Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).     

利用数码相片对冬小麦生物量的试算

数码相片属于地面遥感资料的一种,被广泛应用于实地调查中.然而,目前利用数码相片对植被生物量进行推算的研究很少.利用数码相片提取小麦生长期的小麦覆盖度,结合地面实测叶面积指数(LAI)、归一化植被指数(NDVI)和小麦生物量,分析小麦覆盖度与LAI,NDVI,生物量与小麦覆盖度、LAI,NDVI各自的相关系数,建立各自合适的回归模型推算冬小麦的生物量.结果表明,在小麦生长期,小麦覆盖度与LAI,NDVI的相关系数R2分别达到0.765,0.896,生物量与小麦覆盖度、LAI,NDVI的相关系数R2分别为0.774,0.876,0.712,生物量与其小麦覆盖度、LAI,NDVI之间具有很高的相关性,多元回归分析效果更好,系数R2达0.891.结果说明,在不破坏植被生长状态的情况下,基于数码相片可有效地推算小麦的生物量,这为大面积实地测量和遥感监测作物长势及其生物量估算提供了理论参考依据.     

Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350- to 1000-nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary.     

EVAPOTRANSPIRATION DYNAMICS AT AN ECOHYDROLOGICAL RESTORATION SITE: AN ENERGY BALANCE AND REMOTE SENSING APPROACH1

ABSTRACT: Little work is reported where spatial methods are employed to monitor evapotranspiration (ET) changes as a result of vegetation and wetland restoration. A remote sensing approach with the Surface Energy Balance Algorithm for Land (SEBAL) for estimating ET at The Nature Conservancy's Glacial Ridge prairie‐wetland restoration site in northwestern Minnesota is presented. The calibrated 24‐hour ET from SEBAL was estimated with an average error of prediction of ?4.3 percent. Monthly, interseasonal, and seasonal ET for the period of June to September (2000 to 2003) from three adjacent land‐uses: a hydrologic control preserved wetland; a treated or restored site; and a nontreated or impacted wetland, were used in the study. Results from comparing ET behavior to the preserve suggest restoration efforts have affected monthly and seasonal ET within the treated site. Spatial average standard deviations of the seasonal ET within the preserve, treated, and nontreated sites give 47.3, 75.7, and 109.9 mm, respectively, suggesting hydrologic stabilization within the treated site. Monthly and interseasonal comparisons show similar behavior to that of the seasonal data, where monthly correlations suggest increasing agreement within the treated site, approaching those within the preserve.     

Mixed-Grass Prairie Canopy Structure and Spectral Reflectance Vary with Topographic Position

Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of canopy height in autumn to ensure conservation of grassland structure for wildlife and forage for livestock. However, more comprehensive assessment of vegetation structure could be achieved for mixed-grass prairie by integrating field survey, topographic position (summit, mid and toeslope) and spectral reflectance data. Thus, we examined the variation of mixed-grass prairie structural attributes (canopy leaf area, standing crop mass, canopy height, nitrogen, and water content) and spectral vegetation indices (VIs) with variation in topographic position at the Grand River National Grassland (GRNG), South Dakota. We conducted the study on a 36,000-ha herbaceous area within the GRNG, where randomly selected plots (1?km² in size) were geolocated and included summit, mid and toeslope positions. We tested for effects of topographic position on measured vegetation attributes and VIs calculated from Landsat TM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data collected in July 2010. Leaf area, standing crop mass, canopy height, nitrogen, and water content were lower at summits than at toeslopes. The simple ratio of Landsat Band 7/Band 1 (SR71) was the VI most highly correlated with canopy standing crop and height at plot and landscape scales. Results suggest field and remote sensing-based grassland assessment techniques could more comprehensively target low structure areas at minimal expense by layering modeled imagery over a landscape stratified into topographic position groups.     

Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.     

Use of Radar Remote Sensing (RADARSAT) to Map Winter Wetland Habitat for Shorebirds in an Agricultural Landscape

Many of todays agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while class 3 coverage decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (30% coverage) in December and January, decreasing in March by 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. Also, Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon 97331, USA     

Use of Radar Remote Sensing (RADARSAT) to Map Winter Wetland Habitat for Shorebirds in an Agricultural Landscape

Many of todays agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. Also, Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon 97331, USA     

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.     

LAI inversion algorithm based on directional reflectance kernels

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.     

