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In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. [ABSTRACT FROM AUTHOR]

Exploring the spatiotemporal variation characteristics of vegetation in the confluent area of water systems in western Jinan and its response mechanism to climatic factors is of great significance for the scientific evaluation of the benefits of the water system connectivity project and eco-environmental protection and can provide a reference for ecotourism development in the Jixi wetland park. Based on the Landsat series of images and meteorological data, this study used ENVI to interpret the normalized difference vegetation index (NDVI) of the confluent area from 2010 to 2021, and the spatiotemporal change characteristics and trends of NDVI were quantitatively analyzed. The response of the growing-season NDVI (GSN) to climate factors and its time-lag effect were explored. The results showed that the overall change in the interannual NDVI in the confluent area from 2010 to 2021 was stable. The GSN in the confluent area was significantly positively correlated with precipitation, average temperature, and relative humidity in 37.64%, 25.52%, and 20.87% of the area respectively, and significantly negatively correlated with sunshine hours in 15.32% of the area. There was a time-lag effect on the response of the GSN to climate factors; the response to precipitation and sunshine hours lagged by 1 month, and the response to average temperature and relative humidity was longer. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa subsp. pauca ST53 (XFP), the causal agent of olive quick decline syndrome (OQDS), was thoroughly investigated after a 2013 outbreak in the Salento region of Southern Italy. Some trees from Ogliarola Salentina and Cellina di Nardò, susceptible cultivars in the Gallipoli area, the first XFP infection hotspot in Italy, have resprouted crowns and are starting to flower and yield fruits. Satellite imagery and Normalized Difference Vegetation Index analyses revealed a significant improvement in vegetation health and productivity from 2018 to 2022 of these trees. Lipid molecules have long been recognized as plant defense modulators, and recently, we investigated their role in XFP-positive hosts and in XFP-resistant as well as in XFP-susceptible cultivars of olive trees. Here, we present a case study regarding 36 olive trees (12 XFP-positive resprouting, 12 XFP-positive OQDS-symptomatic, and 12 XFP-negative trees) harvested in 2022 within the area where XFP struck first, killing millions of trees in a decade. These trees were analyzed for some free fatty acid, oxylipin, and plant hormones, in particular jasmonic and salicylic acid, by targeted LC-MS/MS. Multivariate analysis revealed that lipid markers of resistance (e.g., 13-HpOTrE), along with jasmonic and salicylic acid, were accumulated differently in the XFP-positive resprouting trees from both cultivars with respect to XFP-positive OQDS symptomatic and XFP-negative trees, suggesting a correlation of lipid metabolism with the resprouting, which can be an indication of the resiliency of these trees to OQDS. This is the first report concerning the resprouting of OQDS-infected olive trees in the Salento area. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems are vital to arid environments like Saudi Arabia, offering crucial ecological services and enhancing biodiversity. This study investigates the spatial distribution and temporal dynamics of mangrove coverage in the Al Wajh lagoon area, crucial for crafting effective conservation and management strategies. Employing high-resolution Landsat and Sentinel imagery, the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were analysed across 35 mangrove clusters. By 2023, mangroves covered approximately 24.5 km2. Kendall Tau-b Correlation Coefficients confirmed a significant positive trend. The study further explored the relationship between NDVI and surface temperature, revealing an inverse correlation with slope values ranging from -4.03 to -44.1, suggesting that increased mangrove coverage significantly contributes to local cooling, thereby underscoring the climate regulation benefits of mangrove conservation. This positive change in NDVI post-2020 is directly attributable to the conservation efforts embedded within the Red Sea Project, Vision 2030, development phases, demonstrating the critical role of designated conservation zones in mitigating the environmental impacts of large-scale development projects. The findings underscore the need for targeted policies to protect and expand mangrove habitats as part of broader environmental sustainability goals in the region. [ABSTRACT FROM AUTHOR]

