Perform Atmospheric Correction to Sentinel 2 Data with Sen2Cor Automatically

Sen2Cor is a processor for Sentinel-2 Level 2A product generation and formatting. It performs the atmospheric, terrain and cirrus correction of Top-Of- Atmosphere Level 1C input data. Sen2Cor creates Bottom-Of-Atmosphere, optionally terrain and cirrus corrected reflectance images; additional, Aerosol Optical Thickness, Water Vapor, Scene Classification Maps and Quality Indicators for cloud and snow probabilities. Its … More Perform Atmospheric Correction to Sentinel 2 Data with Sen2Cor Automatically

SWOT Analysis as a Basic Index in Spatial Development Projects

For any project with spatial implications, a SWOT analysis is a necessary first step. Given time and budget, an engineer can produce a myriad radically different solutions about the development of a plot of land. If allowed to brainstorm, he can conjure solutions that will cost twice or thrice the budget, solutions that are too taxing on the environment or incompatible with the local customs and necessities. Since brainstorming paves the way for actual project development, the team needs an efficient, rational way to cut the bulk and allow them to focus on the fewer solutions that are for example, within budget, eco-friendly, and most profitable. … More SWOT Analysis as a Basic Index in Spatial Development Projects

Automatic Change Detection Algorithm of Man-made Objects (ACDA)

For the automatic change detection of man-made objects, at first the Chan-Vese model will be used. Code for the model in Python found on GitHub and modified to detect man-made objects and compare them by using multitemporal data. This approach is widely applied in medical applications (example given bellow). The Chan-Vese Model The Chan-Vese level … More Automatic Change Detection Algorithm of Man-made Objects (ACDA)

Map Projections

The best way to represent earth is a globe. Earth is close to a sphere (or an ellipsoid) but it’s not actually one. But globes are hard to carry in your backpack, you can only see one side of the globe, it’s hard to measure distances and they’re just not as convenient as paper maps. … More Map Projections

Soil-Adjasted Vegetation Index (SAVI) Python Script

Empirically derived NDVI products have been shown to be unstable, varying with soil color, soil moisture, and saturation effects from high density vegetation. SAVI is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared wavelengths. The index is given as:   where L is a canopy background adjustment … More Soil-Adjasted Vegetation Index (SAVI) Python Script