Utilizing spectral vegetation indices for yield assessment of tomato genotypes grown in arid conditions

Tomato is among important vegetable crops cultivated in different climates; however, heat stress can greatly affect fruit quality and overall yield. Crop reflectance measurements based on ground reflectance sensor data are reliable indicators of crop tolerance to abiotic stresses. Here, we report on using non-destructive spectral vegetation indices to monitor yield traits of 10 tomato genotypes transplanted on three different dates (Aug. 2, Sept. 3 and Oct. 1) during 2019 growing season in the Riyadh region. The ten genotypes comprised six commercial cultivars–(Pearson Improved, Strain B, Valentine, Marmande VF, Super Strain B, and Pearson early) ––and four local Saudi cultivars (Al-Ahsa, Al-Qatif, Hail and Najran). Spectral reflectance data were utilized using a FieldSpec 3 spectroradiometer in the range of 350–2500 nm to calculate nine vegetation indices (VIs): Normalized Water Band Index (NWBI), Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red Edge Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index (SAVI), Red Edge Normalized Difference Vegetation Index (RENDVI), Renormalized Difference Vegetation Index (RDVI), and Normalized Difference Nitrogen Index (NDNI). VIs and yield parameters (total fruit yield, harvest index) revealed that second transplanting date was optimal for all the genotypes. Valentine showed the best growth performance followed by Najran, Hail, Super Strain B and finally Pearson early. For all the three transplanting dates, Valentine recorded the highest total fruit yield. Additionally, some genotypes had no significant differences in the VIs values or the total fruit yield between the second and third transplanting dates. This study indicated that yield parameters could be linked to rapid, non-destructive hyperspectral reflectance data to predict tomato production under heat stress.     

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.     

Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review

Over the last few years, Deep learning (DL) approaches have been shown to outperform state-of-the-art machine learning (ML) techniques in many applications such as vegetation forecasting, sales forecast, weather conditions, crop yield prediction, landslides detection and even COVID-19 spread predictions. Several DL algorithms have been employed to facilitate vegetation forecasting research using Remotely Sensed (RS) data. Vegetation is an extremely important component of our global ecosystem and a necessary indicator of land cover dynamics and productivity. Vegetation phenology is influenced by lifecycle patterns, seasonality and weather conditions, leading to changes in their spectral reflectance. Various relevant information, such as vegetation indices (VIs), can be extracted from RS data for vegetation forecasting. Therefore, the Normalized Difference Vegetation Index (NDVI) is known as one of the most widely recognized indices for vegetation related studies. This paper reviews the related works on DL-based spatio-temporal vegetation forecasting using RS data over the period between 2015 and 2021. In this review, we present several DL-based studies and discuss DL algorithms and various sources of data that have been used in these studies. The purpose of this work is to highlight the open challenges such as spatio-temporal prediction issues, spatial and temporal non-stationarity, fusion data, hybrid approaches, deep transfer learning and large parameter requirements. We also attempt to figure out the future directions and limits of DL for vegetation forecasting.     

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.     

Satellite observation to assess dengue risk due to Aedes aegypti and Aedes albopictus in a subtropical city of Argentina

Earth observation environmental features measured through remote sensing and models of vector mosquitoes species Aedes aegypti and Ae. albopictus provide an advancement with regards to dengue risk in urban environments of subtropical areas of Argentina. The authors aim to estimate the effect of landscape coverage and spectral indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Water Index [NDWI] and Normalized Difference Built-up Index [NDBI]) on the larvae abundance of Ae. aegypti and Ae. albopictus in Eldorado, Misiones, Argentina using remote satellite sensors. Larvae of these species were collected monthly (June 2016 to April 2018), in four environments: tire repair shops, cemeteries, dwellings and an urban natural park. The proportion of landscape coverage (water, urban areas, bare soil, low vegetation and high vegetation) was determined from the supervised classification of Sentinel-2 images and spectral indices, calculated. The authors developed spatial models of both vector species by generalized linear mixed models. The model's results showed that Ae. aegypti larvae abundance was better modelled by NDVI minimum values, NDBI maximum values and the interaction between them. For Ae. albopictus proportion of bare soil, low vegetation and the interaction between both variables explained better the abundance.     

Does the azimuth orientation of Norway spruce (Picea abies/L./Karst.) branches within sunlit crown part influence the heterogeneity of biochemical,structural and spectral characteristics of needles?

