Normalized Vegetation Index Research Articles

Optimal vegetation coverage from the perspective of ecosystem services in the Qilian Mountains

Abstract The Qilian Mountains (QLMs) serve as a vital ecological security barrier in the northwest China, determining the optimal fractional vegetation cover (FVC) of different land-use types is crucial for ecological restoration and protection. Based on the normalized vegetation index, meteorological data, soil data and land-use data, the InVEST model, bivariate spatial autocorrelation and elastic analysis, we calculated the water conservation, soil conservation and carbon storage, analyzed the spatio-temporal distribution characteristics of FVC and ecosystem services (ES) and the correlation between them. This study aims to evaluate the optimal FVC of the different land-use types and vegetation restoration’s effect on it in the QLMs. The results showed that the FVC and ES showed an increasing trend from 2000 to 2020, with a spatial distribution pattern of high values in the southeast and low values in the northwest. There was a significant and positive correlation between FVC and ecosystem service functions (p < 0.05), FVC had the most obvious effect on soil conservation, followed by water conservation. The optimal FVC varied by land-use type, with its value between 0.4 and 0.48 for forest land, 0.24 and 0.30 for grassland and 0.19 to 0.20 for unused land. In the eastern of the QLMs, nearly 70% of the forests and 50% of the grasslands exceeded the optimal vegetation coverage, while 36% of the grasslands and 62% of the unused lands were under-covered in the western of the QLMs. The findings suggest that differentiated ecological restoration and management measures should be formulated according to the status of vegetation restoration, to enhance the comprehensive capacity of ecosystem services in the QLMs.

Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice.

Exposure to residential green space and semen quality among Chinese men

BackgroundResidential greenness has been linked to various health benefits, but evidence on its association with sperm quality remains limited. MethodsWe enrolled 28,089 adult men from an infertility clinic in Hubei, China, from 2014 to 2020. Residential greenness was estimated using the Normalized Vegetation Index (NDVI) from satellite imagery, averaged within buffers of 250m, 500m and 1000m around each participant’s residential address. We employed multivariate linear regression analysis to evaluate the association between NDVI exposure and semen quality, while controlling for individual characteristics and semen collection season. Additionally, we performed subgroup analyses to investigate potential variations in the association based on individual characteristics. ResultsAn interquartile range increase of 0.243 in NDVI within the 1000m buffer was associated with increases of 1.68% (95% CI: 0.31%, 3.06%) in sperm concentration, 0.43% (95% CI: 0.08%, 0.79%) in progressive motility, and 0.50% (95% CI: 0.14%, 0.87%) in total motility. These associations were consistent across different buffer sizes. The associations were more pronounced during the 70–90 lag days prior to semen collection (spermatogenesis stage) and among men aged ≥ 40 years or those with lower education levels. ConclusionsOur study demonstrated that exposure to residential greenness may act as an innovative protective factor for semen quality.

Temporal and Spatial Variations of Soil Organic Carbon and the Influencing Factors in Shaanxi Province in Recent 30 Years

Soil organic carbon （SOC） variation is a significant indicator for the soil quality dynamic and global carbon cycle. Therefore, it is necessary to study the regional temporal and spatial distribution of SOC pool and the influencing factors. In this study, a total of 540 soil data and environmental variables were collected from Shaanxi Province during a 30-year period from 1985 to 2015, and univariate analysis of variance and path analysis were used to explore the temporal and spatial distribution characteristics of SOC content and the influencing factors of SOC change. The results showed that the SOC contents of Shaanxi Province in both 1985 and 2015 were the highest in central Shaanxi, followed by those in southern Shaanxi, and they were significantly higher than those in northern Shaanxi. From 1985 to 2015, the increase in SOC in southern Shaanxi was the highest （21.28%）, and that in central Shaanxi was 15.33%. The content of SOC in northern Shaanxi was decreased by 10.23%, caused by significant decrements in the bottom horizons of 60-80 cm and 80-100 cm. Compared with that in 1985, the increases in SOC content in the 0-100 cm soil profile （with every 20 cm as a horizon） ranged from 3.21% to 29.39% in 2015. The increase in SOC content of skeletal soils was largest, followed by that of alluvial soils. Correlation analysis and path analysis showed that SOC content was positively correlated with altitude, average annual precipitation, normalized vegetation index, and total nitrogen content and was in significant negative correlation with curvature, bulk density, and pH. Total nitrogen content was the main controlling factor affecting SOC content. The results of the study can provide reference for future carbon management measures in the region.

