Normalized Difference Vegetation Index Values Research Articles

Detection of Fusarium Head Blight in Wheat Using NDVI from Multispectral UAS Measurements and Its Correlation with DON Contamination

Fusarium head blight (FHB) is a serious fungal disease of wheat and other small cereal grains, significantly reducing grain yield and producing mycotoxins that affect food safety. There is a need for disease detection technologies to determine the right time to apply fungicides, as FHB infection begins before visible symptoms appear. Using multispectral remote sensing by an unmanned aircraft system (UAS), wheat plants were observed under field conditions infested with FHB and simultaneously protected with fungicides sprayed with four different types of nozzles, as well as corresponding control plots infested with FHB only. The results showed that the levels of deoxynivalenol (DON) differed significantly between the five treatments, indicating that the control had the highest DON concentration as no fungicide treatment was applied. This study revealed that the assessment of the normalized difference vegetation index (NDVI) after FHB infection could be useful for predicting DON accumulation in wheat, as a significant negative correlation between DON and NDVI values was measured 24 days after anthesis. The decreasing NDVI values at the end of the growth cycle were expected due to senescence and yellowing of the wheat spikes and leaves. Therefore, significant differences in the NDVI were observed between three measurement points on the 13th, 24th, and 45th day after anthesis. Additionally, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge index (NDRE) were in significant positive correlation with the NDVI at 24th day after anthesis. The use of appropriate measurement points for the vegetation indices can offer the decisive advantage of enabling the evaluation of very large breeding trials or farmers’ fields where the timing of fungicide application is particularly important.

Seasonal spatial-temporal trends of vegetation recovery in burned areas across Africa.

Africa is entering a new fire paradigm, with climate change and increasing anthropogenic pressure shifting the patterns of frequency and severity. Thus, it is crucial to use available information and technologies to understand vegetation dynamics during the post-fire recovery processes. The main objective of this study was to evaluate the seasonal spatio-temporal trends of vegetation recovery in response to fires across Africa, from 2001 to 2020. Non-parametric tests were used to analyze MODIS Normalized Difference Vegetation Index (NDVI) products comparing the following three-month seasonal periods: December-February (DJF), March-May (MAM), June-August (JJA), and September-November (SON). We evaluated the seasonal spatial trends of NDVI in burned areas by hemisphere, territory, or country, and by land cover types, and fire recurrences, with a focus on forested areas. The relationships between the seasonal spatial trend and three climatic variables (i.e. maximum air temperature, precipitation, and vapor pressure deficit) were then analyzed. For the 8.7 million km2 burned in Africa over the past 22 years, we observed several seasonal spatial trends of NDVI. The highest proportions of areas with increasing trend (p < 0.05) was recorded in MAM for both hemispheres, with 22.0% in the Northern Hemisphere and 17.4% in the Southern Hemisphere. In contrast, areas with decreasing trends (p < 0.05), showed 4.8-5.5% of burned area in the Northern Hemisphere, peaking in JJA, while the Southern Hemisphere showed a range of 7.1 to 10.9% with the highest proportion also in JJA. Regarding land cover types, 48.0% of fires occurred in forests, 24.1% in shrublands, 16.6% in agricultural fields, and 8.9% in grasslands/savannas. Consistent with the overall trend, the area exhibiting an increasing trend in NDVI values (p < 0.05) within forested regions had the highest proportion in MAM, with 19.9% in the Northern Hemisphere and 20.6% in the Southern Hemisphere. Conversely, the largest decreasing trend (p < 0.05) was observed in DJF in the Northern Hemisphere (2.7-2.9%) and in JJA in the Southern Hemisphere (7.2-10.4%). Seasonally, we found a high variability of regeneration trends of forested areas based on fire recurrences. In addition, we found that of the three climatic variables, increasing vapor pressure deficit values were more related to decreasing NDVI levels. These results indicate a strong component of seasonality with respect to fires, trends of vegetation increase or decrease in the different vegetation covers of the African continent, and they contribute to the understanding of climatic conditions that contribute to vegetation recovery. This information is helpful for researchers and decision makers to act on specific sites during restoration processes.

