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Growing nursery plants in containers usually requires the addition of fertilizer to achieve optimal growth. However, misconceptions among growers often lead them to apply quantities of fertilizers exceeding the recommended dose, resulting in nutrient runoff and increased production costs. Reducing fertilizer application is beneficial to have healthy plants and environment as well as to save on fertilizer costs. In this study, we wanted to determine the minimal level of phosphorus (P) required to grow bedding plants i.e. Lysimachia nummularia 'creeping jenny' (moneywort) and Vinca minor 'Bowles' (periwinkle) in nursery. The plants were grown within a greenhouse in a mixture of peat moss, vermiculite, and rice hulls (3:1:1 v/v) substrates and subjected to various P treatments. The treatments included single dose of 1, 3, or 6 mg/L of P, double doses of 3 or 6 mg/L of P, or a control that received 6 mg/L of P at each irrigation. The results indicated that single dose of 6 mg/L of P had similar fresh weight compared to control in both taxa. Reducing P application further to a single dose of 1 and 3 mg/L reduced fresh weight and leaf area in both groundcovers however did not reduce their stem length or physiological parameters such as Soil Plant Analysis Development (SPAD) and normalized difference vegetation index (NDVI). Phosphorus concentration in the plant tissue at maturity was lower in all treatments than control for both the taxa. During the study period, control plants received 26 times the dose of P compared to plants receiving 6 mg/L of P once. Therefore, this study outlines the possibility of reducing P fertilizer application while growing these groundcovers in nurseries for sale. [ABSTRACT FROM AUTHOR]

The assessment of landslide susceptibility often overlooks the influence of forests on shallow landslide mobility, despite its significance. This study delved into the impact of forest presence on shallow landslide mobility during intense rainfall in Mengdong, China. Field investigations were coupled with the analysis of pre- and post-rainfall remote sensing (RS) images to delineate landslides. The ratio of landslide height (H) to travel distance (L) from a digital elevation model (DEM) were used to calculate landslides mobility. Preceding the event, forest coverage was evaluated using the normalized difference vegetation index (NDVI) derived from multiband RS image. The research identified 1531 shallow landslides in the area, revealing a higher concentration of landslides on slopes with elevated NDVI. Results indicated that disparities in soil permeability and cohesion, generating pore water pressure (PWP), triggered clusters of shallow landslides. Shallow landslides exhibit a higher propensity on slopes with elevated NDVI. The dimensions (height and area) of these identified shallow landslides typically exhibit a positive correlation with NDVI, consequently resulting in longer travel distances for landslides occurring on higher NDVI slopes. The average H/L ratio of all identified landslides was about 0.63. H/L generally increases with NDVI and decreases with landslide area. However, due to river channel restrictions, the H/L increases with slope gradient. The findings suggest that the high permeability of areas with tree roots poses a risk to the shallow stability of slopes, yet trees contribute to mitigating landslide mobility. [ABSTRACT FROM AUTHOR]

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Chlorophyll monitoring is an important topic in phenotypic research. For fruit trees, chlorophyll content can reflect the real-time photosynthetic capacity, which is a great reference for nutrient status assessment. Traditional in situ estimation methods are labor- and time-consuming. Remote sensing spectral imagery has been widely applied in agricultural research. This study aims to explore a transferable model to estimate canopy SPAD across growth stages and tree species. Unmanned aerial vehicle (UAV) system was applied for multispectral images acquisition. The results showed that the univariate model yielded with Green Normalized Difference Vegetation Index (GNDVI) gave valuable prediction results, providing a simple and effective method for chlorophyll monitoring for single species. Reflection features (RF) and texture features (TF) were extracted for multivariate modeling. Gaussian Process Regression (GPR) models yielded better performance for mixed species research than other algorithm models, and the R² of the RF+TF+GPR model was approximately 0.7 in both single and mixed species. In addition, this method can also be used to predict canopy SPAD over various growth stages, especially in the third and fourth stages with R² higher than 0.6. This paper highlights the importance of using RF+TF for canopy feature expression and deep connection exploration between canopy features with GPR algorithm. This research provides a universal model for canopy SPAD inversion which can promote the growth status monitoring and management of fruit trees. [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]

