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Traditional forms of acequia irrigation can be combined with ground based and remote sensing snow measurements and snowmelt runoff modeling to better estimate runoff volumes now and in the future under conditions of climate change. The experience gained over 400 years of irrigating small fields strongly binds communities and strengthens the resolve of acequia associations to contest challenges presented by climate change. Increased density of snow measurements in high elevations of the Rio Grande along with input of real-time data to snowmelt models has led to an improved potential for acequia decision making under the increased temperatures projected for the future by climate models. Acequia communities and similar Native American settlements have shown the willingness to share water during times of severe water shortages in the southwestern U.S. Acequia associations have shown the desire to adopt new forms of hydrologic data and modeling techniques and incorporate them into acequia association approaches. [ABSTRACT FROM AUTHOR]

Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery. [ABSTRACT FROM AUTHOR]

By the early 1900s, concerns were expressed by ranchers, academicians, and federal scientists that widespread overgrazing and invasion of native grassland by woody shrubs were having severe negative impacts upon normal grazing practices in Western America. Ranchers wanted to reverse these trends and continue their way of life and were willing to work with scientists to achieve these goals. One response to this desire was establishment of the USDA Jornada Experimental Range (783 km2) in south central New Mexico by a Presidential Executive Order in 1912 for conducting rangeland investigations. This cooperative effort involved experiments to understand principles of proper management and the processes causing the woody shrub invasion as well as to identify treatments to eradicate shrubs. By the late 1940s, it was apparent that combining the historical ground-based data accumulated at Jornada Experimental Range with rapidly expanding post World War II technologies would yield a better understanding of the driving processes in these arid and semiarid ecosystems which could then lead to improved rangeland management practices. One specific technology was the use of aerial photography to interpret landscape resource conditions. The assembly and utilization of long-term historical aerial photography data sets has occurred over the last half century. More recently, Global Positioning System (GPS) techniques have been used in a myriad of scientific endeavors including efforts to accurately locate historical and contemporary treatment plots and to track research animals including livestock and wildlife. As an incredible amount of both spatial and temporal data became available, Geographic Information Systems have been exploited to display various layers of data over the same locations. Subsequent analyses of these data layers have begun to yield new insights. The most recent technological development has been the deployment of Unmanned Aerial Vehicles (UAVs) that afford the opportunity to obtain high (5 cm) resolution data now required for rangeland monitoring. The Jornada team is now a leader in civil UAV applications in the USA. The scientific advances at the Jornada in fields such as remote sensing can be traced to the original Western America ranching culture that established the Jornada in 1912 and which persists as an important influence in shaping research directions today. [ABSTRACT FROM AUTHOR]

Rangelands cover about 50% of the earth's land surface, are in remote areas and have low population densities, all of which provide an ideal opportunity for remote sensing applications from unmanned aircraft systems (UAS). In this article, we describe a proven workflow for UAS-based remote sensing, and discuss geometric errors of image mosaics and classification accuracies at different levels of detail. We report on several UAS missions over rangelands in Idaho and New Mexico, USA, where we acquired 6-8 cm resolution aerial photography and concurrent field measurements. The geometric accuracies of the image mosaics were in the 1-2 m range, and overall classification accuracies for vegetation maps ranged from 78-92%. Despite current FAA regulations that restrict UAS operations to distances within line-of-sight of the UAS, our results show that UAS are a viable platform for obtaining very high-resolution remote sensing products for applied vegetation mapping of rangelands. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (uAvs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that uAvs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the uiiv mission, image processing and analysis, and accuracy assessment. [ABSTRACT FROM AUTHOR]

