Results for “Land use change” — Page 4

NASA NTRS1976

Urban land use monitoring from computer-implemented processing of airborne multispectral data

Machine processing techniques were applied to multispectral data obtained from airborne scanners at an elevation of 600 meters over central Indianapolis in August, 1972. Computer analysis of these spectral data indicate that roads (two types), roof tops (three types), dense grass (two types), sparse grass (two types), trees, bare soil, and water (two types) can be accurately identified. Using computers, it is possible to determine land uses from analysis of type, size, shape, and spatial associations of earth surface images identified from multispectral data. Land use data developed through machine processing techniques can be programmed to monitor land use changes, simulate land use conditions, and provide impact statistics that are required to analyze stresses placed on spatial systems.

Todd, W. J.↗

NASA NTRS1973

Land use mapping and change detection using ERTS imagery in Montgomery County, Alabama

The feasibility of using remotely sensed data from ERTS-1 for mapping land use and detecting land use change was investigated. Land use information was gathered from 1964 air photo mosaics and from 1972 ERTS data. The 1964 data provided the basis for comparison with ERTS-1 imagery. From this comparison, urban sprawl was quite evident for the city of Montgomery. A significant trend from forestland to agricultural was also discovered. The development of main traffic arteries between 1964 and 1972 was a vital factor in the development of some of the urban centers. Even though certain problems in interpreting and correlating land use data from ERTS imagery were encountered, it has been demonstrated that remotely sensed data from ERTS is useful for inventorying land use and detecting land use change.

Wilms, R. P.↗

NASA NTRS2021

Carolina Coastal Plain Ecological Forecasting: Utilizing NASA Earth Observations to Map Suitable Venus Flytrap Habitat in an Effort to Inform Conservation, Seed Banking, and Reintroduction in the Carolina Coastal Plain and Sandhills Regions

Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.

Monika Rock↗

NASA NTRS2022

Carolina Coastal Plain Ecological Forecasting

Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.

Monika Rock↗

NASA NTRSDate not supplied

Assessing Land Cover Change Dynamics in the Peruvian Amazon to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention

Dengue fever and leishmaniasis are two tropical diseases prevalent in Madre de Dios Region of Peru and have been associated with urbanization and road construction. Rapid land use changes such as mining, timber harvesting, and hydroelectric dam development lead to denser human presence in previously sparsely-populated areas which increases the proximity of human settlements to zoonotic disease vectors. In partnership with the Peruvian Ministries of Health (MINSA), the Environment (MINAM), and other in-country collaborators, a NASA DEVELOP team examined Land Use Land Cover (LULC) changes and reported dengue and leishmaniasis incidence in the Madre de Dios region. This sought to help MINSA and MINAM understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine (GEE) with Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. The team then compared the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate disease incidence and key land cover changes across Madre de Dios’s 11 districts. Follow on work will use these products to develop more detailed outbreak risk maps. These products will allow MINSA, MINAM, and other partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision makers and environmental policy.

Nataly Chacon-Buitrago↗

NASA NTRS1995

A remotely sensed index of deforestation/urbanization for use in climate models

The object of this research is to use indirect measurements, notably thermal infrared, to describe urbanization and deforestation with parameters that can be used to assess, as well as predict, the effects of land use changes on local microclimate. More specifically, we use a new approach for the treatment of remotely sensed data; this is referred to as the 'triangle' method. The name triangle is given because the envelope of data points, when plotted as a function of surface radiant temperature versus vegetation index or fractional vegetation cover, exhibits the shape of a triangle. From the information contained on these 'scatter plots', land use changes can be related to two intrinsic surface variables, the surface moisture availability (M(sub 0))(sup 1) and fractional vegetation cover. Recent work by Carlson et al. indicate that the triangle shape on the scatter plots may be scale similar, suggesting that these two parameters are subject to the same interpretation on differing scales. A second objective in this research is to determine if historical data for Advanced Very High Resolution Radiometer (AVHRR) (NOAA satellite; 1.1 km resolution at nadir) can be used to assess changes in regional land use over time. To this end, two target areas were chosen for the investigation of urbanization and two for deforestation. The former comprise tow areas in Pennsylvania, one a small but rapidly growing population center (State College) and the other a medium-sized urban area which continues to undergo development (Chester County). The two deforestation sites consist of rain forest areas in western and central Costa Rica and a region in the Brazilian Amazon.

