Holly.Rmd

---
title: "Holly_NEONcapstone"
author: "Holly Andrews"
date: "June 23, 2016"
output: html_document
---

#Correlating NDVI to temperature in Harvard Forest using NEON and Landsat data
First, I will import Harvard NDVI data and tower IR temperature data from NEON.  I will import thermal IR rasters from Landsat for Harvard Forest.

##Load libraries and data
```{r load-libraries}
library(raster)
library(ggplot2)
```

```{r import-data}
lidar <- raster("../NEONdata/NEONdata/D01-Massachusetts/HARV/2014/lidar/HARV_lidarCHM.tif")
tower <- read.csv("../NEONdata/HARV_temp/HARV_temp_30min.csv")
thermal <- raster("../NEONdata/NEONdata/D01-Massachusetts/HARV/2014/HARV_thermal.TIF")
lidar[lidar==0] <- NA
x11()
hist(lidar)
x11()
hist(thermal)

plot(thermal)
```

##Check extents

```{r check-extents}
extent(thermal)
extent(lidar)
```

##Clip to smaller extent

```{r clip-extents}
same_extent <- function(raster1,raster2) {
  if (extent(raster1)==extent(raster2)) {
    print("Rasters have same extent")
  } else {
    overlap <- raster::intersect(extent(raster1),extent(raster2))
    raster1 <- crop(raster1,overlap)
    raster2 <- crop(raster2,overlap)
    print("Extents are different, cropping data")
  } 
  raster.stack <- stack(raster1,raster2)
  return(raster.stack)
}


if(extent(lidar)==extent(thermal)) {
  
} else{
    print("Extents are different, cropping data")
    overlap <- intersect(extent(thermal),extent(lidar))
    #now let's crop the lidar data to HSI
    thermal <- crop(thermal,overlap)
    lidar <- crop(lidar,overlap)
  }


thermal <- setExtent(thermal, lidar_aggregated, keepres=FALSE, snap=FALSE)
lidar_aggregated <- setExtent(lidar_aggregated,thermal,keepres=FALSE, snap=FALSE)#check that these should both be FALSE
stack <- brick(lidar_aggregated,thermal)
#check extents again
extent(thermal)
extent(lidar)
extent(lidar_aggregated)

#aggregate rasters
lidar_aggregated <- aggregate(lidar,29.59375)
```

##Plot CHM-thermal regression

```{r plot-regression}
plot(lidar_aggregated,thermal)

lm()
```