Drought Assessment in Kalaburgi District, North-eastern Dry Zone of Karnataka, India by Using Remote Sensing and Google Earth Engine

Drought is a significant natural disaster exacerbated by global warming, leading to severe environmental, economic and societal impacts. As one of the most complex phenomena, drought requires advanced methods for effective monitoring and assessment. Remote sensing indices have proven effective in analyzing drought's geographical and temporal distribution. In this study, the semi-arid nature of the North-Eastern Dry Zone of Karnataka, characterized by low rainfall and high temperature, was examined for its vulnerability to drought. The Google Earth Engine (GEE) platform was utilized, which provides cloud-based access to advanced computational resources for processing multi-temporal satellite data. This approach minimizes the need for extensive data downloads and complex software operations, enabling efficient drought monitoring. In this study, GEE was applied to create and execute customized scripts for drought assessment, thereby accelerating the procedure and minimizing the need for extensive data downloads and complex software operations. The study focused on the North Eastern Dry Zone of Karnataka, particularly Kalaburgi district, employing the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) derived from MODIS data. The analysis revealed severe drought conditions, particularly in 2001, with NDVI values as low as 0.07 at Afzalpur station and 0.06 at Chitapur station, indicating significant vegetation stress. The VCI analysis further supported these findings, with values as low as 0.05 at Afzalpur station and 0.03 at Chitapur station, highlighting the drought's intensity. This integrated approach provides a reliable evaluation of agricultural drought, essential for enhancing drought management and mitigation strategies in the region.

Exploring spatial dynamics of urbanization and solid waste generation in Kota city using the Google Earth Engine.

The current study investigates the relationship between urbanization, solid waste generation, and environmental changes in Kota city from 2000 to 2023. The study employs Google Earth Engine (GEE) to analyze land use and land cover (LULC) classification, normalized difference vegetation index (NDVI), normalized difference modified water index (NDMWI), normalized difference built-up index (NDBI), land surface temperature (LST), and predict future LULC changes up to 2043. The results show that the built-up area increased by 122.38%, correlated with a 294.16% increase in solid waste generation and a significant increase of 24.6% in urban temperature (R2 = 0.9936). Vegetation cover and water resources declined during this period, and NDVI and NDMWI values indicate environmental degradation. Future LULC forecasts for 2043 show that urban expansion will continue, with built-up areas expected to increase by 16.74% at the expense of natural resources. To mitigate these effects, the study emphasizes the need for sustainable urban planning, which includes green infrastructure, advanced waste recycling systems, and strategies to mitigate urban heat islands. These findings provide significant insights for policymakers who seek to balance urban growth with environmental sustainability and proficient waste management.

Analyzing the influence of urban vegetation cover on land surface temperature in Southwestern Nigeria

Urbanization and the expansion of urban infrastructure have led to the development of new land use policies that impact biodiversity and ecosystem services in Nigeria’s rapidly growing cities. Key drivers of this urbanization include population pressure, infrastructure development, rural-to-urban migration, and economic growth. This study investigates the effect of urban vegetation cover on land surface temperature in southwestern Nigeria, using the spectral radiance method, the support vector machine (SVM) algorithm and the normalized difference vegetation index (NDVI). This study analyzed remote sensing data to classify land use changes from 1991 to 2021 based on lithological characteristics. Additionally, the urban vegetation covers (VC) of the two urban centres were assessed through NDVI analysis. The highest NDVI values was recorded in Akure 0.358 to 0.394, and Osogbo had 0.449 to 0.464 while the lowest for Akure − 0.052 to 0.005 and Osogbo had − 0.058 to − 0.009. The analysis of urban land surface temperatures from 1991 to 2021 indicated maximum temperatures of 29.53 to 34.22 °C in Akure and 31.11 to 36.85 °C in Osogbo, with minimum temperatures of 18.22 to 22.48 °C in Akure and 21.72 to 23.66 °C in Osogbo. Primarily, urban land surface temperatures have steadily increased in both cities due to deforestation and urban infrastructural development which have diminished vegetation cover. This research highlights the need for urban green infrastructure and effective planning strategies to mitigate the impacts of rising land surface temperatures.

Effects of land management technology adoptions on land use land cover dynamics using GIS and remote sensing: the case of Goyrie watershed, southern Ethiopia.

