scholarly journals Caracterización Ecológica De Bofedales, Hábitat De Vicuñas Aplicando Metodologías De Teledeteccion Y Sig Estudio De Caso: Reserva De Producción De Fauna Chimborazo

2016 ◽  
Vol 12 (35) ◽  
pp. 105
Author(s):  
Paulina Beatriz Díaz Moyota ◽  
Catalina Margarita Verdugo Bernal ◽  
Carla Sofía Arguello Guadalupe ◽  
Carlos Arturo Jara Santillán ◽  
Byron Ernesto Vaca Barahona ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Production Reserve ◽  
Spatial And Temporal Variability ◽  
Landsat 7 ◽  
Classification Of Images ◽  
Valley Area

This article presents a methodology based on classification of images from Landsat 7 ETM + to classify Andean wetlands known as ¨Bofedal¨ (wetland) located in the Fauna Production Reserve Chimborazo. Five of the seven in-situ geo-referenced bofedales belong to this category and two belong to the altiplano. These georeferenced reservoirs are the principal habitat of the vicuñas that are located within the RPFCH in the jurisdictions of the province of Tungurahua: Río Blanco, ¨Mocha¨ Valley area, 472.26 ha, 4400 m.; Chimborazo Province: Bofedal Quebrada Toni, Urbina area, 16.74 ha, 4301 m, bofedal El Refugio (Hermanos Carrel) at the Nevado Chimborazo, 1.44 ha, 4800msnm, and Curi bofedal Pogyo, Chorrera Mirador, 0.34 ha., 4523 m. and in the Bolivar Province: the wetlands Chag Pogyo, Pulinguí San Pablo, 19.36 ha, 4064 meters above sea level. Bofedal Sinche1, the sector ¨antennas¨, 8.53 ha. 4167 m., And Sinche2, ¨Puente Ayora¨ area, 9.39 ha., 3981 meters, the latter being Chag Pogyo highland bofedales. The seven bofedales represent 0.93% (527.87 ha) of the total area of the RPFCH (56653, 27 ha.). Two images of the satellite Landsat 7 ETM +, from the years 2001 - EarthSat, 2004 - USGS and an orthophoto 2013-2014 - GIS land were used. Georeferenced and rectified to capture the spatial and temporal variability of these ecosystems and define the characterization of bofedales in the reserve. For each image two classification methods were used, the supervised classification being the most efficient when representing the four representative classes in the RPFCH: snow, rock, pajonal and bofedal. Since this classification is oriented to objects that takes into account aspects such as shape and texture and not just the spectral information of each pixel. Allowing to obtain information on the characteristics and spatial distribution of the bofedales which was verified and validated later in the field. This process allows the generation of digital cartography with the identification, delimited and distributed bofedales along the RPFCH, representing a total of approximately 1483.94 ha in the RPFCH. In addition, the Normalized Difference Vegetation Index (NDVI) was applied, which made it possible to differentiate water bodies from other coverages, as well as specifically to know the extent of the reservoirs present in the Reserve, in order to better infer Distribution of vicuñas.

scholarly journals Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity

2016 ◽  
Vol 185 ◽  
pp. 57-70 ◽  
Author(s):  
D.P. Roy ◽  
V. Kovalskyy ◽  
H.K. Zhang ◽  
E.F. Vermote ◽  
L. Yan ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat 7

scholarly journals ESTIMATING CROP DENSITY FROM MULTI-SPECTRAL UAV IMAGERY IN MAIZE CROP

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 619-624 ◽  
Author(s):  
D. Stroppiana ◽  
M. Pepe ◽  
M. Boschetti ◽  
A. Crema ◽  
G. Candiani ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Development Stage ◽  
Local Contrast ◽  
Reference Dataset ◽  
Maize Crop ◽  
Crop Density ◽  
Local Contrast Enhancement

<p><strong>Abstract.</strong> In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over 15 ESU (Elementary Sampling Units) distributed over the field under investigation. The approach proposed for classification of UAV data is based on local contrast enhancement techniques applied to a vegetation index (NDVI-Normalized Difference Vegetation Index) to capture signal from small plants at the early development stage. The output fc map is obtained over grid cells over 70&amp;thinsp;&amp;times;&amp;thinsp;70&amp;thinsp;cm size. The approach proposed here, based on contextual analysis, reduced artefacts due to illumination conditions by better enhancing signal from vegetation compared to, for example, simple band combination such as vegetation index alone (e.g. NDVI). Validation accomplished by a point comparison between estimated (from UAV) and in situ measured FVC values provided R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.73 and RMSE&amp;thinsp;=&amp;thinsp;6%.</p>

scholarly journals Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012–2018)

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Ichirow Kaihotsu ◽  
Jun Asanuma ◽  
Kentaro Aida ◽  
Dambaravjaa Oyunbaatar
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Mongolian Plateau ◽  
Moisture Measurement ◽  
Rainfall Condition ◽  
Soil Moisture Measurement

Abstract This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

scholarly journals The Use of Satellite Information (MODIS/Aqua) for Phenological and Classification Analysis of Plant Communities

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 561 ◽  
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Oleg Yakubailik ◽  
Vlad Soukhovolsky
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Plant Communities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Conditions ◽  
Linear Discriminant ◽  
Canonical Correlations

Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003–2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities.

scholarly journals Spectral signature of brown rust and orange rust in sugarcane

2020 ◽  
pp. 9-20
Author(s):  
Eniel Rodríguez-Machado ◽  
Osmany Aday-Díaz ◽  
Luis Hernández-Santana ◽  
Jorge Luís Soca-Muñoz ◽  
Rubén Orozco-Morales
Keyword(s):  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Brown Rust ◽  
Spectral Signature ◽  
Spatial And Temporal Variability ◽  
Mean Values ◽  
Index Value

