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Shizuoka University (2015)

REMOTE RETRIEVAL OF ECOLOGICAL INDICATORS FOR DETECTING FOREST DROUGHT AND WILDFIRE

CAO Zhenxing

Titre : REMOTE RETRIEVAL OF ECOLOGICAL INDICATORS FOR DETECTING FOREST DROUGHT AND WILDFIRE

Auteur : CAO Zhenxing

Université de soutenance : Shizuoka University

Grade : Doctoral Thesis 2015

Résumé partiel
Events of drought-induced forest mortality and forest fire have been occurred all over the world and will be exacerbated in the future due to rising temperature. Both forest disturbances have substantial influence on the global hydrological and carbon cycle. How to remotely and quantitatively monitor and assess both disturbances have not been effectively addressed until now. Remote sensing provides a reliable and practical means to assess forest disturbances by the retrieval of related indicators. In this study, four indicators, relative water content (RWC), equivalent water thickness (EWT), fluorescence–based quantum yield of PSII (ΔF/F’m) and fuel moisture content (FMC) were retrieved with hyperspectral indices for detecting forest drought and fire, respectively.

Leaf water status information is highly needed for monitoring plant physiological processes and assessing drought stress. In Chapter 2, a leaf dehydration experiment was designed to obtain a relatively comprehensive dataset with ranges that were difficult to obtain in field measurements. RWC and EWT were chosen as the surrogates of leaf water status. Moreover, five common types of hyperspectral indices including : single reflectance (R), wavelength difference (D), simple ratio (SR), normalized ratio (ND) and double difference (DDn) were applied to determine the best indices. The results indicated that values of original reflectance, reflectance difference and reflectance sensitivity increased significantly, particularly within the 350-700 nm and 1300-2500 nm domains, with a decrease in leaf water. The identified best indices for RWC and EWT, when all the species were considered together, were the first derivative reflectance based ND type index of dND (1415, 1530) and SR type index of dSR (1530, 1895), with R2 values of 0.95 (p<0.001) and 0.97 (p<0.001), respectively, better than previously published indices. Even so, different best indices for different species were identified, most probably due to the differences in leaf anatomy and physiological processes during leaf dehydration. Although more plant species and field-measured datasets are still needed in future studies, the recommend indices based on derivative spectra provide a means to monitor drought-induced plant mortality in temperate climate regions.

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