Understanding land use/land cover (LULC) changes is crucial for informing policymakers and planners on the dynamics affecting environmental and resource management. Most past studies highlighted the significance of LULC changes and their driving forces in various locations. However, comprehensive analyses that combine the impact of land management technologies (LMTs) on LULC changes using GIS and remote sensing tools have not been widely addressed. Thus, the study analyzes the effects of LMT adoptions on LULC dynamics and the Normalized Difference Vegetation Index (NDVI) in the Goyrie watershed from 1993 to 2022. It also examines household perceptions of the cause of LULC changes. Methodologically, Landsat 5 TM (1993), Landsat 5 ETM + (2008), and Landsat 8 OLI/TIRS (2022) images were employed to analyze LULC changes and NDVI. Binary logistic regression models were used to identify households' perceptions of the causes of LULC changes. The findings revealed that the Goyrie watershed has experienced significant LULC changes since 1993. During the entire study period, the shares of grassland, shrub land, cultivated land, and settlement areas increased by 89.4%, 8.5%, 53.6%, and 1613.4% from their original sizes, respectively. Conversely, the coverage of bare land and forest land declined by 99.5% and 99.7%, with annual rates of decline of 3.29% and 3.3%, respectively. Throughout the study period, the increasing trends in grassland and shrub land, along with the decline in bare land, were attributed to LMT practices. The NDVI values of moderate and dense vegetation density decreased by 81.8% and 92.2%, respectively, from 1993 to 2022 due to the expansion of settlement areas and cultivated lands. Population pressure, expansion of settlements and agriculture, fuel extraction, LMTs, and policy issues significantly influenced the LULC changes. The study concludes that more sustainable and integrated LMT practices should be essential to managing the related risks of LULC changes.

Retrieval of Vegetation Indices and Vegetation Fraction in Highly Compact Urban Areas: A 3D Radiative Transfer Approach

Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 μm) and near-infrared bands (0.865 μm). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes.

Grapevine and cover crop spectral response to evaluate vineyard spatio-temporal variability

The use of cover crops in vineyards is considered a management strategy that contributes to improving soil structure. Remote sensing techniques of cover crops provide information on spatial variability that can be useful in precision viticulture. This study aims to investigate soil-plant interactions using biometric data, spectral response and soil physico-chemical parameters measured across 2021-2022, with the scope of assessing spatio-temporal variations of cover crop and vineyard. These evaluations were conducted on an on-farm vineyard in a semi-arid Mediterranean environment. During the winter season (T1), the cover crop (Vicia Faba) growth was evaluated. Equally, during the summer season (T2), vineyard monitoring was carried out during the phenological stages of flowering (T2A) and grape ripening (T2B). Multispectral images acquired through unmanned aerial vehicle (UAV) were employed, and subsequently, the normalized difference vegetation index (NDVI) was calculated to obtain crops vigor maps. Through bivariate LISA cluster maps, cover crop vigor maps were compared with those of the vineyard, revealing the presence of significant positive spatial autocorrelation between the two crops. Indeed, in areas where the cover crop exhibited higher dry matter accumulation, grapevine high vigor growth was observed. The spatial association identified in surveys conducted in T1 and T2 was examined considering soil parameters. It emerged that in areas where both the cover crop and the vines exhibited high NDVI values, soil factors such as soil organic carbon (SOC) content and total nitrogen (TN) were higher. Conversely, in areas where both crops showed low NDVI values, they were associated with higher soil bulk density (BD). Moreover, in low vigor zones, higher soil penetration resistance (SPR) values were observed in both T1 and T2, contributing to limiting root elongation and thus reducing overall crop growth. Vegetative growth variability observed through vigor maps and soil physicochemical properties was evaluated through principal component analysis (PCA). Soil parameters considered within these components highlighted their influence on crop growth. This study emphasizes how soil factors directly influence the spatiotemporal variability of crops. Monitoring cover crops allows for early detection of spatial variability, supporting vine growers in choosing sustainable vineyard management techniques.

Moderating effect of green space on relationship between atmospheric particulate matter and cardiovascular and cerebrovascular disease mortality in Ningxia, China.