Association of specific PM2.5 chemical constituents and ozone exposure with pregnancy outcomes in women undergoing assisted reproductive technology treatment in central China

The associations of air pollutants exposure with assisted reproductive technology (ART) pregnancy outcomes are mixed, and the effects of specific components of fine particulate matter (PM2.5) and ozone (O3) are not well understood. We conducted a retrospective longitudinal study to explore the association of PM2.5 constituents and O3 exposure with three ART outcomes among women undergoing ART treatment. The exposure window was segmented into five periods corresponding to the cycle of ovarian stimulation and oocyte retrieval procedure. Generalized linear mixed model (GLMM) was applied to explore the relationships between PM2.5 constituents, O3, Normalized Vegetation Index (NDVI) exposure and three ART outcomes. The combined effect of PM2.5 constituents was evaluated by the quantile g (qg)-computation. We also explored the modifying effect of different covariate. Elevated exposure level of PM2.5 (OR = 0.915, 95% CI: 0.859, 0.974) and its constituents (BC: 0.905, 95% CI: 0.840, 0.975; OM: 0.910, 95% CI: 0.848, 0.976; NO3−: 0.909, 95% CI: 0.850, 0.972, SO42−: 0.905, 95% CI: 0.846, 0.968, and NH4+: 0.902, 95% CI: 0.842, 0.966) exposure throughout the year before oocyte retrieval (period 1) was correlated with a reduced odds ratio (OR) of live birth with statistical significance. Similarly, for each interquartile range (IQR) increase in O3 exposure during periods 2 (85 days prior to oocyte retrieval), 3 (30 days prior to oocyte retrieval), 4 (oocyte retrieval to embryo transfer) and 5 (embryo transfer to hCG test) was significantly related to a decreased OR of live birth. Especially, participants who underwent fresh embryo transfer cycles and received two cleavage-stage embryo transfer, and were younger than 30 years old, showed a higher susceptibility to particulate matter. Findings from this study suggest that PM2.5 constituents and O3 exposure may have adverse effects on the ART outcomes, highlighting the importance of identifying critical exposure periods for various air pollutants and the need for meticulous management of particulate matter.

The association between green space around schools, screen time for entertainment, and adolescent depressive symptoms: A nationwide study from China

BackgroundNatural green environments are beneficial to people's mental health, while too much screen time may be harmful to adolescents' mental health. However, it is not clear how green spaces and screen time affect mental health together in adolescents. MethodsA total of 22,868 adolescents were recruited from October to December 2021 in eight cities in China, through multistage cluster sampling. The Patient Health Questionnaire (PHQ-9) was used to investigate adolescents' depressive symptoms, and the Normalised Vegetation Index for the 200m, 500m and 1000m buffer zones around the school represents the green cover around the school. We used binary logistic regression to analyse the association between green space, screen time and depressive symptoms. ResultsIn fully adjusted models, the highest quartile of NDVI in the 200m, 500m and 1000m buffer zones around the school was a protective factor for depressive symptoms. For less than 2 h of recreational screen time, the highest quartiles of the NDVI and the association with depressive symptoms were negatively correlated for the 200m, 500m and 1000m buffer zones (NDVI200: 0.904(0.848–0.964)), NDVI500: 0.863(0.808–0.921), NDVI1000: 0.862(0.808–0.920)). The highest quartiles of NDVI in all buffers were not associated with depressive symptoms when the screen time was >2 h. The association between green space and depressive symptoms at lower recreational screen time was observed to be stronger in the group of adolescents with a lower family economic status (NDVI200: 0.780(0.695–0.876), NDVI500: 0.838(0.748–0.938), NDVI1000: 0.783(0.698–0.879)). No potential gender differences were observed. ConclusionsFor adolescents with less than 2 h of recreational screen time per day, the greenery around the school had a protective effect on their mental health, especially for adolescents from economically disadvantaged families. Green spaces around schools are significant in improving health inequities.

