scholarly journals VENµS-Derived NDVI and REIP at Different View Azimuth Angles

2022 ◽  
Vol 14 (1) ◽  
pp. 184
Author(s):  
Manuel Salvoldi ◽  
Yaniv Tubul ◽  
Arnon Karnieli ◽  
Ittai Herrmann
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Crop ◽  
Bidirectional Reflectance ◽  
Reflected Light ◽  
Change Vector ◽  
The Stability ◽  
Bidirectional Reflectance Distribution

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles.

scholarly journals Spatiotemporal Assessment of Vegetation Indices and Land Cover for Erbil City and Its Surrounding Using Modis Imageries

2017 ◽  
Vol 10 (1-2) ◽  
pp. 31-39 ◽  
Author(s):  
Shwan O. Hussein ◽  
Ferenc Kovács ◽  
Zalán Tobak
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Natural Vegetation ◽  
Visual Interpretation ◽  
Surrounding Areas ◽  
The Impact

Abstract The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), in addition land cover in and around Erbil city area between the years 2000 and 2015. MODIS satellite imagery and GIS techniques were used to determine the impact of urbanization on the surrounding quasi-natural vegetation cover. Annual mean vegetation indices were used to determine the presence of a spatiotemporal trend, including a visual interpretation of time-series MODIS VI imagery. Dynamics of vegetation gain or loss were also evaluated through the study of land cover type changes, to determine the impact of increasing urbanization on the surrounding areas of the city. Monthly rainfall, humidity and temperature changes over the 15-year-period were also considered to enhance the understanding of vegetation change dynamics. There was no evidence of correlation between any climate variable compared to the vegetation indices. Based on NDVI and EVI MODIS imagery the spatial distribution of urban areas in Erbil and the bare around it has expanded. Consequently, the vegetation area has been cleared and replaced over the past 15 years by urban growth.

scholarly journals Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sang-il Kim ◽  
Do-Seob Ahn ◽  
Kyung-Soo Han ◽  
Jong-Min Yeom
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ocean Color ◽  
Growth Curves ◽  
Surface Reflectance ◽  
Bidirectional Reflectance ◽  
Simulation Accuracy ◽  
Vegetation Growth ◽  
Time Sensitivity ◽  
Bidirectional Reflectance Distribution

The purpose of this study was to optimize a composite method for the Geostationary Ocean Color Imager (GOCI), which is the first geostationary ocean color sensor in the world. Before interpreting the sensitivity of each composite with ground measurements, we evaluated the accuracy of bidirectional reflectance distribution function (BRDF) performance by comparing modeled surface reflectance from BRDF simulation with GOCI-measured surface reflectance according to composite period. The root mean square error values for modeled and measured surface reflectance showed reasonable accuracy for all of composite days since each BRDF composite period includes at least seven cloud-free angular sampling for all BRDF performances. Also, GOCI-BRDF-adjusted NDVIs with four different composite periods were compared with field-observation NDVI and we interpreted the sensitivity of temporal crop dynamics of GOCI-BRDF-adjusted NDVIs. The results showed that vegetation index seasonal profiles appeared similar to vegetation growth curves in both field observations from crop scans and GOCI normalized difference vegetation index (NDVI) data. Finally, we showed that a 12-day composite period was optimal in terms of BRDF simulation accuracy, surface coverage, and real-time sensitivity.

scholarly journals Spatio-temporal pattern of urban forest vegetation density, Medan Baru city, Indonesia

2021 ◽  
Vol 918 (1) ◽  
pp. 012021
Author(s):  
Samsuri ◽  
C A B Ginting ◽  
A Zaitunah ◽  
A Susilowati
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Forest ◽  
Vegetation Indices ◽  
Stand Density ◽  
Development Planning ◽  
Vegetation Density ◽  
Dense Vegetation ◽  
Spatio Temporal

Abstract The growth of urban areas and the population generally requires the guarantee of a healthy and comfortable environment. The expansion of physical developments and urban areas, year after year, can no longer support human existence. In Indonesia, the city should have at least 10% of its surface area committed to private urban forest and 20% for public urban forest. Jakarta is Indonesia’s largest city, and has only 9.98% urban forest coverage. Medan Baru city is facing the same issue, as it continues to grow year after year. The population requires a comfortable environment, which includes safe drinking water and clean, fresh air. As a result, vegetation is an important component of Medan Baru sub-district that offers numerous benefits. It is necessary to conduct research on the analysis of vegetation density in the Medan Baru, using vegetation indices such as Normalized Difference Vegetation Index (NDVI). The research aimed to analyze vegetation density change and mapping the vegetation density of Medan Baru city. The research found the largest area was relatively dense vegetation, about 262.00 hectares (47.87%). The research also found a decrease in urban forest quality, indicated by an increase in the sparse density class of 41.90 hectares and a decrease in the relative-dense vegetation class with 51.65 hectares. This reduction of vegetation density will reduce the urban forest quality by influencing urban forest capability in absorbing carbon dioxide and alleviating the oxygen productivity volume. Areas with lower stand density must be considered in future urban development planning. Moreover, decrease in vegetation density and urban forest area should be a primary consideration in Medan urban forest management.

