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Mapping Leaf Area Index in subtropical upland ecosystems using RapidEye imagery and the randomForest algorithm

Philip Beckschäfer (1)   , Lutz Fehrmann (1), Rhett D Harrison (2), Jianchu Xu (3), Christoph Kleinn (1)

iForest - Biogeosciences and Forestry, Volume 7, Issue 1, Pages 1-11 (2014)
doi: https://doi.org/10.3832/ifor0968-006
Published: Oct 07, 2013 - Copyright © 2014 SISEF

Research Articles


Canopy leaf area, frequently quantified by the Leaf Area Index (LAI), serves as the dominant control over primary production, energy exchange, transpiration, and other physiological attributes related to ecosystem processes. Maps depicting the spatial distribution of LAI across the landscape are of particularly high value for a better understanding of ecosystem dynamics and processes, especially over large and remote areas. Moreover, LAI maps have the potential to be used by process models describing energy and mass exchanges in the biosphere/atmosphere system. In this article we assess the applicability of the RapidEye satellite system, whose sensor is optimized towards vegetation analyses, for mapping LAI along a disturbance gradient, ranging from heavily disturbed shrub land to mature mountain rainforest. By incorporating image texture features into the analysis, we aim at assessing the potential quality improvement of LAI maps and the reduction of uncertainties associated with LAI maps compared to maps based on Vegetation Indexes (VI) solely. We identified 22 out of the 59 image features as being relevant for predicting LAI. Among these, especially VIs were ranked high. In particular, the two VIs using RapidEye’s RED-EDGE band stand out as the top two predictor variables. Nevertheless, map accuracy as quantified by the mean absolute error obtained from a 10-fold cross validation (MAE_CV) increased significantly if VIs and texture features are combined (MAE_CV = 0.56), compared to maps based on VIs only (MAE_CV = 0.62). We placed special emphasis on the uncertainties associated with the resulting map addressing that map users often treat uncertainty statements only in a pro-forma manner. Therefore, the LAI map was complemented with a map depicting the spatial distribution of the goodness-of-fit of the model, quantified by the mean absolute error (MAE), used for predictive mapping. From this an area weighted MAE (= 0.35) was calculated and compared to the unweighted MAE of 0.29. Mapping was done using randomForest, a widely used statistical modeling technique for predictive biological mapping.

  Keywords


Ecosystem Monitoring, Forest and Vegetation Parameters, Leaf Area Index (LAI), Hemispherical Photography, Map Uncertainty, Vegetation Indexes, Image Texture, Xishuangbanna

Authors’ address

(1)
Philip Beckschäfer
Lutz Fehrmann
Christoph Kleinn
Chair of Forest Inventory and Remote Sensing, Georg-August-Universität Göttingen, Büsgenweg 5, D-37077 Göttingen (Germany)
(2)
Rhett D Harrison
Key Laboratory for Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, 666303 Yunnan (China)
(3)
Jianchu Xu
World Agroforestry Centre, ICRAF-Kunming Office, Kunming, 650201 Yunnan (China)

Corresponding author

 
Philip Beckschäfer
philip.beckschaefer@gmail.com

Citation

Beckschäfer P, Fehrmann L, Harrison RD, Xu J, Kleinn C (2014). Mapping Leaf Area Index in subtropical upland ecosystems using RapidEye imagery and the randomForest algorithm. iForest 7: 1-11. - doi: 10.3832/ifor0968-006

Academic Editor

Giorgio Matteucci

Paper history

Received: Feb 06, 2013
Accepted: May 13, 2013

First online: Oct 07, 2013
Publication Date: Feb 03, 2014
Publication Time: 4.90 months

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