Smartphone Digital Photography for Fractional Vegetation Cover Estimation

Gaofei Yin, Yonghua Qu, Aleixandre Verger, Jing Li, Kun Jia, Qiaoyun Xie, Guoxiang Liu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Accurate ground measurements of fractional vegetation cover (FVC) are key for characterizing ecosystem functions and evaluating remote sensing products. The increasing performance of cameras equipped in smartphones opens new opportunities for extensive FVC measurement through citizen science initiatives. However, the wide field of view (FOV) of smartphone cameras constitutes a key source of uncertainty in the estimation of vegetation parameters, which has been largely ignored. We designed a practical method to characterize the FOV of smartphones and improve the FVC estimation. The method was assessed in a mountainous forest based on the comparison with in situ fisheye photographs. After the FOV correction, the agreement of smart-phone and fisheye FVC estimates highly improved: root-mean-square error (RMSE) of 0.103 compared to 0.242 of the original smartphone FVC estimates without considering the FOV effect, mean difference of 0.074 versus 0.213, and coefficient of determination R2 of 0.719 versus 0.353. Smartphone cameras outperform traditional fisheye cameras: the overexposure and low vertical resolution of fisheye photographs introduced uncertainties in FVCestimation while the insensitivity to exposure and high spatial resolution of smartphone cameras make photograph acquisition and analysis more automatic and accurate. The smartphone FVCestimates highly agree with the GF-1 satellite product: RMSE = 0.066, bias = 0.007, and R 2 = 0.745. This study opens new perspectives for the validation of satellite products.

Original languageEnglish
Pages (from-to)303-310
Number of pages8
JournalPhotogrammetric Engineering and Remote Sensing
Volume88
Issue number5
DOIs
Publication statusPublished - May 2022
Externally publishedYes

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