scholarly journals SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR

2019 ◽  
Vol 34 (2) ◽  
pp. 263-270
Author(s):  
Victor Costa Leda ◽  
Aline Kuramoto Golçalves ◽  
Natalia da Silva Lima
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Sao Paulo ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
São Paulo ◽  
Vegetation Indexes ◽  
Production Area ◽  
Remote Sensing Techniques ◽  
Sugarcane Productivity

SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR   VICTOR COSTA LEDA1, ALINE KURAMOTO GOLÇALVES2, NATALIA DA SILVA LIMA3   1 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 2 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 3 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected].   RESUMO: O trabalho objetivou modelar as correlações de produtividade da cana-de-açúcar com índices de vegetação obtidos por meio de análise de imagens orbitais. Para análise, foram elaborados modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, SP. A área ocupa aproximadamente 6000 ha, com altimetrias variando entre 600 e 700 m. Foi constatado que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97, sendo que, em períodos de colheita com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto áreas que apresentaram coeficientes de determinação baixos, podem ser explicadas devido a fatores como, dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.   Palavras-chaves: índices de vegetação, Landsat 8, regressão linear múltipla.   REMOTE SENSING FOR THE SUGARCANE PRODUCTIVITY MODELING   ABSTRACT: The aim of this study was to model the sugarcane productivity correlations with vegetation indexes obtained through orbital image analysis. From the analysis was elaborated      mathematical models to explain sugarcane productivity through geoprocessing and remote sensing techniques. The experiment was carried out in the commercial production area of Agrícola Rio Claro, a partner of the Zilor group, located in the municipalities of Lençóis Paulista and Pratânia, SP, with approximately 6,000 hectares, with altimetry varying between 600 and 700 meters. It was verified that the modeling was satisfactory, varying the coefficient of determination between 0,15 and 0,97. Once      in periods with high determination coefficients, areas of agglomerated form can usually be found, which suggests a lower incidence of variables. While, in periods with low determination coefficients, can be explain due to listed factors that occurred as dispersion of the stands in the area, classes of soil, precipitation and probably different varieties of the crop.   Keywords: vegetation index, landsat8, multiple linear regression.

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

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
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.

scholarly journals Planejamento Ambiental Urbano: a Relação entre a Cobertura Vegetal e Temperatura Superficial na Cidade de São Carlos, São Paulo, Brasil

2021 ◽  
Vol 10 (2) ◽  
pp. 116-131
Author(s):  
Vitor Augusto Luizari Camacho Camacho ◽  
Luiz Eduardo Moschini
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sao Paulo ◽  
Landsat 8 ◽  
São Paulo

A rápida expansão urbana das cidades brasileiras modificou a paisagem natural alterando as condições ambientais e climáticas, a partir disso os estudos que envolvem planejamento urbano, meio ambiente e geotecnologias apresentam soluções as novas demandas. O objetivo deste trabalho consiste em analisar a relação entre a cobertura vegetal e a temperatura da superfície da cidade de São Carlos, São Paulo, Brasil. Foi utilizado imagens do satélite Landsat-8, por meio das técnicas de processamento digital de imagem e sensoriamento remoto. Para a temperatura da superfície foi utilizado a banda 10 (termal) e para a cobertura vegetal as bandas 4 (vermelho) e 5 (infravermelho próximo) pelo índice de vegetação NDVI (Normalized Difference Vegetation Index). O trabalho foi realizado no sistema de informação geográfica QGIS. Como analise foram determinados os coeficientes de correlação e determinação entre os índices a partir de pontos de controle no perímetro urbano. Como resultado foi possível observar uma forte correlação negativa entre cobertura vegetal e temperatura da superfície. Áreas com as maiores temperaturas (37,4°C) estiveram associadas a ausência de vegetação, ao alto grau de adensamento construtivo e impermeabilização do solo. Estudos como este reforçam a importância da cobertura vegetal em áreas urbanas para o controle térmico e bem-estar das populações residentes diante do crescente efeito das mudanças climáticas que afetam os centros urbanos. Propostas e ações de mitigação devem fazer parte de um conjunto de políticas públicas aplicadas as cidades, pensando de forma sistêmica e dinâmica.

scholarly journals Uso de sensores remotos en la determinación del forraje disponible de Urochloa humidicola cv. Llanero bajo pastoreo en la Altillanura colombiana

