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

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

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

Geospatial Analysis of Southern Pine Biome and Pollen Distribution Patterns in Southeastern

The spatial relationship between the parent plants and the distribution of their pollen rain is extremely important for the survival and health of natural ecosystems. 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

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 analyze 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

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

East Texas Forest Inventory (ETFI) Pilot Project: Remote Sensing Phase (Project Report)

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. Prior 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

Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight

Accuracy Assessment on Drone Measured Heights at Different Height Levels

The advancement in unmanned aerial system (UAS) technology has made it possible to attain an aerial unit, commonly known as a drone, at an affordable price with increasing precision and accuracy in positioning and photographing. While aerial photography is the most common use of a drone, many of the models available in the market are also capable of measuring height, the height of the drone above ground, or the altitude above the mean sea level. On board a drone, a barometer is used to control the flight height by detecting the atmospheric pressure change; while a GPS receiver is mainly used to determine the horizontal position of the drone. While both barometer and GPS are capable of measuring height, they are based on different algorithms. Our study goal was to assess the accuracy of height measurement by a drone, with different landing procedures and GPS settings

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