scholarly journals Analysis of Spatiotemporal Features of Cassava Evapotranspiration in Benin Using Integrated FAO-56 Method and Terra/MODIS Data

2020 ◽  
Vol 12 (8) ◽  
pp. 106
Author(s):  
Patrice Koyo ◽  
Jichao Hu ◽  
Martial Amou
Keyword(s):  
Linear Relationship ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Local Level ◽  
Rainfall Pattern ◽  
Trend Test ◽  
Positive Trend ◽  
Positive Linear Relationship ◽  
Country Level ◽  
Terra Modis

This study analyzed the temporal and spatial features of cassava evapotranspiration from 1985 to 2015 in Benin using linear regression, Mann-Kendall trend test, Sen’s slope estimator, and interpolation. The study used basic meteorological data from the Met office of Benin and the Terra/MODIS vegetation index. The estimated crop coefficients (Kc) from FAO and NDVI have shown a strong and positive linear relationship with a correlation coefficient of r = 0.968, while NDVI-Kc presented values slightly lower than FAO-Kc. The rates of crop evapotranspiration (ETc) varied from 1.23 to 7.63 mm/day and 2.92 mm/day on average. At the local level, there were significant upward trends in the seasonal ETc for stations located in the bimodal rainfall pattern area (Cotonou, Bohicon, and Save) and non-significant for stations in the unimodal rainfall pattern area (Kandi, Parakou, and Natitingou). At the country level, both methods revealed a non-significant positive trend in cassava evapotranspiration in the study area while showing a strong and positive linear relationship in variations throughout the growing season, r = 0.956. Cassava’s growth in Benin may encounter in the future the risk of water deficit.

scholarly journals Dynamics of productivity of restored vegetation of the Izykhsky surface mine (Terra Modis)

2020 ◽  
Vol 223 ◽  
pp. 03004
Author(s):  
Alexander Zhukov ◽  
Elena Zhukova
Keyword(s):  
Primary Production ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Ecological Monitoring ◽  
Ecological Niches ◽  
Positive Trend ◽  
Surface Mine ◽  
Processing Information ◽  
Terra Modis

The subject of the study is the course of the dynamics of vegetation productivity on the dumps of the surface mine «Izykhsky» (2000-2019), using Terra Modis. The aim is to identify patterns of seasonal and long-term dynamics of restored vegetation as a result of succession according to images. The methodology included processing information on gross primary production and evapotranspiration, as well as identifying the relationship with meteorological data for 2018-2019. There is a positive trend in the long-term dynamics – in terms of total gross primary production from 1.7 to 5.5 kg/m2/8 days and in evapotranspiration from 1142 to 2784 kg/m2/8 days. The evapotranspiration correlated with the productivity. Since 2016, productivity has reached a plateau, which indicates the development of ecological niches by plants. The phytomass of the restored vegetation was in 1.5 times greater, than the mass of the steppe site. Seasonal dynamics in 2019 showed that communities on dumps have higher productivity, in contrast to the steppe, and one peak in the first ten days of July. The sum of temperatures and productivity had a high relationship (R2 = 0.7) in comparison with the steppe (R2 = 0.2). Terra Modis data can be applied in the field of ecological monitoring of vegetation of coal dumps.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

<p>Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.</p><p>Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.</p><p>To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.</p>

Air quality at open-cast coal mining regions at Hunter Valley, Australia and Ramagundam, India

2021 ◽  
Author(s):  
Harsh Kamath ◽  
Chanchal Chauhan ◽  
Sameer Mishra ◽  
Aariz Ahmed ◽  
Raman Srikanth
Keyword(s):  
Power Plants ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Thermal Power ◽  
Coal Mines ◽  
Export Market ◽  
P Value ◽  
Open Cast ◽  
Monitoring Stations

<p>The upper Hunter Valley region in New South Wales (NSW), Australia has several open-cast coal mines, which supply coal to two large thermal power plants (TPPs) in the area, beside the export market. Long-term Particulate Matter (PM) pollutants and meteorological measurements are recorded by a network of 13 NSW government-owned continuous monitoring stations in the upper Hunter Valley region. The Ramagundam area in the state of Telangana, India has similar pollution source characteristics (coal mines and TPPs), but PM pollutant measurements are largely carried out with manual monitoring stations at 24-hour intervals, not more than twice a week. As the coal and overburden excavation from open-cast coal mines and stack emissions from TPPs lead to local PM pollution, we have used MODIS-MAIAC Aerosol Optical Depth (AOD) at 550 nm and Normalized Difference Vegetation Index (NDVI) along with the local meteorological data such as ambient temperature, relative humidity, wind speed and direction to model PM10 and PM2.5 at the upper Hunter Valley and Ramagundam regions. Our model can explain about 60% of variation in PM10 (p-value < 0.0001), while a similar model is able to explain about 75% of the variation in the PM2.5 (p-value < 0.0001). We will extend our model results from Hunter Valley to Ramagundam area and comment on the potential of using geospatial products such as AOD as a proxy to ground-based pollution measurements in developing countries such as India, where pollution data is scarce.</p>

scholarly journals Monitoring and mapping of seasonal vegetation trend in Tamil Nadu using NDVI and NDWI imagery

