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Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes associated with land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush (Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover in Wyoming, USA. EP maps correspond to the potential natural vegetation cover expected by environmental conditions in the absence of anthropogenic and natural disturbance as represented by the greenest and least disturbed period of the Landsat archive. EP was predicted using regression tree models with inputs of soil maps and spectral data associated with the 75th percentile of the Normalized Difference Vegetation Index in the Landsat archive. We trained our EP models with 2015 component cover maps on ecologically intact sites with relatively lower bare ground than expected. We generated departure of vegetation cover by comparing the EP and 2015 fractional cover. The departures represent land cover change from potential land cover and/or within-state changes in 2015. Next, we converted EP and 2015 fractional cover maps into thematic land cover and evaluated departure to determine if it was great enough to result in land cover change. The 2015 conditions showed reduced shrub, sagebrush, litter, and perennial herbaceous cover and increased bare ground relative to EP. Known disturbances, such as energy development, fires, and vegetation treatments, are clearly visible on the departure maps, but not on EP component maps. The most frequent departure from EP land cover was shrubland conversion to grassland. Land cover departures can be explained only in small part by known disturbance, and instead are ostensibly related to climate and land management practices. These drivers result in land cover departures that broadened the ecotone between shrubland and grassland relative to EP.

We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.

The semi-arid Great Basin region in the Northwest U.S. is impacted by a suite of change agents including fire, grazing, and climate variability to which native vegetation can have low resilience and resistance. Assessing ecosystem condition in relation to these change agents is difficult due to a lack of a consistent and objective Site Potential (SP) information of the conditions biophysically possible at each site. Our objectives were to assess and quantify patterns in ecosystem condition, based on actual fractional component cover and a SP map and to evaluate drivers of change. We used long-term 90th percentile Landsat NDVI (Normalized Difference Vegetation Index) and biophysical variables to produce a map of SP. Ecosystem condition was assessed using two methods, first we integrated fractional components into an index which was regressed against SP. Regression confidence intervals were used to segment the study area into normal, over-, and under-performing relative to SP. Next, the relationships between SP and fractional component cover produced SP expected component cover, from which we mapped the actual cover deviation. Much of the study area is within the range of conditions expected by the SP model, but degraded conditions are more common than those above SP expectations. We found that shrub cover deviation is more positive at higher elevation, while herbaceous cover deviation has the opposite pattern, supporting the hypothesis that more resistant and resilient sites are less likely to change from the shrub dominated legacy. Another key finding was that regions with significant annual herbaceous invasions tend to have lower than expected bare ground and shrub cover.

The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass (Bromus tectorum) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016−2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.

The shortgrass steppe (SGS) occupies the southwestern part of the Great Plains. Half of the land is cultivated, but significant areas remain under natural vegetation. Despite previous studies of the SGS carbon cycle, not all aspects have been completely addressed, including gross productivity, ecosystem respiration, and ecophysiological parameters. Our analysis of 1998 — 2007 flux tower measurements at five Bowen ratio-energy balance (BREB) and three eddy covariance (EC) sites characterized seasonal and interannual variability of gross photosynthesis and ecosystem respiration. Identification of the nonrectangular hyperbolic equation for the diurnal CO2 exchange, with vapor pressure deficit (VPD) limitation and exponential temperature response, quantified quantum yield α, photosynthetic capacity Amax, and respiration rate rd with variation ranges (19 < α < 51 mmol mol-1, 0.48 < Amax < 2.1 mg CO2 m-2 s-1, 0.15 < rd < 0.49 mg CO2 m-2 s-1). Gross photosynthesis varied from 1 100 to 2 700 g CO2 m-2 yr-1, respiration from 900 to 3,000 g CO2 m-2 yr-1, and net ecosystem production from — 900 to 700 g CO2 m-2 yr-1, indicating that SGS may switch from a sink to a source depending on weather. Comparison of the 2004–2006 measurements at two BREB and two parallel EC flux towers located at comparable SGS sites showed moderately higher photosynthesis, lower respiration, and higher net production at the BREB than EC sites. However, the difference was not related only to methodologies, as the normalized difference vegetation index at the BREB sites was higher than at the EC sites. Overall magnitudes and seasonal patterns at the BREB and the EC sites during the 3-yr period were similar, with trajectories within the ± 1.5 standard deviation around the mean of the four sites and mostly reflecting the effects of meteorology.

