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Change in land use in the Phoenix Quadrangle, Arizona between 1970 and 1972 - successful use of a proposed land use classification system
Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (t LU ) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that t LU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.
Land use land cover change affects weather and climate. This paper quantifies land-atmosphere interactions over irrigated and non-irrigated land uses during the Great Plains Irrigation Experiment (GRAINEX). Three coupling metrics were used to quantify some land-atmosphere interactions as it relates to convection. They include: the Convective Triggering Potential (CTP) and Low-Level Humidity Index (HI low ), and the Lifting Condensation Level (LCL) Deficit. These metrics were calculated from the rawinsonde data obtained from the Integrated Sounding Systems (ISS) for Rogers Farm and York Airport along with soundings launched from the Doppler on Wheels (DOW) sites. Each metric was categorized by Intensive Observation Period (IOP), cloud cover, and time of day.
The increasing atmospheric carbon dioxide (CO 2 ) mole fraction affects global climate through radiative (trapping longwave radiation) and physiological effects (reduction of plant transpiration). We use the simulations of the Community Earth System Model (CESM1-BGC) forced with Representative Concentration Pathway 8.5 to investigate climate-vegetation feedbacks from 1850 to the year 2300. Human-induced land use and land cover change (LULCC), through biogeochemical and biogeophysical processes, alter the climate and modify photosynthetic activity. The changing characteristics of extreme anomalies in photosynthesis, referred to as carbon cycle extremes, increase the uncertainty of terrestrial ecosystems to act as a net carbon sink. However, the role of LULCC in altering carbon cycle extremes under business-as-usual (continuously rising) CO 2 emissions is unknown. Here we show that LULCC magnifies the intensity, frequency, and extent of carbon cycle extremes, resulting in a net reduction in expected photosynthetic activity in the future. We found that large temporally contiguous negative carbon cycle extremes are due to a persistent decrease in soil moisture, which is triggered by declines in precipitation. With LULCC and global warming, vegetation exhibits increased vulnerability to hot and dry environmental conditions, increasing the frequency of fire events and resulting in considerable losses in photosynthetic activity. While most regions show strengthening of negative carbon cycle extremes, a few locations show a weakening effect driven by declining vegetation cover or benign climate conditions for photosynthesis. Increasing hot, dry, and fire-driven carbon cycle extremes are essential for improving carbon cycle modeling and estimation of ecosystem responses to LULCC and rising CO 2 mole fractions. Moreover, large aberrations in vegetation productivity represent potential and growing threats to human lives, wildlife, and food security.
Recent research highlights the role of land surface processes in heat waves, droughts, and other extreme events. Here we use an earth system model (ESM) from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the regional impacts of historical anthropogenic land useland cover change (LULCC) on combined extremes of temperature and humidity. A bivariate assessment allows us to consider aridity and moist enthalpy extremes, quantities central to human experience of near-surface climate conditions. We show that according to this model, conversion of forests to cropland has contributed to much of the upper central US and central Europe experiencing extreme hot, dry summers every 2-3 years instead of every 10 years. In the tropics, historical patterns of wood harvesting, shifting cultivation and regrowth of secondary vegetation have enhanced near surface moist enthalpy, leading to extensive increases in the occurrence of humid conditions throughout the tropics year round. These critical land use processes and practices are not included in many current generation land models, yet these results identify them as critical factors in the energy and water cycles of the midlatitudes and tropics.
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Biofuels’ induced land-use change (ILUC) emissions have been widely studied over the past 15 years. Many studies have addressed uncertainties associated with these estimates. These studies have broadly examined uncertainties associated with the choice of economic models, their assumptions and parameters, and a few bio-physical variables. However, uncertainties in land-use emission factors that represent the soil and vegetation carbon contents of various land types across the world and are used to estimate carbon fluxes due to land conversions are mostly overlooked. This paper calls attention to this important omission. It highlights some important sources of uncertainty in land-use emissions factors, explores the range in these factors from established data sources, and compares the influence of their variability on ILUC emissions for several sustainable aviation fuel (SAF) pathways. The estimated land-use changes for each pathway are taken from a well-known computable general equilibrium model, GTAP-BIO. Two well-known carbon calculator models (CCLUB and AEZ-EF) that represent two different sets of emissions factors are used to convert the GTAP-BIO estimated land-use changes to ILUC emissions. The results show that the calculated ILUC emissions obtained from these carbon calculators for each examined SAF pathway are largely different, even for the same amortization time horizon. For example, the ILUC emissions values obtained from the AEZ-EF and CCLUB models for producing jet fuel from corn ethanol for a 25-year amortization period are 24.9 gCO 2 e/MJ and 15.96 gCO 2 e/MJ, respectively. This represents a 60% difference between the results of these two carbon calculators for the same set of land-use changes. The results show larger differences for other pathways as well.
