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

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. <br/> 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. <br/> 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. <br/> 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. <br/> 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. <br/> The inverted Li-Strahler model combined with the regional scaling method, used at a local scale in the Longmenhe study area with QuickBird and Hyperion images, can also be applied at the scale of the Three Gorges region by using the combination of Landsat TM and MODIS images as shown in Chapter 4. For the two years 2002 and 2004, this methodology yields similar accuracies in CC estimation based on 25 field validation samples (R22002=0.614, RMSE2002=0.060; and R22004=0.631, RMSE2004=0.052). The produced map with changes in CC from 2002 to 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam and an increase in CC in other counties implying a positive response to certain policies taken safeguarding forest resources. <br/> The inversion of two canopy reflectance models (the Kuusk-Nilson forest reflectance and transmittance (FRT) model and the Li-Strahler geometric-optical model) for estimating forest CC using Hyperion data in the Longmenhe study area is compared in Chapter 5. The “infeasible” areas (i.e. pixels for which the estimated fraction sunlit background falls not in the range between [0, 1]) from the Li-Strahler model inversion are filled by using a spatial interpolation algorithm based on regression kriging. Validation results (40 field samples) show that the estimated CC by the FRT model inversion has a limited range of variation and is less accurate (R2=0.53, RMSE=0.072) than the estimation by inverting the Li-Strahler model combined with the scaling method and interpolation (R2=0.67, RMSE=0.043). Consequently, in Chapter 6, spatially continuous CC maps for the Three Gorges region in both 2002 and 2004 are produced by integrating the results of Chapter 4 and this spatial interpolation technique. The final improved change map of CC is more suitable to predict and analyze the overall situation of the forest structural change in the whole Three Gorges region. <br/> The main contribution of this work is the integration of the inverted Li-Strahler model, a regional scaling-based endmember extraction method and a spatial interpolation technique to achieve quantitative monitoring of forest canopy structural changes. The approach includes the careful assessment of various scaling aspects namely ranging from multi-spectral to hyperspectral, from high spatial resolution to low spatial resolution, from mono-temporal to multi-temporal and from local to regional study areas. Systematic and structural monitoring of forest ecosystem changes will be feasible at unprecedented quality based on the suggested approach.