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overa...

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated ...

Switchgrass (Panicum virgatum) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e., it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e., NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario-based (“A1B”) land use and land cover map for 2050, the U.S. Geological Survey crop type and Compound Topographic Index (CTI) maps, and long-term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2,528 km2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4,232 km2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development. This article is protected by copyright. All rights reserved.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well agains...

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000−2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses.

Wetlands are critical terrestrial ecosystems in Alaska, covering ~177,000 km2 , an area greater than all the wetlands in the remainder of the United States. To assess the relative influence of changing climate, atmospheric carbon dioxide (CO2 ) concentration, and fire regime on carbon balance in wetland ecosystems of Alaska, a modeling framework that incorporates a fire disturbance model and two biogeochemical models was used. Spatially explicit simulations were conducted at 1-km resolution for the historical period (1950-2009) and future projection period (2010-2099). Simulations estimated that wetland ecosystems of Alaska lost 175 Tg carbon (C) in the historical period. Ecosystem C storage in 2009 was 5,556 Tg, with 89% of the C stored in soils. The estimated loss of C as CO2 and biogenic methane (CH4 ) emissions resulted in wetlands of Alaska increasing the greenhouse gas forcing of climate warming. Simulations for the projection period were conducted for six climate change scenarios constructed from two climate models forced under three CO2 emission scenarios. Ecosystem C storage averaged among climate scenarios increased 3.94 Tg C/yr by 2099, with variability among the simulations ranging from 2.02 to 4.42 Tg C/yr. These increases were driven primarily by increases in net primary production (NPP) that were greater than losses from increased decomposition and fire. The NPP increase was driven by CO2 fertilization (~5% per 100 parts per million by volume increase) and by increases in air temperature (~1% per °C increase). Increases in air temperature were estimated to be the primary cause for a projected 47.7% mean increase in biogenic CH4 emissions among the simulations (~15% per °C increase). Ecosystem CO2 sequestration offset the increase in CH4 emissions during the 21st century to decrease the greenhouse gas forcing of climate warming. However, beyond 2100, we expect that this forcing will ultimately increase as wetland ecosystems transition from being a sink to a source of atmospheric CO2 because of (1) decreasing sensitivity of NPP to increasing atmospheric CO2 , (2) increasing availability of soil C for decomposition as permafrost thaws, and (3) continued positive sensitivity of biogenic CH4 emissions to increases in soil temperature.

On the Ground Cheatgrass (Bromus tectorum L.) dramatically changes shrub steppe ecosystems in the Northern Great Basin, United States. Current-season cheatgrass location and percent cover are difficult to estimate rapidly. We explain the development of a near-real-time cheatgrass percent cover dataset and map in the Northern Great Basin for the current year (2015), display the current years map, provide analysis of the map, and provide a website link to download the map (as a PDF) and the associated dataset. The near-real-time cheatgrass percent cover dataset and map were consistent with non-expedited, historical cheatgrass percent cover datasets and maps. Having cheatgrass maps available mid-summer can help land managers, policy makers, and Geographic Information Systems personnel as they work to protect socially relevant areas such as critical wildlife habitats.

