Studying the vegetation response to simulated leakage of sequestered CO2 using spectral vegetation indices

Measurement of spectral reflectance provides a fast and nondestructive method of stress detection in vegetation. In this shallow subsurface CO₂ release experiment to simulate CO₂ leakage of geologically sequestered CO₂, the radiometric responses of plants to elevated soil CO₂ concentration were monitored using a spectroradiometer. Spectral responses included increased reflectance in the visible spectral region and decreased reflectance in the near-infrared region and thus an altered spectral pattern of vegetation. Visible responses of vegetation include purple discoloration and eventual death of leaves at sites where the soil CO₂ concentration was very high. Derivative analysis identified two features (minimum and maximum) in the 575–580 nm and 720–723 nm spectral regions. The normalized difference first derivative index (NFDI) was defined based on the spectral derivative at the two bands. Four vegetation indices were analyzed with the accumulated soil CO₂ concentration to assess the accumulated impact of high soil CO₂ concentration on vegetation. Results show that with increased soil CO₂ concentration due to the surface CO₂ leakage, (1) the structural independent pigment index (SIPI) increased, indicating a high carotenoid to chlorophyll ratio; (2) the chlorophyll normalized difference vegetation index (Chl NDI) decreased, suggesting a decrease in chlorophyll content with time; (3) pigment specific simple ratios (both PSSRa and PSSRb) were reduced for stressed vegetation compared to that at the control site, indicating a reduction in both chlorophyll a and chlorophyll b; and (4) NFDI was low where plants were stressed. Changes in NFDI during the experiment were 36% and 1% for stressed and control plants, respectively. All four indices were found to be sensitive to stress in vegetation induced by high soil CO₂ concentration.     

基于Hyperion高光谱数据的城市植被胁迫评价

快速获取城市植被的胁迫状态,不仅对城市植被健康状况的维护,而且对城市生态环境的改善具有重要意义．在对受胁迫植被的生理特征和光谱特征进行分析的基础上,利用星载高光谱Hyperion数据,计算出与胁迫相关的14种高光谱植被指数,在此基础上运用BP神经网络算法建立了城市植被胁迫强度分类器,对城市植被的胁迫强度进行了识别与分析．结果表明：城市中心商住区的植被受胁迫程度明显高于城乡结合部和郊区;植被的受胁迫现象在大块绿地外围呈环状分布;构建的植被胁迫强度分类器能够较为准确地反映植被受胁迫的强度信息,可为大面积城市植被胁迫监测提供一种较为可靠而快捷的方法．     

Hyperspectral Detection of a Subsurface CO2 Leak in the Presence of Water Stressed Vegetation

Remote sensing of vegetation stress has been posed as a possible large area monitoring tool for surface CO₂ leakage from geologic carbon sequestration (GCS) sites since vegetation is adversely affected by elevated CO₂ levels in soil. However, the extent to which remote sensing could be used for CO₂ leak detection depends on the spectral separability of the plant stress signal caused by various factors, including elevated soil CO₂ and water stress. This distinction is crucial to determining the seasonality and appropriateness of remote GCS site monitoring. A greenhouse experiment tested the degree to which plants stressed by elevated soil CO₂ could be distinguished from plants that were water stressed. A randomized block design assigned Alfalfa plants (Medicago sativa) to one of four possible treatment groups: 1) a CO₂ injection group; 2) a water stress group; 3) an interaction group that was subjected to both water stress and CO₂ injection; or 4) a group that received adequate water and no CO₂ injection. Single date classification trees were developed to identify individual spectral bands that were significant in distinguishing between CO₂ and water stress agents, in addition to a random forest classifier that was used to further understand and validate predictive accuracies. Overall peak classification accuracy was 90% (Kappa of 0.87) for the classification tree analysis and 83% (Kappa of 0.77) for the random forest classifier, demonstrating that vegetation stressed from an underground CO₂ leak could be accurately discerned from healthy vegetation and areas of co-occurring water stressed vegetation at certain times. Plants appear to hit a stress threshold, however, that would render detection of a CO₂ leak unlikely during severe drought conditions. Our findings suggest that early detection of a CO₂ leak with an aerial or ground-based hyperspectral imaging system is possible and could be an important GCS monitoring tool.     

