Carbon monoxide (CO) is a major air pollutant and a precursor of ozone, influencing atmospheric oxidation and ozone dynamics. It serves as a tracer for tracking pollutant transport. Asia is characterized by the highest CO concentrations in the world, and the CO concentrations there very greatly from year to year. It has been suggested that biomass burning is one of the main drivers for such interannual variation (IAV). This study integrates satellite remote sensing of fires from MODIS, and of CO from MOPITT and AIRS to capture IAV of CO in Asia and the its response to fire activities during 2003-2017. The results show that the IAV of CO total column in Asia is highest over frequent fire regions, including Indo-China, Indonesia and South Siberia. The correlation between the interannual CO and fire activities is highest over forest land cover, while among seasons, the correlation is highest in fall.
An automated spectroscopy system, which is divided into fix-angle and multi-angle subsystems, for collecting simultaneous, continuous and long-term measurements of canopy hyper-spectra in a crop ecosystem is developed. The fix-angle subsystem equips two spectrometers: one is HR2000+ (OceanOptics) covering the spectral range 200–1100 nm with 1.0 nm spectral resolution, and another one is QE65PRO (OceanOptics) providing 0.1 nm spectral resolution within the 730-780 nm spectral range. Both spectrometers connect a cosine-corrected fiber-optic fixed up-looking to collect the down-welling irradiance and a bare fiber-optic to measure the up-welling radiance from the vegetation. An inline fiber-optic shutter FOS-2x2-TTL (OceanOptics) is used to switch between input fibers to collect the signal from either the canopy or sky at one time. QE65PRO is used to permit estimation of vegetation Sun-Induced Fluorescence (SIF) in the O2-A band. The data collection scheme includes optimization of spectrometer integration time to maximize the signal to noise ratio and measurement of instrument dark currency. The multi-angle subsystem, which can help understanding bidirectional reflectance effects, alternatively use HR4000 (OceanOptics) providing 0.1 nm spectral resolution within the 680-800 nm spectral range to measure multi-angle SIF. This subsystem additionally includes a spectrometer Unispec-DC (PPSystems) featuring both up-welling and down-welling channels with 3 nm spectral resolution covering the 300-1100 nm spectral range. Two down-looking fiber-optics are mounted on a rotating device PTU-D46 (FLIR Systems), which can rotate horizontally and vertically at 10° angular step widths. Observations can be used to calculate canopy reflectance, vegetation indices and SIF for monitoring plant physiological processes.
The current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak, which began in 1999, continues to be the leading cause of pine tree mortality in British Columbia. Information regarding the location and spatial extent of the current attack is required for mitigating practices and forest inventory updates. This information is available from spaceborne observations. Unfortunately, the monitoring of the mountain pine beetle outbreak using remote sensing is usually limited to the visible stage at which the expansion of the attack beyond its initial hosts is unpreventable. The disruption of the sap flow caused by a blue-staining fungi carried by the beetles leads to: 1. a decrease in the amount of liquid water stored in the canopy, 2. an increase in canopy temperature, and 3. an increase in shortwave infrared reflectance shortly after the infestation. As such, the potential for early beetle detection utilizing thermal remote sensing is possible. Here we present a first attempt to detect a mountain pine beetle attack at its earliest stage (green attack stage when the foliage remains visibly green after the attack) using the temperature condition index (TCI) derived from Landsat ETM+ imagery over an affected area in British Columbia. The lack of detailed ground survey data of actual green attack areas limits the accuracy of this research. Regardless, our results show that TCI has the ability to differentiate between affected and unaffected areas in the green attack stage, and thus it provides information on the possible epicenters of the attack and on the spatial extent of the outbreak at later stages (red attack and gray attack). Furthermore, we also developed a moisture condition index (MCI) using both shortwave infrared and thermal infrared measurements. The MCI index is shown to be more effective than TCI in detecting the green attack stage and provides a more accurate picture of beetle spread patterns.
Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations
of carbon budget components at local, regional and global scales. These biases result from overlooking subpixel
variability of land surface characteristics. Vegetation heterogeneity is an important factor introducing biases in regional
ecological modeling, especially when the modeling is made on large grids. This study suggests a simple algorithm that
uses subpixel information on the spatial variability of land cover type to correct net primary productivity (NPP) estimates,
made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. The algorithm
operates in such a way that NPP obtained from calculations made at coarse spatial resolutions are multiplied by simple
functions that attempt to reproduce the effects of subpixel variability of land cover type on NPP. Its application to a
carbon-hydrology coupled model(BEPS-TerrainLab model) estimates made at a 1-km resolution over a watershed
(named Baohe River Basin) located in the southwestern part of Qinling Mountains, Shaanxi Province, China, improved
estimates of average NPP as well as its spatial variability.
Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations
of carbon budget components at local, regional and global scales. One of the main causes resulted in these biases is
overlooking of sub-pixel variability of topography, especially in a mountainous area. This study analyzes the
significance of topography to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where
the land surface is considered as homogeneous within each pixel. Its application to a remote sensing process-based
model estimates made at a 1-km resolution over a mountainous forested watershed located in Baohe River Basin in
China. Results of this study show that NPP spatial scaling in complex terrain depends on the amount of the distortion of
the soil moisture field at the coarse resolution, and the spatial redistribution and movement of soil water in complex
terrain tightly affect NPP distribution, suggest that it is indeed necessary to consider topography in NPP spatial scaling.
Forest ecosystem is an important component of terrestrial ecosystem and plays an important role in global changes. Aboveground biomass (AGB) of forest ecosystem is an important factor in global carbon cycle studies. The purpose of this study was to retrieve the yearly Net Primary Productivity (NPP) of forest from the 8-days-interval MODIS-LAI images of a year and produce a yearly NPP distribution map. The LAI, DBH (diameter at breast height), tree height, and tree age field were measured in different 80 plots for Chinese fir, Masson pine, bamboo, broadleaf, mix forest in Liping County. Based on the DEM image and Landsat TM images acquired on May 14th, 2000, the geometric correction and terrain correction were taken. In addition, the "6S"model was used to gain the surface reflectance image. Then the correlation between Leaf Area Index (LAI) and Reduced Simple Ratio (RSR) was built. Combined with the Landcover map, forest stand map, the LAI, aboveground biomass, tree age map were produced respectively. After that, the 8-days- interval LAI images of a year, meteorology data, soil data, forest stand image and Landcover image were inputted into the BEPS model to get the NPP spatial distribution. At last, the yearly NPP spatial distribution map with 30m spatial resolution was produced. The values in those forest ecological parameters distribution maps were quite consistent with those of field measurements. So it's possible, feasible and time-saving to estimate forest ecological parameters at a large scale by using remote sensing.
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