geofis

# Generate tiles from image, then create a layered/stacked 3D perspective.
# Modified from: https://stackoverflow.com/questions/56279466/how-do-i-create-a-figure-displaying-a-stack-of-images-at-an-angle

# Modules
import cv2
import numpy as np

# Load source image
im = cv2.imread('yearly_forest_loss_patches_1_ha_per_100_sq_km.jpg')
im

geofis

---
title: "A Multi-page HTML Document"
author: "Yihui Xie and Romain Lesur"
date: "`r Sys.Date()`"
output:
  pagedreport::paged_grid
  # pagedown::html_paged:
  #   toc: true
  #   # change to true for a self-contained document, but it'll be a litte slower for Pandoc to render
  #   self_contained: false

geofis

Performance comparison between two zonal statistics algorithms: R raster::extract vs. pkextractogr

Packages

library(raster)

geofis

# Gobal forest/non-forest map from TanDEM-X interferometric SAR data.
# Percentages of classes (forest, non-forest, urban) in the Dominican Republic
# Author: José Ramón Martínez Batlle, Universidad Autónoma de Santo Domingo (UASD)
# Related article paper: https://www.sciencedirect.com/science/article/pii/S0034425717305795
# Source data via web: https://download.geoservice.dlr.de/FNF50/
wd <- tempdir()
setwd(wd)
getwd()

#Download and merge tiles

geofis

#Packages
library(sf)

# If GDAL is installed locally...
# listsrs <- read.csv('/usr/share/gdal/2.2/pcs.csv')
# If not:
# Source: https://trac.osgeo.org/gdal/browser/trunk/gdal/data/pcs.csv
# Revision 41888, trunk:
listsrs <- read.csv(
  'https://trac.osgeo.org/gdal/export/41888/trunk/gdal/data/pcs.csv'

geofis

setwd(tempdir())
getwd()

urldatos <- 'http://www.geografiafisica.org/sem201901/geo112/datos_campo_201901/'
clastosatrana <- read.csv(
  paste0(
    urldatos,
    'clastosatr_ana.csv'
  ),
  encoding = "UTF-8",

geofis

setwd(tempdir())
getwd()
urldatos <- 'http://www.geografiafisica.org/sem201901/geo112/datos_campo_201901/'
clastosatr <- read.csv(
  
  paste0(
    
    urldatos,
    
    'clastosatr_carolaine.csv'

geofis

wd <- tempdir()
setwd(wd)
urldatos <- 'http://www.geografiafisica.org/sem201901/geo112/datos_campo_201901/'
clastosall <- read.csv(
  paste0(
    urldatos,
    'joined_csv_files.csv'
  ),
  check.names = F,
  encoding = 'UTF-8')

geofis

#Working directory
wd <- '/home/jr/Documentos/clases_UASD/sem201901/datos_campo/'
alumnas <- list.dirs(wd, full.names = F, recursive = F)
csvfiles <- sapply(
  alumnas,
  function(x){
    fuente <- list.files(
      paste0(
        wd,
        x

geofis

#Convert Excel files of river particle axes length to CSV format using R

#Packages
library(dplyr)
library(readxl)

#Working directory
wd <- '/home/jr/Documentos/clases_UASD/sem201901/datos_campo/'
alumnas <- list.dirs(wd, full.names = F, recursive = F)
i <- NULL