In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC.
A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.
This document summarizes the major findings of a Gulf of Mexico Application Pilot project led by NASA Stennis Space Center (SSC) in conjunction with a regional collaboration network of the Gulf of Mexico Alliance (GOMA). NASA researchers processed and analyzed multi-temporal Landsat data to assess land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, AL between 1974 and 2008. Our goal was to create satellite-based LULC data products using methods that could be transferable to other coastal areas of concern within the Gulf of Mexico. The Mobile Bay National Estuary Program (MBNEP) is the primary end-user, however, several other state and local groups may benefit from the project s data products that will be available through NOAA-NCDDC s Regional Ecosystem Data Management program. Mobile Bay is a critical ecologic and economic region in the Gulf of Mexico and to the entire country. Mobile Bay was designated as an estuary of national significance in 1996. This estuary receives the fourth largest freshwater inflow in the United States. It provides vital nursery habitat for commercially and recreationally important fish species. It has exceptional aquatic and terrestrial bio-diversity, however, its estuary health is influenced by changing LULC patterns, such as urbanization. Mobile and Baldwin counties have experienced a population growth of 1.1% and 20.5% from 2000-2006. Urban expansion and population growth are likely to accelerate with the construction and operation of the ThyssenKrupp steel mill in the northeast portion of Mobile County. Land-use and land-cover change can negatively impact Gulf coast water quality and ecological resources. The conversion of forest to urban cover types impacts the carbon cycle and increases the freshwater and sediment in coastal waters. Increased freshwater runoff decreases salinity and increases the turbidity of coastal waters, thus impacting the growth potential of submerged aquatic vegetation (SAV), which is critical nursing ground for many Gulf fish species. A survey of Mobile Bay SAV showed widespread decreases since the 1940s. Prior to our project, coastal environmental managers in Baldwin and Mobile counties needed more understanding of the historical LULC for properly assessing the impacts of urbanization. In particular, more information on the location and extent of changing urbanization LULC patterns was needed to aid LULC planning and to assess predictions of future LULC patterns. Our products will assist the coastal environmental managers and land-use planners in making better community growth planning decisions. Our project also will help to establish a historical baseline of LULC distributions, which is a fundamental need in any stewardship plan. The primary research objective of our project was to produce historic and current geospatial LULC change products across a 34-year time frame. A multi-decadal coastal LULC change product was the major project deliverable. The geographic extent and nature of change was quantified and assessed for the upland herbaceous, barren, open water, urban, upland forest, woody wetland, and non-woody wetlanddominated land cover types. We focused on regional analyses of decadal-scale urban expansion and watershed-scaled analyses of LULC change for multiple areas of concern to the Mobile Bay NEP (Figure A). We used the following dates to derive LULC classification products from Landsat data: 1974, 1979, 1984, 1988, 1991, 1996, 2001, 2005, and 2008. We assessed the accuracy of our products using randomly sampled locations and digital geospatial reference data including field survey data, high resolution orthorectified aerial photography, high resolution multispectral and panchromatic satellite data displays (from QuickBird and Corona sensors), digital elevation model data, and National Wetlands Inventory wetland cover type data. NOAA s Coastal Change Assessment Program s (C-CAP) and National Land Cover Database (NLCD) procts were used for qualitative comparison in assessing map accuracy. We calculated an average overall classification accuracy of 87% with similar overall accuracies for the older (MSS) and newer (TM and ETM) Landsat LULC products.
