Article Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China (original) (raw)
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Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China
Remote Sensing, 2014
Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to determine whether they could capture the temporal variation and estimate the spatial variability of forest C fluxes derived from eddy covariance tower data. Nineteen indices were divided into four categories of optical indices: broadband, chlorophyll, red edge, and light use efficiency. Correlation tests were performed between the selected vegetation indices, gross primary production (GPP), and ecosystem respiration (Re). Among the 19 indices, five narrowband indices (Chlorophyll Index RedEdge 710, scaled photochemical reflectance index (SPRI)*enhanced vegetation index (EVI), SPRI*normalized difference vegetation index (NDVI), MCARI/OSAVI[705, 750] and the Vogelmann Index), and one broad band index (EVI) had R-squared values with a good fit for GPP and Re. The SPRI*NDVI has the highest significant coefficients of determination OPEN ACCESS Remote Sens. 2014, 6 8987 with GPP and Re (R 2 = 0.86 and 0.89, p < 0.0001, respectively). SPRI*NDVI was used in atmospheric inverse modeling at regional scales for the estimation of C fluxes. We compared the GPP spatial patterns inversed from our model with corresponding results from the Vegetation Photosynthesis Model (VPM), the Boreal Ecosystems Productivity Simulator model, and MODIS MOD17A2 products. The inversed GPP spatial patterns from our model of SPRI*NDVI had good agreement with the output from the VPM model. The normalized difference nitrogen index was well correlated with measured C net ecosystem exchange. Our findings indicated that narrowband bio-indicators based on EO-1 Hyperion images could be used to predict regional C flux variations for Northeastern China's temperate broad-leaved Korean pine forest ecosystems.
Journal of Geophysical Research, 2001
Correct estimation of spatially distributed CO2 flux is of utmost importance for regional and global carbon balance studies. Tower-based instruments provide flux data from a small footprint area and may not be suitable for spatial extrapolation over areas not represented by the towers. In this study we developed a method of combining optical indices from remotely sensed hyperspectral images with flux data from towers covering different vegetation types to make spatially continuous maps of gross CO2 fluxes. Using a simple light-use efficiency model, we tested the ability of spectral indices derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery to estimate photosynthetic fluxes of several boreal forest stands. Because CO 2 flux from terrestrial ecosystems is dependent on both vegetation cover and physiological state, we hypothesized that measures of both forest structure and physiology were important for flux estimation. Consequently, the modeled fluxes considered both the normalized difference vegetation index (NDVI) and a scaled value of the photochemical reflectance index (PRI), both derived from narrowband reflectance. NDVI alone was of limited use in describing the variation in ecosystem fluxes (R 2-0.26). Addition of the PRI, which is related to xanthophyll cycle pigment activity and unrelated to NDVI, improved the agreement between modeled and measured fluxes (R 2 = 0.82). Our results also indicated that simple extrapolation of point-based flux tower data to represent the large-area fluxes of boreal forest may lead to an underestimation of the spatially distributed fluxes, at least for the vegetation types studied in this analysis.
1996
The evaluation and prediction of changes in carbon dynamics at the ecosystem level is a key issue in studies of global change. An operational concept for the determination of carbon fluxes for the Belgian territory is the goal of the presented study. The approach is based on the integration of remotely sensed data into ecosystem models in order to evaluate photosynthetic assimilation and Net Ecosystem Exchange (NEE). Remote sensing can be developed as an operational tool to determine the Fraction of Absorbed Photosynthetically Active Radiation (fPAR). A review of the methodological approach of mapping fPAR dynamics at the regional scale by means of NOAA11-AVHRR/2 data for the year 1990 is given. The processing sequence from raw radiance values to fPAR is presented.