Imagery acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution, the spectral resolution is low when lightweight off-the-shelf digital cameras are used, and the inclusion of texture measures can potentially increase the classification accuracy. Texture measures have been used widely in pixel-based image analysis, but their use in an object-based environment has not been well documented. Our objectives were to determine the most suitable texture measures and the optimal image analysis scale for differentiating rangeland vegetation using UAV imagery segmented at multiple scales. A decision tree was used to determine the optimal texture features for each segmentation scale. Results indicated the following: 1) The error rate of the decision tree was lower; 2) prediction success was higher; 3) class separability was greater; and 4) overall accuracy was higher (high 90% range) at coarser segmentation scales. The inclusion of texture measures increased classification accuracies at nearly all segmentation scales, and entropy was the texture measure with the highest score in most decision trees. The results demonstrate that UAVs are viable platforms for rangeland monitoring and that the drawbacks of low-cost off-the-shelf digital cameras can be overcome by including texture measures and using object-based image analysis which is highly suitable for very high resolution imagery. [ABSTRACT FROM AUTHOR]

High resolution aerial photographs have important rangeland applications, such as monitoring vegetation change, developing grazing strategies, determining rangeland health, and assessing remediation treatment effectiveness. Acquisition of high resolution images by Unmanned Aerial Vehicles (UAVs) has certain advantages over piloted aircraft missions, including lower cost, improved safety, flexibility in mission planning, and closer proximity to the target. Different levels of remote sensing data can be combined to provide more comprehensive information: 15–30 m resolution imaging from space-borne sensors for determining uniform landscape units; < 1 m satellite or aircraft data to assess the pattern of ecological states in an area of interest; 5 cm UAV images to measure gap and patch sizes as well as percent bare soil and vegetation ground cover; and < 1 cm ground-based boom photography for ground truth or reference data. Two parallel tracks of investigation are necessary: one that emphasizes the utilization of the most technically advanced sensors for research, and a second that emphasizes the minimization of costs and the maximization of simplicity for monitoring purposes. We envision that in the future, resource management agencies, rangeland consultants, and private land managers should be able to use small, lightweight UAVs to satisfy their needs for acquiring improved data at a reasonable cost, and for making appropriate management decisions. [ABSTRACT FROM AUTHOR]

Aerial photographs are a type of remote sensing data that are especially valuable for rangeland applications. Advantages of these data include relative ease of interpretation and acquisition, affordability, high resolution (1-2 meters), and provision of a common reference for communication among those involved in rangeland management. Additionally, air photos are especially well suited for analysis of historical rangeland remediation treatments because acquisition of widespread aerial photographic coverage began during the 1930s. Several types of treatments can be easily identified and monitored over time, including contour terraces, brush water spreaders, rootplow seeding, water ponding dikes, shrub removal by grubbing, and grazing restrictions. The use of archived aerial photographs allows the opportunity to recreate the management history of rangeland, as well as to serve as a point of departure for involvement in more sophisticated satellite-based remote sensing systems. [ABSTRACT FROM AUTHOR]

Presents a study which used a linear semiempirical kernel-driven bidirectional reflectance distribution function model to examine the utility of the directional signal in community and cover type differentiation over semi-arid canopies. Description of a community type differentiation; Main challenges for monitoring semi-arid grasslands with remote sensing; Comparison between the abilities of the candidate data sets to identify the classes of interest.

The Jornada Experimental Range and the New Mexico State University Chihuahuan Desert Rangeland Research Center are fruitful areas to study the long-term effects of rangeland remediation treatments which started in the 1930s. A number of diverse manipulations were completed under the direction of federal agency and university scientists, and abundant remote sensing imagery is available to assist in relocating the treatments and evaluating their success. This is particularly important because few of the treatments were maintained following the loss of scientific personnel coinciding with the start of World War II, and most records of Civilian Conservation Corps scientific work were lost with the disbanding of the agency in 1942. Aerial photography, which was systematically used to image the United States beginning in the 1930s, can be used to identify types of treatments, measure areal coverage, estimate longevity, and help plan locations for new experiments. No long-lasting vegetation response could be determined for contour terraces, brush water spreaders, strips grubbed free of shrubs (despite the fact that these strips have remained visible for 65 years), and mechanical rootplowing and seeding. Distinct positive, long-term vegetation responses could be seen in aerial photos for water retention dikes, certain fenced exclosures, and some boundaries where different land management practices meet. It appears from both aerial photos and existing conventional records that experimental manipulation of rangelands has often been ineffective on the landscape scale because treatments are not performed over large enough contiguous areas and hydrological and ecological processes overwhelm the treatments. In addition, treatments are not maintained over time, treatment evaluation periods are sometimes too short, multi-purpose treatments are not used to maximize effects, and treatments are often not located in appropriate sites. [Copyright &y& Elsevier]