Delineation of vegetation shaded ox-bow lakes in Ganges flood plain,India

Floating vegetation cover over the ox-bow lake withstands against its sharp delineation. A good many spectral indices are successfully used for water body delineation. But how far these are applicable in vegetation-shaded ox-bow lakes is a research question. The study also aimed that if the existing indices are not satisfactory and how a new index could be endorsed for resolving the problem. The study additionally monitored the ox-bow lake and vegetation cover area from 1991 to 2021 based on Landsat satellite images.Normalized differences water index (NDWI), Modified NDWI (MNDWI), Re-modified NDWI (RmNDWI), and Normalized Difference Vegetation Index (NDVI) spectral indices were used for delineating ox-bow lakes and multiple accuracy test measures revealed that these are not highly satisfactory. Vegetation inclusive aggregated water index (ViAWI) was built by coupling mentioned spectral indices with the vegetation index and the ensemble map was found more accurate.Monitoring the ox-bow lake area clearly showed that these declined in the last 30 years irrespective of the historical drainage modification legacy of the major rivers to which the ox-bow lakes belonged. Aquatic vegetation cover within ox-bow lakes changed dynamically.The endorsed ViAWI would be a good approach for resolving wetland delineation shaded with floating vegetation and it could be used in other regional units worldwide. Quantitative information regarding ox-bow lake and vegetation cover within ox-bow lakes would be valuable data support for adopting ox-bow lake conservation and restoration planning.     

The independent detection of drought stress and leaf density using hyperspectral resolution data

Measurement of vegetation drought stress or leaf density is essential in ecosystem and agronomic studies. The normalized differential vegetation index (NDVI), a widely used vegetation index in remote sensing, seems to have some limitations as it is known to be affected by both drought stress and leaf density. A field experiment was conducted, using two-year-old potted Quercus serrata (a deciduous tree) and Q. glauca (an evergreen tree), to determine the optimal indices of vegetation drought stress or leaf density that have the least a simultaneous effect, and to test if the existing vegetation indices are useful for independently detecting drought stress or leaf density. The results showed that NDVI and similar indices, which utilize the difference or ratio between the reflectance of red and near infrared bands, such as the ratio vegetation index (RVI), the difference vegetation index (DVI), the atmospherically resistant vegetation index (ARVI), the renormalized difference vegetation index (RDVI), the enhanced vegetation index (EVI), the perpendicular vegetation index (PVI), soil-adjusted vegetation index (SAVI) and the improved variants of SAVI, were effective for the independent detection of leaf density but relatively ineffective for drought stress because they were significantly affected by leaf area index (LAI). Similarly, vegetation indices developed as detectors of vegetation stress, such as the water index (WI), the stress index (SI) and the derivative chlorophyll index (DCI), showed weak correlation (r) and partial correlation (r _p) with leaf water content (LWC). The optimal hyperspectral indices were proposed as (F _502.8 − F _852.0)/(F _502.8 + F _852.0) for LWC (r = 0.847, r _p = 0.849) and R ₇₅₀/R ₅₅₀ (R750R550; Lichtenthaler et al. in J Plant Physiol 148:483–493, 1996) for LAI (r = 0.926, r _p = 0.940) where R _λ and F _λ represent reflectance and first derivatives at wavelength λ nm, respectively. A simulation of lower spectral sampling intervals (ca. 3-nm intervals of original to 10-nm intervals) indicated that it will be necessary to check the appropriateness of the derivative indices approximate to the proposed indices before application because derivative spectra are less smooth as a function of wavelength than reflectance spectra.     

基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

冬小麦冠层叶片氮含量是反映其产量与品质的重要指标,构建高普适性、高精准性冬小麦冠层叶片氮含量高光谱反演模型对提高其监测效率具有重要意义。以不同地点、品种、年份、施氮水平、生育期的大田试验数据为基础,基于两波段光谱植被指数NDRE和550 nm光谱反射率组合构建一个三波段植被指数NEW-NDRE,并与11个传统冬小麦冠层叶片氮素光谱指数进行比较。结果表明: NEW-NDRE及传统植被指数中NDRE、NDDA、RI-1dB与冬小麦冠层叶片氮含量的相关性较好;其中,灌浆初期NEW-NDRE与冬小麦冠层叶片氮含量相关性最好,决定系数R²为0.9,均方根误差(RMSE)为0.4;经独立数据检验,以NEW-NDRE为变量建立的冬小麦冠层叶片氮含量反演模型的平均相对误差(RE)为9.3%,明显低于以NDRE、NDDA、RI-1dB为变量的模型RE。总体上,新构建的NEW-NDRE对冬小麦冠层叶片氮含量的模拟能力显著优于传统指数,减弱了试验条件的限制性,可为精准施肥提供新的技术支撑。     

Future changes in summer MODIS-based enhanced vegetation index for the South-Central United States