Efficient drought monitoring in the Urmia Lake basin (ULB) is imperative to protect its ecosystem, agriculture, and the livelihoods of local communities relying on its water resources, considering the lake's susceptibility to changes in water availability. This study presents a novel approach to address the pressing issue of precise drought monitoring in regions with limited and unevenly distributed weather stations. By utilizing Tropical Rainfall Measuring Mission (TRMM) satellite-derived data and a Geographically Weighted Regression (GWR) model, we have significantly refined the spatial resolution of TRMM-3B43 precipitation (5 km) compared with the original TRMM data (about 25 km). This innovative methodology, which uses the globally available MODIS data of Normalized Difference Vegetation Index (NDVI) and day-night difference in land surface temperature (LSTdn) as independent variables, achieves spatial downscaling, enhancing the spatial resolution to 5 km for the period 2001–2019. The downscaled precipitation data from three models (M1: GWR_NDVI, M2: GWR_LSTdn, and M3: GWR_NDVI-LSTdn) were applied for drought assessment based on the Standardized Precipitation Index (SPI) and modified Rainfall Anomaly Index (mRAI). The findings demonstrate that the M2 model is more accurate than the other models for spatial downscaling of TRMM 3B43 data, with reductions in RMSE and MAE values by 6.04 and 4.16 mm, respectively. Additionally, this downscaling model significantly improves KGE values from 0.56 to 0.92 while achieving the lowest percentage bias. Monthly downscaled TRMM data based on LST across 12 synoptic stations in the basin reveals enhanced accuracy post-downscaling. Spatial analysis of average monthly precipitation maps illustrates a descending rainfall trend from June to September, followed by an ascending trend from October, with peak rainfall in November and December, notably in the western and southwestern regions. This analysis of precipitation trends offers valuable insights into the spatial distribution of rainfall within the basin, revealing variations across regions crucial for effective water resource management. The concentration of higher precipitation levels in the western and southwestern sectors underscores the significance of targeted water conservation and storage efforts. Drought severity analysis unveiled persistent and escalating drought conditions, causing significant impacts across the basin. A comparative assessment of severity classes using SPI and mRAI indices at 12 synoptic stations demonstrated strong agreement. Drought occurrences were a near-annual affair, attaining severe levels during specific years, notably 2008, 2011, and 2019. Spatial analysis revealed widespread drought events affecting nearly half of the basin area, with a noticeable worsening in severity during critical years. This highlights the practical need for adopting drought mitigation strategies and water resources management. [ABSTRACT FROM AUTHOR]

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) RNDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while RNDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of RNDVI-GP and RNDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in RNDVI-GP but an insignificant increase in RNDVI-GT, while no significant temporal changes in RNDVI-GP or RNDVI-GT were observed in the barren land. (3) The fluctuations in RNDVI-GP and RNDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in RNDVI-GP but an increase in RNDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both RNDVI-GP and RNDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations. [ABSTRACT FROM AUTHOR]

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP. [ABSTRACT FROM AUTHOR]

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

In this study, a multilevel drought-induced resistance and resilience analysis (MDRRA) approach was developed to investigate the stability of vegetation in the Yellow River Basin (YRB). MDRRA was quantified by utilizing the Normalized Difference Vegetation Index (NDVI). It was applied to YRB to assess vegetation resistance and resilience to various levels of drought by utilizing precipitation and NDVI data from 2000 to 2019. The results reveal that vegetation resistance and resilience in YRB are affected by drought severity. Monthly and annual changes in SPI over the warm–temperate humid zone of the YRB show a decreasing trend, with rates of 0.001 per decade and 0.034 per decade, respectively; however, the other climatic subregions exhibit an increasing trend, with rates ranging from 0.002 per decade to 0.82 per decade. Over 77.56% of the downstream areas show increases in the annual SPI averages. Drought severity differs across subregions in the YRB. More severe drought events occur in its upper and middle reaches, while less severe ones happen in its lower reaches. As the drought severity increases, the arid and semiarid regions of the mesothermal zone exhibit a decrease in the resistance and resilience indices. MDRRA can help improve the stability and resilience of the ecosystem in the YRB. [ABSTRACT FROM AUTHOR]

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

Dry spells strongly influence biomass production in forest ecosystems. Their effects may last several years following a drought event, prolonging growth reduction and therefore restricting carbon sequestration. Yet, our understanding of the impact of dry spells on the vitality of trees' above-ground biomass components (e.g., stems and leaves) at a landscape level remains limited. We analyzed the responses of Pinus sylvestris and Picea abies to the four most severe drought years in topographically complex sites. To represent stem growth and canopy greenness, we used chronologies of tree-ring width and time series of the Normalized Difference Vegetation Index (NDVI). We analyzed the responses of radial tree growth and NDVI to dry spells using superposed epoch analysis and further explored this relationship using mixed-effect models. Our results show a stronger and more persistent response of radial growth to dry spells and faster recovery of canopy greenness. Canopy greenness started to recover the year after the dry spell, whereas radial tree growth remained reduced for the two subsequent years and did not recover the pre-drought level until the fourth year after the event. Stem growth and canopy greenness were influenced by climatic conditions during and after drought events, while the effect of topography was marginal. The opposite responses of stem growth and canopy greenness following drought events suggest a different impact of dry spells on trees´ sink and source compartments. These results underscore the crucial importance of understanding the complexities of tree growth as a major sink of atmospheric carbon. [ABSTRACT FROM AUTHOR]