Bioenergy aims to reduce greenhouse gas (GHG) emissions and contribute to meeting global climate change mitigation targets. Nevertheless, several sustainability concerns are associated with bioenergy, especially related to the impacts of using land for dedicated energy crop production. Cultivating energy crops can result in synergies or trade-offs between GHG emission reductions and other sustainability effects depending on context-specific conditions. Using the United Nations Sustainable Development Goals (SDGs) framework, the main synergies and trade-offs associated with land use for dedicated energy crop production were identified. Furthermore, the context-specific conditions (i.e., biomass feedstock, previous land use, climate, soil type and agricultural management) which affect those synergies and trade-offs were also identified. The most recent literature was reviewed and a pairwise comparison between GHG emission reduction (SDG 13) and other SDGs was carried out. A total of 427 observations were classified as either synergy (170), trade-off (176), or no effect (81). Most synergies with environmentally-related SDGs, such as water quality and biodiversity conservation, were observed when perennial crops were produced on arable land, pasture or marginal land in the ‘cool temperate moist’ climate zone and ‘high activity clay’ soils. Most trade-offs were related to food security and water availability. Previous land use and feedstock type are more impactful in determining synergies and trade-offs than climatic zone and soil type. This study highlights the importance of considering context-specific conditions in evaluating synergies and trade-offs and their relevance for developing appropriate policies and practices to meet worldwide demand for bioenergy in a sustainable manner.
An airborne multispectral scanner, operating in the same spectral channels as the Landsat Thematic Mapper (TM), was used in a region east of Denver, CO, for a simulation test performed in the framework of using TM to discriminate the level I and level II classes. It is noted that at the 30-m spatial resolution of the Thematic Mapper Simulator (TMS) the overall discrimination for such classes as commercial/industrial land, rangeland, irrigated sod, irrigated alfalfa, and irrigated pasture was superior to that of the Landsat Multispectral Scanner, primarily due to four added spectral bands. For residential and other spectrally heterogeneous classes, however, the higher resolution of TMS resulted in increased variability within the class and a larger spectral overlap.
Aims: Land use change from native grasslands to arable lands globally impacts soil ecosystem functions, including the storage of soil organic carbon (SOC). Understanding the factors affecting SOC changes in topsoil and subsoil due to land use is crucial for effective mitigation strategies. We determined SOC storage and persistence as affected by land use change from native prairies to arable lands. Methods: Here we examined SOC stocks, soil δ 13 C and Δ 14 C signatures, microbial communities (bacteria and fungi), and soil mineral characteristics under native prairies and long-term arable lands (i.e., > 40 years) down to 3 m in the U.S. Midwest. Results: Native prairie soils had higher SOC stocks in the A horizon and 0–50 cm depth increment than arable soils. For both land use types, the δ 13 C and Δ 14 C values significantly decreased with depth, with the latter pointing towards highly stabilized SOC, especially in the B- and C-horizons. Analysis of the microbial communities indicated that the diversity of bacteria and fungi decreased with increasing soil depth. The content of oxalate soluble Al appeared to be the single most important predictor of SOC across horizons and land use types. Conclusion: Our data suggest that most SOC gains and losses and transformation and translocation processes seem to be restricted to the uppermost 50 cm. Increasing SOC retention in the A and B horizons within the 0–50 cm depth would enhance organic material serving as substrate and nutrients for microbes and plants (A horizon) and facilitate long-term SOC storage in the subsoil (B horizon).
Tropical deforestation remains a critical issue given its present rate and a widespread consensus regarding its implications for the global carbon cycle and biodiversity. Nowhere is the problem more pronounced than in the Amazon basin, home to the world's largest intact, tropical forest. This article addresses land cover change processes at household level in the Amazon basin, and to this end adapts a concept of domestic life cycle to the current institutional environment of tropical frontiers. In particular, it poses a risk minimization model that integrates demography with market-based factors such as transportation costs and accessibility. In essence, the article merges the theory of Chayanov with the household economy framework, in which markets exist for inputs (including labor), outputs, and capital. The risk model is specified and estimated, using survey data for 261 small producers along the Transamazon Highway in the eastern sector of the Brazilian Amazon.
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By means of aerial photography and MSS-LANDSAT data a land use/land cover classification was applied to the Tubarao River coastal plain. The following classes were identified: coal related areas, permanently flooded wetlands, periodically flooded wetlands, agricultural lands, bare soils, water bodies, urban areas, forestlands.