results show: 1) There was a positive correlation between SNDVI and SPEI in most areas of the Loess Plateau, whereas, a negative correlation was found between SNDVI and STI. Grasslands shared the highest correlation between SNDVI and SPEI, followed by cropland, and the forests with the lowest correlation. There was no significant difference in the correlation distribution between grassland and cropland SNDVI with SPEI from June to August. However, there was a gradual decrease in the correlation between forest and shrub SNDVI with SPEI. The correlation distribution between grassland and cropland SNDVI with STI remained relatively unchanged, while the correlation between forest and shrub SNDVI with STI gradually increased. 2) The vulnerability values of vegetation to extreme compound dry and hot conditions were 0.51, 0.57, and 0.55 respectively, in June, July, and August, which was significantly higher than those to single drought or high-temperature events. Regions with higher vulnerability were concentrated in the northern part of Shaanxi, Ningxia, eastern Gansu, and Inner Mongolia. 3) The vegetation exhibited a significantly higher vulnerability to extreme compound dry and hot conditions in arid and semi-arid regions, compared with the single extreme drought or high-temperature events. In humid and semi-humid regions, vegetation vulnerability to extreme compound dry and hot conditions was generally comparable or slightly lower than that to single drought or high-temperature events. 4) There was a great variation in the vulnerability of different vegetation types. The vulnerability was ranked in the descending order of grassland, cropland, shrub, forest. The vulnerability of grassland to extreme compound dry and hot conditions increased by 26% to 56% during summer, compared with single drought or high temperature events, while the cropland vulnerability increased by 11% to 24% and 19% to 48%, respectively. Since the SNDVI can only represent the vegetation cover, more vegetation indices (such as gross primary productivity (GPP) and net primary productivity (NPP)) can be expected to assess the vegetation dynamics and their response to extreme events, in order to reduce some uncertainties in the future research. [ABSTRACT FROM AUTHOR]

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]

Taking the MODIS NDVI data, land use data and meteorological data of Heilongjiang Province from 2000 to 2020 as the research object, three main types of land cover, farmland, forest land and grassland, and two climate factors, temperature and precipitation, were extracted to explore the spatial and temporal changes of the vegetation cover index (NDVI) in Heilongjiang Province in the past 21 years and the relationship between vegetation changes and climate factors. Using Theil-Sen and Mann-Kendall trend tests, coefficient of variation, Hurst index, and correlation analysis methods, Using Theil Sen trend estimation, Mann Kendall trend test, coefficient of variation, Hurst index, and correlation analysis methods, this study analyzed the spatiotemporal variation characteristics of NDVI in different seasons and vegetation types in Heilongjiang Province from 2000 to 2020, as well as the relationship between NDVI and temperature and precipitation. The results indicated that, 1) in the past 21 years, the average annual NDVI of the growth season in Heilongjiang Province had been 0. 91, and the high vegetation coverage area was mainly distributed in the central and southern parts of Heilongjiang Province. There were obvious seasonal characteristics of NDVI fluctuations within the year. 2) The NDVI in the province was relatively stable, with the highest stability in grassland NDVI. 3) In the past 21 years, the overall NDVI in Heilongjiang Province had shown a significant upward trend, with a rate of 4. 7% increase every 10 years. The regions showing a significant increasing trend accounted for 35. 55% of the province's area (P<0. 05), mainly distributed in the central, western, and northern parts of Heilongjiang Province. 4) In the future, the overall trend of NDVI in Heilongjiang Province will mainly be continuous changes in the same direction (69. 5%), with 35. 33% likely to develop towards a malignant direction of degradation and 43. 6% likely to develop towards a benign direction of improvement. 5) The vegetation and seasons in Heilongjiang Province were positively correlated with precipitation and temperature (P<0. 05). As a result, the overall vegetation coverage level in Heilongjiang Province has been good in the past 21 years and shows an improvement trend. The development trend of future vegetation cover changes is mainly towards benign improvement, and temperature and precipitation have a positive correlation with vegetation change. [ABSTRACT FROM AUTHOR]

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate. [ABSTRACT FROM AUTHOR]

Leveraging mid-resolution satellite images such as Landsat 8 for accurate farmland segmentation and land change monitoring is crucial for agricultural management, yet is hindered by the scarcity of labelled data for the training of supervised deep learning pipelines. The particular focus of this study is on addressing the scarcity of labelled images. This paper introduces several contributions, including a systematic satellite image data augmentation approach that aims to maintain data population consistency during model training, thus mitigating performance degradation. To alleviate the labour-intensive task of pixel-wise image labelling, we present a novel application of a modified conditional generative adversarial network (CGAN) to generate artificial satellite images and corresponding farm labels. Additionally, we scrutinize the role of spectral bands in satellite image segmentation and compare two prominent semantic segmentation models, U-Net and DeepLabV3+, with diverse backbone structures. Our empirical findings demonstrate that augmenting the dataset with up to 22.85% artificial samples significantly enhances the model performance. Notably, the U-Net model, employing standard convolution, outperforms the DeepLabV3+ models with atrous convolution, achieving a segmentation accuracy of 86.92% on the test data. [ABSTRACT FROM AUTHOR]

Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation's tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future "vegetation-climate" models. [ABSTRACT FROM AUTHOR]

The glacier database provides an opportunity to track its dynamics and address issues linked with water resources, hazards and sustainability. To better understand the dynamics of Eastern Dhauliganga basin, we mapped the clean and debris‐covered glaciers by using satellite images from Landsat‐series and Sentinel 2. The band‐ratio based methods—NDSI (Normalised Difference Snow Index) and NDVI (Normalised Difference Vegetation Index) were used to map the clean ice glaciers. The debris‐covered glaciers were delineated using topographic parameters and thermal band of Landsat TM. The glacier extent and length change were monitored for 25 glaciers in the basin, of which 13 are clean ice and 12 are debris‐covered glaciers. During the time period from 1994 to 2018, higher area loss was observed for clean ice glaciers (±20.6%) compared with debris‐covered glaciers (±10.9%). The clean‐ice glaciers retreated at a rate of 4.8–33.7 m/year, whereas debris‐covered glaciers retreated at a rate of 2.9–28.8 m/year during 1994–2018. [ABSTRACT FROM AUTHOR]

Javan Langur (T. auratus) is well-known as one of endemic primates from Java, Bali and Lombok Islands. The activities of land clearing, vegetation conversing, wild hunting and illegal wildlife trading are the main causes of the extinction of the Javan Langur. It can be used as an important issue for conservation action by making prediction maps of suitable habitat potential, especially for species facing a high risk of extinction in the wild. We were documenting an information about potential habitat for Javan langur using spatial suitability model in order to provide rigorous information as the basis for conservation activities of Javan langur in TNBTS. We used Landsat-8 TM image and geospatial data to support analysis as a representative of environmental parameters in order to develop the habitat model. We were using maximum entropy (MaxEnt) algorithm refers to Javan langur presence or absence. The results showed that the suitability of the Javan langur habitat in TNBTS has an excellent model accuracy level with an AUC (Area Under the Receiver Operating Characteristics) value of 0.964 and a standard deviation of 0.961. Parameters with the highest response values here are elevation, NDVI (Normalised Difference Vegetation Index) and temperatures. [ABSTRACT FROM AUTHOR]

There is valuable information that can be obtained beyond using a fixed crop calendar with coarse spatial resolution. Knowing the dynamics of the timing and location in which a particular crop is planted and harvested, with an annual temporal resolution and a fine spatial resolution, is crucial not only for monitoring crop conditions and production but also for understanding crop management under changing climates. In this study, the Normalized Difference Vegetation Index (NDVI) was utilized to develop a historical crop calendar for paddy in Indonesia with a 1 km resolution from 2001 to 2021. The result of this study is the first dynamic crop calendar that includes information about the planting, peak, and harvesting dates, as crop growth indicators, derived from the analysis of NDVI value fluctuations. Additionally, this dataset also includes the total number of cropping seasons each year. In Indonesia, there are intensive agricultural activities, including two dry cropping seasons that occur after the wet cropping season. However, this dataset is limited only to crops grown during the dry seasons, which typically begin in February and June. This dataset offers significant information at a finer spatiotemporal resolution to enable studies on agricultural fields undergoing climate change, although it is more country–specific than the other established dataset. The annual crop calendar dataset from 2001 to 2021 underscores the significance of examining the variability in cropping seasons over the years. This exploration aims to deepen our comprehension of the interplay between cropping seasons, climatic indicators, and even the social factors influencing farmers' decisions. Furthermore, presented at a 1 km resolution, this dynamic crop calendar underscores the need for a more precise representation of diverse cropping intensities and seasons, particularly within small and fragmented agricultural areas. [ABSTRACT FROM AUTHOR]