2019 ◽  
Vol 11 (1) ◽  
pp. 54-61 ◽  
Author(s):  
P.J. Prajesh ◽  
Balaji Kannan ◽  
S. Pazhanivelan ◽  
K.P. Ragunath
Keyword(s):  
Trend Analysis ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Regional Scale ◽  
Negative Trend ◽  
Positive Trend ◽  
Vegetation Growth ◽  
Kharif Season ◽  
Regression Slope ◽  
Land Covers

In order to monitor vegetation growth and development over the districts and land covers of Tamil Nadu, India during the crop growing season viz., Khairf and Rabi of 2017, Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-day temporal period was used to derive a time series based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for monitoring and mapping terrestrial vegetation trend analysis which showed areas in Tamil Nadu having vegetation greening and vegetation browning. The regression slope values derived from the trend analysis was utilized and the NDVI and NDWI seasonal trend showed majority of area in Tamil Nadu falling under positive trend during the Kharif season (86.52 per cent for NDVI and 90.29 per cent for NDWI). While irrespective of land cover classes, NDVI and NDWI during Kharif season showed a greater positive trend (greening) with least negative trend (browning) for vegetation growth over the land covers whereas during Rabi season it was observed to have a mix of positive trend and negative trend over the land covers. This study was carried out to show that a systematic study can be done for understanding changes over the landscape through the use of high spatial resolution satellite dataset such as MODIS, which provides detailed spatial and temporal description at regional scale. While a trend analysis using regression slope values can be considered for demonstrating the spatial and temporal consistency on land and vegetation dynamics.

scholarly journals Spatio-Temporal Variation Analysis of Landscape Pattern Response to Land Use Change from 1985 to 2015 in Xuzhou City, China

2018 ◽  
Vol 10 (11) ◽  
pp. 4287 ◽  
Author(s):  
Yantao Xi ◽  
Nguyen Thinh ◽  
Cheng Li
Keyword(s):  
Land Use ◽  
Landscape Pattern ◽  
Urban Areas ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Local Level ◽  
Land Use Changes ◽  
Landscape Patterns ◽  
Rapid Urbanization ◽  
Barren Land

Rapid urbanization has dramatically spurred economic development since the 1980s, especially in China, but has had negative impacts on natural resources since it is an irreversible process. Thus, timely monitoring and quantitative analysis of the changes in land use over time and identification of landscape pattern variation related to growth modes in different periods are essential. This study aimed to inspect spatiotemporal characteristics of landscape pattern responses to land use changes in Xuzhou, China durfing the period of 1985–2015. In this context, we propose a new spectral index, called the Normalized Difference Enhanced Urban Index (NDEUI), which combines Nighttime light from the Defense Meteorological Satellite Program/Operational Linescan System with annual maximum Enhanced Vegetation Index to reduce the detection confusion between urban areas and barren land. The NDEUI-assisted random forests algorithm was implemented to obtain the land use/land cover maps of Xuzhou in 1985, 1995, 2005, and 2015, respectively. Four different periods (1985–1995, 1995–2005, 2005–2015, and 1985–2015) were chosen for the change analysis of land use and landscape patterns. The results indicate that the urban area has increased by about 30.65%, 10.54%, 68.77%, and 143.75% during the four periods at the main expense of agricultural land, respectively. The spatial trend maps revealed that continuous transition from other land use types into urban land has occurred in a dual-core development mode throughout the urbanization process. We quantified the patch complexity, aggregation, connectivity, and diversity of the landscape, employing a number of landscape metrics to represent the changes in landscape patterns at both the class and landscape levels. The results show that with respect to the four aspects of landscape patterns, there were considerable differences among the four years, mainly owing to the increasing dominance of urbanized land. Spatiotemporal variation in landscape patterns was examined based on 900 × 900 m sub-grids. Combined with the land use changes and spatiotemporal variations in landscape patterns, urban growth mainly occurred in a leapfrog mode along both sides of the roads during the period of 1985 to 1995, and then shifted into edge-expansion mode during the period of 1995 to 2005, and the edge-expansion and leapfrog modes coexisted in the period from 2005 to 2015. The high value spatiotemporal information generated using remote sensing and geographic information system in this study could assist urban planners and policymakers to better understand urban dynamics and evaluate their spatiotemporal and environmental impacts at the local level to enable sustainable urban planning in the future.

