Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales.

Abstract

There are several important issues in quantitative remote sensing and product authenticity testing, including how well do the ground measurement points represent the remote sensing pixels, how to obtain the relative truth value of pixels, and how much spatial resolution can truly reflect fore-st leaf area index (LAI). In this study, the measured space scope of two plant canopy analyzers [LAI-2200 and tracing radiation and architecture of canopies (TRAC)] were calculated, which were combined with remote sensing images with three different spatial resolutions: GF-2 with 4.1 m spatial resolution, the Sentinel-2 with 10 m spatial resolution, and Landsat-8 OLI with 30 m spatial resolution, to get the relative true value of pixel at each scale. Under the condition of keeping the real observed area consistent with that obtained by remote sensing, the effects of different spatial resolution images for estimating forest LAI were compared and analyzed based on the unary exponential and multiple regression statistical models. Moreover, the optimal statistical models of the three images were tested on 30 m and 100 m scales and the spatial representation of dataset were evaluated, to find the most suitable scale for the description of forest LAI in the study area. The results showed that high resolution did not necessarily fully reflect LAI of forests. The statistical model based on three kinds of resolution images could well estimate forest LAI. Among the three models, the model based on the Sentinel-2 image had the highest accuracy, and the one based on the GF-2 images had the lowest. The test results at 30 and 100 m scales indicated that the forest LAI was overestimated by the GF-2 inversion model, and underestimated by the Landsat-8 inversion model. The statistical model based on Sentinel-2 could well estimated forest LAI in the study area.