scholarly journals Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology

2019 ◽  
Vol 11 (10) ◽  
pp. 1203 ◽  
Author(s):  
Daniel Sousa ◽  
Christopher Small ◽  
Andrew Spalton ◽  
Andy Kwarteng

In monsoonal ecosystems, vegetation phenology is generally modulated by the timing and intensity of seasonal precipitation. Seasonal precipitation is often characterized by substantial interannual variability in both space and time. A rigorous quantitative understanding of the ecology of the landscape requires spatially explicit information regarding the strength of the relationship between seasonal precipitation and vegetation phenology, as well as the interannual variability of the system. For this information to be accurately estimated, it must be based on spatially and temporally consistent measurements. The optical satellite image archive can provide these measurements. Satellite imagery offers observations of both a) atmospheric parameters such as the timing and spatial extent of monsoon cloud cover; and, b) phenological parameters, such as the timing and spatial extent of vegetation green-up and senescence. This work presents a method to capture both atmospheric and phenological parameters from an optical image time series. The method uses Empirical Orthogonal Function (EOF) analysis of a single spectral index for unified characterization of the spatiotemporal dynamics of both monsoon cloud cover and vegetation phenology. This is made possible by leveraging well-understood differences in the visible and near infrared reflectance of green vegetation, soil, and clouds. Image time series are transformed into a temporal feature space (TFS) that is comprised of low-order Principal Components. The structure of the temporal feature space reveals spatiotemporally distinct annual cycles of both cloud cover and vegetation phenology. In order to illustrate this technique, we apply it to the retrospective analysis of a seasonal cloud forest in the Dhofar Mountains of the southern Arabian Peninsula. Our results quantify known (but previously unmapped) local gradients in monsoon duration and vegetation community response. Individual ecological subsystems are also clearly distinguishable from each other, and consistent elevation gradients emerge within each subsystem. Novel observations also emerge, such as regreening/early greening events and spatial patterns in cloud duration. The method is conceptually straightforward and could be applied to characterize other monsoon environments anywhere on Earth.

2020 ◽  
Vol 12 (4) ◽  
pp. 610 ◽  
Author(s):  
Bryce Adams ◽  
Louis Iverson ◽  
Stephen Matthews ◽  
Matthew Peters ◽  
Anantha Prasad ◽  
...  

The Landsat program has long supported pioneering research on the recovery of forest information by remote sensing technologies for several decades, and efforts to improve the thematic resolution and accuracy of forest compositional products remains an area of continued innovation. Recent development and application of Landsat time series analysis offers unique opportunities for quantifying seasonality and trend components among different forest types for developing alternative feature sets for forest vegetation mapping. Within a large forested landscape in Southeastern Ohio, USA, we examined the use of harmonic metrics developed from time series of all available Landsat-8 observations (2013–2019) relative to seasonal image composites, including accompanying spectral components and vegetation indices. A reference dataset among three sources was integrated and used to categorize forest inventory data into seven forest type classes and gradient compositional response. Results showed that the combination of harmonic metrics and topographic variables achieved an accuracy agreement with the reference data of 74.9% relative to seasonal composites (71.6%) and spectral indices (70.3%). Differences in agreement were attributed to improved discrimination of three heterogeneous upland hardwood classes and an early-successional, young forest class, all forest types of primary interest among managers across the region. Variable importance metrics often identified the cosine and sine terms that quantify the seasonality in spectral values in the harmonic feature space, suggesting these aspects best support the characterization of forest types at greater thematic detail than seasonal compositing procedures. This study demonstrates how advanced time series metrics can improve forest type modeling and forest gradient quantifications, thus showcasing a need for continued exploration of such approaches across different forest types.


Author(s):  
Zhiwen Xiao ◽  
Xin Xu ◽  
Huanlai Xing ◽  
Shouxi Luo ◽  
Penglin Dai ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0119811 ◽  
Author(s):  
Sofia Bajocco ◽  
Eleni Dragoz ◽  
Ioannis Gitas ◽  
Daniela Smiraglia ◽  
Luca Salvati ◽  
...  

2021 ◽  
Vol 13 (14) ◽  
pp. 2675
Author(s):  
Stefan Mayr ◽  
Igor Klein ◽  
Martin Rutzinger ◽  
Claudia Kuenzer

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.


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