Normalized Difference Vegetation Indexes Research Articles

Study on the Influence of Vegetation Restoration on Evapotranspiration in Mountainous Areas of the Luan River Basin

The study employed the PML (Penman–Monteith–Leuning) model to simulate the evolution law of long-sequence evapotranspiration in the mountainous region of the Luan River basin. Additionally, this study conducted a quantitative analysis to determine the effect of restoration on evapotranspiration water consumption. From 1981 to 2020, the results indicated that there were significantly less fluctuations in precipitation in the mountainous region of the Luan River basin than there were fluctuations in discharge. The restoration of vegetation in the mountainous region of the Luan River basin caused a mean annual growth rate of 3.47 mm in evapotranspiration. A linear positive correlation was observed between the evapotranspiration and vegetation NDVIs (normalized difference vegetation indexes) in mountainous regions. Specifically, for each 0.01 increase in the NDVI, there was an approximate 8.3 mm increase in evapotranspiration. When comparing the time periods of 1995–2001 and 2002–2020, it was observed that evapotranspiration increased by 70 mm. Furthermore, the evapotranspiration rate in the southeastern region exhibits significant variation, peaking at over 50 mm per year. In contrast, the northwest experiences variations of less than 10 mm per year. A quantitative analysis of the relationship between the evolution of mountain evapotranspiration and the response law of vegetation restoration is presented in this study; this information can be used as a guide when developing practical vegetation restoration strategies.

Arctic Greening Trends: Change Points in Satellite-Derived Normalized Difference Vegetation Indexes and Their Correlation with Climate Variables over the Last Two Decades

In this study, we utilized NDVI data from the moderate resolution imaging spectroradiometer (MODIS) alongside climatic variables obtained from a reanalyzed dataset to analyze Arctic greening during the summer months (June–September) of the last two decades. This investigation entailed a detailed analysis of these changes across various temporal scales. The data indicated a continuous trend of Arctic greening, evidenced by a 1.8% per decade increment in the NDVI. Notably, significant change points were identified in June 2012 and September 2013. A comparative assessment of NDVI pre- and post-these inflection points revealed an elongation of the Arctic greening trend. Furthermore, an anomalous increase in NDVI of 2% per decade was observed, suggesting an acceleration in greening. A comprehensive analysis was conducted to decipher the correlation between NDVI, temperature, and energy budget parameters to elucidate the underlying causes of these change points. Although the correlation between these variables was relatively low throughout the summer months, a distinct pattern emerged when these periods were dissected and examined in the context of the identified change points. Preceding the change point, a strong correlation (approximately 0.6) was observed between all variables; however, this correlation significantly diminished after the change point, dropping to less than half. This shift implies an introduction of additional external factors influencing the Arctic greening trend after the change point. Our findings provide foundational data for estimating the tipping point in Arctic terrestrial ecosystems. This is achieved by integrating the observed NDVI change points with their relationship with climatic variables, which are essential in comprehensively understanding the dynamics of Arctic climate change, particularly with alterations in tundra vegetation.

Precision Nitrogen Fertilization for Opium Poppy Using Combined Proximal and Remote Sensor Data Fusion

Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible and near-infrared reflectance spectroscopy was used to estimate soil pH, organic carbon (OC), soil organic matter (SOM), P, K, Mg, Ca, Na, moisture content (MC), Ca:Mg and K:Mg for one field in Spain. Normalized difference vegetation indexes of the previous crop were retrieved from Sentine-2 images. Rasterization of soil and crop data layers created a spatially homogenous dataset followed by delineation of a management zone (MZ) map using a k-means cluster analysis. MZ clusters were ranked relying on the within-cluster soil fertility attributes. A strip experiment was conducted by creating parallel stripes distributed over the MZ map, over which two SNF treatments (i.e., SNF-Kings approach [KA] and SNF-Robin Hood approach [RHA]) were compared against the uniform rate N (URN) control treatment. In SNF-KA, the highest and lowest N dose was applied in the most and least fertile MZ, respectively, whereas the opposite approach was adopted in the SNF-RHA treatment. Yield and cost–benefit analyses provided both SNF treatments to produce more yield (KA = 2.72 and RHA = 2.74 t ha−1) than the URN (2.64 t ha−1) treatment, leading to increasing gross margins by EUR 91 ha−1 (SNF–KA) and EUR 88.5 ha−1 (SNF–RHA). While SNF-KA reduced N input by 66.54 kg N ha−1, SNF–RHA applied more N by 17.90 kg N ha−1 than URN. Additionally, SNF–RHA attempted to equalize yield responses to N across MZ classes, with a small increase in N input. This study, therefore, suggests adopting SNF–RHA for increasing yield and gross margin and accurate distribution of N according to per MZ N response. Future studies, however, should address the limitations of the current study by delineating MZ maps with the incorporation of additional soil information (e.g., mineral N and clay) for optimizing N doses as well as evaluating agroeconomic performance across multiple sites and years using a full-budget analysis.