The goal of this study was to determine if selected biochemical, structural and spectral properties of Norway spruce needles are influenced by the azimuth orientation of the branch. Three youngest needle age classes from 20 mature (100 years old or older) Norway spruce trees were sampled from upper branches of the sunlit production crown part from each of the 4 cardinal azimuth orientations. Photosynthetic pigments, soluble phenolic compounds and selected spectral and structural characteristics were determined for each needle age class. The content of photosynthetic pigments and soluble phenolic compounds did not differ among needles from different azimuth-oriented branches, nor did the optical reflectance indices Normalized Difference Vegetation Index (NDVI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI)/Optimized Soil-Adjusted Vegetation Index (OSAVI), Red Edge Inflection Point (REIP) and Landsat Thematic Mapper bands 5 and 4 (TM5/TM4). No variation in volume properties, tissue volume proportions and cross-section shape characteristics of 3rd-year needles rejected our hypothesis that there would be variation in needle structural properties according to the azimuth orientation of branches. Consequently, we concluded that a random sampling of similar-aged needles within the sunlit production crown part may be used to study biochemical or structural and spectral needle properties of a mature Norway spruce growing in forest stands without a significant slope. In addition, the results obtained from a branch of one azimuth orientation should be representative for the whole sunlit portion of the crown. Consequences of these findings for Norway spruce health monitoring using remote sensing techniques are discussed.     

The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI

Bamboo is a special forest type in subtropical regions of china. Its huge biomass and carbon storage and its ecological function play an important role in global carbon sink. In this paper, Moso bamboo (Phyllostachys heterocycla var. pubescens) forest in Anji county, Zhejiang province, China was selected to examine the relationships between Landsat Thematic Mapper (TM) data, and aboveground biomass (AGB, Kg), This research indicates that (1) near infrared (TM4) and mid-infrared bands (TM5,TM7) are more important in explaining Moso bamboo AGB than the visible bands. In particular, TM band5 has higher path coefficient with AGB than any other TM bands, implying its important role in explaining Moso bamboo AGB; (2) the Normalized Difference Vegetation Index (NDVI) has weak correlation with Moso bamboo AGB, however, other vegetable indices such as Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index(EVI), and Soil Adjust Vegetation Index (SAVI) which are related to soil adjustment parameters are significantly correlated with Moso bamboo AGB; (3) the new vegetation index developed in this paper is significantly correlated with Moso bamboo AGB (correlation coefficient is 0.48), and has higher correlation coefficient (R) than any other selected vegetation indices, implying that this new vegetation index can better explain Moso bamboo AGB than any other vegetation indices.     

The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI

Bamboo is a special forest type in subtropical regions of china. Its huge biomass and carbon storage and its ecological function play an important role in global carbon sink. In this paper, Moso bamboo (Phyllostachys heterocycla var. pubescens) forest in Anji county, Zhejiang province, China was selected to examine the relationships between Landsat Thematic Mapper (TM) data, and aboveground biomass (AGB, Kg), This research indicates that (1) near infrared (TM4) and mid-infrared bands (TM5,TM7) are more important in explaining Moso bamboo AGB than the visible bands. In particular, TM band5 has higher path coefficient with AGB than any other TM bands, implying its important role in explaining Moso bamboo AGB; (2) the Normalized Difference Vegetation Index (NDVI) has weak correlation with Moso bamboo AGB, however, other vegetable indices such as Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index(EVI), and Soil Adjust Vegetation Index (SAVI) which are related to soil adjustment parameters are significantly correlated with Moso bamboo AGB; (3) the new vegetation index developed in this paper is significantly correlated with Moso bamboo AGB (correlation coefficient is 0.48), and has higher correlation coefficient (R) than any other selected vegetation indices, implying that this new vegetation index can better explain Moso bamboo AGB than any other vegetation indices.     

Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring, analyzing, and mapping temporal and spatial distributions of physiological and biophysical characteristics of vegetation. It is well documented that the NDVI approaches saturation asymptotically under conditions of moderate-to-high aboveground biomass. While reflectance in the red region (rho(red)) exhibits a nearly flat response once the leaf area index (LAI) exceeds 2, the near infrared (NIR) reflectance (PNIR) continue to respond significantly to changes in moderate-to-high vegetation density (LAI from 2 to 6) in crops. However, this higher sensitivity of the rho(NIR) has little effect on NDVI values once the rho(NIR) exceeds 30%. In this paper a simple modification of the NDVI was proposed. The Wide Dynamic Range Vegetation Index, WDRVI = (a * rho(NIR-rho(red))/(a * rho(NIR) + rho(red)), where the weighting coefficient a has a value of 0.1-0.2, increases correlation with vegetation fraction by linearizing the relationship for typical wheat, soybean, and maize canopies. The sensitivity of the WDRVI to moderate-to-high LAI (between 2 and 6) was at least three times greater than that of the NDVI. By enhancing the dynamic range while using the same bands as the NDVI, the WDRVI enables a more robust characterization of crop physiological and phenological characteristics. Although this index needs further evaluation, the linear relationship with vegetation fraction and much higher sensitivity to change in LAI will be especially valuable for precision agriculture and monitoring vegetation status under conditions of moderate-to-high density. It is anticipated that the new index will complement the NDVI and other vegetation indices that are based on the red and NIR spectral bands.     

