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1. The land-use land-cover change–emerging infectious disease nexus reconsidered

   https://doi.org/10.1093/biosci/biaf045  

   Koren, Ore; Chaves, Luis Fernando (April 2025, BioScience)

   Abstract Deforestation due to land-use and land-cover (LULC) change has been linked to increased emerging zoonotic disease risk despite limited local level data on such outbreaks. This Forum reevaluates this risk inference using newly released data on zoonotic disease outbreaks, accounting for Structural One Health features, including socioeconomic development and armed conflict covariates. Event and time series data on disease and forest coverage anomalies at the 0.5-degree level for every month between January 2003 and December 2018 are used to estimate the relationship between LULC and zoonosis using Poisson generalized additive and generalized linear models. Once adjusted for Structural One Health features, outbreak risk is 7%–200% higher in areas that experienced forest cover reversion. These results highlight the importance of accounting for Structural One Health factors when analyzing complex socioecological phenomena such as the LULC–infectious disease nexus. 

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2. 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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3. 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)

   null (Ed.)
4. The land–ocean Arctic carbon cycle

   https://doi.org/10.1038/s43017-024-00627-w  

   Vonk, Jorien E; Fritz, Michael; Speetjens, Niek J; Babin, Marcel; Bartsch, Annett; Basso, Luana S; Bröder, Lisa; Göckede, Mathias; Gustafsson, Örjan; Hugelius, Gustaf; et al (February 2025, Nature Reviews Earth & Environment)
5. The weak land carbon sink hypothesis

   https://doi.org/10.1126/sciadv.adr5489  

   Randerson, James T; Li, Yue; Fu, Weiwei; Primeau, Francois; Kim, Jinhyuk E; Mu, Mingquan; Hoffman, Forrest M; Trugman, Anna T; Yang, Linqing; Wu, Chao; et al (September 2025, Science Advances)

   NA (Ed.)

   Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (−6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO₂gradient, and O₂trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis. 

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