High Normalized Difference Vegetation Index Values Research Articles

Dynamic Change of Vegetation Index and Its Influencing Factors in Alxa League in the Arid Area

While there have been various studies on global vegetation dynamics, limited studies have been conducted to understand vegetation changes in arid areas. Vegetation distribution patterns can be affected by multiple factors, so understanding their interactions can help improve the capability of predicting future vegetation dynamics. This study, therefore, analyzed the dynamic vegetation changes in Alxa League, China, using the Normalized Difference Vegetation Index (NDVI) dataset (2000–2019), with the consideration of land cover types, digital elevation model, air temperature, precipitation, soil moisture, total evaporation, and air quality. The results show that the NDVI in Alxa League is small. Before 2012, the NDVI value fluctuated, while after 2012, the NDVI value dropped sharply and then slowly recovered after 2015. High NDVI values were found in areas with high and frequent human activities (city centers). The NDVI in the northwest region showed a slight degradation trend, and the southeast showed a slight improvement trend. According to the land cover type analysis, the NDVI value was the largest when the land cover type was tree cover, and the NDVI value was the smallest when the land cover type was bare/sparse vegetation. Alxa League was dominated by a bare/sparse vegetation distribution. The terrain analysis indicates that when the height was between 1800 and 3492 m, the NDVI value was the highest, and high NDVI values were mainly distributed in the area with a slope > 25°. When the slope was flat, the NDVI value was the smallest. Considering climate factors, the NDVI was negatively correlated with air temperature, precipitation, soil moisture, and total evaporation in space, and only precipitation and soil moisture were positively correlated in time. Moreover, the population size has a strong positive correlation with the NDVI in this area. The monthly variation of the NDVI and absorbable particulate matter (PM10) was negatively correlated, i.e., strongly negatively correlated in spring, summer, and autumn, but only weakly positively correlated in winter. The seasonal variation of the NDVI was as follows: summer > autumn > spring > winter, and the seasonal variation of PM10 was spring > winter > summer > autumn. The interannual variation of the NDVI and PM10 was positively correlated. This suggests that the absorbable particulate matter (PM10) may be an essential factor for the normalized vegetation index to underestimate the dynamic changes of vegetation in arid regions. This study provides a theoretical basis for the dynamic changes of vegetation in the dry Alxa League.

Monitoring Vegetation Area in Baghdad Using Normalized Difference Vegetation Index

Vegetation monitoring is considered an important application in remote sensing task due to variation of vegetation types and their distribution. The vegetation concentration around the Earth is increase in 5% in 2000 according to NASA monitoring. This increase is due to the Indian vegetable programs. In this research, the vegetation monitoring in Baghdad city was done using Normalized Difference Vegetation Index (NDVI) for temporal Landsat satellite images (Landsat 5 TM& Landsat 8 OIL). These images had been used and utilize in different times during the period from 2000, 2010, 2015 & 2017. The outcomes of the study demonstrate that a change in the vegetation Cover (VC) in Baghdad city. (NDVI) generally shows a low value of plant cover. The highest NDVI values were occur in 2000 and the lowest values for both years 2015-2017. This change is due to a correlation of climate indices such as precipitation, temperature, and dust storms. This study present that (NDVI) method is a powerful and useful way of monitoring vegetation. The calculation of vegetable areas show (43.3, 37.4, 9.1, and 22.7 Km2). The result were evaluated using (Environment for Visualizing Images ENVI) Ver. 4.8 package.

Natural variation in snow depth and snow melt timing in the High Arctic have implications for soil and plant nutrient status and vegetation composition

