The GPS and gravity in situ observations of LMS area were made along roads, all of which were located in deep gorges among high mountains. The computed FGAs at the observation stations are undoubtedly of high accuracy. However, the computed FGAs associated with areas far away from observation stations are considered less accurate, because they are profoundly affected by the local topography (Fig. 3a). More specifically, the variation of elevation will induce a change of gravity, and the magnitude is about 0.3086 mGal per meter. Accordingly, attempting to determine FGAs at the other sides of mountains or into deep gorges by conventional interpolation method (Kriging interpolation used in Fig. 3) can involve considerable error.
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During the construction of EGM2008, only low-resolution terrestrial gravity data are available in some areas, which are referred as the proprietary areas (Pavlis et al. 2012). Unfortunately, China is one of these areas, and the high-resolution data of EGM2008 in China are acquired by using LSC to merge the low-resolution gravity data and the gravity data derived from a digital topography model (DTM 2006.0). DTM 2006.0 is a digital topography model including height, depth and thickness information of land, lake, ocean and ice (Pavlis et al. 2012). The differences between terrestrial gravity and EGM2008 data within China have been compared by several studies. The standard deviation of the differences was found to be about 10 mGal in the Sichuan Basin (Fu et al. 2013), and it increased from below 10 mGal in eastern China to more than 50 mGal in the west (Yang et al. 2012). Both the results of the previous studies and the standard deviation between the measurements and EGM2008 data at the end of the previous paragraph indicate considerable errors exist in EGM2008, not only in the short wavelength, but also in the long wavelength over the LMS area.
The EGM2008 data set may have systematic errors in the LMS area. However, it reflects very well the influence of the regional topography. This can make up for the deficiency of the Kriging interpolation method used for Fig. 3, which cannot reflect the influence of the local topography. Merging the modeled EGM2008 data with in situ observations could render the FGAs in the study area more reliable. This may be particularly relevant for locations far from observation stations. Therefore, the remove-and-restore prediction algorithm based on LSC is used to merge in situ measurements with EGM2008 data. This method is frequently used to derive gravity anomaly fields and terrestrial gravity fields from digital topographic models. During the production of EGM2008, the gravity anomalies derived from DTM2006.0 are considered for the reference field (Pavlis et al. 2012). Similarly, the EGM2008 gravitational model serves as the reference field in our study.
According to the merged data shown in Fig. 8 (a bigger area than the one shown in Fig. 5b), the T e in the LMS area is found to be 6 km, and the load ratios are found to be F 1 = 1 and F 2 = 0 (Fig. 7a, d). These load ratios indicate that the initial load in the LMS area is at the surface of the crust. The coherence between FGAs and topography is high across all wavelengths (Fig. 7c), and the phase of complex admittance (Fig. 7b) is near zero. These findings are consistent with the results of the load ratios, also support that the initial load is at the surface of the crust.
Flexure analysis from two-dimensional admittance between FGAs and topography around LMS area. Error bars are 1σ (standard deviation). a Admittance as a function of wavelength from observed data and forward calculations. F 3 is set to zero. b Complex phase of admittance between FGAs and topography. c Coherence magnitude between FGAs and topography. d Misfit between the admittance deduced from the FGAs around LMS and the model admittance which is a function of T e and F 2. The optimal fit is at T e = 6 km and F 2 = 0
As described above, the T e is acquired by the spectral method, which is the flexure analysis method of the frequency domain. This result is determined by the principal component of the frequency domain, which in the spatial domain, corresponds to areas with greatest changes (in topography or gravity). In our study area, the topography and FGAs are found to vary steeply across the eastern edges of the Tibetan Plateau, which response to the principal component. However, there were relatively mild variations over the Sichuan Basin. Therefore, considering the difference between the Sichuan Basin and eastern Tibetan Plateau, the estimated T e = 6 km is considered suitable for the eastern Tibetan Plateau which includes the LMS area. This result indicates that the strength of the crust of the eastern Tibetan Plateau is low, and it is easy to flexure. Because of the mild variations of the topography, the T e of the Sichuan Basin is not determined by the spectral method but requires a different approach.