Monitoring agriculture by remote sensing enables large‐scale evaluation of biomass production across space and time. The normalized difference vegetation index (NDVI) is used as a proxy for green biomass. Here, we used satellite‐derived NDVI of arable farms in the Netherlands to evaluate changes in biomass following conversion from conventional to organic farming. We compared NDVI and the stability of NDVI across 72 fields on sand and marine clay soils. Thirty‐six of these fields had been converted into organic agriculture between 0 and 50 years ago (with 2017 as reference year), while the other 36 were paired control fields where conventional farming continued. We used high‐resolution images from the Sentinel‐2 satellite to obtain NDVI estimates across 5 years (January 2016–October 2020). Overall, NDVI did not differ between conventional and organic management during the time series, but NDVI stability was significantly higher under organic management. NDVI was lower under organic management in sandy, but not in clay, soils. Organic farms that had been converted less than ~19 years ago had lower NDVI than conventional farms. However, the difference diminished over time and eventually turned positive after ~19 years since the conversion. NDVI, averaged across the 5 years of study, was positively correlated to soil Olsen‐P measured from soil samples collected in 2017. We conclude that NDVI in organic fields was more stable than in conventional fields, and that the lower biomass in the early years since the transition to organic agriculture can be overcome with time. Our study also indicates the role of soil P bioavailability for plant biomass production across the examined fields, and the benefit of combining remote sensing with on‐site soil measurements to develop a more mechanistic understanding that may help us navigate the transition to a more sustainable type of agriculture. [ABSTRACT FROM AUTHOR]

In recent years, with the sustained attention from academia and media to urban air quality and environmental issues, governments and scholars worldwide have been devoted to studying the relationship between air quality and green infrastructure (GI), seeking effective measures to address urban air pollution. This study aims to explore the impact of GI on urban air quality, focusing on analyzing data from Ulsan and Junpo cities in South Korea. Significant statistical significance has been found through correlation analysis between GI area and air pollutants such as nitrogen dioxide, carbon monoxide, particulate matter, and ozone. Specifically, when calculating GI using the Normalized Difference Vegetation Index (NDVI) data, for every 1% increase in GI area, nitrogen dioxide (NO2) decreases by approximately 0.000000001925 ppm. This finding offers important insights for future air quality research and provides empirical evidence for urban planning, emphasizing the significance of green space planning in improving air quality. Additionally, the study suggests that future research should be more diverse, incorporating data from more cities and conducting an in-depth analysis of GI distribution to understand its impact on air quality comprehensively. [ABSTRACT FROM AUTHOR]

Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the "Water Tower of China". We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world. [ABSTRACT FROM AUTHOR]

Several studies have been carried out in the West Africa Sahel; however, there is limited data on agroforestry studies in two distinct ecosystems. Moreover, livelihoods are facing growing uncertainties due to climate change and extensive anthropogenic activities. The majority of the population relies heavily on ecosystem services. The aim of this study was to determine the services provided by agroforestry parklands in two distinct villages. Initially, a survey involved 131 individual farmers (68 in Dan Saga and 63 in Sekoukou village) to highlight the benefits derived from agroforestry parklands for rural communities. Subsequently, a forest inventory employed a systematic random sampling approach along transects, including 40 plots in Dan Saga and 32 plots in Sekoukou (each. Finally, the study utilized the Normalized Difference Vegetation Index (NDVI). The study documented 25 different woody species in Dan Saga and 24 in Sekoukou village. There was a greater prevalence of species from the Fabaceae and Balanitaceae family, followed by Anacardiaceae and Combretaceae. Dendrometric surveys revealed a relatively low tree density (23 stems/ha in Dan Saga and 14 stems/ha in Sekoukou), a bell-shaped diameter distribution indicating a significant dominance of intermediate diameter trees ([20 cm; 30 cm[), and a limited representation of trees with extreme diameters ([0; 10 cm[ and [70 cm; → [). The analysis of variances revealed a significant distinction (P-value < 0.05) in all dendrometric parameters and carbon content, excluding tree density and diameter. The Shannon–Wiener index (reflecting intra-ecosystem diversity) and the Pielou equitability index were relatively low but comparable to the mean Sørensen index. The NDVI of both areas fluctuated between 0 and 1 from 2001 to 2013, illustrating evolving vegetation (biomass) dynamics. This evolution was characterized by minimal vegetation cover in 2001, which subsequently increased by 2013 in both villages. The importance of land restoration in Niger has become a crucial and beneficial activity for the population in recent years. [ABSTRACT FROM AUTHOR]

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change. [ABSTRACT FROM AUTHOR]

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition. [ABSTRACT FROM AUTHOR]

China's vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use. [ABSTRACT FROM AUTHOR]

In Mexico, viticulture represents the second source of employment in the agricultural area after the fruit and vegetable sector. In developed countries, remote sensing is widely used for vineyard monitoring; however, this tool is barely used in the developing countries of Iberoamerica. In this research, our overall objective is to characterise two vineyards in the state of Queretaro (Mexico) using Sentinel-2 and meteorological data, specifically spectral and thermal indices. Results show that spectral indices obtained from Sentinel-2 bands have adequately characterised the phenological dynamics of the different varieties of the vineyards. The Modified Soil-Adjusted Vegetation Index (MSAVI) was adequately used to discriminate between the first stages of vineyards, while the Normalized Difference Vegetation Index (NDVI) was useful for monitoring vineyards during the rest stages of vineyards. Thermal indices have shown that the best grape varieties are those that can adapt to both cooler and warmer temperatures, have a reasonable ripening period, and can produce wines with balanced acidity and flavours. In conclusion, the combination of meteorological (including thermal indices) and remote sensing data (NDVI and MSAVI) provide information for choosing a suitable grape variety for this region. [ABSTRACT FROM AUTHOR]