As the largest freshwater lake in the region and a designated Ramsar site, Loktak lake in northeast India holds paramount ecological and socioeconomic significance, particularly for the indigenous Meitei community. This study delves into the intricate spatiotemporal dynamics of a traditional fishing technique called athaphum practised by the Meitei community in the Loktak. It utilizes the normalized difference vegetation index derived from the remote sensing satellite images to discern the athaphum and its trend over the past three decades (1988–2023). Our findings reveal significant fluctuations in athaphum due to ecological, hydrological and human factors. The findings of this study enhance our understanding of the complexities involved in safeguarding Loktak lake. They shed light on the intricate balance needed to sustain environmental integrity and socioeconomic well-being in the Loktak region. [ABSTRACT FROM AUTHOR]

Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology. [ABSTRACT FROM AUTHOR]

Previous research on watershed assessments has primarily emphasized general hydrological or technical findings, neglecting the spatiotemporal analysis of landuse change and its influence on surface runoff. This study addresses this gap by using a geographic information system (GIS)-based method to analyze surface runoff variations due to land-use change on Tsushima Island, Japan. By leveraging spatiotemporal analysis within a GIS framework, the study interprets satellite imagery (Landsat 8 data, 2014-2024) to assess land use and watershed characteristics. This approach culminates in a comprehensive map depicting potential flood risk across the island. In conclusion, this study offers an advanced method for communicating potential flood risks to the public and policymakers, enabling proactive mitigation strategies for the coming decade. [ABSTRACT FROM AUTHOR]

Background: Tracheal, Bronchus, and Lung (TBL) cancer continues to represent the majority of cancer-related incidence and mortality in United States (U.S.). While air pollutants are considered essential risk factors, both global and national average concentrations of major harmful air pollutants have significantly decreased over the decades. Green space may have a beneficial effect on human health. Methods: We obtained data on national and state-level burden of TBL cancer, the annual average concentration of main air pollutants, and levels of green spaces in 2007, 2013, and 2019. According to generalized estimating equation (GEE), we examine the associations among incidence and mortality of TBL cancer, air pollutants, and greenspaces, represented by the Normalized Difference Vegetation Index (NDVI) in different age groups with models adjusted with meteorological, and socio-demographic. We observed additional effects of the interaction between the NDVI, Ozone, PM2.5, and other factors, which helped us to interpret and understand our results. Also, we collated states that witnessed net increments in forest coverage and conducted the same analysis separately. Results: In our analysis, the majority of associations between NDVI and air pollutants with TBL cancer remained significantly positive, particularly noticeable among individuals aged 20 to 54. However, our findings did not explore air pollution as a potential mediator between greenspace exposure and TBL cancer. While the associations of PM2.5 with TBL cancer remained positive, the other four pollutants showed positive but statistically insignificant associations. Our interaction analysis yielded that there were positive associations between NDVI and ozone, PM2.5, and tobacco use. Max NDVI acts as a protective factor along with high HDI. Additionally, PM2.5 and HDI also showed a negative association. In 18 states with more forest, NDVI acts as a protective factor along with higher health care coverage, better health status, and participation in physical activities. Conclusion: In the state-level of U.S., the effects of total greenspace with TBL cancer are mixed and could be modified by various socio-economic factors. PM2.5 has a direct correlation with TBL cancer and the effects can be influenced by underlying socioeconomic conditions. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia's coastline across the Red Sea. Coastal areas, especially the ones along Saudi's western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro's weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from 'very low' to 'very high.' A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with 'low to moderate' susceptibility is 4,322.8 km2, areas with 'moderate' susceptibility is 5,109.69 km2, areas with 'moderate to high' is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points). [ABSTRACT FROM AUTHOR]

Rock glaciers are geomorphological features often used as a visible expression of mountain permafrost. These are creeping ice-debris with distinct ridge and furrow structures on the surface with a steep frontal slope. Rock glaciers, being the valuable past permafrost indicators, also have utmost hydrological significance in near future. Therefore, mapping of rock glaciers is an important step in order to understand permafrost regimes better. The Himalayas have large occurrences of these features and this study in Himachal Himalayas complied 789 rock glaciers, covering an area of about 336.2 km2. Different labels based on genesis, location, shape, form, surface relief and activity revealed rock glaciers were mainly derived from talus slopes (239) and exhibited tongue shape (377), primarily found in cirques (531). Most of them were classified as simple units (603) with well-developed surface relief (387), and they were found to be predominantly intact (760). The topographical parameters suggest majority of the rock glaciers are located between 4000 and 4800 m with mean elevation to be 4635 m. These rock glaciers are present at gentle to steep slope gradient (0 to 45°) with curvature ranging between −3.5 and 4.5, and majority showing convex curvature. The slope aspect conducive for formation of rock glaciers in Himachal is northerly (N, NW, NE). Principal geology for these rock glaciers belongs to slate, phyllite, quartzarenite, limestone and meta basics. The climatic parameters and indices also affect the rock glaciers occurrence significantly. The mean land surface temperature (LST) for majority rock glaciers lies between 0 and −15°C. While, the mean NDSI of all the rock glaciers varies from 0.04 to 0.68 and mean NDVI varies from −0.06 to 0.08. Overall, the inventory along with labels is a valuable database for understanding the distribution and characteristics of rock glaciers in the Himachal Himalayas. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts. [ABSTRACT FROM AUTHOR]