ABATEMENT OF GROUND WATER PHOSPHATE IN GIANT CANE AND FOREST RIPARIAN BUFFERS1

ABSTRACT: Forest and grass riparian buffers have been shown to be effective best management practices for controlling nonpoint source pollution. However, little research has been conducted on giant cane [Arundinaria gigantea (Walt. Muhl.)], a formerly common bamboo species, native to the lower midwestern and southeastern United States, and its ability to reduce nutrient loads to streams. From May 2002 through May 2003, orthophosphate or dissolved reactive phosphate (DRP) concentrations in ground water were measured at successive distances from the field edge through 12 m of riparian buffers of both giant cane and mixed hardwood forest along three streams draining agricultural land in the Cache River watershed in southern Illinois. Giant cane and mixed hardwood forest did not differ in their DRP sequestration abilities. Ground water DRP concentrations were significantly reduced (14 percent) in the first 1.5 m of the buffers, and there was an overall 28 percent reduction in DRP concentration by 12 m from the field edge. The relatively low DRP reductions compared to other studies could be attributed to high DRP input levels, narrow (12 m) buffer lengths, and/or mature (28 to 48 year old) riparian vegetation.     

EVAPOTRANSPIRATION MEASUREMENTS AND MODELING FOR THREE WETLAND SYSTEMS IN SOUTH FLORIDA1

ABSTRACT: At the Everglades Nutrient Removal project in south Florida, three lysimeters were installed to measure daily evapotranspiration (ET) rates from cattails (Typha domingensis), mixed marsh vegetation, and an open water/algae system. The cattail lysimeter began operation in February 1993. The mixed marsh vegetation lysimeter began operation in January 1994, and the open water lysimeter with occasional algae cover began operation in December 1993. The mean measured ET rate was 3.6 mm, 3.5 mm, and 3.7 mm per day for the cattail, mixed marsh vegetation, and open water/algae system, respectively. High resolution weather data were continuously measured at the site. Six models were applied to estimate daily ET rates of the three systems. The Penman-Monteith equation best estimated ET of cattail and mixed marsh vegetation, and the Penman Combination equation was most suitable for the open water/algae system. Empirical equations based on solar radiation and maximum temperature produced estimates of daily ET from the three systems that are comparable to models that require many more parameters. In cases where limited data is available, the calibrated simple models can be used to estimate ET from wetlands in south Florida.     

Estimating Evapotranspiration for Dryland Cropping Systems in the Semiarid Texas High Plains Using SWAT

The Soil and Water Assessment Tool (SWAT) is one of the most widely used watershed models for simulating hydrology in response to agricultural management practices. However, limited studies have been performed to evaluate the SWAT model's ability to estimate daily and monthly evapotranspiration (ET) in semiarid regions. ET values were simulated using ArcSWAT 2012 for a lysimeter field managed under dryland conditions at the USDA‐ARS Conservation and Production Research Laboratory at Bushland, Texas, and compared with measured lysimeter values from 2000 to 2010. Two scenarios were performed to compare SWAT's performance: (1) use of default plant leaf area index (LAI) values in the embedded plant database and (2) adjusted LAI values. Scenario 1 resulted in an “unsatisfactory” Nash‐Sutcliffe efficiency (NSE) of 0.42 and 0.38 for the calibration and validation periods, respectively. Scenario 2 resulted in a “satisfactory” NSE value for the calibration period while achieving a “good” NSE of 0.70 for the validation period. SWAT generally underestimated ET at both the daily and monthly levels. Overestimation during fallow years may be due to the limitations of the pothole function used to simulate furrow diking. Users should be aware of potential errors associated with using default LAI parameters. Inaccuracies in ET estimation may also stem from errors in the plant stress functions, particularly when evaluating water management practices for dryland watersheds.     

INFLUENCE OF REMOTE SENSING IMAGERY SOURCE ON QUANTIFICATION OF RIPARIAN LAND COVER/LAND USE1

ABSTRACT: This paper compares approaches to quantifying land cover/land use (LCLU) in riparian corridors of 23 watersheds in Oregon's Willamette Valley using color infrared aerial photography (AP) and Thematic Mapper (TM) imagery. For each imagery source, LCLU adjacent to stream networks were quantified across a range of lateral and longitudinal scales. Single‐date AP data had higher LCLU class accuracies than the multi‐date TM data, except for row crops. Correlations among LCLU classes for the two imagery sources increased with increased spatial extent. In general, LCLU proportions for AP and TM differed, but lateral/longitudinal patterns were similar. An aggregated vegetation class comprised of forest, shrub/scrub, and grass/forb was strongly associated with a fish index of biotic integrity (IBI) for both AP and TM data, although AP correlations were higher. Highest fish IBI correlations for both data sources were with the aggregated vegetation class close to the stream and for the longest longitudinal scales. The row crop class was strongly associated with stream nitrate for both data sources, although correlations for the TM data were higher. Stream nitrate correlations were strongest for the widest lateral and longest longitudinal scales. Overall, both single‐date AP and multi‐date TM imagery appear to have potential for use in estimating indicators of stream ecological condition.     