Remote Sensing of Environment, 2004
Carbon fluxes in temperate grassland ecosystems are characterized by large inter-annual variations due to fluctuations in precipitation and land water availability. Since an eddy flux tower has been in operation in the Xilin Gol grassland, which belongs to typical temperate grassland in North China, in this study, observed eddy covariance flux data were used to critically evaluate the biophysical performance of different remote sensing vegetation indices in relation to carbon fluxes. Furthermore, vegetation photosynthesis model (VPM) was introduced to estimate gross primary production (GPP) of the grassland ecosystem for assessing its dependability. As defined by the input variables of VPM, Moderate Resolution Imaging Spectroradimeter (MODIS) and standard data product MOD09A1 were downloaded for calculating enhanced vegetation index (EVI) and land surface water index (LSWI). Measured air temperature (Ta) and photosynthetically active radiation (PAR) data were also included for model simulating. Field CO 2 flux data, during the period from May, 2003 to September, 2005, were used to estimate the "observed" GPP (GPP obs ) for validation. The seasonal dynamics of GPP predicted from VPM (GPP VPM ) was compared quite well (R 2 =0.903, n=111, P<0.0001) with the observed GPP. The aggregate GPP VPM for the study period was 641.5gC·m −2 , representing a ~6% over-estimation, compared with GPP obs . Additionally, GPP predicted from other two typical production efficiency model (PEM) represents either higher overestimation or lower underestimation to GPP obs . Results of this study demonstrate that VPM has potential for estimating site-level or regional grassland GPP, and might be an effective tool for scaling-up carbon fluxes.
Mitigation and Adaptation Strategies for Global Change, 2009
Net primary production (NPP) of terrestrial ecosystems provides food, fiber, construction materials, and energy to humans. Its demand is likely to increase substantially in this century due to rising population and biofuel uses. Assessing national forest NPP is of importance to best use forest resources in China. To date, most estimates of NPP are based on process-based ecosystem modeling, forestry inventory, and satellite observations. There are little efforts in using spatial statistical approaches while large datasets of in-situ observed NPP are available for Chinese forest ecosystems. Here we use the surveyed forest NPP and ecological data at 1,266 sites, the data of satellite forest coverage, and the information of climate and topography to estimate Chinese forest NPP and their associated uncertainties with two geospatial statistical approaches. We estimate that the Chinese forest and woodland ecosystems have total NPP of 1,325±102 and 1,258±186 Tg C year −1 in 1.57 million km 2 forests with a regression method and a kriging method, respectively. These estimates are higher than the satellite-based estimate of 1,034 Tg C year −1 and almost double the estimate of 778 Tg C year −1 using a process-based terrestrial ecosystem model. Cross-validation suggests that the estimates with the kriging method are more accurate. Our developed geospatial statistical models could be alternative tools to provide national-level NPP estimates to better use Chinese forest resources.
Spatial distribution of carbon balance in forest ecosystems across East Asia
Agricultural and Forest Meteorology, 2008
a g r i c u l t u r a l a n d f o r e s t m e t e o r o l o g y 1 4 8 ( 2 0 0 8 ) 7 6 1 -7 7 5 a b s t r a c t The objective of this paper is to clarify what kind of environmental factors that regulate net ecosystem production (NEP), gross primary production (GPP), and ecosystem respiration (RE) in forest ecosystems across East Asia. Study sites were widely distributed and included diverse ecosystems, such as evergreen and deciduous, coniferous and broadleaf, planted and natural forests, from subarctic to tropical zones. We measured NEP using the eddy covariance technique at 13 forest sites in East Asia.
Agricultural and Forest Meteorology, 2017
In this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China. Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R 2 = 0.70 and RMSE = 24.52 tones ha −1). We then obtained one seasonal cycle (2011) of GPP (R 2 = 0.88 and RMSE = 5.02 gC m −2 8d −1) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (Biome-BioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R 2 = 0.75 and RMSE = 1. 27 gC m −2 d −1 for GPP, and R 2 = 0.61 and RMSE = 1.17 gC m −2 d −1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R 2 = 0.73 and RMSE = 46.65 g m −2 year −1). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step, we conducted a Monte Carlo analysis of uncertainties for interannual forest AGB estimates based on errors in the above forest AGB map, NPP estimates, and the conversion of NPP to an AGB increment.
