ABSTRACT
Fire is a major disturbance factor in Daxing'anling region, with important impacts on carbon balance of forest ecosystems. Fire severity and the distinction of microclimates induced by different topography are the primary factors driving the restoration of post-fire net primary productivity (NPP). In this study, we examined the influence of fire severity and topographic factors on the restoration of forest NPP in the Genhe forest region. The spatial and temporal restoration process of post-fire NPP were simulated by combining with MTCLIM and 3PGS model based on multiyear Landsat TM satellite (2008-2012) and climate (1980-2010) data. The results showed that the 3PGS-MTCLIM model could precisely estimate the spatial distribution of NPP at small scales, with a good correlation between simulated and observed values (R2=0.828). The percentage of declined NPP in the year following the fire ranged 43%-80%, and the average NPP recovery period for this region was about 10 years by comparing pre- and post-fire NPP. Fire severity had significant impacts on post-fire recovery. The stronger the fire intensity, the longer the recovery period was needed. The NPP recovered relatively slower after a period of fast-speed recovery. Among the three topographic factors, elevation was the strongest one affecting forest NPP restoration, followed by slope and aspect.
Subject(s)
Environmental Monitoring , Fires , Forests , Trees , China , EcosystemABSTRACT
The sensitivity of biochemical effects on leaf reflectance is vital for retieving biochemical parameters with remote sensing. In this study, the chlorophyll and water absorption coefficients of the commonly used model LIBERTY (leaf incorporation biochemistry exhibiting reflectance and transmittance yields) were calibrated using field measured needle spectral reflectance curves based on a look up table (LUT) method. A novel spectra reflectance fitting method were presented by involving a new index (named as yellow index, YI), which could obviously improve the fitting accuracy of Pinus yunnanensis reflection spectrum at highly-stressed status. As a global sensitivity analysis method, the EFAST (extended Fourier amplitued sensitivity test) was implemented to quantitatively assess the sensitivity of biochemical parameters on needle reflectance. Results show that: (1) the reflectauce spectrum of healthy needles (R2=0.999ï¼RMSEï¼0.01), slightly stressed needles (R2=0.991, RMSEï¼0.02) and moderately stressed needles (R2=0.992ï¼RMSEï¼0.03) are simulated fairly well by calibrated LIBERTY model which has less potential in fitting the reflectance spectrum of seriously stressed needles (R2=0.803ï¼RMSEï¼0.1). (2) the reflectance spectrum of seriously stressed needles can be successfully simulated by our proposed spectrum reflectance fitting method (R2=0.991, RMSE<0.03), because YI can quantitatively describe different degrees of stress, and (3) the sensitivity of leaf reflectance to chlorophyll and water parameters decreases with the degree of stress; while the sensitivity to other biochemical parameters is increasing, which includ baseline absorption, albino absorption, Lignin and Cellulose content, and nitrogen content, increases with the stress degree. Needle reflectance spectrum also have sensitivive bands for these parameters. For example, the albino absorption have a significant effect on needle reflectance in 505ï½565 and 705ï½850 nm). In addition, Albino absorption and chlorophyll also have significant effects on needle reflectance in visible region for seriously stressed needles, which indicates that the prior knowledge of the albino absorption level can help obtain the valid inversion result of chlorophyll content.
Subject(s)
Pinus , Chlorophyll , Needles , Nitrogen , Plant Leaves , Spectrum Analysis , WaterABSTRACT
Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in the People's Republic of China, accounting for 90% of human cases reported globally. In this study, a landscape epidemiologic approach, combined with geographic information system and remote sensing techniques, was applied to increase our understanding of HFRS due to Hantaan virus and its relationship with landscape elements in China. The landscape elements considered were elevation, normalized difference vegetation index (NDVI), precipitation, annual cumulative air temperature, land surface temperature, soil type, and land use. Multivariate logistic regression analysis showed that HFRS incidence was remarkably associated with elevation, NDVI, precipitation, annual cumulative air temperature, semihydromorphic soils, timber forests, and orchards. These findings have important applications for targeting HFRS interventions in mainland China.
