Using multi-angle modis data to observe vegetation dynamics in the Amazon Forest

• Autor(a) principal: Yhasmin Mendes de Moura
• Data de Publicação: 2015
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações do INPE
• Texto Completo: http://urlib.net/sid.inpe.br/mtc-m21b/2016/01.19.10.55

Seasonality and drought in Amazon rainforests have been controversially discussed in the literature, partially due to a limited ability of current remote sensing techniques to detect drought impacts on tropical vegetation. Detailed knowledge of vegetation structure is required for accurate modeling of terrestrial ecosystem. However, direct measurements of the three dimensional distribution of canopy elements using LiDAR are not widely available, especially in the Amazon region. This thesis explores a novel multi-angle remote sensing approach to determine changes in vegetation structure from differences in directional scattering (anisotropy) observed from the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data, atmospherically corrected using the Multi-Angle Implementation Atmospheric Correction Algorithm (MAIAC). Chapter 1 presents a general overview of the topic, followed by a theoretical background of the most important types of remote sensing data used in this thesis (Chapter 2). Chapter 3 describes the retrieval of BRDF from MODIS data. Chapters 4 and 5 present two distinct approaches using multi-angular MODIS data. In Chapter 4, the potential of using MODIS anisotropy for modeling vegetation roughness from directional scattering of visible and near-infrared (NIR) reflectance was evaluated across different forest types. Derived estimates were compared to independent measures of canopy roughness (entropy) obtained from the: 1) airborne laser scanning (ALS), 2) spaceborne LiDAR Geoscience Laser Altimeter System (GLAS), and 3) spaceborne SeaWinds/QSCAT. GLAS-derived entropy presented strong seasonality and varied between different forest types. Results from Chapter 4 showed linear relationships between MODIS-derived anisotropy and ALS-derived entropy with a coefficient of determination (r$^{2}$) of 0.54 and a root mean squared error (RMSE) of 0.11, even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy (0.5$\leq$r$^{2}$$\leq$0.61; p<0.05), with similar slopes and offsets found throughout the season. The RMSE varied between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements ($\sigma$$^{0}$) from SeaWinds/QuikSCAT were also significant (r$^{2}$=0.59, RMSE=0.11). Results also showed a strong linear relationship of the anisotropy with field- (r$^{2}$=0.70) and LiDAR-based (r$^{2}$=0.88) estimates of leaf area index (LAI). In Chapter 5, the method was used to analyze seasonal changes in the Amazonian forests, comparing them to spatially explicit estimates of onset and length of dry season obtained from the Tropical Rainfall Measurement Mission (TRMM). The results of Chapter 5 showed an increase in vegetation greening during the beginning of dry season (7\% of the basin), which was followed by a decline (browning) later during the dry season (5\% of the basin). Anomalies in vegetation browning were particularly strong during the 2005 and 2010 drought years (10\% of the basin). The magnitude of seasonal changes was significantly affected by regional differences in onset and duration of the dry season. Seasonal changes were much less pronounced when assuming a fixed dry season from June through September across the Amazon basin. The findings reconcile remote sensing studies with field-based observations and model results, supporting the argument that tropical vegetation growth increases during the beginning of the dry season, but declines after extended dry season and drought periods. Overall, we concluded that multi-angle approaches, as the one used in this thesis, are suitable to extrapolate measures of canopy structure across different forest types, and may help quantify drought tolerance and seasonality in the Amazonian forests.