MANGROVE MONITORING USING NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI): CASE STUDY IN NORTH HALMAHERA, INDONESIA

Community-based ecotourism is an appropriate approach which is effectively preserved mangrove sustainability. As an effort to identify the priority areas of mangrove development, mangrove density levels need to be analyzed. The distribution of mangroves with the lowest average density value needs to be prioritized as a preservation and conservation strategy through community-based ecotourism concept. This article aims to identify the distribution of mangroves using the normalized difference vegetation index (NDVI) model in North Halmahera Regency, Indonesia. Also, discuss the sustainable livelihoods' perspective. This study adopted a mixed-method. Data processing is divided into two stages: first stage, mapping the distribution of mangroves based on density levels; second stage, triangulation. Landsat 8 operational land imager (OLI) in 2013 and 2021 were used as primary data to get the NDVI value, and categorized mangrove density level in Pilawang Cape, Kumo Island, Kakara Island, Maiti Island, and Tagalaya Island. The findings show that Pilawang Cape has the lowest NDVI value. The average NDVI value in 2021 is 0.22 which can be categorized as a rare mangrove forest according to the mangrove standard damage criteria. Thus, Mangrove area in Pilawang Cape needs to be prioritized as a preservation and conservation strategy through community-based ecotourism concept.

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NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

Sustainability Agri Food and Environmental Research ◽

10.7770/safer-v0n0-art2174 ◽

2020 ◽

Author(s):

Perminder Singh ◽

Ovais Javeed

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Spectral Signature ◽

Landsat 8 ◽

Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining various features based upon their spectral signature such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also used for the mapping of changes in land use and land cover. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature

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Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

GEOGRAPHY ENVIRONMENT SUSTAINABILITY ◽

10.24057/2071-9388-2019-112 ◽

2019 ◽

Vol 12 (4) ◽

pp. 175-187

Author(s):

Thanh Tien Nguyen

Keyword(s):

Rural Areas ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Classification Method ◽

Landsat 8 ◽

Landsat Images ◽

Fractional Vegetation Cover ◽

Multi Temporal

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

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IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

Present Environment and Sustainable Development ◽

10.15551/pesd2019132013 ◽

2019 ◽

Vol 13 (2) ◽

pp. 179-186

Author(s):

Paul Macarof ◽

Florian Statescu ◽

Cristian Iulian Birlica ◽

Paul Gherasim

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

European Space Agency ◽

Condition Index ◽

Landsat 8 ◽

Natural Phenomena ◽

Space Agency ◽

Vegetation Condition Index ◽

Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

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LAND COVER BASED WATERSHED HEALTH ASSESSMENT

AGROFOR ◽

10.7251/agreng1803047h ◽

2019 ◽

Vol 3 (3) ◽

Cited By ~ 2

Author(s):

Zeinab HAZBAVI ◽

Seyed Hamidreza SADEGHI ◽

Mehdi GHOLAMALIFARD

Keyword(s):

Environmental Protection Agency ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Health Assessment ◽

Primary Data ◽

Management Objectives ◽

Healthy State ◽

Landsat Satellite ◽

Watershed Health

The adoption of appropriate managerial approaches mainly depends upon propermonitoring and consequent assessment of ecosystems health. Towards that, thewatershed health monitoring has gained recognition among regulating agenciessuch as Environmental Protection Agency (EPA). However, its importance has notbeen considerably taken into account by authorities in developing countries wherethe outcome of such approach is essentially needed for effective and efficientmanagement of the ever-degrading ecosystems. To this end, the present articleintroduces a simple and standardized approach of describing the overall watershedhealth situation using risk based RelResVul framework. Towards this, threeindicators of reliability (Rel), resilience (Res) and vulnerability (Vul) have beenconceptualized and calculated based on the normalized difference vegetation index(NDVI) for the Shazand Watershed, Markazi Province, Iran, as a case study. NDVIis an important and commonly used vegetation index in research on globalenvironmental change. The primary data collected to create NDVI maps was multispectralsatellite images of path 165 and rows of 36 and 37, with a spatialresolution of 30 m from the Landsat Satellite images for the sample year of 2014.The results of RelResVul analysis showed that the overall condition of the ShazandWatershed health in terms of Rel, Res and Vul was healthy, un-healthy andmoderately healthy, respectively with scores of 0.82, 0.17 and 0.50 out of 1.0. Theaverage watershed health index based on RelResVul framework was also obtained0.34 varying from 0.04 to 0.46. Hence, it can be concluded that the ShazandWatershed was in relatively un-healthy state from view of vegetation cover. Themaintenance and recovery of the Shazand Watershed health should be consideredas fundamental step to reach the integrated watershed management objectives.

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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.