scholarly journals Innovative Trend Analysis of Precipitation in the Lake Issyk-Kul Basin, Kyrgyzstan

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 332 ◽  
Author(s):  
Yilinuer Alifujiang ◽  
Jilili Abuduwaili ◽  
Balati Maihemuti ◽  
Bilal Emin ◽  
Michael Groll
Keyword(s):  
Trend Analysis ◽  
Resource Planning ◽  
Kendall Test ◽  
Trend Test ◽  
Lake Basin ◽  
Positive Trend ◽  
Monthly Precipitation ◽  
Innovative Trend Analysis ◽  
Mann Kendall Test ◽  
Increasing Trends

The analysis of various characteristics and trends of precipitation is an essential task to improve the utilization of water resources. Lake Issyk-Kul basin is an upper alpine catchment, which is more susceptible to the effects of climate variability, and identifying rainfall variations has vital importance for water resource planning and management in the lake basin. The well-known approaches linear regression, Şen’s slope, Spearman’s rho, and Mann-Kendall trend tests are applied frequently to try to identify trend variations, especially in rainfall, in most literature around the world. Recently, a newly developed method of Şen-innovative trend analysis (ITA) provides some advantages of visual-graphical illustrations and the identification of trends, which is one of the main focuses in this article. This study obtained the monthly precipitation data (between 1951 and 2012) from three meteorological stations (Balykchy, Cholpon-Ata, and Kyzyl-Suu) surrounding the Lake Issyk-Kul, and investigated the trends of precipitation variability by applying the ITA method. For comparison purposes, the traditional Mann–Kendall trend test also used the same time series. The main results of this study include the following. (1) According to the Mann-Kendall trend test, the precipitation of all months at the Balykchy station showed a positive trend (except in January (Zc = −0.784) and July (Zc = 0.079)). At the Cholpon-Ata and Kyzyl-Suu stations, monthly precipitation (with the same month of multiple years averaged) indicated a decreasing trend in January, June, August, and November. At the monthly scale, significant increasing trends (Zc > Z0.10 = 1.645) were detected in February and October for three stations. (2) The ITA method indicated that the rising trends were seen in 16 out of 36 months at the three stations, while six months showed decreasing patterns for “high” monthly precipitation. According to the “low” monthly precipitations, 14 months had an increasing trend, and four months showed a decreasing trend. Through the application of the ITA method (January, March, and August at Balykchy; December at Cholpon-Ata; and July and December at Kyzyl-Suu), there were some significant increasing trends, but the Mann-Kendall test found no significant trends. The significant trend occupies 19.4% in the Mann-Kendall test and 36.1% in the ITA method, which indicates that the ITA method displays more positive significant trends than Mann–Kendall Zc. (3) Compared with the classical Mann-Kendall trend results, the ITA method has some advantages. This approach allows more detailed interpretations about trend detection, which has benefits for identifying hidden variation trends of precipitation and the graphical illustration of the trend variability of extreme events, such as “high” and “low” values of monthly precipitation. In contrast, these cannot be discovered by applying traditional methods.

scholarly journals Nutrient management on leaf area index of potato-mungbean- T. aman rice cropping pattern

2015 ◽  
Vol 39 (3) ◽  
pp. 515-527 ◽  
Author(s):  
MAH S Jahan ◽  
MAR Sarkar ◽  
M Salim
Keyword(s):  
Leaf Area Index ◽  
Linear Relationship ◽  
Leaf Area ◽  
Nutrient Management ◽  
Agricultural Research ◽  
Management Practice ◽  
Research Centre ◽  
Cropping Pattern ◽  
Area Index ◽  
Positive Linear Relationship

