Environmental and Vegetation Drivers of Seasonal CO2 Fluxes in a Sub-arctic Forest–Mire Ecotone

Unravelling the role of structural and environmental drivers of gross primary productivity (GPP) and ecosystem respiration (R _eco) in highly heterogeneous tundra is a major challenge for the upscaling of chamber-based CO₂ fluxes in Arctic landscapes. In a mountain birch woodland-mire ecotone, we investigated the role of LAI (and NDVI), environmental factors (microclimate, soil moisture), and microsite type across tundra shrub plots (wet hummocks, dry hummocks, dry hollows) and lichen hummocks, in controlling net ecosystem CO₂ exchange (NEE). During a growing season, we measured NEE fluxes continuously, with closed dynamic chambers, and performed multiple fits (one for each 3-day period) of a simple light and temperature response model to hourly NEE data. Tundra shrub plots were largely CO₂ sinks, as opposed to lichen plots, although fluxes were highly variable within microsite type. For tundra shrub plots, microsite type did not influence photosynthetic parameters but it affected basal (that is, temperature-normalized) ecosystem respiration (R ₀). PAR-normalized photosynthesis (P ₆₀₀) increased with air temperature and declined with increasing vapor pressure deficit. R ₀ declined with soil moisture and showed an apparent increase with temperature, which may underlie a tight link between GPP and R _eco. NDVI was a good proxy for LAI, maximum P ₆₀₀ and maximum R ₀ of shrub plots. Cumulative CO₂ fluxes were strongly correlated with LAI (NDVI) but we observed a comparatively low GPP/LAI in dry hummocks. Our results broadly agree with the reported functional convergence across tundra vegetation, but here we show that the role of decreased productivity in transition zones and the influence of temperature and water balance on seasonal CO₂ fluxes in sub-Arctic forest–mire ecotones cannot be overlooked.     

Spatial Patterns and Natural Recruitment of Native Shrubs in a Semi-arid Sandy Land

Passive restoration depending on native shrubs is an attractive approach for restoring desertified landscapes in semi-arid sandy regions. We sought to understand the relationships between spatial patterns of native shrubs and their survival ability in sandy environments. Furthermore, we applied our results to better understand whether passive restoration is feasible for desertified landscapes in semi-arid sandy regions. The study was conducted in the semi-arid Mu Us sandy land of northern China with the native shrub Artemisia ordosica. We analyzed population structures and patterns of A. ordosica at the edges and centers of land patches where sand was stabilized by A. ordosica-dominated vegetation. Saplings were more aggregated than adults, and both were more aggregated at the patch edges than at the patch centers. At the patch edges, spatial association of the saplings with the adults was mostly positive at distances 0.3–6.6 m, and turned from positive to neutral, and even negative, at other distances. At the patch centers, the saplings were spaced almost randomly around the adults, and their distances from the adults did not seem to affect their locations. A greater number of A. ordosica individuals emerged at the patch edges than at the patch centers. Such patterns may have resulted from their integrative adjustment to specific conditions of soil water supply and sand drift intensity. These findings suggest that in semi-arid sandy regions, native shrubs that are well-adapted to local environments may serve as low-cost and competent ecological engineers that can promote the passive restoration of surrounding patches of mobile sandy land.     

Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest

Aims 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.Methods Daily photosynthetic capacity was assessed by in situ canopy leaf area index (LAI), (LAI × V cmax [single-leaf photosynthetic capacity]), and the daily maximum rate of gross primary production (GPP max) was estimated by an ecosystem carbon cycle model. We examined five VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red vegetation index (GRVI), chlorophyll index (CI) and canopy chlorophyll index (CCI), which were obtained by the in situ measurements of canopy spectral reflectance.Important findings Our in situ observation of leaf and canopy characteristics, which were analyzed by an ecosystem carbon cycling model, revealed that their phenological changes are responsible for seasonal and interannual variations in canopy photosynthesis. Significant correlations were found between the five VIs and canopy photosynthetic capacity over the seasons and years; four of the VIs showed hysteresis-type relationships and only CCI showed rather linear relationship. Among the VIs examined, we applied EVI–GPP max relationship to EVI data obtained by Moderate Resolution Imaging Spectroradiometer to estimate the temporal and spatial variation in GPP max over central Japan. Our findings would improve the accuracy of satellite-based estimate of forest photosynthetic productivity in fine spatial and temporal resolutions, which are necessary for detecting any response of terrestrial ecosystem to meteorological fluctuations.     

