scholarly journals Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

2021 ◽  
Vol 13 (5) ◽  
pp. 840
Author(s):  
Ernesto Sanz ◽  
Antonio Saa-Requejo ◽  
Carlos H. Díaz-Ambrona ◽  
Margarita Ruiz-Ramos ◽  
Alfredo Rodríguez ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Vegetation Indices ◽  
Aridity Index ◽  
Rangeland Degradation ◽  
Temperature And Precipitation ◽  
Lag Effect ◽  
Relationship Of ◽  
The Relationship

Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

scholarly journals Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

2020 ◽  
Vol 12 (8) ◽  
pp. 1332 ◽  
Author(s):  
Linghui Guo ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Yuan Jiang ◽  
Yongling Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Inner Mongolia ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Growing Season ◽  
Climate Fluctuations ◽  
Temperature And Precipitation ◽  
Lag Effect

An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.

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 Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

2018 ◽  
Vol 8 (9) ◽  
pp. 1435 ◽  
Author(s):  
Xiaochen Zou ◽  
Iina Haikarainen ◽  
Iikka Haikarainen ◽  
Pirjo Mäkelä ◽  
Matti Mõttus ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Imaging Spectroscopy ◽  
Leaf Angle ◽  
Field Crops ◽  
Leaf Chlorophyll ◽  
Simple Ratio ◽  
The Relationship

Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

Relationship of soil moisture with solar-induced chlorophyll fluorescence and normalized difference vegetation index in different phenological stages

2020 ◽  
Author(s):  
Qiu Shen ◽  
Jianjun Wu ◽  
Leizhen Liu ◽  
Wenhui Zhao
Keyword(s):  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maturity Stage ◽  
Phenological Stages ◽  
Active Radiation ◽  
Relationship Of ◽  
The Relationship

&lt;p&gt;As an important part of water cycle in terrestrial ecosystem, soil moisture (SM) provides essential raw materials for vegetation photosynthesis, and its changes can affect the photosynthesis process and further affect vegetation growth and development. Thus, SM is always used to detect vegetation water stress and agricultural drought. Solar-induced chlorophyll fluorescence (SIF) is signal with close ties to photosynthesis and the normalized difference vegetation index (NDVI) can reflect the photosynthetic characteristics and photosynthetic yield of vegetations. However, there are few studies looking at the sensitivity of SIF and NDVI to SM changes over the entire growing season that includes multiple phenological stages. By making use of GLDAS-2 SM products along with GOME-2 SIF products and MODIS NDVI products, we discussed the detailed differences in the relationship of SM with SIF and NDVI in different phenological stages for a case study of Northeast China in 2014. Our results show that SIF integrates information from the fraction of photosynthetically active radiation (fPAR), photosynthetically active radiation (PAR) and SIF&lt;sub&gt;yield&lt;/sub&gt;, and is more effective than NDVI for monitoring the spatial extension and temporal dynamics of SM on a short time scale during the entire growing season. Especially, SIF&lt;sub&gt;PAR_norm&lt;/sub&gt; is the most sensitive to SM changes for eliminating the effects of seasonal variations in PAR. The relationship of SM with SIF and NDVI varies for different vegetation cover types and phenological stages. SIF is more sensitive to SM changes of grasslands in the maturity stage and &amp;#160;rainfed&amp;#160;croplands&amp;#160; in the senescence stage than NDVI, and it has significant sensitivities to SM changes of forests in different phenological stages. The sensitivity of SIF and NDVI to SM changes in the senescence stages stems from the fact that vegetation photosynthesis is relatively weaker at this time than that in the maturity stage, and vegetations in the reproductive growth stage still need much water. Relevant results are of great significance to further understand the application of SIF in SM detection.&lt;/p&gt;

Estimation of sugar beet biomass and yield comparing NDVI measurements and physical soil parameters

2021 ◽  
pp. 100-109
Author(s):  
Koç Mehmet Tuğrul
Keyword(s):  
Sugar Beet ◽  
Soil Sample ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Analysis ◽  
Soil Parameters ◽  
Growing Seasons ◽  
Relationship Of ◽  
The Relationship ◽  
Moderate Positive Correlation

This study was conducted to estimate the relationship of soil sample analysis and satellite imagery with sugar beet yield (BY). The red NDVI obtained monthly from Landsat OLI satellite images during the 2017 and 2018 sugar beet growing seasons were used to establish relationships between imagery and georeferenced soil sample analyses and sugar beet harvest sites. The study was carried out in the field of Sugar Institute Ilgın Experiment Station, Turkey, in 2017 and 2018. Soil samples were obtained in a 0.4 ha grid, and sugar beet yield and recoverable sugar yield (RSY) were obtained from the same sampling areas. The results showed that there were relationships between some soil analysis factors and BY and beet quality. The overall results showed that the amount of clay, electric conductivity (EC), and organic matter in the field might be indicators of BY and beet quality. A statistically significant moderate positive correlation was also obtained between NDVI (Normalized Difference Vegetation Index) images and BY and RSY values in all images obtained by satellite near the harvest date.

scholarly journals TREE AGE AS ADJUSTMENT FACTOR TO NDVI

2018 ◽  
Vol 41 (3) ◽  
Author(s):  
Elias Fernando Berra ◽  
Denise Cybis Fontana ◽  
Tatiana Mora Kuplich
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Range ◽  
Vegetation Indices ◽  
Tree Age ◽  
Stand Age ◽  
Adjustment Factor ◽  
Biophysical Parameters ◽  
Wood Volume ◽  
The Relationship

ABSTRACT This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalyptus and Pinus stands’ wood volume was investigated. The adjustment factor caused an increase in NDVI dynamic range values, since older stands tended to be assigned with highest NDVI values, while younger ones tended to be forced to assume lower NDVI values. As a result, direct and significant relationship between NDVI_a and wood volume could be maintained for wider ranges of wood volume. However, it was observed that NDVI_a was only statistically superior to NDVI and SAVI when a detailed age dataset is available. It is conclude that, the stand age has potential to improve NDVI sensitivity to biophysical parameters allowing that quantitative estimates could be made since young to adult stands.

