scholarly journals Detecção de Mudança da Vegetação de Caatinga (Change Detection of Vegetation Caatinga)

2013 ◽  
Vol 5 (6) ◽  
pp. 1473 ◽  
Author(s):  
Paulo Roberto Megna Francisco ◽  
Iêde De Brito Chaves ◽  
Lúcia Helena Garófalo Chaves ◽  
Eduardo Rodrigues Viana de Lima
Keyword(s):  
Change Detection ◽  
River Basin ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Dry Period ◽  
Caatinga Biome ◽  
Semi Arid

A caatinga é um bioma de grande diversidade que cobre a maior parte da área de clima semiárido brasileiro. Várias técnicas já foram utilizadas com o objetivo de determinar quantitativamente e qualitativamente o estado da vegetação a partir de imagens de satélite e índices de vegetação foram desenvolvidos para auxiliar no mapeamento da vegetação e otimizar os parâmetros presentes nas medidas multiespectrais utilizadas com esse fim. Este trabalho teve como objetivo mapear a vegetação da caatinga, e selecionar um índice de vegetação usando o IBVL para validação dos resultados e detectar mudanças ocorridas. Concluiu-se que o melhor índice que se correlaciona com a cobertura vegetal da caatinga foi o Normalized Difference Vegetation Index, do período seco, e que a metodologia utilizada mostrou-se eficiente para caracterização, classificação e separação em 9 classes. A maior recuperação ocorreu em áreas de drenagem e em declividade mais acentuada. A classe detectada de não mudança ocorreu em áreas de menor cobertura vegetal e de solos propensos à erosão. Estimou-se que 38,71% da área da bacia do rio Taperoá esteja em processo de desertificação.Palavras-chave: Semiárido, Geoprocessamento, Degradação. Change Detection of Vegetation Caatinga ABSTRACTThe caatinga biome is a large diversity that covers most of the area of Brazilian semi-arid climate. Several techniques have been used in order to determine quantitatively and qualitatively the state of vegetation from satellite images and vegetation indices were developed to assist in vegetation mapping and optimizing the parameters present in the multispectral measurements used for this purpose. This study aimed to map the vegetation of the caatinga, and select a vegetation index using IBVL to validate the results and detect changes. It was concluded that the best index that correlates with the vegetation of the caatinga was the Normalized Difference Vegetation Index, the dry period, and that the methodology used was efficient for characterization, classification and separation into nine classes. The best recovery occurred in areas of drainage and steeper slope. The class detected no change occurred in areas with less vegetation cover and soils prone to erosion. It was estimated that 20.21% of the area of the river basin Taperoá is in an advanced process of desertification.Keywords: Semiarid, Geoprocessing, Degradation.

scholarly journals Análise espectral e avaliação de índices de vegetação para o mapeamento da caatinga

2015 ◽  
Vol 10 (3) ◽  
pp. 01
Author(s):  
Paulo Roberto Megna Francisco ◽  
Iede De Brito Chaves ◽  
Lucia Helena Garofalo Chaves ◽  
Eduardo Rodrigues Viana de Lima ◽  
Bernado Barbosa da Silva
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Spectral Indices ◽  
Font Size ◽  
Dry Period ◽  
Analysis And Evaluation ◽  
Caatinga Vegetation

