Lloyd P. Queen

Deriving Fuel Mass by Size Class in Douglas-fir (Pseudotsuga menziesii) Using Terrestrial Laser Scanning

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r 2 = 0.898; SE 22.7%) and branchwood (r 2 = 0.937; SE 28.9%), as well as for total mass (r 2 = 0.940; SE 25.5%). Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d 2 . Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing.

Estimating near-surface air temperature with NOAA AVHRR

A technique is presented for producing estimates of near-surface air temperature (Ta) in complex terrain based on biweekly composite data from the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR). Results are tested against independently derived DAYMET gridded meteorological data. The model utilizes radiant surface temperature (Ts) extrapolations as a surrogate for Ta, and digital terrain information is used to adjust temperatures as a function of environmental adiabatic lapse rates. The Earth–Sun–sensor geometry information is used to constrain air temperature estimates in three different implementations. Increasing the complexity of geometric constraints on the model resulted in increased accuracies of the predictions, and yet caused a decrease in the number of days Ta estimates could be made. When comparing DAYMET surfaces to the satellite estimates of Ta, correlation coefficients (R) as high as 0.742 and standard errors of the estimate of 2.73 were observed in the final model implementation. The logic presented here serves as a useful technique for relatively simple derivation of near-surface air temperatures in a variety of remote sensing applications.

Using Laser Altimetry-based Segmentation to Refine Automated Tree Identification in Managed Forests of the Black Hills, South Dakota

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy height model (CHM) smoothing decisions and crown width estimation for each stand condition ranging from open savannah to multi-strata stands. Results show that CHM smoothing improves stem predictions for low density stands and no CHM smoothing better detects stems in dense even-aged stands, specifically dominant and co-dominant trees (R 2 � 0.61, RMSE � 20.91 stems with smoothing; R 2 � 0.85, RMSE � 46.02 stems with no-smoothing; combined smoothed CHM for low density and unsmoothed CHM for high density stands R 2 � 0.88, RMSE � 28.59 stems). At a threshold of approximately 2,200 stems ha � 1 , stem detection accuracy is no longer obtainable in any

Geographic information system concepts for land management

A Geographical Information System (GIS) has recently been developed for the assessment of the agricultural potential of the Southern African Development Community (SADC) region. The main value of this GIS is the integration of agricultural resource information from the SADC countries in order to support regional planning. The development of GIS technology makes it possible to compile, store, retrieve, analyse and display vast quantities of spatial data on, inter alia, the climate, topography, soils and infrastructure of the region. This article aims to give background information on the nature and general application of a GIS. Attention is given to the capabilities of a GIS, the spatial questions that drive analyses, basic database requirements, analytical and operational functions, as well as the applications of a GIS in land reform. More detail on the spatial agricultural resource data captured and its use by means of the SADC GIS will be described in a later article.

Development of spatially explicit descriptive models of watershed mass wasting

The Nemadji River contributes 525,000 metric tons of sediment per year to Lake Superior. This sediment results in a number of environmental impacts to the Nemadji/Superior fluvial system. Basin hydrology has been changed through logging, burning, clearing for agriculture and road construction. These activities are hypothesized to have reduced evapotranspiration rates and to have decreased the soils resistance to shear forces. A GIS was established to analyze landscape data for nine representative subwatersheds within the Nemadji basin. Predictor variables were examined in buffer zones on each side of the stream in each watershed. Output from the GIS was used as input to linear regression models used to quantify the relationships between the frequency of soil mass movement and landscape characteristics. Results show no significant relationships between watershed area, percent coniferous cover, percent of stream channel protected in coniferous cover, channel gradient, time of concentration, road density, mean weighted watershed slope, or number of beaver dams and frequency of mass soil movement. Significant relationships were found between slump frequency and total forested area and percent non-forested land. The results of this investigation have important implications for the maintenance and restoration of forest land in the red clay area. Keywords: mass soil wasting, forest cover, geographic information system

Mapping potential of digitized aerial photographs and space images for site-specific crop management

In site-specific crop management, treatments (e.g., fertilizer and herbicides) are applied precisely where they are needed. Global positioning system receivers allow accurate navigation of field implements and creation of crop yield maps. Remote sensing products help producers explain the wide range of yields shown on these maps and become the basis for digitized field management maps. Previous sources of remote sensing products for agriculture did not provide services that generated a sustained demand by crop producers, often because data were not delivered quickly enough. Public Access Resource Centers could provide a nearly uninterrupted electronic flow of data from NASAs MODIS and other sensors that could help producers and their advisors monitor crop conditions. This early warning/opportunity system would provide a low-cost way to discover conditions that merit examination on the ground. High-spatial-resolution digital aerial photographs or data from new commercial satellite companies would provide the basis for site-specific treatments. These detailed data are too expensive to acquire often and must be timed so as to represent differences in water supply characteristics and crop yield potentials. Remote sensing products must be linked to specific prescriptions that crop produces use to control operations and improve outcomes.