2021 ◽  
Vol 9 (3) ◽  
pp. 376-382
Author(s):  
Raúl Alejandro Díaz Giraldo ◽  
Mauricio Álvarez de León ◽  
Otoniel Pérez López
Keyword(s):  
Near Infrared ◽  
Agricultural Research ◽  
Coefficient Of Determination ◽  
Research Centre ◽  
Landsat 8 ◽  
Pastoral Systems ◽  
Forage Availability ◽  
Vegetation Indexes ◽  
Remote Sensing Techniques ◽  
Sentinel 2A

Modernization of pastoral systems based on the use of Urochloa species in the Colombian Eastern Llanos need the use of remote sensing techniques from satellite platforms to estimate amount of offered forage. In the Carimagua Research Centre of the Colombian Corporation for Agricultural Research (Agrosavia), an Urochloa humidicola cv. Llanero pasture was evaluated using Landsat 8 and Sentinel 2A images. The NDVI, SAVI, EVI y GNDVI vegetation indexes determined by using the blue, green, red and near infrared bands; and the results analyzed with the R free software, to relate those indexes with forage availability field measures taken during the dry season. Forage availability ranged between 290 and 656 kg DM ha-1 and the vegetation indexes for the Landsat 8 and Sentinel 2A sensors were: NDVI = 0.67 (±0.037) and 0.69 (±0.061); SAVI = 0.48 (±0.048) and 0.41 (±0.046); EVI = 0.70 (±0.052) and 0.41 (±0.047); y GNDVI = 0.60 (±0.028) and 0.70 (±0.034), respectively. The relationships between vegetation indexes and forage availability were linear. The Coefficient of Determination (R2= 0.56‒0.72) and the Mean Square Error (MSR =63.95‒80.16) of the prediction equations were used. In conclusion, under the conditions of the study, the EVI for Landsat 8 and NDVI for Sentinel 2A were considered adequate for estimating forage availability of Urochloa humidicola cv. Llanero.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals COMPARISON OF SAFER AND METRIC-BASED ACTUAL EVAPOTRANSPIRATION MODELS IN A SUBTROPICAL AREA OF BRAZIL

Irriga ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 48-55
Author(s):  
Cesar De Oliveira Ferreira Silva ◽  
Rodrigo Lilla Manzione ◽  
Jos´é Luiz Albuquerque Filho
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Low Cost ◽  
Simple Algorithm ◽  
Sao Paulo ◽  
Actual Evapotranspiration ◽  
Landsat 8 ◽  
São Paulo ◽  
Subtropical Area ◽  
Internal Calibration

