Detection of waterholes by Vegetation Index in the habitat of bighorn sheep (Ovis Canadensis) in Baja California

10.7287/peerj.preprints.2999v1 ◽

2017 ◽

Author(s):

Jonathan Gabriel Escobar-Flores ◽

Jorge Torres ◽

Raúl Valdez ◽

Sergio Álvarez Cárdenas ◽

Patricia Galina Tessaro ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Bighorn Sheep ◽

Ovis Canadensis ◽

Arid Ecosystems ◽

Landsat 8 ◽

Desert Bighorn Sheep ◽

False Color ◽

Juncus Acutus ◽

Color Composite

The desert bighorn sheep is adapted to the extreme conditions of arid ecosystems. The amount and distribution of watering holes is an essential component of the habitat of this species. With information provided by people in Sierra Santa Isabel a database of potential sites watering sites was obtained, which was taken as reference of spectral information for water and vegetation. Two images of Landsat 8-OLI were processed; the first corresponded to the end of the drought and the second rainy season of 2013. A false-color composite was made between bands where water has an absorption behavior (band 5 and 7) and a Normalized Difference Vegetation Index (NDVI). Field visits to the existence of 15 watering holes of which 11 had evidence of use by the bighorn were confirmed. The abundance of plant species Tamarix ramosissima, Juncus acutus, Typha domingensis and Psorathamnus spinosus contributed substantially NDVI values and facilitated the detection of watering holes.

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Waterhole detection using a vegetation index in desert bighorn sheep (Ovis canadensis cremnobates) habitat

PLoS ONE ◽

10.1371/journal.pone.0211202 ◽

2019 ◽

Vol 14 (1) ◽

pp. e0211202 ◽

Cited By ~ 1

Author(s):

Jonathan Gabriel Escobar-Flores ◽

Sarahi Sandoval ◽

Raul Valdez ◽

Eahsan Shahriary ◽

Jorge Torres ◽

...

Keyword(s):

Vegetation Index ◽

Bighorn Sheep ◽

Ovis Canadensis ◽

Desert Bighorn Sheep

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Developing Forest Cover Composites through a Combination of Landsat-8 Optical and Sentinel-1 SAR Data for the Visualization and Extraction of Forested Areas

Journal of Imaging ◽

10.3390/jimaging4090105 ◽

2018 ◽

Vol 4 (9) ◽

pp. 105 ◽

Cited By ~ 3

Author(s):

Ram Sharma ◽

Keitarou Hara ◽

Ryutaro Tateishi

Keyword(s):

Vegetation Index ◽

Forest Cover ◽

Normalized Difference Vegetation Index ◽

Short Wave ◽

Forest Monitoring ◽

Landsat 8 ◽

Blue Color ◽

Forested Areas ◽

Spatio Temporal ◽

Color Composite

Mapping the distribution of forested areas and monitoring their spatio-temporal changes are necessary for the conservation and management of forests. This paper presents two new image composites for the visualization and extraction of forest cover. By exploiting the Landsat-8 satellite-based multi-temporal and multi-spectral reflectance datasets, the Forest Cover Composite (FCC) was designed in this research. The FCC is an RGB (red, green, blue) color composite made up of short-wave infrared reflectance and green reflectance, specially selected from the day when the Normalized Difference Vegetation Index (NDVI) is at a maximum, as the red and blue bands, respectively. The annual mean NDVI values are used as the green band. The FCC is designed in such a way that the forested areas appear greener than other vegetation types, such as grasses and shrubs. On the other hand, the croplands and barren lands are usually seen as red and water/snow is seen as blue. However, forests may not necessarily be greener than other perennial vegetation. To cope with this problem, an Enhanced Forest Cover Composite (EFCC) was designed by combining the annual median backscattering intensity of the VH (vertical transmit, horizontal receive) polarization data from the Sentinel-1 satellite with the green term of the FCC to suppress the green component (mean NDVI values) of the FCC over the non-forested vegetative areas. The performances of the FCC and EFCC were evaluated for the discrimination and classification of forested areas all over Japan with the support of reference data. The FCC and EFCC provided promising results, and the high-resolution forest map newly produced in the research provided better accuracy than the extant MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Type product (MCD12Q1) in Japan. The composite images proposed in the research are expected to improve forest monitoring activities in other regions as well.

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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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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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A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

10.31219/osf.io/65xkt ◽

2020 ◽

Author(s):

Toby N. Carlson ◽

George Petropoulos

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Heat Fluxes ◽

Surface Model ◽

Landsat 8 ◽

Simple Method ◽

Practical Applications ◽

Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

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Desert Bighorn Sheep: Changes in Genetic Variation Over Time and the Impact of Merging Populations

Journal of Fish and Wildlife Management ◽

10.3996/082013-jfwm-055 ◽

2014 ◽

Vol 5 (1) ◽

pp. 3-13 ◽

Cited By ~ 4

Author(s):

Philip W. Hedrick ◽

John D. Wehausen

Keyword(s):

Genetic Variation ◽

New Mexico ◽

Monitoring And Evaluation ◽

Bighorn Sheep ◽

Ovis Canadensis ◽

Mexican Population ◽

Desert Bighorn Sheep ◽

Two Generations ◽

The Impact ◽

Over Time

Abstract Founder effects, genetic bottlenecks, and genetic drift in general can lead to low levels of genetic diversity, which can influence the persistence of populations. We examine genetic variation in two populations of desert bighorn sheep Ovis canadensis from New Mexico and Mexico to measure change over time and evaluate the impact of introducing individuals from one population into the other. Over about three generations, the amount of genetic variation in the New Mexico population increased. In contrast, over about two generations the amount of genetic variation in the Mexican population decreased by a great extent compared with an estimate from another Mexican population from which it is primarily descended. The potential reasons for these changes are discussed. In addition, although both populations have low genetic variation, introduction of Mexican rams into the New Mexico population might increase the amount of genetic variation in the New Mexico population. Overall, it appears that management to increase genetic variation might require substantial detailed monitoring and evaluation of ancestry from the different sources and fitness components.

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