Winter Soil Respiration from Different Vegetation Patches in the Yellow River Delta, China

Vegetation type and density exhibited a considerable patchy distribution at very local scales in the Yellow River Delta, due to the spatial variation of soil salinity and water scarcity. We proposed that soil respiration is affected by the spatial variations in vegetation type and soil chemical properties and tested this hypothesis in three different vegetation patches (Phragmites australis, Suaeda heteroptera and bare soil) in winter (from November 2010 to April 2011). At diurnal scale, soil respiration all displayed single-peak curves and asymmetric patterns in the three vegetation patches; At seasonal scale, soil respiration all declined steadily until February, and then increased to a peak in next April. But, the magnitude of soil respiration showed significant differences among the three sites. Mean soil respiration rates in winter were 0.60, 0.45 and 0.17 μmol CO(2) m(-2) s(-1) for the Phragmites australis, Suaeda heteroptera and bare soil, respectively. The combined effect of soil temperature and soil moisture accounted for 58-68 % of the seasonal variation of winter soil respiration. The mean soil respiration revealed positive and linear correlations with total N, total N and SOC storages at 0-20 cm depth, and plant biomass among the three sites. We conclude that the patchy distribution of plant biomass and soil chemical properties (total C, total N and SOC) may affect decomposition rate of soil organic matter in winter, thereby leading to spatial variations in soil respiration.     

Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing

The terrestrial carbon cycle is one of the foci in global climate change research. Simulating net primary productivity (NPP) of terrestrial ecosystems is important for carbon cycle research. In this study, China's terrestrial NPP was simulated using the Boreal Ecosystem Productivity Simulator (BEPS), a carbon-water coupled process model based on remote sensing inputs. For these purposes, a national-wide database (including leaf area index, land cover, meteorology, vegetation and soil) at a 1 km resolution and a validation database were established. Using these databases and BEPS, daily maps of NPP for the entire China's landmass in 2001 were produced, and gross primary productivity (GPP) and autotrophic respiration (RA) were estimated. Using the simulated results, we explore temporal-spatial patterns of China's terrestrial NPP and the mechanisms of its responses to various environmental factors. The total NPP and mean NPP of China's landmass were 2.235 GtC and 235.2 gCm(-2)yr(-1), respectively; the total GPP and mean GPP were 4.418 GtC and 465 gCm(-2)yr(-1); and the total RA and mean RA were 2.227 GtC and 234 gCm(-2)yr(-1), respectively. On average, NPP was 50.6% of GPP. In addition, statistical analysis of NPP of different land cover types was conducted, and spatiotemporal patterns of NPP were investigated. The response of NPP to changes in some key factors such as LAI, precipitation, temperature, solar radiation, VPD and AWC are evaluated and discussed.     

Visitor Assessment of the Mandatory Alternative Transportation System at Zion National Park

Transportation infrastructure in national parks has historically been designed for the automobile. With more vehicles in the parks, visitors found themselves in circumstances more reminiscent of a city than a park. Traffic jams, overcrowding, illegal parking, horn honking, and idling vehicles became common, creating stress and contributing to air and noise pollution, the very things visitors were hoping to get away from. Park managers began searching for alternatives, including shuttle systems. Many national parks have implemented optional shuttle systems, but relatively few have completely closed roads to vehicles, transporting visitors on mandatory shuttles. Zion National Park instituted a mandatory shuttle system in May 2000 to relieve crowding and congestion in the main canyon and to protect natural resources. Taking a longitudinal approach, attributes of the shuttle (e.g., crowding, accessibility, freedom, efficiency, preference, and success) were assessed with experiential park factors (e.g., scenic beauty, naturalness, solitude, tranquility, air quality, and soundscape) in 2000, 2003, and 2010 by surveying shuttle-riding park visitors. While visitors initially reported a few reservations about the shuttle system, by 2003, the majority rated the system successful. Ratings of all shuttle-related variables, except crowding, improved over the decade. Improvements were greatest for freedom, accessibility, and efficiency. Multiple regression found overall shuttle success to be mediated by preference, freedom, accessibility, efficiency, and comfort. Experiential variables assessing park conditions followed a similar pattern, with improved ratings as the decade progressed. Results provide important insights into the visitor experience with mandatory alternative shuttle systems in national parks.     

