Skip to content

Module1.md

Latest commit

 

History

History
133 lines (83 loc) · 4.27 KB

Module1.md

File metadata and controls

133 lines (83 loc) · 4.27 KB
Raw

Remote Environmental Sensing

Geographic Information Databases

The collection, organization and processing of environmental data is a step that requires equipment installed in the field. Such equipment can be expensive or difficult to access for some research groups in certain regions or countries. For this reason, we decided to insert a routine for collecting climatic data through the NASA POWER base, which can access information on a daily scale anywhere on the globe.

The Raster Package also offers a digital platform for downloading files in raster format of climatic data (from the WorldClim database) and SRTM (elevation) using only geographical coordinates (Latitude and Longitude).

Remote Data Collection

To facilitate the use by researchers, especially in the field of genetics and plant breeding, we have integrated these platforms in the functions below:

  • Preparing de informations (latitude, longitude, start day and end date)
lat = c(-13.05,-12.32,-18.34,-18.90,-23.03)  # vector of latitude WGS84
lon = c(-56.05,-55.42,-46.31,-49.56,-51.02)  # vector of lontitude WGS84
env = c("NM","SO","PM","IP","SE")            # vector of environment/site ID
plant.date = c("2015-02-15","2015-02-13",    # vector of start period
                                 "2015-02-26","2015-03-01",
                                 "2015-02-19") 
harv.date =rep("2015-06-15",5)               # vector of end period
  • So we can use this information to collect weather data from NASAPOWER
df.clim <- get_weather(env.id = env,lat = lat,lon = lon, start.day = plant.date,end.day = harv.date, asdataframe = F) # returns a list of dataframes by environments
                  
df.clim <- get_weather(env.id = env,lat = lat,lon = lon,start.day = plant.date,end.day = harv.date,country = 'BRA') # returns a dataframe with all environments by default

head(df.clim)

The argument country were setted to collect elevation data from Brazil (BRA). For other countries please consult the ISO in the following table:

Menu

Raw Data Processing

Additional variables (ecophysiological)

  • Basic processing of get_weather()
df.clim <-processWTH(env.data = df.clim)

Raw Data Summary

  • Basic summary statistics for environmental data
summaryWTH(df.clim)
# or
summaryWTH(df.clim,env.id = 'env')
  • Summary a particular environmental variable
summaryWTH(df.clim,env.id = 'env',var.id = 'T2M')
summaryWTH(df.clim,env.id = 'env',var.id = c('T2M','T2M_MAX')) # or more than one
  • Summary by time intervals. Dividing the development cycle into time intervals (e.g., phenology), whether phenological or fixed time intervals (e.g. 10-day intervals) helps to understand the temporal variation of environmental factors during the crop growth cycle.
summaryWTH(df.clim,env.id = 'env',by.interval = T)
  • Summary by time intervals given by time.window argument.
summaryWTH(df.clim,env.id = 'env',by.interval = T,time.window = c(0,14,35,60,90,120))
  • Summary by time intervals given by time.window and names.window.
summaryWTH(df.clim,env.id = 'env',by.interval = T,time.window = c(0,14,35,60,90,120), names.window = c('P-E','E-V1','V1-V4','V4-VT','VT-GF','GF-PM'))
  • Returns only mean values
summaryWTH(df.clim,env.id = 'env',statistic = 'mean')
  • Returns only sum values
summaryWTH(df.clim,env.id = 'env',statistic = 'sum')
  • Returns quantile values (default = 25%, 50% and 75%)
summaryWTH(df.clim,env.id = 'env',statistic = 'quantile')
  • For specific quantiles (e.g., 20%, 76% and 90%)
summaryWTH(df.clim,env.id = 'env',statistic = 'quantile',probs = c(.20,.76,.90))

Menu