title: Sampling for landscape elements--a case study from Lower Saxony, Germany.
abstract: The estimation of coverage, i.e., the proportion of the total area in a study region covered by a given target class, is essential to many aspects of environmental monitoring. We analyze and compare the efficiency of different sample-based approaches for the estimation of coverage of different land cover classes from aerial imagery in a case study in Lower Saxony, Germany on the basis of the estimated standard errors. A complete delineation of vegetation classes in n = 279 aerial photo plots of 400 × 400 m thrown onto the study region of 1,117.7 km(2) in accordance with a systematic grid is compared to different configurations of line intercept sampling and clusters of points. The observation designs under study are characterized by different complexity and total size of the observation units and therefore also to the efforts related to yield a single observation. Especially for those classes that cover a relatively large proportion of the sampling frame, our results show that difference in performance between the different designs are negligible. A cluster of four transects of 200 m each allows estimating the area of land cover classes with high coverage with nearly similar precision as a complete mapping of fixed area plots of 16 ha each. Clusters of points show unexpected high precision for the estimated coverage of land cover classes with relatively high coverage.
authors.affiliation: Forest Inventory and Remote Sensing, Faculty of Forest Science and Forest Ecology, University of Göttingen, Büsgenweg 5, 37077, Göttingen, Germany, lfehrma@gwdg.de.
authors.forename: Lutz
authors.initials: L
authors.lastname: Fehrmann
authors.forename: Dominik
authors.initials: D
authors.lastname: Seidel
authors.forename: Benjamin
authors.initials: B
authors.lastname: Krause
authors.forename: Christoph
authors.initials: C
authors.lastname: Kleinn
mesh_terms: D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005858:Germany; D010944:Plants; D058998:Remote Sensing Technology