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought. [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]

Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter ω is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between ω and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter ω for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter ω established for the three subwatersheds were reasonable (R2 = 0.60, 0.80, and 0.40), and parameter ω was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development. [ABSTRACT FROM AUTHOR]

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology's robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping. [ABSTRACT FROM AUTHOR]

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map. [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]

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an 'all-in-one' Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data. [ABSTRACT FROM AUTHOR]

Massive accumulations of invasive brown algae Rugulopteryx okamurae are exacerbating environmental and socio-economic issues on the Mediterranean and potentially Atlantic coasts. These golden tides, likely intensified by global change processes such as changes in wind direction and intensity and rising temperatures, pose increasing challenges to coastal management. This study employs the Normalized Difference Vegetation Index (NDVI), with values above 0.08 from Level-2 Sentinel-2 imagery, to effectively monitor these strandings along the coastline of Los Lances beach (Tarifa, Spain) in the Strait of Gibraltar Natural Park from 2018 to 2022. Los Lances beach is one of the most affected by the R. okamurae bioinvasion in Spain. The analysis reveals that wind direction determines the spatial distribution of biomass accumulated on the shore. The highest average NDVI values in the western patch were observed with south-easterly winds, while in the eastern patch, higher average NDVI values were recorded with south-westerly, westerly and north-westerly winds. The maximum coverage correlates with elevated temperatures and minimal rainfall, peaking between July and October. Leveraging these insights, we propose a replicable methodology for the early detection and strategic pre-shore collection of biomass, which could facilitate efficient coastal cleanup strategies and enhance biomass utility for biotechnological applications. This approach promises cost-effective adaptability across different geographic areas impacted by golden tides. [ABSTRACT FROM AUTHOR]

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS's blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. [ABSTRACT FROM AUTHOR]

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone. [ABSTRACT FROM AUTHOR]

The ecological environment of the Yellow River Delta is undergoing serious degradation due to the pressures of economic development and population growth. To improve and protect the ecological environment, it is crucial to accurately assess and monitor its eco-environmental quality. With consideration of the characteristics of terrestrial salinization in the region and the need for long-term ecological monitoring, we first utilized Google Earth Engine (GEE) to construct the Improved Remote Sensing Ecological Index (IRSEI). The IRSEI is based on the Remote Sensing Ecological Index (RSEI), which consists of the Normalized Difference Vegetation Index (NDVI), WET, Land Surface Temperature (LST), and Normalized Difference Built-Up and Soil Index (NDBSI), as well as the Net Primary Productivity (NPP) index. The entropy weighting method was employed to construct the IRSEI for assessing the eco-environmental quality of the Yellow River Delta. The validity of the index was verified through image entropy and contrast assessment. We then employed the Hurst exponent, Sen's slope estimation, and Coefficient of Variation (CV) to calculate the range of variation of the IRSEI in the Yellow River Delta over a 20-year period to analyze the spatio-temporal evolution of the ecological quality and its distribution pattern. Furthermore, we conducted a comprehensive analysis combining the Geographically and Temporally Weighted Regression (GTWR) model and Geodetector to understand the influence of drivers such as topography, soil, and climate on the IRSEI, considering both the temporal and spatial dimensions. The results indicate that: (1) The proposed IRSEI demonstrates higher reliability, adaptability, and sensitivity compared to RSEI in monitoring the eco-environmental quality of the Yellow River Delta. (2) From 2000 to 2020, the eco-environmental quality of the Yellow River Delta remained generally stable, with a spatial distribution resembling a "Y" shape, showing significant improvement, particularly in Lijin County and its surrounding areas. However, the middle and eastern estuary exhibited a declining trend in eco-environmental quality. (3) The impact of driving factors on the eco-environmental quality varied across the four subordinate regions of the Yellow River Delta, indicating spatial heterogeneity. Factors such as FVC, Soil, LST, JS, and Srad significantly influenced and explained the spatial differentiation of eco-environmental quality in the region. The proposed IRSEI demonstrates better monitoring capabilities in the Yellow River Delta compared to RSEI, providing a scientific basis for land use planning and ecological protection in the area. [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]

