Assessing interpreter's disagreements in land cover reference data collection from historical Landsat time series in Amazon

• Published in: International journal of remote sensing Vol. 45; no. 15; pp. 5192 - 5223
• Main Authors: Reis, Mariane S., de Barros, Lucélia S., Rodrigues Neto, Manoel R., de Moraes, Dayane Rafaela V., Moreira, Noeli Aline P., Alves, Gabriel Mikael R., Adorno, Bruno V., Messias, Cassiano Gustavo, Dutra, Luciano V., Rennó, Camilo D., Sant'Anna, Sidnei João S., Escada, Maria Isabel S.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02.08.2024; Taylor & Francis Ltd
• Subjects: Accuracy; Borders; Data collection; Deforestation; Forest degradation; image mosaic; interpretation; Interpreters; Land cover; Landcover change; Landsat; Landsat LDCM; Pixels; Remote sensing; Satellite imagery; Satellite observation; Temporal resolution; Time dependence; Time series; Vegetation
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2024.2373340

Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field data can be very rare for multi-temporal analysis, many studies use samples visually interpreted from Remote Sensing images. This subjective process is prone to errors in interpretation, leading to uncertainties not fully quantified in the reference sample collection of historical time series. We evaluated the agreement rate among independent interpreters in the identification of land cover classes assigned to pixels for yearly Landsat observations from 1984 to 2020, in the Lower Tapajós Basin region (within the Brazilian Amazon). Considering 10 classes, we found that sets of three interpreters completely disagreed on the label of 14.8% of the pixels for each year, and completely agreed in less than half (39.6%). These values change to 2.7% and 68.8% when we consider only five classes in the analysis. We observed time-dependent variations in agreement for specific land cover classes: whereas forest areas tend to present an increase in the disagreement rate over time, land cover types that happen only in previously deforested areas tend to present a higher proportion of pixels with better agreement rates. Disagreements tend to be higher in pixels identified as borders or near borders between targets. However, there is a non-negligible amount of disagreements in pixels identified as not borders caused by limitations derived from the unit of analysis, image composition techniques, and intrinsic similarities among classes. We highlight problems with the identification of classes that happen in complex spatiotemporal patterns (Secondary Vegetation, Small-scale Agriculture, and Degraded Forest). These results alert to possible inconsistencies in reference samples collected for the accuracy assessment of land cover time series and the correspondent values of the calculated accuracy indexes.