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. Journal of Plant Nutrition. 28 . 1 : 173 – 182 . 30. Roderick , M. – Smith , R. – Cridland , S. : 1996 . The precision of the NDVI derived from AVHRR observations . Remote Sensing

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Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Penuelas, J., Valentini, R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Applic. 5 :28

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between normalized difference vegetation index (NDVI) and other traits of tropical testcross maize ( Zea mays L.) hybrids under drought and well-watered conditions . J. Appl. Agric. Res. 6 : 173 – 180

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The objective of the work reported is the development of red-edge methodology in order to characterize agricultural vegetation types and the determination of relationships between different vegetation (high biomass, low biomass) and thermal images. Therefore, the aim was to calculate red-edge position (REP) values and compare them to traditional vegetation indices (NDVI) and thermal images. Images were taken by a DAIS 7915 airborne imaging spectrometer that was equipped with an additional thermal imaging system. An exponential relationship was found between the on-curve-evaluation based (REP) and the broad band vegetation indices (NDVI). A linear relationship was determined between surface temperature differences ( ΔT s ) of the vegetation and NDVI values. A logarithmic relationship was found between surface temperature differences ( ΔT s ) of the vegetation of the canopy and red-edge position (REP). NDVI and REP are suitable vegetation indices when there are several bands available in the spectral range of 600-800 nm. REP was found to be a suitable method for analyzing and characterizing vegetated surfaces.

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Application of the appropriate N fertilizer rate for wheat production is needed to improve and sustain productivity. Different methods have been developed over time to estimate these needs. The objective of this work was to evaluate the relationship basal N rate at planting — NDVI (normalized difference vegetative index) by means of a spline regression to estimate further N needs of spring wheat. Experiments were established in two planting systems; permanent beds and conventional in solid stands. Three flat N rates (25, 50, and 75 kg N ha −1 , and 30, 60 and 90 kg Nha −1 for permanent beds and conventional planting, respectively) plus an unfertilized check plot were applied according to three N timing treatments (whole rate at planting or end of tillering, and split at planting and at the end of tillering). Before the application of N treatments at the end of tillering, plots were divided into two halves to apply variable N rates according to the first segment of the spline model. Results indicated that parameter estimates from the spline regression vary within each planting system. However, variable N rates estimated for each year and location were lower than flat N rates. In spite of those differential fertilizer rates, grain yield resulting for the application of variable N rates were similar to flat N rates. Pooled data analysis suggests that NDVI readings greater than 0.56 and 0.65 for permanent beds and conventional planting, respectively, the application of N fertilizer at the end of tillering can be excluded as grain yield will not be modified.

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We elaborated and tested a novel operative framework for sampling and analysing fine-scale pattern of plant composition and biomass. We combined presence/absence sampling of plant species with non-destructive biomass estimation. In an open perennial sand grassland, we used 46 m long circular transects consisting of 0. 05 m by 0. 05 m adjoining elementary sampling units. This arrangement allows us to scale across a range of 0. 05 to 20 m. For measuring aboveground green biomass, we applied digital camera sensitive to red and near infrared parts of light spectrum, and we calculated normalised differential vegetation index (NDVI). We used information statistics proposed by Juhász-Nagy to study the association between spatial patterns of production and species composition. Since information statistical functions applied require binary data, we transformed NDVI data into one or several binary variables. We found that not only dominant species but subordinate gap species were also associated to high biomass, although the strength of association varied across scales. Most of the significant associations were detected at fine scales, from 0. 05 to 0. 25 m. At the scales commensurable with quadrat sizes usually applied in grasslands, i. e., from 0. 5 to 2. 0 m, we could hardly find any significant associations between species composition and biomass. We concluded that the novel methods applied proved reliable for studying fine-scale relationships between species composition and biomass.

