scholarly journals Nitrogen management in wheat based on the normalized difference vegetation index (NDVI)

2018 ◽  
Vol 48 (9) ◽  
Author(s):  
André Luis Vian ◽  
Christian Bredemeier ◽  
Marcos Alexandre Turra ◽  
Cecília Paz da Silva Giordano ◽  
Elizandro Fochesatto ◽  
...  
Keyword(s):  
Technical Efficiency ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Experiments ◽  
Shoot Biomass ◽  
Sensor Field ◽  
N Dose ◽  
Plant Emergence ◽  
The Moment ◽  
The Relationship

ABSTRACT: Biomass production and nitrogen (N) accumulated in wheat shoots may be used for quantifying optimal topdressing nitrogen doses. The objective of this study was to develop and validate models for estimating the amount of biomass and nitrogen accumulated in shoots and the N topdressing dose of maximum technical efficiency in wheat using the normalized difference vegetation index (NDVI) measured by an active optical canopy sensor. Field experiments were carried out in two years and treatments consisted of N doses applied at plant emergence and as topdressing. NDVI, shoot biomass and N accumulated in shoots at the growth stage of six fully expanded leaves and grain yield were evaluated, being determined the topdressing N dose of maximum technical efficiency (DMTE). The NDVI was positively correlated to shoot biomass and N content in shoots and models for the relationship between these variables were developed and validated. The DMTE was negatively correlated with the NDVI value evaluated at the moment of N topdressing application. Thus, NDVI evaluation by an active optical canopy sensor can be used for nitrogen fertilization in variable rate, allowing the adjustment of applied N doses in different areas within a field.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals The Effect of Surrounding Greenness on Type 2 Diabetes Mellitus: A Nationwide Population-Based Cohort in Taiwan

2020 ◽  
Vol 18 (1) ◽  
pp. 267
Author(s):  
Hui-Ju Tsai ◽  
Chia-Ying Li ◽  
Wen-Chi Pan ◽  
Tsung-Chieh Yao ◽  
Huey-Jen Su ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Population Based ◽  
Research Database ◽  
Individual Level ◽  
The Relationship

This study determines whether surrounding greenness is associated with the incidence of type 2 diabetes Mellitus (T2DM) in Taiwan. A retrospective cohort study determines the relationship between surrounding greenness and the incidence of T2DM during the study period of 2001–2012 using data from the National Health Insurance Research Database. The satellite-derived normalized difference vegetation index (NDVI) from the global MODIS database in the NASA Earth Observing System is used to assess greenness. Cox proportional hazard models are used to determine the relationship between exposure to surrounding greenness and the incidence of T2DM, with adjustment for potential confounders. A total of 429,504 subjects, including 40,479 subjects who developed T2DM, were identified during the study period. There is an inverse relationship between exposure to surrounding greenness and the incidence of T2DM after adjustment for individual-level covariates, comorbidities, and the region-level covariates (adjusted HR = 0.81, 95% CI: 0.79–0.82). For the general population of Taiwan, greater exposure to surrounding greenness is associated with a lower incidence of T2DM.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

scholarly journals Assessing water demand with remote sensing for two coriander varieties

2018 ◽  
Vol 36 (3) ◽  
pp. 266-273
Author(s):  
Euseppe Ortiz ◽  
Enrique A. Torres
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Demand ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Digital Camera ◽  
Crop Coefficient ◽  
Remote Sensors ◽  
The Relationship ◽  
Important Basis

The use of remote sensing to determine water needs has been successfully applied by several authors to different crops, maintaining, as an important basis, the relationship between the normalized difference vegetation index (NDVI) and biophysical variables, such as the fraction of coverage (fc) and the basal crop coefficient (Kcb). Therefore, this study quantified the water needs of two varieties of coriander (UNAPAL Laurena CL and UNAPAL Precoso CP) based on the response of fc and Kcb, using remote sensors and a water balance according to the FAO-56 methodology. A Campbell Scientific meteorological station, a commercial digital camera and a portable spectro radiometer were used to obtain information on the environmental conditions and the crop. By means of remote sensing associated with a water balance, it was found that the water demand was 156 mm for CL and 151 mm for CP until the foliage harvest (41 d after sowing); additionally, the initial Kcb was 0.14, the mean Kcb was 1.16 (approximately) and the final Kcb was 0.71 (approximately).

scholarly journals Sugarcane spatial-temporal monitoring and crop coefficient estimation through NDVI

2019 ◽  
Vol 23 (5) ◽  
pp. 330-335 ◽  
Author(s):  
Angelo B. Alface ◽  
Silvio B. Pereira ◽  
Roberto Filgueiras ◽  
Fernando F. Cunha
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Management ◽  
Crop Coefficient ◽  
Irrigated Agriculture ◽  
Sugarcane Crop ◽  
Coefficient Estimation ◽  
Sentinel 2A ◽  
The Relationship

ABSTRACT The use of satellite images as a complement in irrigation management constitutes a primordial basis in the decision-making process for irrigated agriculture. In this context, the present study aimed to monitor through Normalized Difference Vegetation Index (NDVI) an irrigated sugarcane field belonging to the Mafambisse company, located at the District of Nhamatanda/Sofala, Republic of Mozambique, and establish its relationship with the crop coefficient established by FAO (kcFAO) and fit a regression model to estimate crop coefficient (kc) from the relationship between NDVI and kcFAO. The study was conducted using a series of Sentinel-2A/MSI images, relative to the period from October 2016 to October 2017. Based on the NDVI images generated, it was possible to monitor the sugarcane crop in the field and analyse the sensitivity of the index to its vegetative vigor. A similar pattern was observed between kcFAO profiles and NDVI values, which allowed the adjustment to be performed, demonstrating that this index is an alternative to obtain the crop coefficient.