Gillies, Robert R.↗

NASA NTRS2017

Synergy Between Land Use and Climate Change Increases Future Fire Risk in Amazon Forests

Tropical forests have been a permanent feature of the Amazon basin for at least 55 million years, yet climate change and land use threaten the forest's future over the next century. Understory forest fires, which are common under the current climate in frontier forests, may accelerate Amazon forest losses from climate-driven dieback and deforestation. Far from land use frontiers, scarce fire ignitions and high moisture levels preclude significant burning, yet projected climate and land use changes may increase fire activity in these remote regions. Here, we used a fire model specifically parameterized for Amazon understory fires to examine the interactions between anthropogenic activities and climate under current and projected conditions. In a scenario of low mitigation efforts with substantial land use expansion and climate change – Representative Concentration Pathway (RCP) 8.5 – projected understory fires increase in frequency and duration, burning 4–28 times more forest in 2080–2100 than during 1990–2010. In contrast, active climate mitigation and land use contraction in RCP4.5 constrain the projected increase in fire activity to 0.9–5.4 times contemporary burned area. Importantly, if climate mitigation is not successful, land use contraction alone is very effective under low to moderate climate change, but does little to reduce fire activity under the most severe climate projections. These results underscore the potential for a fire-driven transformation of Amazon forests if recent regional policies for forest conservation are not paired with global efforts to mitigate climate change.

Le Page, Yannick↗

NASA NTRS2020

Land management and climate change determine second‐generation bioenergy potential of the US Northern Great Plains

Bioenergy with carbon capture and storage (BECCS) has been proposed as a potential climate mitigation strategy raising concerns over trade‐offs with existing ecosystem services. We evaluate the feasibility of BECCS in the Upper Missouri River Basin (UMRB), a landscape with diverse land use, ownership, and bioenergy potential. We develop land‐use change scenarios and a switchgrass (Panicum virgatum L.) crop functional type to use in a land‐surface model to simulate second‐generation bioenergy production. By the end of this century, average annual switchgrass production over the UMRB ranges from 60 to 210 Tg dry mass/year and is dependent on the Representative Concentration Pathway for greenhouse gas emissions and on land‐use change assumptions. Under our simple phase‐in assumptions this results in a cumulative total production of 2,000–6,000 Tg C over the study period with the upper estimates only possible in the absence of climate change. Switchgrass yields decreased as average CO2 concentrations and temperatures increased, suggesting the effect of elevated atmospheric CO2 was small because of its C4 photosynthetic pathway. By the end of the 21st century, the potential energy stored annually in harvested switchgrass averaged between 1 and 4 EJ/year assuming perfect conversion efficiency, or an annual electrical generation capacity of 7,000–28,000 MW assuming current bioenergy efficiency rates. Trade‐offs between bioenergy and ecosystem services were identified, including cumulative direct losses of 1,000–2,600 Tg C stored in natural ecosystems from land‐use change by 2090. Total cumulative losses of ecosystem carbon stocks were higher than the potential ~300 Tg C in fossil fuel emissions from the single largest power plant in the region over the same time period, and equivalent to potential carbon removal from the atmosphere from using biofuels grown in the same region. Numerous trade‐offs from BECCS expansion in the UMRB must be balanced against the potential benefits of a carbon‐negative energy system.

climate change↗

NASA NTRS2019

Multimodel Analysis of Future Land Use and Climate Change Impacts on Ecosystem Functioning

Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to −0.021 ± 0.007 and −0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered.

land use change↗

NASA NTRS1973

Urban and regional land use analysis: CARETS and Census Cities experiment package

The author has identified the following significant results. Areas of post 1970 and 1972 land use changes were identified solely from the Skylab imagery from comparisons with 1970 land use maps. Most land use changes identified involved transition from agriculture to single family residential land use. The second most prominent changes identified from the Skylab imagery were areas presently under construction. Post 1970 changes from Skylab were compared with the 1972 changes noted from the high altitude photographs. A good correlation existed between the change polygons mapped from Skylab and those mapped from the 1972 high altitude aerial photos. In addition, there were a number of instances where additional built-up land use not noted in the 1972 aerial photo as being developed were identified on the Skylab imagery. While these cases have not been documented by field observation, by correlating these areas with the appearance of similar land use areas whose identity has been determined, we can safely say that we have been able to map further occurrences of land use change beyond existing high altitude photo coverage from the Skylab imagery. It was concluded that Skylab data can be used to detect areas of land use change within an urban setting.

Alexander, R. H.↗

NASA NTRS2019

Multimodel Analysis of Future Land Use and Climate Change Impacts on Ecosystem Functioning

Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to −0.021 ± 0.007 and −0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered.