Snow cover is a key component in Arctic ecosystems and will likely be affected by changes in winter precipitation. Increased snow depth and consequent later snowmelt leads to greater microbial mineralization in winter, improving soil and vegetation nutrient status. We studied areas with naturally differing snow depths and date of snowmelt in Adventdalen, Svalbard. Soil properties, plant leaf nutrient status, and species composition along with the normalized difference vegetation index (NDVI) were compared for three snowmelt regimes (Early, Mid, and Late). We showed that (1) Late regimes (snow beds) had wetter soils, higher pH, and leaves of Bistorta vivipara (L.) Delarbre and Salix polaris Wahlenb. had higher concentration of nutrients (nitrogen and δ15N). Little to no difference was found in soil nutrient concentrations between snowmelt regimes. (2) Late regimes had highest NDVI values, whereas those of Early and Mid regimes were similar. (3) Vegetation composition differed between Early and Late regimes, with Dryas octopetala L. and Luzula arcuata subsp. confusa (Lange) characterizing the former and Equisetum arvense L. and Eriophorum scheuchzeri Hoppe the latter. (4) Trends for plant nutrient contents were similar to those found in a nearby snow manipulation experiment. Snow distribution and time of snowmelt played an important role in determining regional environmental heterogeneity, patchiness in plant community distribution, their species composition, and plant phenology.

Assessing Spatial and Temporal Distribution of Algal Blooms Using Gini Coefficient and Lorenz Asymmetry Coefficient

Algal bloom in an inland lake is characterized by significant spatial and temporal dynamics. Accurate assessment of algal bloom distribution and dynamics is highly required for tracing the causes of and creating countermeasures for algal bloom. Satellite remote sensing provides a fast and efficient way to capture algal bloom distribution at a large scale, but it is difficult to directly derive accurate and quantitative assessment based on satellite images. In this study, the Gini coefficient and Lorenz asymmetry coefficient were introduced to examine the spatio-temporal algal bloom distribution of Chaohu Lake, the fifth largest inland lake in China. A total of 61 remote sensing images from three satellite sensors, Landsat, Gaofen, and Sentinel were selected to obtain algal bloom distributions. By dividing remote sensing images into 0.01°*0.01° grid cells, the normalized difference vegetation index (NDVI) for each grid cell was derived, forming a spatial and time series database for quantitative analysis. Two coefficients, Gini coefficient and Lorenz asymmetry coefficient, were used to evaluate the overall intensity, unevenness, and attribution of algal bloom in Chaohu Lake from 2011 to 2020. The Gini coefficient results show a large variety of algal bloom in the spatial and temporal scales of Chaohu lake. The lake edge and northwestern part had longer lasting and more severe algal bloom than the lake center, which was mainly due to nutrient import, especially from three northwestern tributaries that flow through the upstream city. The Lorenz asymmetry coefficient revealed the exact source of the unevenness. Spatial uncertainties were mostly caused by the tiny areas with high NDVI values, accounting for 53 cases out of 61 cases. Temporal unevenness in northwestern and northeastern parts of the lake was due to the most severe breakout occurrences, while unevenness in the lake center was mainly due to the large number of light occurrences. Finally, the advantage of Gini coefficient and Lorenz asymmetry coefficient are discussed by comparison with traditional statistical coefficients. By incorporating the two coefficients, this paper provides a quantitative and comprehensive assessment method for the spatial and temporal distribution of algal bloom.

Изменение лесных массивов северо-восточного региона Азербайджана по космическим снимкам

Among the most valuable natural resources of any country are its forest reserves. They need to be preserved. The need for forest protection also exists in Azerbaijan, where there is less forest per inhabitant than in neighboring countries. The use of modern data acquisition and processing methods as well as geographical information system technologies has reduced research in this area to a set of standard procedures. The article describes the stages of processing satellite images available in the public domain, using the case study of the north-eastern region of Azerbaijan, in order to compile forest cover maps over a number of different years. The Landsat images obtained during the summer seasons in different years from 1987 to 2018 are studied. The images covered the territory of 5 neighboring districts in the north-east of Azerbaijan. Preliminary processing of the images included radiometric calibration and atmospheric correction and was carried out using the ENVI software and the FLAASH module. The article also shows the final processing of images using the ArcGIS program in order to determine the areas covered by forests in different years. The analysis was based on the calculation of the Normalized Difference Vegetation Index (NDVI). The index was calculated for all considered satellite images. Then sections of images with high NDVI values were highlighted, vectorized, and the areas of the resulting polygons were found. Thus, a separate thematic layer is created for each year, showing the area of forest cover that year, i.e. 3 layers in total. The data obtained were summarized in a table, from which a diagram showing the dynamics of the forest area in the region was created. The data also became the basis for a thematic electronic map of forest loss. The continuation of this process has been described. For citation: Mamedaliyeva V.M. Changes in Forested Areas of the North-Eastern Region of Azerbaijan Revealed by Satellite Images. Lesnoy Zhurnal [Russian Forestry Journal], 2022, no. 1, pp. 88–97. DOI: 10.37482/0536-1036-2022-1-88-97

