scholarly journals Quantitative - Spatial Analysis of Soil Loss by Laminar Erosion and Comparison with NDVI Method of the Municipalities of Mandaguaçu and Presidente Castelo Branco / PR

2021 ◽  
Vol 17 (4) ◽  
Author(s):  
Diogo Yukio Uema ◽  
Renan Valério Eduvirgem ◽  
Fernando Luiz de Paula Santil
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
Soil Loss ◽  
Satellite Images ◽  
Vegetation Index ◽  
Digital Tools ◽  
Map Algebra ◽  
Quantitative Spatial Analysis ◽  
Possible Cause ◽  
Soil Loss Equation

Environmental monitoring is essential for society to maintain balance with the environment, given this fact, several methods from the science of geoprocessing and remote sensing were created to assist in the task. The present work used the Universal Soil Loss Equation to verify the spatiality of laminar erosions in the municipalities of Mandaguaçu and Presidente Castelo Branco, both in Paraná, in addition to comparing their results with data from the Digital Vegetation Index (NDVI) to obtain relationships between the loss of soils and the development or lack of vegetation. The results identified several foci of laminar erosion in the study area, as a possible cause, soils from the highly erodible Caiuá Sandstone can be considered, as well as topographic factors of the EUPS LS factor. It should be noted that the use of digital tools, satellite images and map algebra is not essential for environmental monitoring studies.

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

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.

scholarly journals Silting in the dense reservoir network of the Pereira de Miranda catchment

2014 ◽  
Vol 34 (4) ◽  
pp. 671-682 ◽  
Author(s):  
Daniely Lira ◽  
Cristian Toledo ◽  
George Mamede
Keyword(s):  
Water Availability ◽  
Soil Loss ◽  
Erosion Rate ◽  
Satellite Images ◽  
Storage Capacity ◽  
System Structure ◽  
And Storage ◽  
Total Sediment ◽  
System Water ◽  
Soil Loss Equation

This study aims to analyze the impacts of the reservoir network within Pereira de Miranda - CE catchment (also called Pentecoste) over sediment transport and storage capacity of the system. The survey of the "damming" was carried out using satellite images. We identified 502 erosion units, derived from overlaying maps of the Universal Soil Loss Equation parameters, which allowed the estimation of localized erosion in the basin and identification of areas potentially generating sediment. In order to estimate silting in Pentecoste reservoir, different system structure scenarios were considered. An average erosion rate of 59 t ha-1year-1 was estimated. According to the model, the silting of Pentecoste reservoir may vary from 1.1 to 2.6% per decade, depending on the scenario considered. It is also observed that the reservoirs upstream can retain up to 58% of the sediment that would reach the Pentecoste reservoir. Very small reservoirs with a capacity of up to 100,000 m³, although representing only 1.83% of the system water availability, are able to retain almost 8% of total sediment produced.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

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

scholarly journals Detection of changes of the soil use and vegetation in the low San Francisco

2019 ◽  
Vol 9 (5) ◽  
pp. 310
Author(s):  
Douglas Alberto De Oliveira Silva ◽  
Frederico abraão Costa Lins ◽  
Jhon Lennon Bezerra da Silva ◽  
Landson Carlos da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Environmental Monitoring ◽  
Satellite Images ◽  
Environmental Changes ◽  
Vegetation Indices ◽  
Operating Cost ◽  
Semiarid Region ◽  
Landsat 8 ◽  
Biophysical Parameters

The quantification and spatialization of environmental degradation is an essential element in the planning of agricultural activities and in the management of the water and natural resources in the semiarid. Thus, the detection of changing land use conditions is necessary for understand with more accurately the dynamics of the different types of soil coverage. Remote sensing techniques make it possible to evaluate this type of environmental monitoring in a practical and efficient manner, and low operating cost in a short time. The objective of this study was to monitor and evaluate the environmental changes caused about the Caatinga vegetation coverage by remote sensing using satellite images in the municipality of Petrolina, semiarid region of Pernambuco state. The study was developed using two Landsat-8 satellite images, processed using SEBAL algorithm steps, in the development of thematic maps of the surface biophysical parameters. The maps expressed the spatial distribution of the albedo parameters and surface temperature, and of the NDVI and SAVI vegetation indices, which served for highlight the dynamics of environmental changes in the Caatinga natural environment of semiarid region. The results showed increased of the albedo and surface temperature when there was a decrease in vegetation indices. This behavior was mainly favored by the region's dry season, which coincides with the satellite's days of passage. The biophysical parameters are effective in the spatial monitoring of semiarid regions, highlighting the spatial variability of the soil uses, identifying possibly degraded areas. Remote sensing environmental monitoring is a viable alternative for mitigate environmental changes caused by anthropogenic actions and drought events. 

