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1. 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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2. A Land-use/Land Cover Classification of Baltimore City in 1953

   https://doi.org/10.6073/pasta/84b59c8e375e3915cc62dea2cf73627a  

   Lagrosa IV, John J; Sonti, Nancy; Grove, J Morgan (January 2022, 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 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. 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 an ecologically and socially important moment in the post-war history of the city. It 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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3. 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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4. Land Use and Land Cover Shape River Water Quality at a Continental Caribbean Land-Ocean Interface

   https://doi.org/10.3389/frwa.2022.737920  

   Clark, Kasey E.; Bravo, Viviana D.; Giddings, Sarah N.; Davis, Kristen A.; Pawlak, Geno; Torres, Mark A.; Adelson, Anne E.; César-Ávila, Carolina I.; Boza, Ximena; Collin, Rachel (March 2022, Frontiers in Water)

   Land use and land cover (LULC) can significantly alter river water, which can in turn have important impacts on downstream coastal ecosystems by delivering nutrients that promote marine eutrophication and hypoxia. Well-documented in temperate systems, less is known about the way land cover relates to water quality in low-lying coastal zones in the tropics. Here we evaluate the catchment LULC and the physical and chemical characteristics of six rivers that contribute flow into a seasonally hypoxic tropical bay in Bocas del Toro, Panama. From July 2019 to March 2020, we routinely surveyed eight physical and chemical characteristics (temperature, specific conductivity, salinity, pH, dissolved oxygen (DO), nitrate and nitrite, ammonium, and phosphate). Our goals were to determine how these physical and chemical characteristics of the rivers reflect the LULC, to compare the water quality of the focal rivers to rivers across Panama, and to discuss the potential impacts of river discharge in the Bay. Overall, we found that the six focal rivers have significantly different river water characteristics that can be linked to catchment LULC and that water quality of rivers 10 s of kilometers apart could differ drastically. Two focal catchments dominated by pristine peat swamp vegetation in San San Pond Sak, showed characteristics typical of blackwater rivers, with low pH, dissolved oxygen, and nutrients. The remaining four catchments were largely mountainous with >50% forest cover. In these rivers, variation in nutrient concentrations were associated with percent urbanization. Comparisons across Panamanian rivers covered in a national survey to our focal rivers shows that saltwater intrusions and low DO of coastal swamp rivers may result in their classification by a standardized water quality index as having slightly contaminated water quality, despite this being their natural state. Examination of deforestation over the last 20 years, show that changes were <10% in the focal catchments, were larger in the small mountainous catchments and suggest that in the past 20 years the physical and chemical characteristics of river water that contributes to Almirante Bay may have shifted slightly in response to these moderate land use changes. (See supplementary information for Spanish-language abstract). 

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5. Three Regimes of Temperature Distribution Change Over Dry Land, Moist Land, and Oceanic Surfaces

   https://doi.org/10.1029/2020GL090997  

   Duan, Suqin Q.; Findell, Kirsten L.; Wright, Jonathon S. (December 2020, Geophysical Research Letters)

   Abstract Climate model simulations project different regimes of summertime temperature distribution changes under a quadrupling of CO₂for dry land, moist land, and oceanic surfaces. The entire temperature distribution shifts over dry land surfaces, while moist land surfaces feature an elongated upper tail of the distribution, with extremes increasing more than the corresponding means by ∼20% of the global mean warming. Oceanic surfaces show weaker warming relative to land surfaces, with no significant elongation of the upper tail. Dry land surfaces show little change in turbulent sensible (SH) or latent (LH) fluxes, with new balance reached with compensating adjustments among downwelling and upwelling radiative fluxes. By contrast, moist land surfaces show enhanced partitioning of turbulent flux toward SH, while oceanic surfaces show enhanced partitioning toward LH. Amplified warming of extreme temperatures over moist land surfaces is attributed to suppressed evapotranspiration and larger Bowen ratios. 

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