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Association between Green Space Structure and the Prevalence of Asthma: A Case Study of Toronto

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115852 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5852

Author(s):

Yuping Dong ◽

Helin Liu ◽

Tianming Zheng

Keyword(s):

Air Pollution ◽

Vegetation Index ◽

Green Space ◽

Normalized Difference Vegetation Index ◽

Chronic Inflammatory Disease ◽

Space Structure ◽

Tree Diversity ◽

Underlying Mechanisms ◽

Index Tree

Asthma is a chronic inflammatory disease that can be caused by various factors, such as asthma-related genes, lifestyle, and air pollution, and it can result in adverse impacts on asthmatics’ mental health and quality of life. Hence, asthma issues have been widely studied, mainly from demographic, socioeconomic, and genetic perspectives. Although it is becoming increasingly clear that asthma is likely influenced by green spaces, the underlying mechanisms are still unclear and inconsistent. Moreover, green space influences the prevalence of asthma concurrently in multiple ways, but most existing studies have explored only one pathway or a partial pathway, rather than the multi-pathways. Compared to greenness (measured by Normalized Difference Vegetation Index, tree density, etc.), green space structure—which has the potential to impact the concentration of air pollution and microbial diversity—is still less investigated in studies on the influence of green space on asthma. Given this research gap, this research took Toronto, Canada, as a case study to explore the two pathways between green space structure and the prevalence of asthma based on controlling the related covariates. Using regression analysis, it was found that green space structure can protect those aged 0–19 years from a high risk of developing asthma, and this direct protective effect can be enhanced by high tree diversity. For adults, green space structure does not influence the prevalence of asthma unless moderated by tree diversity (a measurement of the richness and diversity of trees). However, this impact was not found in adult females. Moreover, the hypothesis that green space structure influences the prevalence of asthma by reducing air pollution was not confirmed in this study, which can be attributed to a variety of causes.

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Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

Remote Sensing ◽

10.3390/rs12122015 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2015 ◽

Cited By ~ 2

Author(s):

Manuel Ángel Aguilar ◽

Rafael Jiménez-Lao ◽

Abderrahim Nemmaoui ◽

Fernando José Aguilar ◽

Dilek Koc-San ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Surface Reflectance ◽

Remote Sensing Techniques ◽

Image Pairs ◽

Study Sites ◽

High Level ◽

Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

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ANALISIS PERBANDINGAN METODE NORMALIZED VEGETATION INDEX DAN MAXIMUM LIKELIHOOD CLASSIFICATION UNTUK ANALISIS RUANG TERBUKA HIJAU (Studi Kasus di Kota Pekalongan, Jawa Tengah)

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1164 ◽

2021 ◽

pp. 513

Author(s):

Mohammad Slamet Sigit Prakoso ◽

Rizki Dwi Safitri

Keyword(s):

Maximum Likelihood ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Maximum Likelihood Classification

Ruang Terbuka Hijau (RTH) adalah suatu tempat yang luas dan terbuka yang dimaksudkan untuk penghijauan suatu kota, di mana di dalamnya ditumbuhi pepohonan. Dalam analisis ruang terbuka hijau dapat menggunakan beberapa metode, di antaranya yaitu metode Normalized Difference Vegetation Index (NDVI) dan metode Maximum Likelihood Classification. Tujuan penelitian ini untuk mengetahui perbedaan hasil dari analisis metode NDVI dan Maximum Likelihood Classification yang digunakan untuk mengetahui ruang terbuka hijau di Kota Pekalongan. Metode yang digunakan pada penelitian ini yaitu dengan menggunakan metode NDVI dan metode Maximum Likelihood Classification. Data yang digunakan yaitu Citra Landsat 8 OLI. Pengolahan data menggunakan software Arcgis 10.3. Hasil dari pengolahan berupa peta ruang terbuka hijau dari masing - masing metode. Secara kuantitatif dari hasil perhitungan luas metode NDVI, luas permukiman sebesar 3.016,53 ha, persawahan 609,39 ha, hutan kota 573,3 ha, dan badan air seluas 482,04 ha. Sedangkan untuk metode Maximum Likelihood Classification didapatkan hasil luas permukiman 2.278,26 ha, persawahan 1.141,83 ha, hutan kota 738,18 ha, dan badan air seluas 522,99 ha. Berdasarkan luasan RTH terhadap luas Kota Pekalongan, pada metode NDVI sebesar 25,2%, sedangkan untuk metode Maximum Likelihood Classification sebesar 40,1%. Dari hasil analisis diperoleh perbedaan luasan yang cukup signifikan yaitu pada luasan persawahan dan permukiman. Perbedaan hasil analisis terjadi akibat perbedaan klasifikasi warna citra pada saat pengolahan data.

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ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

ECOTROPHIC Jurnal Ilmu Lingkungan (Journal of Environmental Science) ◽

10.24843/ejes.2018.v12.i01.p12 ◽

2018 ◽

Vol 12 (1) ◽

pp. 93

Author(s):

Made Arya Bhaskara Putra ◽

I Wayan Nuarsa ◽

I Wayan Sandi Adnyana

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Image Data ◽

Research Area ◽

Rice Production ◽

Landsat 8 ◽

Estimation Model ◽

Accuracy Test ◽

Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

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Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-7-297-2018 ◽

2018 ◽

Vol 7 (4) ◽

pp. 297-306 ◽

Cited By ~ 4

Author(s):

Amal Y. Aldhebiani ◽

Mohamed Elhag ◽

Ahmad K. Hegazy ◽

Hanaa K. Galal ◽

Norah S. Mufareh

Keyword(s):

Saudi Arabia ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Floristic Composition ◽

Water Source ◽

Shannon Index ◽

Landsat 8 ◽

Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

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