Evaluating Tiffs (Toolbox for LiDAR Data Filtering and Forest Studies) in Deriving Forest Measurements from LiDAR Data
Recent advances in LiDAR (Light Detection and Ranging) technology have allowed for the remote sensing of important forest characteristics to be more reliable and commercially available. Studies have shown that this technology can adequately estimate forest characteristics such as individual tree locations, tree heights, and crown diameters. These values are then used to estimate biophysical properties of forests, such as basal area and timber volume. This study assessed the capability of a commercially available program, Tiffs (Toolbox for Lidar Data Filtering and Forest Studies), to accurately estimate forest characteristics, as compared to data collected at the plot level using traditional timber sampling methods. We found a high, positive correlation coefficient (r) of 0.8223 for tree heights, between the LiDAR-derived measurements and the field measurements, which is somewhat promising. However, we found low correlations in tree count per plot (r = 0.1777) and tree crown radius (r = 0.1517), between the LiDAR-derived measurements and the field measurements, results which are far from satisfactory.
Full text International Journal of Mathematical and Computational Forestry & Natural-Resources Sciences, Vol. 2, No. 2, P. 145-152
Multitemporal Analysis Using Landsat Thematic Mapper (TM) Bands for Forest Cover Classification
in East Texas
Land cover maps have been produced using satellite imagery to monitor forest resources since the launch of Landsat 1. Research has shown that stacking leaf-on and leaf-off imagery (combining two separate images into one image for processing) may improve classification accuracy. It is assumed that the combination of data will aid in differentiation between forest types. In this study we explored potential benefits of using multidate imagery versus single-date imagery for operational forest cover classification as part of an annual remote sensing forest inventory system. Landsat Thematic Mapper (TM) imagery was used to classify land cover into four classes. Six band combinations were tested to determine differences in classification accuracy and if any were significant enough to justify the extra cost and increased difficulty of image acquisition. The effects of inclusion/exclusion of the moisture band (TM band 5) also were examined. Results show overall accuracy ranged from 72 to 79% with no significant difference between single and multidate classifications. We feel the minimal increase (3.06%) in overall accuracy, coupled with the operational difficulties of obtaining multiple (two), useable images per year, does not support the use of multidate stacked imagery. Additional research should focus on fully utilizing data from a single scene by improving classification methodologies.
Full text Southern Journal of Applied Forestry, Vol. 32, No. 1, P. 21-27
A Standardized, Cost-Effective, and Repeatable Remote Sensing Methodology to Quantify Forested Resources in Texas
A standardized remote sensing methodology was evaluated for its use in quantifying the forested resources of the state of Texas in a timely and cost-effective manner. Landsat data from 2002 were used to create a land cover base map encompassing a four-county study area in East Texas. Site-specific and non-site-specific accuracy assessments of the classified map indicate that overall the 2002 base map accuracy of 72.78% was within acceptable remote sensing standards for Landsat data and that forest cover types derived from 2002, 1987, and 1980 Landsat data were within 4.4, 0.5, and 7.4% agreement with Forest Inventory and Analysis Program data collected in 1988, 1988, and 1980 respectively. A classified image representing five age class distributions for all forest cover types, derived through a Boolean manipulation of forest cover type maps from 2002, 1997, 1992, 1987, 1984, 1980, and 1974, indicates that overall map accuracy for age class distributions based on 30-m Landsat data from 1974 through 2002 was 58.69%. Overall, results indicate that remote sensing in conjunction with ground truthing can accurately quantify forest composition and age distributions using standardized and readily available data.
