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1. Land Use and Land Cover Mapping in the Era of Big Data

   https://doi.org/10.3390/land11101692  

   Zhang, Chuanrong; Li, Xinba (October 2022, Land)

   We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales. 

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2. The Effect of Land Albedo on the Climate of Land-dominated Planets in the TRAPPIST-1 System

   https://doi.org/10.3847/1538-4357/abbe04  

   Rushby, Andrew J.; Shields, Aomawa L.; Wolf, Eric T.; Laguë, Marysa; Burgasser, Adam (December 2020, The Astrophysical Journal)

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3. The size of the land carbon sink in China

   https://doi.org/10.1038/s41586-021-04255-y  

   Wang, Yilong; Wang, Xuhui; Wang, Kai; Chevallier, Frédéric; Zhu, Dan; Lian, Jinghui; He, Yue; Tian, Hanqin; Li, Junsheng; Zhu, Jianxiao; et al (March 2022, Nature)
4. Analysis of Land Use and Land Cover Changes through the Lens of SDGs in Semarang, Indonesia

   https://doi.org/10.3390/su14137592  

   Kelly-Fair, Mira; Gopal, Sucharita; Koch, Magaly; Pancasakti Kusumaningrum, Hermin; Helmi, Muhammad; Khairunnisa, Dinda; Kaufman, Les (July 2022, Sustainability)

   Land Use and Land Cover Changes (LULCC) are occurring rapidly around the globe, particularly in developing island nations. We use the lens of the United Nations’ Sustainable Development Goals (SDG) to determine potential policies to address LULCC due to increasing population, suburbia, and rubber plantations in Semarang, Indonesia between 2006 and 2015. Using remote sensing, overlay analysis, optimized hot spot analysis, expert validation, and Continuous Change Detection and Classification, we found that there was a spread of urban landscapes towards the southern and western portions of Semarang that had previously been occupied by forests, plantations, agriculture, and aquaculture. We also witnessed a transition in farming from agriculture to rubber plantations, a cash crop. The implications of this study show that these geospatial analyses and big data can be used to characterize the SDGs, the complex interplay of these goals, and potentially alleviate some of the conflicts between disparate SDGs. We recommend certain policies that can assist in preserving the terrestrial ecosystem of Semarang (SDG 15) while creating a sustainable city (SDG 11, SDG 9) and providing sufficient work for individuals (SDG 1) in a growing economy (SDG 8) while simultaneously maintaining a sufficient food supply (SDG 2). 

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5. A Land-use/Land Cover Classification of Baltimore City in 1927

   https://doi.org/10.6073/pasta/28793d6417db548f26679ab0e5197fb8  

   Lagrosa IV, John J; Sonti, Nancy; Grove, J Morgan (January 2021, Environmental Data Initiative)

   Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region. 

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