Quantifying the influence of dominant factors on the long-term sandstorm weather - A case study in the Yellow River Basin during 2000–2021

Sandstorm is a disastrous weather phenomenon that often occurs in arid and semi-arid areas, endangering the ecological environment and affecting people's lives and property safety seriously. Since the 21st century, the sandstorm weather in the Yellow River Basin has ameliorated obviously. However, the causes of the long-term trends in sandstorms during 21st century were still unknown. In this study, fifteen influencing factors from five aspects: ecology, meteorology, hydrology, geography and man-made were selected to comprehensively analyze the driving mechanism of sandstorm activities in the Yellow River Basin since the 21st century, and the effect of each influencing factor on sandstorm weather was quantified. The results indicated that ecological, meteorological and geographical factors had dominant impacts on the spatio-temporal variation of sandstorms during 2000–2021, while hydrological and human factors played little role in the long-term variation of sandstorms. Sandstorms frequently occurred in semi-desert or grassland or non-high vegetation covered areas in spring. Vegetation coverage, precipitation, surface pressure, surface roughness, and soil moisture content were negatively correlated with sandstorms, while wind speed, friction velocity, evaporation, and soil temperature were positively correlated with sandstorms. Precipitation, runoff, evaporation, soil moisture content, soil temperature, and surface temperature indirectly acted on normalized brightness temperature dust index (NBTDI) and sandstorms by changing soil texture. Normalized Vegetation Index (NDVI) had direct negative effects on NBTDI, while wind speed 10 m (WS10m), slope of sub-gridscale orography (SOSGO), and forecast surface roughness (FSR) had direct positive effects on NBTDI. This study comprehensively revealed the dominant factors and their driving mechanism of sandstorm weather in the Yellow River Basin since the 21st century, which had practical application value for the prevention of sandstorms.

Monitoring of Vegetation Drought Index in Laibin City Based on Landsat Multispectral Remote Sensing Data

Due to the impact of global warming, drought has caused serious damage to China’s ecological environment and social status. This article selects Laibin City in the Guangxi Zhuang Autonomous Region as the research area, utilizing multispectral remote sensing data as the data source and Landsat series image data for relevant preprocessing. It calculates the monthly normalized vegetation index (NDVI) and surface temperature (LST) data for Laibin City. Based on the ecological environment and surface coverage conditions of the research area, the ratio vegetation index (RVI), normalized vegetation moisture index (NDWI), temperature vegetation drought index (TVDI), and conditional vegetation temperature drought index (VTCI) were selected to calculate and invert the drought monitoring results of Laibin City. The drought monitoring results were obtained and overlaid with the vegetation area map to generate the vegetation drought monitoring results of Laibin City. Based on the climate, geography, and ecological characteristics of the monitored area in Laibin City, a specific analysis will be conducted to develop an appropriate TVDI index drought level, and generate vegetation drought level result maps for Laibin City in 2021, 2022, and 2023. Then, a detailed analysis of the vegetation drought situation in Laibin City is conducted according to time and space. Among them, in the past three years, the vegetation areas in Laibin City have experienced drought seasons mostly in summer and autumn. The interannual drought is mainly mild drought, and the proportion of areas with mild drought shows a relatively stable trend. In conclusion, TVDI proves to be a valuable tool for monitoring vegetation drought in Laibin City, offering insights for efficient water resource management strategies.

A novel red-edge vegetable index for paddy rice mapping based on Sentinel-1/2 and GF-6 images

ABSTRACT Accurate paddy rice mapping is crucial for ensuring food security and guiding agricultural production. Vegetation indices are extensively employed to map paddy rice. However, most traditional normalized indices tend to be oversaturated during periods of lush vegetation due to normalization errors, resulting in uncertainties in paddy rice mapping. To address this issue, we introduce a novel red-edge rice index (RERI) in this study; this index comprises information from red, near-infrared, and red-edge bands without normalization. To extract single- and double-cropping rice features from potential rice areas, we employ GF-6 and Sentinel-2 images based on the proposed RERI and the random forest algorithm. The proposed method is validated in the Dingcheng District of Changde city, China, and the results are compared with those based on three normalized vegetation indices. The results show that the RERI yielded the highest levels of the accuracy for all the metrics, achieving an overall accuracy (OA) of 92.50% and a kappa coefficient of 0.8875. The RERI exhibited F1 scores of 92.26% for single-cropping rice, 93.00% for double-cropping rice, and 92.28% for non-rice areas. Our results highlight the potential of using the RERI for rice identification, and the effectiveness of our method for rice extraction is demonstrated.

