GitHub topics: planetscope

Repositories

sertit/eoreader

Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.

Language: Python - Size: 64.3 MB - Last synced at: about 10 hours ago - Pushed at: 9 days ago - Stars: 311 - Forks: 29

AidenIGSchore/IntFire

Code for calculating IntFire

Language: R - Size: 19.5 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

GISLab-ELTE/WasteDetection

Waste Detection and Change Analysis based on Multispectral Satellite Imagery

Language: Python - Size: 15 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 5 - Forks: 0

torhaa1/Planetscope_preprocessing

Notebooks for preprocessing and analysis of Planetscope 4 band data/imagery, using rasterio and fiona.

Language: Jupyter Notebook - Size: 2.7 MB - Last synced at: 5 months ago - Pushed at: 11 months ago - Stars: 9 - Forks: 2

j0nflores/planet-hma-streams

This repo contains implementations of water classification methods to detect small proglacial streams in High Mountain Asia (HMA) using high-resolution PlanetScope imagery.

Language: Python - Size: 36.8 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 3 - Forks: 1

j-desloires/eo-crops

A collection of python code to download and preprocess satellite images for crop monitoring at field-level

Language: Python - Size: 4.63 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 8 - Forks: 1

As the population increases, demand for food increases too, which has led to large-scale land conversion to improve livestock production in Colombia. Fulfilling these criteria of increasing demand in a sustainable way is a challenge and remote sensing data provides an accurate method to support this task. In this study, Planet Scope multispectral satellite datasets and coincident field measurements acquired over test fields in the study area (Patía) of September 2018 was be used. Fresh and dry weight biomass was calculated and forage quality analyses, crude protein (CP), in vitro dry matter digestibility (IVDMD), Ash and standing biomass dry weight (DM) was carried out in the forage nutritional quality laboratory of International Centre for Tropical Agriculture (CIAT). Field data was related to the remote sensing data using the random forest regression algorithm. R was required for the statistical analysis, to figure out the model performance for IVDMD, CP, Ash and DM. This project also investigated the spatial distribution of livestock which is affected by quality and area of potential forage zones. The R2 values of the regression models were 0.74 for IVDMD, 0.69 for CP, 0.38 for Ash and 0.49 for DM using a predictor combination of vegetation indices, simple ratios and bands.

Language: R - Size: 97.7 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 1