Downloading MODIS data on R

Searching to download MODIS data to use on spatial analyses 🌎 in R, I found this amazing package called MODIStsp1. Here is the code I used to download Terra MODIS yearly Land Cover data for two time periods. library(MODIStsp) library(terra) library(sf) library(tidyverse) MODIS MODIS data (see: Products) are usually distributed in different spatial resolution (250/500m/1km) and temporal composite (daily/8-day/16-day/monthly/yearly) combinations for both sensors. ‘MOD’ are products from the Terra satellite.

IUCN distribution data

IUCN species distribution maps are widely used to conduct spatial analyses. However, these data are not always as accurate in some areas of the globe. Uruguay, in particular, has a severe lack of information on the distribution of its species. Luckily, last year the first comprehensive open-access biodiversity database in the country was made available by Biodiversidata. We are going to use this data to check how well represented are these species in the IUCN Red List database.

GBIF data in Latin America

In our recent Data Paper, we showed that Uruguay ranks amongst the countries of Latin America with the lowest levels of available data on their biodiversity in the Global Biodiversity Information Facility GBIF. Also, that most of the records that we found in GBIF belong to the eBird initiative, the world’s largest biodiversity-related citizen science project. The extensive contribution provided by eBird to GBIF highlights the enormous role that data provided by citizens play in the development of global biodiversity datasets, while at the same time, points out the critical taxonomical biases encountered in GBIF for the region.

R Code used for the Biodiversidata Project

As part of my PhD project I have written some scripts for the Biodiversidata Project. This are useful scripts for biodiversity data cleaning, processing and quality controlling. 1) Retrieving Conservation Status and Population Trend (IUCN) The script contains a function that takes a species list as input and returns a dataframe with 3 columns containing Species Name, Conservation Status and Popultaion Trend, according to the IUCN Red List. The run will return the result of the search for each species in the list, printing in the console screen a ‘CHECK’ warning when the species name is not found in the Red List search.