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Remote Sens. 2018, 10(2), 179; https://doi.org/10.3390/rs10020179

Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

1
Graduate School of Science, Tohoku University, 6-3, Aramaki Aza-Aoba, Aoba-ku, Sendai 980-8578, Japan
2
River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
*
Author to whom correspondence should be addressed.
Received: 7 December 2017 / Revised: 19 January 2018 / Accepted: 23 January 2018 / Published: 26 January 2018
(This article belongs to the Section Forest Remote Sensing)
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Abstract

An accurate estimation of the leaf area index (LAI) by satellite remote sensing is essential for studying the spatial variation of ecosystem structure. The goal of this study was to estimate the spatial variation of LAI over a forested catchment in a mountainous landscape (ca. 60 km2) in central Japan. We used a simple model to estimate LAI using spectral reflectance by adapting the Monsi-Saeki light attenuation theory for satellite remote sensing. First, we applied the model to Landsat Operational Land Imager (OLI) imagery to estimate the spatial variation of LAI in spring and summer. Second, we validated the model’s performance with in situ LAI estimates at four study plots that included deciduous broadleaf, deciduous coniferous, and evergreen coniferous forest types. Pre-processing of the Landsat OLI imagery, including atmospheric correction by elevation-dependent dark object subtraction and Minnaert topographic correction, together with application of the simple model, enabled a satisfactory 30-m spatial resolution estimation of forest LAI with a maximum of 5.5 ± 0.2 for deciduous broadleaf and 5.3 ± 0.2―for evergreen coniferous forest areas. The LAI variation in May (spring) suggested an altitudinal gradient in the degree of leaf expansion, whereas the LAI variation in August (mid-summer) suggested an altitudinal gradient of yearly maximum forest foliage density. This study demonstrated the importance of an accurate estimation of fine-resolution spatial LAI variations for ecological studies in mountainous landscapes, which are characterized by complex terrain and high vegetative heterogeneity. View Full-Text
Keywords: LAI; Landsat OLI; phenology; complex terrain; forest structure; Monsi-Saeki theory; atmospheric correction; dark object subtraction LAI; Landsat OLI; phenology; complex terrain; forest structure; Monsi-Saeki theory; atmospheric correction; dark object subtraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Melnikova, I.; Awaya, Y.; Saitoh, T.M.; Muraoka, H.; Sasai, T. Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring. Remote Sens. 2018, 10, 179.

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