The influence of urban greenspaces on people’s physical activity: A population-based study in Spain

Past research has described positive associations between exposure to urban greenspaces and people's physical activity. However, there is variation in the relationship since it may differ according to the type of physical activity, socio-economic factors and use, as well as intrinsic characteristics of greenspaces. This study assesses the influence of urban greenspaces on distinct types of physical activity accounting for indicators such as vegetation quantity, tree cover density and green surface. The study combines data of a survey across Spain (n = 2063) with data derived from satellite imagery – including normalized difference vegetation indexes (NDVI), tree cover density and land-use cover data. A generalized linear mixed model was used to evaluate the association between urban greenspaces and physical activity as well as to evaluate the effect of main socio-economic determinants. After adjustment for potential confounders, greater availability in greenspace was found to be related with decreased sedentary time and increased walking. Besides exposure to urban greenspaces, physical activity was found to be associated with household income, pro-environmental attitudes, lifestyles and eating habits. The results also showed that exposure to greenspaces in rural areas had considerably weaker effect than in urban areas. The results suggest that efforts should be made to provide access to new greenspaces where possible, in order to foster walking and improve population health.

Modeling Colonial Paternalism: GIS and Multispectral Satellite Imagery at Kingstown, British Virgin Islands

GIS modeling and analysis of multispectral satellite imagery are applied to a former plantation in the British Virgin Islands (BVI), which, in 1831, became a settlement of free Africans who lived within slavery-based British colonialism. A map of the settlement represents the paternalist British government ideal for this community—an “experiment” for controlling a postemancipation peasantry—and the techniques discussed here allow clearer understanding of the way these ideals would have interacted with the physical and social landscape of the BVI had they been implemented. The residents were certainly aware of their situation, and this study does not mean to imply that they simply adopted the plan they were handed. Instead, our goal is to interrogate the implications of the plan itself. We combine least cost path (LCP), Normalized Difference Vegetation Indexes (NDVI), and other technical analyses to show the interaction of the British plan and the BVI landscape in order to describe the context in which the Kingstown community was built and maintained. Although schematic, this study quantifies at least some of the barriers the community overcame and contributes in a limited way to broader considerations of the place of land and landscape in structures of colonialism.

Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns

Abstract. The Yonsei Aerosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 min AHI or 1 h GOCI data at 6 km×6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-Earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE.

Relationship between multi-scale climate factors and performance of ecological engineering on the Loess Plateau, China

The long-term “Grain-to-Green Program” (GGP) on China’s Loess Plateau is a major global ecological engineering project which has significantly boosted vegetation renewal. Some studies have found that the rate of restoration is quite rapid during the implementation of ecological engineering, however, the influence of multi-scale climatic conditions on the performance of ecological engineering is unclear. In this study, multiple sources of remote sensing data were used to estimate the dynamics of vegetation structural and functional indicators, water-related local climatic factors, and atmospheric circulation factors. These datasets were also used to detect possible causes for vegetation restoration on the Loess Plateau over the past 20 years. The results show that widespread increases in rates of normalized difference vegetation indexes (NDVI), leaf area indexes (LAI), gross primary production (GPP), and aboveground biomass carbon (ABC) during 2000–2016 were significantly higher than before 2000. GPP was significantly correlated with rainfall and surface runoff on a monthly scale, and there were significant positive correlations between GPP and atmospheric circulation. Our results demonstrate that both vegetation structural and functional indicators rapidly increase, and ecological engineering greatly accelerated vegetation restoration after 2000. Local climatic conditions and atmospheric circulation patterns enhance vegetation growth and impact of ecological engineering.

Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images.

Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong'an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2days for wheat; 2, 5, 3, and 4days for single-season corn; and 2, 5, 2, and 2days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.

Time-series modelling of dengue incidence in the Mekong Delta region of Viet Nam using remote sensing data.

