Plots are extensively used to study forest dynamics and these observations can be scaled-up such as for the modelling of regional processes. However, concerns have been expressed regarding using plots to reflect landscape-scale vegetation characteristics and dynamics. In Chapter 2, we have shown that the Fushan Forest Dynamics Plot (FFDP, 25 ha) did not represent the entire variation of the Fushan Experimental Forest (FEF, 1097 ha). The FFDP was a subsection of its landscape vegetation, as the plot disproportionally reflected the highest values of the vegetation indices (VIs) measured across the FEF. While the FFDP covered a subset of the lower values of elevation and slope angles in the FEF, the plot was representative for the topographic position index. Similarly, the entire range of responses to cyclones (ΔVIs) within the FEF was not entirely represented although the plot fared relatively well at covering ΔVIs variation in comparison with its representation of VIs. It implies that forest responses to disturbance are sampled better than forest cover variation.
Interestingly, there was not a trend for either under- or over-exposure to damages across the five typhoons as the representation of the FFDP for the FEF varied among these events. However, for Typhoon Nari – for which images had the lowest cloud cover – the results suggested that the FFDP is less affected than the FEF. Disturbance frequencies were also not accurately represented by the FFDP. The reserve had proportionally more pixels that were frequently disturbed.
Although the FFDP did not represent disturbances at the landscape scales, the use of minimal Euclidean distances to compare the FFDP with multiple alternative strategies showed that alternative designs based on wider elevational gradient did not improve topography and disturbance representativeness. Typhoon damage distribution was complex and even if it varied with topography, better representation of elevation alone did not provide better representation of cyclone disturbances in Fushan. Hence, although the current plot did not represent the entirety of the FEF, it was better than alternative plots that covered the same area. Full representation of the
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landscape disturbances may not be attainable through a permanent plot of limited area, but the study showed that the current plot provided a satisfying representation in comparison with alternative strategies that were tested. Moreover, a unique plot laid in a relatively gentle terrain offers the advantage of easier surveys in comparison with subplots dispersed across the landscape.
The results of the second study, in Chapter 3, indicated that typhoons caused different landscape patterns of disturbances. Although the five typhoons led to drops in NDVI, NDII, EVI, and SAVI in the Fushan forest, the spatial distribution of disturbances changed widely among typhoons as suggested by the low inter-typhoons correlations that remained low even for typhoons of similar tracks and strengths. This study indicates that there were complex interactions between typhoon properties, vegetation conditions and topography. Indeed, topographical variables explained only a small proportion of the variation in ΔVIs across the forest, and topography was a better explicator of variation of ΔVIs for the two typhoons that passed close to the site – Herb and Nari. For each typhoon, the directions of the relationships between topographical variables and vegetation indices were consistent across ΔVIs. However, the direction changed among typhoons, even between Typhoons Dujuan and Soudelor which had similar tracks. Nevertheless, typhoons caused consistent changes in the vegetation cover, with all typhoons increased vegetation heterogeneity. Moreover, areas of the Fushan forest with more vegetation cover were also more severely affected by typhoons, as pre-typhoon VI and ΔVI were negatively correlated.
Vegetation indices based on the same spectral bands were moderately to strongly correlated for each disturbance event. On the other hand, vegetation indices using different spectral bands had displayed weaker correlations, implying that variation of water content (detected through SWIR) may not have exactly followed the variation of green cover (NIR-Red) after typhoon passage. No vegetation index remained more sensible than other indices for all typhoons; therefore, multiple vegetation indices should be used when studying several disturbance events in future studies.
68 Future works
Representativeness assessment is critical for the upscaling of current plots data to broader scales. Hence, the method described in Chapter 2 can be used to study the landscape-representativeness of forest plots in other regions that are prone to cyclones such as Palanan (Philippines), Luquillo (Puerto Rico), Laupahoehoe (Hawaii), or Ngardok (Palau). These regions are subject to different cyclones frequencies, and these forests have shown varying levels of resistance and resilience. Hence, good representation is necessary if the plot aims to sample its landscape dynamics. Cyclone prone forests (such as these cited previously) also differ in their topography and inter-comparison of their plot representativeness, along with the analysis of alternative plotting strategies, may suggest different suitable plot designs. Moreover, the method used in this study should be improved. First by including more cyclones in order to detect possible trends in both disturbance intensity and frequency representation. This can be attained using multiple sensors imagery (such as Sentinel 2, EO-1, SPOT) although it would limit inter-events comparisons because of differences in sensor sensitivities. In addition, it is worth to note that Sentinel-2 satellites will likely improve the monitoring of future disturbances since their revisit time is of 5 days (two satellites) against 16 days for Landsat 7 and 8, hence increasing the chance to obtain non-cloudy images. Secondly, more images can be used to analyse individual disturbances, as multiple images following cyclone passage will give indications regarding delayed mortality and recovery through time series analyses. Third, additional variables should be included to compute Euclidean distances, such as soil type and moisture, rainfall, but also LiDAR-derived forest structure.
While typhoons caused significant drops in VI values, the ecological significance of such variation (usually < 5%) is unknown. Future research, combining ground observations (e.g., LAI) and remote sensing may help linking field-based measurements to remotely sensed variables (e.g., VIs) and assess if the relationship remains the same across disturbances. In turn, relationships between remotely sensed values and forest features would help upscaling plot observations to its wider
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landscape and improve the imperfect representativeness of plots as their data alone likely do not reflect the range of disturbances in their landscapes.
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