Morphological and ecological data confirm Reithrodontomys cherrii as a distinct species from Reithrodontomys mexicanus

The integrative taxonomy approach has recently been widely suggested in systematic studies. Lines of evidence such as the geometric morphometrics and ecological analyses have been useful for discriminating between genetically well-differentiated species. Within the genus Reithrodontomys, R. mexicanus is one of the more taxonomically complex species, being considered a cryptic species complex. R. cherrii was considered a subspecies of R. mexicanus, until molecular evidence raised it to the species-level. Herein, we evaluate these two forms using morphological and ecological data based on the premise that they constitute genetically differentiated species. We carried out geometric morphometric analyses on dorsal and ventral views of the skull. Landmark and semi-landmark configurations for both views of the skull were selected based on previous studies of cricetid rodents. We tested the presence of sexual dimorphism, and the skull shape and size differences between species on both cranial views. Additionally, we characterized the environmental space of each species habitat using bioclimatic variables, elevation, and the Normalized Difference Vegetation Index (NDVI). Females and males of R. mexicanus and R. cherrii did not show sexual dimorphism in shape or size of both skull views. We found significant differences between the two species in both shape and size of the skull. Cranial structures of the ventral view were more useful to differentiate both species. R. mexicanus exhibited a broader environmental space than R. cherrii, with relatively similar values of temperature and elevation, but not of precipitation. The pairwise comparison showed significant differences in the majority of the environmental variables analyzed. Although for each view, we found statistical differences in the skull shape of R. cherrii and R. mexicanus, the ventral side showed major resolutive power differentiating both species. Our findings suggest that R. cherrii tends to have a larger skull than R. mexicanus. However, the morphological and pelage coloration similarity between these species reported in the past, could explain the previous inclusion of R. cherrii as a subspecies of R. mexicanus. R. mexicanus occurs in a variety of vegetation-types coinciding with the broader environmental space that it occupies compared to that of R. cherrii. The natural areas where both species are distributed were associated with high NDVI values. Our results complement the molecular evidence and, under an integrative taxonomy approach, support R. cherrii as a different species from R. mexicanus.

ANALYZING THE IMPACT OF BUILT UP AND GREEN SPACES ON LANDSURFACE TEMPERATURE WITH SATELLITE IMAGES IN JALANDHAR SMART CITY

Land use in developing nations is shifting as a result of rapid urbanisation. The ecosystem is being harmed by the unplanned, urban development of cities. Utilizing data from Landsat, the present research examined the spatiotemporal urban growth in Jalandhar City and its impact on variations in land surface temperature (LST). The results indicate that urban regions have increased while urban green spaces have shrunk (UGS). UGS and LST have been found to be inversely correlated. Where there was a low percentage of urban areas and a high percentage of accessible green spaces, it was observed that the LST decreased. The link between spectral variability and changes in vegetation growth rate was studied using the NDVI (Normalized Difference Vegetation Index). It works on tracking the growth of green vegetation and spotting variations in the amount of greenery. Where NDVI value was low, the scatter plots indicate higher surface temperatures. The dense urban regions with little available vegetation cover were linked to the low NDVI number and the NDVI is negative in the urban area of Jalandhar. High NDVI values indicate regions with dense vegetation and low surface temperatures. The research findings of present study could be applied to urban administration, planning, management and research projects.