This study explores the moderating effect of green space on the association between atmospheric particulate matter (PM) and cardiovascular and cerebrovascular disease (CCVD) mortality. Data on CCVD mortality, PM, meteorological factors, and the Normalized Difference Vegetation Index (NDVI) of green spaces in Ningxia from 2010 to 2020 were collected. A time-series generalized additive mixed-effect model (GAMM) was applied to analyze the exposure-response relationship between PM and CCVD mortality. The moderating effect of green spaces was examined using green space buffers of different radii (300m, 500m, 1000m, and 2000 m) and density. There were 150,356 CCVD deaths in Ningxia during the study period. The annual mean concentrations of PM2.5 and PM10 were 44.44μg/m³ and 105.30μg/m³, respectively, with an annual mean NDVI value of 0.25 within a 500m radius buffer. An increase of 10μg/m³ in PM2.5 and PM10 concentrations was significantly associated with an elevated risk of CCVD mortality, with the strongest excess risk (ER) observed at lag07 lag. The ER for PM2.5 was 1.43% (95% CI: 0.97%, 1.89%), and for PM10 was 0.55% (95% CI: 0.38%, 0.72%). The interaction analysis indicated that higher green space density could moderate the association between PM exposure and CCVD mortality risk. and as the green space buffer zone expanded, the interaction on CCVD mortality risk progressively strengthened. The independent moderation analysis indicated that an increase in green space buffer zone was associated with a reduced risk, and as green space density increased from Q1 to Q3, the ER for PM2.5-related CCVD mortality decreased from 1.56% to 0.6%, while the ER for PM10-related CCVD mortality decreased from 0.53% to 0.09%. In conclusion, atmospheric PM is associated with increased CCVD mortality risk, while larger green space buffers and higher green space density significantly moderated this association.

Evolution of Vegetation Coverage in the Jinan Section of the Basin of the Yellow River (China), 2008–2022: Spatial Dynamics and Drivers

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth.

A health relevant approach for assessing urban green spaces

This study constitutes a first step towards a better understanding of the associations between human Health and Well-Being (H&WB) and urban green space characteristics. Utilizing various Key Performance Indicators for two green spaces in the city of Chania, we report their capacities in terms of greenness availability, accessibility, usage, air temperature reduction, as well as air pollution removal. Our report consists of in-situ measurements, modelling results and questionnaire data. Despite Municipal Garden of Chania’s (MGC) lower total carbon storage, it demonstrates denser vegetation, leading to efficient carbon sequestration and higher Normalized Difference Vegetation Index values. Accessibility favors MGC, with both spaces offering similar attractiveness, safety and acoustic comfort. Saint Apostoles Park (SAP) proves more effective in air pollutant reduction. Temperature differentials between green and urban areas are pronounced, with SAP and MGC showing lower temperatures. The findings underscore the importance of strategic urban planning to bolster human H&WB.

Threshold changes in the production potential of bush clumps along piosphere gradients in arid thicket mosaics

Various studies have noted threshold changes in vegetation composition and structure and soil physical and chemical properties in the Albany Thicket biome of South Africa. The aim of this study is to assess if these changes to the environment have transformed the ecosystem’s forage production potential. To estimate forage production potential we compare three models that make use of the mean and variance of the Normalized Difference Vegetation Index (NDVI) obtained from two WorldView-2 satellite images. For the models, NDVI values were sampled along four independent piosphere gradients that vary in their intensity of use and rest from herbivory. Our results indicate that the model that makes use of NDVI variance after a good rainfall year is able to discern changes in the forage production potential that corroborate threshold changes in the vegetation and soil environment. Our results support the growing body of evidence that increases in the variance of ecosystem processes and services are important indicators of impending threshold changes in social-ecological systems.

Predicting Schistosoma Haematobium Risk Distribution in Zambia Using Geographic Information System (GIS) and Remote Sensing (RS) Technologies

Introduction: This study explores the spatial distribution and climatic influences on schistosomiasis in Zambia using remote-sensed data, specifically focusing on the Normalized Difference Vegetation Index (NDVI) and maximum temperature (Tmax). Material and Methods: By analysing prevalence data over a decade (2002-2012) and employing Geographic Information System (GIS) and Remote Sensing (RS) technologies, we identified regions conducive to the transmission of the disease. Results: The findings revealed significant variability in schistosomiasis prevalence across Zambia. Climatic cut-offs for schistosomiasis transmission were established, highlighting NDVI values between 134 - 153 and Tmax ranges of 19.4 – 27.6°C as optimal for sustaining transmission. Conclusion: The study underscores the pressing need for comprehensive control strategies that extend beyond school-based treatments, addressing the broader community and targeting at-risk populations. Furthermore, the established climatic cut-offs provide a framework for future research and public health initiatives aimed at reducing the burden of schistosomiasis in Zambia. This work emphasizes the intricate relationship between climate, health interventions, and disease dynamics, calling for adaptive strategies in the face of climate change.

Assessing the Feasibility of Using Remote Sensing Data and Vegetation Indices in the Estimation of Land Subject to Consolidation.

