Artificial Surface Materials at Golm

Research Question

Which artificial surface material types expanded the most and how much (in ) between 2018-2024?

Definition

Artifical Surface Materials: Materials, that are located on the surface and are either Man-made or deliberately placed there by humans.

Study area Campus Golm

Workflow

1. Preprocessing of data
2. Create training data
3. Classification
4. Change detection
5. Change analysis

Classes

Datasets

Classification Method

1. Read raster bands
2. Apply PCA to reduce spectral dimensionality
3. Rasterize training polygons into labeled pixels
4. Extract labeled pixels as training samples
5. Train Random Forest on labeled data
6. Classify all pixels using trained model
7. Validate with validation data

Classification Implementation

• rasterio
• geopandas
• sklearn.decomposition.PCA
• sklearn.ensemble.RandomForestClassifier

Classification Training Data

Classification Training Data

Classification Results

Classification Confusion Matrix

Classification Accuracy

Change Detection Method

Change Detection Results

11
12
13
...
88

Change Analysis 2018-2024

Challenges

• Datasets had different amount of Bands
• Deciding on the right classes
• Classification with Enmap Box, unsatisfactory

Thank you, for your attention!

Sources

Balsamo et al. “Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review.” Remote Sensing 10, no. 12 (December 2018): 2038. https://doi.org/10.3390/rs10122038.

Guanter et al. “The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation.” Remote Sensing 7, no. 7 (July 2015): 8830–57. https://doi.org/10.3390/rs70708830.

Ilehag et al. “KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials.” Remote Sensing 11, no. 18 (January 2019): 2149. https://doi.org/10.3390/rs11182149.

Lindeni et al. “Imaging Spectroscopy of Urban Environments.” Surveys in Geophysics 40, no. 3 (May 1, 2019): 471–88. https://doi.org/10.1007/s10712-018-9486-y.

Okujeni et al. “Support Vector Regression and Synthetically Mixed Training Data for Quantifying Urban Land Cover.” Remote Sensing of Environment 137 (October 1, 2013): 184–97. https://doi.org/10.1016/j.rse.2013.06.007.

Zhao et al. “ASI: An Artificial Surface Index for Landsat 8 Imagery.” International Journal of Applied Earth Observation and Geoinformation 107 (March 1, 2022): 102703. https://doi.org/10.1016/j.jag.2022.102703.