Detecting changes in forest floor habitat after canopy disturbance

A massive ice storm hit northeastern North America in 1998, dropping more than 100 mm of freezing rain at its epicenter in southern Quebec, Canada. There has been extensive study of which trees and areas received the most damage, but the biodiversity consequences of this damage at landscape scales have not received much attention. We assessed the effectiveness of seven remotely sensed vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index, Difference Vegetation Index, Renormalized Difference Vegetation Index, Atmospherically Resistant Vegetation Index, Green Normalized Difference Vegetation Index and Visible Atmospheric Resistant Index—for modeling the coarse woody debris (CWD) influx in an old growth forest reserve at the storm’s epicenter; NDVI was the best predictor of CWD influx. We categorized the geospatial CWD predictions from the NDVI-derived model to map the spatial distribution of sun-exposed, moist-shaded, dry-shaded and wet CWD microhabitats on the forest floor. Moist-shaded, dry-shaded and wet patches of CWD were large and well connected, but sun-exposed patches were small and sparse. Since these microhabitats affect the distribution and abundance of saproxylic insects, wood-rotting fungi, salamanders, birds, small burrowing mammals and plant species dependent on nurse-logs for establishment, the CWD influx from the 1998 ice storm may have revitalized local populations of these taxa through increased habitat availability as well as increased dispersal within the reserve.     

基于辐射传输模型的叶绿素含量定量反演

利用基于叶片内部辐射传输机制的PROSPECT模型模拟大量不同生化含量和叶肉结构的叶片光谱,研究利用高光谱植被指数定量反演叶绿素含量的可行性和精度,并比较各指数的稳定性和抗干扰能力。结果显示,各指数在对叶绿素的敏感性方面相差不大,除三角植被指数(TVI)外,其它指数均随叶绿素含量的增加而减小。叶片水分含量的差异对各指数的影响很小,干物质次之,叶肉结构影响最大。在抵抗干物质影响和叶肉结构影响方面,结构无关色素指数(SIPI)明显优于其它四种指数,吸收中心波深归一化后的面积指数(ABNC)次之。通过使用叶片光学模型的模拟光谱来研究叶绿素含量变化的光谱响应及其影响因素和反演策略,具有较强的理论性和普适性。研究结果与实际观测相吻合,方法简单易行。     

Inter-annual variability in Delaware Bay brackish marsh vegetation,USA

Reports of sudden marsh browning, or even dieback, suggest that the many heretofore “healthy” coastal marshes have reached some tipping point with respect to sea level rise, necessitating better and more widespread monitoring. In this paper, we examine spatial and temporal variations in marsh vegetation cover, substrate wetness, and sediment exposure for mesohaline to oligohaline marshes in Delaware Bay over a 15-year period (1993–2008) using three spectral indices (the Normalized Difference Vegetation Index, the Normalized Difference Water Index, and the Normalized Difference Soil Index) based on Landsat Thematic Mapper and Enhanced Thematic Mapper + imagery. In general, degrading marsh areas show low percentages of vegetation cover compared to bare marsh substrate, and substrate wetness tends to be high. But this characterization is not consistent from one year to the next, and in marshes that are in incipient stages of degradation, apparent vegetation health can improve substantially for a few years. Detailed transect data collected from July to September in an area of Bombay Hook National Wildlife Refuge, where little marsh loss was evident, document considerable variability in vegetation dynamics. The marshes along the transect kept pace with the major transgressive pulse of the 1990s, but as the rate of sea level rise decreased after 2000, vegetation vigor fell, especially in 2004, the year after Hurricane Isabel. The years of maximum vegetation cover, 2003 and 2005, coincided with short-term, sea level high stands and/or very wet and cooler summers. We theorize that after keeping up with the dramatic rise in sea level during the 1990s, marsh surface elevations in these microtidal systems are now too high to allow adequate flushing of sulfides and low dissolved oxygen waters except for high precipitation events or short-term sea level rises. If this situation were to continue, it could affect the “health” of marshes that otherwise were accommodating high rates of sea level rise well.     

