Retrieval of seasonal variation in photosynthetic capacity from multi-source vegetation indices

Seasonal information on photosynthetic-capacity parameters (maximum carboxylation velocity, V_cmax; and maximum rate of electron transport, J_max) plays an important role in accurate simulation of carbon fixation in gas-exchange models. Exact inclusion of seasonal information on photosynthetic-capacity parameters into the models has been an irresolvable challenge. This paper investigated the relationships between vegetation indices (from multiple sources) and photosynthetic-capacity parameters of three beech forest stands (Fagus crenata) along an elevation gradient in the cold-temperate zone of Japan, over the entire growing season of 2006. Diverse vegetation indices were examined in terms of spectral, spatial and temporal scales; ranging from meteorological sensor-based broadband indices to hyperspectral data-based narrowband indices, to simulated MODIS (MODerate-resolution Imaging Spectroradiometer) indices based on hyperspectral data, and finally satellite-borne MODIS vegetation indices. Regression analysis revealed that all examined indices, with the exception of the downloaded MODIS products, had significant regression relationships with photosynthetic parameters (P < 0.001) when all data were pooled. Among the different indices, the simulated MODIS NDVI (Normalized Difference Vegetation Index) performed the best for both V_cmax and J_max (R² = 0.81 and 0.73, respectively). Site differences were apparent, as the simulated MODIS NDVI performed the best in exponential regressions for the 550 m site, while broadband NDVI performed best in exponential regression models for the 900 m site. The broadband SR (Simple Ratio) in relation to V_cmax performed best with respect to a linear model, whereas the broadband NDVI with J_max performed the best in an exponential model for the 1500 m site. The results reveal that vegetation indices which are obtained across different scales nevertheless retain tight relationships with canopy-scale photosynthetic-capacity parameters. The established relationships were inversely applicable to derive seasonal trajectories of photosynthetic-capacity parameters. Thus, new insight and confidence is gained for using remotely estimated photosynthetic parameters, even though most previous research works were limited on linking of vegetation indices with biophysical parameters. The control effect of physiological capacity on reflectance and further on vegetation indices has not been adequately established and thus needs further orientation for rigorous research work.     

北京城区道路系统路网空间特征及其与LST和NDVI的相关性

城市道路系统是人类活动最为频繁的场所之一,研究路网空间特征与相关环境指标的关系,可为定量分析城市建设过程对城市生态环境的影响以及为城市基础设施规划管理提供方法。对北京市五环内各级道路抽样调查及面向对象的高分辨率遥感影像识别,建立道路空间信息数据库,运用TM影像反演获得地表温度(LST)及归一化植被指数(NDVI),研究4种不同分析网格下城市路网特征与两者的空间相关性。结果表明,在较大分析网格下(4×4),路网空间指标与LST mean及NDVI mean相关性显著;中等网格下,除LST与NDVI的平均值外,路网空间指标还可与两者最小值建立相关性;在较小窗口下(32×32),除平均值与最小值,还可建立特定道路指标与LST max及NDVI max的联系;各路网指标中,单位面积道路总长与LST及NDVI相关性最强;加权道路结点数在8×8和32×32网格分析中,与NDVI的相关性高于该指标未加权值,而结点指标均不适合与LST max建立联系;在各分析网格下,4项道路指标均与NDVI mean呈极显著负相关,表明道路绿地建设规模还不足以对NDVI的强度及分布产生较大影响。     

NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin,USA

In this study, we developed a new approach that adjusted normalized difference vegetation index (NDVI) pixel values that were near saturation to better characterize the cropland performance (CP) in the Greater Platte River Basin (GPRB), USA. The relationship between NDVI and the ratio vegetation index (RVI) at high NDVI values was investigated, and an empirical equation for estimating saturation-adjusted NDVI (NDVI_sat__adjust) based on RVI was developed. A 10-year (2000–2009) NDVI_sat__adjust data set was developed using 250-m 7-day composite historical eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. The growing season averaged NDVI (GSN), which is a proxy for ecosystem performance, was estimated and long-term NDVI non-saturation- and saturation-adjusted cropland performance (CP_non__sat__adjust, CP_sat__adjust) maps were produced over the GPRB. The final CP maps were validated using National Agricultural Statistics Service (NASS) crop yield data. The relationship between CP_sat__adjust and the NASS average corn yield data (r = 0.78, 113 samples) is stronger than the relationship between CP_non__sat__adjust and the NASS average corn yield data (r = 0.67, 113 samples), indicating that the new CP_sat__adjust map reduces the NDVI saturation effects and is in good agreement with the corn yield ground observations. Results demonstrate that the NDVI saturation adjustment approach improves the quality of the original GSN map and better depicts the actual vegetation conditions of the GPRB cropland systems.     

Integrating Remote Sensing and GIS for Prediction of Winter Wheat (Triticum aestivum) Protein Contents in Linfen (Shanxi), China

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI_4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI_4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R² (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (R_RMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.     

Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

Monitoring soil respiration (R_s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R_s to the regional scales. In this study, we estimated the spatial distribution of R_s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R_s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R_s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R_s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R_s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R_s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R_s predictor model was validated by comparing it with the field measured R_s using an independent dataset. The TM-calibrated MODIS-estimated R_s was within an accuracy of field measured R_s with R² of 0.78 and root mean square error of 1.45 gC m⁻² d⁻¹. At the peak growing season of alpine grasslands, R_s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R_s in this study.     

Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago

Numerous ecological studies, including of the polar environment, are now using the remotely sensed normalized difference vegetation index (NDVI, e.g. PAL-NDVI or MODIS-NDVI) as a proxy of vegetation productivity rather than performing direct vegetation assessments. Even though previous data strongly suggested a saturation of NDVI at high biomass values, few studies have explicitly included this characteristic in the modelling process. Here, we developed a generalized non-linear model to explicitly model the relationship between temporal variations of NDVI (Pathfinder AVHRR Land 8 km dataset) and empirical field data. We illustrated our approach on the Kerguelen archipelago by using a green biomass index (point-intercept protocol) sampled at a small scale relative to PAL-NDVI data, and in presence of spatial (water) and temporal (cloud contamination, snow) heterogeneity, i.e. field conditions encountered in many ecological studies. We showed a strong relationship (r _pred.obs = 0.89 [0.77; 0.95]_95%) between this index and the seasonal component of NDVI time series (NDVI_comp). Despite the absence of lignified species in the stand, the NDVI_comp reached an asymptote (0.54 ± 0.05) for high values of green biomass index stressing the need to account for non-linearity when relating NDVI and plant measurements. We provided here a new methodological framework to standardize comparisons between studies assessing performance of NDVI as a proxy of vegetation data.

An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province,China

Grassland monitoring is important for both global change research and regional sustainable development. Gross primary production (GPP) is one of the key factors for understanding grass growing conditions. Methods for estimating GPP are plentiful, and the light use efficiency (LUE) model based on remote sensing data is widely used. The MODIS GPP product, which is employed by the National Aeronautics and Space Administration (NASA), is calculated using the LUE model and the surface reflection data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra/Aqua satellite. The MODIS GPP product harbors its own uncertainties arising from the sources and parameters, such as FPAR and light use efficiency (ɛ). In this study, we propose an improved indicator for monitoring grassland based on MODIS GPP and NDVI data. Fractional vegetation coverage and the percentage of grass area (1 km²) were used to reduce the mixed pixel effect. A function of NDVI was used to simulate the light use efficiency and FPAR. The modified GPP data were calculated and validated with in situ measured data from the Sichuan province, China, 2011. The results indicated that the modified GPP data were a more accurate indicator for monitoring grassland than previous indicators, and the precision of grass production simulated by SsGPP_ndvi reached 85.6%. Spatial statistic results were consistent with the practical condition in most cases. Since MODIS data are available twice a day, the improved indicator can meet the actual requirement of grassland monitoring at regional scale.     