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With continuing proliferation of human influences on landscapes, there is mounting incentive to undertake quantification of relationships between spatial patterns of human populations and vegetation. In considering such quantification, it is apparent that investigations must be conducted at different scales and in a comparative manner across regions. At the broader scales it becomes necessary to utilize remote sensing of vegetation for comparative studies against map referenced census data. This paper explores such an approach for the urbanized area in the Tokyo vicinity. Vegetation is represented by the normalized difference vegetation index (NDVI) as determined from data acquired by the thematic mapper (TM) sensor of the Landsat satellite. Sparseness of vegetation is analyzed in relation to density of human residence, first by regression analysis involving stratified distance zones and then by the recent echelon approach for characterization of surfaces. Echelons reveal structural organization of surfaces in an objective and explicit manner. The virtual surface determined by census data collected on a grid is shown to have structural correspondence with the surface representing vegetation greenness as reflected in magnitude of NDVI values computed from red and infrared bands of image data.

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Litter mass represents a key factor in the process of carbon sequestration. Pine plantations are known to accumulate high amounts of litter, which may act as real carbon sink only if it persists for long time. Thus, predicting litter mass by means of robust and straightforward models which convey information from several ecological predictors become crucial in this framework. The aim of this paper was to test for relationships between environmental predictors and pine litter mass (total branch, needle and cone) by Generalized Linear Models, exploring the contribution of different environmental variables in describing patterns of pine litter mass. Different predictors accounting for seasonality, spatial and geomorphological variability, pine stand properties, remotely sensed derived biomass were taken into account. Considering total litter mass, observed vs. predicted values showed a statistically highly significant relation (p < 0.001) by retaining four variables: elevation, latitude, stand age and season. Similar results were achieved for the needle litter mass, which represented anyway the largest fraction of the litter. Regarding branch litter mass, only stand age appeared to be a significant variable. For cone litter mass, no variables were statistically significant in explaining its variance. Potential ecological background processes responsible for the correlations between variables are discussed.

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Szennyvíziszap kihelyezés rövidtávú következményeinek értékelési lehetősége Sentinel-2 alapú szántóföldi vegetációmonitoring alapján

Evaluate the short-term effects of sewage sludge disposal based on Sentinel-2 vegetation monitoring

Agrokémia és Talajtan
Authors: Kovács Ferenc and Ladányi Zsuzsanna

– 2899 Szabó , A ., Tamás , J ., Nagy , A ., Adeniyi , O. D . 2019 . Wheat Yield Prediction Based On MODIS NDVI Time Series Data In The Wider Region Of A Cereal Processing Plant . Natural Resources and Sustainable Development . 9 . ( 2 ) 193

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Our aim was to describe vegetation heterogeneity at a regional scale in northeastern Patagonia and to identify the environmental variables associated to it. The study area encompasses 13 144 km2 and is characterized by a mixture of species typical of Patagonian steppes and Monte Desert. We performed 48 vegetation relevés, which were randomly assigned to a training set and to a validation set (32 and 16 relevés, respectively). Training set was subjected to cluster analysis, which allowed the identification of two plant communities one related to Patagonian steppes and another to the Monte desert. We derived 3 attributes of the seasonal curve of the NDVI as indicators of ecosystem function: the seasonal amplitude (SA), the date of the maximum (DOM), and the large seasonal integral (LSI). We explored the relationship between NDVI attributes and communities by classification tree analysis. LSI was the strongest predictor among NDVI attributes, separating both communities without misclassification errors. Patagonian steppes occupy areas with higher LSI. The partial RDA analysis explained 38.1% of total data variation, of which 16.5% was ascribed to environment, 7.9% to space, and 13.7% to spatial component of environment. Patagonian steppes are closer to the coast, in areas exhibiting higher annual precipitation and lower annual temperature range than Monte deserts. Our results indicate the occurrence of two plant communities in the study area and highlight the significance of climatic variables to explain their spatial distribution. As most scenarios of future climate predict greater annual thermal amplitude in the study area, the limit between both communities could be displaced eastward.

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