NDVI

Sonia  Nazneen

NDVI

Question What is NDVI? How to calculate NDVI? Discuss the application of NDVI in Remote Sensing? Answer Remote sensing phenology studies use data gathered by satellite sensors that measure wavelengths of light absorbed and reflected by green plants. Certain pigments in plant leaves strongly absorb wavelengths of visible (red) light. The leaves themselves strongly reflect wavelengths of near-infrared light, which is invisible to human eyes. As a plant canopy changes from early spring growth to late-season maturity and senescence, these reflectance properties also change. Many sensors carried aboard satellites measure red and near-infrared light waves reflected by land surfaces. Using mathematical formulas (algorithms), scientists transform raw satellite data about these light waves into vegetation indices. A vegetation index is an indicator that describes the greenness — the relative density and health of vegetation — for each picture element, or pixel, in a satellite image. Although there are several vegetation indices, one of the most widely used is the Normalized Difference Vegetation Index (NDVI). Remote Sensing Remote sensing is defined as the technique of obtaining information about objects through the analysis of data collected by special instruments that are not in physical contact with the objects of investigation. According to Barrett & Curtis (1976) “Remote sensing is the observation of a target by a device separated from it by some distance.” According to Paul J. Gibson (2000) “Remote Sensing can be defined as the acquisition and recoding of information about an object without being in direct contact with that object.” Examples of remote-sensing methods include aerial photography, radar, and satellite imaging. NDVI The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared bands of the electromagnetic spectrum, and is adopted to analyze remote sensing measurements and assess whether the target being observed contains live green

vegetation or not. NDVI has found a wide application in vegetative studies as it has been used to estimate crop yields, pasture performance, and rangeland carrying capacities among others. It is often directly related to other ground parameters such as percent of ground cover, photosynthetic activity of the plant, surface water, leaf area index and the amount of biomass. NDVI was first used in 1973 by Rouse et al. from the Remote Sensing Centre of Texas A&M University. Calculating NDVI Measuring NDVI Satellite maps of vegetation show the density of plant growth over the entire globe. The most common measurement is called the Normalized Difference Vegetation Index (NDVI). NDVI values range from +1.0 to -1.0. Areas of barren rock, sand, or snow usually show very low NDVI values (for example, 0.1 or less). Sparse vegetation such as shrubs and grasslands or senescing crops may result in moderate NDVI values (approximately 0.2 to 0.5). High NDVI values (approximately 0.6 to 0.9) correspond to dense vegetation such as that found in temperate and tropical forests or crops at their peak growth stage. By transforming raw satellite data into NDVI values, researchers can create images and other products that give a rough measure of vegetation type, amount, and condition on land surfaces around the world. NDVI is especially useful for continental- to global-scale vegetation monitoring because it can compensate for changing illumination conditions, surface slope, and viewing angle. That said, NDVI does tend to saturate over dense vegetation and is sensitive to underlying soil color. NDVI values can be averaged over time to establish "normal" growing conditions in a region for a given time of year. Further analysis can then characterize the health of vegetation in that place relative to the norm. When analyzed through time, NDVI can reveal where vegetation is thriving and where it is under stress, as well as changes in vegetation due to human activities such as deforestation, natural disturbances such as wild fires, or changes in plants’ phenological stage. Visible and Near-Infrared Vegetation appears very different at visible and near-infrared wavelengths. In visible light (top), vegetated areas are very dark, almost black, while desert regions (like the Sahara) are light. At near-infrared wavelengths, the vegetation is brighter and desserts are about the same. By comparing visible and infrared light, scientists measure the relative amount of vegetation.

Acute poverty has a severe impact on children in India, where 30% of all children living in extreme poverty worldwide are born. The truth is that 36% of the world's poorest children reside in South Asia, with India hosting 84 percent of this population. Besides, more than 45 million children in India are affected by the COVID-19 pandemic's extreme poverty, which accounts for 30% of all children worldwide. Childhood poverty, which is frequently associated with accelerated aging, may have a significant impact on immune system function, which may lead to dysregulation of inflammatory processes in response to foreign substances and a change to unfavorable proinflammatory states. The term "Metabolic Syndrome" (MetS) describes a group of disorders, such as high blood pressure, high blood sugar, insulin resistance (IR) and elevated adiposity, that frequently co-occur and increase the risk of stroke, type 2 diabetes (T2DM), and cardiovascular diseases. An extensive incidence of IR among children exhibiting MetS was found in an Indian cross-sectional investigation. Over time, the scientific community has become more cognizant of the critical role the immune system plays in maintaining systemic metabolic homeostasis. The maintenance of excellent "metabolic health" over the course of a person's life depends critically on this interaction between the immune and metabolic systems. Two major stress-signaling pathways that contribute to immunological dysregulation in children during poverty are the Autonomic Nervous System (ANS) and the Hypothalamic-Pituitary-Adrenal (HPA) axis. Prolonged HPA axis activation brought on by poverty-induced stress can directly contribute to the pathophysiology of T2DM. Early traumatic events and lifestyle modifications induced by poverty may also have an impact on how quickly telomeres shorten throughout the course of a person's lifespan. Telomere shortening brought on by immune system aging slows down T-and B-cell population renewal and clonal proliferation, aggravating MetS. Early-life nutrition results in long-lasting alterations in DNA methylation that have an effect on a person's health and aging-related disorders throughout their lifetime. In order to further validate the causal relationship between these crucial intersecting events that the article seeks to capture during poverty, additional research will be needed to collect data on the prevalence of MetS, immunological parameters, including retrospective and prospective longitudinal studies in larger Indian cohorts.

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NDVI