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]

The identification of dominant driving factors for different ecosystem services (ESs) is crucial for ecological conservation and sustainable development. However, the spatial heterogeneity of the dominant driving factors affecting various ESs has not been adequately elucidated, particularly in ecologically fragile regions. This study employed the integrated valuation of ESs and trade-offs (InVEST) model to evaluate four ESs, namely, water yield (WY), soil conservation (SC), habitat quality (HQ), and carbon storage (CS), and then to identify the dominant driving factors of spatiotemporal differentiation of ES and further to characterize the spatial heterogeneity characteristics of the dominant driving factors in the eco-fragile areas of the upper Yellow River, China from 2000 to 2020. The results demonstrated that WY exhibited northeast-high and northwest-low patterns in the upper Yellow River region, while high values of SC and CS were distributed in central forested areas and a high value of HQ was distributed in vast grassland areas. The CS, WY, and SC exhibited decreasing trends over time. The most critical factors affecting WY, SC, HQ, and CS were the actual evapotranspiration, precipitation, slope, and normalized difference vegetation index, respectively. In addition, the effects of different factors on various ESs exhibited spatial heterogeneity. These results could provide spatial decision support for eco-protection and rehabilitation in ecologically fragile areas. [ABSTRACT FROM AUTHOR]

Changes in vegetation status generally also represents changes in the ecological health of islands and reefs (IRs). However, studies are limited of drivers and trends of vegetation change of Nansha Islands, China and how they relate to climate change and human activities. To resolve this limitation, we studied changes to the Normalized Difference Vegetation Index (NDVI) vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions. Trends of vegetation greenness were analyzed using Sen' s slope and Mann-Kendall test at two spatial scales (pixel and island), and driving factor analyses were performed by time-lagged partial correlation analyses. These were related to impacts from human activities and climatic factors under normal (temperature, precipitation, radiation, and Normalized Difference Built-up Index (NDBI)) and extreme conditions (wind speed and latitude of IRs) from 2016 to 2022. Results showed: 1) among the 22 IRs, NDVI increased/decreased significantly in 15/4 IRs, respectively. Huayang Reef had the highest NDVI change-rate (0.48%/mon), and Zhongye Island had the lowest (−0.29%/mon). Local spatial patterns were in one of two forms: dotted-form, and degradation in banded-form. 2) Under normal conditions, human activities (characterized by NDBI) had higher impacts on vegetation-greenness than other factors. 3) Under extreme conditions, wind speed (R2 = 0.2337, P < 0.05) and latitude (R2 = 0.2769, P < 0.05) provided limited explanation for changes from typhoon events. Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations 'Ocean Decade' initiative. [ABSTRACT FROM AUTHOR]

The temporal behaviors of land surface temperature (LST) coupled with its associated parameters play a crucial role in determining the microclimate at the city scale. The increasing pattern of LST and consequent changes in biophysical parameters (parameters specify the amalgamation of living system with their physical characteristics including vegetation, water, built-up, bareness and drought parameters) at monthly, seasonal and annual time spans and from regional to global scale need to be comprehensively evaluated. The present study deals with LST estimation along with other spectral indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Multi-band Drought Index (NMDI) using Landsat series datasets from 1991 to 2022 of Aligarh city, Uttar Pradesh, India. The spatial pattern of LST indicates that the areas having water bodies and dense vegetation are colder such as the Aligarh Muslim University Campus and the catchment of Ganga canal areas, whereas the areas of high urbanization and bare grounds reflect high LST trends. Study finds a positive correlation of LST with NDBI (R2-0.56), NDBaI (R2-0.22) and NDWI (R2-0.22), whereas a negative correlation with NDVI (R2-0.35), MNDWI (R2-0.36) and NMDI (R2-0.41). Land use land cover (LULC)-based change detection in land cover classes was found consistent with the obtained results for spectral indices and LST patterns in the study area. Finally, the cross-validation using Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis-based products of earth skin temperature and rainfall showed a good fit between observed and reanalysis products. [ABSTRACT FROM AUTHOR]

