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

详细信息	查看全文 | 推荐本文 |

• 作者：Paul L. Delamater^a ; ^{delamate@msu.edu} ; Joseph P. Messina^a ; ^b ; Jiaguo Qi^a ; ^b ; Mark A. Cochrane^c
• 关键词：Fractional coverage ; fc ; Percent vegetation cover ; Visual estimation ; NDVI ; Spectral mixture analysis
• 刊名：International Journal of Applied Earth Observation and Geoinformation
• 出版年：2012
• 期刊代码：91_03032434
• 类别：ear
• 出版时间：August, 2012
• 卷：18
• 期：Complete
• 页码：504-514
• 文件大小：2074 K

摘要

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.

Our study found a significant, positive linear relationship (尾 = 0.795, r² > 0.84, and p < 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.