White lupine (Lupinus albus L.) is a well-known green manure crop in Hungary, but the production of seeds can be badly impacted by weeds. The sweet white lupine 'Nelly' was grown on acidic sandy soil, and experimental plots were treated with different herbicides. Flumioxazin (0.06 kg ha−1), pendimethalin (5 L ha−1), dimethenamid-P (1.4 L ha−1), pethoxamid (2 L ha−1), clomazone (0.2 L ha−1), metobromuron (3 L ha−1), and metribuzin (0.55 L ha−1) were applied pre-emergence (1–2 days after sowing). Imazamox was also tested and applied post-emergence (1 L ha−1) when some basal leaves were clearly distinct (BBCH 2.3). In this paper, the weed control efficiency and the phytotoxicity of herbicides applied to lupine are examined. Vegetation index datasets were collected 12 times using a manual device and 2 times using an unmanned aerial vehicle (UAV). The phytotoxicity caused by herbicides was visually assessed on several occasions throughout the breeding season. The frequency of weed occurrence per treatment was assessed. The harvested seed yields, in kg ha−1, were analyzed after the seeds were cleaned. The herbicides metribuzin and imazamox caused extensive damage to white lupine. While pendimethalin, dimethenamid-P, pethoxamid, and clomazone were outstanding in several measured indicators, the final ranking which summarizes all the variables showed that only the pethoxamid and clomazone treatments performed better than the control. Metribuzin and imazamox were highly phytotoxic to white lupine. In the future, it would be appropriate to integrate more post-emergence active substances into trials, and the pre-emergence herbicides involved in this study should be further tested. [ABSTRACT FROM AUTHOR]

We hereby present VolcFire, a new cellular automaton model for fire propagation aimed at the creation of fire hazard maps for fires of volcanic origin. The new model relies on satellite-derived input data for the topography, land-use, fuel, and humidity information, and produces probabilistic maps of fire propagation simulating fire spread. The model contains several simplifications compared to the current state-of-the-art, limiting its usability to plan fire-fighting interventions during an event in favour of a reduced computational load. The accuracy and reliability of the model are also discussed by presenting its ability to reproduce two recent fires on Stromboli island, with good spatial fit (Brier score of 0.146 ± 0.002 for the 3 July 2019 volcanic fire, and of 0.073 ± 0.001 for the 25 May 2022 anthropogenic fire) and less than 1.5 % variation across multiple simulations for the same event. [ABSTRACT FROM AUTHOR]

Water resources must be preserved because they are vital for agricultural, industrial, and domestic use, especially in tropical forest regions. Given the importance of the Ulu Muda Forest Reserve to the Kedah state in sustaining paddy cultivation for the country, this study aimed to provide reliable forest classification method and produce Normalized Difference Vegetation Index-Maximum Likelihood Classification of forest density map for Ulu Muda Forest Reserve. Here, the study reports measurements of the satellite remote sensing index for the forest based the image date. The forest recorded NDVI index of 0.72 to 0.98 for high density, NDVI index of 0.61 to 0.71 for moderate and NDVI index of 0.26 to 0.60 for low density area. The index showed higher value, which is comparable with reports of other tropical forest elsewhere. The results were influenced using satellite remote sensing pre-processing, processing, and classification. As a result, based on the satellite image indices approach, upper threshold of the higher density, the forest can exhibit higher capability for sustaining its ecological and hydrological functions. Given that most of the tropical forest biome has sustained this threshold, forest management should be sustained at this state, and more measures should be taken for example increase of protection forest classification in Rancangan Pengurusan Hutan (RPH) that is one of yearly planning report in states of Peninsular Malaysia. In the meantime, Geographical Information System (GIS) capacity should be mandate looking forwards for more utilisation in mapping for planning and projection of forest resource management activities. [ABSTRACT FROM AUTHOR]