Shadows in remote sensing images contain crucial information about various features on the ground. In this study, a method for detecting building shadows in GF-2 images based on improved quick shift was proposed. First, six feature variables were constructed: first principal component (PC1), brightness component (I), normalized difference shadow index (NDSI), morphological shadow index (MSI), normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Then, the image was segmented to obtain homogeneous objects, which were then classified using a random forest model. Two improvements were added to the quick shift algorithm: using PC1, I, and MSI as input data instead of RGB images; and adding Canny edge constraints. Validation in six research areas yields Kappa coefficients of 0.928, 0.896, 0.89, 0.913, 0.879, and 0.909, confirming method feasibility. In addition, comparative experiments demonstrate its effectiveness and robustness across different land cover types while mitigating the segmentation scale effect. [ABSTRACT FROM AUTHOR]

Cyclones pose significant threats to coastal regions, triggering widespread ecological and hydrological changes. This study presents an impact assessment of cyclone Biparjoy, which originated in the Arabian Sea and made landfall on the Gujarat coast of India on June 16, 2023. The research encompasses flood delineation and vegetation impact assessment in the Kachchh and Devbhoomi Dwarka districts of Gujarat, India. Sentinel-1A (VV polarized) imagery is used to precisely map the extent of inundation caused by cyclone Biparjoy. The total flooded area for Kachchh and Devbhoomi Dwarka was calculated to be 6556.73 km2 and 104.49 km2, respectively. The most affected LULC class in Kachchh is found to be bare ground (38.95%) and rangeland (38.94%) which is the major part of the Northeastern Rann region. In Dwarka, most waterlogging has been seen in the cropland (33.04%). The classification of the water and non-water pixels for the pre- and post-images is validated using the ROC curve. The accuracy was 93.2% and 89.5% for pre- and post-images classifications, respectively. Furthermore, vegetation impact was investigated to estimate the cyclone's ecological consequences. Alterations in vegetation density and overall health were estimated by calculating Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from both pre- and post-cyclone Landsat-8 OLI images. The cyclone-induced damage is further assessed for the mangrove trees in Kori Creek. This work contributes to understanding the ecological repercussions of such extreme weather events. [ABSTRACT FROM AUTHOR]

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city. [ABSTRACT FROM AUTHOR]

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure. [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]

The selection of disaster-inducing factors and the construction of assessment models are two important stages in landslide susceptibility assessment. Existing research has neglected the influence of time-varying dynamic factors on landslide susceptibility. On the contrary, the best assessment model that fits the characteristics of disaster-pregnant environment in a certain area needs to be determined through a large number of comparative studies. In this paper, Boshan District, China was taken as the research area, and the assessment factor combination of static disaster-inducing factors and the change value of dynamic disaster-inducing factors in the current year was constructed. Landslide susceptibility modeling was carried out by random forest (RF), logistic regression (LR), support vector machine (SVM), stacking ensemble and convolutional neural network (CNN), and the assessment model with the highest accuracy was determined based on the area under the curve (AUC) method. Based on the geodetector, the interpretation degree of land use to landslide susceptibility and the relationship between the change information of dynamic disaster-inducing factors and the spatial distribution of landslide susceptibility were analyzed. The results show that CNN outperforms other models in the performance of modeling, with an AUC value of 0.920. The extremely high sensitive zones for landslides in the Boshan District were mainly distributed in the northwest, south, and east regions. The overall explanatory power of land use on landslide susceptibility shows a declining trend, suggesting that the spatial layout of land use in the Boshan District has been optimized. The rapidly growing population has destroyed the geological and hydrological environment and increased the probability of landslide susceptibility; Multiple acute changes of the normalized difference vegetation index (NDVI) in the opposite direction were more likely to trigger landslides, so it is not suitable to carry out logging, planting and other work repeatedly in a short time. This research can provide a theoretical foundation for land use planning and landslide prevention policies in the Boshan District. [ABSTRACT FROM AUTHOR]