Effect of different seedling growing methods on the SPAD, NDVI values and some morphological parameters of four sweet corn (Zea mays L.) hybrids

Abstract The main goal of our investigation was to determine the relationship between different growing methods of sweet corn seedlings and some physiological and morphological parameters of four hybrids in order to get information about the ability of their stress tolerance in a two-year experiment (2019, 2020). Seedlings were grown with and without pre-conditioning. Pre-conditioning is based on growing young plants exposing them to cold stress. Seedling emergence percentage, plant height, total leaf number, the total mass of fresh aboveground biomass, and ear length were determined as well as Soil Plant Analysis Development (SPAD) and Normalized Difference Vegetation Index (NDVI) values. In 2019, the pre-conditioned seedlings were more tolerant to cold stress for most of the tested parameters. Overall, the SPAD and NDVI values of the pre-conditioned seedlings were considered better in both years, however, it was not verified for all the studied hybrids. Among the hybrids, ‘Strongstar’ had the highest benefit from pre-conditioning compared to the standard growing method in terms of resulting in 17.5% higher plants, 13.1% longer ears, and 10.4% higher SPAD values in 2019. In 2020, when the cold stress was not so dominant, ‘Gyöngyhajnal’ gained the most from pre-conditioning with 9.7% higher plants, 32% more fresh aboveground biomass, 6.8% longer ears, 3.6% higher SPAD, and 9.3% higher NDVI values. More emphasis should be placed on the choice of stress-tolerant hybrids as well as on the seedling growing method and the date of transplanting to improve adaptation to the more frequent weather extremes.

Canopy spectral characteristics of typical invasive and native plants in the coastal wetland of Yancheng City, China

Spartina alterniflora (S. alterniflora) is a dominant invasive alien species that occurs in Yancheng Wetland National Nature Reserve, the largest coastal wetland in China. It expands rapidly and exerts great threats to local ecosystem. The main native species there are Phragmites australis (P. australis) and Suaeda salsa (S. salsa), respectively. In order to monitor their dynamics, it is of great significance to analyze their spectral discrimination. Canopy spectra of these typical species were measured in July and October in order to compare species differences, as well as the seasonal variation. The Normalized Difference Vegetation Index (NDVI) was calculated based on canopy spectra. The significances of differences in spectral characteristic among species were tested using the one-way analysis of variance (ANOVA) (P < 0.05). The results showed that the visible (VI) bands were the optimum wavelengths for species discrimination in both seasons. S. alterniflora always had the greatest green peak height and red absorption depth. S. salsa had no obvious green peak, but an obvious red reflection peak. P. australis generally had intermediate values. Spectra in the near-infrared (NIR) bands were not appropriate for delineating S. alterniflora and P. australis in summer, as they showed similar values in these wavelengths. But NIR bands could be used to distinguish S. salsa in summer, as it had significantly lower reflectance in NIR bands than the others. Meanwhile, the reflectance in short-wavelength infrared (SWIR) bands became another suitable approach for distinguishing species in autumn. S. salsa had significantly higher values than the others in SWIR regions. Significant NDVI differences between different species proved that it could improve the species discrimination. S. alterniflora always had the highest NDVI values, while S. salsa had the lowest values. The seasonal trend of canopy spectra was also revealed. With plant maturity, the reflectance values in green bands and NIR bands decreased significantly, but increased significantly around the yellow-red wavelengths. The green peak heights, red absorption depths, and NDVI values of the species decreased remarkably. Furthermore, S. alterniflora and P. australis illustrated obvious ‘blue shift’ of the red edge with senescence. These results pointed out the the potential bands and appropriate spectral parameters appropriate for species discrimination, and highlighted the influence of seasonality on spectral information. They provided an important basis for salt marshes identification and dynamic monitoring on a large scale.

Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles

AbstractUnmanned Aerial Vehicles (UAVs) offer new opportunities for accurate, repeatable vegetation assessments, which are needed to adaptively manage restored habitat. We used UAVs, ground surveys, and satellite imagery to evaluate vegetation metrics for three riparian restoration sites along the Colorado River in Mexico and we compared the data accuracy and efficiency (cost and time requirements) between the three methods. We used an off‐the‐shelf UAV coupled with a multispectral sensor to determine Normalized Difference Vegetation Index (NDVI) and vegetation cover. We were unable to accurately classify vegetation by individual species, but by grouping riparian species of interest (cottonwood‐willow, mesquite, shrubs), we achieved high overall model accuracies of 87–96% across sites (Kappa = 0.82–0.95). Producer’s and user’s accuracies were moderate to high for target vegetation classes (69–100%). UAV and ground‐survey vegetation percent cover differed due to differences in methodologies (UAVs measure aerial cover; ground surveys measure foliar cover) and sources of error for each method. Correlations between UAV and ground survey vegetation cover were moderate (rs(90) = 0.24–0.58, p &lt; 0.05). UAV NDVI (0.50–0.61) was significantly higher than Landsat NDVI (0.40–0.45) for all sites (p &lt; 0.0001), likely due to presence of shadows with high NDVI values in UAV imagery. UAV NDVI, Landsat NDVI and UAV total vegetation cover were strongly correlated (rs(90) = 0.72–0.85, p &lt; 0.05). UAV surveys were more labor‐ and cost‐ intensive than ground surveys in the first year, but were slightly less so in the second year. We conclude that UAVs can provide efficient, accurate assessments of riparian vegetation, which can be used in restoration site management. Due to UAV limitations to assess vegetation in a multi‐layered canopy and inability to classify individual riparian species with similar spectral signals, we recommend a combined approach of UAV and ground surveys.