Characteristics of spatial and temporal changes in ecosystem service value and threshold effect in Henan along the Yellow River, China

Ecosystem services (ES), which serve as the link between human activities and natural ecosystems, are crucial for maintaining ecological balance and supporting economic development. The Ecosystem Services Value (ESV) is an indicator used to quantifying ecosystem services. Accurately estimating the dynamics of ESV and the threshold effects of influencing factors is of great importance for the sustainable development of the region. This study constructs an ESV valuation model based on the equivalent factor method, incorporating construction land, and corrects the initial valuation results in three dimensions. By constructing a random forest model and integrating partial dependence analysis, Elevation, Evapotranspiration, Normalized Difference Vegetation Index, and the proportion of built-up land area were identified as significant factors in determining ecologically vulnerable areas within the study region. The results show that: (1) The total ESV in Henan along the Yellow River in 2000, 2005, 2010, 2015, and 2020 was 204.612 billion yuan, 249.775 billion yuan, 239.100 billion yuan, 234.868 billion yuan and 202.767 billion yuan, respectively. The overall trend of change is first growth and then decline, with spatial features remaining relatively stable, and ESV polarization becoming more pronounced. (2) The spatial clustering characteristics of ESV in Henan along the Yellow River are remarkable, and the cold and hot spot areas show a bidirectional expansion trend. The cold spot areas expand substantially around the construction land, whilethe hot spot areas show a small expansion around the western mountain areas and the vegetation areas along the Yellow River, but the overall growth is slower. (3) The main factors affecting ESV include three natural factors and one anthropogenic factor, namely evapotranspiration (ET), the proportion of built-up land area (POB), normalized vegetation index (NDVI) and elevation (ELEV). ESV is at a high level when ELEV>718 m, ET<90 mm, NDVI>0.7, and POB<25.67 % approximately. The study results can provide an important theoretical basis for the development of comprehensive ecological protection measures. Furthermore, they can contribute to the sustainable development of the Yellow River Basin.

Does ambient particulate matter 1 increase the risk of gastric cancer in the northwest of China?

Gastric cancer (GC) remains a significant health concern in Gansu province, China, with morbidity and mortality rates surpassing national averages. Despite the recognized health risks associated with ambient particulate matter with an aerodynamic diameter <1 μm (PM1), the relationship between PM1 exposure and GC incidence has not been extensively studied. Data on GC cases from 2013 to 2021 were gathered from 262 hospitals in Gansu, China. Concurrently, data on the normalized vegetation index (NDVI), gross domestic product (GDP), drinking and smoking behavioral index (DSBI), PM1, PM2.5, and PM2.5-1 were collected. Utilizing a Bayesian conditional autoregressive (CAR) combined generalized linear model (GLM) with quasi-Poisson regression, we evaluated the impact of PM1, PM2.5, PM2.5-1, NDVI, DSBI, and GDP on GC morbidity while adjusting for potential confounders. Our analysis indicated that exposure to PM1 (μg/m3) is significantly positively correlated with GC incidence in regions with an overall age-standardized incidence rate (ASIR) >40 (relative risks [RR]: 1.023, 95% confidence intervals [CI, 1.007, 1.039]), male ASIR >50 (RR: 1.014, 95% CI [1.009, 1.019]), and female ASIR >20 (RR: 1.010, 95% CI [1.002, 1.018]). PM2.5, PM2.5-1, DSBI, and GDP were positively correlated with GC incidence, while NDVI was negatively correlated in the study regions. Our findings provided evidence of a positive correlation between PM1 exposure and GC incidence in high-risk areas of GC within arid regions. Further research is warranted to elucidate the complex nonlinear relationships between environmental factors and GC. These insights could inform strategies for improving the control and prevention of GC in Gansu and similar regions.