Evaluating the response of vegetation to climate change is relevant to improving the management of both human and natural systems. Here, we quantify the response of the MODIS-based enhanced vegetation index (EVI) to temperature, precipitation, and large-scale natural variability across the South-Central U.S. for summer (JJA) from 2000 to 2013. We find statistically significant relationships between climate and EVI that vary across the region and are distinct for each land cover type: the mean coefficient of determination (R²) between EVI and climate is greatest for pasture (0.61 ± 0.13) and lowest for forest (0.55 ± 0.14). Among the climate variables, three-month cumulative precipitation has the strongest influence on summer vegetation, particularly in semi-arid west Texas and eastern New Mexico. Summer monthly maximum temperature plays an important role in the eastern half of Texas and Oklahoma, moderated by the influence of both Atlantic and Pacific teleconnection indices over inter-annual time scales. Based on these relationships, we train, cross-validate, and, where statistically significant relationships exist, combine this multivariate predictive model with projected changes in teleconnection indices and statistically-downscaled temperature and precipitation from 16 CMIP5 global climate models to quantify future changes in EVI. As global mean temperature increases, projected EVI decreases, indicative of stressed and dry vegetation, particularly for grasslands as compared to other land types, and in Oklahoma and western, central and Gulf Coast Texas for mid- and end-of-century. These trends have potentially important implications for agriculture and the regional economy, as well as for ecosystems and endemic species that depend on vegetation.     

Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots

Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. Manual inoculations were timed to simulate infestation of corn, Zea mays L., by first and second flights of adult O. nubilalis. The ability of spectral vegetation indices to detect O. nubilalis-inoculated plots improved as the growing season progressed, with multiple spectral vegetation indices able to identify infested plots in late August and early September. Our findings also indicate that for detecting O. nubilalis-related plant stress in corn, spectral vegetation indices targeting carotenoid and anthocyanin pigments are not as effective as those targeting chlorophyll. Analysis of image data suggests that feeding and stem boring by O. nubilalis larvae may increase the rate of plant senescence causing detectable differences in plant biomass and vigor when compared with control plots. Further, we identified an approximate time frame of 5-6 wk postinoculation, when spectral differences of manually inoculated "second" generation O. nubilalis plots seem to peak.     

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.     

基于MODIS数据的松嫩平原西部芦苇湿地地上生物量遥感估算

芦苇作为湿地生态系统中重要的群落类型,其地上生物量是衡量湿地生态系统质量的关键指标。应用面向对象的土地覆盖分类技术,基于多季相Landsat8 OLI遥感数据,提取松嫩平原西部芦苇湿地分布信息;依托野外实测芦苇地上生物量数据(AGB)和同期MODIS数据源的NDVI、EVI、RVI、MSAVI和WDVI 5种光谱植被指数,探讨不同光谱植被指数对芦苇AGB的敏感性,进而构建松嫩平原西部芦苇AGB遥感估算最优模型,并进行芦苇AGB遥感反演及空间格局分析。结果表明:2014年松嫩平原西部地区芦苇总面积为1653 km~2,其中扎龙湿地自然保护区内芦苇分布面积最大(1178km~2),占区域芦苇总面积的71.3%;所选取的5种植被指数均与芦苇AGB呈极显著正相关(P0.01),基于EVI构建的指数曲线模型为松嫩平原西部芦苇AGB反演的最优模型(R2=0.55)。研究区芦苇平均AGB为372.1g/m~2,AGB总量为6.14×105t,其中扎龙湿地自然保护区内芦苇AGB总量为4.38×105t;各保护区芦苇平均AGB由大到小依次为:向海保护区(469.7 g/m~2)大布苏保护区(454.1 g/m~2)莫莫格保护区(373.0 g/m~2)扎龙保护区(372.4 g/m~2)查干湖保护区(369.8 g/m~2);松嫩平原西部芦苇AGB总体呈现南高北低的分布格局,将为湿地生态系统管理与保护及芦苇资源的合理利用提供科学依据。     

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。     

Vegetation vulnerability to hydrometeorological stresses in water-scarce areas using machine learning and remote sensing techniques

