Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

Joseph St Peter,Jason Drake,Paul Medley,John Hogland,Nathaniel Anderson

doi:10.3390/ijgi7030107

Abstract

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.

Highlights

Land cover classification is a common remote sensing process that assigns classes to geographic areas based on remotely sensed data
This study describes a methodology to produce a fine resolution land cover classification that quantifies and maps the spatial patterns of land cover types across a broad extent
To minimize the number of dimensions used in our modeling stage, we performed a PCA using National Agriculture Imagery Program (NAIP) spectral values and texture variables

Summary

Introduction

Land cover classification is a common remote sensing process that assigns classes to geographic areas based on remotely sensed data. Classifications are typically conducted on a per-cell basis and fit into two broad categories, supervised or unsupervised. In forest management, land cover classifications are frequently used to inform management activities such as timber harvest [6], forest restoration [7], fire risk mitigation [8], and preservation of rare habitats [9]. From land cover classification datasets, relevant objectives such as locating forested and non-forested areas [10] or determining the proportion of impervious surface occupying landscape [11] can be quickly addressed. Land cover classifications can be used as a component of more complex analyses of landscape characteristics [12] and can be used to describe important characteristics of forest and woodland ecosystems, such as percent canopy cover, understory composition within open forests, and the degree of fragmentation

Methods

Results

Conclusion