NDVI, risk assessment and developing countries

The Normalized Difference Vegetation Index (NDVI) estimates the greenness of plants covering the surface of the Earth by measuring the light reflected by the vegetation into space. The main idea behind the NDVI is that visible and near-infrared light is absorbed in different proportions by healthy and unhealthy plants: a green plant will reflect 50% of the near infrared-light it receives and only 8% of the visible light while an unhealthy plant will reflect respectively 40% and 30%. NDVI can then be used to quantitatively compare vegetation conditions across time and space (and indeed is quite widely used, a Google Scholar search on NDVI produced 60,500 hits).

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Thursday, 14 February 2013

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Twitter: frbailo

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blogroll


RSS r-bloggers.com

  • Mapping Covid-19 cases: a Shiny app
    R lets you create charts and graphs in image form. But the Shiny package lets you create those same charts and graphs in interactive format. I created my first Shiny chart: a world map of confirmed Covid-19 cases. Check it out here. Unfortunately I cannot embed the app into this website right now, so the […]
  • SR2 Chapter 3 Medium
    SR2 Chapter 3 Medium Posted on 5 April, 2020 by Brian Tags: statistical rethinking, solutions, grid approximation, posterior probability, posterior predictive probability, hpdi, binomial Category: statistical-rethinking-2 Here’s my solution to the medium exercises in chapter 3 of McElreath’s Statistical Rethinking, 2nd edition. \(\DeclareMathOperator{\dbinomial}{Binomial} \DeclareMathOperator{\dbernoulli}{Bernoulli} \DeclareMathOperator{\dpoisson}{Poisson} \DeclareMathOperator{\dnormal}{Normal} \DeclareMathOperator{\dt}{t} \DeclareMathOperator{\dcauchy}{Cauchy} \DeclareMathOperator{\dexponential}{Exp} \DeclareMathOperator{\duniform}{Uniform} \DeclareMathOperator{\dgamma}{Gamma} \DeclareMathOperator{\dinvpamma}{Invpamma} \DeclareMathOperator{\invlogit}{InvLogit} \DeclareMathOperator{\logit}{Logit} \DeclareMathOperator{\ddirichlet}{Dirichlet} […]
  • On the “correlation” between a continuous and a categorical variable
    Let us get back on the Titanic dataset, loc_fichier = "http://freakonometrics.free.fr/titanic.RData" download.file(loc_fichier, "titanic.RData") load("titanic.RData") base = base[!is.na(base$Age),] On consider two variables, the age (the continuous one) and the survivor indicator (the qualitative one) X = base$Age Y = base$Survived It looks like the age might be a valid explanatory variable in the logistic regression, summary(glm(Survived~Age,data=base,family=binomial)) […]
  • D is for dummy_cols
    For the letter D, I'm going to talk about the dummy_cols functions, which isn't actually part of the tidyverse, but hey: my posts, my rules. This function is incredibly useful for creating dummy variables, which are used in a variety of ways, including...
  • Caching in R
    Introduction Caching intermediate objects in R can be an efficient way to avoid re-evaluating long-running computations. The general process is always the same: run the chunk of code once, store the output to disk, and load it up the next time the same chunk is run. There are, of course, multiple packages in R to […]

RSS Simply Statistics

  • Is Artificial Intelligence Revolutionizing Environmental Health?
    NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience, and Elizabeth Boyle, National Academies of Sciences, Engineering, and Medicine. […]
  • You can replicate almost any plot with R
    Although R is great for quickly turning data into plots, it is not widely used for making publication ready figures. But, with enough tinkering you can make almost any plot in R. For examples check out the flowingdata blog or the Fundamentals of Data Visualization book. Here I show five charts from the lay press […]
  • So You Want to Start a Podcast
    Podcasting has gotten quite a bit easier over the past 10 years, due in part to improvements to hardware and software. I wrote about both how I edit and record both of my podcasts about 2 years ago and, while not much has changed since then, I thought it might be helpful if I organized […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Career advice for a future statistician
    Gary Ruiz writes: I am a first-year math major at the Los Angeles City College in California, and my long-term educational plans involve acquiring at least one graduate degree in applied math or statistics. I’m writing to ask whether you would offer any career advice to someone interested in future professional work in statistics. I […]
  • Interesting y-axis
    Merlin sent along this one: P.S. To be fair, when it comes to innumeracy, whoever designed the above graph has nothing on these people. As Clarissa Jan-Lim put it: Math is hard and everyone needs to relax! (Also, Mr. Bloomberg, sir, I think we will all still take $1.53 if you’re offering).
  • Model building is Lego, not Playmobil. (toward understanding statistical workflow)
    John Seabrook writes: Socrates . . . called writing “visible speech” . . . A more contemporary definition, developed by the linguist Linda Flower and the psychologist John Hayes, is “cognitive rhetoric”—thinking in words. In 1981, Flower and Hayes devised a theoretical model for the brain as it is engaged in writing, which they called […]