More Like this

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. 

   more » « less
2. 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). 

   more » « less
3. Theory and the future of land-climate science

   https://doi.org/10.1038/s41561-024-01553-8  

   Byrne, Michael P; Hegerl, Gabriele C; Scheff, Jacob; Adam, Ori; Berg, Alexis; Biasutti, Michela; Bordoni, Simona; Dai, Aiguo; Geen, Ruth; Henry, Matthew; et al (November 2024, Nature Geoscience)
4. 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.)
5. Sensitivity of Surface Temperature to Land Use and Land Cover Change‐Induced Biophysical Changes: The Scale Issue

   https://doi.org/10.1029/2019GL084861  

   Li, Dan; Wang, Liang (August 2019, Geophysical Research Letters)

   Abstract While the signs of the sensitivities of surface temperature (T_s) to land use and land cover change‐induced biophysical changes are relatively well understood, their exact magnitude and how their magnitude depends on the scale characterizing the size of the change remain elusive. In this study, we compare the sensitivities of surface temperature to changes in surface albedo and surface water availability from three analytical/semianalytical models, which are designed for small (<1 km), intermediate (from ∼1 to ∼10 km), and large (>10–20 km) scales. Results suggest that the sensitivities of surface temperature to biophysical changes are scale dependent due to atmospheric feedbacks. Our results demonstrate that it is important to consider the scale and the associated atmospheric feedbacks when quantifying the sensitivities of surface temperature to biophysical changes. 

   more » « less