Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations

Abstract

The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyper-spectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3-5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.

Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR

“…Seasonality effects on plant phenology can reduce our ability to extract biophysical information from remote sensing observations in the Amazon, e.g., [47]. Without tree-by-tree phenology field data to support a proper analysis, we are unable to determine the exact impact of seasonality on our detection, but we expect that it may have a minor to moderate impact.…”

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

“…In addition, two recent studies also have shown that leaf spectroscopy provides an accurate, rapid means to assess leaf age at both individual and community scales Wu et al, 2017). Furthermore, some studies also suggest that it is possible that spectroscopy-based models of leaf V c,max25 and age could be extended to the canopy scale by leveraging imaging spectroscopy instrumentation on tower, unmanned aerial systems and manned airborne platforms De Moura et al, 2017). These developments highlight the potential to map changes in V c,max25 and leaf age over unprecedented spatial and temporal scales.…”

Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis

“…In this context, remote sensing data offer a viable means to explore spatial and temporal information, providing a range of insights about the tropical ecosystems (Chambers et al, 2007;Yang et al, 2013). The spectral indices and time series approaches have been widely used across the Amazon basin detecting seasonal changes of rainforest vegetation (Hilker et al, 2015;Moura et al, 2017), urban mapping (Lu et al, 2011), sediment dynamic in the rivers (Park and Latrubesse, 2014;Lobo et al, 2016), cropland and pasture monitoring (Arvor et al, 2012;Arantes et al, 2016;Aguiar et al, 2017). While optical sensors allow the synoptic view of the landscape dynamics (Barbosa et al, 2015), the top-of-atmosphere (TOA) radiance measured by satellite sensors is not only dependent on the ground properties, but is often distorted by atmospheric constituents (Okin and Gu, 2015;Vermote and Kotchenova, 2008).…”