Development and Application of an Agricultural Intensity Index to Invertebrate and Algal Metrics from Streams at Two Scales

Research was conducted at 28‐30 sites within eight study areas across the United States along a gradient of nutrient enrichment/agricultural land use between 2003 and 2007. Objectives were to test the application of an agricultural intensity index (AG‐Index) and compare among various invertebrate and algal metrics to determine indicators of nutrient enrichment nationally and within three regions. The agricultural index was based on total nitrogen and phosphorus input to the watershed, percent watershed agriculture, and percent riparian agriculture. Among data sources, agriculture within riparian zone showed significant differences among values generated from remote sensing or from higher resolution orthophotography; median values dropped significantly when estimated by orthophotography. Percent agriculture in the watershed consistently had lower correlations to invertebrate and algal metrics than the developed AG‐Index across all regions. Percent agriculture showed fewer pairwise comparisons that were significant than the same comparisons using the AG‐Index. Highest correlations to the AG‐Index regionally were ?0.75 for Ephemeroptera, Plecoptera, and Trichoptera richness (EPTR) and ?0.70 for algae Observed/Expected (O/E), nationally the highest was ?0.43 for EPTR vs. total nitrogen and ?0.62 for algae O/E vs. AG‐Index. Results suggest that analysis of metrics at national scale can often detect large differences in disturbance, but more detail and specificity is obtained by analyzing data at regional scales.     

The effects of military tank traffic on prairie: A management model

The effect of various frequencies and seasons of military tank traffic on native mixed-grass prairie was examined in a randomized and replicated field experiment. Vegetation (in 10×10 m plots) was subjected to tank traffic at the following rates: (a) one pass per day of training from May until August; (b) one pass per day in May and June; (c) one pass per day in July and August; (d) one pass every three weeks from May until August; (e) zero (control). Species composition and the amount of bare ground were found to vary significantly with traffic frequency. Plant species alien to North America invaded plots subjected to spring driving. Regression analysis showed spring driving to produce more bare ground than summer driving. The regression models suggested that much higher intensities of training could be conducted without damage if spring driving were avoided. Regression models were also used to estimate the frequency of traffic associated with a significant change in species composition, where species composition was expressed as the ratio ofBouteloua gracilis toStipa spartea, an indicator of disturbance-induced change in prairie vegetation. This relationship predicted the capacity of the vegetation of a training area of any given width to support tank traffic without changing species composition. The predictive ability of the model was tested by comparing predicted traffic capacities with the amount of traffic actually applied to two training areas in 1986. Where traffic capacity was exceeded, the model successfully predicted a significantly higher frequency of bare ground and ratio ofBouteloua gracilis toStipa spartea.     

Measuring Plant Diversity in the Tall Threetip Sagebrush Steppe: Influence of Previous Grazing Management Practices

In July 2000, a 490-ha wildfire burned a portion of a long-term grazing study that had been established in 1924 at the US Sheep Experiment Station north of Dubois, Idaho, USA. Earlier vegetation measurements in this tall threetip sagebrush (Artemisia tripartita spp. tripartita) bunchgrass plant community documented significant changes in vegetation due to grazing and the timing of grazing by sheep. A study was initiated in May 2001 using 12 multiscale modified Whittaker plots to determine the consequences of previous grazing practices on postfire vegetation composition. Because there was only one wildfire and it did not burn all of the original plots, the treatments are not replicated in time or space. We reduce the potential effects of psuedoreplication by confining our discussion to the sample area only. There were a total of 84 species in the sampled areas with 69 in the spring-grazed area and 70 each in the fall- and ungrazed areas. Vegetation within plots was equally rich and even with similar numbers of abundant species. The spring-grazed plots, however, had half as much plant cover as the fall- and ungrazed plots and the spring-grazed plots had the largest proportion of plant cover composed of introduced (27%) and annual (34%) plants. The fall-grazed plots had the highest proportion of native perennial grasses (43%) and the lowest proportion of native annual forbs (1%). The ungrazed plots had the lowest proportion of introduced plants (4%) and the highest proportion of native perennial forbs (66%). The vegetation of spring-grazed plots is in a degraded condition for the environment and further degradation may continue, with or without continued grazing or some other disturbance. If ecosystem condition was based solely on plant diversity and only a count of species numbers was used to determine plant diversity, this research would have falsely concluded that grazing and timing of grazing did not impact the condition of the ecosystem.