COMPARISON OF SAFER AND METRIC-BASED ACTUAL EVAPOTRANSPIRATION MODELS IN A SUBTROPICAL AREA OF BRAZIL     CÉSAR DE OLIVEIRA FERREIRA SILVA1; RODRIGO LILLA MANZIONE2 E JOSÉ LUIZ ALBUQUERQUE FILHO3   1Agronomical Sciences Faculty, Universidade Estadual Paulista, Av. Universitária, 3780, Altos do Paraíso, 18610-034, São Paulo/SP, Brazil, [email protected]. 2School of Sciences and Engineering, Universidade Estadual Paulista, Rua Domingos da Costa Lopes,780, Jd. Itaipu, 17602-496, Tupã/SP, Brazil, [email protected]. 3Department of Hydrogeology, Institute of Technological Research (IPT), Av. Prof. Almeida Prado, 532, Cid. Universitária – Butantã, 05508-901, São Paulo/SP, Brazil, [email protected].     1 ABSTRACT   Remote sensing algorithms are well known to estimate surface energy fluxes in regional to global scales with low cost. The remote sensing approach has an advantage of estimating evapotranspiration (ET) on larger spatiotemporal scales when compared with traditional methods. This study compared the result of ET estimates from the “Simple Algorithm for Evapotranspiration Retrieving” (SAFER) and “Mapping Evapotranspiration at high Resolution with Internal Calibration” (METRIC) models on varied land uses of a subtropical area located in Southeast Brazil by using a image from the sensor OLI of LANDSAT-8. The results showed similarity of ET estimate from both models, although slight deviation especially at high ET values. It happened due differences as the need of anchor pixel in METRIC, which requires two points with extrem thermohydrological conditions in the same area. Minimum ground data requirement is the major advantage of the METRIC over the SAFER model. The maximum value, the sum and ET range by METRIC was higher than SAFER. This study has considered both models feasible for estimation of ET from satellite data in the study area.   Keywords: remote sensing, modelling, superficial temperature, Landsat-8, agriwater.                                                            SILVA, C. O. F.; MANZIONE, R. L.; ALBUQUERQUE FILHO, J. L. COMPARAÇÃO DE MODELOS DE EVAPOTRANSPIRAÇÃO REAL SAFER E METRIC EM UMA ÁREA SUBTROPICAL DO BRASIL     2 RESUMO   Algoritmos de sensoriamento remoto são conhecidos por estimar fluxos de energia de superfície em escalas regionais a globais com baixo custo. A abordagem de sensoriamento remoto tem a vantagem de estimar a evapotranspiração (ET) em escalas espaço-temporais maiores que os métodos tradicionais. Este estudo compara o resultado da estimativa de ET do “Simple Algorithm for Evapotranspiration Retrieving” (SAFER) e “Mapping Evapotranspiration at high Resolution with Internal Calibration” (METRIC) em variados usos da terra de uma área subtropical localizada no Sudeste do Brasil usando uma imagem do sensor OLI do satélite LANDSAT-8. Os resultados mostraram similaridade da estimativa da ET em ambos os modelos, embora houvesse desvio, especialmente em altos valores de ET. Isto é devido a diferenças como a necessidade de âncora de pixel no modelo METRIC, que necessita de dois pontos com condições termohidrológicas extremas em uma mesma área. A exigência mínima de dados terrestres é a principal vantagem do METRIC sobre o modelo SAFER. Esse estudo considerou ambos os modelos viáveis ​​para a estimativa de ET a partir de dados de satélite na área de estudo. Neste estudo, o valor máximo, a soma e a variação do ET pelo METRIC foram maiores que o do SAFER.   Palavras-chave: sensoriamento remoto, modelagem, temperatura de superfície, Landsat-8, agriwater.

scholarly journals Quantification and Analysis of Impervious Surface Area in the Metropolitan Region of São Paulo, Brazil

2019 ◽  
Vol 11 (8) ◽  
pp. 944 ◽  
Author(s):  
Fernando Kawakubo ◽  
Rúbia Morato ◽  
Marcos Martins ◽  
Guilherme Mataveli ◽  
Pablo Nepomuceno ◽  
...  
Keyword(s):  
Surface Area ◽  
Vegetation Index ◽  
Impervious Surface ◽  
Sao Paulo ◽  
Metropolitan Region ◽  
Integrated Analysis ◽  
Landsat 8 ◽  
São Paulo ◽  
Impervious Surface Area ◽  
Pixel Scale

The growing intensity of impervious surface area (ISA) is one of the most striking effects of urban growth. The expansion of ISA gives rise to a set of changes on the physical environment, impacting the quality of life of the human population as well as the dynamics of fauna and flora. Hence, due to its importance, the present study aimed to examine the ISA distribution in the Metropolitan Region of São Paulo (MRSP), Brazil, using satellite imagery from the Landsat-8 Operational Land Imager (OLI) instrument. In contrast to other investigations that primarily focus on the accuracy of the estimate, the proposal of this study is—besides generating a robust estimate—to perform an integrated analysis of the impervious-surface distribution at pixel scale with the variability present in different territorial units, namely municipalities, sub-prefecture and districts. The importance of this study is that it strengthens the use of information related to impervious cover in the territorial planning, providing elements for a better understanding and connection with other spatial attributes. Reducing the dimensionality of the dataset (visible, near-infrared and short-wave infrared bands) by Karhune–Loeve analysis, the first three principal components (PCs) contained more than 99% of the information present in the original bands. Projecting PC1, PC2 and PC3 onto a series of two-dimensional (2D) scatterplots, four endmembers—Low Albedo (Dark), High Albedo (Substrate), Green Vegetation (GV) and Non-Photosynthetic Vegetation (NPV)—were visually selected to produce the unmixing estimates. The selected endmembers fitted the model well, as the propagated error was consistently low (root-mean-square error = 0.005) and the fraction estimates at pixel scale were found to be in accordance with the physical structures of the landscape. The impervious surface fraction (ISF) was calculated by adding the Dark and Substrate fraction imagery. Reconciling the ISF with reference samples revealed the estimates to be reliable (R2 = 0.97), regardless of an underestimation error (~8% on average) having been found, mostly over areas with higher imperviousness rates. Intra-pixel variability was combined with the territorial units of analysis through a modification of the Lorenz curve, which permitted a straightforward comparison of ISF values at different reference scales. Good adherence was observed when the original 30-m ISF was compared to a resampled 300-m ISF, but with some differences, suggesting a systematic behavior with the degradation of pixel resolution tending to underestimate lower fractions and overestimate higher ones; furthermore, discrepancies were bridged with the increase of scale analysis. The analysis of the IFS model also revealed that, in the context of the MRSP, gross domestic product (GDP) has little potential for explaining the distribution of impervious areas on the municipality scale. Finally, the ISF model was found to be more sensitive in describing impervious surface response than other well-known indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI).

