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 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 Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

scholarly journals ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 223-227 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Shou-Tsung Wu ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Aerial Images ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Shadow Area ◽  
Vegetation Indexes ◽  
Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

scholarly journals Identificação de Ilhas de Calor na Cidade de Campina Grande-PB Utilizando Sensoriamento Remoto (Identification of Heat Islands in Campina Grande City-PB Using Remote Sensing)

2016 ◽  
Vol 9 (2) ◽  
pp. 614 ◽  
Author(s):  
Elânia Daniele Silva Araújo
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Rural Areas ◽  
Urban Areas ◽  
Spatial And Temporal Variability ◽  
Heat Islands ◽  
Social Nature ◽  
Spatial Changes ◽  
Remote Sensing Techniques ◽  
The City

A intensa urbanização causa diversos problemas de natureza ambiental, climática e social. O crescimento não planejado da população urbana e a remoção da vegetação são fatores que intensificam estes problemas. As temperaturas na cidade são significativamente mais quentes do que as suas zonas rurais circundantes devido às atividades humanas. As intensas mudanças espaciais em áreas urbanas, promovem significativo aumento na temperatura, causando o chamado efeito de Ilha de Calor Urbano (ICU). Campina Grande é uma cidade de tamanho médio que experimentou um crescimento desordenado, desde o tempo do comércio de algodão e, como qualquer cidade de grande ou médio porte, sofre alterações em seu espaço. Dessa forma, este estudo teve por objetivo analisar a variabilidade espaço-temporal da temperatura da superfície (Ts) e detectar ICU, através de técnicas de sensoriamento remoto. Para o efeito, foram utilizadas imagens dos satélites Landsat 5 e 8, dos anos de 1995, 2007 e 2014. Aumentos da Ts foram bem evidentes e foram detectadas duas ICU. Campina Grande mostra um padrão de tendência: o crescimento urbano não planejado é responsável por mudanças no ambiente físico e na forma e estrutura espacial da cidade, o que se reflete sobre o microclima e, em última análise, na qualidade de vida das pessoas.   ABSTRACT The intense urbanization causes several problems of environmental, climate and social nature. The unplanned growth of urban population and the vegetation removal are factors that deepen these problems. Temperatures in the city are significantly warmer than its surrounding rural areas due to human activities. Large spatial changes in urban areas promote significant increase in temperature, causing the so-called Urban Heat Island effect (UHI). Campina Grande is a medium-sized town that experienced an uncontrolled growth since the time of the cotton trade and like any large or medium-sized city, undergoes changes in its space. Therefore, this study aimed to analyze surface temperature spatial and temporal variability and to detect potential UHI, through remote sensing techniques. Spectral images from Landsat 5 and 8 satellites were used. Using images from years 1995, 2007 and 2014, considerable increases in temperature were identified and two UHI were recognize. Campina Grande shows a trend pattern: the urban unplanned growth is responsible for changes in the physical environment and in the form and spatial structure of the city, reflecting on people quality of life. Keywords: change detection, surface temperature, heat islands, urbanization.   

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls

2015 ◽  
Vol 3 (2) ◽  
pp. 58-67 ◽  
Author(s):  
Jan Rudolf Karl Lehmann ◽  
Keturah Zoe Smithson ◽  
Torsten Prinz
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Vegetation Index ◽  
Archaeological Site ◽  
Unmanned Aerial Systems ◽  
Surrounding Water ◽  
Normalised Difference Vegetation Index ◽  
Remote Sensing Techniques ◽  
Aerial Systems

Remote sensing techniques have become an increasingly important tool for surveying archaeological sites. However, budgeting issues in archaeological research often limit the application of satellite or airborne imagery. Unmanned aerial systems (UAS) provide a flexible, quick, and more economical alternative to commonly used remote sensing techniques. In this study, the buried features of the archaeological site of the Kleinburlo monastery, near Münster, Germany, were identified using high-resolution color–infrared (CIR) images collected from a UAS platform. Based on these CIR images, a modified normalised difference vegetation index (NDVIblue) was calculated, showing reflectance spectra of vegetation anomalies caused by water stress. In the presented study, the vegetation growing on top of the buried walls was better nourished than the surrounding plants because very wet conditions over the days previous to data collection caused higher levels of water stress in the surrounding water-drenched land. This difference in water stress was a good indicator for detecting archaeological remains.

scholarly journals MONITORING DESERTIFICATION IN SOME REGIONS OF IRAQ USING GIS TECHNIQUES

2021 ◽  
Vol 52 (3) ◽  
pp. 620-625
Author(s):  
Y. K. Al-Timimi
Keyword(s):  
Remote Sensing ◽  
Environmental Economic ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Marked Decrease ◽  
Social Systems ◽  
Sand Dunes ◽  
Rate Of Increase ◽  
Remote Sensing Techniques

Desertification is one of the phenomena that threatening the environmental, economic, and social systems. This study aims to evaluate and monitor desertification in the central parts of Iraq between the Tigris and Euphrates rivers through the use of remote sensing techniques and geographic information systems. The Normalized difference vegetation index NDVI and the crust index CI were used, which were applied to two of the Landsat ETM + and OLI satellite imagery during the years 1990 and 2019. The research results showed that the total area of ​​the vegetation cover was 2620 km2 in 1990, while there was a marked decrease in the area Vegetation cover 764 km2 in 2019, accounting for 34.8% (medium desertification) and 10.2% (high desertification), respectively. Also, the results showed that sand dunes occupied an area of ​​767 km2 in 1990, while the area of ​​sand dunes increased to 1723 km2 in 2019, with a rate of 10.2%) medium desertification (and 22.9% (severe desertification), respectively. It was noted that the overall rate of decrease in vegetation cover was 21.33 km2year-1 while the overall rate of increase in ground erosion in the area is 10.99 km2year-1.

scholarly journals RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 887-892
Author(s):  
X. F. Sun ◽  
X. G. Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Urban Areas ◽  
Vegetation Index ◽  
Conditional Random Field ◽  
Remote Sensing Data ◽  
Intermediate Step ◽  
Sensing Data ◽  
3D Geometry

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

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