scholarly journals A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images

2021 ◽  
Vol 11 (8) ◽  
pp. 3472
Author(s):  
Geunsang Lee ◽  
Jeewook Hwang ◽  
Sangho Cho
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Landscape Management ◽  
Urban Regeneration ◽  
Urban Vegetation ◽  
Multispectral Images ◽  
Long Run

Unmanned aerial vehicles (UAVs) equipped with high-resolution multispectral cameras have increasingly been used in urban planning, landscape management, and environmental monitoring as an important complement to traditional satellite remote sensing systems. Interest in urban regeneration projects is on the rise in Korea, and the results of UAV-based urban vegetation analysis are in the spotlight as important data to effectively promote urban regeneration projects. Vegetation indices have been used to obtain vegetation information in a wide area using the multispectral bands of satellites. UAV images have recently been used to obtain vegetation information in a more rapid and precise manner. In this study, multispectral images were acquired using a UAV equipped with a Micasense RedEde MX camera to analyze vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Blue Normalized Difference Vegetation Index (BNDVI), Red Green Blue Vegetation Index (RGBVI), Green Red Vegetation Index (GRVI), and Soil Adjusted Vegetation Index (SAVI). However, in the process of analyzing urban vegetation using the existing vegetation indices, it became clear that the vegetation index values of long-run steel roofing, waterproof coated roofs, and urethane-coated areas are often similar to, or slightly higher than, those of grass. In order to improve the problem of misclassification of vegetation, various equations were tested by combining multispectral bands. Kappa coefficient analysis showed that the squared Red-Blue NDVI index produced the best results when analyzing vegetation reflecting urban land cover. The novel vegetation index developed in this study will be very useful for effective analysis of vegetation in urban areas with various types of land cover, such as long-run steel roofing, waterproof coated roofs, and urethane-coated areas.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

2021 ◽  
Vol 13 (4) ◽  
pp. 766
Author(s):  
Yuanmao Zheng ◽  
Qiang Zhou ◽  
Yuanrong He ◽  
Cuiping Wang ◽  
Xiaorong Wang ◽  
...  
Keyword(s):  
Urban Development ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Development Planning ◽  
Urban Land ◽  
Sustainable Urban Development ◽  
Land Use Patterns ◽  
Water Index ◽  
Nighttime Light

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

Comparing Effects of FOXO3A and Residing in Urban Areas on Longevity: A Gene-Environment Interaction Study

2021 ◽  
Author(s):  
John S Ji ◽  
Linxin Liu ◽  
Lijing Yan ◽  
Yi Zeng
Keyword(s):  
Protective Effect ◽  
Effect Size ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fine Particulate Matter ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Forkhead box O3 (FOXO3A) is a candidate longevity gene. Urban residents are also positively associated with longer life expectancy. We conducted a gene-environment interaction to assess the synergistic effect of FOXO3A and urban/rural environments on mortality. We included 3085 older adults from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used single nucleotide polymorphisms (SNPs) rs2253310, rs2802292, and rs4946936 to identify the FOXO3A gene and classified residential locations as "urban" and "rural." Given the open cohort design, we used the Cox-proportional hazard regression models to assess the mortality risk. We found the minor allele homozygotes of FOXO3A to have a protective effect on mortality [HR (95% CI) for rs4946936 TT vs. CC: 0.807 (0.653, 0.996); rs2802292 GG vs TT: 0.812 (0.67, 0.985); rs2253310 CC vs. GG: 0.808 (0.667, 0.978)]. Participants living in urban areas had a lower risk of mortality [HR of the urban vs. the rural: 0.854 (0.759, 0.962)]. The interaction between FOXO3A and urban and rural regions was statistically significant (pinteraction&lt;0.01). Higher air pollution (fine particulate matter: PM2.5) and lower residential greenness (Normalized Difference Vegetation Index: NDVI) both contributed to higher mortality. After adjusting for NDVI and PM2.5, the protective effect size of FOXO3A SNPs was slightly attenuated while the protective effect size of living in an urban environment increased. The effect size of the beneficial effect of FOXO3 on mortality is roughly equivalent to that of living in urban areas. Our research findings indicate the effect of places of residence and genetic predisposition of longevity are intertwined.

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