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1. Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)

   https://doi.org/10.5194/essd-15-1005-2023  

   Li, Xiaoyong ; Tian, Hanqin ; Lu, Chaoqun ; Pan, Shufen ( January 2023 , Earth System Science Data)

   Abstract. The land of the conterminous United States (CONUS) hasbeen transformed dramatically by humans over the last four centuries throughland clearing, agricultural expansion and intensification, and urban sprawl.High-resolution geospatial data on long-term historical changes in land useand land cover (LULC) across the CONUS are essential for predictiveunderstanding of natural–human interactions and land-based climatesolutions for the United States. A few efforts have reconstructed historicalchanges in cropland and urban extent in the United States since themid-19th century. However, the long-term trajectories of multiple LULCtypes with high spatial and temporal resolutions since the colonial era(early 17th century) in the United States are not available yet. Byintegrating multi-source data, such as high-resolution remote sensingimage-based LULC data, model-based LULC products, and historical censusdata, we reconstructed the history of land use and land cover for theconterminous United States (HISLAND-US) at an annual timescale and 1 km × 1 km spatial resolution in the past 390 years (1630–2020). The results showwidespread expansion of cropland and urban land associated with rapid lossof natural vegetation. Croplands are mainly converted from forest, shrub,and grassland, especially in the Great Plains and North Central regions.Forest planting and regeneration accelerated the forest recovery in theNortheast and Southeast since the 1920s. The geospatial and long-termhistorical LULC data from this study provide critical information forassessing the LULC impacts on regional climate, hydrology, andbiogeochemical cycles as well as achieving sustainable use of land in thenation. The datasets are available at https://doi.org/10.5281/zenodo.7055086 (Li et al., 2022). 

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2. Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh

   https://doi.org/10.3389/fenvs.2023.1144686  

   Islam, Md Sariful ; Crawford, Thomas W. ; Shao, Yang ( April 2023 , Frontiers in Environmental Science)

   Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988–2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments. 

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3. Linking Global Land Use/Land Cover to Hydrologic Soil Groups From 850 to 2015

   https://doi.org/10.1029/2019GB006356  

   Wang, Pei‐Ling ; Feddema, Johannes J. ( March 2020 , Global Biogeochemical Cycles)

   The characteristics of soils in part determine human‐induced land use and land cover (LULC) change, and together, soil properties and LULC directly impact global water, energy, and biogeochemical cycles. Plant health and the exchange of energy, water, and biogeochemical components at the surface interface are partly controlled by soil properties. Different soil types modify vegetation responses to existing climate forcings, and each soil type also responds differently to the same land‐use practice. Currently, Earth system models often use single soil columns with averaged properties. This leads to uncertainties in assessing LULC impacts. To improve the estimates of land surface change in Earth System Models, we link an existing LULC data set to four hydrologic soil groups from 850 to 2015, based on demonstrated soil preferences for eight LULCs under current conditions. We conclude that humans prefer hydrologic soil groups in order from B, D, C, to A. This ranking was applied to construct the history of LULC on each soil type at the 0.5° grid resolution. Results primarily distribute croplands to hydrologic soil group B in 850, while hydrologic soil group A has the most undisturbed area. Over time, good soils (hydrologic soil groups B and D) experience increased use for cropland areas, while poor soils (hydrologic soil groups C and A) are occupied predominantly by increasing areas in grazing land and secondary nonforests. The results provide better land surface characteristics to improve Earth systems modeling.

    

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4. 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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5. A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape

   https://doi.org/10.3390/land10090994  

   Nedd, Ryan ; Light, Katie ; Owens, Marcia ; James, Neil ; Johnson, Elijah ; Anandhi, Aavudai ( September 2021 , Land)

   Land is a natural resource that humans have utilized for life and various activities. Land use/land cover change (LULCC) has been of great concern to many countries over the years. Some of the main reasons behind LULCC are rapid population growth, migration, and the conversion of rural to urban areas. LULC has a considerable impact on the land-atmosphere/climate interactions. Over the past two decades, numerous studies conducted in LULC have investigated various areas of the field of LULC. However, the assemblage of information is missing for some aspects. Therefore, to provide coherent guidance, a literature review to scrutinize and evaluate many studies in particular topical areas is employed. This research study collected approximately four hundred research articles and investigated five (5) areas of interest, including (1) LULC definitions; (2) classification systems used to classify LULC globally; (3) direct and indirect changes of meta-studies associated with LULC; (4) challenges associated with LULC; and (5) LULC knowledge gaps. The synthesis revealed that LULC definitions carried vital terms, and classification systems for LULC are at the national, regional, and global scales. Most meta-studies for LULC were in the categories of direct and indirect land changes. Additionally, the analysis showed significant areas of LULC challenges were data consistency and quality. The knowledge gaps highlighted a fall in the categories of ecosystem services, forestry, and data/image modeling in LULC. Core findings exhibit common patterns, discrepancies, and relationships from the multiple studies. While literature review as a tool showed similarities among various research studies, our results recommend researchers endeavor to perform further synthesis in the field of LULC to promote our overall understanding, since research investigations will continue in LULC. 

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