Full text Southern Journal of Applied Forestry, Vol. 32, No. 1, P. 12-20
Using Remotely Sensed Data to Quantify Contaminated Brine Sites in Southwest Texas
Although field checking of contaminated brine sites is relatively straight forward, the ability to field check a large and expansive area like southwest Texas can be time consuming and expensive. A more robust method is needed to accurately quantify brine contaminated sites in a more timely, efficient and cost effective manner. The overall goal of the project was to test a remote sensing methodology to accurately quantify the spatial extent and total acreage of contaminated brine sites in southwest Texas as a result of oil exploration. Landsat ETM+ data of southwest Texas were obtained and classified using supervised classification methodology with a maximum likelihood classification algorithm. Supervised classified was chosen since brine contaminated soil areas have distinct spectral signatures, especially in the dry season, which are easily distinguishable as training sites. Results indicate that Landsat ETM+ data can be an effective tool to use in quantifying previously unknown brine contaminated areas larger than 2 acres in southwest Texas to ascertain the spatial extent of contaminated brine sites as an aid in land reclamation/restoration.
Full text Presented in: the 21st Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment, Terre Haute, Indiana
Geospatial Analysis of Southern Pine Biome and Pollen Distribution Patterns in Southern United States
The spatial relationship between the parent plants and the distribution of their
pollen rain is extremely important for the survival and health of natural eco-systems. In our modern societies there is a continuous and extensive need for
wood products, therefore, the health and productivity of the forest ecosystems
should be primary concerns for practitioners and researchers. Southern yellow pine forested biomes consist of four major pine species that have been extremely important as American timber sources and as income for the lumber industry. Currently, the intensive harvesting and exploitation of southern pine forests have created a series of highly fragmented forest biome regions. As the distance between individual forest patches increases, the potential intensity of gene transfer decreases. The result is forested patches with limited gene plasticity, which can affect the health of individual trees and of the natural forested ecosystems. The purpose of this research is to establish correlation between spatial distributions of pine forest biome and dispersion of pine pollen. Once the relationship between the pollen rain distributional data and the vegetational biomes are determined, then those correlations will enable researchers to produce projected pollen rain distribution maps for certain regions of North America where existing pollen rain data is absent.
Full text GEOGRAFICKY CASOPIS, Vol. 58, No. 4, P. 239-258
Spatial Autocorrelation and Pseudoreplication in Fire Ecology
Fire ecologists face many challenges regarding the statistical analyses of their studies. Hurlbert (1984) brought the problem of pseudoreplication to the scientific community’s attention in the mid 1980’s. Now, there is a new issue in the form of spatial autocorrelation. Spatial autocorrelation, if present, violates the traditional statistical assumption of observational independence. What, if anything, can the fire ecology community do about this new problem? An understanding of spatial autocorrelation, and knowledge of available methods used to reduce the effect of spatial autocorrelation and pseudoreplication will greatly assist fire ecology researchers.
Full text Fire Ecology, Vol. 2, No. 2, P. 107-118
Correlation between Pollen Dispersion and Forest Spatial Distribution Patterns in the Southeastern United States
The pollen that falls to the surface at any given point is called the pollen rain. For most regions of the world the pollen rain provides a fairly reliable record of the plants that produce and disperse airborne pollen within a radius of about 30 km from the sampled location. To some extent the local pollen rain can also reflect limited information about the insect-pollinated plants living in a region. For some regions of North America, existing studies of the pollen rain and the regional vegetation associated with those data demonstrate a reliable relationship between these two vegetation aspects. For other regions of North America pollen rain studies exist but they have not been linked or correlated with the regional vegetation. In many others areas of North America there are no existing pollen rain studies. One objective of this project is to develop a method using geographic information systems to correlate existing pollen rain data with remote sensing based on classified vegetation patterns, especially in the forested biomes of North America. In addition, spatial interpolation methods will be used in GIS to predict the pollen rain in other regions where remote sensing data is available but no pollen rain data currently exist. Once completed, these correlations can be used to produce actual and projected pollen rain distributions for many regions of North America. Understanding the relationships between pollen rain data and the vegetation biomes they represent will then enable researchers and practitioners to use existing fossil pollen records to map past environmental changes in forested regions of North America and to predict future global changes of the biosphere. A secondary benefit of this research is that it will provide actual and projected pollen rain maps for North America. Those maps will permit law enforcement agencies to use pollen as a geographical marker and powerful forensic tool in their effort to solve crimes and catch potential terrorists before they can commit violent acts of destruction.