Asynchronous Response of Tropical Forest Leaf Phenology to Seasonal and El Ni?o-Driven Drought

The Hawaiian Islands are an ideal location to study the response of tropical forests to climate variability because of their extreme isolation in the middle of the Pacific, which makes them especially sensitive to El Niño-Southern Oscillation (ENSO). Most research examining the response of tropical forests to drought or El Niño have focused on rainforests, however, tropical dry forests cover a large area of the tropics and may respond very differently than rainforests. We use satellite-derived Normalized Difference Vegetation Index (NDVI) from February 2000-February 2009 to show that rainforests and dry forests in the Hawaiian Islands exhibit asynchronous responses in leaf phenology to seasonal and El Niño-driven drought. Dry forest NDVI was more tightly coupled with precipitation compared to rainforest NDVI. Rainforest cloud frequency was negatively correlated with the degree of asynchronicity (Δ_NDVI) between forest types, most strongly at a 1-month lag. Rainforest green-up and dry forest brown-down was particularly apparent during the 2002–003 El Niño. The spatial pattern of NDVI response to the NINO 3.4 Sea Surface Temperature (SST) index during 2002–2003 showed that the leeward side exhibited significant negative correlations to increased SSTs, whereas the windward side exhibited significant positive correlations to increased SSTs, most evident at an 8 to 9-month lag. This study demonstrates that different tropical forest types exhibit asynchronous responses to seasonal and El Niño-driven drought, and suggests that mechanisms controlling dry forest leaf phenology are related to water-limitation, whereas rainforests are more light-limited.     

Osmotic adjustment in sorghum: I. Mechanisms of diurnal osmotic potential changes

Osmotic adjustment, defined as a lowering of osmotic potential (ψ_π) due to net solute accumulation in response to water stress, has been considered to be a beneficial drought tolerance mechanism in some crop species. The objective of this experiment was to determine the relative contribution of passive versus active mechanisms involved in diurnal ψ_π changes in sorghum (Sorghum bicolor L. Moench) leaf tissue in response to water stress. A single sorghum hybrid (cv AT×623 × RT×430) was grown in the field under variable water supplies. Water potential, ψ_π, and relative water content were measured diurnally on expanding and the uppermost fully expanded leaves before flowering and on fully expanded leaves during the grain-filling period. Diurnal changes in total osmotic potential (Δψ_π) in response to water stress was 1.1 megapascals before flowering and 1.4 megapascals during grain filling in comparison with 0.53 megapascal under well-watered conditions. Under water-stressed conditions, passive concentration of solutes associated with dehydration accounted for 50% (0.55 megapascal) of the diurnal Δψ_π before flowering and 47% (0.66 megapascal) of the change during grain filling. Net solute accumulation accounted for 42% (0.46 megapascal) of the diurnal Δψ_π before flowering and 45% (0.63 megapascal) of the change during grain filling in water-stressed leaves. The relative contribution of changes in nonosmotic volume (decreased turgid weight/dry weight) to diurnal Δψ_π was less than 8% at either growth stages. Water stress did not affect leaf tissue elasticity or partitioning of water between the symplasm and apoplasm.     

The uniqueness of the plant mitochondrial potassium channel

The ATP-inhibited Plant Mitochondrial K⁺ Channel (PmitoK_ATP) was discovered about fifteen years ago in Durum Wheat Mitochondria (DWM). PmitoKATP catalyses the electrophoretic K⁺ uniport through the inner mitochondrial membrane; moreover, the co-operation between PmitoK_ATP and ⁺/H⁺ antiporter allows such a great operation of a K⁺ cycle to collapse mitochondrial membrane potential (ΔΨ) and ΔpH, thus impairing protonmotive force (Δp). A possible physiological role of such ΔΨ control is the restriction of harmful reactive oxygen species (ROS) production under environmental/oxidative stress conditions. Interestingly, DWM lacking Δp were found to be nevertheless fully coupled and able to regularly accomplish ATP synthesis; this unexpected behaviour makes necessary to recast in some way the classical chemiosmotic model. In the whole, PmitoK_ATP may oppose to large scale ROS production by lowering ΔΨ under environmental/oxidative stress, but, when stress is moderate, this occurs without impairing ATP synthesis in a crucial moment for cell and mitochondrial bioenergetics. [BMB Reports 2013; 46(8): 391-397]     

Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco

African horse sickness (AHS) is a vector-borne, infectious disease of equids caused by African horse sickness virus (AHSV) . The only proven field vector of the virus is the biting midge Culicoides imicola. Following a recent epizootic (1989–91) of AHS in Morocco, light traps and automatic weather stations were operated for 2 years at twenty-two sites distributed over much of the country. The annually-averaged mean daily trap catch of C. imicola at these sites was negatively correlated with wind speed, and positively correlated with the average and mean annual minimum NDVI (Normalized Difference Vegetation Index, a remotely sensed measure of vegetation activity). There were no significant correlations between the mean daily trap catch and air temperature, soil temperature, relative humidity, saturation deficit, rainfall, altitude or the mean annual maximum or range of NDVI. The best two-variable model, which combined Windspeed_MnAvMn (the average daily minimum wind speed of the least windy month) and NDVI_min (the average annual minimum NDVI) as predictors, explained over 50% of the variance in the annually-averaged mean daily trap catch of C. imicola. There was a significant, positive correlation between minimum wind speed at night and the daily mortality rate of adult female C. imicola and it is suggested that the relationship between wind speed and the abundance of C. imicola arises from effects on adult mortality or dispersal. Considering several climatic variables, in North Africa NDVI_min was most significantly correlated with total annual rainfall. It is suggested that the relationship between NDVI_min and the abundance of C. imicola arises from the impact of soil moisture on both. It is proposed that areas of Morocco with higher levels of soil moisture in late summer or autumn provide more, larger and/or more enduring breeding sites for C. imicola, as well as supporting more photosynthetically active vegetation and hence having higher NDVI.     

黄土高原不同植被覆被类型NDVI对气候变化的响应

植被与气候是目前研究生态与环境的重要内容。为探究黄土高原地区植被与气候因子之间的响应机制,利用线性趋势分析、Pearson相关分析、多元线性回归模型以及通径分析的方法,对黄土高原2000—2015年全区和不同植被覆被类型区内NDVI与气候因子的变化趋势以及相互作用关系进行分析。植被覆被分类数据和植被指数数据分别来源于ESA CCI-LC(The European Space Agency Climate Change Initiative Land Cover)以及MODND1T/NDVI(Normalized Difference Vegetation Index)。结果表明:(1) 2000—2015年黄土高原全区植被年NDVI_(max)显著增加的区域占总面积的74.25%,不同植被覆被类型年NDVI_(max)分别为常绿阔叶林常绿针叶林落叶阔叶林落叶针叶林镶嵌草地农田镶嵌林地草地灌木,并且都呈显著增加趋势,其中常绿阔叶林和农田增加幅度最大,为0.012/a。(2)黄土高原全区NDVI与气温、日照、降水和相对湿度等气候因子之间没有显著相关性,但在不同植被覆被类型区,气候因子对NDVI存在显著作用,且不同植被覆被类型差异明显。(3)在全区和不同植被覆被类型区NDVI仅对降水的响应比较一致,气温无论在整个区域尺度还是不同植被覆被类型区对植被的影响均不显著。(4)常绿阔叶林、落叶阔叶林、常绿针叶林及镶嵌林地等以乔木为主的植被覆被类型受年均相对湿度和年总日照时数的显著负效应驱动,草地、镶嵌草地等以草本为主的植被覆被类型则受到年总降水量的显著正效应影响。这说明对植被类型进行区分,更有利于揭示气候对植被的作用机制。     

Mapping Current and Potential Distribution of Non-Native Prosopis juliflora in the Afar Region of Ethiopia

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m² spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species- occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km² in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km² (AUC  = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.     