The values of vegetation indices can provide a new source of data for use in the estimation of land to be consolidated. The results of research work carried out so far indicate a significant advantage of low-volume imaging over satellite methods when it comes to calculating vegetation index values. This paper analyses multispectral images for the areas of selected croplands acquired via the Sentinel-2 satellite and an unmanned aerial vehicle (UAV) equipped with a multispectral camera. The research work consisted of evaluating NDVI (Normalised Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) values depending on the type of crop grown, the size of the cultivated area and the method of data acquisition. The data obtained were used to assess their potential use in the estimation of land to be consolidated. The effect of land consolidation is primarily to create more favourable living conditions and increase agricultural productivity. The results of the study showed that it would be preferable to use multispectral images acquired using UAVs rather than those from Sentinel satellites. This is due to the insufficient resolution of the satellite data, the correlation of NDVI and SAVI values at only a satisfactory level and the low accuracy of the data obtained for small registered plots of land.

Dataset Construction for Monitoring the Effects of Ecosystem Restoration Projects

Climate change have increased the necessity for developing climate adaptation strategies. Ecological restoration projects have been implemented to restore the structure and function of degraded natural environments, thus enabling adaptation to the climate crisis. However, due to the lack of subsequent monitoring and evaluation, the effectiveness and success of these restoration efforts remain unverified. Verification of restoration effectiveness is essential for establishing ecological restoration and adaptation policies. Therefore, this study catalogs 338 cases of ecosystem restoration projects (including Ecosystem Conservation Fund Return Projects, Jayeon Madang Restoration Projects, and Urban Ecological Corridor Restoration Projects) conducted between 2010 and 2023 to effectively select monitoring sites. Using satellite-based spatial data, we quantified the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference moisture index (NDMI) and land surface temperature (LST). This study shows that higher NDVI values significantly lower LST and NDMI, indicating that the more vegetation restored in ecosystems, the more effectively it reduces surface temperatures. The NDVI across all land cover types averaged above 0.2, corresponding to a high vegetation cover density. Specifically, forests exhibited significantly higher NDVI and NDMI, whereas bare land and used area showed significantly higher NDBI and LST. Over the time series, NDVI and NDMI increased, and NDBI decreased, suggesting the positive effects of restoration. The ranges of NDVI, NDBI, NDMI and LST values by land cover type for the 338 restoration project sites provided in this study can be utilized for selecting specific monitoring sites and verifying effectiveness.

Estimation of Nitrogen Absorption of Rice Plants Based on Remote Sensing

One of the indicators of maintaining the quality of rice plants is monitoring and managing nitrogen requirements. Nitrogen (N) absorption in rice plants can be detected by remote sensing technology using Sentinel 2-A satellite imagery data using the NDRE (Normalized Difference Red Edge) method. This research aims to determine a mathematical model to predict nitrogen absorption in rice plants. This study uses the NDRE (Normalized Difference Red Edge) index value. The image used is Sentinel 2 imagery, namely channels 4 and 8 to see the Normalized Difference Vegetation Index (NDVI) value. Besides, the Normalized Difference Red-Edge Index (NDRE) is channels 5 and 8. The results of spatial and tabular data processing are analyzed per pixel and time trend to obtain patterns during one phase of the planting period. Based on the analysis of the NDVI and NDRE values of rice plants in Nagari Singkarak, the NDVI index pattern is in line with the NDRE index. At the beginning of planting (age ± 1 month) and the ripening period (age &gt; ± 90 days) the NRDE value of rice plants is dominated by the low category NDRE value (red color). While when the rice is ± 60 days old, it is dominated by the high category NDRE value (green color). The estimation model for nitrogen uptake of rice plants in Nagari Singkarak based on NDRE data is y = 141.37 X + 0.0412, with a correlation coefficient (r) value of 0.97, which indicates a high correlation between NDRE values and nitrogen uptake.

Recovering NDVI over lake surfaces: Initial insights from CYGNSS data enhanced by ERA-5 inputs

The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous water bodies, such as Taihu Lake. Sensors operating in the microwave band can effectively mitigate this issue, particularly the emerging Global Navigation Satellite System Reflectometry (GNSS-R), which offers high temporal resolution and cost-effectiveness. In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. We also examine the effective range of GNSS-R data within this application scenario. Meanwhile, the Weighted Linear Regression-Laplacian Prior Regulation Method (WLR-LPRM) image gap-filling algorithm is used as a benchmark to evaluate recovery accuracy. The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. Compared to the previous work on GNSS-R algal bloom detection with overall accuracy of 0.82, this work shows significant improvement in both accuracy and utility. The recovery of lake surface NDVI provides detailed insights into algal blooms, including quantifiable metrics such as the amount and spatial distribution, which are crucial for effective monitoring and management. Additionally, the recovered image textures exhibit high clarity and closely resemble the reference NDVI images. Experimental evaluation using simulated and actual cloud blocks indicates the model’s robustness to recover NDVI under varying cloud cover conditions. In summary, this study demonstrates the capability of GNSS-R aided by supplementary data for recovering missing NDVI values on lake surfaces when optical observations are absent for the first time.

