10 Remote Sensing Interview Questions and Answers

Remote sensing is a critical technology used to gather and analyze data about the Earth’s surface without direct contact. It leverages satellite or aerial sensor technologies to detect and classify objects on Earth, making it invaluable in fields such as environmental monitoring, agriculture, urban planning, and disaster management. The ability to interpret and utilize remote sensing data is becoming increasingly important as the demand for accurate and timely information grows.

This article provides a curated selection of interview questions designed to test your knowledge and problem-solving skills in remote sensing. By working through these questions, you will gain a deeper understanding of key concepts and be better prepared to demonstrate your expertise in this evolving field.

Remote Sensing Interview Questions and Answers

1. Explain the difference between passive and active remote sensing systems.

Passive remote sensing systems capture data using natural energy sources like sunlight, detecting radiation emitted or reflected by Earth’s surface. Examples include optical and thermal infrared sensors. Active systems, such as radar and LiDAR, emit their own energy to illuminate targets, allowing data collection regardless of time or weather.

2. Describe how NDVI (Normalized Difference Vegetation Index) is calculated and its applications.

NDVI (Normalized Difference Vegetation Index) measures plant growth and vegetation cover using the formula: NDVI = (NIR – RED) / (NIR + RED), where NIR is near-infrared reflectance and RED is red band reflectance. Values range from -1 to +1, with higher values indicating healthier vegetation. Applications include monitoring crops, deforestation, natural disaster impacts, drought conditions, and urban planning.

3. Explain the concept of atmospheric correction and why it is necessary.

Atmospheric correction removes atmospheric effects on reflectance values recorded by remote sensing instruments, which can distort data accuracy. Methods include Radiative Transfer Models like 6S and MODTRAN, Empirical Line Calibration using ground truth data, and Dark Object Subtraction, which estimates atmospheric effects using areas with near-zero reflectance.

4. How does LiDAR technology work and what are its primary applications?

LiDAR technology emits laser pulses that bounce back after hitting objects, measuring the return time to calculate distances and create detailed 3D maps. Components include a laser, scanner and optics, photodetector and receiver electronics, and GPS and IMU for location data. Applications include topographic mapping, forestry, urban planning, autonomous vehicles, and archaeology.

5. Describe the process of image classification.

Image classification categorizes pixels in an image into land cover classes. Steps include data collection, preprocessing, feature extraction, training data selection, applying classification algorithms, post-processing, and accuracy assessment. Algorithms like Support Vector Machines, Random Forest, and K-means clustering are commonly used.

6. Discuss the role of machine learning and provide an example of its application.

Machine learning analyzes remote sensing data to identify patterns and make predictions. A common application is land cover classification, where models categorize land cover types based on satellite imagery. For example, a Random Forest classifier can be trained to predict land cover types with high accuracy.

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np

X = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]])
y = np.array([0, 1, 0])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')

7. Design a workflow to monitor deforestation using multi-temporal satellite imagery. Include data sources, preprocessing steps, and analysis methods.

To monitor deforestation using multi-temporal satellite imagery, the workflow includes data sources, preprocessing, and analysis methods.

Data Sources:
– Satellite Imagery: Use Landsat and Sentinel-2 for high-resolution, multi-spectral images.
– Ancillary Data: Include topographic maps, land cover maps, and climate data.

Preprocessing Steps:
– Data Acquisition: Download images for specific time periods.
– Radiometric and Geometric Correction: Adjust for sensor noise and align images with geographic coordinates.
– Cloud Masking: Remove cloud-covered areas.
– Image Normalization: Account for lighting and sensor differences.

Analysis Methods:
– Change Detection: Use techniques like image differencing to identify deforestation.
– Classification: Categorize land cover types using algorithms like Random Forest.
– Time Series Analysis: Monitor trends using indices like NDVI.
– Validation: Use ground truth data for accuracy.

8. Describe the principles and applications of Synthetic Aperture Radar (SAR).

Synthetic Aperture Radar (SAR) uses radar to create images or 3D reconstructions. It transmits microwave signals and receives reflections, using the Doppler effect and signal processing to achieve high resolution. Applications include environmental monitoring, military surveillance, topographic mapping, and ice and snow monitoring.

9. What is change detection in remote sensing and how is it performed?

Change detection in remote sensing compares images from different times to identify landscape changes. Steps include image preprocessing, registration, applying change detection algorithms like image differencing or change vector analysis, and post-processing to interpret results.

10. Discuss the role of cloud computing in processing large remote sensing datasets.

Cloud computing processes large remote sensing datasets by offering scalable resources. Platforms like AWS, GCP, and Azure provide tools for data storage, processing, and machine learning. Cloud computing allows distributed data processing, cost-effective resource use, and enhanced collaboration and accessibility.