Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 15; p. 3040
• Main Authors: Shrestha, Binita, Stephen, Haroon, Ahmad, Sajjad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: Algorithms; Cities; Classification; Coordinates; data fusion; Data integration; Datasets; Developing countries; Dielectric properties; Environmentalists; Geographical locations; Growth rate; Image resolution; impervious surface extraction and mapping; Land cover; land cover classification; LDCs; Radar; Radar imaging; random forest classifier; Remote sensing; Satellites; Sensors; Sentienel-2 MSI; Sentinel-1 SAR; Urban planning; Urbanization; Water supply; Islamabad Pakistan; Rawalpindi Pakistan; Pakistan

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countrie...