Investigating phenological changes using MODIS vegetation indices in deciduous broadleaf forest over continental U.S. during 2000–2008

Vegetation phenology describing the seasonal cycle of plants is currently one of the main concerns in the study of climate change and carbon balance estimation in ecosystems. Remote sensing techniques which can capture canopy reflectance allow vegetation photosynthetic capacity to be assessed. In this study, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were used to identify onset date of greenness in deciduous broadleaf forest (DBF) over the continental United States from year 2000 to 2008. Onset dates determined by these three indices were compared with North American First Leaf Lilac Phenology Data over the same time period. NDVI has a best agreement with the field data among the three vegetation indices. Spatial analysis was performed on the onset dates predicted with NDVI. Four major conclusions were drawn from this study are: 1) onset dates are not only dependent on latitude but also associated with ecoregions and altitudes; 2) onset of greenness moves northward gradually and the average change of onset dates along latitude is about 3 days per degree. Interannual variability of onset dates is greater at higher latitudes (> 43 °N) than at lower latitudes (≤ 43 °N); 3) at the same latitude, DBF in mountain area tends to green up latter and coastal forest tends to green up earlier than other ecoregions; and 4) the impact of altitude is more obvious when the range of elevation achieves more than 1000 m. These conclusions provide insight for assessing vegetation indices in determining onset date of greenness at regional scale, and can be exploited to analyze the impacts of climate change on terrestrial ecosystem.     

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.     

Study of Spectral Response Characteristics of Oilseed Rape (<i>Brassica napus</i>) to Particulate Matters Based on Hyper-Spectral Technique

Haze is mainly caused by the suspended particulate matters in the air, of which the particulate matters pollution harms leaf vegetables. In this paper, oilseed rapes at four different growing periods were investigated in a simulated particulate pollution environment. In combination of hyper-spectral technology and micro examination, the response of hyper-spectral characteristics of the leaf to particulate matters was investigated in-depth. The hyperspectral, chlorophyll content, net photosynthetic rate and stomatal conductance of leaf were obtained. The deposition and adsorption of particulate matters on the leaf were observed by Environmental Scanning Electron Microscope (ESEM). Normalized difference vegetation index (NDVI), modified red edge normalized (mNDVI705) and modified red edge simple ratio index (mSR705) were selected as characteristic parameters and the range of 510 nm~620 nm as the sensitive band. 16 methods were used to establish the physiological information inversion model. The main results were as follows: Under the influence of particulate matters, the spectral reflectance decreased as a whole. With the increase of leaf age, the phenomenon of blue shift aggravated. The amplitude of yellow and blue edge decreased with overall decreasing vegetation indices. The furrows and irregular band protrusions in leaves were favorable for keeping particulate matters. With longer affecting time and more deposition of particle matters on the leaf, the stomatal opening became smaller. After comparing, principal component regression (PCR) + multiple scatter correction (MSC) + second derivative (SD) + Savitzky-Golay smooth (SG), and partial least square (PLS) + multiple scatter correction (MSC) + first derivative (FD) + Savitzky-Golay smooth (SG) were determined the best method to establish the inversion model of chlorophyll content and net photosynthetic rate respectively. This study may bring novel ideas for the diagnosis and analysis of the physiological response of leaf vegetables under particulate matters pollution using hyper-spectral technology.     

Increase in seagrass distribution at Bourgneuf Bay (France) detected by spatial remote sensing

The changes in spatial distribution of intertidal Zostera noltii seagrass beds were studied with multispectral visible-infrared remote sensing in Bourgneuf Bay (France) over a 14-year period, between 1991 and 2005. Six SPOT satellite images acquired at low tide were calibrated using in situ spectroradiometric data and processed with the Normalized Difference Vegetation Index (NDVI). A steady and linear increase in meadow areas was observed between 1991 and 2005 with total surfaces colonized by Z. noltii increasing from 208 to 586 ha, respectively. A greater increase in the densest part of the meadow (NDVI > 0.4) was also observed: it represented only 15% of total meadow surfaces in 1991 vs. 35% in 2005. The seagrass expansion took place mainly towards the lower part of the intertidal zone, while in the upper intertidal zone the meadow appeared strictly limited by the +4 m (Lowest Astronomical Tide) bathymetric level. The influence of Z. noltii above-ground biomass variations on spectral reflectance was analyzed experimentally by spectrometry. Z. noltii displays a characteristic steep slope from 700 to 900 nm, increasing with increasing biomass. A quantitative relationship obtained experimentally between NDVI and the dry weight of leaves was used to produce a biomass distribution map. The distribution of Bourgneuf Bay intertidal seagrass beds is certainly constrained by the water turbidity and we suggest that tidal flat accretion could be a significant variable explaining the observed expansion downwards. With very limited spatial interactions, oyster aquaculture cannot be considered as a threat, while a recent increase in recreational hand fishing of Manila clams within the beds could become problematic.     