Thirty-two years of mangrove forest land cover change in Parita Bay, Panama

Mangrove forests have experienced a rapid decline. However, the rate of loss has decreased in recent years due to enhanced conservation and nature regeneration. The dynamics of mangrove forests in Panama have not been monitored since the year 2000, despite a significant loss during the 1980s. The objectives of our study were to quantify changes in mangrove cover and identify the dominant drivers of change in Parita Bay, Panama. Temporal changes in mangrove cover and the Normalized Difference Vegetation Index (NDVI) were determined using the supervised classification method on Landsat satellite images from 1987 to 2019. We identified a 4.7% increase in the mangrove area of Parita Bay during the 32 years; the mangrove forests were also considered healthy as reflected by high NDVI values. However, the conversion of mangroves to other land cover types resulted in a 1.26% decline in mangrove cover from 1987 to 1998. Moreover, the area of aquaculture and saltpans almost doubled during this period. During the following two decades, the conversion of other land cover classes (water, other vegetation, and bare soil) increased the mangrove area by 6%, and the annual rate of increase was greater during the second decade (0.43% year−1). From 2009 to 2019, mangroves declined at an annual rate of 0.11% in protected areas and increased at an annual rate of 0.50% in unprotected areas. Despite the regeneration potential of mangrove forests, our study highlights the need to continually manage and protect mangrove forests in order to facilitate their expansion in Parita Bay.

Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions

To impart sustainability to modern intensive farming systems, environmental pollution caused by nitrogenous fertilizers in needs to be reduced by optimizing their doses. To estimate the grain yield and nutrtional quallity of wheat, the normalized difference vegetation index (NDVI) and chlorophyll content (SPAD) are potential screening tools to identify the N deficiency and screen out the promising cultivars. The two-year field study was comprised with five levels of nitrogen (N) (control, 50, 100, 150 and 200 kg N ha−1) and two durum wheat genotypes (Sena and Svevo). The experimental design was split-plot, in which N levels were placed in the main plots, while wheat genotypes were arranged in sub-plots. To predict the yield and quality traits, the NDVI and SPAD values recorded at heading, anthesis and milky growth stages were taken as response variables. The results revealed that N fertilization significantly influenced the SPAD and NDVI attributed traits of durum wheat, except NDVI at milky stage (NDVI-M) during the first year. The maximum value of NDVI was recorded by 150 kg N ha−1, while control treatment gave the minimum value. The grain yield was increased with the increasing dose of the N up to 100 kg N ha−1 (4121 kg ha−1), and thereafter, it was declined with further increased of N levels. However, the variation between the genotypes was not significant, except NDVI and SPAD values at the milky stage. The genotype Svevo had the highest NDVI values at all growth stages, while the genotype Sena recorded the maximum SPAD values during both years. Similarly, the N levels significantly influenced the quality traits (protein, wet gluten, starch test weight and Zeleny sedimentation) of both genotypes. The highly significant relationship of SPAD and NDVI with the grain yield and yield attributes showed their reliability as indicators for determining the N deficiency and selection of superior wheat genotypes for ensuring food security under climate change scenario.

Novel sensor-based method (quick test) for the in-season rapid evaluation of herbicide efficacy under real field conditions in durum wheat

AbstractOptimum herbicide use is a key factor affecting the success of any integrated weed management strategy. The main objective of the current study was to implement a method based on spectrometer measurements for the in situ evaluation of herbicide efficacy and the detection of potentially herbicide-resistant weeds. Field trials were conducted in Greece between 2018 and 2020 in several durum wheat fields (Triticum durum Desf.). In all trials, the overall effect of herbicide application on the recorded Normalized Difference Vegetation Index (NDVI) values (at 1 and 2 wk after treatment [WAT]) was significant (P ≤ 0.001). For the majority of the surveyed fields, low NDVI values were recorded after 2,4-D application and a mixture of clopyralid + florasulam from 1 WAT, suggesting their increased efficacy. In several cases, the application of pyroxsulam + florasulam resulted in significant NDVI reductions at 2 WAT. As observed at the end of the growing seasons, the herbicides that reduced NDVI resulted in lower weed biomass. Strong correlations were observed between weed aboveground biomass and NDVI (2 WAT). In particular, R2 values were 0.8234 to 0.8649, 0.8453, 0.8595, 0.8149, and 0.8925 for the Aliartos, Thiva, Domokos, Larissa, and Orestiada fields, respectively. The overall effects of herbicide application on wheat grain yield were also significant (P ≤ 0.001). Pot experiments confirmed that the high NDVI values in some cases could be attributed to the presence of herbicide-resistant weeds. For instance, the resistance indices of two accessions of catchweed bedstraw (Galium aparine L.) to mesosulfuron-methyl + iodosulfuron-methyl-sodium ranged between 9.7 and 13.2, whereas one sterile oat [Avena sterilis L. ssp. ludoviciana (Durieu) Gillet &amp; Magne] accession was 8.8 times more resistant to fenoxaprop-p-ethyl than a susceptible one. The present study is targeted at making a significant contribution toward establishing cause–effect relationships and presenting a useful tool for developing more effective weed management practices in more arable crops and under different soil and climatic conditions.

Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage

In order to control the spread of COVID-19, China had implemented strict lockdown measures. The closure of cities had had a huge impact on human production and consumption activities, which had greatly reduced population mobility. This article used air pollutant data from 341 cities in mainland China and divided these cities into seven major regions based on geographic conditions and climatic environment. The impact of urban blockade on air quality during COVID-19 was studied from the perspectives of time, space, and season. In addition, this article used Normalized Difference Vegetation Index (NDVI) to systematically analyze the characteristics of air pollution in the country and used the Pearson correlation coefficient to explore the relationship between NDVI and the air pollutant concentrations during the COVID-19 period. Then, linear regression was used to find the quantitative relationship between NDVI and AQI, and the fitting effect of the model was found to be significant through t test. Finally, some countermeasures were proposed based on the analysis results, and suggestions were provided for improving air quality. This paper has drawn the following conclusions: (1) the concentration of pollutants varied greatly in different regions, and the causes of their pollution sources were also different. The region with the largest decline in AQI was the Northeast China (60.01%), while the AQI in the southwest China had the smallest change range, and its value had increased by 1.72%. In addition, after the implementation of the city blockade, the concentration of NO2 in different regions dropped the most, but the increase in O3 was more obvious. (2) Higher vegetation coverage would have a beneficial impact on the atmospheric environment. Areas with higher NDVI values have relatively low AQI. There is a negative correlation between NDVI and AQI, and an average increase of 0.1 in NDVI will reduce AQI by 3.75 (95% confidence interval). In the case of less human intervention, the higher the vegetation coverage, the lower the local pollutant concentration will be. Therefore, the degree of vegetation coverage would have a direct or indirect impact on air pollution.