scholarly journals ANÁLISE COMPARATIVA ENTRE TÉCNICAS DE SENSORIAMENTO REMOTO PARA MENSURAÇÃO DA VEGETAÇÃO URBANA NO MUNICÍPIO DE ALEGRE, ES

2020 ◽  
Vol 15 (01) ◽  
pp. 156-177
Author(s):  
Ivo Augusto Lopes Magalhães ◽  
Osmar Abílio de Carvalho Junior ◽  
Alexandre Rosa dos Santos
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Vegetation Index ◽  
Urban Vegetation ◽  
Adjustment Factor ◽  
High Spatial Resolution Image ◽  
Ndvi Index ◽  
Vegetation Indexes ◽  
Remote Sensing Techniques ◽  
Model Technique

Objetivou-se com este estudo, comparar os resultados obtidos por meio de técnicas de sensoriamento remoto orbital, no intuito de mensurar a vegetação arbórea no município de Alegre, ES. Utilizou-se uma imagem de alta resolução espacial do satélite GeoEye-1 e determinou-se a fotointerpretação da vegetação como técnica modelo a ser comparada perante os índices de vegetação NDVI, SAVI e classificadores de imagens por Distância Euclidiana e Isoseg. Os Índices de Vegetação e os classificadores foram fatiados em três classes; vegetação urbana, pastagem e áreas urbanas. Por meio da fotointerpretação a vegetação urbana foi mensurada em 68 ha. Já por meio do índice de vegetação SAVI com fator de ajuste L 0,25 obteve 66,46 ha, correspondendo a 11,73% do perímetro urbano, entretanto, o índice NDVI subestimou a vegetação urbana em 19,13 ha quando comparado à área mapeada com o SAVI 0,25. Para a região em estudo o índice SAVI com fator de ajuste ao solo 0,25 e o classificador Isoseg podem ser usados para substituir a fotointerpretação, pois apresentaram áreas de vegetação urbana mensurada com valores aproximados, além de serem menos onerosos para obtenção do mapeamento da vegetação. Palavras-chave: Geoprocessamento; fotointerpretação; mapeamento urbano.   COMPARATIVE ANALYSIS BETWEEN TECHNIQUES OF REMOTE SENSING IN MEASUREMENT OF VEGETATION URBAN IN MUNICIPALITY OF ALEGRE, ES Abstract The objective of the study was to compare the results obtained by means of orbital remote sensing techniques, in order to measure the arboreal vegetation in municipality of Alegre, ES. A high spatial resolution image of the GeoEye-1 satellite was used and the vegetation photointerpretation was determined as a model technique to be compared to NDVI, SAVI vegetation indexes and Euclidian Distance and Isoseg image classifiers.The Vegetation Indexes and the classifiers were sliced ​​into three classes; Urban vegetation, pasture and urban areas. Through the photointerpretation the urban vegetation was measured in 68 ha. However, the SAVI vegetation index with adjustment factor L 0.25 obtained 66.46 ha, corresponding to 11.73% of the urban perimeter, however, the NDVI index underestimated the urban vegetation by 19.13 ha when compared to the area Mapped with SAVI 0.25. For the study area, the SAVI index with soil adjustment factor 0.25 and the Isoseg classifier can be used to replace the photointerpretation, since they presented areas of urban vegetation measured with approximate values, besides being less expensive to obtain the mapping of the vegetation.  Keywords: Geoprocessing; photointerpretation; urban mapping.   ANÁLISIS COMPARATIVO ENTRE LAS TÉCNICAS DE TELEDETECCIÓN PARA LA MEDICIÓN DE LA VEGETACIÓN EN URBAN ALEGRE, ES Resumen El objetivo de este estudio fue comparar los resultados obtenidos por medio de técnicas de teledetección con el fin de medir la vegetación arbórea en la ciudad de Alegre, ES. Se utilizó una imagen de alta resolución espacial GeoEye-1 vía satélite y determinó la fotointerpretación de técnica de modelado de la vegetación que se compara con las imágenes de NDVI, SAVI y clasificadores de distancia euclídea y Isoseg. El índice de vegetación y clasificadores se cortaron en tres clases; la vegetación urbana, pastos y áreas urbanas. A través de la interpretación de fotografías vegetación urbana se midió en 68 ha. Ya través del índice de vegetación SAVI con factor de ajuste L obtenido 66,46 0,25 ha, que corresponde al 11,73% de la zona urbana, sin embargo, el índice NDVI subestimar la vegetación urbana en 19.13 ha, frente a la zona mapeada con el SAVI 0,25. Para la región en estudio el factor de ajuste del índice suelo SAVI 0,25 y clasificador Isoseg se pueden utilizar para reemplazar la interpretación de fotografías, como áreas presentados de la vegetación urbana medidos con valores aproximados, y son menos costosos de obtener el mapeo la vegetación. Palavras clave: Geoprocesamiento; fotointerpretación; la cartografía urbana.

