A hybrid visual estimation method for the collection of ground truth fractional coverage data in a humid tropical environment

作者：Delamater Paul L^*; Messina Joseph P; Qi Jiaguo; Cochrane Mark A

来源：International Journal of Applied Earth Observation and Geoinformation, 2012, 18: 504-514.

DOI：10.1016/j.jag.2011.10.005

• Fractional coverage
• fc
• Percent vegetation cover
• Visual estimation
• NDVI
• Spectral mixture analysis

A substantial body of research exists exploring the spectral unmixing of remotely sensed image data. Specifically, we refer to the attempts and successes to model the percent vegetation cover (2-dimensional horizontal density) within a pixel, known as fractional coverage (fc). With this paper, we present a hybrid visual estimation method for fc field data collection in the complex landscapes found in humid tropical environments. The method includes a scalable theoretical model of fc, integrates the visual estimation technique with hemispherical photography collection, and is conducted over a systematic ground collection area. We present results from a case study conducted in the humid tropical region of Ecuador. Specifically, we report on the relationship between fc data modeled using a linear NDVI transformation and observed fc data collected using our hybrid visual estimation method. %26lt;br%26gt;Our study found a significant, positive linear relationship (beta = 0.795, r(2)%26gt;0.84, and p%26lt;0.001) between modeled and observed fc values. Because the accuracy of both modeled and observed values are unknown, a full validation of the proposed method of collection is not possible. Therefore, we conduct an error assessment, identifying limitations in the modeling method (e.g., non-linear relationship between modeled and true values and potential for saturation) and hybrid ground-truth collection method (e.g., subjectivity of visual estimation and positional errors in the ground collection area) that explain the deviation from a 1:1 relationship. We believe the proposed method of ground truth data collection is a significant contribution towards efforts to validate biophysical information gained from remotely sensed data.

• 出版日期2012-8