The quantitative understanding of vegetation vulnerability as a major example of terrestrial ecosystems under hydrometeorological stress is essential for environmental risk preparedness and mitigation strategies. The aim of this study was to develop a new quantitative vegetation vulnerability map using benchmark and standalone machine learning (ML) algorithms (e.g., RF, SVM and Maxent), as well as influencing variables (evaporation, rainfall, maximum temperature, slope degree, elevation, topographic wetness index, distance from river, aspect, land use), in the South Baluchistan basin, Iran. An ensemble model was developed based on selected standalone ML algorithms. A vegetation vulnerability index (VVI), based on remote sensing indices (NDVI, VCI, LST, and TCI), was used to monitor vegetation conditions and changes. Five evaluation metrics for the confusion matrix (accuracy, precision, bias, Probability of Detection (POD), False Alarm Ratio (FAR)) and ROC-AUC were used to measure the predictive performance of the ensemble model and VVI. The optimum values for accuracy, precision, bias, POD, FAR, and ROC-AUC were obtained as 0.89, 0.88, 1.02, 0.91, 0.11, and 0.946, respectively for the ensemble model. Based on remote sensing data, VVI achieved a 0.923 prediction rate in vegetation vulnerability mapping (the efficiency of the ensembled model was somewhat better than VVI). Based on the results obtained from the ensemble model, precipitation (PRD = 20.61), maximum temperature (PRD = 12.31), evaporation (PRD = 5.53), and distance from the river (PRD = 2.62) were found to be the most important variables. The methodology as presented in this study provides valuable information in a large area and can be easily modified for other case studies by adding different influencing variables.     

高光谱植被指数与水稻叶面积指数的定量关系

基于不同水稻品种、施氮水平和不同生育期下的大田试验,确立了水稻叶面积指数（LAI）与冠层光谱特征参数的定量关系.结果表明：水稻叶面积指数与部分高光谱植被指数存在良好的相关性,其中原始光谱组成的2波段差值指数（DI）形式相关性最好,其次为比值（RI）和归一化（NI）植被指数.相关最好的原始光谱植被指数是由近红外波段组成的差值指数DI(854,760),相关最好的一阶导数光谱植被指数是红光和近红外光组成的导数差值指数DI(D₆₇₆, D₇₇₈),但总体上导数光谱指数不如原始光谱指数与LAI关系密切.独立试验数据检验结果表明,以差值指数DI(854,760)为变量建立的水稻LAI监测模型具有较好的表现,可用于水稻LAI的估测.     

基于TM遥感影像的陕北黄土区结构化植被因子指数提取

根据结构化植被因子指数的概念,以TM影像为信息源,探讨了利用遥感技术提取陕北黄土区结构化植被因子指数（C_s）的途径与方法.结果表明：在陕北黄土区,C_s能更好地描述植被群落的水土保持效益,其与绿度植被指数（归一化植被指数NDVI、修正土壤调整植被指数MSAVI）和黄度植被指（归一化差异衰败指数NDSVI、归一化耕作指数NDTI）等单一的遥感植被指数虽然均存在良好的相关关系,但用绿度与黄度植被指数相结合可综合反映植被的水土保持功能,能较好地克服单一指数在描述植被控制水土流失中的不足;MSAVI、NDTI分别是基于遥感影像提取C_s较为理想的绿度和黄度植被指数;根据群落结构化植被因子指数与遥感植被指数的关系推算区域尺度上的结构化植被因子指数是可行的,但由于不同地区植物物候期的差异,要使该方法在其他地区适用,仍需开展相应的率定和验证工作.     

Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region,Türkiye

Above-ground biomass (AGB) is an important component for identifying carbon stocks, monitoring the impacts of climate change, and evaluating merchantable timber. Accurate prediction of forest AGB is central to the correct interpretation of these components and to produce usable data for planners and researchers. In this study, remotely sensed time-series data derived from Landsat 8 (reflectance (R) and vegetation indices (VI)), topographic (T) and climate (C) data were used as independent variables to predict AGB of pure Calabrian pine (Pinus brutia Ten.) stands using multiple regression analysis (MLR) and support vector machines (SVM) methods. The AGB modeling was done by using independent variables individually and by combining variables, and the AGB maps of the most successful models obtained from MLR and SVM methods were produced. It was determined that the most successful variable group was the VI when the independent variables were used one by one (MLR Training R² = 0.50, SVM Training R² = 0.67). The most successful predictions in AGB modeling were obtained with combining all independent variables and using the SVM method (Training R² = 0.85, Validation R² = 0.69). In the combination of independent variables, VI and C data made the greatest contribution to the success of the AGB prediction. The ‘green leaf index’ vegetation indices had the most significant effect on the modeling AGB. In this study, T and C in addition to spectral data has increased the AGB estimation performance. It has been found that the SVM method yielded higher model accuracy than MLR method in predicting AGB. Overall, the spectral data and the SVM method can contribute to improving the accuracy of AGB estimates and provide an effective approach towards the capability for forest ecosystem monitoring.     