scholarly journals Monitoring environmental supporting conditions of a raised bog using remote sensing techniques

2018 ◽  
Vol 380 ◽  
pp. 9-15 ◽  
Author(s):  
Saheba Bhatnagar ◽  
Bidisha Ghosh ◽  
Shane Regan ◽  
Owen Naughton ◽  
Paul Johnston ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Environmental Conditions ◽  
Vegetation Index ◽  
Limiting Factor ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Raised Bog ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Abstract. Conventional methods of monitoring wetlands and detecting changes over time can be time-consuming and costly. Inaccessibility and remoteness of many wetlands is also a limiting factor. Hence, there is a growing recognition of remote sensing techniques as a viable and cost-effective alternative to field-based ecosystem monitoring. Wetlands encompass a diverse array of habitats, for example, fens, bogs, marshes, and swamps. In this study, we concentrate on a natural wetland – Clara Bog, Co. Offaly, a raised bog situated in the Irish midlands. The aim of the study is to identify and monitor the environmental conditions of the bog using remote sensing techniques. Environmental conditions in this study refer to the vegetation composition of the bog and whether it is in an intact (peat-forming) or degraded state. It can be described using vegetation, the presence of water (soil moisture) and topography. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in properties of vegetation. This study uses mid-resolution data from Sentinel-2 MSI, Landsat 8 OLI for VI analysis. An initial study to delineate the boundary of the bog using the combination of edge detection and segmentation techniques namely, entropy filtering, canny edge detection, and graph-cut segmentation is performed. Once the bog boundary is defined, spectra of the delineated area are studied. VIs like NDVI, ARVI, SAVI, NDWI, derived using Sentinel-2 MSI and Landsat 8 OLI are analysed. A digital elevation model (DEM) was also used for better classification. All of these characteristics (features) serve as a basis for classifying the bog into broad vegetation communities (termed ecotopes) that indicate the quality of raised bog habitat. This analysis is validated using field derived ecotopes. The results show that, by using spectral information and vegetation index clustering, an additional linkage can be established between spectral RS signatures and wetland ecotopes. Hence, the benefit of the study is in understanding ecosystem (bog) environmental conditions and in defining appropriate metrics by which changes in the conditions can be monitored.

scholarly journals O CLIMA URBANO DE FEIRA DE SANTANA –BA ATRAVÉS DA TEMPERATURA DA SUPERFÍCIE E DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA -NDVI

2018 ◽  
Vol 10 (3) ◽  
pp. 94-98
Author(s):  
Jovana Mariano Damasceno ◽  
Margarete Cristiane de Costa Trindade Amorim
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Thermal Structure ◽  
Urban Climate ◽  
Landsat 8 ◽  
Land Uses ◽  
Remote Sensing Techniques ◽  
Climate Studies