Analysis of the spatial heterogeneity of glacier melting in Tibet Autonomous Region and its influential factors using the K-means and XGBoost-SHAP algorithms

This study employed machine learning to comprehensively analyze glacier melting in Tibet Autonomous Region (TAR) and its vital influencing factors. Existing machine learning research often lacks detailed explanations, leading to generalized predictions without considering essential driving factors necessary for yielding an insightful understanding of glacier melting dynamics. To overcome these limitations and fulfill multi-level analysis requirements for comprehending glacier melting, this study identifies factors contributing to glacier melting heterogeneity and assesses distinct melting causes in three spatial melted glacier clusters. We utilized K-means unsupervised classification to cluster Tibet melted glaciers into three categories based on temperature, sunshine hours, evapotranspiration, precipitation, normalized vegetation index, and slope. XGBoost algorithm explores the nonlinear relationships of glacier melting with these features and Shapley values were used for model transparency, quantifying feature's influence on the melting process. Investigating geographical heterogeneity among clusters enhanced our understanding of the observed changes. High fitting accuracy (>0.98) enhanced the result reliability, as well. The results show that Tibetan glaciers melt significantly from 2010 to 2020, and the cluster analysis reveals its unique melting characteristics. Melting glaciers in the same cluster are not only similar in characteristics, but also in spatial and geographical distribution, with two of the clusters concentrating in the eastern part of TAR, and the third cluster scattered in the western part of the country. the XGBoost-SHAP analysis efficiently quantifies the contribution of each cluster feature to the glacier melting, revealing the different roles of different clustered features.

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

Research on landslide hazard assessment based on improved analytic hierarchy process optimizing multiple rainfall indicators

Rainfall is the key factor that leads to landslide, so this study introduces multiple rainfall indexes to optimize the rainfall model in view of the single evaluation index of the rainfall model for landslide hazard assessment. In this study, Xiangxiang City of Hunan Province was selected as the study area, and eight types of susceptibility assessment factors including slope, aspect, elevation, normalized vegetation index (NDVI), road, fault, lithology and land use were extracted. By analyzing the characteristics of local rainfall, six types of rainfall induced assessment factors were selected for hazard assessment of the study area. The two types of evaluation factors were substituted into the improved AHP and RF combined weighting models respectively to obtain the susceptibility zoning map and rainfall induced model of the study area, and finally superimposed to obtain the hazard zoning map of the study area.Using ROC curve and hazard zoning in the studied area test results, the results show that:The AUC value of the multi-rainfall index is 17.7% higher than that of the single rainfall index, and the AUC value of the improved AHP is 6% higher than that of the traditional AHP method. It is verified by the disaster points on the day of extreme rainfall in the study area, and the actual occurrence of the disaster points is basically consistent with the hazard evaluation and zoning of the multi-rainfall index. Therefore, the rainfall model of landslides is optimized by using multiple rainfall indexes, which significantly improves the rationality of landslide hazard assessment.The study of multiple rainfall induced indicators can fill the knowledge gap in the current field, provide new insights and understanding for the field, and provide assistance for predicting and preventing landslides in related areas.

Impacts of the land use transition on ecosystem services in the Dongting Lake area

