Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring (Second Edition)

Dear Colleagues,

In the last decade, there has been an explosion of data from both remote sensing and other sources of geospatial data (e.g., citizen science, low-cost sensors, mobile phones), which could benefit the mapping and monitoring of land cover and land use. The opening up of the Landsat archive, the spatial and temporal richness of the data now available from Sentinel satellites, and the proliferation of small satellites photographing the Earth provide novel opportunities for characterizing the land surface, particularly in relation to land use. By integrating remote sensing with other sources of big geospatial data and machine learning/data fusion, we can create new data sets on land use, e.g., land use management intensity [1], forest management [2], and drivers of tropical deforestation [3], all of which fill significant gaps regarding land use information.  Much of the recent work performed in this area has focused on urban applications, but there is also potential for other land cover and land use types.

This Special Issue aims to compile state-of-the-art research in this field. We welcome papers that present methods and applications that integrate remote sensing with geospatial big data in the mapping and monitoring of land use, including change detection.

References

1. Dou, Y.; Cosentino, F.; Malek, Z.; Maiorano, L.; Thuiller, W.; Verburg, P.H. A new European land systems representation accounting for landscape characteristics. Landscape Ecol. 2021, 36, 2215–2234. https://doi.org/10.1007/s10980-021-01227-5
2. Lesiv, M.; Schepaschenko, D.; Buchhorn, M.; See, L.; Dürauer, M.; Georgieva, I.; Jung, M.; Hofhansl, F.; Schulze, K.; Bilous, A.; et al. Global forest management data for 2015 at a 100 m resolution. Sci. Data 2022, 9, 199. https://doi.org/10.1038/s41597-022-01332-3
3. Laso Bayas, J.C.; See, L.; Georgieva, I.; Schepaschenko, D.; Danylo, O.; Dürauer, M.; Bartl, H.; Hofhansl, F.; Zadorozhniuk, R.; Burianchuk, M.; et al. Drivers of tropical forest loss between 2008 and 2019. Sci. Data 2022, 9, 146. https://doi.org/10.1038/s41597-022-01227-3

Dr. Myroslava Lesiv
Dr. Linda See
Dr. Dmitry Schepaschenko
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data fusion
• machine learning
• remote sensing
• geospatial big data

• Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring in Land (10 articles)