Cheatgrass ( Bromus tectorum L.) is a highly invasive species in the Northern Great Basin that helps decrease fire return intervals. Fire fragments the shrub steppe and reduces its capacity to provide forage for livestock and wildlife and habitat critical to sagebrush obligates. Of particular interest is the greater sage grouse ( Centrocercus urophasianus ), an obligate whose populations have declined so severely due, in part, to increases in cheatgrass and fires that it was considered for inclusion as an endangered species. Remote sensing technologies and satellite archives help scientists monitor terrestrial vegetation globally, including cheatgrass in the Northern Great Basin. Along with geospatial analysis and advanced spatial modeling, these data and technologies can identify areas susceptible to increased cheatgrass cover and compare these with greater sage grouse priority areas for conservation (PAC). Future climate models forecast a warmer and wetter climate for the Northern Great Basin, which likely will force changing cheatgrass dynamics. Therefore, we examine potential climate-caused changes to cheatgrass. Our results indicate that future cheatgrass percent cover will remain stable over more than 80% of the study area when compared with recent estimates, and higher overall cheatgrass cover will occur with slightly more spatial variability. The land area projected to increase or decrease in cheatgrass cover equals 18% and 1%, respectively, making an increase in fire disturbances in greater sage grouse habitat likely. Relative susceptibility measures, created by integrating cheatgrass percent cover and temporal standard deviation datasets, show that potential increases in future cheatgrass cover match future projections. This discovery indicates that some greater sage grouse PACs for conservation could be at heightened risk of fire disturbance. Multiple factors will affect future cheatgrass cover including changes in precipitation timing and totals and increases in freeze-thaw cycles. Understanding these effects can help direct land management, guide scientific research, and influence policy.

Fire can be a significant driver of permafrost change in boreal landscapes, altering the availability of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to fire-induced change. As fire frequency is expected to increase in the high latitudes, methods to understand the vulnerability and resilience of different landscapes to permafrost degradation are needed. We present a combination of multiscale remote sensing, geophysical, and field observations that reveal details of both near-surface ( 1 m) impacts of fire on permafrost. Along 11 transects that span burned-unburned boundaries in different landscape settings within interior Alaska, subsurface electrical resistivity and nuclear magnetic resonance data indicate locations where permafrost appears to be resilient to disturbance from fire, areas where warm permafrost conditions exist that may be most vulnerable to future change, and also areas where permafrost has thawed. High-resolution geophysical data corroborate remote sensing interpretations of near-surface permafrost and also add new high-fidelity details of spatial heterogeneity that extend from the shallow subsurface to depths of about 10 m. Results show that postfire impacts on permafrost can be variable and depend on multiple factors such as fire severity, soil texture, soil moisture, and time since fire.

Cultivating annual row crops in high topographic relief waterway buffers has negative environmental effects and can be environmentally unsustainable. Growing perennial grasses such as switchgrass (Panicum virgatum L.) for biomass (e.g., cellulosic biofuel feedstocks) instead of annual row crops in these high relief waterway buffers can improve local environmental conditions (e.g., reduce soil erosion and improve water quality through lower use of fertilizers and pesticides) and ecosystem services (e.g., minimize drought and flood impacts on production; improve wildlife habitat, plant vigor, and nitrogen retention due to post-senescence harvest for cellulosic biofuels; and serve as carbon sinks). The main objectives of this study are to: (1) identify cropland areas with high topographic relief (high runoff potentials) and high switchgrass productivity potential in eastern Nebraska that may be suitable for growing switchgrass, and (2) estimate the total switchgrass production gain from the potential biofuel areas. Results indicate that about 140,000 hectares of waterway buffers in eastern Nebraska are suitable for switchgrass development and the total annual estimated switchgrass biomass production for these suitable areas is approximately 1.2 million metric tons. The resulting map delineates high topographic relief croplands and provides useful information to land managers and biofuel plant investors to make optimal land use decisions regarding biofuel crop development and ecosystem service optimization in eastern Nebraska.

The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.

