Artificial neural networks to detect forest fire prone areas in the southeast fire district of Mississippi

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires

Resource Analysis of Small-diameter Tree Above-ground Biomass in Mississippi

Small diameter trees refer to the trees with diameter at breast height (DBH) ranging from 5 to 11 inches. This research focuses on the resource analysis and spatial distribution of small-diameter tree (SDT) volume in Mississippi by a set of grouping variables including DBH class, species, stand size, forest cover type, ownership, and county groups. Regression and spatial interpolation techniques were used to predict the SDT volume for pine, hardwood, and mixed forest covers. Regression analysis resulted in a low regression coefficient (R2) without inventory data for all the forest cover types. The mean SDT volume to the total volume was greatest for pine (0.6), followed by mixed (0.4), and hardwood (0.3) forest cover. Non-spatial estimates indicated the total volume within respective groups. The spatial and non-spatial estimates of SDT resources can guide forest management personnel to effectively focus their management efforts

ARTIFICIAL NEURAL NETWORKS TO DETECT FOREST FIRE PRONE AREAS IN THE SOUTHEAST FIRE DISTRICT OF MISSISSIPPI

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires

Abstract Comparative Analysis of Spectral Unmixing and Neural Networks for Estimating Small Diameter Tree Above-Ground Biomass in the State of Mississippi

The accumulation of small diameter trees (SDTs) is becoming a nationwide concern. Forest management practices such as fire suppression and selective cutting of high grade timber have contributed to an overabundance of SDTs in many areas. Alternative value-added utilization of SDTs (for composite wood products and biofuels) has prompted the need to estimate their spatial availability. Spectral unmixing, a subpixel classification approach, and artificial neural networks (ANN) are being utilized to classify SDT biomass in Mississippi. The Mississippi Institute for Forest Inventory (MIFI) data base biomass (volume per acre) estimates will be used to check the accuracy and compare the two classification procedures. A suitable and accurate classification approach will be vital to understanding the spatial distribution as well as availability of SDTs and would benefit both forest industries and forest managers in proper utilization and forest health restoration