Geology topics

Xiaoyang Zhang

Publications and source records attributed to Xiaoyang Zhang.

4 recordsLinked to original sources

USGS2023

Using simulated GEDI waveforms to evaluate the effects of beam sensitivity and terrain slope on GEDI L2A relative height metrics over the Brazilian Amazon Forest

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by LiDAR sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR sensor collects data systematically over the Earth’s surface from the International Space Station. Since GEDI became operational, it has collected billions of ~25 m diameter footprints. This massive dataset has been used to create higher level gridded and non-grided products. However, GEDI’s ~25 m footprints can be subject to errors associated with effects of geolocation, terrain slope, and beam sensitivity, among others, which are likely transferred to the downstream products. This study aims to (1) evaluate the effect of beam sensitivity and terrain slope on the accuracy of relative heights (RH) of GEDI product L2A version 2 through comparison with discrete-return airborne LiDAR data collected over transects in the Brazilian Amazon Forest biome, (2) assess GEDI’s geolocation uncertainty and investigate its combined effects with beam sensitivity and terrain slope, and (3) re-evaluate beam sensitivity and terrain slope effects on the GEDI L2A RHs using footprints that were geolocation-adjusted through a simple novel approach. The analysis separates GEDI footprints by acquisition time, i.e., daytime, nighttime, and combined (all-data). The discrete-return airborne LiDAR point clouds are used to derive terrain slope within the GEDI footprints, and to simulate GEDI waveforms and derive RHs comparable to the GEDI L2A product. Results indicate that terrain slope only causes significant effects on GEDI data collected during daytime because solar radiation affects waveform signal-to-noise ratio. Beam sensitivity causes significant effects on nighttime and all-data GEDI L2A RHs. If geolocation uncertainty is considered, the effects of beam sensitivity and terrain slope have just minor changes. Geolocation-adjusted data continue showing significant effects on nighttime and all-data RH differences caused by beam sensitivity but produce unbiased results. This study improves the understanding of how beam sensitivity, terrain slope and their combined effect with geolocation uncertainty may affect the GEDI L2A RH collected over the Brazilian Amazon forest during daytime and nighttime.

Brazilian Amazon Forest↗

USGS2021

Investigation of land surface phenology detections in shrublands using multiple scale satellite data

Shrublands occupy about 13% of the global land surface, contain about one-third of the biodiversity, store about half of the global terrestrial carbon, and provide many ecosystem services to a large amount of world's human population and livestock. Because phenology is a sensitive indicator of the response of shrubland ecosystems to climate change, the alteration of ecosystems following species invasions, and the dynamics of shrubland ecosystem function, biodiversity, and carbon budgets, it is critical to monitor and assess phenological dynamics in shrubland ecosystems. However, most current land surface phenology (LSP) products derived from satellite data do not provide phenology detections in some semiarid shrublands where the amplitude of seasonal vegetation greenness is small. Therefore, we investigated the LSP detection using multiple spatial resolution satellite data and examined the impacts of spatial scales and shrubland ecosystem components (shrub and herb cover) on LSP detections over the western United States. Specifically, greenup onset date (GUD) in shrublands was detected from 30 m Harmonized Landsat and Sentinel-2 (HLS) data and 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) data to quantify scale effects. The GUD spatial patterns were explored with 30 m pixel variations in shrubland ecosystem components. The results show that GUD values varied with percent vegetation cover and shifted to earlier dates with increasing vegetation cover, demonstrating that satellite observations were not able to capture greenup onset until a threshold of green vegetation cover is reached. GUD was mostly undetectable from both HLS and VIIRS pixels with vegetation cover less than 10% and became fully detectable with vegetation covers larger than 50%. Similarly, the differences of GUD between HLS and VIIRS detections also decreased with increased vegetation cover. As a result of high shrubland heterogeneity, GUD from 30 m HLS pixels could be partially detected within a 500 m pixel despite GUD being undetectable from VIIRS time series. Moreover, vegetation cover heterogeneity also made it difficult for GUD at 30 m to be aggregated to coarse scales (such as to 500 m VIIRS pixels). These findings have significant implications to the detection and characterization of shrubland LSP responses to environmental and climate changes.

Nevada, Utah↗

USGS2017

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

International Journal of Applied Earth Observation↗

USGS2014

A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest

Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m × 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring.

Wisconsin↗