Matthew B. Rigge

Spatial variability and landscape controls of near‐surface permafrost within the Alaskan Yukon River Basin

The distribution of permafrost is important to understand because of permafrosts influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earths surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.

Linking Phenology and Biomass Productivity in South Dakota Mixed-Grass Prairie

Abstract Assessing the health of rangeland ecosystems based solely on annual biomass production does not fully describe the condition of the plant community; the phenology of production can provide inferences about species composition, successional stage, and grazing impacts. We evaluated the productivity and phenology of western South Dakota mixed-grass prairie in the period from 2000 to 2008 using the normalized difference vegetation index (NDVI). The NDVI is based on 250-m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Growing-season NDVI images were integrated weekly to produce time-integrated NDVI (TIN), a proxy of total annual biomass production, and integrated seasonally to represent annual production by cool- and warm-season species (C3 and C4, respectively). Additionally, a variety of phenological indicators including cool-season percentage of TIN were derived from the seasonal profiles of NDVI. Cool-season percentage and TIN were combined to generate vegetation classes, which served as proxies of the conditions of plant communities. TIN decreased with precipitation from east to west across the study area. However, the cool-season percentage increased from east to west, following patterns related to the reliability (interannual coefficient of variation [CV]) and quantity of midsummer precipitation. Cool-season TIN averaged 76.8% of the total TIN. Seasonal accumulation of TIN corresponded closely (R2 > 0.90) to that of gross photosynthesis data from a carbon flux tower. Field-collected biomass and community composition data were strongly related to TIN and cool-season percentage. The patterns of vegetation classes were responsive to topographic, edaphic, and land management influences on plant communities. Accurate maps of biomass production, cool- and warm-season composition, and vegetation classes can improve the efficiency of land management by facilitating the adjustment of stocking rates and season of use to maximize rangeland productivity and achieve conservation objectives. Further, our results clarify the spatial and temporal dynamics of phenology and TIN in mixed-grass prairie.

Monitoring the status of forests and rangelands in the Western United States using ecosystem performance anomalies

The effects of land management and disturbance on ecosystem performance (i.e. biomass production) are often confounded by those of weather and site potential. The current study overcomes this issue by calculating the difference between actual and expected ecosystem performance (EEP) to generate ecosystem performance anomalies (EPA). This study aims to delineate and quantify average EPA from 2000–2009 within the Greater Platte and Upper Colorado River Basins, USA. Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images averaged over the growing season (GSN) served as a proxy of actual ecosystem performance. Yearly EEP was determined with rule-based piecewise regression tree models of abiotic data (climate, soils, elevation, etc.), independently created for each land cover. EPA were calculated as the residuals of the EEP to GSN relationship, and characterized as normal performing, underperforming, and overperforming at the 90% confidence level. Validation revealed that EPA values were related to biomass production (R 2 = 0.56, P = 0.02) and likely to the proportion of biomass removed by livestock in the Nebraska Sandhills. Overall, 60.6% of the study area was (normal) performing near its EEP, 3.0% was severely underperforming, 5.0% was highly overperforming, and the remainder was slightly underperforming or overperforming. Generally, disturbances such as fires, floods, and insect damage, in addition to high grazing intensity, result in a negative EPA. Conversely, mature stands and appropriate management often result in positive EPA values. This method provides information critical to land managers to evaluate the appropriateness of previous management practices and restoration efforts and quantify disturbance impacts. Results are at a scale sufficient for many of the large management units of the region and for locating areas needing further investigation. Applications of EPA data to monitoring invasive species, grazing impacts, and vulnerability to plant community shifts have been suggested by land management professionals.

Effects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest

A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000–2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30–40 years following fire, and deciduous stands generally have higher EPA than coniferous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

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 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

Detecting the Influence of Best Management Practices on Vegetation Near Ephemeral Streams With Landsat Data

Abstract Various best management practices (BMPs) have been implemented on rangelands with the goals of controlling nonpoint source pollution, reducing the impact of livestock in ecologically important riparian areas, and improving grazing distribution. Providing off-stream water sources to livestock in pastures, cross-fencing, and rotational grazing are common rangeland BMPs that have demonstrated success in drawing livestock grazing pressure away from streams. We evaluated the effects of rangeland BMP implementation with six commercial-scale pastures in the northern mixed-grass prairie. Four pastures received a BMP suite consisting of off-stream water, cross-fencing, and deferred-rotation grazing, and two pastures did not receive BMPs. We hypothesized that the BMPs increased the quantity of riparian vegetation cover relative to the conditions in these pastures during the pre-BMP period and to the two pastures that did not receive BMPs. We used a series of 30-m Landsat normalized difference vegetation index (NDVI) images to track the spatial and temporal changes (1984–2010, n = 24) in vegetation cover, to which NDVI has been well correlated. Validation indicated that the remotely sensed signal from in-channel vegetation was representative of ground conditions. The BMP suite was associated with a 15% increase in the in-channel NDVI (0–30 m from stream centerline) and 18% increase in the riparian NDVI (30–180 m from stream center line). Conversely, the in-channel and riparian NDVI of non-BMP pastures declined 30% and 18% over the study period. The majority of change occurred within 2 yr of BMP implementation. The patterns of in-channel NDVI among pastures suggested that BMP implementation likely altered grazing distribution by decreasing the preferential use of riparian and in-channel areas. We demonstrated that satellite imagery time series are useful in retrospectively evaluating the efficacy of conservation practices, providing critical information to guide adaptive management and decision makers.