Detecting open vegetation in a forested landscape: pollen and remote sensing data from New England, USA
The proportional cover of forest and grassland vegetation, known as landscape openness, has been particularly difficult to reconstruct because of differences in pollen productivity and transport between the two vegetation types. To begin to calibrate landscape openness in eastern North America, we collected 2 1 samples of surface sediments front small ponds (less than 60 ha) in the Upper Connecticut River Valley of New England, USA. Pollen assemblages from these surface samples were compared with vegetation composition around each pond assessed with three different remote sensing methods: land cover digitized from aerial photographs, the National Land Cover Database and a spectral mixture analysis of vegetation structure using LANDSAT data. A principal component analysis indicates that most of the variation in land cover among sites is captured by pollen data and each of the three vegetation methods. Because spectral mixture analysis contains at least as much vegetation information as the other two techniques, while avoiding the problem of classifying vegetation into categories, it is better for comparison with pollen data and detection of landscape openness. Even though there is a relationship between non-arboreal pollen percentages and a metric of landscape openness, it is inadvisable to use pollen percentages alone to inter landscape openness across regions either in modern or ancient times. Quantitative assessments and reconstructions of plant abundance at a higher taxonomic resolution will require detailed vegetation Surveys to correlate with pollen data from Surface samples and possibly simulations of modern and ancient landscapes. Open areas in a landscape that is over 75% forested can be detected, allowing for future quantitative reconstructions of landscape openness.
Keywords: landscape openness, forested area, land cover, New England, pollen, remote sensing, vegetation structure, spectral mixture analysis
