Integrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring [Editorial]

See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, & Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990 (2024). Integrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring [Editorial]. Land 13 (6) e769. 10.3390/land13060769.

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Abstract

The last few decades have seen an explosion in the availability of remotely sensed and geospatial big data, which are defined by the 3 Vs: a large volume of data; a variety of different forms of data; and the rapid velocity of data arrival. The term big data is particularly applicable to remote sensing. The opening of the Landsat archive, the spatially and temporally rich data now available from the Sentinel satellites, and the proliferation of small satellites photographing the Earth all provide new opportunities for characterizing and monitoring the Earth’s surface.
New sources of geospatial big data (as well as regular geospatial data) can also benefit the mapping and monitoring of land cover and land use. These include data from authoritative sources, e.g., data from official censuses and surveys, as well as data generated by citizens, both actively and passively. Citizen science and volunteered geographic information can provide data on land cover and use through initiatives such as OpenStreetMap (OSM), Geo-Wiki, and many other projects that involve volunteers monitoring the environment or landscape features. Mobile phones and low-cost sensors can provide new streams of information through mobile apps that facilitate data collection or that collect information in the background, as well as a variety of different sensors that are being used for environmental monitoring. Data from social media, including geotagged photographs from sites such as Flickr or street-level photographs from providers such as Google Street View and Mapillary, can be processed using computer vision and segmentation to extract information related to land cover and land use.
The logical progression of this field of study is the integration of remote sensing with these different sources of geospatial data using various machine learning and data fusion approaches to create new data sets on land cover and land use. Much of the previous integration work in this area has focused on urban areas because of the large number of geospatial data sets available for cities. Yet, there is considerable potential for creating better data sets in other domains as well. One example is the mapping of land use intensities, which involved integrating Corine land cover and other remotely sensed data sets with statistical and other geospatial data sources to produce a map for Europe with a 1 km resolution. Another example is the recently produced global map of forest management, which used data crowdsourced via the Geo-Wiki platform to train a classifier with satellite imagery to produce a wall-to-wall map of forest management at a 100 m resolution.
The purpose of this Special Issue is to bring together the latest papers on methods and applications that integrate remote sensing with geospatial data in the mapping and monitoring of land cover and land use. This includes applications that span different types of land cover and land use as well as those that focus on change detection. The next section summarizes the papers included in this Issue.

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