Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

Barry R Middleton,Laura M Norman

doi:10.1117/1.jrs.12.026017

Barry R Middleton, Laura M Norman

Open Access

https://doi.org/10.1117/1.jrs.12.026017

Copy DOI

Abstract

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000 km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

Highlights

Remote sensing technology is often used to document and classify existing vegetation data at the regional scales.[1]
A multitiered unsupervised classification generated over 900 classes, and these classes were compared individually with the Southwest Regional Gap Analysis Project (SWReGAP) map and recoded to coincide with a class that represented the largest majority of the shared pixels
A slight improvement (53% versus 44%) was measured in the overall accuracy of the unsupervised classification map versus the SWReGAP map using the limited field data from 2017, the field data collected during the previous SWReGAP mapping effort allowed for an analysis depicting the opposite

Summary

Introduction

Remote sensing technology is often used to document and classify existing vegetation data at the regional scales.[1] Vegetation/land cover maps can be developed either at a community level or species level by discerning spectral characteristics and translating them into classes.[2] Large-scale projects are often constrained by the limited availability of high-resolution imagery and depend on lower resolution remotely sensed imagery as inputs that create lower resolution outputs.[3]. A large mapping effort was undertaken to classify vegetation in the USA by the National Gap Analysis Program (GAP), where gap analysis was defined as a method for identifying “gaps” in conservation land and/or water locations.[6] This was improved upon in the five southwestern states (Arizona, Colorado, New Mexico, Nevada, and Utah), by the Southwest Regional Gap Analysis

Methods

Results

Discussion

Conclusion