How accurately do Sentinel-2 (S2) images describe vine row spatial variability? Can they produce effective management zones (MZs) for precision viticulture? S2 and UAV datasets acquired over two years for different drip-irrigated vineyards in the Colli Morenici region (northern Italy) were used to assess the actual need to use UAV-NDVI maps instead of S2 images to obtain effective MZ maps. First, the correlation between S2 and UAV-NDVI values was investigated. Secondly, contingency matrices and dichotomous tables (considering UAV-MZ maps as a reference) were developed to compare MZ maps produced using S2 and UAV imagery. Moreover, data on grape production and quality were analyzed through linear discrimination analyses (LDA) to evaluate the effectiveness of S2-MZs and UAV-MZs to explain spatial variability in yield and quality data. The outcomes highlight that S2 images can be quite good tools to manage fertilization based on the within-field vigor variability, of which they capture the main features. Nevertheless, as S2-MZs with low and high vigor were over-estimated, S2-MZ maps cannot be used for high-accuracy input management. From the LDA results, the UAV-MZs appeared slightly more performant than the S2-MZs in explaining the variability in grape quality and yield, especially in the case of low-vigor MZs. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, irrigation is crucial to mitigate water stress and yield loss. However, the overexploitation of water resources by the agricultural sector together with the climate change effects can lead to water scarcity. Effective regional water management depends on estimating irrigation demand using maps of irrigable areas or national and regional statistics of irrigated areas. These statistical data are not always of reliable quality because they generally do not reflect the updated spatial distribution of irrigated and rainfed fields. In this context, remote sensing provides reliable methods for gathering useful agricultural information from derived records. The combined use of optical and radar Earth Observation data enhances the probability of detecting irrigation events, which can improve the accuracy of irrigation mapping. Hence, we aimed to utilize Sentinel-1 (VV and VH) and Sentinel-2 (NDVI) data to classify irrigated fruit trees and rainfed ones in a study area located in the Castilla La-Mancha region in Spain. To obtain these time-series data from Sentinel-1 and Sentinel-2, which constitute the input data for the classification algorithms, a tool has been developed for automating the download from the Sentinel Hub. This tool downloads products organized by tiles for the region of interest and for the entire required time-series, ensuring the spatial repeatability of each pixel across all products and dates. The classification of irrigated plots was carried out by SVM Support Vector Machine. The employed methodology displayed promising results, with an overall accuracy of 88.4%, indicating the methodology's ability to detect irrigation over orchards that were declared as non-irrigated. These results were evaluated by applying the change detection method of the σ p 0 backscattering coefficient at plot scale. [ABSTRACT FROM AUTHOR]

The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C. [ABSTRACT FROM AUTHOR]

Simple Summary: Flower-visitor communities and plant-pollinator interactions are species- and region-specific and may vary spatially and temporally. We studied flower visitor interactions with curry plants, considering both daily fluctuations and differences across zones characterized by varying vegetation densities (i.e., dense, medium-density, and low-density vegetation). The visitors' richness, diversity, and abundance were higher in the area with dense vegetation. Specifically, between 10.00 h and 14.00 h, these parameters reached their peak, contrasting with lower activity observed during early mornings and late afternoons. For most visitors, the flower handling time was lower, and the visitation rate was higher in dense vegetation areas (at 10.00–14.00 h) than in medium- and low-density vegetation areas. The proportions of foraging categories varied over time, with higher ratios of mixed foragers observed in the early morning. Bee species, such as Apis cerana, Apis dorsata, Halictus acrocephalus, Nomia iridescens, and Tetragonula iridipennis, and butterfly species, such as Appias libythea, were the most effective pollinators of curry plants. The effectiveness of pollinators also varied spatially and remained region-specific. In conclusion, flower-visitor communities and plant-pollinator interactions varied spatially and temporally. The reproductive success of flowering plants relates to flower-visitor communities and plant-pollinator interactions. These traits are species- and region-specific and vary across regions, pollinator groups, and plant species. However, little literature exists on the spatiotemporal variation in visitor activity, especially in India. Here, we aimed to depict the spatial and temporal variation in visitor activity on the curry plants (Bergera koenigii). Data were collected at different daytime slots from three vegetation zones (confirmed by field surveys and normalized difference vegetation index values in remote sensing)—dense, medium-density, and low-density vegetation in West Bengal, India. The visitors' richness, diversity, and abundance were higher in the area with dense vegetation. Considering daytime patterns, higher values for these parameters were obtained during 10.00–14.00 h. For most visitors, the flower handling time was shorter, and the visitation rate was higher in dense vegetation areas (at 10.00–14.00 h) than in medium- and low-density vegetation areas. The proportions of different foraging categories varied over time. Vital pollinators were Apis cerana, Apis dorsata, Appias libythea, Halictus acrocephalus, Nomia iridescens, and Tetragonula iridipennis. However, the effectiveness of pollinators remained region-specific. Therefore, it can be concluded that floral visitors' richness, diversity, abundance, and plant-visitor interactions varied spatially with their surrounding vegetation types and also changed daytime-wise. [ABSTRACT FROM AUTHOR]

Single phenological measures, like the average rate of phenological advancement, may be insufficient to explain how climate change is driving trends in animal populations. Here, we develop a multifactorial concept of spring phenology—including the onset of spring, spring duration, interannual variability, and their temporal changes—as a driver for population dynamics of migratory terrestrial species in seasonal environments. Using this conceptual model, we found that effects of advancing spring phenology on animal populations may be buffered or amplified depending on the duration and interannual variability of spring green‐up, and those effects are modified by evolutionary and plastic adaptations of species. Furthermore, we compared our modelling results with empirical data on normalized difference vegetation index‐based spring green‐up phenology and population trends of 106 European landbird finding similar associations. We conclude how phenological changes are expected to affect migratory bird populations across Europe and identify regions that are particularly prone to suffer population declines. [ABSTRACT FROM AUTHOR]