Based on the in situ joint gravity and GPS observations conducted in the past several years, a dense network was constructed between the Sichuan Basin and the eastern Tibetan Plateau (Fig. 2). Then, the regional FGAs and Bouguer gravity anomalies of the study area are updated. The accuracy of the computed FGAs at and near the observation stations is high. Yet the one associated with areas far away from the observation stations is not high, because FGAs can be profoundly affected by the local topography. To render FGAs for areas far from observation stations more accurate (Fig. 2), the in situ observations are merged with EGM2008 data using a remove-and-restore algorithm (Pavlis et al. 2012). The new FGAs fields show pairs of positive and negative anomalies along the edges of the Sichuan Basin and the eastern Tibetan Plateau (Fig. 5b), in much more detail than the results found using EGM2008 data alone.
The new FGAs are used to calculate T e and F in the LMS area through two-dimensional admittance analysis (McKenzie 2003). The results show that the T e in the LMS area is 6 km. Profile analysis indicate the T e in Sichuan Basin exceed 30 km. Small T e in the LMS area and great T e in Sichuan Basin support the fluid crust model of the eastern Tibetan Plateau crust flowing over strong Sichuan Basin lithosphere (Copley and McKenzie 2007). F 1 is the load ratio indicated the initial load from the surface of the crust, and F 2 is the load ratio indicated the initial load from the interface between the lower and upper crust. F 1 = 1 and F 2 = 0 indicate that the lithospheric flexure of the LMS area is entirely attributed to the surface load. This is consistent with the crustal shortening model and the fluid crust model of the uplifting of the LMS. This study recommends a uplifting mechanism of the LMS, which combines the crustal shortening model and the fluid crust model. The eastern Tibetan crust with a low strength was not only thrusting over the Sichuan basin crust with a high strength, but also breaking and shortening simultaneously. With this processing, the LMS was uplifting and forming one of the steepest topographic marginal areas around the Tibetan Plateau.
Abstract: Assessing the status of tropical dry forest habitats using remote sensing technologies is one of the research priorities for Neotropical forests. We developed a simple method for mapping vegetation and habitats in a tropical dry forest reserve, Mona Island, Puerto Rico, by integrating the Normalized Difference vegetation Index (NDvI) from Landsat, topographic information, and high-resolution Ikonos imagery. The method was practical for identifying vegetation types in areas with a great variety of plant communities and complex relief, and can be adapted to other dry forest habitats of the Caribbean Islands. NDvI was useful for identifying the distribution of forests, woodlands, and shrubland, providing a natural representation of the vegetation patterns on the island. The use of Ikonos imagery allowed increasing the number of land cover classes. As a result, sixteen land-cover types were mapped over the 5 500 ha area, with a kappa coefficient of accuracy equal to 79 %. This map is a central piece for modeling vertebrate species distribution and biodiversity patterns by the Puerto Rico Gap Analysis Project, and it is of great value for assisting research and management actions in the island. Rev. Biol. Trop. 56 (2): 625-639. Epub 2008 June 30.
Another problem for mapping tropical vegetation using satellite data is the spectral confusion, in which more than one vegetation type shows similar spectral responses. Segmenting the image into regions with potentially different vegetation using variables such as topography, temperature, rainfall, substrate, etc, has shown its value for alleviating this problem (Vogelman et al. 1998, Helmer et al. 2002). In the case of Mona, relief is the principal variable explaining the distribution of the plant communities (Cintron and Rogers 1991).
In this paper we evaluate the use of Landsat NDVI, topographic information, and high-resolution remotely sensed data for mapping land cover and habitats on Mona Island, and we evaluated how this technology can be applied to other regions.
Additional information included a previous vegetation map by Cintrón and Rogers (1991) made by visual interpretation of blackand-white aerial photos from the 1960s and 1970s, and digitized by Ramos (2004) at a scale 1:35.000. ADD structural parameters. We also used a layer of the depression forests developed from the Ikonos as part of ongoing research of vegetation in Mona (Martinez et al. 2005), but we only considered depressions larger than the Landsat pixel. Although Cintrón and Roger (1991) vegetation map did not overlay well with the rest of the data, it was still functional for visually interpreting the vegetation patterns and the topography of the island. Finally, we used the Sensitivity of Coastal and Inland Resources to Spilled Oil Atlas for Puerto Rico (NOAA et al. 2000) to classify the coastal shorelines. Remote sensing analysis was conducted using ERDAS 8.7 software (Leica Geosystems GIS & Mapping LLC). 2ff7e9595c
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