利用不同分辨率卫星影像的NDVI数据估算叶面积指数(LAI) ——以岷江上游为例

短周期的低分辨率遥感数据为大面积估算LAI及季节动态和物候趋势提供了有利工具,但基于高分辨率LAI的遥感估算模型在低分辨率遥感数据上应用有很大的不确定性。研究利用LAI-2000冠层分析仪与跟踪辐射和冠层结构测量仪(TRAC),测定了岷江上游流域范围内490块野外调查样地（50m×50m样方）的LAI数据,结合同期较高精度卫星数据（TM）建立了不同植被类型的LAI-NDVI算法,在经过传感器的相对校正后,将这种算法应用到同期分辨率较低的MODIS数据和SPOT VEGETATION数据上。结果表明,30m 分辨率的TM LAI的均值为4.53,250m MODIS LAI的均值为3.55,1000m VGT LAI的均值为4.20,随着栅格分辨率的降低,总体标准差有增加的趋势,并且LAI值也有不同程度的低估,其中MODIS LAI值被低估约22%。但利用TM LAI数据验证MODIS 和VGT LAI数据后发现,250m的MODIS数据预测误差在30%左右,1000m的SPOT数据预测误差则高达50%,空间重采样分析表明,栅格分辨率的降低是导致预测误差扩大的主要原因,而这也是岷江流域植被分布破碎化的体现。     

Spatial variation in regional CO2 exchange for the Kuparuk River Basin, Alaska over the summer growing season

The spatial and temporal patterns in CO₂ flux for the Kuparuk River Basin, a 9200‐km² watershed located in NE Alaska were estimated using the Regional Arctic CO₂ Exchange Simulator (RACES) for the 1994–1995 growing seasons. RACES uses non‐linear models and a Geographical Information System database (GIS) consisting of the normalized difference vegetation index (NDVI) and dynamic temperature and radiation maps. The spatial and temporal patterns in the NDVI during both growing seasons suggest that ecosystem development occurred 2–4 weeks earlier and was relatively more rapid in the southern portion of the Kuparuk River Basin. Rates of gross primary production (GPP) and whole‐ecosystem respiration (R) were 2–4 fold higher in the southern basin than along the arctic coastal plain depending on time of year. The higher rate of GPP estimated for the southern basin was primarily due to higher NDVI values, while the higher R estimated for the southern basin was due in part to higher temperature and the NDVI. While GPP and R showed strong latitudinal trends, spatial and temporal trends in net ecosystem CO₂ exchange (NEE) were much more variable. Thus, while spatial trends in carbon gain (GPP) and loss (R) were highly correlated, small spatial and temporal differences in these large fluxes (GPP and/or R) lead to corresponding large spatial variations in the NEE.     

Impact of tropospheric ozone on the Euro‐Mediterranean vegetation

The impact of ozone (O₃) on European vegetation is largely under‐investigated, despite huge areas of Europe are exposed to high O₃ levels and which are expected to increase in the next future. We studied the potential effects of O₃ on photosynthesis and leaf area index (LAI) as well as the feedback between vegetation and atmospheric chemistry using a land surface model (ORCHIDEE) at high spatial resolution (30 km) coupled with a chemistry transport model (CHIMERE) for the whole year 2002. Our results show that the effect of tropospheric O₃ on vegetation leads to a reduction in yearly gross primary production (GPP) of about 22% and a reduction in LAI of 15–20%. Larger impacts have been found during summer, when O₃ reaches higher concentrations. During these months the maximum GPP decrease is up to 4 g C m^?2 day^?1, and the maximum LAI reduction is up to 0.7 m² m^?2. Since CHIMERE uses the LAI computed by ORCHIDEE to estimate the biogenic emissions, a LAI reduction may have severe implications on the simulated atmospheric chemistry. We found a large change in O₃ precursors that however leads to small changes in tropospheric O₃ concentration, while larger changes have been found for surface NO₂ concentrations.     