Analysing forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices

Wildfires have major effects on forest dynamics, succession and the carbon cycle in the boreal biome. They are a significant source of carbon emissions, and current observed changes in wildfire regimes due to changes in climate could affect the balance of the boreal carbon pool. A better understanding of postwildfire vegetation dynamics in boreal forests will help predict the future role of boreal forests as a carbon sink or source. Time series of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Shortwave Infrared Index (NDSWIR) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite were used to investigate whether characteristic temporal patterns exist for stands of different ages in the Siberian boreal forests and whether their postwildfire dynamics are influenced by variables such as prewildfire vegetation cover. Two types of forests, evergreen needle‐leaf (ENF) and deciduous needle‐leaf (DNF), were studied by analysing a sample of 78 burned forest areas. In order to study a longer time frame, a chronosequence of burned areas of different ages was built by coupling information on location and age provided by a forest burned area database (from 1992 to 2003) to MODIS NDVI and NDSWIR time series acquired from 2001 to 2005. For each of the burned areas, an adjacent unburned control plot representing the same forest type was selected, with the aim of separating the interannual variations caused by climate from changes in NDVI and NDSWIR behaviour due to a wildfire. The results suggest that it takes more than 13 years for the temporal NDVI and NDSWIR signal to recover fully after wildfire. NDSWIR, which is associated to canopy moisture, needs a longer recovery period than NDVI, which is associated to vegetation greenness. The results also suggest that variability observed in postwildfire NDVI and NDSWIR can be explained partially by the dominant forest type: while 13 years after a fire NDVI and NDSWIR are similar for ENF and DNF, the initial impact appears to be greater on the NDVI and NDSWIR of ENF, suggesting a faster recovery by ENF.     

Accuracy of the AVHRR vegetation index as a predictor of biomass,primary productivity and net CO2 flux

The Normalized Difference Vegetation Index (NDVI) or greenness index, based on the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA-7 satellite, has been widely interpreted as a measure of regional to global vegetation patterns. This study provides the first rigorous, quantitative evaluation of global relationships between the NDVI and geographically representative vegetation data-bases, including field metabolic measurements and carbon-balance results from global simulation models. Geographic reliability of the NDVI is judged by comparing NDVI values for different surface types with a general global trend and by statistical analysis of relationships to biomass amounts, net and gross primary productivity, and actual evapotranspiration. NDVI data appear to be relatively reliable predictors of primary productivity except in areas of complex terrain, for seasonal values at high latitudes, and in extreme deserts. The strength of the NDVI-productivity relationship seems comparable to that of earlier climate-based productivity models. Little consistent relationship was found, across different vegetation types, between NDVI and biomass amounts or net biospheric CO₂ flux.Abbreviations AET= Actual Evapotranspiration - AVHRR= Advanced Very High Resolution Radiometer - GPP= Gross Primary Production - GVI= Global Vegetation Index - NDVI= Normalized Difference Vegetation Index - NPP= Net Primary Production     

中国不同植被区对极端气候的响应差异

分析不同区域植被对极端气候的响应对于加深对植被与气候之间关系的理解以及制定应对极端气候条件的措施尤为重要。基于2001—2020年气候数据和归一化植被指数(NDVI)数据,以植被区划为分析单元,分析中国8个植被区的NDVI和27个极端气候指数的时空变化趋势,探究各植被区植被NDVI对极端气候的响应特征与差异性。结果表明：(1)整个研究区及各植被区的平均NDVI年最大值呈显著增加趋势,其中,温带针叶、落叶阔叶混交林区增加趋势最明显,青藏高原高寒植被区增加趋势最弱。(2)极端高温指数多呈升高趋势。极端降水指数在研究区东部呈升高趋势,在西南部呈减少趋势。(3)在不同植被区对NDVI影响最大的极端气候指数不同,其中在寒温带针叶林区影响最大的指数为温暖时间持续指数(WSDI);在温带针叶、落叶阔叶混交林区和热带季风雨林、雨林区影响最大的指数为最高低温(TNx);在暖温带落叶阔叶林区和亚热带常绿阔叶林区为简单降水强度指数(SDII);在温带草原区为最高高温(TXx);在温带荒漠区为年总降水量(PRCPTOT);在青藏高原高寒植被区为结冰天数(ID)。     

