Detecting volume changes in municipal solid waste landfill using airborne laser scanning

Accurate and operative monitoring of municipal solid waste (MSW) landfills is critical for operational safety, spatial planning, and regulatory compliance. Traditional point-based surveying methods are precise, but they are limited in spatial density coverage and hence, efficiency. The objective of the study is to evaluate the application of airborne laser scanning (ALS) for detecting volume changes at operational MSW landfill in the Czech Republic. Specifically, the study determines optimal spatial resolution of digital terrain model (DTM) from ALS for estimation of landfill volume, estimates uncertainties related to slope steepness and different vegetation cover affecting the accuracy of ALS-derived DTM, and formalizes and applies the method for detecting landfill volume changes using ALS. Two ALS datasets (10 points/m2) were collected in a five-month interval and processed at multiple spatial resolutions (0.3 m to 1.5 m). GPS reference points were measured for ALS data co-registration and to assess the accuracy of ALS-derived elevations. Positional errors and their propagation into elevation errors were quantified, and vegetation-induced uncertainties were considered. Results indicate that DTM resolutions of 0.3–0.8 m provide the most reliable estimates of volume change, especially in heterogeneous areas such as vegetated slopes. Differences in the standard deviation (SD) of elevation changes for selected areas at the landfill between the DTM resolution of 0.3 m and coarser resolutions were minimal for stable surfaces such as roads and compacted waste (0.01–0.02 m), but higher for vegetated areas, where the SD increased by up to 0.10 m due to surface roughness and variable laser penetration through the canopy. Comparison with independent GPS reference points showed that finer DTMs (0.3 and 0.5 m) reduced both bias and variability (mean differences ≤ 0.23 m, SD ≤ 0.24 m), whereas coarser DTMs (0.8 and 1.5 m) increased systematic errors due to surface smoothing and vegetation-induced misclassification. The findings recommend acquiring ALS data in early spring or late autumn, when vegetation cover is minimal and the influence of canopy on DTM accuracy is reduced. The study presents a novel workflow integrating ALS data with error modeling to improve landfill monitoring protocols. While the workflow was demonstrated on a specific site, it has potential for adaptation and application in other MSW landfills.