ObjectiveThis study aims to enhance the capacity of dengue prediction by investigating the relationship of dengue incidence with climate and environmental factors in the Mekong Delta region (MDR) of Viet Nam by using remote sensing data.MethodsTo produce monthly data sets for each province, we extracted and aggregated precipitation data from the Global Satellite Mapping of Precipitation project and land surface temperatures and normalized difference vegetation indexes from the Moderate Resolution Imaging Spectroradiometer satellite observations. Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region.ResultsThe final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. As a result, the obtained model presents a good fit at a regional level with the correlation value of 0.65 between predicted and reported dengue cases; nevertheless, its performance declines at the subregional scale.ConclusionWe demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. Results indicated that this approach could be an effective method to predict regional dengue incidence and its trends.

Trends, change points and spatial variability in extreme precipitation events from 1961 to 2017 in China

Abstract Extreme precipitation events vary with different sub-regions, sites and years and show complex characteristics. In this study, the temporal variations, trends with significance and change points in the annual time series of 10 extreme precipitation indices (EPIs) at 552 sites and in seven sub-regions were analyzed using the modified Mann–Kendall test and sequential Mann–Kendall analysis. Three representative (extremely wet, normal and extremely dry) years from 1961 to 2017 were selected by the largest, 50%, and smallest empirical frequency values in China. The spatiotemporal changes in the EPIs during the three representative years were analyzed in detail. The results showed that during 1961–2017, both the consecutive wet or dry days decreased significantly, while the number of heavy precipitation days had no significant trend, and the other seven wet EPIs increased insignificantly. The abrupt change years of the 10 EPIs occurred 32 and 40 times from 1963 to 1978 and from 1990 to 2016, respectively, regardless of sub-region. The extremely dry (or wet) events mainly occurred in western (or southwestern) China, implying a higher extreme event risk. The extremely wet, normal and extremely dry events from 1961 to 2017 occurred in 2016, 1997 and 2011 with empirical frequencies of 1.7%, 50% and 98.3%, respectively. In addition, 1998 was the second-most extremely wet year (empirical frequency was 3.7%). The monthly precipitation values were larger from February to August in 1998, forming a much earlier flood peak than that of 2016. The 10 EPIs had close connections with Normalized Difference Vegetation Indexes during the 12 months of 1998 and 2016. This study provides useful references for disaster prevention in China.

A New Remote Sensing-Based System for the Monitoring and Analysis of Growth and Gas Exchange Rates of Photosynthetic Microorganisms Under Simulated Non-Terrestrial Conditions.

Oxygenic photosynthetic microorganisms are a focal point of research in the context of human space exploration. As part of the bioregenerative life-support systems, they could have a key role in the production of breathable O2, edible biomasses and in the regeneration of CO2 rich-atmospheres and wastewaters produced by astronauts. The test of the organism’s response to simulated physico-chemical parameters of planetary bodies could also provide important information about their habitability potential. It is believed that the success of future planetary and space missions will require innovative technologies, developed on the base of preliminary experiments in custom-made laboratory facilities. In this context, simulation chambers will play a pivotal role by allowing the growth of the microorganisms under controlled conditions and the evaluation in real-time of their biomass productivity and impact on atmosphere composition. We here present a system capable of addressing these requirements with high replicability and low costs. The setup is composed by three main parts: 1) a Star Light Simulator, able to generate different light intensities and spectra, including those of non-solar stars; 2) an Atmosphere Simulator Chamber where cultures of photosynthetic microorganisms can be exposed to different gas compositions; 3) a reflectivity detection system to measure from remote the Normalized Difference Vegetation Indexes (NDVI). Such a setup allows us to monitor photosynthetic microorganism’s growth and gas exchange performances under selected conditions of light quality and intensity, temperature, pressure, and atmospheres simulating non-terrestrial environments. All parameters are detected by remote sensing techniques, thus without interfering with the experiments and altering the environmental conditions set. We validated the setup by growing cyanobacteria liquid cultures under different light intensities of solar illumination, collecting data on their growth rate, photosynthetic activity, and gas exchange capacity. We utilized the reflectivity detection system to measure the reflection spectra of the growing cultures, obtaining their relative NDVI that was shown to correlate with optical density, chlorophyll content, and dry weight, demonstrating the potential application of this index as a proxy of growth.

