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write a lab report following the instructions below and by using R studio and USGS to make NDVI map

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write a lab report following the instructions below and by using R studio and USGS to make NDVI map.

Requirements: all requirements |

.doc file

Hey, as we talked I need the answer in 10 hours because I have to turn it in that time.

Also this is the code for r

# Example to perform system set-up checks

#The Universal Transverse Mercator (UTM) conformal projection uses

#a 2-dimensional Cartesian coordinate system to give locations on

#the surface of the Earth. Like the traditional method of latitude and longitude,

#it is a horizontal position representation, i.e. it is used to identify locations

#on the Earth independently of vertical position. However, it differs from that method in several respects.

# Code was created by Xiaofeng Xu in March 2017

# A small bug corrected by Xiaofeng Xu on April 5, 2018

# latest update on March 18, 2021

# work on MAC, for PC, some specific changes are required

library(rgdal)

getGDALVersionInfo()

library(raster)

# To get only the files with .tif extension

#list.files(pattern = glob2rx('*.tif'))

# Or if you are familiar with regular expressions

setwd("/Users/xxuadmin/BUSINESS/TEACHING/2021_Spring_BIO535/3rdField_Trip/project3")

#list.files(pattern = './downloaded/*.tif$')

sandiego <- brick('LC08_L1TP_040037_20210221_20210303_02_T1_refl.tif')

## Plot the first band of Landsat

pdf("layer1.pdf")

plot(sandiego, 1,main=" ")

title("landsat 8 data - san diego region Band 1")

dev.off()

## Plot the second band of Landsat

pdf("layer2.pdf")

plot(sandiego, 2,main=" ")

title("landsat 8 data - san diego region Band 2")

dev.off()

## Plot the third band of Landsat

pdf("layer3.pdf")

plot(sandiego, 3,main=" ")

title("landsat 8 data - san diego region Band 3")

dev.off()

list <- list.files(path='sandiego/', full.names=TRUE)

SDStack <- stack(list)

pdf("NDVI.pdf") #,width=300,height=300)

writeRaster(x=SDStack, filename='SDStack.grd', datatype='INT2S',overwrite=TRUE)

ndvi <- (SDStack[[2]] - SDStack[[1]]) / (SDStack[[2]] + SDStack[[1]])

colors = c("blue","gold","darkgoldenrod","grey","lightgreen","green","darkgreen")

plot(ndvi,breaks=c(-1,0,0.075,0.15,0.20,0.25,0.30,0.6),col=colors, main = "Xiaofeng Xu's coure project #3 (San Diego NDVI 3/3/2021)")

dev.off()

 

Guideline for the report for the 3rd project for BIO535 plant ecology in Spring 2021. The report should be written with three sections: (1) Objectives; (2) Methodology; (3) Result. In the methodology section, the key information of data source and data processing method should be clearly described. In the result section, please include the NDVI map generated with the R script and > 3 photos showing the three landscape types in the field (water, relatively dense vegetation and relatively patch vegetation, or bare soil and desert). The grading criteria for the report: 5 points for the format (different sections), 15 points for the results, 10 points for write-up (clear in describing your objective and the methods used, and the key results). 5 points will be deducted if: the reports are late. Course project #3 Mapping spatial distribution of vegetation with Landsat 8 First section – preparation user account 1. Create an account at USGS website for downloading the Landsat 8 images https://ers.cr.usgs.gov/login?redirectUrl=https://ers.cr.usgs.gov/ Second section – Download the landsat 8 images 2. Open Google Chrome or IE. Google “USGS LandSat”, click “EarthExplorer-USGS” 3. You will see the below website https://earthexplorer.usgs.gov/, 1 4. At the website of https://earthexplorer.usgs.gov/, left column, click “Decimal” option. In the popup window, then click Add Coordinates of San Diego (32.7 N, 117.2W; 32.7, -117.2). You may need to create an account to proceed. 5. Choose the dataset as