LandCoM: Land cover mapping
Simon Ricard
Researcher
Description
The LandCoM project aims to develop an automated method for evaluating by remote sensing the percentage of soil cover by crop residues and cover crops.
Objective(s)
Develop and validate a method for analyzing satellite remote sensing images to distinguish and quantify in the early spring period the percentage of soil cover by grassland, cover crops and crop residues on a return of annual field crops.
Carried out in collaboration with INRS-ETE, the analytical approach of CarTéCoS is based on a three-scale methodology:
- micro-scale: the collection and analysis of digital photos taken using smart phones;
- mesoscale: the collection and analysis of high-resolution spatial multispectral images obtained by a drone;
- macro-scale: the analysis of multi-source and multi-temporal Sentinel 2 and Landsat-8 (optical) and Sentinel 1 radar images by automatic deep learning algorithms in the Google Earth Engine © cloud platform.
From 2021 to 2023
Project duration
Field crops
Activity areas
Partners
- MAPAQ, PRIME-VERT, Sous-volet 2.2 Approche interrégionale
- INRS-ETE
- Opticonseils
- GestrieSol
- ProConseil
This may interest you
Determining the ideal time to spread pig manure to improve crop yield and protect soil, water, and air quality
To provide information on the fertilizing value and environmental impact of spreading pig manure at different times, the project compared the effect of mineral fertilizer in early fall, late fall, and in the spring.
A technology showcase of irrigation management decision-support tools for use in a variety of contexts
Showcase that will present and compare, in an impartial manner, a number of decision-support tools and technologies.
Researcher: Carl Boivin
Testing biofungicides against white mold in soybean crops – an economic analysis
Testing the efficacy of four biofungicides against Sclerotinia in soybeans.
Researcher: Luc Belzile