Wood distillate (WD) has recently emerged as a promising bio-stimulant for sustainable legume crop production, owing to its ability to enhance seed yield and quality. However, no studies exist on the effects of WD on chickpea plants at pre-harvesting stages, hindering the farmers' ability to acquire valuable knowledge on the early action of WD on the plants' status and preventing the establishment of proactive measures to optimize WD use in agriculture. In this study, two multispectral, thermographic and spectroradiometric surveys, along with in-situ measurements of specific plant biometric traits, were conducted across the reproductive stage of field-grown chickpea in order to evaluate the early involvement of WD on plant health. The acquired multispectral images were used to calculate the Normalized Difference Vegetation Index (NDVI), revealing a notable ~35% increase in NDVI scores of WD-treated plants at the onset of physiological maturity, and indicating an improved plant status compared to the control (water-treated) plants. Moreover, control and WD-treated plants exhibited distinct spectral signatures across the visible, near-infrared (NIR) and short-wave infrared (SWIR) spectra, suggesting potential changes in their photosynthetic capacity, structural properties and water content both at the leaf and at the pod level. Furthermore, WD-treated plants showed a 25% increase in pod production, particularly at the beginning of seed maturity, suggesting that enhancements in plant status were also reflected in higher pod yields. These results point to a beneficial effect of WD on plant health during the preliminary stages of seed formation and indicate that a combination of both multispectral and spectroradiometric analyses can provide critical insights on the status of chickpea crops at pre-harvesting stages. In addition, these findings emphasize the importance of analyzing pre-harvesting stages to gain insights into the early involvement of WD in promoting plant health and, ultimately, in predicting final crop yields. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive evaluation of the effects of these policies at a regional scale and an analysis of the changes in policy implementation stages to optimize the strategic direction of regional ecological policies. In this study, using mathematical statistics and spatial analysis, we analysed the spatial and temporal characteristics of the Normalized Difference Vegetation Index (NDVI) in Qinghai Province from 2000 to 2023. Further, by systematically reviewing ten major ecological policies currently or previously implemented in the region, we explored the response of vegetation restoration to these policies through both horizontal and vertical evaluations by reasonably delineating the policy study sub-zones. The study identified distinct stages of policy implementation—regreening, stabilizing, and natural recovery—and correlated these stages with the efficacy of policy impacts. Our findings indicate significant vegetation coverage improvements across Qinghai Province over the past two decades, with all ecological policies positively influencing the environment. The main contribution of this study is that it comprehensively evaluates the impact of multiple ecological policies on vegetation restoration at the regional scale, providing a reference for the formulation and adjustment of subsequent ecological policies. [ABSTRACT FROM AUTHOR]

Since 2002, numerous sustainable land management (SLM) interventions have been implemented in Ethiopia, such as agroforestry, area closure, forage development, gully rehabilitation, and conservation agriculture. In addition, watershed-based developments contributed comprehensively to a better use of existing natural resources. This study determined the impact of Integrated Watershed Management (IWM) on land use/land cover for the Yesir watershed in Northern Ethiopia. Supervised image classification algorithms were applied to a time series of Landsat 5 (2002) and Landsat 8 (2013 and 2022) images to produce land use/land cover maps. A Geographic Information System was applied to analyze and map changes in land use/land cover for settlements, agricultural land, grazing land, and land covered with other vegetation. In focus group discussions, the time series maps were analyzed and compared with the integrated watershed management practices to analyze their impacts. The results document that integrated watershed management practices have contributed to a significant change in land use/land cover in the study area over the past 20 years. The quantitative analysis of land use/land cover between the years 2002 and 2022 only revealed a downward trend in agricultural land. Considering the value of the Normalized Difference Vegetation Index (NDVI) as a biophysical feature for the increase of green mass, this indicator also documents an improvement in land use/land cover with regard to sustainable land management and consequently poverty alleviation. [ABSTRACT FROM AUTHOR]

Protected areas in South Asia face significant challenges due to human disturbance and deforestation. The ongoing debate surrounds the recent surge in illegal encroachment of forest buffer zones in the Musali divisional secretariat division (DSD), which has led to a significant loss of forest cover over the past three decades. In this context, detecting changes in forest cover, assessing forest health, and evaluating environmental quality are crucial for sustainable forest management. As such, our efforts focused on assessing forest cover dynamics, forest health, and environmental conditions in the DSD from 1988 to 2022. We employed standardized image processing techniques, utilizing Landsat-5 (TM) and Landsat-8 (OLI) images. However, the forest area in the DSD has shown minimal changes, and environmental conditions and forest health have illustrated considerable spatial-temporal variations over the 34 years. The results indicated that 8.5 km2 (1.9%) of forest cover in the DSD has been converted to other land use classes. Overall, the Normalized Difference Vegetation Index (NDVI) has declined over time, while Land Surface Temperature (LST) exhibits an increasing trend. The regression results demonstrated a robust inverse relationship between LST and NDVI. The declining vegetation conditions and the increasing LST contribute to an increase in environmental criticality. The derived maps and indices will be beneficial for forest authorities in identifying highly sensitive locations. Additionally, they could enable land use planners to develop sustainable land management strategies. [ABSTRACT FROM AUTHOR]

Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forest pests pose a major threat to ecosystem services worldwide, requiring effective monitoring and management strategies. Recently, satellite remote sensing has emerged as a valuable tool to detect defoliation caused by these pests. Lymantria dispar, a major forest pest native to Japan, Siberia, and Europe, as well as introduced regions in North America, is of particular concern. In this study, we used Sentinel-2 satellite imagery to estimate the defoliation area and predict the distribution of L. dispar in Toyama Prefecture, central Japan. The primary aim was to understand the spatial distribution of L. dispar. The normalized difference vegetation index (NDVI) difference analysis estimated a defoliation area of 7.89 km2 in Toyama Prefecture for the year 2022. MaxEnt modeling, using defoliation map as occurrence data, identified the deciduous forests between approximately 35° and 50° at elevations of 400 m and 700 m as highly suitable for L. dispar. This predicted suitability was also high for larval locations but low for egg mass locations, likely due to differences in larval habitats and ovipositing sites. This study is the first attempt to utilize NDVI-based estimates as a proxy for MaxEnt. Our results showed higher prediction accuracy than a previous study based on the occurrence records including larvae, adults, and egg masses, indicating better discrimination of the distribution of L. dispar defoliation. Therefore, our approach to integrating satellite data and species distribution models can potentially enhance the assessment of areas affected by pests for effective forest management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ecological environment of the Hexi Corridor in China is fragile owing to its unique geographical conditions. Since the 21st century, the construction of highway projects in the Hexi Corridor has increased with the implementation of China's western development policy. The impact of highway construction on vegetation along roads is uncertain and requires attention. In this study, the normalized difference vegetation index (NDVI) was obtained based on remote sensing data, and the correlations between the vegetation index, climate changes, and highway construction from 2000 to 2019 were analyzed. The results showed that the NDVI of the Hexi Corridor showed a significant increasing trend, and the spatial difference was obvious, which was largely controlled by climatic conditions. Generally, the vegetation index was negatively correlated with temperature, but positively correlated with precipitation, and the correlation between the NDVI and precipitation was greater than that of temperature. The impact of highways on vegetation is a long-term process. In the eastern region of the Hexi Corridor, where the ecological environment is better, highway construction promoted vegetation development. However, in the western region with high desertification, the damage caused by highway construction to vegetation was serious, and the recovery rate of degraded vegetation was slow after the completion of highway construction. Although the vegetation development in the Hexi Corridor was mainly affected by precipitation, in the western region, highway construction was the main factor controlling vegetation disturbance within a certain range on both sides, and its contribution to vegetation disturbance reached 60%. The research results can improve understanding of the disturbance effect of highway construction on vegetation in arid areas. [ABSTRACT FROM AUTHOR]

Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Equatorial Africa (EQA) region and their responses to climate factors from 1982 to 2021. The NDVI values declined at a rate of 0.00023 year−1, while the precipitation (P) and mean temperature (TMEAN) values increased at rates of 0.22 mm year−1 and 0.22 °C year−1, respectively. The mean minimum temperature (TMIN) had a higher rate of 0.2 °C year−1 than the mean maximum temperature (TMAX) at 0.02 °C year−1. An abrupt change analysis showed that the TMAX, P, and NDVI breakpoints occurred in 2000, 2002, and 2009, respectively; TMEAN and TMIN breakpoints occurred in 2001. The NDVI trends declined in forest and cropland areas but increased in shrubland and grassland areas. The summer NDVI trends declined for all vegetation types and were reversed in the winter season. The NDVI positively correlated with the P (r = 0.50) and TMEAN (r = 0.60). All seasonal analyses varied across four seasons. A temporal analysis was conducted using partial correlation analysis (PCR), and the results revealed that TMIN had a greater impact on the NDVI (PCR = −0.45), followed by the TMAX (PCR = 0.31) and then the P (PCR = −0.19). The annual trend showed that areas with significant greening were consistent with stronger wetter and weaker warming trends. Both precipitation and temperature showed a positive relationship with vegetation in semi-arid and arid regions but a negative relationship with humid regions. Our findings improve our insight into scientific knowledge on ecological conservation. [ABSTRACT FROM AUTHOR]

