An Object-Based Approach to Map Young Forest and Shrubland Vegetation Based on Multi-Source Remote Sensing Data

Chadwick D Rittenhouse,Zhe Zhu,Dustin Riordan,Shi Qiu,Nathaniel Mikle,Elana H Berlin

doi:10.3390/rs14051091

Abstract

Many remote sensing studies have individually addressed afforestation, forest disturbance and forest regeneration, and considered land use history. However, no single study has simultaneously addressed all of these components that collectively constitute successional stages and pathways of young forest and shrubland at large spatial extents. Our goal was to develop a multi-source, object-based approach that utilized the strengths of Landsat (large spatial extent with good temporal coverage), LiDAR (vegetation height and vertical structure), and aerial imagery (high resolution) to map young forest and shrubland vegetation in a temperate forest. Further, we defined young forest and shrubland vegetation types in terms of vegetation height and structure, to better distinguish them in remote sensing for ecological studies. The multi-source, object-based approach provided an area-adjusted estimate of 42,945 ha of young forest and shrubland vegetation in Connecticut with overall map accuracy of 88.2% (95% CI 2.3%), of which 20,953 ha occurred in complexes ≥2 ha in size. Young forest and shrubland vegetation constituted 3.3% of Connecticut’s total land cover and 6.3% of forest cover as of 2018. Although the 2018 estimates are consistent with those of the past 20 years, concerted efforts are needed to restore, maintain, or manage young forest and shrubland vegetation in Connecticut.

Highlights

We identified three categories of vegetation based on ecological processes of succession, disturbance and regeneration, and hydrology, each of which contributes to the establishment or maintenance of young forest and shrubland vegetation (Table 1)
The segmentation, classification, and interpretation procedure resulted in 5,121,477 superpixels statewide, of which interpreters reviewed 165,067 pre-classified superpixels that occurred in vegetation complexes ≥1 ha in size (Supplemental Table S3)
Interpreters did not review 36,235 superpixels that were pre-classified as palustrine scrub-shrub and forested wetland as they had >50% spatial overlap with National Wetland Inventory (NWI) polygons

Summary

Introduction

Forests constitute 31% of the Earth’s land surface [1]. Their importance cannot be overstated as providers of essential ecosystem services for biodiversity [2], the hydrologic cycle [3,4], carbon sequestration and storage [5], and for human livelihoods [6]. The remote sensing literature has devoted much attention to mapping forest cover dynamics to identify and better understand forest succession, disturbance, loss, and degradation. Examples include identifying ecological states of forest succession [8], mapping forest disturbance at continental [9,10] to global extents [11,12], identifying hotspots of forest loss [13,14] and forest gain [15], as well as conversion of forests to plantation forests [16]

Objectives

Methods

Results

Discussion

Conclusion