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.

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.

A APLICAÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) PARA ANÁLISE DA DEGRADAÇÃO AMBIENTAL DA ÁREA DE INFLUÊNCIA DIRETA DO AÇUDE CASTANHÃO

2019 ◽  
Vol 21 (2) ◽  
pp. 674-685
Author(s):  
Amanda Menezes De Albuquerque ◽  
José Robério Cabral Ribeiro ◽  
Marta Celina Linhares Sales
Keyword(s):  
Remote Sensing ◽  
Environmental Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Spatial Location ◽  
Biophysical Parameters ◽  
Spectral Image ◽  
Vegetation Indexes ◽  
The Relationship

O aumento da degradação ambiental de terras secas vem conduzindo à erosão dos solos e desertificação, o uso intenso e predatório dos recursos naturais nessas áreas acaba impossibilitando a sobrevivência das comunidades que vivem nessas regiões. O estado do Ceará tem cerca de 92% de seu território inserido no semiárido, a pesquisa foi desenvolvida na Área de Influência Direta do Açude Castanhão – AIC. A através do registro de imagens, tornou-se possível às análises de relacionamento entre localização espacial de alvos do meio ambiente, variação espectral da imagem e variação da cobertura vegetal dos solos. A utilização do sensoriamento remoto e de índices de vegetação como o Índice de Vegetação da Diferença Normalizada (NDVI), facilita a obtenção e modelagem de parâmetros biofísicos das plantas, como a área foliar, biomassa e porcentagem de cobertura do solo, fornecendo importantes informações sobre a Degradação Ambiental da área.Palavras-chave: Degradação; Sensoriamento Remoto; Cobertura Vegetal. ABSTRACTThe increased environmental degradation of dry lands has led to soil erosion and desertification, the intense and predatory use of natural resources in these areas makes it impossible to survive the communities living in these regions. The state of Ceará has about 92% of its territory inserted in the semi-arid, the research was developed in the Area of Direct Influence of Castanhão - AIC. A through image registration, it became possible to analyze the relationship between spatial location of environmental targets, spectral image variation and variation of soil cover. The use of remote sensing and vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) facilitates the obtaining and modeling of plant biophysical parameters such as leaf area, biomass and percentage of soil cover, providing important information on the Environmental Degradation of the area.Keywords:Degradation; Remote Sensing; Vegetal Cover.

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 Sensitivity of vegetation indices in relation to parameters of Norway spruce stands

2017 ◽  
Vol 59 (2) ◽  
pp. 85-98 ◽  
Author(s):  
Aneta Modzelewska ◽  
Krzysztof Stereńczak ◽  
Monika Mierczyk ◽  
Sylwia Maciuk ◽  
Radomir Bałazy ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Range ◽  
Vegetation Indices ◽  
Stand Density ◽  
Spatial Changes ◽  
Spruce Stands ◽  
Stand Height ◽  
Stable Index ◽  
Stand Characteristics

AbstractThe main goal of this research is to shed further light on the sensitivity of the vegetation indices to spatial changes of stand parameters. The analysis was done within mountain forests in the Sudetes and the Beskids in southern Poland. Some 1327 stands were analysed with more than 70 percent of spruce contribution in the species composition. The response of selected vegetation indices was verified in relation to the alterations of spruce participation, stand height, volume, stand density and diameter. The following indices were analysed: Normalized Difference Vegetation Index, Normalized Difference Red Edge Index, Green Normalized Difference Vegetation Index and Wide Dynamic Range Vegetation Index. Indices were calculated based on the Rapid Eye (Black Bridge) images. All the analysed stand characteristics influence the values of vegetation indices. In general: mean height, diameter at breast height, volume and spruce participation are the most negatively correlated with the indices. Density is a variable that, in general, cannot directly be used for indices correction, because it is hard to find any stable trend. NDRE is the most stable index for the analysis of stand characteristics.

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.

scholarly journals Effect of the Illumination Angle on NDVI Data Composed of Mixed Surface Values Obtained over Vertical-Shoot-Positioned Vineyards

2021 ◽  
Vol 13 (5) ◽  
pp. 855
Author(s):  
Pedro C. Towers ◽  
Carlos Poblete-Echeverría
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Area Index ◽  
Factors Affecting ◽  
Soil Fractions ◽  
Illumination Direction ◽  
Moderate Resolution ◽  
Canopy Size

Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.

scholarly journals Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

2019 ◽  
Vol 11 (9) ◽  
pp. 1073 ◽  
Author(s):  
Pedro C. Towers ◽  
Albert Strever ◽  
Carlos Poblete-Echeverría
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Dynamic Range ◽  
Spectral Response ◽  
Incident Light ◽  
Vegetation Indices ◽  
Area Index

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

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.

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