Krause, A.↗

NASA NTRS1975

CARETS: A prototype regional environmental information system. Volume 5: Interpretation, compilation and field verification procedures in the CARETS project

The production of the CARETS map data base involved the development of a series of procedures for interpreting, compiling, and verifying data obtained from remote sensor sources. Level II land use mapping from high-altitude aircraft photography at a scale of 1:100,000 required production of a photomosaic mapping base for each of the 48, 50 x 50 km sheets, and the interpretation and coding of land use polygons on drafting film overlays. CARETS researchers also produced a series of 1970 to 1972 land use change overlays, using the 1970 land use maps and 1972 high-altitude aircraft photography. To enhance the value of the land use sheets, researchers compiled series of overlays showing i cultural features, county boundaries and census tracts, surface geology, and drainage basins. In producing Level I land use maps from Landsat imagery, at a scale I of 1:250,000, interpreters overlaid drafting film directly on Landsat color composite transparencies and interpreted on the film. They found that such interpretation involves pattern and spectral signature recognition. In studies using Landsat imagery, interpreters identified numerous areas of change but also identified extensive areas of "false change," where Landsat spectral signatures but not land use had changed.

Field verification procedures↗

NASA NTRS2024

Platte River Water Resources: Assessing Urban Flood Vulnerability to Select Restoration Sites for Urban Woods and Prairies in the Great Plains

In the Platte River Basin, wetlands provide ecosystem services such as flood mitigation and wildlife habitat. However, increasing urban development in the area has impacted natural floodplain processes, leading to a decline in wildlife habitat and an elevated flood risk for nearby communities. To address this issue, Audubon Great Plains’ Urban Woods and Prairies (UWP) Initiative focuses on restoring vital habitats within urban areas to protect bird species and reduce flood hazards. Our project used remotely sensed data, including Landsat 8 Operational Land Imagery (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture Radar (SAR), to assess land use and land cover from 2013 to 2023, as well as flood extent. Broad-scale analysis of the LULC showed some changes in land use patterns across the Central Platte River Basin, with the most notable being a decrease in Agricultural land coverage and an increase in Vegetation and Grassland coverage. Land use changes varied across 13 focal cities across the entire basin. In particular, developed land in Grand Island, NE, nearly tripled from 2019 to 2023, making it a good possible candidate for restoration efforts. We overlaid a flood extent map with the LULC classifications in Grand Island to identify possible restoration sites under UWP. This data will inform Audubon Great Plains in identifying potential restoration sites in key cities.

Jennifer Mathis↗

NASA NTRS2019

Global Carbon Budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E(FF)) are based on energy statistics and cement production data, while emissions from land use change (E(LUC)), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S(OCEAN)) and terrestrial CO2 sink (S(LAND)) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B(IM)), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), E(FF) was 9.5±0.5 GtC/yr, E(LUC) 1.5±0.7 GtC/yr, G(ATM) 4.9±0.02 GtC/yr (2.3±0.01 ppm/yr), S(OCEAN) 2.5±0.6 GtC/yr, and S(LAND) 3.2±0.6 GtC/yr, with a budget imbalance B(IM) of 0.4 GtC/yr indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E(FF) was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC/yr, reaching 10 GtC/yr for the first time in history, E(LUC) was 1.5±0.7 GtC/yr, for total anthropogenic CO2 emissions of 11.5±0.9 GtC/yr (42.5±3.3 GtCO2). Also for 2018, G(ATM) was 5.1±0.2 GtC/yr (2.4±0.1 ppm/yr), S(OCEAN) was 2.6±0.6 GtC/yr, and S(LAND) was 3.5±0.7 GtC/yr, with a B(IM) of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in E(FF) of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC/yr persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013).

global carbon budget 2019↗

NASA NTRS2022

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (E FOS ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate (G ATM ) is computed from the annual changes in concentration. The ocean CO 2 sink (S OCEAN ) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO 2 sink (S LAND ) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B IM ), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, E FOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr −1 (9.9 ± 0.5 GtC yr −1 when the cement carbonation sink is included), and E LUC was 1.1 ± 0.7 GtC yr −1 , for a total anthropogenic CO 2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr −1 (40.0 ± 2.9 GtCO 2 ). Also, for 2021, G ATM was 5.2 ± 0.2 GtC yr −1 (2.5 ± 0.1 ppm yr −1 ), S OCEAN was 2.9 ± 0.4 GtC yr −1 , and S LAND was 3.5 ± 0.9 GtC yr −1 , with a B IM of −0.6 GtC yr −1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO 2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in E FOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO 2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr −1 persist for the representation of annual to semi-decadal variability in CO 2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO 2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Pierre Friedlingstein↗

NASA NTRS1998

The Net Exchange Between Terrestrial Ecosystems and the Atmosphere as a Result of Changes in Land Use