A Benchmark Test for Ecohydrological Models of Interannual Variability of NDVI in Semi-arid Tropical Grasslands

Pulses of aboveground net primary productivity (ANPP) in response to discrete precipitation events are an integral feature of ecosystem functioning in arid and semi-arid lands. Yet, the usefulness of nonlinear, ecohydrological pulse response functions to predict regional-scale patterns of annual ANPP at decadal scales remains unclear. Here, we assessed how different pulse response (PR) models compete with simple linear statistical models to capture variability in yearly integrated values of Normalized Difference Vegetation Index (NDVI_int), a remotely sensed proxy of annual ANPP. We examined 24-year-long time series of NDVI_int calculated from Advanced Very High Resolution Radiometer (AVHRR) NDVI for 350,000 km² of tropical grasslands in northern Australia. Based on goodness-of-fit statistics, PR models clearly outperformed statistical models when parameters were optimized for each site but all models showed the same error magnitude when all sites were combined in ensemble simulations or when the models were evaluated outside the calibration period. PR models were less biased and their performance did not deteriorate in the driest areas compared to linear models. Increasing the complexity of PR models to provide a better representation of soil water balance and its feedback with plant growth did not improve model performance in ensemble simulations. When error magnitude, bias, and sensitivity to parameter uncertainty were all considered, we concluded that a low-dimensional PR model was the most robust to capture NDVI_int variability. This study shows the potential of long time series of AVHRR NDVI to benchmark process-oriented models of interannual variability of NDVI_int in water-controlled ecosystems. This opens new avenues to examine at the global scale and over several decades the causal relationships between climate and leaf dynamics in the grassland biome.     

基于多时相MODIS数据的东北地区一季稻面积提取

利用2007-2008年MODIS/Terra陆表反射率数据提取的归一化植被指数(NDVI)、增强植被指数(EVI)及陆表水分指数(LSWI),分析了东北地区水稻、旱田、林地、湿地和水体5种不同下垫面在作物生长季的动态变化,同时结合水稻发育期观测数据,建立了东北地区一季稻面积提取模型,并制作了东北地区水稻种植面积分布图.以辽宁盘锦为试验区,利用ALOS数据提取结果对该模型进行了试验,提取精度达到89.5％,结果表明该方法可以较高精度进行大范围的一季稻种植面积提取.     

The Human Mitochondrial DNA Depletion Syndrome Gene MPV17 Encodes a Non-selective Channel That Modulates Membrane Potential

The human MPV17-related mitochondrial DNA depletion syndrome is an inherited autosomal recessive disease caused by mutations in the inner mitochondrial membrane protein MPV17. Although more than 30 MPV17 gene mutations were shown to be associated with mitochondrial DNA depletion syndrome, the function of MPV17 is still unknown. Mice deficient in Mpv17 show signs of premature aging. In the present study, we used electrophysiological measurements with recombinant MPV17 to reveal that this protein forms a non-selective channel with a pore diameter of 1.8 nm and located the channel''s selectivity filter. The channel was weakly cation-selective and showed several subconductance states. Voltage-dependent gating of the channel was regulated by redox conditions and pH and was affected also in mutants mimicking a phosphorylated state. Likewise, the mitochondrial membrane potential (Δψ_m) and the cellular production of reactive oxygen species were higher in embryonic fibroblasts from Mpv17^−/− mice. However, despite the elevated Δψ_m, the Mpv17-deficient mitochondria showed signs of accelerated fission. Together, these observations uncover the role of MPV17 as a Δψ_m-modulating channel that apparently contributes to mitochondrial homeostasis under different conditions.