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors that contribute to these shifts. The goal of this study is to assess the spatial and temporal variations in vegetation covering and the partitioning of its drivers in the Minjiang River Basin on the eastern edge of the Tibetan Plateau between 2000 and 2022. The Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, and other techniques were used to look at the features of temporal and geographical changes in regional vegetation coverage as well as potential development trends. The climatic influences leading to the spatial differentiation of vegetation NDVI (Normalized Difference Vegetation Index) were quantified through partial and complex correlation analyses of NDVI with temperature and precipitation. The results of the study showed that (1) the NDVI of the watershed performed well with a stable upward trend, indicating that the vegetation growth was generally good; (2) the spatial analysis showed that the coefficient of variation of the NDVI reached 0.092, which highlighted the stability of the vegetation change in the region; (3) the future development trend of the vegetation coverage in the watershed is low, and there is a certain degree of ecological risk; and (4) the main driver of the vegetation coverage is the non-climate factor, distributed in most parts of the watershed; (5) the climate driver shows localized influence, especially concentrated in the southwest, downstream and part of the upstream areas of the watershed. [ABSTRACT FROM AUTHOR]

Rice is a globally important food crop, and it is crucial to accurately and conveniently obtain information on rice fields, understand their spatial patterns, and grasp their dynamic changes to address food security challenges. In this study, Chongqing's Yongchuan District was selected as the research area. By utilizing UAVs (Unmanned Aerial Vehicles) to collect multi-spectral remote sensing data during three seasons, the phenological characteristics of rice fields were analyzed using the NDVI (Normalized Difference Vegetation Index). Based on Sentinel data with a resolution of 10 m, the NDVI difference method was used to extract rice fields between 2019 and 2023. Furthermore, the reasons for changes in rice fields over the five years were also analyzed. First, a simulation model of the rice harvesting period was constructed using data from 32 sampling points through multiple regression analysis. Based on the model, the study area was classified into six categories, and the necessary data for each region were identified. Next, the NDVI values for the pre-harvest and post-harvest periods of rice fields, as well as the differences between them, were calculated for various regions. Additionally, every year, 35 samples of rice fields were chosen from high-resolution images provided by Google. The thresholds for extracting rice fields were determined by statistically analyzing the difference in NDVI values within the sample area. By utilizing these thresholds, rice fields corresponding to six harvesting regions were extracted separately. The rice fields extracted from different regions were merged to obtain the rice fields for the study area from 2019 to 2023, and the accuracy of the extraction results was verified. Then, based on five years of rice fields in the study area, we analyzed them from both temporal and spatial perspectives. In the temporal analysis, a transition matrix of rice field changes and the calculation of the rice fields' dynamic degree were utilized to examine the temporal changes. The spatial changes were analyzed by incorporating DEM (Digital Elevation Model) data. Finally, a logistic regression model was employed to investigate the causes of both temporal and spatial changes in the rice fields. The study results indicated the following: (1) The simulation model of the rice harvesting period can quickly and accurately determine the best period of remote sensing images needed to extract rice fields. (2) The confusion matrix shows the effectiveness of the NDVI difference method in extracting rice fields. (3) The total area of rice fields in the study area did not change much each year, but there were still significant spatial adjustments. Over the five years, the spatial distribution of gained rice fields was relatively uniform, while the lost rice fields showed obvious regional differences. In combination with the analysis of altitude, it tended to grow in lower areas. (4) The logistic regression analysis revealed that gained rice fields tended to be found in regions with convenient irrigation, flat terrain, lower altitude, and proximity to residential areas. Conversely, lost rice fields were typically located in areas with inconvenient irrigation, long distance from residential areas, low population, and negative topography. [ABSTRACT FROM AUTHOR]

The identification and systematic monitoring of water spread regions using remote sensing techniques necessitates precise temporal resolution datasets. Landsat data are one of the most popular data sources that provides satellite derived images of the Earth's surface for examining the satellite-based indices of a specific region. In the present research study, Normalized Difference Water Index is employed to identify the surface area of 11 system and non-system lakes in a part of Coimbatore region over the time span of 32 years (1990–2022). Based on the analysis, the water spread region in 1990 is seen to be 13.01 ha which gradually increased to 435.17 ha in 2022. This clearly indicates that there is substantial difference in the rainfall statistics over the years. Computations for cross-validation are done using other indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI). A 500 m buffer is created and calculated for vegetation and built-up indices like Chinna kulam 1 and 2, Kumaraswamy Lake, Valankulam, Singanallur and Sengulam lakes which show the decreasing trend of NDVI. Simultaneously, NDBI increased in 2000, which shows that measures were taken to tackle the encroachment of lakes in the city and the concerted efforts of the Governmental authorities and Non-governmental organizations (NGOs) were fruitful. A decreasing trend is noted in NDBI during 2011 and 2020. The microclimatic conditions and advection current of waterbodies along with boating facilities and parks in some lakes offer city dwellers a tranquil recreational area with respite from vehicular pollution and heat generation from the concrete jungle. This study will be helpful in facilitating policy decisions for future planning and sustainable development of water bodies. It will also be beneficial in devising rehabilitation measures for irrigation tanks and will aid in bringing about agrarian stability of tanks in urban locales. [ABSTRACT FROM AUTHOR]

