vegetation index
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2023 ◽  
Vol 83 ◽  
Author(s):  
C. B. Alvarenga ◽  
G. S. M. Mundim ◽  
E. A. Santos ◽  
R. B. A. Gallis ◽  
R. Zampiroli ◽  
...  

Abstract Water magnetization and geoprocessing are increasingly utilized tools in weed management. Our objective was to study the influence of water magnetization on herbicide efficiency and to verify whether there is a relationship between control scores and the normalized difference vegetation index (NDVI). In the laboratory experiment, water was subjected to magnetization and evaluated with respect to four characteristics. In the field experiment, plots of Brachiaria grass were subjected to treatments in a factorial scheme (6 × 2 + 1). Six herbicidal factors (doses of glyphosate and glyphosate + 2,4-D) and the magnetization or absence of magnetization of the spray solution were evaluated and compared against the control treatment (without spraying). Weed control assessments were carried out six times. Images were obtained using an embedded multispectral camera to determine the NDVI values. Data related to water characteristics were analyzed using the t test. Weed control and NDVI data were subjected to analysis of variance and are presented in regression graphs. Dispersion analysis of NDVI data was performed according to the control scores. The magnetization process decreased the pH of the water and increased the surface tension, but it did not influence the control scores or the NDVI. As the glyphosate dose was increased, the control scores were higher and the NDVI values were lower. Magnetized water did not affect the biological efficiency of the herbicides, and there was a strong correlation between the control scores and the NDVI values.


Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 257
Author(s):  
Peng Guo ◽  
Jiqiang Lyu ◽  
Weining Yuan ◽  
Xiawan Zhou ◽  
Shuhong Mo ◽  
...  

This study examined the Chabagou River watershed in the gully region of the Loess Plateau in China’s Shaanxi Province, and was based on measured precipitation and runoff data in the basin over a 52-year period (1959–2010), land-use types, normalized difference vegetation index (NDVI), and other data. Statistical models and distributed hydrological models were used to explore the influences of climate change and human activity on the hydrological response and on the temporal and spatial evolution of the basin. It was found that precipitation and runoff in the gully region presented a downward trend during the 52-year period. Since the 1970s, the hydrological response to human activities has become the main source of regional hydrological evolution. Evapotranspiration from the large silt dam in the study area has increased. The depth of soil water decreased at first, then it increased by amount that exceeded the evaporation increase observed in the second and third change periods. The water and soil conservation measures had a beneficial effect on the ecology of the watershed. These results provide a reference for water resource management and soil and water conservation in the study area.


2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Annie Doubleday ◽  
Catherine J. Knott ◽  
Marnie F. Hazlehurst ◽  
Alain G. Bertoni ◽  
Joel D. Kaufman ◽  
...  

Abstract Background Neighborhood greenspaces provide opportunities for increased physical activity and social interaction, and thus may reduce the risk of Type 2 diabetes. However, there is little robust research on greenspace and diabetes. In this study, we examine the longitudinal association between neighborhood greenspace and incident diabetes in the Multi-Ethnic Study of Atherosclerosis. Methods A prospective cohort study (N = 6814; 2000-2018) was conducted to examine the association between greenspace, measured as annual and high vegetation season median greenness determined by satellite (Normalized Difference Vegetation Index) within 1000 m of participant homes, and incident diabetes assessed at clinician visits, defined as a fasting glucose level of at least 126 mg/dL, use of insulin or use of hypoglycemic medication, controlling for covariates in stages. Five thousand five hundred seventy-four participants free of prevalent diabetes at baseline were included in our analysis. Results Over the study period, 886 (15.9%) participants developed diabetes. Adjusting for individual characteristics, individual and neighborhood-scale SES, additional neighborhood factors, and diabetes risk factors, we found a 21% decrease in the risk of developing diabetes per IQR increase in greenspace (HR: 0.79; 95% CI: 0.63, 0.99). Conclusions Overall, neighborhood greenspace provides a protective influence in the development of diabetes, suggesting that neighborhood-level urban planning that supports access to greenspace--along with healthy behaviors--may aid in diabetes prevention. Additional research is needed to better understand how an area’s greenness influences diabetes risk, how to better characterize greenspace exposure and usage, and future studies should focus on robust adjustment for neighborhood-level confounders.


2022 ◽  
Vol 14 (2) ◽  
pp. 394
Author(s):  
Dan Li ◽  
Yuxin Miao ◽  
Curtis J. Ransom ◽  
G. Mac Bean ◽  
Newell R. Kitchen ◽  
...  

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.


MAUSAM ◽  
2022 ◽  
Vol 53 (1) ◽  
pp. 53-56
Author(s):  
P. R. JAYBHAYE ◽  
M. C. VARSHNEYA ◽  
T. R. V. NAIDU

Spectral characteristics were studied at pod development stage (75 DAS) in summer groundnut, at Pune, in western Maharashtra plain zone. A simple regression model (yield vs. vegetation index, R2= 0.94) and another multiple regression model (yield vs. B: R, G: R, NIR: R and VI, R2= 0.99) were developed to predict the yields of summer groundnut. The yield prediction model based on spectral ratios at pod development stage (75 DAS) is helpful in forecasting the yield of summer groundnut, one month in advance, in western Maharashtra plain zone.


2022 ◽  
Vol 14 (2) ◽  
pp. 343
Author(s):  
Fujue Huang ◽  
Xingsheng Xia ◽  
Yongsheng Huang ◽  
Shenghui Lv ◽  
Qiong Chen ◽  
...  

The northeastern margin of the Qinghai–Tibet Plateau (QTP) is an agricultural protection area in China’s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices’ time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices—normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)—maintained high mapping potential; (2) under the optimal threshold, >88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.


Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 100
Author(s):  
Maohong Wei ◽  
Hailing Li ◽  
Muhammad Adnan Akram ◽  
Longwei Dong ◽  
Ying Sun ◽  
...  

Drylands are expected to be affected by greater global drought variability in the future; consequently, how dryland ecosystems respond to drought events needs urgent attention. In this study, the Normalized Vegetation Index (NDVI) and Standardized Precipitation and Evaporation Index (SPEI) were employed to quantify the resistance of ecosystem productivity to drought events in drylands of northern China between 1982 and 2015. The relationships and temporal trends of resistance and drought characteristics, which included length, severity, and interval, were examined. The temporal trends of resistance responded greatest to those of drought length, and drought length was the most sensitive and had the strongest negative effect with respect to resistance. Resistance decreased with increasing drought length and did not recover with decreasing drought length in hyper-arid regions after 2004, but did recover in arid and semi-arid regions from 2004 and in dry sub-humid regions from 1997. We reason that the regional differences in resistance may result from the seed bank and compensatory effects of plant species under drought events. In particular, this study implies that the ecosystem productivity of hyper-arid regions is the most vulnerable to drought events, and the drought–resistance and drought–recovery interactions are likely to respond abnormally or even shift under ongoing drought change.


Author(s):  
A. Malah ◽  
H. Bahi ◽  
H. Radoine ◽  
M. Maanan ◽  
H. Mastouri

Abstract. By 2050, Most of the world’s population will live in cities, this demographic explosion will lead to significant urban development at the expanse of natural land which may harm the environmental quality. Consequently, assessing and modeling the urban environmental quality (UEQ) is requisite for efficient urban sprawl control and better city planning and management. The present study proposes a methodology to model and assess the environment of the urban system by developing the urban environmental quality index (UEQI) based on remote sensing data. Five environmental indicators were derived from the Landsat OLI image namely, Modified Normalized Difference Impervious Surface Index (MNDISI), Modified Normalized Difference, Water Index (MNDWI), Normalized difference vegetation Index (NDVI), Normalized difference built-up Index (NDBI) and Soil adjusted vegetation index (SAVI). Using the Principal Component Analysis (PCA) the urban environmental quality index was computed for the 17 communes of Casablanca city. The UEQI values were spatially mapped under three classes (good, moderate, and poor). The results obtained from the analysis showed a significant difference in the term of UEQI values among the communes. In addition, the environmental quality is inadequate in communes with fewer green spaces and more impervious surfaces. The outcomes of this work can serve as an efficient tool to determine the most critical interventions to be made by the authority for current and future urban planning and land/resource management.


Author(s):  
R. Lambarki ◽  
E. Achbab ◽  
M. Maanan ◽  
H. Rhinane

Abstract. Accelerated urban growth has affected many of the planet's natural processes. In cities, most of the surface is covered with asphalt and cement, which has changed the water and air cycles. To restore the balance of urban ecosystems, cities must find the means to create green spaces in an increasingly gray world. Green spaces provide the city and its inhabitants a better living environment. This article uses Nador city as a case study area, this project consists in studying the possibility for the roofs to receive vegetation. The first axis of this project is the quantification of the current vegetation cover at ground level by calculating the Normalized Difference Vegetation Index (NDVI) based on Satellite images Landsat 8, then the classification of the LiDAR point cloud, and the generation of a digital surface model (DSM) of the urban area. This type of derived data was used as the basis for the various stages of estimating the potential plant cover at the roof level. In order to study the different possible scenarios, a set of criteria was applied, such as the minimum roof area, the inclination and the duration of the sunshine on the roof, which is calculated using the linear model of angstrom Prescott based on solar radiation. The study shows that in the most conservative scenario, 21771 suitable buildings that had to be redeveloped into green roofs, with an appropriate surface area of 369.26Ha allowing a 63,40% increase in the city's green space by compared to the current state contributing to the improvement of the quality of life and urban comfort. The average budget for the installation of green roofs in a building with a surface area of 100 m2 varies between 60000dh and 170000dh depending on the type of green roofs used, extensive or intensive. These results would enable planners and researchers in green architecture sciences to carry out more detailed planning analyzes.


Author(s):  
A. Limon-Ortega ◽  
A. Baez-Perez

Abstract Environmental conditions contribute to a large percentage of wheat yield variability. This phenomenon is particularly true in rainfed environments and non-responsive soils to N. However, the effect of P application on wheat is unknown in the absence of N fertilizer application. This study was conducted from 2012 to 2019 in permanent beds established in 2005. Treatments were arranged in a split-plot design and consisted of superimposing three P treatments (foliar, banded and broadcast application) plus a check (0P) within each one of four preceding N treatments (applied from 2005 to 2009). Foliar P generally showed a greater response than granular P treatments even though the soil tests high P (>30 mg/kg). Precipitation estimated for two different growth intervals explained through regression procedures the Years' effect. Seasonal precipitation (224–407 mm) explained variation of relative yield, N harvest index (NHI) and P agronomic efficiency (AE). Reproductive stage precipitation (48–210 mm) explained soil N supply. In dry years, foliar P application improved predicted relative yield 14% and AE 155 kg grain/kg P compared to granular P treatments. Similarly, soil N supply increased 15 kg/ha in dry moisture conditions during the reproductive stage. The NHI consistently improved over the crop seasons. This improvement was relatively larger for 0 kg N/ha. On average, NHI increased from about 0.57 to 0.72%. Normalized difference vegetation index (NDVI) readings at the booting growth stage were negatively associated with NHI. Foliar P in this non-responsive soil to N showed the potential to replace granular P sources. However, the omission of granular P needs to be further studied to estimate the long-term effect on the soil P test.


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