<p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-size: 12.0pt; font-family: 'Times New Roman','serif';">A caatinga é um bioma de grande diversidade que cobre a maior parte da área de clima semiárido brasileiro. Várias técnicas já foram utilizadas com o objetivo de determinar quantitativamente e qualitativamente o estado da vegetação a partir de imagens de satélite, e índices de vegetação foram desenvolvidos para auxiliar no mapeamento da vegetação, otimizando parâmetros de medidas espectrais utilizadas com esse fim. Este trabalho teve como objetivo analisar e avaliar índices espectrais (NDVI, SAVI e EVI) para mapear a vegetação de caatinga. Concluiu-se que o melhor índice que se correlaciona com a cobertura vegetal da caatinga foi o Normalized Difference Vegetation Index (NDVI), para o período seco, e o padrão de resposta espectral do período seco diminuiu os confundimentos de alvos de vegetação da caatinga. Estimou-se que 29,7% da área da bacia do rio Taperoá esteja em processo avançado de desertificação.</span></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"> </p><p align="center"><strong><em>Spectral analysis and evaluation of vegetation indices for mapping caatinga</em></strong></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"> </p><p><strong>ABSTRACT: </strong>The caatinga biome is a large diversity that covers most of the area of Brazilian semi-arid climate. Several techniques have been used in order to determine quantitatively and qualitatively the state of vegetation from satellite images and vegetation indices were developed to assist in vegetation mapping, optimizing spectral measurement parameters used for this purpose. This study aimed to analyze and evaluate spectral indices (NDVI, SAVI and EVI) to map the caatinga vegetation. It was concluded that the best index that correlates with the caatinga vegetation was the Normalized Difference Vegetation Index (NDVI) for the dry period, and the pattern of spectral response of the dry period decreased confounding targets of caatinga vegetation. It was estimated that 29.7% of the area of the river basin Taperoá is in advanced process of desertification.<strong></strong></p><p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: justify;"><span style="font-size: 12.0pt; font-family: 'Times New Roman','serif';"><br /></span></p>

scholarly journals Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

2011 ◽  
Vol 3 (3) ◽  
pp. 157
Author(s):  
Daniel Rodrigues Lira ◽  
Maria do Socorro Bezerra de Araújo ◽  
Everardo Valadares De Sá Barretto Sampaio ◽  
Hewerton Alves da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Digital Processing ◽  
Sparse Vegetation ◽  
New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

Separation of Different Vegetation Types in ASTER and Landsat Satellite Images Using Satellite‐derived Vegetation Indices

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Mazlan Hashim ◽  
Sharifeh Hazini
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Vegetation Indices ◽  
Critical Issue ◽  
Vegetation Types ◽  
Enhanced Vegetation Index ◽  
Density Slicing ◽  
Landsat Satellite

Separation of different vegetation types in satellite images is a critical issue in remote sensing. This is because of the close reflectance between different vegetation types that it makes difficult segregation of them in satellite images. In this study, to facilitate this problem, different satellite derived vegetation indices including: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index 2 (EVI2) were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-5 TM data. The obtained NDVI, EVI, and EVI2 images were then analyzed and interpreted in order to evaluate their effectiveness to discriminate rice and citrus fields from ASTER and Landsat data. In doing so, the Density Slicing (DS) classification technique followed by the trial and error method was implemented. The results indicated that the accuracies of ASTER NDVI and ASTER EVI2 for citrus mapping are about 75% and 65%, while the accuracies of Landsat NDVI and Landsat EVI for rice mapping are about 60% and 65%, respectively. The achieved results demonstrated higher performance of ASTER NDVI for citrus mapping and Landsat EVI for rice mapping. The study concluded that it is difficult to detect and map rice fields from satellite images using satellite-derived indices with high accuracy. However, the citrus fields can be mapped with the higher accuracy using satellite-derived indices.

scholarly journals Maize Crop Coefficient Estimation Based on Spectral Vegetation Indices and Vegetation Cover Fraction Derived from UAV-Based Multispectral Images

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 668
Author(s):  
Mariana de Jesús Marcial-Pablo ◽  
Ronald Ernesto Ontiveros-Capurata ◽  
Sergio Iván Jiménez-Jiménez ◽  
Waldo Ojeda-Bustamante
Keyword(s):  
Temporal Resolution ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Coefficient ◽  
Irrigation Scheduling ◽  
Multispectral Images ◽  
Maize Crop ◽  
Spectral Models

Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of crop evapotranspiration (ETc). Several methodologies have shown a high correlation between the Vegetation Indices (VIs) and the crop coefficient (Kc). This work analyzes the estimation of the crop coefficient (Kc) as a spectral function of the product of two variables: VIs and green vegetation cover fraction (fv). Multispectral images from experimental maize plots were classified to separate pixels into three classes (vegetation, shade and soil) using the OBIA (Object Based Image Analysis) approach. Only vegetation pixels were used to estimate the VIs and fv variables. The spectral Kcfv:VI models were compared with Kc based on Cumulative Growing Degree Days (CGDD) (Kc-cGDD). The maximum average values of Normalized Difference Vegetation Index (NDVI), WDRVI, amd EVI2 indices during the growing season were 0.77, 0.21, and 1.63, respectively. The results showed that the spectral Kcfv:VI model showed a strong linear correlation with Kc-cGDD (R2 > 0.80). The model precision increases with plant densities, and the Kcfv:NDVI with 80,000 plants/ha had the best fitting performance (R2 = 0.94 and RMSE = 0.055). The results indicate that the use of spectral models to estimate Kc based on high spatial and temporal resolution UAV-images, using only green pixels to compute VI and fv crop variables, offers a powerful and simple tool for ETc assessment to support irrigation scheduling in agricultural areas.

scholarly journals Monitoring Vegetation Patterns at the Syrdarya River Basin from 2000 to 2015 using MODIS Data

2021 ◽  
pp. 27-36
Author(s):  
K. Samarkhanov ◽  
J. Abuduwaili ◽  
T. Ahmed
Keyword(s):  
Surface Temperature ◽  
River Basin ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Seasonal Temperature ◽  
Cropland Area

The dependence of vegetation condition dynamics as expressed by Normalized Difference Vegetation Index (NDVI) from hydro-climatic factors (Multiyear precipitation, land surface temperature) in the Syrdarya River Basin (SRB) was analyzed for the period of 16 years from 2000 to 2015. The analysis demonstrated a different correlation between NDVI and hydrometric parameters. According to experimental analyses, the average NDVI values reached a maximum in April and minimum in October, while the annual average values of land surface temperature were observed maximum in June and minimum in October. Correlation between precipitation and NDVI was positive and extraordinarily strong in Spring while the correlation between Land Surface Temperature (LST) and NDVI was found negative and strong. Correlation between LST and NDVI changed from positive in spring to negative in summer due to an increase in seasonal temperature and found a decrease of vegetation cover throughout the Syrdarya river basin. Desert vegetation area in plain part of SRB decreased while NDVI of cropland area in Syrdarya and Shu river basins remained the same or increased. Hydro-climatic factors negatively affected a decrease in vegetation cover, which leads to desertification processes.

scholarly journals Spacialization of the Degradation Risk in the Natural Monument of the São Francisco River

2019 ◽  
Vol 9 (4) ◽  
pp. 204
Author(s):  
Douglas Alberto De Oliveira Silva ◽  
Suzana Maria Gico Lima Montenegro ◽  
Pabrício Marcos Oliveira Lopes ◽  
Gabriel Siqueira Tavares Fernandes ◽  
Ênio Farias de França e Silva ◽  
...  
Keyword(s):  
Decision Tree ◽  
Change Detection ◽  
San Francisco ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Detection Technique ◽  
Landsat 8 ◽  
Decision Tree Classification ◽  
Natural Monument

Understanding changes related to environmental degradation by parameters such as Normalized Difference Vegetation Index (NDVI), Surface Albedo (α) and Moving Standard Deviation Index (MDSI) has been of great relevance in the study of environmental impacts. The objective of the present study was to analyze the evolution of soil degradation and also of the soil use and occupation in the San Francisco River Natural Monument, using surface data and images from Landsat-5 and Landsat-8, for the 1987, 1997, 2007 and 2017 years. Remote sensing techniques were used to estimate indices such as NDVI, albedo (α) and MDSI. The change detection technique and decision tree classification based on predefined rules in NDVI, albedo and MDSI were applied to infer degradation, soil use and occupation. There was a significant increase in degradation, especially for areas with high degradation. Vegetation indices showed the lowest values for areas of low vegetation and exposed soil, being the highest values found for Caatinga dense vegetation. It was concluded that the change detection technique and decision tree classification were efficient in identifying the degradation during the study period. The change detection technique algorithm was more sensitive to water bodies than the change intensity technique.