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

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

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

The Tibetan Plateau (TP) serves as a crucial ecological barrier in Asia, with vegetation playing a pivotal role in the terrestrial ecosystem by facilitating energy exchange between the land and atmosphere, regulating climate, and participating in the carbon cycle. In this study, we analyze the characteristics of surface vegetation on the TP in the growing season during 1982–2018 using satellite remote sensing data obtained from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Forcing Dataset (CMFD). We investigate how these characteristics respond to climate change under different warming and humidification conditions across the TP. The main conclusions are as follows. (1) The normalized difference vegetation index (NDVI) values on the TP exhibit a gradual decrease from southeast to northwest during the growing season. There is a significant overall increasing trend at a climate tendency rate of 0.01·decade−1 (p < 0.01) from 1982 to 2018, characterized by a notable mutation of around 1998. Over the past 37 years, a polarized trend of vegetation was observed on the TP, with notable improvement in its central and eastern regions. However, there has been noticeable degradation in northwestern TP, specifically within the Kunlun Mountains and Qaidam Basin. (2) The climate of the TP demonstrates distinct regional disparities in terms of warming and humidification characteristics before and after 1998. During the period of 1982–1998 (1998–2018), the temperature increase is primarily concentrated in the northern (southern) TP, while precipitation increase is mainly observed in the southern and northwestern (northeastern and western) regions of the TP. (3) The responses of surface vegetation to climate factors exhibit significant variations across diverse climatic backgrounds. It is noteworthy that moisture conditions have a substantial impact on the response of vegetation to air temperature on the TP. During the period of 1982–1998, under relatively insufficient moisture conditions, a positive correlation was observed between air temperature and surface vegetation in the humid and semi-humid regions of the southeastern TP, while a negative correlation was found in the semi-arid regions of northeastern TP. During 1998–2018, as moisture conditions became relatively sufficient, surface vegetation in the semi-arid regions showed positive correlations with both temperature and precipitation. However, surface vegetation in the humid and semi-humid regions exhibited a significant negative correlation with precipitation. During this period, the synergistic effects between warm and humid climates in the semi-arid regions of northeastern TP and warm and dry climates in humid and semi-humid regions of southeastern TP substantially enhanced surface vegetation on the TP. Furthermore, our results indicate that thermal factors (air temperature) primarily influence variations in surface vegetation within the high-altitude arid region of the TP. During 1998–2018, a significant cooling trend was observed in the northwestern TP, which could potentially account for the degradation of surface vegetation in the Kunlun Mountains. The findings of this study establish a scientific basis for the sustainable development of grassland ecosystems on the TP. [ABSTRACT FROM AUTHOR]

Introduction: The vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region. Methods: This study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001-2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework. Results: The study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greeningto-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region. [ABSTRACT FROM AUTHOR]

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]

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]

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

A smart agricultural system is necessary for monitoring crop growth and stress conditions using near-ground-based remote sensing techniques. Crop growth can be estimated using several standard crop growth parameters. However, obtaining timed sequential data for observing leaf area index (LAI) is challenging, and normalized difference vegetation index (NDVI) estimation in crop fields requires the installation of sensors on frames structure that are taller than the crop. Canopy light transmittance (CLT) indicates the degree of decrease in the amount of light passing through some material. It was conventionally used to understand canopy radiative transfer. This study examined the viability of CLT as a novel crop parameter for monitoring crop growth conditions. The CLT, LAI, and NDVI of a garlic field were recorded for five years. The correlation between daily CLT and LAI was higher than that between NDVI and LAI. Thus, CLT has the potential to sequentially estimate crop LAI values, particularly for capturing the temporal patterns of LAI. In addition, like NDVI, CLT showed sensitivity in representing the canopy structure and the amount of biomass because CLT is conceptually related to the sky gap fraction. Thus, CLT has the potential to serve as a novel growth parameter for continuous crop growth monitoring. [ABSTRACT FROM AUTHOR]

Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term. [ABSTRACT FROM AUTHOR]

The greenery of floodplain meadows in arid regions, such as Mongolia, is influenced by climate, hydrology, and land use. In this study, we analyzed the NDVI (Normalized Difference Vegetation Index) of two floodplain meadows located along the South Tamir and Tamir Rivers using LANDSAT images. Our goal was to observe NDVI spatial changes, variations, and mean values in mid-August every six years from 1991 to 2015 and to identify the factors driving these differences. To achieve this, we conducted variance analysis to identify changes in NDVI and implemented Principal Component Analysis to determine the influence of hydro-meteorological factors and grazing intensity. Our findings indicate a significant decrease in greenness, as measured by pixel-scale NDVI, during the late summer period. This decrease was consistently observed, except for a series of harsh winters that followed relatively dry summers, resulting in a disastrous event called dzud, which led to the death of livestock. The decrease in NDVI was amplified by lower precipitation in June, higher temperatures and wind speed in July, and increased precipitation in August, along with a higher frequency of days with convective rain. Our findings have important implications for managing grazing in Mongolia's grasslands, promoting sustainable land use, and mitigating sandstorms. The variance and average values of NDVI at the pixel level can serve as reliable markers of sustainable pasture management in areas where other vegetation measures are limited. [ABSTRACT FROM AUTHOR]