Atlantic Forest loss caused by the world´s largest tailing dam collapse (Fundão Dam, Mariana, Brazil)

The collapse of Fundão dam in Mariana, Minas Gerais, Brazil, released more than 50 million cubic meters of ore tailings into the environment, representing the world's largest mining disaster. Three analyses estimating the forest loss to the ore tailings were produced soon after the collapse but the values varied threefold between them due to differences in objective and spatial resolution. Our aim was to estimate the riverside forest loss due to the flooding of the ore tailings. We analyzed Landsat Normalized Difference Vegetation Indexes (NDVI) with the digital elevation model (DEM) specific for flooded forest and limited to analyze an area floodable and contiguous from the water stream. Our forest loss quantification resulted in the same order of magnitude than the two of previous estimates. The area other than forest flooded by the ore tailings accounted for 1176.6 ha. The loss of the forest area due to the collapse was 457.6 ha and concentrated along the first 74 km from the Fundão Dam.

Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.

Temporal and Spatial Variations of Vegetation Cover in Xinjiang from 2002 to 2015 and Their Response to Climate

In this paper, the dataset of normalized difference vegetation indexes (NDVIs) in the arid region in Xinjiang from 2002 to 2015 as well as the climate data from 52 meteorological stations are utilized and the temporal and spatial variations of NDVI in recent years and their response to temperature and precipitation are analyzed in combination with various methods such as the maximum value analysis, correlation analysis and spatial analysis. It is concluded that in the past 14 years, the annual maximum NDVIs of Xinjiang presented a moderate rising tendency; Under the influence of the global background, the temperature and precipitation also showed different degrees of increase, which showed a significant increase in temperature. The annual maximum NDVI had a significant correlation with the annual precipitation (correlation coefficient: 0.634), but no obvious correlation was identified between the annual maximum NDVI and the annual average temperature (correlation coefficient: 0.279). To this end, regarding to the climatic factors, the influence of precipitation on the vegetation cover is higher than that of temperature.

Compositing the Minimum NDVI for MODIS Data

The maximum and minimum normalized difference vegetation indexes (NDVIs) describe two extremes of vegetation greenness during a predefined period. A maximum NDVI image can be composited easily via the direct selection of the maximum NDVI from multiple observations without the need to mask out cloud or snow. But, a minimum NDVI image cannot be built in a similar manner. In this paper, an approach was proposed to composite the minimum NDVI (the least vegetation greenness) image. The minimum spectral index that consists of the green (555 nm) and SWIR bands (2130 nm) from MODIS data, which was named here as the Brown Vegetation Index (BVI), was taken as a proxy to composite the minimum vegetation NDVI. This composite method performs well on a global scale for the NDVIs that were derived from MODIS land surface reflectance (MOD09A1) products. The BVI-based minimum NDVI was compared with the direct selection of the minimum NDVI after excluding contaminated observations using a refined cloud/snow mask. The comparison shows that the difference for 97% of the minimum NDVI between the two approaches is within the range of ±0.1 NDVI unit. Various potential spectral indices for compositing the minimum NDVI were compared, which demonstrated the BVI-based approach was top rated. Several examples demonstrated that the composited minimum NDVI is valuable and effective for identifying evergreen forests, monsoon forests, and double cropping. The minimum NDVI combined with the maximum NDVI would simplify the way to describe intraannual vegetation changes.

EVOLUTION OF CERRADO VEGETAL COVER ON A RIVER ISLAND BASED ON ORBITAL IMAGING DATA

This study aimed to assess vegetal cover evolution on a river island within the Ecological Station of (EEP), by remote sensing. For this purpose, Normalized Difference Vegetation Indexes were generated for Landsat 1 (1973) and Landsat 5 (1984, 1990, 2000 and 2011) images. Five landscape units were identified in the field: bare soil, Rough savanna, Typical savanna, Forested savanna and Evergreen dry woods. Only Forested savanna and Evergreen dry woods showed poor spectral splitting, being thus considered as a forestry complex. Changes throughout time have occurred in all units, with decreasing in bare soil areas (-2.56 ha year−1), Rough savanna (-0.66 ha year−1) and Typical savanna (-0.94 ha year−1) and with an increase in the Forested savanna (5.97 ha year−1). Thus, the method used in this study was effective for sorting and assessing plant cover of the landscape units studied over time.

