Dr. Cristina Monterisi Profile

Dr. Cristina Monterisi

at Politecnico di Bari

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Proceedings Article | 12 September 2021 Paper

Google Earth Engine for land surface albedo estimation: comparison among different algorithms

A. Capolupo, C. Monterisi, C. Barletta, E. Tarantino

Proceedings Volume 11856, 118560F (2021) https://doi.org/10.1117/12.2597666

KEYWORDS: Earth observing sensors, Landsat, Satellites, Algorithm development, Sensors, Satellite imaging, Error analysis, Data acquisition, Vegetation

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Albedo has long been recognized as a relevant bio-geophysical variable to model Earth surface and it was involved in all the climate simulation models. Therefore, the correct modelling of albedo is essential to reduce the error propagation in the prediction algorithms. To meet such a purpose, different methods have been developed over the past years. Among them, the simplified approach proposed by Liang in 2000 and the corrected algorithm introduced by Silva et al. (2016) are commonly used. To the best of our knowledge, the outcomes produced by applying such techniques have not been investigated yet. The present paper is intended to explore the potentialities of Google Earth Engine (GEE) platform in estimating land surface albedo from three medium-resolution geospatial data gathered by different Landsat sensors in diverse acquisition periods. Java-script code was developed to numerically implement the above-mentioned algorithms in GEE environment. Their performances were compared and the error committed adopting the simplified method was quantified. As a result, the corrected algorithm reported more accurate values. Nevertheless, its complexity implies a high implementation difficulty and, consequently, a higher processing time is required to handle the data. Conversely, the simplified approach allowed to estimate land surface albedo in a short time. Quantifying the error committed using the simplified approach allows us to correct its results, improving their accuracy. Although obtained results are preliminary, this research enhanced the possibility to model the albedo by adopting the simplified algorithm after correcting it. This implies to reduce error propagation and, simultaneously, to speed up the data handling.

Proceedings Article | 26 August 2020 Paper

Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform

Alessandra Capolupo, Cristina Monterisi, Mirko Saponaro, Eufemia Tarantino

Proceedings Volume 11524, 1152419 (2020) https://doi.org/10.1117/12.2571228

KEYWORDS: Earth observing sensors, Landsat, Vegetation, Mining, Image classification, Satellites, Image processing, Satellite imaging, Clouds, Data processing

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Timely and accurate maps of land cover changes are crucial for understanding the evolution of Earth's features and, consequently, the relationships between individual and collective needs. Therefore, this information is extremely important to develop future planning strategies and tackle environmental issues. This paper aims to exploit the use of Google Earth Engine (GEE) platform to examine land cover changes over a period of about fiftheen years in the pilot site of Siponto, an historical municipality in Puglia, Southern Italy. Six atmospherically corrected Landsat data, two for each selected mission (5, 7 and 8), were collected: the former was acquired in fall and the latter in spring. Land cover information was automatically extracted from each image through the implementation of an innovative Landsat Images Classifications algorithm (LICA) based on spectral indices analysis. Six classes (water, built-up, mining areas, bare soil, dense and sparse vegetation) were detected from each image, with an average overall accuracy higher than 85%. Land cover changes were assessed comparing classification maps of the same season, showing bare soil areas as the most altered ones, having been converted into arable lands in consideration of the adavantageous geomorphological features of the investigated site. This is also confirmed by the historical events experienced by the area.

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