Climate variability is one of the major factors affecting the supply of ecosystem services and the well-being of people who rely on them. Despite the substantial effects of climate variability on ecosystem goods and services, empirical researches on these effects are generally lacking. Thus, this study examines the spatiotemporal impacts of climate variability on selected ecosystem services in Maze National Park and its surroundings, in southwestern Ethiopia. We conducted climate trend and variability analysis by using the Mann-Kendall (MK) trend test, Sen's slope estimator, and innovative trend analysis (ITA). Relationships among ecosystem services and climate variables were evaluated using Pearson's correlation coefficient (r), while partial correlation was used to evaluate the relationship among key ecosystem services and potential evapotranspiration (PET). The MK tests show a decreasing trend for both mean annual and main rainy season rainfall, with Sen's slope (β) = -0.721 and β = -0.1.23, respectively. Whereas, the ITA method depicted a significant increase in the second rainy season rainfall (Slope(s) = 1.487), and the mean annual (s = 0.042), maximum (s = 0.024), and minimum (s = 0.060) temperature. Spatial correlations revealed significant positive relationships between ecosystem services and the mean annual rainfall and Normalized Difference Vegetation Index (NDVI), while negative correlations with the mean annual temperature. Additionally, temporal correlations highlighted positive relationships among key ecosystem services and the main rainy season rainfall. The maximum and minimum temperatures and ecosystem services were negatively correlated; whereas, there was strong negative correlations between annual (r = -0.929), main rainy season (r = -0.990), and second rainy season (r = -0.814) PET and food production. Thus, understanding the spatiotemporal variability of climate and the resulting impacts on ecosystem services helps decision-makers design ecosystem conservation and restoration strategies to increase the potential of the ecosystems to adapt to and mitigate the impacts of climate variability. [ABSTRACT FROM AUTHOR]

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model's impressive precision when generating the pollution risk map. [ABSTRACT FROM AUTHOR]

Land use land cover plays an essential role in altering regional micro and macro climate, diminishing biodiversity, and significantly influences Land Surface Temperature (LST). However, no research has been conducted in the Shigar district to investigate LULC changes, particularly the correlation between LST and vegetation/snow cover at higher elevated regions. Therefore, this study was carried out in the Shigar district of Gilgit Baltistan Region, Pakistan, due to its extreme climatic variability. In this study, we investigated the spatiotemporal variability of LULC and its impacts on LST in the Shigar district, utilizing Landsat data from 1993 to 2020. To study the variable LST, thermal bands of Landsat imagery were used. LULC was determined by classifying the images using the supervised maximum likelihood classification method. The vegetation and snow cover were also extracted by using the indices of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Snow Index (NDSI), respectively. The results showed that the Shigar district had experienced severe land cover variations in the years of 1993, 2003, 2010, and 2020. Specifically, the agricultural area gradually increased from 1993 to 2020. The forest/vegetation cover also increased in the study area. In contrast, snow cover and water bodies were drastically reduced during this period. The overall vegetation increased within the region and the LST values were found to be decreased in 2003 and 2010, whereas the LST rose in 2020. In addition, the correlation between NDVI and LST was found to be significant negative in areas with high vegetation cover. Conversely, it was weakly positive in other areas with minor vegetations. These findings suggest that green cover plays a crucial role in regulating temperature. Additionally, a negative correlation between LST and NDSI was observed with rising elevation. This study will be helpful in landscape and urban planning, watershed management, and policymaking for climate change variability. [ABSTRACT FROM AUTHOR]

Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development. [ABSTRACT FROM AUTHOR]

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. [ABSTRACT FROM AUTHOR]

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions. [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]