We summarize the results of a recent interagency assessment of land carbon dynamics in Alaska, in which carbon dynamics were estimated for all major terrestrial and aquatic ecosystems for the historical period (1950-2009) and a projection period (2010-2099). Between 1950 and 2009, upland and wetland (i.e., terrestrial) ecosystems of the state gained 0.4 Tg C/yr (0.1% of net primary production, NPP), resulting in a cumulative greenhouse gas radiative forcing of 1.68 × 10-3 W/m2 . The change in carbon storage is spatially variable with the region of the Northwest Boreal Landscape Conservation Cooperative (LCC) losing carbon because of fire disturbance. The combined carbon transport via various pathways through inland aquatic ecosystems of Alaska was estimated to be 41.3 Tg C/yr (17% of terrestrial NPP). During the projection period (2010-2099), carbon storage of terrestrial ecosystems of Alaska was projected to increase (22.5-70.0 Tg C/yr), primarily because of NPP increases of 10-30% associated with responses to rising atmospheric CO2 , increased nitrogen cycling, and longer growing seasons. Although carbon emissions to the atmosphere from wildfire and wetland CH4 were projected to increase for all of the climate projections, the increases in NPP more than compensated for those losses at the statewide level. Carbon dynamics of terrestrial ecosystems continue to warm the climate for four of the six future projections and cool the climate for only one of the projections. The attribution analyses we conducted indicated that the response of NPP in terrestrial ecosystems to rising atmospheric CO2 (~5% per 100 ppmv CO2 ) saturates as CO2 increases (between approximately +150 and +450 ppmv among projections). This response, along with the expectation that permafrost thaw would be much greater and release large quantities of permafrost carbon after 2100, suggests that projected carbon gains in terrestrial ecosystems of Alaska may not be sustained. From a national perspective, inclusion of all of Alaska in greenhouse gas inventory reports would ensure better accounting of the overall greenhouse gas balance of the nation and provide a foundation for considering mitigation activities in areas that are accessible enough to support substantive deployment.

Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map near-surface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of ~ 85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies.

Quantification of aboveground biomass AGB in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year 2008–2010 composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature BT. We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error MAE of 25.7 Mg ha−1 relative MAE 47.5% and a mean bias error MBE of 4.3 Mg ha−1 relative MBE 7.9%. The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

Understanding cheatgrass (Bromus tectorum) dynamics in the Northern Great Basin rangelands, USA, is necessary to effectively manage the region's lands. This study's goal was to map and monitor cheatgrass performance to identify where and when cheatgrass dieoff occurred in the Northern Great Basin and to discover how this phenomenon was affected by climatic, topographic, and edaphic variables. We also examined how fire affected cheatgrass performance. Land managers and scientists are concerned by cheatgrass dieoff because it can increase land degradation, and its causes and effects are not fully known. To better understand the scope of cheatgrass dieoff, we developed multiple ecological models that integrated remote sensing data with geophysical and biophysical data. The models' R2 ranged from 0.71 to 0.88, and their root mean squared errors (RMSEs) ranged from 3.07 to 6.95. Validation of dieoff data showed that 41% of pixels within independently developed dieoff polygons were accurately classifie...

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

It is important to understand how upland ecosystems of Alaska, which are estimated to occupy 84% of the state (i.e., 1,237,774 km2 ), are influencing and will influence state-wide carbon (C) dynamics in the face of ongoing climate change. We coupled fire disturbance and biogeochemical models to assess the relative effects of changing atmospheric carbon dioxide (CO2 ), climate, logging and fire regimes on the historical and future C balance of upland ecosystems for the four main Landscape Conservation Cooperatives (LCCs) of Alaska. At the end of the historical period (1950-2009) of our analysis, we estimate that upland ecosystems of Alaska store ~50 Pg C (with ~90% of the C in soils), and gained 3.26 Tg C/yr. Three of the LCCs had gains in total ecosystem C storage, while the Northwest Boreal LCC lost C (-6.01 Tg C/yr) because of increases in fire activity. Carbon exports from logging affected only the North Pacific LCC and represented less than 1% of the state's net primary production (NPP). The analysis for the future time period (2010-2099) consisted of six simulations driven by climate outputs from two climate models for three emission scenarios. Across the climate scenarios, total ecosystem C storage increased between 19.5 and 66.3 Tg C/yr, which represents 3.4% to 11.7% increase in Alaska upland's storage. We conducted additional simulations to attribute these responses to environmental changes. This analysis showed that atmospheric CO2 fertilization was the main driver of ecosystem C balance. By comparing future simulations with constant and with increasing atmospheric CO2 , we estimated that the sensitivity of NPP was 4.8% per 100 ppmv, but NPP becomes less sensitive to CO2 increase throughout the 21st century. Overall, our analyses suggest that the decreasing CO2 sensitivity of NPP and the increasing sensitivity of heterotrophic respiration to air temperature, in addition to the increase in C loss from wildfires weakens the C sink from upland ecosystems of Alaska and will ultimately lead to a source of CO2 to the atmosphere beyond 2100. Therefore, we conclude that the increasing regional C sink we estimate for the 21st century will most likely be transitional.