Analysis and Evaluation of principal climatic factors of NDVI in the Yarlung Zangbo River Basin

The normalized difference vegetation index (NDVI) of vegetation covers is influenced by many climatic factors such as precipitation and average temperature. In this paper, NDVI is calculated on the basis of waveband characteristic of NOAA/AVHRR data for the Yarlung Zangbo River Basin. Then, the spatial-temporal change of NDVI is analyzed. The basin's NDVI is in consistency with season from 2011 to 2013.Large value zone of NDVI is mainly distributed in the lower reaches and part of the middle reaches. Meanwhile, point-data of principal climatic factors are interpolated to spatial grid data with Kriging method consistent with NDVI based on DEM of the Yarlung Zangbo River Basin. Then, the relation of precipitation and normalized difference vegetation indexes or average air temperature is analyzed with F checking and two-sample variance test in the basin. The result is P=0, which shows that correlation analysis has larger confidence. The linear and logarithmic correlation of precipitation and normalized difference vegetation indexes or average air temperature is analyzed at confidence level of 0.05. The conclusion is that precipitation and normalized difference vegetation indexes and average air temperature in the basin vary with seasons and are closely correlated. The linear correlation coefficient of precipitation and normalized difference vegetation indexes is 0.8, and that of NDVI and average air temperature is about 0.77.And the logarithmic correlation coefficient of precipitation and normalized difference vegetation indexes is 0.71, and that of NDVI and average air temperature is 0.7.

PARTIÇÃO DA PRECIPITAÇÃO PLUVIAL EM UMA MICROBACIA HIDROGRÁFICA OCUPADA POR MATA ATLÂNTICA NA SERRA DA MANTIQUEIRA, MG

http://dx.doi.org/10.5902/1980509815739A análise do comportamento da precipitação e sua interação nas diferentes etapas do ciclo hidrológico em bacias hidrográficas florestadas são essenciais para a determinação do balanço hídrico, devido à sua relevante participação nos processos hidrológicos e à sua variação espaço-temporal em função de fatores edáficos, topográficos, climáticos e de vegetação. Devido à heterogeneidade da Mata Atlântica, aliada à variabilidade espaço-temporal da precipitação, tornam-se fundamentais mecanismos que permitam a descrição, bem como a interligação dos componentes do balanço hídrico nesse ambiente. Nesse contexto, este estudo foi realizado com o objetivo de analisar a partição da precipitação pluvial em uma microbacia hidrográfica inteiramente ocupada por um remanescente de Mata Atlântica, durante os anos hidrológicos de 2009-2010 e de 2010-2011, correlacionando-a com a evolução sazonal dos ecossistemas, identificada pela aplicação das diferenças dos índices de vegetação por diferença normalizada (IVDN). Observou-se maior percentual de precipitação interna em períodos com menores precipitações. Constatou-se também maior capacidade de armazenamento de água do dossel vegetativo da Mata Atlântica no período chuvoso. Ainda, foi verificada correlação entre a capacidade de armazenamento de água do dossel da Mata Atlântica e a regeneração vegetal sinalizada pelo IVDN, podendo-se associar este comportamento aos processos responsáveis pelo desenvolvimento da Mata Atlântica.

Estimation of Nitrogen Uptake and Yield of Tobacco (Nicotiana tobacum L.) by Reflectance Indices of Ground-based Remote Sensors

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for predicting yield, biomass, and nitrogen stress during growing season. The objectives of this study were: 1) to assess biomass and nitrogen (N) status of tobacco (Nicotiana tabacum L.) plants under N stress using ground-based remote sensors; and 2) to evaluate the feasibility of spectral reflectance indices for estimating an application rate of N and predicting yield of tobacco. Dry weight (DW), N content, and N uptake at the 40th and 50th day after transplanting (DAT) were positively correlated with chlorophyll content and normalized difference vegetation indexes (NDVIs) from all sensors (P th DAT ranged from 0.72 to 1.0 and showed the same positively quadratic regression with N application rate explaining 84% for the variability of N rate. These results suggest that use of reflectance indices measured with ground-based remote sensors may assist in determining application rate of fertilizer N at the critical season and estimating yield in mid-season.

NDVI Changes of the Nansi Lake in Shandong Province of China

The normalized difference vegetation indexes (NDVI) of the Nansi Lake wetland in Shandong province is are calculated on the basis of MSS, TM and ETM+ remote sensing data collected by the Landsat satellites. The characteristics of seasonal vegetation activity of the Nansi Lake during 1973-2011 were revealed. The seasonal variation characteristics of wetland vegetation in Nansi Lake are not identical in the last 40 years. The NDVI in spring (P=0.0216) first decreased and then increased; The NDVI in summer (P=0.0007) and winter (P=0.0189) present a significant decreasing trend. The variation of the NDVI in autumn is small.