A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li–Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li–Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km2 area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r 2 =0.78, RMSE=0.013, N =3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r 2 =0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets. [Copyright &y& Elsevier]

Accurately mapping community types is one of the main challenges for monitoring arid and semi-arid grasslands with remote sensing. The multi-angle approach has been proven useful for mapping vegetation types in desert grassland. The Multi-angle Imaging Spectro-Radiometer (MISR) provides 4 spectral bands and 9 angular reflectance. In this study, 44 classification experiments have been implemented to find the optimal combination of MISR multi-angular data to mine the information carried by MISR data as effectively as possible. These experiments show the following findings: 1) The combination of MISR''s 4 spectral bands at nadir and red and near infrared bands in the C, B, and A cameras observing off-nadir can obtain the best vegetation type differentiation at the community level in New Mexico desert grasslands. 2) The k parameter at red band of Modified–Rahman–Pinty–Verstraete (MRPV) model and the structural scattering index (SSI) can bring useful additional information to land cover classification. The information carried by these two parameters, however, is less than that carried by surface anisotropy patterns described by the MRPV model and a linear semi-empirical kernel-driven bidirectional reflectance distribution function model, the RossThin–LiSparseMODIS (RTnLS) model. These experiments prove that: 1) multi-angular reflectance raise overall classification accuracy from 45. 8% for nadir-only reflectance to 60. 9%. 2) With surface anisotropy patterns derived from MRPV and RTnLS, an overall accuracy of 68. 1% can be obtained when maximum likelihood algorithms are used. 3) Support Vector Machine (SVM) algorithms can raise the classification accuracy to 76. 7%. This research shows that multi-angular reflectance, surface anisotropy patterns and SVM algorithms can improve desert vegetation type differentiation importantly. [Copyright &y& Elsevier]

Abstract: This work examines the application of a geometric-optical canopy reflectance model to provide measures of woody shrub abundance in desert grasslands at the landscape scale. The approach is through inversion of the non-linear simple geometric model (SGM) against 631 nm multi-angle reflectance data from the Compact High Resolution Imaging Spectrometer (CHRIS) flown on the European Space Agency''s Project for On-Board Autonomy (Proba) satellite. Separation of background and upper canopy contributions was effected by a linear scaling of the parameters of the Walthall bidirectional reflectance distribution function model with the weights of a kernel-driven model. The relationship was calibrated against a small number of sample locations with highly contrasting background/upper canopy configurations, before application over an area of about 25 km2. The results show that with some assumptions, the multi-angle remote sensing signal from CHRIS/Proba can be explained in terms of a combined soil–understory background response and woody shrub cover and exploited to map this important structural attribute of desert grasslands. [Copyright &y& Elsevier]

Knowledge of the surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces, there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation, the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TLR) observations, it is possible to estimate the spectral emissivity variation for these surfaces. We present data from the TIMS (Thermal Infrared Multispectral Scanner) instrument, which has six channels in the 8- to 12-µm region. T1MS is a prototype of the TIR portion of the ASTER (Advanced Spacebome Thermal Emission and Reflection radiometer) instrument on NASA's Terra (EOS-AM1) platform launched in December 1999. The Temperature Emissivity Separation (TES) algorithm, developed for use with ASTER data, is used to extract the temperature and six emissivities from the six channels of TIMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jomada Experimental Range in New Mexico. The Jomada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data presented here are from flights at a range of altitudes from 800 to 5000 m, yielding a pixel resolution from 3 to 12 m. The resulting spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity for the quartz rich soils of the site. The derived surface temperatures agree with ground measurements within the standard deviations of both sets of observations. The results for the 10.8- and 11.7-µm channels show limited variation of the emissivity values over the mesquite and grass sites indicating that split window approaches... [ABSTRACT FROM AUTHOR]