СОРТОВАЯ СПЕЦИФИЧНОСТЬ ОПТИКО-БИОЛОГИЧЕСКИХ СВОЙСТВ И ФИТОМЕТРИЧЕСКИХ ПАРАМЕТРОВ ПОСЕВОВ ОЗИМОЙ ПШЕНИЦЫ

Identification of varietal specificity of opto-biological properties of field crops is necessary for adequate interpretation of satellite images, rapid determination of phytometric parameters of crops, indication of the level of their development and justification of corrective technological solutions. The aim of the research was to evaluate the opto-biological properties and phytometric parameters of diverse biological varieties of winter wheat; identify their relationship and inter-variety variability in high-tech reference crops based on satellite and ground monitoring. Field studies were conducted in 2019-2020 on the site of competitive ecological variety testing laid on the experimental field of the Orenburg State Agrarian University in the zone of the southern steppes of the Orenburg Cis-Urals (Preduralye). During the study, we found that agrocenoses of different winter wheat varieties differ markedly in opto-biological properties expressed in normalized difference vegetation index (NDVI) values. Varieties ‘Grom’, ‘Don 95’, ‘Bystritsa’, ‘Spartak’, ‘Stanichnaya’ and ‘Sintetik’ were characterized by the lowest average NDVI values during the growing season (0.57-0.59). Higher NDVI values (0.67) were observed in the crops of ‘Zhemchuzhina Povolzhya’, ‘Kolos Orenburzhya’, ‘Orenburgskaya 105’, ‘Rifey’ and ‘Pionerskaya 32’ varieties. The variability between the varieties according to the lowest (0.51 – ‘Grom’, 0.52 – ‘Don 95’) and the highest (0.69 – ‘Kolos Orenburzhya’, 0.70 – ‘Pionerskaya 32’, 0.69 – ‘Orenburgskaya 105’) NDVI values during the maximum assimilation apparatus development reached 0.17-0.19 units (phase of full spring tillering). By the heading, when NDVI was 0.78–0.79 and 0.85 units, respectively, this difference somewhat leveled but, in general, remained significant – at the level of 0.08-0.11 units (10.9–14.9 %). The largest leaf area (30 thousand m2/ha) was typical for the crops of ‘Saratovskaya 17’, ‘Kolos Orenburzhya’, ‘Orenburgskaya 105’, ‘Rifey’ and ‘Pionerskaya 32’. There was no direct inter-variety relationship between the leaf area and NDVI of winter wheat crops (R 2= 0.38).

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

A field experiment examined the wheat response following soybean due to urea-N fertilization at sowing (0, 20, 40, and 60 kg N ha−1) and top dressing (0, 30, 60, and 90 kg N ha−1). Normalized difference vegetation index (NDVI) measurements using a GreenSeeker active sensor were taken at different growth stages, at various sensor heights above the canopy, and at different times of day. Both the shoot dry matter yield and the grain yield were not affected by N fertilization at sowing and increased with increasing N rates at top dressing. A maximum economic yield was obtained at 55 kg N ha−1 in top dressing, causing a 33% increase in grain yield and an economic return of US$ 189.50 ha−1. Differences in NDVI were found before and after top-dressing N fertilization, indicating that GreenSeeker was efficient in monitoring wheat N nutrition. NDVI varied depending on the measurement distance, stabilizing from 0.30 to 1.20 m above the canopy. Because of the influence of incident radiation, higher NDVI values were obtained at the beginning and end of the day. The results suggest that for the wheat cultivar Quartzo following soybean under minimum soil disturbance, while there is no need to apply N at sowing, an important increase in grain yield can be obtained with top-dressing N fertilization. In addition, to improve the accuracy in developing N fertilizer recommendation models using GreenSeeker, a consistent protocol for spectral reflectance readings, mainly regarding the time of day, is required.

Bangalore: Urban heating or urban cooling?

Bangalore, one of the Indian metropolitan cities, has witnessed rapid urbanization during the last decade with an overwhelming spatial growth of 696 sq. km to 2185 sq.km within a span of ten years (2006 to 2016). With this exponential rate of urban growth, the city is experiencing a shift in its micro- climatic condition. This paper aims to identify the surface urban heat island (SUHI) effect by comparing normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and the fraction of impervious surface area (ISA) as indicators of SUHI. Landsat 8 datasets were used to estimate the land surface temperature (LST) for two different seasons representing winter (January) and summer (April) using radiative transfer algorithm. A spatial map of ISA was generated using normalized spectral mixture analysis (NSMA) showing a standard error of 9.25%. Correlations between NDVI, NDBI and fraction of ISA with LST indicate a strong linear negative relationship between LST and ISA with a coefficient of correlation 0.85 and 0.94 s for January and April respectively and is contrary to the conventionally accepted positive SUHI phenomenon. The relationship between LST and NDVI was in particular negative at areas bearing high NDVI values. Results indicate that ISA is quantitatively a better metric indicating that the city experiences cool island effect indicative of high vegetative cover presence in the Bangalore Urban (BU) area. Also, NDVI is greatly affected by seasonal variation and NDBI shows mixing of soil characters and hence failed in providing conclusive results.

