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Wageningen University (2008)

Quantitative remote sensing for monitoring forest canopy structural variables in the Three Gorges region of China

Zeng, Y.

Titre : Quantitative remote sensing for monitoring forest canopy structural variables in the Three Gorges region of China

Auteur : Zeng, Y.

Université de soutenance : Wageningen University

Grade : PhD thesis 2008

Résumé partiel
Bridging various scales ranging from local to regional and global, remote sensing has facilitated extraordinary advances in modeling and mapping ecosystems and their functioning. Since forests are one of the most important natural resources on the terrestrial Earth surface, accurate and up-to-date information on forest structure and its changes are essential for many aspects of forest management. In particular the quantitative monitoring of forest structure using remote sensing techniques strongly supports conservation strategies that take into account biodiversity and the impact of the global carbon cycle. China is a vast country with abundant forest resources. This thesis focuses in particular on the Three Gorges region of China, where currently major changes are taking place in the forest ecosystem. Certainly, the Three Gorges region is widely known due to the construction of the Three Gorges Dam. But the Chinese government also puts great importance on eco-environmental aspects of the Three Gorges Dam project and has therefore implemented a long-term investigation intending to monitor the changing environment. Within the Three Gorges region, the Longmenhe forest nature reserve has been selected as one of the main study sites for this thesis. This forest nature reserve is dominated by subtropical broadleaved and coniferous forests and the pilot study in the reserve enables monitoring forest structural variables as well as detecting their changes in the whole Three Gorges region. Quantitative retrieval methods for assessing forest canopy structural variables using remote sensing are commonly grouped into statistical and physical approaches. Inverting physical-based canopy reflectance models for estimating forest variables generally can be applied at different sites and with different sensors. Dealing with scales and scaling currently is one of the central issues in quantitative remote sensing. A better understanding of the different spectral, spatial and temporal scales and a further study on scaling the information from local to regional scales are necessary. Therefore, the main objective of this thesis is to develop a methodology for quantitatively monitoring forest canopy structural variables and their change by integrating multiple scale remote sensing techniques. In Chapter 2, the potential of hyperspectral EO-1 Hyperion data combined with the inverted physical-based Li-Strahler geometric-optical model for retrieving mean crown closure (CC) and mean crown diameter (CD) as forest canopy structural variables in the Longmenhe forest nature reserve is studied. One of the most important inputs for the model inversion is the fractional contribution of sunlit background (Kg), which is obtained by using linear spectral unmixing methods based on image-derived endmembers of the viewed scene components (sunlit and shaded canopy, sunlit and shaded background). Validation results (37 field samples) show confidence (R2CC=0.61, RMSECC=0.046, R2CD=0.39 and RMSECD=0.984) in the approach selected. Chapter 3 studies the feasibility of up-scaling from very high spatial resolution data (QuickBird) to high spatial resolution hyperspectral data (Hyperion) for extracting the endmembers of sunlit canopy, sunlit background and shadow. It can be concluded that the regional scaling-based endmembers calculated in the overlapping region of QuickBird and Hyperion using the linear unmixing model are the best ones to be used in combination with the Li-Strahler model inversion for mapping CC and CD in the Longmenhe forest nature reserve. Additionally, the estimation of CC is better than that of CD by inverting the Li-Strahler model on a per-pixel basis.

Mots clés : canopy / forests / remote sensing / scaling / china / forest structure / imaging spectroscopy


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