亚热带地区马尾松林碳储量的遥感估算——以长汀河田盆地为例

全球气候的变化已使得人类日益关注森林生态系统的碳储量变化.以福建省长汀县河田盆地为例,开展马尾松林碳储量估算模型的研究.通过2010年的野外样地调查获得了马尾松林的实测数据,并将其与同年的ALOS遥感影像对应样地的植被光谱信息进行比较.通过研究5种遥感植被指数与马尾松林碳储量之间的相关关系,从中选取了基于归一化植被指数(NDVI)的研究区最佳马尾松林碳储量反演模型.精度分析表明,该模型平均相对误差为-1.95％,均方根误差为3.01 t/hm2,因此可以有效地用于反演研究区的马尾松林碳储量.利用该模型反演出河田盆地2010年马尾松林的总碳储量为114.58×104 t,碳密度为34.92 t./hm2.     

黄土高原典型区植被冗亏

选择了黄土高原中部典型区——泾河流域为研究区域。采用Holdridge潜在蒸发方法计算出了泾河流域的气候干燥度指数,构建了遥感植被指数与气候干燥度指数之间的回归模型。通过该模型反衍出了泾河流域潜在植被指数,提出以该指数为基础的植被冗亏格局的评价新方法。通过该评价方法发现,在泾河流域西北部的大部分子流域的植被冗亏指数小于-0.2,植被亏缺较为严重;在自然环境较差的流域北部,植被冗亏指数介于-0.20到-0.10之间,植被亏缺较轻;而流域东南部山区的大部分子流域的植被冗亏指数介于-0.10到0.10之间,植被亏缺最轻。从植被冗亏的时间尺度上分析,植被亏缺主要发生在植被生长旺盛的6~9月份,其中农田植被亏缺最大,冗亏指数在7月份可达到-0.51;稀疏草原植被亏缺较小,其冗亏指数最小值在-0.18左右;森林植被的冗亏指数接近于0。     

中国东部南北样带主要植被类型物候期的变化

植被物候期的变化是全球变化研究的热点问题, 因为物候过程是反映植被对气候变化响应的最直接和最敏感的生态学过程之一, 大尺度植被物候学过程主要以植被的季节动态体现其对气候变化的长期适应过程。基于NOAA/AVHRR从1982年至2006年的双周归一化植被指数NDVI (Normalized Difference Vegetation Index)数据, 依托中国东部南北样带, 对主要植被类型的物候过程进行模拟, 并计算了主要物候现象(包括返青起始期、休眠起始期和生长季长度)的发生时间和演变趋势。结果表明: 返青起始期显著提前的植被有温带针叶林(TCF, 0.56 d·a^-1)、温带草丛(TG, 0.66 d·a^-1)、亚热带热带针叶林(STCF, 0.46 d·a^-1)、亚热带落叶阔叶林(SDBF, 0.58 d·a^-1)和亚热带热带草丛(STG, 0.89 d·a^-1); 休眠起始期显著推迟的植被有寒温带温带针叶林(TCTCF, 0.32 d·a^-1)、SDBF (0.80 d·a^-1)和温带落叶阔叶林(TDBF, 0.18 d·a^-1); 此外, 大部分植被类型的生长季长度都有所延长, 但延长的方式不同: TCF (0.77 d·a^-1)是由于返青起始期显著提前造成的; TCTCF (0.38 d·a^-1)和TDBF (0.36 d·a^-1)是由于休眠起始期显著推迟造成的; TG (0.76 d·a^-1)、STCF (0.83 d·a^-1)、SDBF (1.40 d·a^-1)和STG (1.30 d·a^-1)等是由于返青起始期提前和休眠起始期推迟共同造成的。对温度和降水的变化进行分析发现, 温度对南北样带上植被物候的影响较大, 而降水对物候的影响相对较小, 不同植被类型对温度的响应各异。在南北样带上存在的热量梯度, 使得整条样带上植被的物候现象也表现出时间梯度, 从返青起始期发生的时间上比较, 从北向南逐渐推迟, 即寒温带植被>温带植被>亚热带植被; 休眠起始期和生长季长度则正好相反, 亚热带植被>温带植被>寒温带植被。     

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate?Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors.Important findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI 250 m). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB =^-10.80 + 139.13 MSAVI 250 m), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136g m ?2. Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1×1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.