基于多角度植被指数的马尾松林LAI反演方法

CHRIS/PROBA是目前具有最高空间分辨率（17 m×17 m）的星载多角度高光谱数据,该款数据在反演植被垂直结构参数,如树高、叶面积指数（leaf area index,LAI）等方面具有重要的应用前景。基于四尺度几何光学模型得到马尾松（Pinus massoniana Lamb.）冠层的归一化差分植被指数（normalized difference vegetation index,NDVI）各向异性分布规律,利用CHRIS红光特征波段和近红外特征波段构建一种新型多角度植被指数（normalized hotspot-dark-spot difference vegetation index,NHDVI）,并将其应用于CHRIS数据对马尾松林的LAI遥感估算上。结果显示：（1）相比归一化差分植被指数（NDVI）与土壤调节植被指数（soil adjusted vegetation index,SAVI）而言,NHDVI能很好地融合光谱信息与角度信息,与地面实测LAI的决定系数达到0.7278;（2）利用NHDVI-LAI统计回归模型方法来反演LAI值,将得到的LAI值与地面实测值进行相关性分析,结果拟合优度达到0.8272,均方根误差RMSE为0.1232。与传统植被指数相比,包含角度信息的多角度植被指数对LAI的反演在精度上有较大提升,同时比基于辐射传输模型的反演方法更简易、实用。     

Tight coupling between leaf area index and foliage N content in arctic plant communities

The large spatial heterogeneity of arctic landscapes complicates efforts to quantify key processes of these ecosystems, for example productivity, at the landscape level. Robust relationships that help to simplify and explain observed patterns, are thus powerful tools for understanding and predicting vegetation distribution and dynamics. Here we present the same linear relationship between Leaf area index (LAI) and Total foliar nitrogen (TFN), the two factors determining the photosynthetic capacity of vegetation, across a wide range of tundra vegetation types in both northern Sweden and Alaska between leaf area indices of 0 and 1 m² m^–2, which is essentially the entire range of leaf area index values for the Arctic as a whole. Surprisingly, this simple relationship arises as an emergent property at the plant community level, whereas at the species level a large variability in leaf traits exists. As the relationship between LAI and TFN exists among such varied ecosystems, the arctic environment must impose tight constraints on vegetation canopy development. This relationship simplifies the quantification of vegetation productivity of arctic vegetation types as the two most important drivers of productivity can be estimated reliably from remotely sensed NDVI images.     

Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity

Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m²/m² and biases of 0.42–0.55 m²/m² for all three simulated datasets and an RMSE of 1.22 m²/m² with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties.     

Estimation of leaf area index and canopy openness in broad-leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography

We estimated leaf area index (LAI) and canopy openness of broad-leaved forest using discrete return and small-footprint airborne laser scanner (ALS) data. We tested four ALS variables, including two newly proposed ones, using three echo types (first, last, and only) and three classes (ground, vegetation, and upper vegetation), and compared the accuracy by means of correlation and regression analysis with seven conventional vegetation indices derived from simultaneously acquired high-resolution near-infrared digital photographs. Among the ALS variables, the ratio of the “only-and-ground” pulse to “only” pulse (OGF) was the best estimator of both LAI (adjusted R ² = 0.797) and canopy openness (adjusted R ² = 0.832), followed by the ratio of the pulses that reached the ground to projected lasers (GF). Among the vegetation indices, the normalized differential vegetation index (NDVI) was the best estimator of both LAI (adjusted R ² = 0.791) and canopy openness (adjusted R ² = 0.764). Resampling analysis on ALS data to examine whether the estimation of LAI and canopy openness was possible with lower point densities revealed that GF maintained a high adjusted R ² until a fairly low density of about 0.226 points/m², while OGF performed marginally when the point density was reduced to about 1 point/m², the standard density of high-density products on the market as of February 2008. Consequently, the ALS variables proposed in the present study, GF and OGF, seemed to have great potential to estimate LAI and canopy openness of broad-leaved forest, with accuracy comparable to NDVI, from high-resolution near-infrared imagery.     