Full text Presented in: the 5th Southern Forestry and Natural Resources GIS Conference, Asheville, North Carolina.
Using GIS for Selecting Trees for Thinning
Thinning removes trees within a stand to regulate the level of site occupancy and subsequent stand development. Before thinning is applied, foresters determine the amount of residual growing stock, the spatial distribution of the residual trees, and the criteria used to select trees to cut. In this study, a portion of a loblolly pine (Pinus taeda) plantation was surveyed through a complete tree tally with the coordinates of each individual tree recorded. The dataset was then processed in a GIS program composed in Arc Marco Language (AML) applying a moving circular quadrat system superimposed over the study area. In each quadrant, tree attributes including DBH (nearest 0.1 inch), basal area (sq ft per ac), and density (trees per unit area) were utilized as determining factors for tree selection. A 3D visualization before and after thinning was created with a goal of equal distribution of trees across the stand.
Full text Presented in: the 25th Annual ESRI International User Conference, San Diego, California.
Predicting Ordinary Kriging Errors Caused by Surface Roughness and Dissectivity
The magnitude of kriging errors varies in accordance with the surface properties. The purpose of this paper is to determine the association of ordinary kriging (OK) estimated errors with the local variability of surface roughness, and to analyse the suitability of probabilistic models for predicting the magnitude of OK errors from surface parameters. This task includes determining the terrain parameters in order to explain the variation in the magnitude of OK errors. The results of this research indicate that the higher order regression models, with complex interaction terms, were able to explain 95 per cent of the variation in the OK error magnitude using the least number of predictors. In addition, the results underscore the importance of the role of the local diversity of relief properties in increasing or decreasing the magnitude of interpolation errors. The newly developed dissectivity parameters provide useful information for terrain analysis. Our study also provides constructive guides to understanding the local variation of interpolation errors and their dependence on surface dissectivity.
Full text Earth Surface Processes and Landforms, Vol. 30, No. 5, P. 601-612
Forest Landscape Changes in East Texas from 1974 to 2002
production has been one of the most important industries in east
Full text Presented in: the 4th Southern Forestry and Natural Resources GIS Conference, Athens, Georgia.
Advanced Digital Terrain Analysis Using Roughness-Dissectivity Parameters in GIS
The local variation of terrain properties causes profound changes in the biosphere, microclimate, hydrologic cycle, and in the distribution of human activities on this planet. With the dawn of computerized technology, the terrain is represented in a digital form and new methods are needed to effectively describe, evaluate and quantify terrain properties. The purpose of this project is to develop new methods and procedures for terrain analyses within a GIS environment. The focus is to develop tools for capturing the local terrain variability. The selected parameters such as the hypsometric integral (modified Martonne’s index), roughness index, and basic statistical measures (mean, range and variance) are combined with newly developed dissectivity parameters, drainage lengths and landuse characteristics in one unified package and programmed in GIS using the ARC Macro Language (AML). The digital terrain data from this analysis can then be correlated with other spatial information to determine the influence of terrain properties on the ecosystem or other variables of interest including the human systems.
Full text Presented in: the 24th Annual ESRI International User Conference, San Diego, California.
East Texas Forest Inventory (ETFI) Pilot Project: Remote Sensing Phase
The overall goal of the project was to test a methodology to
accurately quantify the forest resources of East Texas based on the premise that
the quantification and qualification of forest resources is crucial to: (1)
managing the resources wisely by providing timely and accurate information; and
(2) proper forest resource assessment is crucial to the economic development and
sustainability of East Texas communities.
quantification and qualification of forest resources in East Texas have relied
on measurements taken at field plots recorded either by the Texas Forest Service
(TFS) or the United States Forest Service (USFS) via the Southern Forest
Inventory and Analysis Program (SFIA). However, for field plot measurements to
be effective with respect to time and cost, plots must be physically located
with data collected and analyzed in a timely manner. Inaccessible or remote
areas, required to validate sampling procedures, may prove difficult to measure.