The interactions between the normalised difference vegetation index (NDVI), the normalised difference built-up index (NDBI), and land surface temperature (LST) are complex. The assessment of land use/land cover (LULC) changes in the North-western region of Algeria between 1995 and 2021 confirms the direct influence of these factors on surface thermal processes. The use of new information technologies, particularly remote sensing coupled with GIS, favourably contributes to the processing of a large volume of data and to the use of specific methods aimed at confirming and/or disproving the hypotheses put forward. The application of LULC classification methods clearly highlights the magnitude of transformations, predominantly in favour of intensified urbanisation over the past two decades. Indeed, agricultural lands have experienced a reduction of 17.45%, while urbanised areas have nearly doubled. This phenomenon can, in part, be attributed to the mass migration of populations from inland areas to the coast, not only due to climate change: secondary for political problems between 1990 and 2001. Similarly, barren lands have increased by 10.45%. These changes have real implications for ecosystems (mainly loss of biodiversity) and the climate (pollution, GHG emissions, and rising ambient temperatures). The estimation of average LST from multiple satellite scenes reveals an increasing trend, rising from 36.6 °C in 1995 to 40.35 °C in 2021. The direct relationship between LST and NDVI and between LST and NDBI confirms the close association between land use change and increasing surface temperatures. The Pearson coefficient between LST and NDVI showed a negative correlation, ranging between -0.52 and -0.47, while it was positively correlated between LST and NDBI, with values around 0.66. The emergence of hotspots in the region, confirmed by the results of analysis employing the Getis-Ord G* method, is marked by clearly increasing spatial envelopes. This phenomenon is associated with a distinct reduction in vegetation cover density, coupled with an increased vulnerability to drought conditions. These initial results argue in favour of preserving green and blue networks and, more largely, ecosystems. [ABSTRACT FROM AUTHOR]

[Objective] The Grand Canal is a complex hydraulic project in the eastern China. This paper studies the flooding and its detrimental impacts in the lowland areas of the canal. [Method] The research was conducted in the Pichang-Tanxin Plain, using multi-source remote sensing data and ground surveys to analyze flooding events. We calculated the normalized difference water body index (NDWI) and the normalized difference vegetation index (NDVI) before and after flooding to assess flood severity, impact on crop growth, as well as the efficacy of hydraulic facilities in mitigating flooding effects. [Result] The NDVI difference between 2004 and 2005 for crops in flooded and non-flooded areas was 0.5 and 0.25, respectively, indicating floodings detrimentally affected crop growth and development. NDWI analysis from 2003 to 2017 demonstrated that the Taierzhuang Control Gate project mitigated flooding impacts on nearby farmlands. Flooding also affected crops in non-inundated regions, with lowland crops more adversely impacted than those outside lowlands. The hydraulic facilities significantly reduced economic losses caused by flooding. [Conclusion] Remote sensing is an effective tool for monitoring inundated areas and assessing the consequences of flooding on crops. The construction of hydraulic facilities plays a crucial role in reducing the detrimental effects of flooding in lowland regions of the Grand Canal. [ABSTRACT FROM AUTHOR]

The primary goal of asthma management is to achieve and maintain asthma control, which can be influenced by environmental factors. This longitudinal study aimed to construct a comprehensive environmental indicator to predict asthma control in children with asthma in Palermo, Italy. The study included 179 asthmatic children aged 5–16 years. The Normalized Difference Vegetation Index (NDVI) was used to measure green cover, and the Coordination of Information on the Environment (CORINE) framework was used to assess land use based on each home address. A land use regression (LUR) model centered on the home address estimated NO2 exposure for each child using GIS. An environmental indicator, including environmental and personal exposure, was formulated using an additive value model approach. A logistic regression mixed model assessed the association between the environmental indicator and uncontrolled asthma. A probability map of uncontrolled asthma was constructed. In conclusion, a comprehensive environmental indicator proved effective in identifying areas at higher and lower risk of uncontrolled asthma. [ABSTRACT FROM AUTHOR]

This research analyses the long-term vegetation trends in Shada Mountain across six elevation zones, utilizing Landsat 5, 7, 8, and 9 imageries processed via Google Earth Engine and R. The study managed differences in images resolution through meticulous calibration and image processing techniques. The study is structured around two objectives: examining the relationship between vegetation and its proximity to streams and land surface temperature and analyzing trends in the Normalized Difference Vegetation Index (NDVI). Regression analysis revealed a negative correlation between vegetation and proximity to streams in lower zones (1-3), with no significant effect in higher zones (4-6). NDVI trend analysis indicated an overall increase in vegetation across most zones, with the exception of zone 5, which displayed a negative trend (slope -0.0025). The findings reveal that the decline is particularly pronounced among key tree species such as Ficus cordata subsp. salicifolia and Acacia asak, suggesting potential impacts from climate change and land use alterations. These zone-specific insights deepen our understanding of the dynamic ecological processes in semi-arid environments and guide targeted environmental management and conservation efforts. [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]