Using Information Theory to Determine Optimum Pixel Size and Shape for Ecological Studies: Aggregating Land Surface Characteristics in Arctic Ecosystems

Quantifying vegetation structure and function is critical for modeling ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Land surface heterogeneity is commonly characterized using rectangular pixels, whose length scale reflects that of remote sensing measurements or ecological models rather than the spatial scales at which vegetation structure and function varies. We investigated the ‘optimum’ pixel size and shape for averaging leaf area index (LAI) measurements in relatively large (85 m² estimates on a 600 × 600-m² grid) and small (0.04 m² measurements on a 40 × 40-m² grid) patches of sub-Arctic tundra near Abisko, Sweden. We define the optimum spatial averaging operator as that which preserves the information content (IC) of measured LAI, as quantified by the normalized Shannon entropy (E _S,n) and Kullback–Leibler divergence (D _KL), with the minimum number of pixels. Based on our criterion, networks of Voronoi polygons created from triangulated irregular networks conditioned on hydrologic and topographic indices are often superior to rectangular shapes for averaging LAI at some, frequently larger, spatial scales. In order to demonstrate the importance of information preservation when upscaling, we apply a simple, validated ecosystem carbon flux model at the landscape level before and after spatial averaging of land surface characteristics. Aggregation errors are minimal due to the approximately linear relationship between flux and LAI, but large errors of approximately 45% accrue if the normalized difference vegetation index (NDVI) is averaged without preserving IC before conversion to LAI due to the nonlinear NDVI-LAI transfer function. Author Contributions:  PS devised and undertook the analyses and wrote the paper. MW devised and implemented the measurement plan, and reviewed the analysis. LS assisted in the spatial data analysis and derivation of the terrain indices. RAB provided the macro-scale dataset. APB generated the DEM from aircraft data. JGE provided meteorological data. MvW generated the micro-scale field data with the help of Lorna Street and Sven Rasmussen. All authors contributed to the text.     

Modeling gross primary productivity of alpine meadow in the northern Tibet Plateau by using MODIS images and climate data

The Vegetation Photosynthesis Model (VPM) was used to simulate the gross primary productivities (GPP) of the alpine meadow ecosystem in the northern Tibet Plateau at three different spatial resolutions of 0.5 km, 1.5 km and 2.5 km, respectively. The linear relationships between enhanced vegetation indices (EVI) and GPP, with higher correlative coefficients, were better than those between normalized difference vegetation indices (NDVI) and GPP at the three resolutions. VPM could well simulate the seasonal changes and inter-annual variations of GPP, with similar trends at the three resolutions. There were significant differences (P < 0.0001) among the three modeled GPP with the three resolutions. Therefore, the modeled GPP at high resolution could not be directly extrapolated to low resolution, and vice versa. The contribution levels of different model parameters, including photosynthetically active radiation (PAR), air temperature (Ta), NDVI, EVI and land surface water indices (LSWI), to modeled GPP could vary with spatial resolution based on multiple stepwise linear regression analysis. This indicated that it was important to choose parameters properly and consider their effects on modeled GPP.     

Construction of 3D landscape indexes based on oblique photogrammetry and its application for islands

