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Day 1 of 30DayMapChallenge: « Points » (previously).
IGN is scanning France using LIDAR. More than half the country is available for downloading currently. Let’s play with points around the Aiguille du Midi…
library(readr)
library(dplyr)
library(purrr)
library(fs)
library(lidR)
dir_create("tiles")
Selecting 4 squares on the selection map allows to generate a catalog of 1✕1 km tiles to download.
read_lines("liste_dalle.txt") |>
tibble(url = _) |>
mutate(file = path("tiles", path_file(url))) |>
pwalk((url, file) download.file(url, file))
Read and manipulate the LAZ files with the help of the {lidR} package. We only keep a region of interest 500 m around the peak.
ctg <- readLAScatalog("tiles")
roi <- clip_circle(ctg, x = 1001400, y = 6538400, radius = 500)
The point cloud has already been classified so we can extract only the ground (although at this altitude there is no vegetation).
ground <- filter_ground(roi) plot(ground)
We display 38 millions points (50 points/m²)!
And then we can create a digital terrain model and a hillshade map…
dtm <- rasterize_terrain(roi, 2, tin(), pkg = "terra")
dtm_prod <- terra::terrain(dtm, v = c("slope", "aspect"), unit = "radians")
dtm_hillshade <- terra::shade(slope = dtm_prod$slope, aspect = dtm_prod$aspect)
plot(dtm_hillshade, col = gray(0:50/50), legend = FALSE)
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Continue reading: Lidar
Analysis of LIDAR Scanning and Its Implications
The text describes the use of LIDAR (Light Detection and Ranging) technology by IGN (National Institute of Geographic and Forest Information) to scan France, with over half the country already available for download. A clear example of utilizing this data is demonstrated with the scanning and digital modeling of a region surrounding the Aiguille du Midi peak. The software package used for data manipulation and visualization is R, a popular language for statistical analysis and visualization.
Long-Term Implications
Continuous scanning of terrain at the scale being done by IGN has multiple long-term applications and implications. Completing the scanning of the entirety of France will lead to precise, detailed, and readily available topographical data. Such a database can be instrumental for a variety of sectors including cartography, urban planning, forestry management, and disaster risk assessment.
Improved Urban Planning
The precise information provided by LIDAR technology could be incredibly useful for urban planners. It would enable them to make more informed decisions about infrastructure development, land-use planning, and transportation networks.
Forestry Management
Forestry management could also benefit significantly from the detailed elevation data. It could enable better logging planning, disease detection, fire risk management, and overall conservation efforts.
Disaster Risk Assessment
Having accurate information about the topography could significantly improve predictions about flood paths, landslide risk zones, and earthquake effects for disaster risk assessments.
Possible Future Developments
LIDAR technology’s vast potential implies possible future advancements and improvements, especially with applications of machine learning and artificial intelligence.
Integration of Machine Learning and AI
Integration of machine learning algorithms with LIDAR data could result in automated and advanced data analysis methods. These could recognize patterns, predict scenarios, or classify landscapes without needing intensive manual labor.
Expanding Coverage
A future development could involve expanding the scale of LIDAR projects beyond France to a European or even global scale. Such an extensive, detailed global terrain database would be beneficial for climatology, geography, and geological studies.
Actionable Advice
For stakeholders or businesses interested in terrain and topography, it would be essential to stay updated with the developments in LIDAR technology. Furthermore, investing in skills like R programming and data visualization could be beneficial to leverage this emerging data source fully. Training in machine learning and AI could also be useful for enhanced data analysis.