scholarly journals Classifying vineyards from satellite images: a case study on Burgundy’s Côte d’Or

OENO One ◽  
2014 ◽  
Vol 48 (4) ◽  
pp. 247 ◽  
Author(s):  
Jorge R. Ducati ◽  
Magno G. Bombassaro ◽  
Jandyra M. G. Fachel
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Vegetation Index ◽  
Pinot Noir ◽  
Grape Variety ◽  
Spectral Bands ◽  
Remote Sensing Techniques ◽  
Grape Varieties

<p style="text-align: justify;"><strong>Aim</strong>: To use Remote Sensing imagery and techniques to differentiate categories of Burgundian vineyards.</p><p style="text-align: justify;"><strong>Methods and results</strong>: A sample of 201 vine plots or “climats” from the Côte d’Or region in Burgundy was selected, consisting of three vineyard categories (28 Grand Cru, 74 Premier Cru, and 99 Communale) and two grape varieties (Pinot Noir and Chardonnay). A mask formed by the polygons of these vine plots was made and projected on four satellite images acquired by the ASTER sensor, covering the Côte d’Or region in years 2002, 2003 (winter image), 2004 and 2006. Mean reflectances were extracted from pixels within each polygon for each of the nine spectral bands (visible and infrared) covered by ASTER. The database had a total of 797 reflectance spectra assembled over the four images. Statistical discriminant analysis of percentage classification accuracy was made separately for Côte de Nuits and Côte de Beaune, and for each year. Results showed that for individual years and Côtes, classification accuracy for vineyard category was as high as 73.7% (Beaune 2002) and as low as 66.7% (Beaune 2003). There were no significant differences in accuracy between spring, summer and winter images. Classification accuracy for grape variety in Côte de Beaune over the four study years was between 73.5% for Pinot Noir climats in 2004 and 91.9% for Chardonnay climats in 2006, including the winter image. Concerning the vegetation index NDVI, there were no significant differences between vineyard categories.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Satellite data is shown to be functional to reveal vineyard quality. Spectral differences between categories of Burgundian vineyards are at least partially due to terroir characteristics, which are transmitted to vine and vine canopy.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: This work indicates that Remote Sensing techniques can be used as an auxiliary tool for the monitoring of vineyard quality in established viticultural regions and for the study of quality potential in new regions.</p>

scholarly journals Remote Sensing Analysis In RUSLE Erosion Estimation

2018 ◽  
Author(s):  
Ketut Wikantika
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Soil Loss ◽  
Spatial Information ◽  
Universal Soil Loss Equation ◽  
Large Area ◽  
Coverage Area ◽  
Erosion Processes ◽  
Long Time ◽  
Soil Loss Equation

Soil erosion is a major issue in various hemispheres. It is because erosion affects the survival of ecosystem. Diverse human actions, e.g., bushes burning and illegal logging, play a role in accelerating erosion. Climate factor such as rain intensity has also an influence in the release of soil particles. Therefore, a regular identification of those factors that affect erosion processes is highly needed in order to keep an environmental sustainable. Different areas in Indonesia have different erosion variable characteristics. One of the characteristics is indicated by the varieties ofvegetation cover, where a loose vegetation cover causes soil surfaces open for a long time period. Till now, researches dealing with the modeling of erosions with wide area coverage are few, since erosion observations have always been conducted by direct observations in the field, hence time consuming. Therefore, an erosion mapping model that is applied in a wide coverage area and the up to date of data is needed. Spatially, erosions can be depicted in a form of spatial information system model describing their potential class levels. There are several erosion models that can be used to find out the erosion occurring on a land, among others Universal Soil Loss Equation (USLE) model or its modification Revised Universal Soil Loss Equation (RUSLE). RUSLE erosion model consists of rainfall, soil erodibility, vegetation cover, slope gradient and length, and support practice factors. Recent technology in remote sensing allowed vegetation cover to beanalysed from satellite imagery, make the possibility of erosion analysis in large area in shorter time.

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