中国东部10个海岛植被近自然度评价

植被近自然度评价是森林近自然恢复的重要理论基础。海岛因特殊的生物地理环境,其植被结构和功能有别于陆地植被,当前还未有从植物生理生态角度,联合植物功能性状的海岛植被近自然度评价指标体系。基于中国东部10个典型海岛的74个植物群落,以植被信息、土壤属性和多样性特征（物种和功能多样性）构建海岛植被近自然度评价指标体系。利用敏感性分析筛选出13个反映海岛植被近自然度的中、高敏感性指标,基于筛选指标用层次分析法初次构建了中国东部海岛植被近自然度评价指标体系,并计算典型海岛的植被近自然度综合指数和划定植被近自然度等级。结果显示：海岛植被近自然度综合评价指标的权重：植被信息 > 多样性特征 > 土壤属性,其中植物自然构成系数、土壤含水量、Shannon-Wiener指数、Rao二次熵指数的权重较大;典型海岛的植被近自然度综合指数在0.345-0.611间,其中大金山岛的植被近自然度最高,属半天然林,北长山岛最低,为近人工林,其他海岛为远天然林;中亚热带的海岛植被近自然度较高,暖温带和南亚热带的海岛植被近自然度较低。本研究基于陆地植被近自然度评价指标,联合植物功能多样性对我国东部海岛植被近自然度进行综合评价,为海岛植被的生态状况提供定量依据,以及为海岛植被保护与管理和近自然恢复提供理论支撑。     

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.     

NDVI‐based vegetation monitoring in Mau forest complex,Kenya

The normalized difference vegetation index (NDVI) measures vegetation health and density using plant reflectance characteristics recorded by satellite imagery. Dekadal NDVI data were obtained for January 1999–December 2009 from 1‐km resolution SPOT‐VEGETATION sensor for closed woody vegetation type in four blocks of the Mau forest complex. Vegetation response to yearly seasonal variations was plotted and used to compare deviations by specific years. Subnormal vegetation conditions were recorded by the standardized vegetation index (SVI) and persistently low SVI values indicated a drought season or degraded vegetation. The general linear trend of the vegetation was plotted for the study period to identify trends towards degradation or vegetation recovery. Analysis of variance was used to compare forest blocks and shows spatial vegetation variations and also among years to identify vegetation variations with time. Rainfall data recorded for 2002–2009 in east Mau were used to confirm rainfall‐related vegetation variations block. Results show that NDVI patterns within an year follow cyclic trends with a strong dependence on rainfall seasons. The forest vegetation indicated negligible changes over the study period but effects of extended dry periods in 2000 and 2009 were evident. There were significant differences (P < 0.05) in NDVI between forest blocks. East Mau had significantly inferior vegetation that can be attributed to forest type, level of human degradation prior to the study and the lower rainfall. There were significant variations (P < 0.05) of NDVI among years but the forests showed a natural resilience to disturbance and can retain original vegetation vigour once stress is removed. The study proposes further monitoring of the forests including other vegetation types that are more vulnerable to climatic variations and anthropogenic effects.     

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。     

Modifying living space: an experimental study of the influences of vegetation on aquatic invertebrate community structure

In temporary freshwater systems, the type of vegetation within a system can influence community structure. Vegetation not only provides physical structure, but can also contribute to changes in abundance and quality of food and in water quality through decomposition. An experiment was undertaken using natural and artificial vegetation in small mesocosms to examine the influence of the physical structure of vegetation on invertebrate community structure in terms of water quality, food abundance, and physical structure. It was predicted that invertebrate community structure would be identical in natural and artificial treatments if the effect of vegetative decomposition was negligible. Furthermore, invertebrate community structure in bare ground treatments would be identical to those with vegetation if the physical structure of vegetation has no significant effect. Five treatments were used: a bare ground control, artificial vegetation (×2), and natural vegetation treatments (grass, eucalypt leaf litter). Water quality, food abundance, and invertebrate abundance were examined after six weeks of inundation. All treatments had high water temperatures (34–40°C), and natural vegetation treatments had slightly higher conductivity (208–316 mS cm⁻¹) and lower turbidity (40–231 NTU) than other treatments (47–156 mS cm⁻¹ and 55–400 NTU, respectively). The physical structure of artificial vegetation did not significantly influence invertebrate community structure compared to the bare ground treatment, whereas treatments with decomposing natural vegetation had relatively low abundances of microcrustaceans (0–96 individuals/mesocosm) and relatively high abundances of chironomids (192–1576 individuals/mesocosm) compared to other treatments (>100 microcrustaceans/mesocosm if present, and <370 chironomids/mesocosm, respectively). This suggests that food availability had greater importance than physical structure in determining community structure in these small aquatic ecosystems. Handling editor: S. M. Thomaz