Pemetaan Kondisi Ekologi Perkotaan Skala Mikro Menggunakan Citra Landsat 8 di Kota Semarang

Semarang City, as a capital city of a province in Indonesia, has experienced intensive rural or suburban migration to urban robinareas. Consequently, the land surface temperature (LST) is getting warmer in urbanized areas and leading to microscale temperature variation from time to time. The objectives of this study are 1) to investigate the geographical distribution of LST and vegetation covers in Semarang, and 2) to map microscale urban ecological condition with regards to LST based on urban thermal field variance index (UTFVI). Landsat 8 data was utilized to derive LST as well as vegetation covers by means of normalized difference vegetation index (NDVI). Next, UTFVI was classified based LST. This study revealed that relatively low NDVI values which mainly consists of built up areas and grassland are predominantly concentrated in the northern and central parts of Semarang. High NDVI values representing vegetation covers are predominantly located in the southern portion of Semarang. The most ecologically depressed areas are mainly distributed in the central portions of the city toward the northern portions of coastal areas. Prioritized sub-districts to be ecologically restored are Ngaliyan, Semarang Utara and Semarang Barat. Upcoming studies should emphasize on finding suitable measures to restore ecologically stressed areas.

Vegetation Dynamics with Elevation in Southern European Russia

In this research work vegetation dynamics was examined for spring-summer (may–august) season based on Moderate Resolution Imaging Spectroadiometer (MODIS) satellite data generated Normalized Difference Vegetation Index (NDVI) with elevation from 2014 to 2018 in Southern part of European Russia. The resulting divergent groups with respect to decreasing, increasing or no trends presented significant differences in vegetation dynamics and correlation between elevation and NDVI values. Results indicate that in the year 2016, vegetation was highly degraded with low NDVI values from less than 0 to 0.20. In comparing of elevation maximum vegetation was present from 100 to 250 m elevation, below and above this range was very low vegetation. In high elevation from 100 to 250m, low NDVI values slightly shift in high NDVI values, which show healthy vegetation on high altitude. Field observation also conform high NDVI values to high elevation. The MODIS time series with 250m resolution is appropriate for vegetation dynamics study for a large, inaccessible area with low coast, within less time for long term changes in vegetation growth.

MAPPING OF SARGASSUM DISTRIBUTION IN THE EASTERN COAST OF SOUTHERN LEYTE USING SENTINEL 2 SATELLITE IMAGERY

Abstract. Sargassum is a brown seaweed distributed in the Philippines and recognized as an additional source of income for fishing communities. Due to uncontrolled harvesting of the seaweed, the Department of Agriculture regulated its collection and harvesting by imposing seasonal restrictions. Hence, the need to identify the locations and cover of healthy Sargassum is vital to address the demand in the market while maintaining ecological balance in the marine ecosystem. Two Sentinel-2 satellite imagery (10 m resolution) acquired on December 08, 2017 (peak growth) and May 27, 2018 (senescence stage) were used to map the presence of Sargassum in the eastern coast of Southern Leyte. Supervised classification using maximum likelihood algorithm and accuracy assessment were conducted before generating the map. Three classes were considered namely Sargassum, clouds and land. Furthermore, Anselin Local Moran’s I (cluster and outlier analysis) was conducted to determine which areas have significant clustering of “healthy” Sargassum using the normalized difference vegetation index (NDVI). For both image dates, high classification accuracies of Sargassum were obtained in the islands. However, there are misclassifications of Sargassum in Silago (UA = 78.72%) and Hinunangan (PA = 82.35%) using the May image. Furthermore, misclassification of Sargassum were obtained in Silago (PA = 93.6%) and Hinundayan (PA = 96.23%) using the December image. Clusters of high NDVI values are more evident in December. Healthy Sargassum are apparent in the coast of Silago and mostly found near shore and in rocky substrates.