Relationship of Neighborhood Greenness to Alzheimer’s Disease and Non-Alzheimer’s Dementia Among 249,405 U.S. Medicare Beneficiaries

2021 ◽  
pp. 1-10
Author(s):  
William W. Aitken ◽  
Joanna Lombard ◽  
Kefeng Wang ◽  
Matthew Toro ◽  
Margaret Byrne ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Odds Ratio ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Population Based ◽  
Dade County ◽  
Medicare Beneficiaries ◽  
Relationship Of ◽  
The Relationship

Background: Neighborhood greenness (vegetative presence) has been linked to multiple health outcomes, but its relationship to Alzheimer’s disease (AD) and non-Alzheimer’s (non-AD) dementia has been less studied. Objective: This study examines the relationship of greenness to both AD and non-AD dementia in a population-based sample of Medicare beneficiaries. Methods: Participants were 249,405 US Medicare beneficiaries aged >  65 living in Miami-Dade County, FL, from 2010 to 2011. Multi-level analyses examined the relationship of greenness, assessed by mean Census block level Normalized Difference Vegetation Index (NDVI), to odds of each of AD, Alzheimer’s disease and related dementias (ADRD), and non-AD dementia, respectively. Covariates included age, gender, race/ethnicity, number of comorbid health conditions, and neighborhood income. Results: Higher greenness was associated with reduced risk of AD, ADRD, and non-AD dementia, respectively, adjusting for individual and neighborhood sociodemographics. Compared to the lowest greenness tertile, the highest greenness tertile was associated with reduced odds of AD by 20%(odds ratio, 0.80; 95%CI, 0.75–0.85), ADRD by 18%(odds ratio, 0.82; 95%CI, 0.77–0.86), and non-AD dementia by 11%(odds ratio, 0.89; 95%CI, 0.82–0.96). After further adjusting for number of comorbidities, compared to the lowest greenness tertile, the highest greenness tertile was associated with reduced odds of AD (OR, 0.94; 95%CI, 0.88–1.00) and ADRD (OR, 0.93; 95%CI, 0.88–0.99), but not non-AD dementia (OR, 1.01; 95%CI, 0.93–1.08). Conclusion: High neighborhood greenness may be associated with lower odds of AD and ADRD. Environmental improvements, such as increasing neighborhood vegetation, may be a strategy to reduce risk for AD and possibly other dementias.

scholarly journals Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing

2020 ◽  
Vol 12 (4) ◽  
pp. 671 ◽  
Author(s):  
Matthew Dannenberg ◽  
Xian Wang ◽  
Dong Yan ◽  
William Smith
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Microwave Remote Sensing ◽  
Self Organizing Maps ◽  
Growth Cycles ◽  
Growing Seasons ◽  
Temperature And Precipitation ◽  
Optical Fluorescence

Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.

Use of linguistic estimates and vegetation indices to assess post-fire vegetation regrowth in woodland areas

2010 ◽  
Vol 19 (1) ◽  
pp. 94 ◽  
Author(s):  
Carol R. Jacobson
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Moisture Index ◽  
Index Data ◽  
Subset Regression ◽  
Simple Ratio ◽  
The Relationship ◽  
Non Destructive

This study examined an area of woodland that was recovering from severe fire in Royal National Park (NSW, Australia). A non-destructive method of field sampling is required for vulnerable recovering vegetation and therefore classification of digital photographs using linguistic terms was trialled. The linguistic data for three vegetation strata (canopy, shrub and ground) were converted to crisp scores and compared with vegetation index data derived from remotely sensed imagery. All possible subset regression was used to test the proposition that the combined vegetation scores (independent variables) would explain the values of NDVI (Normalized Difference Vegetation Index) and NDMI (Normalized Difference Moisture Index). Vegetation scores for the three strata were also combined using simplified weighting ratios to assess broad relationships between the indices and field data. The combined vegetation scores explained ~60% of the variation in the vegetation index data and inclusion of variables representing multiple strata explained more of the variation than any single variable. The precise value of the weights used to combine the layers did not affect the strength of the association. A simple ratio is proposed that may be useful to estimate woodland parameters under similar conditions, by inversion of the relationship with vegetation index data.

Association Analysis of NDVI Changes and Topographic Factors

2013 ◽  
Vol 333-335 ◽  
pp. 1205-1208
Author(s):  
De Li Liu ◽  
Ya Shuang Zhang ◽  
Nan Lin
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Topographic Factors ◽  
Gis Software ◽  
Relationship Of ◽  
The Relationship ◽  
Slope Direction

Based on the TM remote sensing data of the Huadian city in 1991 and 2011 and based on the DEM data,using the normalized difference vegetation index (NDVI) change classification method,to Extraction the elevation,slope,slope direction data and the vegetation index data of the study area.Then using the spatial analysis function of GIS software to overlay the two different period NDVI data and analysis the NDVI change of area and spatial. Using the same method to overlay and analysis the relationship of NDVI data and elevation,slope,slope direction.Research shows that the variation of NDVI in the study area has relationship with the topographic factors change.

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