Albert Rango

Remote sensing in hydrology

Remote sensing provides a means of observing hydrological state variables over large areas. The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar. Methods for estimating the hydrometeorlogical fluxes, evapotranspiration and snowmelt runoff, using these state variables are also described. Published by Elsevier Science Ltd.

Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery

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.

A simple energy budget algorithm for the snowmelt runoff model

The snowmelt runoff model (SRM) uses a degree-day approach for melting snow in a basin. A simple radiation component was combined with the degree-day approach (restricted degree-day method) in an effort to improve estimates of snowmelt and reduce the need to adjust the melt factor over the ablation season. A daily energy balance model was formulated that requires not only the input of radiation but also measurements of daily wind speed, air temperature, and relative humidity. The three approaches for computing snowmelt, namely, the degree-day, restricted degree-day, and daily energy balance model were tested at the local scale by comparing melt rates with lysimeter outflow measurements. Because radiation measurements are not often available, a simple model for simulating shortwave and longwave components of the radiation balance that requires minimal information (i.e., daily cloud cover estimates, air temperature, and relative humidity) was developed. It was found that clouds and their effects on daily insolation at the surface can produce significant differences between measured and model estimates. In the comparisons of snowmelt estimates with the lysimeter outflow, the restricted degree-day method yielded melt rates that were in better agreement with the observed outflow than the degree-day method and were practically the same as estimates given by the energy balance model. A sensitivity analysis of runoff generated with SRM using as input the local snowmelt computations given by the three models and measured outflow from the lysimeter was performed for a basin. A comparison of the synthetic hydrographs for the basin suggests that a radiation-based snowmelt factor may improve runoff predictions at the basin scale.

Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management

Rangeland comprises as much as 70% of the Earths land surface area. Much of this vast space is in very remote areas that are expensive and often impossible to access on the ground. Unmanned Aerial Vehicles (UAVs) have great potential for rangeland management. UAVs have several advantages over satellites and piloted aircraft: they can be deployed quickly and repeatedly; they are less costly and safer than piloted aircraft; they are flexible in terms of flying height and timing of missions; and they can obtain imagery at sub-decimeter resolution. This hyperspatial imagery allows for quantification of plant cover, composition, and structure at multiple spatial scales. Our experiments have shown that this capability, from an off-the-shelf mini-UAV, is directly applicable to operational agency needs for measuring and monitoring. For use by operational agencies to carry out their mandated responsibilities, various requirements must be met: an affordable and reliable platform; a capability for autonomous, low altitude flights; takeoff and landing in small areas surrounded by rugged terrain; and an easily applied data analysis methodology. A number of image processing and orthorectification challenges have been or are currently being addressed, but the potential to depict the land surface commensurate with field data perspectives across broader spatial extents is unrivaled.

Parameter values for snowmelt runoff modelling

Parameters which appear frequently in snowmelt runoff models are analyzed with the aim of facilitating their evaluation. Results of runoff computations by the Snowmelt Runoff Model (SRM) carried out at various institutes, universities and agencies on 24 basins ranging in size from 0.77 to 4000 km2 and in elevation from 171 to 6000 m a.s.l. from 11 countries are reviewed. Based on this review, the physically and hydrologically understandable range of parameter values is assessed for the degree-day factor, runoff coefficient, temperature lapse rate, critical temperature (rain-snow), time lag, and recession coefficient. Consideration of SRM parameter values in these past applications may prove valuable for SRM applications on other basins and for initial selection of related parameter values in other snowmelt runoff models.

Acquisition, Orthorectification, and Object-based Classification of Unmanned Aerial Vehicle (UAV) Imagery for Rangeland Monitoring

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 UAV mission, image processing and analysis, and accuracy assessment.

Snow water equivalent estimation by microwave radiometry

Abstract Snow water equivalent (SWE) is one of the most important parameters for accurate prediction of snowmelt runoff. Conventionally, SWE is monitored using observations made at widely scattered points in or around specific watersheds. Remote sensors, which provide data with better spatial and temporal coverage, can be used to improve the SWE estimates. Microwave radiation, which can penetrate through a snowpack, may be used to infer the SWE. Calculations made from a microscopic scattering model are used to simulate the effect of varying SWE on the microwave brightness temperature. Data obtained from truck mounted, airborne and spaceborne systems from various tests sites have been studied. The simulated SWE compares favorably with the measured SWE for dry snowpacks. In addition, whether or not the underlying soil is frozen or thawed may be discriminated using the polarization information obtained by spaceborne sensors.

INCORPORATING RADIATION INPUTS INTO THE SNOWMELT RUNOFF MODEL

Process-based, distributed-area snowmelt runoff models operating at small scales are essential to understand subtle effects of climate change, but require data not commonly available. Temperature index models operating over large areas provide realistic simulations of basin runoff with operationally available data, but lack rigorous physically based algorithms. A compromise between the two types of models is required to provide realistic evaluations of basin response to environmental changes in cold regions. One adaptation that is uniformly required for snowmelt models is the use of remotely sensed data, either as input or in model validation. At a minimum, snowmelt forecasting models need to incorporate snowcover extent information, which is currently obtained operationally. As more remote sensing capabilities come on line, models should accept upgraded information on snow water equivalent; additional remotely sensed information on landcover, frozen soil, soil moisture, cloudiness and albedo would also be useful. Adaptations to the semi-distributed snowmelt runoff model (SRM) are underway to make it more physically based for use in large area studies. A net radiation index has been added to the model, which formerly used only a temperature (degree-day) index to melt snow from a basins elevation zones. The addition of radiation to the SRM allows the basin to be subdivided into hydrological response units by general aspect (orientation) as well as elevation. Testing of the new radiation-based SRM with measured radiation from a small research basin is the first step towards large scale simulations. Results from the W-3 research basin in Vermont, USA are promising. In the radiation version, the factor that multiplies the degree-day index is estimated independently of model output and is held constant throughout the season, in contrast with the degree-day version, where the corresponding factor is allowed to increase throughout the season. Without calibrating or optimizing on this important parameter, the goodness-of-fit measure R 2 is improved in two out of six test years when the radiation version of the SRM is used in place of the degree-day version in melt season simulations. When the accumulation of error is eliminated with periodic updating of streamflow, more significant improvement is noted with radiation included.

Temperature and emissivity separation from multispectral thermal infrared observations

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 (TIR) 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. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASAs 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 Jornada Experimental Range in New Mexico. The Jornada 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 may be possible for conditions like these.

RESEARCH ARTICLE: Using Unmanned Aerial Vehicles for Rangelands: Current Applications and Future Potentials

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.