Urbanization-induced land use transitions (LUTs) result in a decline in ecosystem services, which has implications for regional ecological security. In order to explore the relationship between ecosystem services and land use transition, this paper utilizes the InVEST model, a geographically weighted regression (GWR) model, to examine the impact of land use transition on ecosystem services in the Dongting Lake area (DLA). The results showed that 1) with the change in urbanization development, the average values of land use transition intensity (LUI) in 2000, 2010, and 2020 are 237.99, 235.82, and 238.92, respectively. Land use dynamics (LUD) show a tendency to increase and then decrease, with average values of 5.58 and 5.62 for the periods 2000–2010 and 2010–2020, respectively, and the transformation of land use shows obvious spatio-temporal heterogeneity. 2) Habitat quality and carbon sequestration showed a downward trend. In contrast, food supply followed an upward trend; soil conservation (SC) and water yield (WY) services initially increased and decreased later. The overall spatial changes in habitat quality and carbon sequestration appear to be insignificant. Food supply shows significant differences in the plains compared to other areas, while soil conservation and water yield service show significant changes in places other than the DLA. 3) From 2000 to 2020, land use transition dynamics, population density, GDP density, night lighting, and transition intensity had mainly negative effects on ecosystem services. Only the Normalized Vegetation Index (NDVI) showed a positive effect on ecosystem services. The results of the research will provide valuable references for the development and implementation of spatial ecological restoration planning and land use policies in the national territory.

Foliar Spraying with ZnSO4 or ZnO of Vitis vinifera cv. Syrah Increases the Synthesis of Photoassimilates and Favors Winemaking.

Zinc enrichment of edible food products, through the soil and/or foliar application of fertilizers, is a strategy that can increase the contents of some nutrients, namely Zn. In this context, a workflow for agronomic enrichment with zinc was carried out on irrigated Vitis vinifera cv. Syrah, aiming to evaluate the mobilization of photoassimilates to the winegrapes and the consequences of this for winemaking. During three productive cycles, foliar applications were performed with ZnSO4 or ZnO, at concentrations ranging between 150 and 1350 g.ha-1. The normal vegetation index as well as some photosynthetic parameters indicated that the threshold of Zn toxicity was not reached; it is even worth noting that with ZnSO4, a significant increase in several cases was observed in net photosynthesis (Pn). At harvest, Zn biofortification reached a 1.2 to 2.3-fold increase with ZnSO4 and ZnO, respectively (being significant relative to the control, in two consecutive years, with ZnO at a concentration of 1350 g.ha-1). Total soluble sugars revealed higher values with grapes submitted to ZnSO4 and ZnO foliar applications, which can be advantageous for winemaking. It was concluded that foliar spraying was efficient with ZnO and ZnSO4, showing potential benefits for wine quality without evidencing negative impacts.

Refining Long-Time Series of Urban Built-Up-Area Extraction Based on Night-Time Light—A Case Study of the Dongting Lake Area in China

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning.

Study on Vegetation Coverage Based on Binary Pixel Model

Vegetation coverage (FVC) is an important indicator of ecosystem change and provides an indicator for the study of land cover change. In this paper, taking Zhengzhou City as an example, the 2015 Landsat 8 OLI remote sensing images were used as data sources, normalized vegetation index (NDVI) was used as a parameter to extract information from the study area, and the pixel binary model was used to calculate vegetation coverage. The results showed that the vegetation cover was higher in the west and lower in the east, and some forest land was changed into cultivated land from west to east.

An Image Identification System for Agricultural Items Based on Machine Vision

The purpose of this work was to use machine learning classification models and hyperspectral camera technologies to create a model of surface damage to garlic. 140 of the 184 garlic plants of which 44 were used for test validation were pre-treated for surface damage. First, we examined the data in ENVI under various damage scenarios using the normalised vegetation index (NDVI) approach. 579 pixels were then chosen for the training of the logistic regression model. Finally, we used 54 garlic bulbs to practically validate the model. Although tiny regions could not be precisely identified, the mouldy portion of the garlic’s surface could be identified using the NDVI technique. 90% accuracy was attained using the 90% classification model constructed using the logistic regression approach. Garlic’s surface damage, even at first mild ones, was correctly identified. The creation of this model for identifying garlic damage lowers the cost of detecting garlic damage and broadens the use of hyperspectral technologies in garlic detection.

Application of Remote Sensing Technologies for Nature Reserves Monitoring

The scientific article is devoted to the urgent problem of conservation of natural resources and ecosystems through the effective use of information technologies, especially methods of remote sensing of the Earth. Examples of the use of Landsat 8, Sentinel-2 and Resurs-P satellite systems for monitoring the state of nature, including the detection and analysis of wildfires, are given. Attention is also paid to the use of vegetation indices, such as the normalized vegetation index, to assess the consequences of fires in nature reserves.