scholarly journals Pendugaan Intensitas Serangan Penyakit Blas pada Tanaman Padi Melalui Pendekatan Citra NDVI (Normalized Difference Vegetation Index)

2019 ◽  
Vol 7 (2) ◽  
pp. 287
Author(s):  
I Made Prasetia Candra Andika ◽  
I Made Anom Sutrisna Wijaya ◽  
Ida Bagus Putu Gunadnya
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Technology Development ◽  
Blast Disease ◽  
Aerial Photo ◽  
Rice Plants ◽  
Processing Data ◽  
Arc Gis ◽  
The Relationship ◽  
Attack Intensity

Penyakit blas merupakan salah satu penyakit yang berbahaya bagi tanaman padi. Penyakit ini bisa menyerang di setiap fase pertumbuhan. Perhitungan intensitas serangan penyakit blas saat ini masih  dilakukan secara manual. Diperlukan pengembangan teknologi dalam pendugaan intensitas serangan penyakit blas melalui citra NDVI. Penelitian ini bertujuan untuk (1) untuk mendapatkan ketinggian foto udara NDVI terbaik, (2) untuk mendapatkan umur tanaman padi dengan intensitas serangan penyakit blas tertinggi, (3) untuk mendapatkan hubungan antara intensitas serangan penyakit blas dengan nilai NDVI tanaman padi. Penelitian ini menggunakan Drone DJI Phantom 4 dengan lensa NDVI. Pengolahan data menggunakan Web Drone Deploy dan software Arc Gis 10.3. Berdasarkan dari hasil analisis, detail terbaik dari pembesaran 200% mendapatkan akuisisi ketinggian dari citra NDVI adalah 20 meter dengan ukuran piksel 1,4732 cm/pixel. Pertumbuhan intensitas serangan penyakit blas tertinggi  terjadi pada umur 98 hari setelah tanam. Hubungan antara intensitas serangan penyakit blas dengan nilai NDVI memiliki koefisien determinasi sebesar 0,986. Persamaan regresi didapatkan dalam penelitian ini adalah y = -23345x3 + 21191x2- 6416,8x + 665,07 dengan akurasi sebesar sebesar 91,74%.   Blast is one of disease that is dangerous for rice plants. This disease can attack in every phase of growth. Calculation of the intensity of blast disease attacks is still done manually. Technology development is needed in estimating the intensity of blast disease attacks through NDVI imagery. This study purpose (1) to get the best NDVI aerial photo altitude, (2) to get the age of rice plants with the highest attack intensity of blast disease, (3) to get a relationship between the intensity of blast disease and the NDVI value of rice plants. This study use Drone DJI Phantom 4 with lens NDVI. Processing data using Web Drone Deploying and Arc Gis 10.3 software. Based on the analysis results, the best detail of 200% zooming results obtained altitude of the NDVI image acquisition that is 20 m with pixel density of 1,4732 cm/pixel. The highest intensity of blast disease attacks occurs at the age of 98 days after planting. The relationship between the intensity of blast disease and NDVI value has a determination coefficient of 0.986. The regression equation obtained in this study is y = -23345x3 + 21191x2- 6416,8x + 665,07 with an estimated accuracy of 91,74%.

scholarly journals Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

2019 ◽  
Vol 11 (3) ◽  
pp. 706 ◽  
Author(s):  
Xinbing Wang ◽  
Yuxin Miao ◽  
Rui Dong ◽  
Zhichao Chen ◽  
Yanjie Guan ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Experiments ◽  
Management Strategies ◽  
N Fertilizer ◽  
Yield Potential ◽  
Maize Production ◽  
N Management

Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP0), response index to side-dress N based on harvested yield (RIHarvest), and side-dress N agronomic efficiency (AENS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP0 (R2 = 0.44–0.78) than only using NDVI or RVI (R2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AENS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AENS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha−1 and 70 $ ha−1, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer’s typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.

scholarly journals An Analysis of Vegetation Change Trends and Their Causes in Inner Mongolia, China from 1982 to 2006

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Baolin Li ◽  
Wanli Yu ◽  
Juan Wang
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Anthropogenic Factors ◽  
Simple Linear Regression ◽  
Ordos Plateau ◽  
Hetao Plain ◽  
Avhrr Data ◽  
The Relationship ◽  
Very High

This paper presents the vegetation change trends and their causes in the Inner Mongolian Autonomous Region, China from 1982 to 2006. We used National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to determine the vegetation change trends based on regression model by fitting simple linear regression through the time series of the integrated Normalized Difference Vegetation Index (NDVI) in the growing season for each pixel and calculating the slopes. We also explored the relationship between vegetation change trends and climatic and anthropogenic factors. This paper indicated that a large portion of the study area (17%) had experienced a significant vegetation increase at the 0.05 level from 1982 to 2006. The significant vegetation increase showed no positive link with precipitation and was mainly caused by human activities. In or to the south of Horqin Sandy Land, in the Hetao Plain, and at the northern foothills of the YinshanMountain, the significant NDVI increase trends were mainly caused by the increase of the millet yield per unit of cropland. In the east of Ordos Plateau, the significant NDVI increase trends were mainly determined by the fencing and planting of grasses and trees on grassland.

scholarly journals Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

2013 ◽  
Vol 10 (6) ◽  
pp. 7963-7997 ◽  
Author(s):  
A. McNally ◽  
C. Funk ◽  
G. J. Husak ◽  
J. Michaelsen ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Drought ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
The Relationship ◽  
African Sahel

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

Sign in / Sign up

Export Citation Format

Share Document