The general purpose of this research was to improve and update (to 1990) estimates of the net flux of carbon between the world's terrestrial ecosystems and the atmosphere from changes in land use (e.g., deforestation and reforestation). The estimates are important for understanding the global carbon cycle, and for predicting future concentrations of atmospheric CO2 that will result from emissions. The emphasis of the first year's research was on the northern temperate zone and boreal forests, where the greatest discrepancy exists between estimates of flux. Forest inventories suggest net sinks of 0.6 PgC/yr; inversion analyses based on atmospheric data and models suggest much larger sinks 2-3.6 PgC/yr (e.g., Tans et al. 1990, Ciais et al. 1995). The work carried out with this grant calculated the flux attributable to changes in land use. The estimated flux was somewhat smaller than the flux calculated from inventory data suggesting that environmental changes have led to a small accumulation of carbon in forests that exceeds the accumulation expected from past rates of harvest. Two publications have described these results (Houghton 1996, 1998). The large difference between these estimates and those obtained with atmospheric data and models remains unexplained. The recent estimate of a 1.7 PgC/yr sink in North America, alone (Fan et al. 1998), is particularly difficult to explain. That part of the sink attributable to land-use change, however, is defined as a result of this grant.

Houghton, R. A.↗

NASA NTRS2010

Use of Land Use Land Cover Change Mapping Products in Aiding Coastal Habitat Conservation and Restoration Efforts of the Mobile Bay NEP

The Mobile Bay region has undergone significant land use land cover change (LULC) over the last 35 years, much of which is associated with urbanization. These changes have impacted the region s water quality and wildlife habitat availability. In addition, much of the region is low-lying and close to the Gulf, which makes the region vulnerable to hurricanes, climate change (e.g., sea level rise), and sometimes man-made disasters such as the Deepwater Horizon (DWH) oil spill. Land use land cover change information is needed to help coastal zone managers and planners to understand and mitigate the impacts of environmental change on the region. This presentation discusses selective results of a current NASA-funded project in which Landsat data over a 34-year period (1974-2008) is used to produce, validate, refine, and apply land use land cover change products to aid coastal habitat conservation and restoration needs of the Mobile Bay National Estuary Program (MB NEP). The project employed a user defined classification scheme to compute LULC change mapping products for the entire region, which includes the majority of Mobile and Baldwin counties. Additional LULC change products have been computed for select coastal HUC-12 sub-watersheds adjacent to either Mobile Bay or the Gulf of Mexico, as part of the MB NEP watershed profile assessments. This presentation will include results of additional analyses of LULC change for sub-watersheds that are currently high priority areas, as defined by MB NEP. Such priority sub-watersheds include those that are vulnerable to impacts from the DWH oil spill, as well as sub-watersheds undergoing urbanization. Results demonstrating the nature and permanence of LULC change trends for these higher priority sub-watersheds and results characterizing change for the entire 34-year period and at approximate 10-year intervals across this period will also be presented. Future work will include development of value-added coastal habitat quality assessment products that will be used by the MB NEP and its partners in the planning of coastal conservation and restoration activities.

Spruce, Joseph P.↗

NASA NTRS2007

Using NASA Techniques to Atmospherically Correct AWiFS Data for Carbon Sequestration Studies

Carbon dioxide is a greenhouse gas emitted in a number of ways, including the burning of fossil fuels and the conversion of forest to agriculture. Research has begun to quantify the ability of vegetative land cover and oceans to absorb and store carbon dioxide. The USDA (U.S. Department of Agriculture) Forest Service is currently evaluating a DSS (decision support system) developed by researchers at the NASA Ames Research Center called CASA-CQUEST (Carnegie-Ames-Stanford Approach-Carbon Query and Evaluation Support Tools). CASA-CQUEST is capable of estimating levels of carbon sequestration based on different land cover types and of predicting the effects of land use change on atmospheric carbon amounts to assist land use management decisions. The CASA-CQUEST DSS currently uses land cover data acquired from MODIS (the Moderate Resolution Imaging Spectroradiometer), and the CASA-CQUEST project team is involved in several projects that use moderate-resolution land cover data derived from Landsat surface reflectance. Landsat offers higher spatial resolution than MODIS, allowing for increased ability to detect land use changes and forest disturbance. However, because of the rate at which changes occur and the fact that disturbances can be hidden by regrowth, updated land cover classifications may be required before the launch of the Landsat Data Continuity Mission, and consistent classifications will be needed after that time. This candidate solution investigates the potential of using NASA atmospheric correction techniques to produce science-quality surface reflectance data from the Indian Remote Sensing Advanced Wide-Field Sensor on the RESOURCESAT-1 mission to produce land cover classification maps for the CASA-CQUEST DSS.

Holekamp, Kara L.↗