A field experiment was conducted at Regional Wheat Research Centre of the Bangladesh Agricultural Research Institute, Joydebpur, Gazipur, Bangladesh for 2 consecutive years during 2006-07 and 2007-08. The objective was to find out the optimum nutrient management practice on leaf area index of each component crop of potato-mungbean-T. Aman rice cropping pattern. Twelve nutrient management treatments were tested in RCBD with 3 replications. Treatments combination based on cropping pattern were T1=HYG (0-198-44- 194-24-6-1.2 for potato; 0-24-40-48-24-3-1.2 for mungbean ; 0-80-16-44-12-2-0 for T.Aman rice ), T2=MYG (0-140-34-138-18-4.5-0.9 for potato; 0-20-36-40- 20-2-1 for mungbean ; 0-56-12-32-8-1.5-0 for T.Aman rice), T3=IPNS (10000- 168-38-170-18-6-1.2 for potato ; 5000-9-37-36-21-3-1.2 for mungbean ; 5000- 65-13-32-9-2-0 for T.Aman rice), T4=STB (0-171-40-164-22-5-1 for potato; 0- 20-36-40-22-2-1 for mungbean ; 0-68-15-37-11-2-0 for T.Aman rice), T5=FP (0- 97-16-91-0-0-0 for potato ; 0-6-5-4-0-0-0 for mungbean ; 0-39-37-12-0-0-0 for T.Aman rice), T6=CON (0-0-0-0-0-0-0 for potato, mungbean and T.Aman rice) kg/ha CDNPKSZnB, T7=HYG+CRI, T8=MYG+CRI, T9=IPNS+CRI, T10=STB+CRI, T11=FP+CRI, T12=CON+CRI for potato-mungbean T.Aman rice cropping pattern, respectively. Average of two years data showed that HYG+CRI treatment gave maximum LAI followed by HYG, IPNS+CRI, IPNS, STB+CRI, and STB treatments at 60 days after planting (DAP) for potato, at 50 days after sowing (DAS) for mungbean, at 60 days after transplanting (DAT) for T.Aman rice, respectively. For potato, there was a significant (p?0.01) and positive linear relation between the LAI at 60 DAP and the tuber yield. While there was a significant (p?0.01) and positive linear relationship between the LAI at 50 DAS and seed yield of mungbean. In case of T.Aman rice, there was a significant (p?0.05) as well as positive linear relationship between the LAI at 60 DAT and the grain yield of rice. DOI: http://dx.doi.org/10.3329/bjar.v39i3.21994 Bangladesh J. Agril. Res. 39(3): 515-527, September 2014

scholarly journals Trends and trend reversal detection in 2 decades of tropospheric NO<sub>2</sub> satellite observations

2019 ◽  
Vol 19 (9) ◽  
pp. 6269-6294 ◽  
Author(s):  
Aristeidis K. Georgoulias ◽  
Ronald J. van der A ◽  
Piet Stammes ◽  
K. Folkert Boersma ◽  
Henk J. Eskes
Keyword(s):  
Economic Recession ◽  
Western World ◽  
Positive Trend ◽  
Country Level ◽  
Socioeconomic Changes ◽  
Tropospheric No2 ◽  
Environmental Protection Policies ◽  
Scanning Imaging ◽  
The Uk ◽  
Trend Reversal

Abstract. In this work, a ∼21-year global dataset from four different satellite sensors with a mid-morning overpass (GOME/ERS-2, SCIAMACHY/ENVISAT, GOME-2/Metop-A, and GOME-2/Metop-B) is compiled to study the long-term tropospheric NO2 patterns and trends. The Global Ozone Monitoring Experiment (GOME) and GOME-2 data are “corrected” relative to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data to produce a self-consistent dataset that covers the period April 1996–September 2017. The highest tropospheric NO2 concentrations are seen over urban, industrialized, and highly populated areas and over ship tracks in the oceans. Tropospheric NO2 has generally decreased during the last 2 decades over the industrialized and highly populated regions of the western world (a total decrease of the order of ∼49 % over the US, the Netherlands, and the UK; ∼36 % over Italy and Japan; and ∼32 % over Germany and France) and increased over developing regions (a total increase of ∼160 % over China and ∼33 % over India). It is suggested here that linear trends cannot be used efficiently worldwide for such long periods. Tropospheric NO2 is very sensitive to socioeconomic changes (e.g., environmental protection policies, economic recession, warfare, etc.) which may cause either short-term changes or even a reversal of the trends. The application of a method capable of detecting the year when a reversal of trends happened shows that tropospheric NO2 concentrations switched from positive to negative trends and vice versa over several regions around the globe. A country-level analysis revealed clusters of countries that exhibit similar positive-to-negative or negative-to-positive trend reversals, while 29 out of a total of 64 examined megacities and large urban agglomerations experienced a trend reversal at some point within the last 2 decades.

scholarly journals An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase

2020 ◽  
Vol 9 (11) ◽  
pp. 625
Author(s):  
Quan Xiong ◽  
Xiaodong Zhang ◽  
Wei Liu ◽  
Sijing Ye ◽  
Zhenbo Du ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Distributed Storage ◽  
Meteorological Data ◽  
Data Access ◽  
Henan Province ◽  
Geospatial Data ◽  
Experimental Result ◽  
Raster Data ◽  
Encoding Method ◽  
Original Grid

Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase.

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