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

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

In 2023, most parts of the world experienced exceptional heat. In particular, anomalous warm temperatures and heatwave events were evidenced across South America during the second half of the year. The situation was particularly critical in the Amazon region in terms of not only hydrometeorological drought but also ecological and socioeconomic impacts. In this study, remote-sensing data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to observe the changes in temperature and vegetation across Amazonia during the exceptional drought of 2023. This analysis was based on anomalies in the land surface temperature (LST) and vegetation indices: the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). The amplitude of the LST (AMP-LST), an indicator of the energy partitioning between the latent and sensible heat flux, and fire counts were also considered. The results show widespread and extreme warming across Amazonia during the austral spring in 2023, accompanied by a decline in vegetation greenness, water stress conditions across northern Amazonia, and an enhanced fire occurrence across central and northern Amazonia. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of intangible cultural heritage in Fujian Province, China, and the factors that influence it were explored using multiple spatial scales. The samples include five batches of Chinese national-level and six batches of Chinese provincial-level intangible cultural heritage items, totaling 554. The samples involve city-scale, county-scale, and traditional dwellings, and the analysis uses various methods, such as gravity migration theory and the GeoDetector model. The results show that, in terms of historical timescales, the moving trajectory of the center of gravity of the intangible cultural heritage distribution in Fujian Province involves reciprocation from north to south, and the center moves south in time from the pre-Qin period to the modern period. Spatially, intangible cultural heritage in Fujian Province forms an overall aggregate pattern, among which traditional skills fall into an aggregate pattern; five categories, including traditional drama, fall into an aggregate-random pattern; and four categories, including traditional medicine, fall into a random pattern. The traditional art category and other intensive distribution areas are located in coastal areas, and the traditional dance category is scattered in northwestern Fujian Province. Experiments on the factors influencing intangible cultural heritage distribution were conducted at the city and county levels. At the city level, human geography factors, such as the resident population, urbanization rate, and gross domestic product, have a greater influence on distribution. At the county level, the influence of physical geographical factors clearly increases. The dominant factors for coastal cities are the normalized difference vegetation index (NDVI), average precipitation, and coastline, and that for inland cities is elevation. From the perspective of categories, the dominant factors of traditional music and Quyi are human geography factors, and those of other types of intangible cultural heritage are the synergistic effects of physical geography and human geography. The conclusions of this study are helpful for systematically protecting and developing the intangible cultural heritage industry and improving research theories and methodological systems of the spatiotemporal distribution and mechanism influencing intangible cultural heritage. [ABSTRACT FROM AUTHOR]

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security. [ABSTRACT FROM AUTHOR]

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water column were identified from remote sensing-derived chlorophyll concentrations, and surface HAB scums were classified based on the Normalized Difference Vegetation Index (NDVI) band ratio index. Using imagery from the Moderate Resolution Imaging Spectroradiometer sensor on the Aqua satellite (MODIS-Aqua) from 2002 to 2022, we generated daily estimates of the HAB and surface scum extents for each region, which were then averaged to generate mean annual extents. We observed a significant decline in the Saginaw Bay mean annual HAB extents over the 21-year study period. Otherwise, no significant changes were observed over this period in any region for either the HAB or surface scum mean annual extents, thus suggesting that HABs are not increasing in the Great Lakes. Despite the lack of increasing trends, the blooms are still recurring annually and causing a negative impact on the nearby communities; thus, we believe that it is crucial to continue studying Great Lakes HABs to monitor the impact of current and future abatement strategies. [ABSTRACT FROM AUTHOR]

Introduction: Heat stress negatively affects wheat production in several ways, mainly by reducing growth rate, photosynthetic capacity and reducing spike fertility. Modeling stress response means analyzing simultaneous relationships among traits affecting the whole plant response and determinants of grain yield. The aim of this study was to dissect the diverse impacts of heat stress on key yield traits and to identify the most promising sources of alleles for heat tolerance. Methods: We evaluated a diverse durum wheat panel of 183 cultivars and breeding lines from worldwide, for their response to long-term heat stress under field conditions (HS) with respect to non stress conditions (NS), considering phenological traits, grain yield (GY) and its components as a function of the timing of heat stress and climatic covariates. We investigated the relationships among plant and environmental variables by means of a structural equation model (SEM) and Genetic SEM (GSEM). Results: Over two years of experiments at CENEB, CIMMYT, the effects of HS were particularly pronounced for the normalized difference vegetation index, NDVI (-51.3%), kernel weight per spike, KWS (-40.5%), grain filling period, GFP (-38.7%), and GY (-56.6%). Average temperatures around anthesis were negatively correlated with GY, thousand kernel weight TKW and test weight TWT, but also with spike density, a trait determined before heading/anthesis. Under HS, the correlation between the three major determinants of GY, i.e., fertile spike density, spike fertility and kernel size, were of noticeable magnitude. NDVI measured at medium milk-soft dough stage under HS was correlated with both spike fertility and grain weight while under NS it was less predictive of grain weight but still highly correlated with spike fertility. GSEM modeling suggested that the causal model of performance under HS directly involves genetic effects on GY, NDVI, KWS and HD. Discussion: We identified consistently suitable sources of genetic resistance to heat stress to be used in different durum wheat pre-breeding programs. Among those, Desert Durums and CIMMYT'80 germplasm showed the highest degree of adaptation and capacity to yield under high temperatures and can be considered as a valuable source of alleles for adaptation to breed new HS resilient cultivars. [ABSTRACT FROM AUTHOR]