Satellite based remote sensing, which has the ability to acquire information about earth’s resources from a distance, can provide accurate information concerning forested resources in a more timely manner due to high temporal resolution and synoptic perspective. Satellite based remotely sensed data for natural resources, available since 1972, can provide a historical perspective of resources, as well as forest composition maps, forest age class assessments and biometric measurements in a timely and repetitive manner. Hence, this study was initiated to assess value of remote sensed (satellite) data for rapid assessment of important forest resource attributes.
Project Report Appendix
Using GIS for Forest Recreation Planning on the Longleaf Ridge Special Area of the Angelina National Forest, East Texas
Longleaf Ridge Special Area (LRSA) located in the Angelina National Forest is the westernmost example of a longleaf pine savanna community. Ecologically, the area is one of the most diverse communities in Texas. Due to its size, abundant natural and historical resources, numerous outdoor recreation opportunities exist.
In this study, GIS was used to develop a forest recreation concept plan on LRSA. Most of the geospatial data came from public entities. Information for demand analysis on forest recreation was obtained from the 2000 National Survey on Recreation and the Environment database. U.S. Forest Service recreation fee envelope data were analyzed to depict existing recreational use.
To minimize impacts from recreation development, overlay analysis was executed in GIS to identify limitations. Based on the recreation demand, existing resources, and the limitation composite, a conceptual site design was proposed for recreation use on LRSA.
Full Text PhD dissertation, Stephen F. Austin State University, Nacogdoches, Texas.
Geospatial Analysis of Reflectance and NDVI Values in the Angelina Forest Ecosystem
The aerial photographs and subsequently remote sensed imagery have been used for decades in classified landcover mapping, forest inventory, management, and evaluation of renewable resources. However, the implementation of geostatistical methods in remote sensing is of a newer date. In this study the variogram modeling is used to analyze the spatial structure of a forest canopy. The biomass and wood production can be evaluated in the studied area using NDVI (normalized difference vegetation index) values and kriging.
The study area is located within the Angelina National Forest in the Neches River Basin. The Angelina Forest is an important part of the East Texas Ecosystem and plays a significant role in all aspects of the natural and industrial development of this region including timber production, forage, wildlife, recreation and as a water resource.
Full Text Presented in: the Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado.
Assessment of Kriging Accuracy in the GIS Environment
The demand for spatial data is on the rise. However, even the latest technology cannot guarantee an error free database in Geographic Information System (GIS). In natural resources the point field sampling is often used for spatially oriented projects and interpolation methods are implemented to predict the values in an unsampled location and to generate maps. In order to evaluate the performance of Kriging interpolation in GIS the Kriging errors were analyzed and compared to the four other interpolation methods using fundamental statistical parameters. The sensitivity of ordinary Kriging interpolation in the GIS environment was evaluated with respect to the resolution of the predicted grid and conclusions were drawn for applications in spatial analysis.
Full Text Presented in: the 21st Annual ESRI International User Conference, San Diego, California.
Propagation of Errors in Spatial Analysis
In most spatially oriented projects, the conversion of data from analog to digital form used to be an extremely time-consuming process. At present, industrial and research institutions continue to accumulate large quantities of data that are easily accessible to users worldwide, and consequently less time is spent for data input. In addition, the introduction of Internet2 rapidly increased the transfer of spatial data through the electronic highway and opened new avenues for collaboration among research institutions and scientists. It is apparent that this trend will continue in the future. New regional and national centers for spatial data are being established with the objective of providing data to natural resource institutions and developing a high-resolution database of regional significance. Therefore the questions of spatial data accuracy and quality are of utmost importance. The purpose of this paper is to discuss the propagation of errors, outline the major trends and problems that are encountered during spatial data analysis, and demonstrate the propagation of errors during raster data conversion in a GIS environment. The results of this study will contribute to an understanding of errors emanating from the conversion of irregularly spaced points to regular grids using different interpolation methods.
Full Text Presented in: the 24th Applied Geographic Conferences Vol. 24, Fort Worth, Texas.