Caiti Steele

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.

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.

A cross-comparison of field, spectral, and lidar estimates of forest canopy cover

A common challenge when comparing forest canopy cover and similar metrics across different ecosystems is that there are many field- and landscape-level measurement methods. This research conducts a cross-comparison and evaluation of forest canopy cover metrics produced using unmixing of reflective spectral satellite data, light detection and ranging (lidar) data, and data collected in the field with spherical densiometers. The coincident data were collected across a ~25 000 ha mixed conifer forest in northern Idaho. The primary objective is to evaluate whether the spectral and lidar canopy cover metrics are each statistically equivalent to the field-based metrics. The secondary objective is to evaluate whether the lidar data can elucidate the sources of error observed in the spectral-based canopy cover metrics. The statistical equivalence tests indicate that spectral and field data are not equivalent (slope region of equivalence = 43%). In contrast, the lidar and field data are within the acceptable error margin of most forest inventory assessments (slope region of equivalence = 13%). The results also show that in plots where the mean lidar plot heights are near zero, each of modeled remotely sensed estimates continues to report canopy cover >21% for lidar and >30% for all investigated spectral methods using near-infrared bands. This suggests these metrics are sensitive to the presence of herbaceous vegetation, shrubs, seedlings, saplings, and other subcanopy vegetation.

Production of vegetation spatial-structure maps by per-object analysis of juniper encroachment in multitemporal aerial photographs

The remote sensing of vegetation, which has predominantly applied methods that analyze each image pixel as independent observations, has recently seen the development of several methods that identify groups of pixels that share similar spectral or structural properties as objects. The outputs of “per-object” rather than “per-pixel” methods represent characteristics of vegetation objects, such as location, size, and volume, in a spatially explicit manner. Before decisions can be influenced by data products derived from per-object remote sensing methods, it is first necessary to adopt methodologies that can quantify the spatial and temporal trends in vegetation structure in a quantitative manner. In this study, we present one such methodological framework where (i) marked point patterns of vegetation structure are produced from two per-object methods, (ii) new spatial-structural data layers are developed via moving-window statistics applied to the point patterns, (iii) the layers are differenced to highlight spatial-structural change over a 60 year period, and (iv) the resulting difference layers are evaluated within an ecological context to describe landscape-scale changes in vegetation structure. Results show that this framework potentially provides information on the population, growth, size association (nonspatial distribution of large and small objects), and dispersion. We present an objective methodological comparison of two common per-object approaches, namely image segmentation and classification using Definiens software and two-dimensional wavelet transformations.

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Remote-sensing techniques can detect and up-scale leaf-level physiological responses to large areas, and provide significant and reliable information on water use and irrigation management. The objectives of this study were to screen leaf-level physiological changes that occur during the cyclic irrigation of pecan orchards to determine which responses best represent changes in moisture status of plants and link plant physiological changes to remotely sensed surface reflectance data derived from the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (ETM+). The study was conducted simultaneously on two southern New Mexico mature pecan orchards. For both orchards, plant physiological responses and remotely sensed surface reflectance data were collected from trees that were either well watered or in water deficit. Remotely sensed variables included reflectance in band 1, the ratio between shortwave infrared (SWIR) bands (B5:B7), the normalized difference vegetation index, and SWIR moisture indices. Midday stem water potential (Ψsmd) was the best performing leaf-level physiological response variable for detecting moisture status in pecans. The B5:B7 ratio positively and significantly correlated with Ψsmd in five of six irrigation cycles while multiple linear regression weighted with six remotely sensed surface reflectance variables revealed a significant relationship with moisture status in all cycles in both orchards (R2 > 0.73). Because changes in the B5:B7 band ratio and multiple regression of spectral variables correlate with the moisture status of pecan orchards, we conclude that remotely sensed data hold promise for detecting the moisture status of pecans.

Rangeland resource assessment, monitoring, and management using Unmanned Aerial Vehicle-based remote sensing

Civilian applications of Unmanned Aerial Vehicles (UAV) have rapidly been expanding recently. Thanks to military development many civil UAVs come via the defense sector. Although numerous UAVs can perform civilian tasks, the regulations imposed by FAA in the national airspace system and military equivalent agencies in restricted airspace need to be closely considered and followed in order to make progress in civilian applications. Personnel at the Jornada Experimental Range have developed approaches to abide by FAA and military regulations. Because of this, the enormous potential of UAVs for rangeland assessment, monitoring, and management is starting to be realized.

Sensitivity of the snowmelt runoff model to underestimates of remotely sensed snow covered area

Three methods for estimating snow covered area (SCA) from Terra MODIS data were used to derive conventional depletion curves for input to the Snowmelt Runoff Model (SRM). We compared the MOD10 binary and fractional snow cover products and a method for estimating sub-pixel snow cover using spectral mixture analysis (SMA). All three methods underestimated SCA and this contributed to underestimates in runoff modeled by SRM. The closest relationship between measured and computed runoff was achieved when SRM was run with conventional depletion curves derived from the MODIS binary snow cover product (R2 = 0.91). Although the MODIS fractional snow cover product and SMA did not perform as well as the binary snow cover product (R2 = 0.70 and R2 = 0.72 respectively) we anticipate that either of these methods may be reworked to better account for forest cover in our study area and so improve SCA estimates.

Diverse landscapes, diverse risks: synthesis of the special issue on climate change and adaptive capacity in a hotter, drier Southwestern United States

Assessing regional-scale vulnerability of agricultural systems to climate change and variability is vital in securing food and fiber systems, as well as sustaining rural livelihoods. Farmers, ranchers, and forest landowners rely on science-based, decision-relevant, and localized information to maintain production, ecological viability, and economic returns. This paper synthesizes the collection of research on the future of agricultural production in the Southwestern United States. A variety of assessment methods indicate the diverse impacts and risks across the Southwest, often related to water availability, which drives adaptive measures in this region. Sector- or species-specific adaptive measures have long been practiced in this region and will continue to be necessary to support agricultural production as a regional enterprise. Diversification of crop selection and income source imparts climate resilience. Building upon biophysical vulnerability through incorporating social and economic factors is critical to future adaptation planning efforts. The persistence and adaptive capacity of agriculture in the water-limited Southwest serves as an instructive example for producers outside the region expecting drier and warmer conditions and may offer solutions to reduce future climate impacts.