Modeled historical land use and land cover for the conterminous United States

Sohl, Terry L.; Reker, Ryan R.; Bouchard, Michelle; Sayler, Kristi L.; Dornbierer, Jordan; Wika, Steve; Quenzer, R.; Friesz, Aaron M.

doi:10.1080/1747423x.2016.1147619

Cited by 86 publications

(76 citation statements)

References 41 publications

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“…We selected a 250 m cell size (6.25 ha) because this extent is several times larger than our estimated co‐registration error and corresponds to the coarsest‐resolution dataset used in later analyses (i.e., Sohl et al. ). This 250 m resolution is also relevant for land managers, approximating the size of individual treatment units that are used in planning forest management activities in the NFR (Addington ).…”

Section: Methodsmentioning

confidence: 99%

“…To account for co-registration error and to permit additional analyses of the potential drivers of forest change, we aggregated the resolution of the 1 m classified maps to 250 m in each date. We selected a 250 m cell size (6.25 ha) because this extent is several times larger than our estimated co-registration error and corresponds to the coarsest-resolution dataset used in later analyses (i.e., Sohl et al 2016). This 250 m resolution is also relevant for land managers, approximating the size of individual treatment units that are used in planning forest management activities in the NFR (Addington 2018).…”

Section: Data Aggregationmentioning

confidence: 99%

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Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range

et al. 2019

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Recent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.

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Section: Methodsmentioning

confidence: 99%

Section: Data Aggregationmentioning

confidence: 99%

Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range

et al. 2019

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“…The model calibration process was limited by the amount and quality of available land use, water‐catchment characteristics, climate, and TSS data; put simply low‐quality data into the model equals low‐quality estimates out of the model. Land use data that informed each submodel were estimated data from 1947 to 2012 and lacked some degree of spatial accuracy (Sohl et al, ); therefore, any errors in the land use data limited the calibration process of each submodel. For example, errors in spatially explicit annual land use could amplify errors in hydrologic and TSS responses over time as annual hydrologic responses vary depending on land use hydrologic characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Existing modeling methods such as optimization and spatial/temporal analysis (interpolation) used in previously published land use land cover modeling and natural resource models, facilitated the determination of uncertain parameters (i.e., constants) as well as the creation of continuous datasets used as input data over time (e.g. RUSLE; Sohl et al, ; Turner, et al, ). Additionally, a GIS (ArcGIS 10.3.1™) based approach in combination with Program R™ (R‐3.2.5) was used to delineate data by water‐catchment; R™ greatly decreased the processing time of spatially explicit annual land‐use data.…”

Section: Methodsmentioning

confidence: 99%

A spatial landscape scale approach for estimating erosion, water quantity, and quality in response to South Dakota grassland conversion

Menendez

Wuellner

Turner

et al. 2019

Natural Resource Modeling

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Conversion of grassland to cropland has been linked to many complex environmental challenges in natural resource systems. South Dakota is a mosaic of grasslands, wetlands, and cropland that has experienced tremendous land use change over the past 10 years and is expected to continue for the next 50 years. The rate of future conversion may vary greatly depending on economic, policy, and social factors. Land conversion influences cumulative erosion from arable soils which could impact hydrologic flow and water quality.Quantifying future changes for these three externalities is important to understand the possible long-term consequences of grassland conversion. A system dynamics model was developed to address the dynamic complexity of these natural resource systems by capturing its structure and behavior and was able to adequately replicate historical changes in erosion, discharge, and total suspended solids from 1947 to 2012. Recommendations for resource managers• Resource managers should apply this tool for problems that require quantitative assessment of environmental Present address Hector M. Menendez III, Department of Animal Science, Kleberg Center, Texas A&M University-College Station, TAMU 2471, College Station, TX 77843.consequences to be coupled with economic, policy, and social factors that influence long-term land-use change decisions.• The model can be used to evaluate alternative policies and indicate the magnitude of change for three critical environmental factors using different long-term grassland conversion, climate, and tillage (conservation and conventional) patterns.• Model output of four spatially explicit water-catchments that span South Dakota from east to west: Big Sioux, James, Bad, and Belle Fourche rivers can be used to quantify differences between unique natural resource systems.• The model is adequate for the purpose of generating forecast for future annual erosion (t·ha −1 ·year −1 ), discharge (million cubic meters), and total suspended solids (mg/L) under different potential future grassland conversion rates and should be leveraged by managers to gain insight into future landscape scale consequences of grassland conversion in South Dakota.• Potential for additional natural resource applications.

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“…Yearly land cover projections from 1938 to 2002 were obtained from the US Geological Survey (USGS) Modeled Historical Land Use and Land Cover for the Conterminous United States at 250 m resolution [38]. In general, Landsat imagery was used in combination with other ancillary data to project historical land cover types on a yearly basis across the United States [39,40]. Five land cover types, including forest, grassland, cropland, pasture, and wetland, were investigated for their effects on the changes of SOC stock.…”

Section: Environmental Covariatesmentioning

confidence: 99%

Climate and Land-Use Change Effects on Soil Carbon Stocks over 150 Years in Wisconsin, USA

2019

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Soil organic carbon is a sink for mitigating increased atmospheric carbon. The international initiative “4 per 1000” aims at implementing practical actions on increasing soil carbon storage in soils under agriculture. This requires a fundamental understanding of the soil carbon changes across the globe. Several studies have suggested that the global soil organic carbon stocks (SOCS) have decreased due to global warming and land cover change, while others reported SOCS may increase under climate change and improved soil management. To better understand how a changing climate, land cover, and agricultural activities influence SOCS across large extents and long periods, the spatial and temporal variations of SOCS were estimated using a modified space-for-time substitution method over a 150-year period in the state of Wisconsin, USA. We used legacy soil datasets and environmental factors collected and estimated at different times across the state (169,639 km2) coupled with a machine-learning algorithm. The legacy soil datasets were collected from 1980 to 2002 from 550 soil profiles and harmonized to 0.30 m depth. The environmental factors consisted of 100-m soil property maps, 1-km annual temperature and precipitation maps, 250-m remote-sensing (i.e., Landsat)-derived yearly land cover maps and a 30-m digital elevation model. The model performance was moderate but can provide insights on understanding the impacts of different factors on SOCS changes across a large spatial and temporal extent. SOCS at the 0–0.30 m decreased at a rate of 0.1 ton ha−1 year−1 between 1850 and 1938 and increased at 0.2 ton ha−1 year−1 between 1980 and 2002. The spatial variation in SOCS at 0–0.30 m was mainly affected by land cover and soil types with the largest SOCS found in forest and wetland and Spodosols. The loss between 1850 and 1980 was most likely due to land cover change while the increase between 1980 and 2002 was due to best soil management practices (e.g., decreased erosion, reduced tillage, crop rotation and use of legume and cover crops).

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Modeled historical land use and land cover for the conterminous United States

Cited by 86 publications

References 41 publications

Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range

Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range

A spatial landscape scale approach for estimating erosion, water quantity, and quality in response to South Dakota grassland conversion

Climate and Land-Use Change Effects on Soil Carbon Stocks over 150 Years in Wisconsin, USA

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