Landscape indexes are quantitative indicators that reflect the composition and spatial configuration of landscape structures. However, the current two-dimensional (2D) spatial analysis methods lack accuracy in assessing patch characteristics due to the lack of three-dimensional (3D) information. Following the “Patch-Class-Landscape” framework, eight 3D landscape indexes were built to quantitatively describe spatial landscape features and two sensitivity indexes were developed to identify the differences between 2D and 3D structures. Based on two types of oblique photogrammetry data, validation and comparison studies were conducted for Tianheng Island and Sichang Island. The results found the following: (1) At the patch level, the 3D shape index (TPSI) of vegetation was generally higher than that of buildings, with an R² of 0.634, and the classification index (TCI) showed remarkable performance in identifying patch type. The patch type was likely to be building or vegetation when TCI approached 33, respectively, with a classification accuracy of 90% after verification. (2) At the class level, the 3D percentage of landscape (TPLAND) of grassland and arbor types on the two islands were quite different, reflecting significant differences in the dimensionality of the vegetation landscapes, as influenced by different climatic zones. Moreover, the 3D landscape shape index (TLSI) and other shape-related indexes had higher exponential sensitivity coefficient (ESC) values, due to the higher amount of 3D shape information they carry. (3) At the landscape level, the two 3D Shannon indexes (TSHDI and TSHEI) did not significantly change compared with their 2D counterparts, implying that these two indexes, as larger-scale landscape indicators, had lower sensitivity when extra-dimensional information was added. Overall, the 3D landscape indexes can better present 3D information at different landscape levels. As a potential and effective assessment tool and it will be applied to improve existing spatial planning and landscape management.     

Modeling gross primary productivity of alpine meadow in the northern Tibet Plateau by using MODIS images and climate data

The Vegetation Photosynthesis Model (VPM) was used to simulate the gross primary productivities (GPP) of the alpine meadow ecosystem in the northern Tibet Plateau at three different spatial resolutions of 0.5 km, 1.5 km and 2.5 km, respectively. The linear relationships between enhanced vegetation indices (EVI) and GPP, with higher correlative coefficients, were better than those between normalized difference vegetation indices (NDVI) and GPP at the three resolutions. VPM could well simulate the seasonal changes and inter-annual variations of GPP, with similar trends at the three resolutions. There were significant differences (P < 0.0001) among the three modeled GPP with the three resolutions. Therefore, the modeled GPP at high resolution could not be directly extrapolated to low resolution, and vice versa. The contribution levels of different model parameters, including photosynthetically active radiation (PAR), air temperature (Ta), NDVI, EVI and land surface water indices (LSWI), to modeled GPP could vary with spatial resolution based on multiple stepwise linear regression analysis. This indicated that it was important to choose parameters properly and consider their effects on modeled GPP.     

基于MODIS NDVI数据的陕西省植被绿度时空变化及人类活动影响

气候和人类活动是控制和影响植被空间分布及其变化的基本驱动力。利用2001-2018年的MODIS NDVI和1999-2018年的降雨时间序列数据,分析了陕西省NDVI的时空变化规律。采用TSS-RESTREND （Time Series Segmentation and Residual Trend）算法剥离了气候要素（降雨）对植被NDVI的影响,分析了人类活动对植被变化的影响程度和区域。（1）2001-2018年间,陕西省NDVI呈显著增加,全省平均增加速率为0.006/a;（2）相比18年来的平均值,77.29%的区域大于均值。其中,陕北的榆林市、延安市大于均值的区域较大,分别为97.52%和89.03%,秦巴山区次之,为73.91%。2012年之后,NDVI高值向北逐年推进趋势明显。（3）全省NDVI增加的区域达71.77%,而陕北地区的增加量明显大于关中平原区和陕南秦巴山地,其中陕北的榆林NDVI增加区域为72.11%,延安为86.44%,均超过了全省平均水平。（4）总体上陕西全省呈变绿趋势。榆林市和延安市的变绿区域明显多于关中平原和秦巴山地,延安和榆林的剧烈增长区域分别为55.46%和34.34%,而陕南为41.03%,说明处于湿润气候区的陕南地区也有显著变绿趋势。     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

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.     

