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

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.

Download Full-text

DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2014.v11.a2598 ◽

2017 ◽

Vol 11 (1) ◽

pp. 21

Author(s):

Nanik Suryo Haryani ◽

Sayidah Sulma ◽

Junita Monika Pasaribu

Keyword(s):

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Dry Season ◽

Wet Season ◽

Multi Temporal ◽

Acid Sludge ◽

Landsat Satellite ◽

Solid Form

The solid form of oil heavy metal waste is known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI. In this research, NDVI is analyzed using the remote sensing data on dry season and wet season. Between 1997 to 2012, NDVI value in dry season is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995). The high NDVI value shows the leaf health or thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.

Download Full-text

Temporal Patterns in Illumination Conditions and Its Effect on Vegetation Indices Using Landsat on Google Earth Engine

Remote Sensing ◽

10.3390/rs12020211 ◽

2020 ◽

Vol 12 (2) ◽

pp. 211 ◽

Cited By ~ 2

Author(s):

Pablo Martín-Ortega ◽

Luis Gonzaga García-Montero ◽

Nicole Sibelet

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Temporal Patterns ◽

Google Earth ◽

Surface Reflectance ◽

Enhanced Vegetation Index ◽

Google Earth Engine ◽

Level 1 ◽

Irregular Topography

Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984–2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.

Download Full-text

Crop coefficient of pomegranate in the Comarca Lagunera

Journal-Agrarian and Natural Resource Economics ◽

10.35429/janre.2020.6.4.1.6 ◽

2021 ◽

pp. 1-6

Author(s):

Norma Guadalupe Sifuentes-Morín ◽

José Alfredo Montemayor-Trejo ◽

Alan Joel Servín-Prieto ◽

Jorge Arnaldo Orozco-Vidal

Keyword(s):

Agricultural Development ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Regional Scale ◽

Economic Value ◽

Crop Coefficient ◽

Production Costs ◽

Promising Tool ◽

Landsat Satellite

For agricultural development, water is the most important thing, so today farmers are looking for crops that have some degree of resistance to drought and high economic value such as pomegranate, however, there is poor literature on its production. The Crop Coefficient (Kc) helps us determine the water requirement during plant development, which is critical for reducing production costs and saving water. The objective of this study was to know the Kc during the phenological development of the pomegranate, in an orchard located in the municipality of Gómez Palacio, Durango, Mexico, using 8 Landsat satellite images and geographic information systems. The estimation of Kc based on the Normalized Difference Vegetation Index (NDVI), was performed as proposed by Calera (2016). The KC values obtained range from 0.33 to 0.65. Its evolution with satellite images is consistent according to the development stages of the crop. The relationship between the NDVI and KC may be a promising tool for farmers to estimate water use of pomegranate trees on a regional scale based on satellite imagery.

Download Full-text

Monitoring of Forty Years of Agricultural Expansion in the Oum Er Rbia Valley (Morocco). The Use of Google Earth Engine Compared to Sentinel Application Platform

10.20944/preprints202111.0007.v1 ◽

2021 ◽

Author(s):

María Adell ◽

José Antonio Domínguez-Gómez ◽

Juan Soria

Keyword(s):

Satellite Images ◽

Vegetation Index ◽

Plant Cover ◽

Vegetation Indices ◽

Google Earth ◽

Arid Zones ◽

Us Government ◽

Google Earth Engine ◽

The Moment ◽

Landsat Satellite

Agriculture in Morocco has been extensive until the middle of the 20th century due to the distribution of rainfall and the availability of water. In the middle of the last century hydraulic works were built that allowed the transition to intensive agriculture by the increase of irrigated areas, allowing that in the territories where there is water for irrigation and the climate allows it, the crops adapt to the demands of the market. The objective of the study is to assess by satellite images the land cover between 1985 and 2020, analyzing the changes in cultivation areas, as well as the changes in desert, sub-desert and forest areas of the Oum Er Rbia hydrological basin in Morocco. Landsat satellite images have been used since 1984 by the US government (Aerospace and Geological Agencies). A series of vegetation indices (NDVI, RVI, TNDVI and EVI) have been used; among which TNDVI (Transformed Normalized Vegetation Index) stands out for its better accuracy, which has allowed us to distinguish vegetation in cultivated and forest areas, as well as arid zones. In addition, the study has compared the use of two methodologies to calculate changes in the coverage of the Earth’s surface, has used local image processing from the Sentinel Application Platform tool and has also used the Google Earth Engine tool. The latter being the most optimal, although at the moment it has great limitations. In both methodologies and in the different indices it has been possible to observe during these 35 years as the cultivated area has increased (related to the availability of water by the construction of reservoirs and canals), how plant cover has improved in forest areas, and a range of variations in arid areas.

Download Full-text

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

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/895/1/012013 ◽

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.

Download Full-text

STUDI KERAPATAN MANGROVE DAN PERUBAHAN GARIS PANTAI TAHUN 1989-2018 DI PESISIR PROVINSI JAMBI

Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) ◽

10.29244/jpsl.10.2.138-152 ◽

2020 ◽

Vol 10 (2) ◽

pp. 138-152

Author(s):

Eva Achmad ◽

Nursanti Nursanti ◽

Marwoto ◽

Fazriyas Fazriyas ◽

Dwi Putri Jayanti

Keyword(s):

Mangrove Forest ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Low Density ◽

Shoreline Changes ◽

Average Change ◽

Coastal Province ◽

Analysis System ◽

Landsat Satellite

The density of mangrove cover is one of the factors that influence changes in shoreline both accretion and abrasion. This study aims to determine the effect of changes in density of mangrove cover on shoreline changes in 1989-2018 in the Coastal Province of Jambi. The method used is the interpretation of Landsat satellite images in 1989, 2000 and 2018 using NDVI (Normalized Difference Vegetation Index) and overlaying images to see shoreline changes and DSAS (Digital Shoreline Analysis System) to calculate the area of change. The results showed that there had been a change in shoreline both accretion and abrasion in several locations that had different mangrove densities in the period 1989-2018. The results showed that accretion occured in 6 locations with an average change of Kota Sebrang 771 m, Tungkal Ilir 240.65 m, Kuala Betara 153.73 m, Mendahara 167.78 m, Kuala Jambi 169.35 m and Nipah 57.3 m, while abrasion occurs at 2 locations with an average change in Sabak Timur -41.8 m and Sadu -36.55 m. Where in the 6 locations that had accretion, mangrove density dominantly was in a close-densed and moderate state and only a few are in a low-densed condition. Meanwhile, the 2 locations that had abrasion were in a moderate state and have a low density mangrove forest.

Download Full-text

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

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v5i6.232834 ◽

2013 ◽

Vol 5 (6) ◽

pp. 1473 ◽

Cited By ~ 1

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.

Download Full-text

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

Revista Verde de Agroecologia e Desenvolvimento Sustentável ◽

10.18378/rvads.v10i3.3046 ◽

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

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. Spectral analysis and evaluation of vegetation indices for mapping caatinga ABSTRACT: 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.

Download Full-text

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

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.

Download Full-text

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.548 ◽

2019 ◽

Vol 21 (2) ◽

pp. 1310-1320

Author(s):

Cícera Celiane Januário da Silva ◽

Vinicius Ferreira Luna ◽

Joyce Ferreira Gomes ◽

Juliana Maria Oliveira Silva

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Temperature Measurement ◽

Rainy Season ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Low Density ◽

Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia. ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

Download Full-text