Advancing Sugarcane Farm Management Through NDVI-Based Color Mapping and Drone Imaging

Precision agriculture has emerged as a key strategy for boosting crop productivity and optimizing resource use. This study leverages advanced imaging and machine learning to enhance the management of sugarcane farms. Using drones, high-resolution RGB images of sugarcane fields are captured and transformed into multispectral images through a Generative Adversarial Network (GAN), revealing critical spectral data for plant health assessment. The Normalized Difference Vegetation Index (NDVI) is derived from these multispectral images and serves as a vital measure of vegetation health. This NDVI data, combined with farmer-reported yield information, creates a comprehensive dataset linking NDVI values to actual crop yields. To predict sugarcane yield from NDVI values, we trained a feedforward neural network on this integrated dataset. The proposed method not only enhances prediction accuracy but also provides valuable insights into the connection between NDVI metrics and crop performance. The model was validated using individual field images, enabling precise yield predictions for different field sections. This study highlights the effectiveness of integrating drone imagery, machine learning, and remote sensing in precision agriculture. The combination of NDVI data with yield information provides a robust tool for optimizing sugarcane production, improving farm management decisions, and advancing agricultural sustainability.

Temporal Analysis of Mangrove Canopy Cover of High Resolution Satellite Imagery on the West Coast of Bangkalan Regency, Madura East Java

Mangroves are plants that live in muddy substrates, muddy sand, sand to tidal areas. These plants have important roles for marine organisms, carbon sequestration, as shoreline protectors from waves or erosion and sediment filters before entering the sea. The influence of human activity in the form of conversion of mangrove land to other forms or caused by natural disasters is a dangerous threat to the mangrove ecosystem. The magnitude of mangrove cover change is an important component so information is needed on changes in the spatial and temporal distribution of mangrove cover. This study aims to temporally analyze mangrove cover using high-resolution satellite imagery on the West Coast of Bangkalan Regency. This research uses secondary data in the form of Planetscope high-resolution satellite images from 2018 - 2023. The data will be processed to determine changes in mangrove area using Fv (Fractional Vegetation Cover). Calculation of the Fv (Fractional Vegetation Cover) value is done from the results of the minimum and maximum values of NDVI (Normalized Difference Vegetation Index). The results showed that there was a change in mangrove cover which was initially included in the FVC (Full Vegetation Coverage) class in 2022 with an area of 41.12 Ha to the HVC (High Vegetation Coverage) class with an area of 84.82 Ha in 2020. At the Arosbaya location, the highest Fv value occurred in 2019 with an area of 206.48 Ha. Reduction of mangrove area occurred in Sepulu and Arosbaya sub-districts due to land use change and sedimentation.

The Preliminary Study of Environmental Variations Around the Du-Ku Highway Since 2000

Highways and their surrounding areas in mountainous and plateau regions are particularly susceptible to environmental changes, which can significantly impact their safety. In the context of global warming, the magnitude of environmental changes around highways has been further amplified. These environmental disturbances pose substantial risks to highway infrastructure in mountainous regions. By using satellite data and remote sensing techniques, this study focused on the environmental variations of the Du-Ku Highway (DKH) in the Tianshan Mountains and the preliminary revealed shifts in surface water, land surface temperature (LST), normalized difference vegetation index (NDVI), and temperature vegetation dryness index (TVDI) since 2000. The quantitative results showed that the water bodies with area between 0.1 and 0.5 ha showing the most significant growth around the DKH. The LST values are primarily distributed between 280 and 285 K, while the NDVI values are mostly below 0.4, and the TVDI is mainly concentrated at the two extremes. In the context of global warming and its amplified impact on mountainous and plateau regions, these findings offer critical insights that can directly support mountainous highway construction and maintenance strategies by identifying environmental indicators, providing a scientific foundation for making data-driven decisions.