2000-2010年黄河流域植被覆盖的时空变化

黄河流域位于干旱、半干旱和半湿润地区,生态环境脆弱,近年来,在气候变化和人类活动影响下,植被覆盖状况发生了变化。因此需要对黄河流域植被覆盖的变化进行监测,进而掌握流域植被的动态变化特征。在此背景下,利用2000-2010年的250 m分辨率的MOD13Q1数据来研究黄河流域植被覆盖区域的NDVI时空变化特征。采用Theil-Sen Median趋势分析和Mann-Kendall检验来研究NDVI的变化趋势特征,通过对Theil-Sen Median趋势分析和Mann-Kendall检验的结果和Hurst指数的结果的叠加,来研究NDVI的可持续特征。研究表明：1）从空间分布上看,黄河流域NDVI呈现出西部和东南部高,北部低的特征;2）从时间变化特征上看,2000-2010年植被覆盖区域年均NDVI均值在0.3-0.4之间波动,其中2000-2004年NDVI波动较大,但自2005年以来NDVI呈现快速增长的趋势;3）从变化趋势上看,2000-2010年黄河流域植被改善的区域远远大于退化的区域,改善的区域占植被覆盖区域总面积的62.9%,退化的区域占27.7%,9.4%的区域NDVI稳定不变;4）从可持续性来看,86.0%的植被覆盖区域NDVI呈现正向可持续性,即NDVI的可持续性较强;由变化趋势与Hurst指数的耦合信息得出,持续改善的面积占植被覆盖区域总面积的53.7%,持续稳定不变的区域占7.8%,持续退化的区域占24.5%,另外14.0%的区域未来变化趋势无法确定,持续退化和未来变化趋势无法确定区域的植被变化状况需要研究人员继续关注。     

An improved approach for remotely sensing water stress impacts on forest C uptake

Given that forests represent the primary terrestrial sink for atmospheric CO₂, projections of future carbon (C) storage hinge on forest responses to climate variation. Models of gross primary production (GPP) responses to water stress are commonly based on remotely sensed changes in canopy ‘greenness’ (e.g., normalized difference vegetation index; NDVI). However, many forests have low spectral sensitivity to water stress (SSWS) – defined here as drought‐induced decline in GPP without a change in greenness. Current satellite‐derived estimates of GPP use a vapor pressure deficit (VPD) scalar to account for the low SWSS of forests, but fail to capture their responses to water stress. Our objectives were to characterize differences in SSWS among forested and nonforested ecosystems, and to develop an improved framework for predicting the impacts of water stress on GPP in forests with low SSWS. First, we paired two independent drought indices with NDVI data for the conterminous US from 2000 to 2011, and examined the relationship between water stress and NDVI. We found that forests had lower SSWS than nonforests regardless of drought index or duration. We then compared satellite‐derived estimates of GPP with eddy‐covariance observations of GPP in two deciduous broadleaf forests with low SSWS: the Missouri Ozark (MO) and Morgan Monroe State Forest (MMSF) AmeriFlux sites. Model estimates of GPP that used VPD scalars were poorly correlated with observations of GPP at MO (r² = 0.09) and MMSF (r² = 0.38). When we included the NDVI responses to water stress of adjacent ecosystems with high SSWS into a model based solely on temperature and greenness, we substantially improved predictions of GPP at MO (r² = 0.83) and for a severe drought year at the MMSF (r² = 0.82). Collectively, our results suggest that large‐scale estimates of GPP that capture variation in SSWS among ecosystems could improve predictions of C uptake by forests under drought.     

表征亚热带常绿林光合作用季节变化特征的多种植被指数

利用遥感方法可以在区域尺度反演地表植被的光合生理状况和生产力变化,但亚热带常绿林冠层结构季节变化较小,传统的光谱植被指数对植被光合作用难以准确捕捉。利用2014—2015年中国科学院广东省鼎湖山森林生态试验站多角度自动光谱观测系统的光谱反射数据,分别反演传统冠层结构型植被指数(NDVI)、光合生理生化型植被指数(CCI)和叶绿素荧光型植被指数(NDFI_(685)和NDFI_(760)),并利用不同类型植被指数的组合,构建多元线性回归模型。结果表明:亚热带常绿针阔混交林三种类型植被指数均与GPP的动态变化有显著的相关性,其中,NDVI是表征GPP较优的植被指数(R~2=0.60,P0.01),其次为CCI(R~2=0.55,P0.01),而NDFI能够作为辅助指数,有效提高NDVI(R~2=0.68,P0.001)和CCI(R~2=0.67,P0.001)表征GPP的程度。多个植被指数参与构建的多元回归模型能够有效提高亚热带地区常绿林GPP季节动态变化的拟合精度,提升遥感精确评估亚热带森林生产力的能力。