Conservation and land use planning in humid tropical lowland
forests urgently need accurate remote sensing techniques to distinguish among floristically different
forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain
forest types can be accurately discriminated by a non-parametric
k nearest neighbors (
k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of
forest (regrowth
forest). Three floristically defined
forest types were formed through clustering the old-growth
forest plots (
n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four
forest types did not adequately distinguish two old-growth
forest classes, so they were merged into a single
forest class. The resulting three
forest classes were most accurately classified by the
k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the
k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the
k-nn classifier can accurately distinguish floristically and structurally different rain
forest types. The classification accuracies were higher for the
k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.
相似文献