scholarly journals Assessment of soil contamination using remote sensing data in the Tamsag-Bulag oil field, Mongolia

2021 ◽  
Vol 895 (1) ◽  
pp. 012013
Author(s):  
S Gantumur ◽  
G V Kharitonova ◽  
A S Stepanov ◽  
K N Dubrovin
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Oil Field ◽  
Oil Contamination ◽  
Vegetation Growth

Abstract Although field surveus represent an essential method for determining oil contamination of soils and soil cover, the use of remote sensing techniques has become one of the main trends over recent years due to their economic and temporary advantages. The fundamental basis of this approach is the assessment of changes in vegetation cover by vegetation indices as indicator. In this study, the problems of assessment of the soil cover contamination during oil production are considered. It is aimed to select and evaluate objective criteria for soil cover contamination with oil in the Tamsag–Bulag field (Eastern Gobi, Mongolia). For this purpose, during the period of maximum vegetation growth, various vegetation indices were investigated at test sites (4 km2) from 2015 to 2019. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used with Sentinel-2 and MODIS of the Terra satellite images at 30 and 250 m resolution, respectively. The monitoring of the land quality with satellite images via NDVI and SAVI allows us to assess the area of oil contamination of the soils and soil cover. The significant increase in the values of the NDVI and SAVI at a distance of more than 4 km from the center of the Tamsag-Bulag oil field is shown. The obtained results indicate the possibility of assessment and monitoring the state of the oil-ed territories of the Eastern Gobi by NDVI и SAVI using satellite images.

The Use of the Normalized Difference Vegetation Index to Analyze the Influence of Vegetation Cover Changes on the Streamflow in the Manhuaçu River Basin, Brazil

2020 ◽  
Vol 34 (6) ◽  
pp. 1933-1949 ◽  
Author(s):  
Uilson Ricardo Venâncio Aires ◽  
Demetrius David da Silva ◽  
Michel Castro Moreira ◽  
Carlos Antônio Alvares Soares Ribeiro ◽  
Celso Bandeira de Melo Ribeiro
Keyword(s):  
River Basin ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index

scholarly journals Analysis of vegetation condition and its relationship with meteorological variables in the Yarlung Zangbo River Basin of China

2018 ◽  
Vol 379 ◽  
pp. 105-112 ◽  
Author(s):  
Xianming Han ◽  
Depeng Zuo ◽  
Zongxue Xu ◽  
Siyang Cai ◽  
Xiaoxi Gao
Keyword(s):  
River Basin ◽  
Vegetation Cover ◽  
Socioeconomic Development ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Variables ◽  
Weak Sustainability ◽  
Vegetation Condition ◽  
Lower Elevation ◽  
Yarlung Zangbo River

Abstract. The Yarlung Zangbo River Basin is located in the southwest border of China, which is of great significance to the socioeconomic development and ecological environment of Southwest China. Normalized Difference Vegetation Index (NDVI) is an important index for investigating the change of vegetation cover, which is widely used as the representation value of vegetation cover. In this study, the NDVI is adopted to explore the vegetation condition in the Yarlung Zangbo River Basin during the recent 17 years, and the relationship between NDVI and meteorological variables has also been discussed. The results show that the annual maximum value of NDVI usually appears from July to September, in which August occupies a large proportion. The minimum value of NDVI appears from January to March, in which February takes up most of the percentage. The higher values of NDVI are generally located in the lower elevation area. When the altitude is higher than 3250 m, NDVI began to decline gradually, and the NDVI became gradual stabilization as the elevation is up to 6000 m. The correlation coefficient between NDVI and precipitation in the Yarlung Zangbo River Basin is greater than that with temperature. The Hurst index of the whole basin is 0.51, indicating that the NDVI of the Yarlung Zangbo River Basin shows a weak sustainability.

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