The use of remote sensing techniques for urban climate studies has advanced over recent years. In this sense, the objective of this study was to identify the influence exerted by the different land uses and coverages in the thermal structure of the urban surface in Feira de Santana-BA. For elaboration of the map of the surface temperature were used calculations for conversion of digital values of the image of Landsat 8 satellite to temperature in degrees Celsius (°C) in the software Idrisi. The vegetation mapping was prepared by the calculation of the vegetation index of normalized difference (NDVI), on the same software. Analyzing the results,itwas possible to perceive that the highestsurface temperature aredirectlyrelated to land use,and thatthe vegetation is fundamental to decrease those temperatures. Thereby, remotesensingtechniques are very useful for urban climate studies.

scholarly journals Geotecnologias Aplicadas à Análise de Ilhas de Calor de Superfície em Cidades do Interior do Estado de São Paulo

2019 ◽  
Vol 12 (6) ◽  
pp. 2041
Author(s):  
Gislene Figueiredo Ortiz Porangaba ◽  
Margarete Cristiane de Costa Trindade Amorim
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Rural Areas ◽  
Urban Areas ◽  
Sao Paulo ◽  
Surface Heat ◽  
Landsat 8 ◽  
São Paulo ◽  
Heat Islands

A qualidade dos ambientes urbanos tem se mostrado de maneira inadequada para parcela significativa da população, devido ao fato de não se considerar, no seu processo de expansão territorial, as características físicas desses ambientes. No intuito de amenizar os problemas relacionados à qualidade desses ambientes, particularmente no que se refere às características da temperatura e à geração de ilhas de calor de superfície, a comunidade científica vem desenvolvendo formas de análise para auxiliar no planejamento ambiental das cidades tendo o sensoriamento remoto como grande aliado. Nas análises acerca das ilhas de calor de superfície, o sensoriamento remoto auxilia na representação da temperatura dos alvos urbanos em relação ao entorno próximo e na avaliação da cobertura vegetal, que é um importante elemento para amenizar as ilhas de calor superficiais. Nesse sentido, no presente artigo tem-se por objetivo analisar a cobertura vegetal por meio do Índice de Vegetação por Diferença Normalizada (NDVI) e sua interferência na temperatura da superfície nas cidades de Assis, Cândido Mota, Maracaí e Tarumã (São Paulo/Brasil). Para isso foram utilizadas imagens do satélite Landsat 8, banda 10, para o cálculo da temperatura dos alvos e as bandas 4 e 5 para o cálculo do NDVI.  Pode-se concluir por meio da análise do NDVI que a vegetação exuberante e/ou ativa (alto NDVI) tem papel fundamental na amenização das temperaturas dos alvos. Por outro lado, NDVI baixo, devido à alta densidade construtiva nas áreas urbanas ou em período de estiagem, particularmente nas áreas rurais próximas, favorece o aquecimento superficial. Heat Islands in Cities in the Interior of the State of São Paulo, Brazil A B S T R A C TThe quality of urban environments has proven inappropriate for a significant portion of the population due to a failure to consider, in their territorial expansion process, the physical characteristics of these environments. In order to mitigate issues related to the quality of these environments, particularly regarding characteristics of temperature and generation of surface heat islands, the scientific community has developed analysis methods to assist in the environmental planning of cities, using remote sensing as a key ally. In the analysis of surface heat islands, remote sensing assists in the representation of the temperature of urban targets in relation to the near surroundings and the assessment of the vegetation, which is a key element to mitigate surface heat islands. In this sense, this paper aims to analyze the vegetation cover using the Normalized Difference Vegetation Index (NDVI) and its interference on surface temperature in the cities of Assis, Cândido Mota, Maracaí and Tarumã (São Paulo, Brazil). To do that, we used images from the Landsat 8 satellite, band 10, to calculate the temperature of the targets, and bands 4 and 5, to calculate the NDVI. It can be concluded through the NDVI analysis that the exuberant and/or active vegetation (high NDVI) plays a key role in reducing temperatures in the targets. On the other hand, a low NDVI, due to the high building density in urban areas or the dry season, particularly in nearby rural areas, favors surface heating.Keywords: Remote sensing, NDVI, Surface temperature, Urban climate.

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