Develop a Python script to automate the analysis of satellite imagery for detecting and quantifying deforestation in a specified region over a given time period, incorporating image processing libraries and statistical methods for change detection.
technical screen · 15-20 minutes
How to structure your answer
MECE Framework: 1. Data Acquisition & Preprocessing: Utilize Google Earth Engine API for satellite imagery (Landsat/Sentinel), apply radiometric calibration, atmospheric correction, and cloud masking. 2. Feature Extraction: Implement spectral indices (NDVI, NDWI) to highlight vegetation and water bodies. 3. Change Detection: Employ unsupervised classification (K-means) or supervised learning (Random Forest) on multi-temporal imagery. Calculate difference images or post-classification comparison. 4. Quantification & Reporting: Compute deforested area in hectares, generate time-series plots, and export georeferenced shapefiles. 5. Validation: Use ground truth data or high-resolution imagery for accuracy assessment (e.g., confusion matrix).
Sample answer
To automate deforestation analysis, I'd implement a Python script using a MECE approach. First, for Data Acquisition & Preprocessing, I'd use the earthengine-api to access Landsat or Sentinel imagery, applying xarray and rioxarray for efficient data handling, radiometric correction, and cloud masking. Next, for Feature Extraction, I'd calculate spectral indices like NDVI and EVI using numpy to enhance vegetation signals. For Change Detection, I'd employ a supervised classification approach using scikit-learn's Random Forest, training it on known deforestation polygons. Alternatively, a post-classification comparison of land cover maps from different time periods would be effective. Finally, for Quantification & Reporting, I'd calculate the total deforested area in hectares, generate time-series plots using matplotlib, and export results as GeoJSON or shapefiles using geopandas. This robust script would provide quantifiable, actionable insights into deforestation trends.
Key points to mention
- • Data acquisition and pre-processing (atmospheric correction, cloud masking, radiometric calibration)
- • Choice of spectral indices (NDVI, EVI, NBR) and their relevance to deforestation
- • Change detection algorithms (e.g., image differencing, principal component analysis, machine learning classifiers)
- • Spatial analysis and quantification of deforested areas
- • Error assessment and validation of results
Common mistakes to avoid
- ✗ Neglecting atmospheric correction, leading to false positives/negatives in change detection.
- ✗ Not accounting for cloud cover or shadows, which can significantly skew results.
- ✗ Using a single threshold for change detection across diverse landscapes without calibration.
- ✗ Failing to validate the model's output with ground truth data or higher-resolution imagery.
- ✗ Ignoring the computational efficiency for large datasets.