Precision agriculture, making use of the spatial and temporal variability of cultivable land, allows farmers to refine fertilization, control field irrigation, estimate planting productivity, and detect pests and disease in crops. To that end, this paper identifies the spectral reflectance signature of brown rust (Puccinia melanocephala) and orange rust (Puccinia kuehnii), which contaminate sugar cane leaves (Saccharum spp.). By means of spectrometry, the mean values and standard deviations of the spectral reflectance signature are obtained for five levels of contamination of the leaves in each type of rust, observing the greatest differences between healthy and diseased leaves in the red (R) and near infrared (NIR) bands. With the results obtained, a multispectral camera was used to obtain images of the leaves and calculate the Normalized Difference Vegetation Index (NDVI). The results identified the presence of both plagues by differentiating healthy from contaminated leaves through the index value with an average difference of 11.9% for brown rust and 9.9% for orange rust.

scholarly journals Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1025 ◽  
Author(s):  
Jung-il Shin ◽  
Won-woo Seo ◽  
Taejung Kim ◽  
Joowon Park ◽  
Choong-shik Woo
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Method ◽  
Burn Severity ◽  
Spectral Angle Mapper ◽  
Before And After ◽  
Uav Images ◽  
Uav Image

Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mosaic reflectance image. Hundreds of samples were collected for each burn severity class, and they were used as training and validation samples for classification. Maximum likelihood (MLH), spectral angle mapper (SAM), and thresholding of a normalized difference vegetation index (NDVI) were used as classifiers. In the results, all classifiers showed high overall accuracy. The classifiers also showed high accuracy for classification of the burned surface, even though there was some confusion among spectrally similar classes, unburned pine, and unburned deciduous. Therefore, multispectral UAV images can be used to analyze burn severity after a forest fire. Additionally, NDVI thresholding can also be an easy and accurate method, although thresholds should be generalized in the future.

scholarly journals Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

2013 ◽  
Vol 10 (6) ◽  
pp. 7963-7997 ◽  
Author(s):  
A. McNally ◽  
C. Funk ◽  
G. J. Husak ◽  
J. Michaelsen ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Drought ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
The Relationship ◽  
African Sahel

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

scholarly journals ΑΞΙΟΛΟΓΗΣΗ TOY ΔΕΙΚΤΗ ΒΛΑΣΤΗΣΗΣ TVI ME ΤΗ ΣΥΝΔΡΟΜΗ ΤΗΣ ΘΕΩΡΙΑΣ ΠΙΘΑΝΟΤΗΤΩΝ

2004 ◽  
Vol 36 (3) ◽  
pp. 1338
Author(s):  
Γ. Αιμ. Σκιάνης ◽  
Δ. Βαϊόπουλος ◽  
Κ. Νικολακόπουλος
Keyword(s):  
Probability Theory ◽  
Standard Deviation ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Environmental Research ◽  
Theoretical Predictions ◽  
Landsat 7 ◽  
The Right ◽  
Western Greece

In the present paper the statistical behaviour of the Transformed Vegetation Index TVI is studied. TVI is defined by: (equation No1) - or, alternatively, by: (equation No2) u is the numerical value of the vegetation index, χ and y are the brightness values of the near infrared and red zones, respectively. Relation (1) defines the vegetation index TVI. Relation (2) defines the vegetation index TVI'. Using appropriate distributions to describe the histograms of χ and y channels, and taking into account certain theorems from probability theory, the expressions for the distributions of TVI and TVI' values are deduced. According to these expressions, the standard deviation of TVI image is larger than that of TVI', as well as NDVI (Normalized Difference Vegetation Index). The prevailing value of the TVI' histogram is located at the right part of the tonality range. Therefore, according to the mathematical analysis, the TVI image has a better contrast than that of the NDVI and TVI' images. The TVI' has a diffuse luminance. The theoretical predictions were tested with a Landsat 7 ETM image of Zakynthos Island (western Greece) and they were found to be in accordance with the satellite data. It was also observed that lineaments with a dark tonality are expressed more clearly in the TVI image than in the TVI' image. The general conclusion is that the TVI vegetation index is preferable from TVI', since the former produces images with a larger standard deviation and a better contrast than the latter. The results and conclusions of this paper may be useful in geological and environmental research , for mapping regions with a different vegetation cover.

scholarly journals Analisis Perubahan Penggunaan Lahan Menggunakan Metode NDVI (Normalized Difference Vegetation Index) pada Kecamatan Natar, Kabupaten Lampung Selatan

2021 ◽  
Vol 2 (1) ◽  
pp. 17-22
Author(s):  
Fattur Rachman
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Sector ◽  
Land Conversion ◽  
Field Observations ◽  
Land Uses ◽  
Vegetation Density ◽  
Index Method ◽  
Landsat 7 ◽  
Open Land

Natar District is one of the districts in South Lampung Regency which has an area of 213.77 km2 or around 21,377 HA. In the agricultural sector, most of the land in Natar District is dominated by maize and paddy fields. This study aims to determine changes in land use in 2002, 2009 and 2019 in Natar District, South Lampung Regency. This study uses imagery from Landsat 7 and 8 processed in the NDVI (Normalized Difference Vegetation Index) method with the formula "NDVI = (NIR-RED) / (NIR + RED)". After processing the data, field observations were made to 30 sample points which were spread evenly throughout the Natar District. In this study, the results showed that land conversion to open land increased every year, on the other hand the area of land with low to moderate vegetation density decreased every year. In field observations, it was found that various land uses ranging from settlements, markets, and various uses for agricultural and plantation land.

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

Sign in / Sign up

Export Citation Format

Share Document