Estimating Daily Forest Carbon Fluxes Using a Combination of Ground and Remotely Sensed Data
Journal of Geophysical Research: Biogeosciences, 2015
Several studies have demonstrated that Monteith's approach can efficiently predict forest gross primary production (GPP), while the modeling of net ecosystem production (NEP) is more critical, requiring the additional simulation of forest respirations. The NEP of different forest ecosystems in Italy was currently simulated by the use of a remote sensing driven parametric model (modified C-Fix) and a biogeochemical model (BIOME-BGC). The outputs of the two models, which simulate forests in quasi-equilibrium conditions, are combined to estimate the carbon fluxes of actual conditions using information regarding the existing woody biomass. The estimates derived from the methodology have been tested against daily reference GPP and NEP data collected through the eddy correlation technique at five study sites in Italy. The first test concerned the theoretical validity of the simulation approach at both annual and daily time scales and was performed using optimal model drivers (i.e., collected or calibrated over the site measurements). Next, the test was repeated to assess the operational applicability of the methodology, which was driven by spatially extended data sets (i.e., data derived from existing wall-to-wall digital maps). A good estimation accuracy was generally obtained for GPP and NEP when using optimal model drivers. The use of spatially extended data sets worsens the accuracy to a varying degree, which is properly characterized. The model drivers with the most influence on the flux modeling strategy are, in increasing order of importance, forest type, soil features, meteorology, and forest woody biomass (growing stock volume). In particular, several investigations have shown that Monteith's approach is suited to simulate the gross primary production (GPP) of forest ecosystems [e.g., Veroustraete et al., 2002; Gilabert et al., 2015]. This approach, which combines remotely sensed estimates of the fraction of photosynthetically active radiation absorbed by vegetation (fAPAR) with meteorological data, provides the theoretical foundation for the most widely used methods for predicting GPP, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP algorithm [Heinsch et al., 2003]. The modeling of forest net ecosystem production (NEP) is, instead, a much more critical issue, requiring the additional simulation of both autotrophic and heterotrophic respiration rates. Such simulation is theoretically and practically complex, since these rates are nonlinearly dependent on several environmental factors which are spatially variable and difficult to assess over wide areas (forest type, density, age, etc.) [Waring and Running, 2007].
Journal of Plant Ecology, 2013
Understanding of the ecophysiological dynamics of forest canopy photosynthesis and its spatial and temporal scaling is crucial for revealing ecological response to climate change. Combined observations and analyses of plant ecophysiology and optical remote sensing would enable us to achieve these studies. In order to examine the utility of spectral vegetation indices (VIs) for assessing ecosystem-level photosynthesis, we investigated the relationships between canopy-scale photosynthetic productivity and canopy spectral reflectance over seasons for 5 years in a cool, temperate deciduous broadleaf forest at 'Takayama' super site in central Japan.
Spatiotemporal dynamics of forest net primary production in China over the past two decades
Global and Planetary Change, 2008
Forest ecosystems play an important role in global carbon cycle regulation. Clarifying the dynamics and mechanism of carbon sink is of both scientific and political importance. In this paper, we have investigated the spatiotemporal change of forest net primary production (NPP) in China for recent two decades based on the geographically weighted regression (GWR) with a cumulative remote sensing index, the maximum normalized difference vegetation index (NDVI max ). GWR is a recently developed regression method with special emphasis on spatial non-stationarity. Outputs of forest NPP at three different stages was generated by the GWR model with NDVI max for the 1980s, early and late 1990s which were consequently analyzed. Our results indicated a wave-like pattern of change in forest NPP in the three stages with a trough-like depression for the early 1990s.