Explain how you would assess the accuracy of land cover classification using remote sensing data.

Sample interview questions: Explain how you would assess the accuracy of land cover classification using remote sensing data.

Sample answer:

Assessment of Land Cover Classification Accuracy Using Remote Sensing Data

1. Reference Data Collection:

Acquire high-resolution aerial imagery, field surveys, or LiDAR data as reference data.
Ensure the reference data covers a representative sample of the study area and encompasses the range of land cover classes of interest.

2. Sample Dataset Preparation:

Divide the remote sensing data into training and testing datasets.
The training dataset is used to build the classification model, while the testing dataset is used for accuracy assessment.

3. Classification Model Development:

Utilize a supervised classification algorithm such as Maximum Likelihood, Support Vector Machines, or Random Forest to build a classification model.
Train the model using the training dataset, optimizing model parameters for optimal accuracy.

4. Accuracy Assessment:

Calculate overall accuracy as a percentage of correctly classified pixels: (Correctly Classified Pixels / Total Pixels) x 100%
Compute class-specific accuracy to evaluate the performance for each land cover class: (Correctly Classified Pixels for Class / Total Pixels for Class) x 100%
Consider additional metrics such as precision, recall, and F1-score for a more comprehensive understanding of classification performance.

5. Error Matrix Analysis: